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TRANSPORTATION IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS Mouyid BIN ISLAM Kunnawee KANITPONG Research Associate, Thailand Accident Research Center Asian Institute of Technology Pathumthani, Thailand Assistant Professor, Transportation Engineering Program Manager, Thailand Accident Research Center Asian Institute of Technology Pathumthani, Thailand (Received February 4, 2008) The rising trend of motorization and improving socio-economic status of Thai people directly influences the aggravating road safety situation with fatalities and permanently disabled injuries of about 130,000 and 500,000 respectively over the past decades. An estimated annual cost from road crashes amounts to about US$2,500 million, 3.4 percent of Gross National Product (GNP), undoubtedly inflicts Thailand with a burning public health concern in the South East Asian region. This paper addresses an in-depth study through crash investigation and reconstruction which has not yet been practised in Thailand to identify the contributory factors in road crashes by the concerned authorities. This research attempts to establish the linkage between the causes and consequences with event classification of an investigated case by highlighting the dynamic driving situation with initial traveling speed, pre-impact and post-impact speed of the involved vehicles to describe the crash scenario. Moreover, inaccurate risk assessment and late evasive action, absence of street-light facilities, inadequate lane marking and visibility were also outlined as major risk factors increasing the severity of crash and injury in this investigated case. Key Words: Investigation, Reconstruction, In-depth analysis, Event tree, Factors 58 IATSS RESEARCH Vol.32 No.2, 2008 1.00 50% 0.90 45% 0.80 Injuries per accident 40% 0.70 35% 0.60 30% 0.50 25% 20% 0.40 0.30 Fatality Index 0.00 15% 10% 0.20 0.10 Fatality Index Road safety becomes a major public health concern when the statistics show that more than 3,000 people around the world succumb to death daily due to road traffic injury1. In addition, road crashes lead to the global economic losses as estimated in road traffic injury costs of US$518 billion per year 2. The huge economic losses are an economic burden for developing countries. It is reflected that the road crash costs are estimated to be US$100 billion in developing countries which is twice the annual amount of development aid to such countries 2. Considering within South East Asian countries, the economic growth rate of Thailand continues to move upward with an aggravating road traffic situation due to the heavy negative impact of a higher level of motorization. Over 130,000 fatalities and nearly 500,000 people were permanently disabled due to road crashes over past decades 3. The economic losses due to the road crashes are; therefore, considerably high, costing approximately US$2,500 million per year (about US$0.3 million per hour), or 3.4 percent of the Gross National Product (GNP). An Asian Development Bank country report 3 focused on the seriousness of the road accident problem which is shown in Figure 1 with an upward trend of inju- ries per accident whereas fatalities per accident remained constant with small fluctuations from 1993-2002. However, the fatality index declined to 16 percent in 2002 from 27 percent in 1993 over this period of time. The collection and use of accurate and comprehensive data related to road accidents is very important to road safety management 4. The road accident data are necessary not only for statistical analysis in setting priority targets but also for in-depth study in identifying the contributory factors to have a better understanding of the chain-of-events. Having the inconsistencies in the aims of the police and the road safety engineers, the data analFatalities or Injuries per Accident 1. INTRODUCTION Fatality per accident 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 5% 0% Fig. 1 Trends in casualties per accident and fatality index (Tanaboriboon 2004) IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS ysis and its interpretation usually does not result in proper countermeasures. Sometimes a lack of proper knowledge of crash and proper training of the police officers in charge on systematic data collection procedures from a crash scene adds to the diverging nature of the role of the police and the road safety professionals. These problems have become a burning issue for developing countries addressing road safety without completed crash data due to the negligence of the concerned authorities. A study 5 clearly indicates this limitation - “the reactions are mostly on major accidents, but the interests would fade away rapidly and the problem still remains”. 2. BACKGROUND The identification of factors affecting road crashes obtained from the crash investigation and reconstruction has not been conducted in practice in the Asian countries. The goal of this study was to initiate this road safety practice in Thailand by addressing the timely need for an in-depth study for road accidents. The accident investigation involves the inspection of crash scenes and the documentation of all necessary and available information of each component (i.e. human, vehicle, and road-environment). Accident reconstruction is defined by Baker and Fricke 6 as “…the efforts to determine from whatever information is available, how the accident occurred”. Accident reconstruction approach works backward from the evidence of the crash investigation and the remains of the crash to look into the scenario of before (pre crash), during (crash) and after the crash (post crash). The sequential analysis of end results to the initial condition of the events can establish “how” and “why” a particular type of crash occurs. Mathematics and Newtonian physics are applied in this analysis. It can be stated that crash reconstruction goes back to investigate the contributory factors and/or causes behind the crash event based on major and minor physical clues left behind at the crash scene. The techniques of crash reconstruction, trajectory and damage based analysis by using physics simplifies the determination of many important parameters of crash events. Moreover, to obtain a reliable conclusion, detailed information encompassing the system components needs to be thoroughly investigated. The information necessary for reconstruction starts with the crash scene 7. The answers to the questions of ‘why’, ‘what’, ‘when’ and ‘how’ should lead the reconstruction process to build up the real scenario of the pre-crash, crash, and post crash 7. Photographing of important clues and videotaping of the crash M. BIN ISLAM, K. KANITPONG scene plays a vital role for the reconstruction. Injury information from occupant medical reports can be verified with the trajectory of the occupants found inside the involved vehicles at the scene. Therefore, an “open mind” investigative attitude is very crucial to search for all the detailed information from the scene 7. 3. OBJECTIVES The purpose of this study was to conduct an indepth study focusing on the application of event analysis through crash investigation and reconstruction. The objectives of this study were the followings: 1. To identify the contributory factors based on the findings obtained from crash investigation and reconstruction by using a case study; 2. To apply an event analysis in establishing the links between the events to describe the crash scenario based on the available information. This case was selected to conduct an in-depth analysis of the crash investigation and reconstruction because of the following reasons: 1. A fatal case between a bus and a pickup truck vehicle to understand the crash mechanism of two structurally different (incompatible) vehicles and crash and injury severity of the involved vehicles. 2. Good example for the evasive actions taken by the driver (i.e. bus driver) in the form of the skid marks on the road surface to calculate back the traveling speed of the bus. 3. The total number of buses and pickup truck vehicles combined involved in all crashes increased from 34,650 in 2003 to 36,816 in 2005 8. But the severity of injury from an angled head-on collision like this case is very challenging to investigate the leading factors to prevent such crashes. 4. METHODOLOGY The in-depth analysis for the contributory factors through event classification requires a scientific methodology to follow systematic research. The conceptual framework designed for this study is presented in Figure 2. The investigation team was always on the alert for crash news. Independent News Network (INN) was considered a primary source of road accidents where the team was motivated to investigate the case. It should be noted that the safety precautions of the investigation team were maintained while conducting the investigation proIATSS RESEARCH Vol.32 No.2, 2008 59 Crash Notification Crash News Report Crash Data Collection TRANSPORTATION Crash Investigation resent possible ways for a dysfunctional behavior to manifest itself in the dimensions of time, space and energy whereas the causes are interpreted from the reasoning 9. To apply DREAM analysis in this study, the possible connections between factors behind the events were established which attempts to explain the observed consequences or the event phenotype9. Figure 2 shows the steps followed in this study to figure out the complete analysis. 5. ACCIDENT IN-DEPTH ANALYSIS - A CASE STUDY Crash Analysis Crash Reconstruction 5.1 Results and Findings Development of Event Tree Contributory Factors Fig. 2 Conceptual framework for the study cess. After the arrival at the crash location, the necessary information was collected with field sketches, drawings, and photographs of the crash scene and damaged vehicles from different angles. The physical evidence (e.g. tire masks, broken glass, oil and blood spatter, etc) at the crash scene were carefully collected particularly for the skid marks that were measured according to direction and coordinates from a reference point. The roadside infrastructure was referenced and taken into consideration. In addition, the concerned police station was contacted and their reports were gathered. Damaged vehicles taken to the police station were also carefully investigated. The evidence at the crash scene, interviews of eyewitnesses in the vicinity of the crash location, and the police reports were gathered and interpreted to visualize the events prior to the crash according to the available information. The trajectory based reconstruction was carried out to determine the traveling, pre-impact and postimpact speed including direction of force acting on the involved vehicles. Based on the concept of Driving Reliability and Error Analysis Method (DREAM), the genotypes (i.e. cause) and phenotypes (i.e. consequences) of the case study were segregated. The inseparable driver behavior from the context was the main focus which encompassed the effects and the causes of the effects. The effects rep- 60 IATSS RESEARCH Vol.32 No.2, 2008 General information of the case The crash occurred between a bus and a pickup truck on an undivided 2-lane 2-way straight section of Chiang Rak-Bang sai Arts and Craft Center rural highway at about 9 p.m. A total of nine casualties, eight fatalities and one serious injury, were reported in this angled head-on crash between two vehicles. All of the causalities were the occupants of the pickup truck. The pickup truck truck was severely damaged with little deformation in the bus. 5.2 Pre-crash information Driver Information Pickup truck: The pickup truck driver was 37 yearold male. Nine passengers were inside the pickup truck. A seatbelt was used by the driver according to the evidence from the investigation process. He was traveling at about 55 kph outbound to Bangsai. Bus: The bus driver was male (age not reported). He was trying to pass the motorcycle in front of him. He was traveling at about 65 kph inbound to Bangsai. Vehicle Information Pickup truck: The 4-wheel pickup truck was locally modified to provide a roof and seats for the passengers in the back. The seating rows were arranged along both sides of the vehicle for the convenience of passengers. The pickup truck was used for public transport. The body was silver color. Bus: The body structure of the 6-wheel bus was locally modified. It was white and blue painted. Road-Environment Information Geometry: Chiang Rak-Bang sai Arts and Crafts Center Road is a 2-lane 2-way road in a rural setting. The lane width is 2.7 meters in each direction with 1.8 meter wide shoulders in both directions. The road was level. Surface: The pavement surface was asphalt, and the surface condition was dry during the investigation. Lane markings: The yellow marking (dashed) was a lane separating the two lanes, and the white marking IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS (solid) was a lane-shoulder separation in both directions. The lane markings were not clearly visible considering the road surface as a background. Roadside furniture and area: The small trees and electricity transmission poles were found along the straight section of the road. In addition, election campaign boards, information boards, and some traffic signs were observed along the road near the crash scene. There was a sheltered ‘Bus Stop’ on the opposite side of an election campaign board. No street light was found on the road. Two minor connecting roads (i.e. access road) were also observed on the opposite side of the road section at the crash scene. A cut-section was on both sides of the embankment of the road section. 5.3 In-crash information Driver Information Pickup truck: As the pickup truck was traveling in its own lane, there was no sudden expectancy to slow down. Suddenly, the bus appeared into the right of way (lane) of the pickup truck. Due to ‘sudden and unexpected’ situation, the pickup truck did not have time to brake and avoid the collision (no skid marks by the pickup truck were found on the driving lane of the road). The pre-impact speed of the pickup truck was about 55 kph. The direction of force (PDOF) passing through the centriod of the damaged portion was 11.5 degree (clockwise) with respect to the longitudinal axis of the vehicle. Figure 3 shows the driving direction of the pickup truck. Bus: Consequently, the bus was going to the right lane from its driving lane (left lane). Suddenly, the bus D E C B A Note: A: Initial Direction of Travel (pickup truck); B: Rear Right and Left Tire at Point of Rest (pickup truck); C: Blood; D: Debris and Oil Spatter and E: Initial Direction of Travel (bus). Fig. 3 Driving path of pickup truck (southeast bound) M. BIN ISLAM, K. KANITPONG J I H F G Note: F: Initial Direction of Travel (bus); G: Right Front Tire Mark (bus); H: Rear Right Tire Mark (bus); I: Rear Right and Left Tire Mark at Point of Rest (bus) and J: Initial Direction of Travel (pick-up). Fig. 4 Driving path of bus (northwest bound) driver found that the pickup truck was approaching at a very close distance. Therefore, the bus driver applied the brakes in 0.63 seconds and made 9.8 meters of skid marks before the crash. However, the bus could not avoid the collision with the pickup truck. The bus was traveling at about 65 kph and slowed down to 47 kph within a very short time (i.e. 0.63 seconds). Figure 4 shows the driving direction of the bus. 5.4 Post-crash information Driver Information Pickup truck: The post-impact speed of the pickup truck was estimated to be about 37 kph. Due to severe intrusion and damage of the passenger compartment there were eight fatalities and one serious injury of the occupants. Bus: The post-impact speed of the bus was estimated to be about 24 kph. Due to the geometry and mass incompatibility between the vehicles, the bus sustained minor damage compared to the pickup truck. Vehicle Information Pickup truck: Since pickup truck collided with the bus, the damage was very severe. The impact force of the bus was high enough to crush the major portion of crush zone and passenger compartment of the pickup truck. The measurements were not directly made due to the crash severity of the pickup truck. The external damage with internal intrusion of the damage resulted in eight fatalities out of nine passengers inside the pickup truck. The calculated delta-V for the pickup truck was about 89 kph. Figure 5 (a) shows the extent of damage of pickup truck from different angles. IATSS RESEARCH Vol.32 No.2, 2008 61 TRANSPORTATION Bus: The bus was higher than the pickup truck in terms of vehicle geometry and only the right side of the bus was damaged at the bumper and the right front fender. The direction of force (PDOF) passing through the centriod of the damaged portion was 8.4 degree (clockwise) with respect to the longitudinal axis of the vehicle. The extent of damage of bus was comparatively less. The calculated delta-V for the bus was about 24 kph. Figure 5 (b) shows the extent of damage of the bus from different angles. Road-Environment Information Pickup truck: The pickup truck was stopped after going backward on the left side for 6.8 meters from the point of impact. The rest position of the pickup truck was found at the crash scene on its driving lane but close to the shoulder. Bus: The bus stopped after crossing 2.8 meters ahead from the point of impact on the opposing lane (right lane which is right-of-way of pick-up). Figure 6 shows a schematic drawing of the crash scene when the vehicles were at their rest positions. 5.5 Reconstruction by simulation The trajectory of the crash involved vehicles was determined by using hand calculations. The results were obtained and used as input in the reconstruction simulation software, PC-Crash, to simulate different events to demonstrate the consequences leading the crash. In the simulation package, 3D features were applied in the reconstruction process. The 100 meters-straight section of road following the curve was set as an input for the crash scene. Trees, electric poles, information sign boards, advertisement boards, two abutting roads, and the small sheltered ‘Bus Stop’ for the passengers were included as inputs for the crash scene data. Length of broken lines and gap between them were set as 3 meters and 6 meters, respectively. The plan view is illustrated in Figure 7. a) 1.8m 2 Lans @2.7m 1.8m 20m N b) 10m Level Grade Cross Slope 5 percent Reference Point Electric Pole B1A 1F-07 10K 11.4m Fig. 5 a) Damaged pickup truck (left: front) and (right: right-corner), and b) Damaged bus (left: front) and (right: right-corner) Fig. 6 Schematic drawing of crash scene A D C B Note: A: Initial position of bus, B: Initial position of pickup truck, C: Point of rest, D: Initial position of motorcycle. Fig. 7 Plan view of crash scene input 62 IATSS RESEARCH Vol.32 No.2, 2008 IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS M. BIN ISLAM, K. KANITPONG The traveling speed, path, direction, and relative positions of the vehicles (i.e. pickup truck, the bus and motorcycle) were set according to the investigation and values obtained from reconstruction. Figure 8 shows the snapshot of simulation when the bus was trying to start the passing maneuver. Figure 9 shows the separation of the bus and the pickup truck after full impact and both vehicles were starting to slow down to their respective rest positions. As shown in Figure 10, the speed vs. distance relationship of the bus shows the events for bus before collision (e.g. Perception-Identification-Emotion-Volition (PIEV) distance and pre-crash braking), at collision (e.g. point of impact) and after collision (e.g. point of rest). Pickup truck: It was traveling at about 55 kph along the straight section of the undivided 2-lane 2-way rural road. The pickup truck driver was observing the oncoming vehicles in the opposite lane. There was a motorcycle coming in the opposite lane. During night time driving under no street light condition, the driver could have a glare problem to some extent. Consequently, the pickup truck driver paid less attention to the following vehicle (i.e. bus). The driver did not expect any opposing vehicle would enter the right-of-way of the pickup truck. However, the driver had little time (e.g. due to un-expectancy and glare) to react and no time to apply the brakes. Since no skid marks by the pickup truck were found during the investigation, the speed vs. distance profile of the pickup truck was not plotted. Fig. 8 Snapshot of starting passing maneuver of bus (Left: Plan view, Right: Bus driver’s view) Fig. 9 Snapshot of separation of the bus and pickup truck after full impact (Left: Plan view, Right: Bus driver’s view) 70 Initial Travel Speed (V* = 65 kph) 60 Speed (kph) 50 40 30 20 Pt. of No Escape Pt. of Actual Perception 10 Pt. of Operator Action POI POR 0 0 5 10 15 20 25 30 35 40 45 Perception/Recation (PIEV = 1.5 sec) 11.08 m 50 55 60 65 Braking 9.8 m 27.12 m 11.08 m SSD = 48 m SSD = 48 m Distnace (m) Note: PIEV: Perception-Identification-Emotion-Volition, POI: Point of Impact, POR: Point of Rest, SSD: Safe Stopping Distance. Fig. 10 Speed vs. distance profile for bus IATSS RESEARCH Vol.32 No.2, 2008 63 TRANSPORTATION Bus: It was traveling at about 65 kph after crossing the curve upstream. The required safe Stopping Sight Distance (SSD) was 48 m. Considering PIEV time of 1.5 second, PIEV distance was about 27 m and pre-impact skidding took 9.8 m which made about 37 m in total with initial traveling speed of 65 kph. According to the calculations, the bus driver could avoid the crash if he started braking at about 11 m before the actual braking position as shown in Figure 10. At x = 38 m, the bus driver applied braking and collided with the pickup truck at x = 48 m with about 47 kph. Due to the misjudgment of the bus driver to the pickup truck speed which was traveling (pickup truck: 55 kph) 10 kph lower than the bus (bus: 65 kph) and also with no street lighting resulted in too late decision making by the bus driver to pass the leading vehicle (i.e. motorcycle). The misjudgment of a gap between the vehicles (i.e. bus and motorcycle) and high traveling speed of the bus resulted in passing and entering to the opposing lane. The late decision of passing maneuver of the bus led to the collision with the oncoming pickup truck even though the bus was applying the brakes. All these factors influenced the bus driver to cross the point of no escape at x > 0 as shown in Figure 10. 5.6 Application of ‘Event Tree’ concept The sequences of the events presented in Figure 11 (for Pickup truck) and 12 (for Bus) are based on the phys- Events Prior to Crash Lack of street light facilities Less conspicuity due to absence of street lights, presence of shadow of trees and advertisement boards Observation of oncoming vehicles Observing oncoming small profile-motorcycle in the opposite lane Misjudgment of speed of motorcycle Fast closing distance of single headlight of small profile-motorcycle and pickup truck Driving along straight section Traveling in long straight section for long time with speed (55 km/hr) in undivided 2-lane, 2-way road Point of No Escape ical evidence found at the crash scene, damaged vehicles, information of the crash investigation, results of the crash reconstruction, interview of eye witnesses and local people, and police reports. Following the concept of DREAM analysis, possible factors and observable consequences were presented in Figures 11 and 12. The branching out of these consequences from their causes led to the collision. Pickup truck: Lack of street light facilities, misjudgment of speed of oncoming vehicles, and normal driving expectancy on the straight road affected unclear vision of the pickup truck driver, less attention to following vehicles and unexpected state for the pickup truck driver. The later caused less time to react and literally no time to brake to avoid the oncoming bus from the opposite direction. All these factors and consequences are interlinked to one another. The event tree for the pickup truck is presented in Figure 11 with events prior to crash vs. time line. Bus: Lack of street light facilities, together with misjudgment of speed of oncoming vehicles, and less attention in driving caused a faulty line of vision of the bus driver, misjudgment of gap between the leading and following vehicles, and temptation of higher speed. Consequently, the misjudgment of the gap caused improper plan of driving and temptation of higher speed to pass the leading vehicles causing a late decision for passing. This late decision of passing made the passing vehicle applied late braking to avoid the collision with the oncoming vehicle Unclear vision of pickup truck driver Sudden oncoming headlights of bus within short interval of motorcycle’s single headlight Less recovery time for pickup truck driver Sudden and unexpected maneuvering of bus into the “wrong lane” Less attention to following vehicle Paying less attention to succeeding two headlights of bus followed by single headlight of motorcycle Unexpected state Keeping the proper lane for the vehicles as designated for respective right-of-way Point of Actual Perception Time Line Fig. 11 Event tree for pickup truck 64 IATSS RESEARCH Vol.32 No.2, 2008 No time to brake Not having time to brake to avoid collision with bus Point of Operator Action IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS Events Prior to Crash Speeding in straight section Speeding up of bus in straight long section after crossing the curve upstream Misjudgment of speed of pickup truck Bus driver estimated speed of pickup truck slower for safe passing distance M. BIN ISLAM, K. KANITPONG Passing got priority Paying more attention to pass the leading vehicle (i.e. motorcycle) in undivided 2-lane 2-way road Temptation of high speed Bus driver was tempted to drive fast to pass the slow moving vehicles ahead Misjudgment of gap Bus driver estimated the gap between bus and leading vehicle (i.e. motorcycle) for safe passing Late decision of passing Late in starting to pass the motorcycle in front of bus Late braking in avoidance “Flinching” due to sudden appearance of hazard (i.e. pickup truck) in the opposite lane and applying brake late Less attention in driving Bus driver paid less attention in driving during nighttime in undivided 2-lane 2-way road Lack of street light facilities Less conspicuity in the line of vision for bus driver for safe passing sight distance due to no street lights along the road Faulty line of vision of bus driver Going apart of single taillight of motorcycle and oncoming two headlights of pickup truck in the dark section of 2-lane roadway Point of No Escape Improper plan in driving Overtaking maneuver and sudden appearance of oncoming pickup truck Point of Actual Perception Point of Operator Action Time Line Fig. 12 Event tree for bus from the opposite direction. The event tree for the bus is presented in Figure 12 with events prior to crash vs. time line. 6. SUMMARY OF FINDINGS The summary of event analysis could lead to listing the factors of the system components. The human factor of the pickup truck, where the pickup truck driver could have braked as an evasive action (but actually he did not in the real situation) was influenced by the unexpectancy of the oncoming vehicle (i.e. bus) into its (i.e. pickup truck) own right of way. Less attentive driving on the undivided 2-lane 2-way highway at night in the absence of street light facilities is also added. For the bus driver, the misjudgment of a gap between bus and leading vehicle (i.e. motorcycle) together with late passing decision of the passing vehicle led to late braking to avoid the collision. For the vehicle aspect, the compatibility of the bus and smaller vehicle such as a pickup truck in this case could be taken seriously in terms of mass and geometry of the vehicles. In addition, the locally modified body structure of the bus could possibly have late braking response due to the old age of the vehicle. For the road and environment aspect, no street light facilities particularly during night time driving on the undivided 2-lane 2-way rural highway could lead to the dif- ficulty in distinguishing the lane separation. Shadows of static objects (e.g. campaign boards, information boards) also contributed to the faulty decisions of the pickup truck and bus drivers. In addition, no speed limit signs along that road section were found during the investigation process. Possible factors from the summary of event analysis could be listed as follows under system components (i.e. human, vehicle and road-environment): Human Pickup truck: - Unexpected maneuvering of bus into “wrong lane” in 2-lane road - Paying less attention to the oncoming vehicles in the opposite lane in undivided road during nighttime driving Bus: - Misjudgment of distance and speed of the leading vehicle (i.e. motorcycle) - Late in overtaking for small profile vehicle (i.e. motorcycle) - Inattentive driving for oncoming vehicles from the opposite lane in 2-lane-2-way undivided road Vehicle Bus: - Complete stop by applying brakes did not occur due to having short braking time (0.63 sec) - No “crash compatible design” between large vehicle IATSS RESEARCH Vol.32 No.2, 2008 65 TRANSPORTATION (i.e. bus) to smaller vehicle (i.e. pickup truck) Road and Environment Pickup truck: - Lack of street lighting - Lack of conspicuity of the static roadside objects during nighttime - Difficulty in distinguishing the lane separation clearly due to lack of reflective devices for 2-lane road - No “Speed Limit” sign along the roadside Bus: - Lack of street lighting - Unclear vision due to shadow of small trees, advertisement boards, information sign along road - No “Speed Limit” sign or warning sign of curvature ahead along the long straight section 7. CONCLUSIONS The event analysis obtained from crash investigation and reconstruction can be applied to determine the possible contributory factors in the fatal road crash. These contributory factors are generalized according to the available information of the system components. The event tree analysis mainly deals with what were the events and factors that came to the drivers’ attention and decision making to influence their behavior during driving in the pre-crash stage. The factors found based on the event analysis can be concluded as follows: Human Factor - Cognitive behavior particularly judgment and decision-making based on analytical aspects of reaction were found to be important particularly in this case. This behavior was highly dependent on the inaccurate risk assessment. Both drivers had other causes which influenced them to take the risk. - Sensorimotor behavior includes experiences related to sensory and motor channels. This is another aspect of human behavior, which highly contributes to crashes. This effect was found to be important in this case where evasive action (i.e. braking action) was perceived later than required. In this case, the pick-up driver could not react in an appropriate time to avoid collision when there was a mistake in passing maneuver happened by the bus. Road and Environment Factor - It was found to have a potential effect on road crashes. The visibility, geometry, lane markings, surface condition, and street light facilities have a potential influence on the drivers to perceive and react in a dynamic driving condition. The interaction of road and environ- 66 IATSS RESEARCH Vol.32 No.2, 2008 ment is quite complex with driving behavior and performance. In this case, absence of street lights, unclear lane marking were found to increase the risk of crash and its severity. The vehicle factor particularly the vehicular defects were not thoroughly investigated in this case. Nevertheless, the vehicle compatibility of bus and pickup truck focusing on a safer vehicle design is highlighted in this study. It is expected that more advanced vehicle investigation will be further analyzed in the future. Since this study was undertaken as a pilot study for future in-depth studies, the limitations and a more systematic way of analysis will be adopted to understand better about the pre-crash events and the contributory factors. Human factors are supposed to be the leading contributory factor in any crash analysis including this case study. Nevertheless, vehicle and road-environment are also crucial in influencing driver behavior in pre-crash and the crash phase. These two factors require more attention in investigation and in-depth analysis in particular for developing countries where vehicle and road-environment are still not of the same standards as developed countries10. This in-depth analysis through a demonstrated case study eventually indicates the improvements in highway design and facilities need to be addressed to ensure safer roads in Thailand. Since this study was initially conducted in Thailand, the findings and review of this study could play an important role as a pilot study for further research studies. Considering this pilot study, the objectives and scope of the research could be broadened to be implemented in other projects to establish a firm research base. The benefits of such in-depth study envisioned with better understanding of the interrelationship of the system components in road crashes eventually could be followed in other developing countries in Asia. REFERENCES 1. World report on road traffic injury prevention: summary, http:// www.who.int/world-health day/2004/infomaterials/world_report/en/summary_en_rev.pdf, (July 24, 2004). (2004). 2. Pan American Health Organization (PAHO). http://www.paho. org/English/DD/PIN/whd04_main.htm, (July 24,2004). (2004). 3. Tanaboriboon, Y. The Status of Road Safety in Thailand. ADBASEAN Regional Safety Program, Country Report: CR09, Final Report, Thailand. (2004). 4. Ogden, K.W. Safer Roads: A Guide to Road Safety Engineering, Ashgate Publishing Limited, England. pp.69-70. (2002). 5. Tanaboriboon, Y., Suriyawongpaisal, P., and Chadbunchachai, IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS M. BIN ISLAM, K. KANITPONG W. Traffic Accident Study: Thailand and Japan Experiences. Final Report Submitted to the Sumitomo Foundation, Japan. (1997). 6. Baker, J., and Fricke, L., Process of Traffic Accident Reconstruction. Traffic Accident Reconstruction (L. Fricke, ed.), Evanston, IL, Northwestern University Traffic Institute. (1990). 7. Van Kirk, D. J. Vehicular Accident Investigation and Reconstruction. CRC press. USA. pp.15-19. (2001). 8. Royal Thai Police crash statistics, www.royalthaipolice.go.th/ crime/traff_main.htm. 9. Huang, Y., H., and Ljung, M. Factors Influencing the Causation of Accidents and Incidents. Human Factors in Design, edited by D. de Waard, K.A. Brookhuis and C.M. Weikert, Maastricht, Shaker. (2004). 10. Islam, M.B., and Tanaboriboon, Y. “Crash Investigation and Reconstruction…The New Experience in Developing Countries: Thailand Case Study”. Proceeding of the 13th International Conference on Road Safety on Four Continents (CD-Rom), Warsaw, Poland, pp.874-884. (2005). ACKNOWLEDGEMENTS The authors sincerely acknowledge late Professor Yordphol Tanaboriboon, who had the vision of such innovative thinking in the context of Thai road safety research. This study was completely supervised by late Professor Yordphol Tanaboriboon. This pilot study was made possible for the team members’, particularly Mr. Sarttrawut Ponboon and Mr. Nuttapong Boontob, motivation and hard work for such vigorous activity. The investigation team described in this paper is presently working under Thailand Accident Research Center (TARC) in Thailand. IATSS RESEARCH Vol.32 No.2, 2008 67 JOURNAL OF PREVENTIVE MEDICINE AND HYGIENE 2006; 47: 69-73 BRIEF REVIEW Biological and behavioral factors affecting driving safety R. VIVOLI, M. BERGOMI, S. ROVESTI, P. BUSSETTI, G.M. GUAITOLI University of Modena and Reggio Emilia, Department of Public Health Sciences, Modena, Italy Key words Biological factors • Behavioral factors • Driving safety The European Commission estimates that car crash-related costs in Europe are around 160 billion euros, approximately 2% of the Gross Domestic Product [1]. In several countries, car crashes are the first cause of death among subjects aged 15-30, with a direct heavy impact on the years of life lost; in young subjects car crashes also represent one of the major causes of disability [2]. Several driver characteristics and driving behaviors due to age, diet, alcohol consumption, circadian rhythms, drug intake and diseases may contribute to a reduced alertness and induce drowsiness with dangerous consequences on driving ability thus increasing the risk of car crashes. It can be estimated that human factors concerning the psychophysical condition of the driver are involved in 60-80% of road accidents [2]. Crash involvement rates on a population basis are higher among males than females in all age groups [3]. This observation may be related to the fact that females drive fewer kilometres/year, drive mainly in town and for short journeys, rarely in bad weather and usually drive small engine cars. Males drive for a higher number of kilometres/year, on motorways for long distance driving and drive trucks or large engine cars. Most accidents involve subjects under 25 years (35%), whereas subjects aged over 70 years are involved in ap- proximately 3% of car crashes, as expected considering that the percentage of drivers over 70 years of age is small compared to other age groups [3]. Taking into account the distance travelled (Fig. 1), crash rates in older subjects are higher than in the middle-aged and comparable to those of young subjects; crash rates in females are slightly higher than in males in all age groups [3]. Our study on truck drivers disclosed that the reaction times worsen (rS = 0.337; p = 0.034) and the number of correct answers decreases as age increases (rS = -0.354; p = 0.025) even in a limited age-range (Fig. 2) (unpublished data). Socioeconomic factors such as low social class and low educational level, family conditions (divorced or with divorced parents), job loss and social isolation and several behavioral and psychophysical factors are considered predisposing factors to traffic accidents [4]. Several psychotropic substances taken for recreational (alcohol and illicit drugs) or medical purposes can impair driving performance either by disturbing the information processing mental function, promoting risk taking behaviour, or by increasing response time [4]. Commonly administered therapeutic drugs, such as antihistaminics, antihypertensives, cardiac glycosides, diuretics, antidiabetic agents and antibiotics may cause Fig. 1. Driver crash involvement by age and sex, Western Australia 1989-1992 (crashes per 100 million km driven) [3]. Reprinted from Accid Anal Prev 1998;30:379-87, with permission from Elsevier. 69 R. VIVOLI ET AL. Fig. 2. Relationship between age and driver performance (reaction time and number of correct answers) by Vienna Determination Test. weakness or other side effects, thereby increasing the risk of a road accident [4]. Although relatively few reports have addressed the role of medical conditions in road accident involvement, it has been suggested that several diseases and disabilities may impair driving performance [4]. Among behavioral factors, alcohol plays an important role in car crashes, and accidents involving alcohol are more likely to result in injuries and deaths than crashes where alcohol is not a factor [4-6]. A large proportion of accidents are attributable to alcohol (in Europe about 20%) mainly in young people: the intake of alcoholic beverages when associated with narcotics use may represent the most dangerous combination that when is increases the risk of serious crashes [4]. Sleepiness while driving and/or falling asleep at the wheel are other important risk factors for injuries from car crashes, though the exact role of these factors has yet to be fully elucidated [7]. Estimates of the proportion of road traffic accidents due to sleepiness while driving vary widely between nations, ranging from 1% to 33% [7]. This contribution is most likely underesti- Fig. 3. Reasons for sleep loss [11]. Reprinted from Accid Anal Prev 1997;29:463-9, with permission from Elsevier. mated, due to the lack of a standardised definition of “sleep-related vehicle accidents” and/or due to insurance-related problems. The percentage of road traffic accidents ascribed to sleepiness in Italy has been estimated at around 21% [8]. Individuals at highest risk of sleep-related accidents are generally young subjects, particularly males, individuals with undiagnosed or untreated sleep disorders, subjects who use sleep-inducing drugs, shift workers or truck drivers with long periods of driving, a high frequency of night driving and lack of adequate rest (< 6 hours of sleep) [9, 10]. Sleep loss is also an important risk factor in city accidents occurring on short trips and the main reasons for sleep loss (Fig. 3) are work and partying/social events [11]. As regards the distribution of sleep-related vehicle accidents by time of day, Figure 4 shows three peaks, two during the early morning (at around 02-03, and 06-07) when traffic flow rates are low, and another one in the mid-afternoon (16-17) at a time of high traffic density [12]. Sleepiness decreases performance, reducing the vigilance level and impairing attention and reaction times [13]. Even a low and generally ‘safe’ level of alcohol consumption may exacerbate driving impairment due to sleepiness [14]. A poorly investigated aspect of road accidents is the role of the driver’s chronotype in the induction of sleepiness. Several studies have shown interindividual differences in the spontaneous sleep-wake cycle and it has been suggested that this variability may influence individual levels of performance. In particular it has been observed that performance differences between the two chronotypes (morning-type or evening-type) are related to the time of day [15]. Recent studies have shown that peaks of melatonin synthesis occur on average three hours earlier among individuals with morning chronotype compared with evening chronotypes [16-18]. The phase and magnitude of the melatonin secretion cycle, at the beginning or 70 FACTORS AFFECTING DRIVING SAFETY Fig. 4. Incidence of sleep related vehicle accidents and traffic flow rates by hour of day [12]. Reprinted from BMJ 1995;310:565-7, with permission from the BMJ Publishing Group. end of the work shift, may represent a marker of maladjustment and stress condition among vehicle drivers [19, 20]. Among the human risk factors of road accidents, several psychological and behavioral factors seem to be associated with an increased risk of motor-vehicle accidents [4, 21]. Several attempts have been made to analyse the role of these factors in triggering accidents and it has been suggested that certain personality traits can affect “driving style”. To shed more light on the relative contribution of personality factors and driving behaviors in accident involvement, a model distinguishing distal (i.e. personality factors) and proximal (i.e. aberrant driving behaviors) predictors of traffic accidents has been proposed (Fig. 5) [21]. Some personality traits, such as impulsiveness and sensation seeking have been implicated as major factors in the risk-taking disposition. In particular, the “Sensation Seeking” trait related to pursuit of “strong” sensations and of an adventurous and hazardous life, seems to increase the risk of road traffic accidents. “Sensation Seeking”, a personality trait that seems to be genetically based, is more pronounced among young males, and is related to behaviors such as reckless driving, sometimes associated with excess alcohol consumption [22, 23]. “Sensation Seeking” is directly correlated to hazardous driving behaviours such as high speed, infringement of safety distance and other rules of the highway code, driving after excess alcohol intake, etc. [24, 25]. It has been hypothesized that the interaction between stable personality factors and transient elements such as stressful events, fatigue or drinking might play a major role in crash causation [26]. Normal car driving on the road, especially under difficult conditions, is considered one of the most significant stressors of everyday life and is influenced by several individual and environmental factors. Consequently, the driver’s performance, as well as road safety, may be affected by the stress induced by driving. Significant changes in stress hormones, such as catecholamines (in particular adrenaline) and cortisol or both, have been detected in studies carried out on bus, truck and racing car drivers [27-33]. The finding that adrenaline excretion rates were significantly correlated to anxiety scores in both truck and racing car drivers suggests that the degree of adrenergic response is influenced by the psychological profile [31, 33]. Driving motor-vehicles under stressful environmental conditions (long-distance driving, traffic or weather conditions) may trigger a major activation of the car- Fig. 5. Proposed contextual mediated model [21]. Reprinted from Accid Anal Prev 2003;35:949-64, with permission from Elsevier. 71 R. VIVOLI ET AL. diovascular system. Our study on truck drivers showed an increase in heart rate and onset of supraventricular extrasystoles during conditions of traffic jams and fog [31]. In addition, the marked increase in urinary levels of thromboxane B2 found in truck drivers at the end of the working-shift suggests that the stressful conditions of long distance driving might interact with the release of this modulator of platelet function [32]. In conclusion, among the human factors related to driving safety, some individual characteristics such as age and gender and lifestyle features such as alcohol and drug intake increase the risk of being involved in motor-vehicle crashes. Young male subjects with particular personality traits (aggressiveness, sensation seeking) are likely to have aberrant driving behaviors (driving speed, violations, alcohol abuse) that increase the probability of road accidents. A large proportion of traffic accidents can be ascribed to drowsiness or falling asleep that usually hit the driver in the early morning hours. Among the factors affecting sleepiness, driver chronotype may influence driving safety, mainly at certain times of day, since individual variability related to the sleep-wake cycle has been associated with changes in performance rhythms. Although the large literature data on traffic accidents, the potential causative role of several factors overviewed in this paper (personality traits, chronotype and others) needs to be clarified in future researches. A better understanding of the human factors affecting motor-vehicle accidents is required to adopt appropriate measures to increase driving safety. References [1] Commissione delle Comunità Europee. Comunicazione della Commissione - Programma di azione europeo per la sicurezza stradale - Dimezzare il numero di vittime della strada nell’Unione europea entro il 2010: una responsabilità condivisa. http://europa.eu.int/scadplus/leg/it/lvb/l24257.htm [2] Taggi F, Giustini M, Fondi G, Macchia T, Chiarotti M. L’epidemiologia degli incidenti stradali (I): i dati di base e i fattori di rischio. In: Atti della 53a Conferenza del Traffico e della Circolazione, Stresa, 1-4 ottobre 1997, pp. 67-79. [3] Ryan GA, Legge M, Rosman D. Age related changes in drivers’ crash risk and crash type. Accid Anal Prev 1998;30:37987. [4] Petridou E, Moustaki M. Human factors in the causation of road traffic crashes. Eur J Epidemiol 2000;16:819-26. [5] Fabbri A, Marchesini G, Morselli-Labate AM, Rossi F, Cicognani A, Dente M, et al. Positive blood alcohol concentration and road accidents. A prospective study in an Italian emergency department. 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Sleep deprivations and irregular work schedules in transport operations. In: Proceedings of the 2nd Pan-Pacific Conference on Ergonomics in Occupational Safety Health, Wuhan (China), 1-5 November 1992, p. 135-40. Bougrine S, Mollard R, Ignazi G, Coblentz A. Appropriate use of bright light promotes a durable adaptation to night-shifts and accelerates readjustment during recovery after a period of night-shifts. Work Stress 1995;9:314-26. Sümer N. Personality and behavioral predictors of traffic accidents: testing a contextual mediated model. Accid Anal Prev 2003;35:949-64. Zuckerman M, Kuhlman DM. Personality and risk-taking: common biosocial factors. J Pers 2000;68:999-1029. Iversen H, Rundmo T. Personality, risky driving and accident involvement among Norwegian drivers. Pers Individ Dif 2002;33:1251-63. Jonah BA. Sensation seeking and risky driving: a review and synthesis of the literature. Accid Anal Prev 1997;29:651-65. Jonah BA, Thiessen R, Au-Yeung E. Sensation seeking, risky driving and behavioral adaptation. Accid Anal Prev 2001;33:679-84. Elander J, West R, French D. Behavioral correlates of individual differences in road-traffic crash risk: an examination of methods and findings. Psychol Bull 1993;113:279-94. van der Beek AJ, Meijman TF, Frings-Dresen MH, Kuiper JI, Kuiper S. Lorry drivers’ work stress evaluated by catecholamines excreted in urine. Occup Environ Med 1995;52:464-9. Sluiter JK, van der Beek AJ, Frings-Dresen MHW. Work stress and recovery measured by urinary catecholamines and cortisol excretion in long distance coach drivers. Occup Environ Med 1998;55:407-13. FACTORS AFFECTING DRIVING SAFETY [29] Matthews G, Dorn L, Hoyes TW, Davies DR, Glendon AI, Taylor RG. Driver stress and performance on a driving simulator. Hum Factors 1998;40:136-49. [30] Vivoli G, Bergomi M, Caselgrandi E. Biochemical and psychological study of stress in bus drivers. In: Proceedings of the Workshop on Effects of Automation on Operator Performance (A. Coblentz Ed.), Paris, 27-28 October 1986, Commission of the European Communities, Medical and Public Health Research Programme, p. 80-96. ■ Received on June 17, 2005. Accepted on January 5, 2006. ■ This paper was presented at the Fourth European Conference on Travel Medicine, Rome, 29-31 March 2004. ■ Correspondence: Dr Roberto Vivoli, Department of Public Health Sciences, University of Modena and Reggio Emilia, via Campi 287, 41100 Modena, Italy. Tel. +39 059 2055460 – Fax +39 059 2055483 – E-mail: vivoli@unimo.it 73 [31] Vivoli G, Bergomi M, Rovesti S, Carrozzi G, Vezzosi A. Biochemical and haemodynamic indicators of stress in truck drivers. Ergonomics 1993;36:1089-97. [32] Bergomi M, Vivoli R, Rovesti S, Malagoli C, Pecone L, Vivoli G. Biological indicators of stress in lorry drivers. Epidemiology 2002;13:S170-1. [33] Rovesti S, Vivoli R, Bergomi M, Vivoli G. Biological indicators of stress in racing car drivers. Heavy Vehicle Systems, Special Series, Int J Vehicle Design 1997;4:340-52. Accident Analysis and Prevention 34 (2002) 357– 365 www.elsevier.com/locate/aap Effects of road geometry and traffic volumes on rural roadway accident rates Matthew G. Karlaftis *, Ioannis Golias Department of Transportation Planning and Engineering, Faculty of Ci6il Engineering, National Technical Uni6ersity of Athens, 5 Iroon Polytechnion Street, 157 93 Zografon, Athens, Greece Received 16 May 2000; received in revised form 1 February 2001; accepted 28 February 2001 Abstract This paper revisits the question of the relationship between rural road geometric characteristics, accident rates and their prediction, using a rigorous non-parametric statistical methodology known as hierarchical tree-based regression. The goal of this paper is twofold; first, it develops a methodology that quantitatively assesses the effects of various highway geometric characteristics on accident rates and, second, it provides a straightforward, yet fundamentally and mathematically sound way of predicting accident rates on rural roads. The results show that although the importance of isolated variables differs between two-lane and multilane roads, ‘geometric design’ variables and ‘pavement condition’ variables are the two most important factors affecting accident rates. Further, the methodology used in this paper allows for the explicit prediction of accident rates for given highway sections, as soon as the profile of a road section is given. © 2002 Elsevier Science Ltd. All rights reserved. Keywords: Accident rates; Rural roads; Hierarchical tree based regression 1. Introduction Road safety modelling has attracted considerable research interest in the past four decades because of its wide variety of applications and important practical implications. Public agencies, such as State Departments of Transportation, may be interested in identifying accident-prone areas to promote safety treatments. Transportation engineers may be interested in identifying those factors (traffic, geometric, etc.) that influence accident frequency and severity to improve roadway design and provide a safer driving environment. The very high cost of highway accidents paid by societies around the world makes highway safety improvement an important objective of transportation engineering. Highway safety specialists can influence traffic safety either through means such as road rules, law enforcement, and education, or by applying local traffic control and geometry improvements. An over* Corresponding author. Tel.: + 30-1-6711203; fax: 7721327. E-mail address: mgk@central.ntua.gr (M.G. Karlaftis). whelming majority of previous studies have indicated that improvements to highway design could produce significant reductions in the number of crashes. Recognizing this, the Federal Highway Administration (FHWA) promotes safety and accident investigation by encouraging States to pursue the development of Safety Management Systems (SMS). And, although SMSs are not Federally required as of 1996, most States continue to work on their development, suggesting the need for improving on existing empirical models for accident measurement. Following a long line of studies concerned with identifying major factors contributing to highway accidents, this paper revisits the problem of the relationship between rural road geometric characteristics, accident rates and their prediction, using a rigorous non-parametric statistical methodology known as hierarchical tree-based regression (HTBR).1 The goal of this paper is not only to develop a methodology that quantitatively assesses the effects of various rural road geomet- + 30-11 For information on the process of functional road classification the reader should refer to US DOT (1969). 0001-4575/02/$ - see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 0 0 1 - 4 5 7 5 ( 0 1 ) 0 0 0 3 3 - 1 358 M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 ric characteristics on accident rates, but also to provide a straightforward, yet fundamentally and mathematically sound way of predicting accident rates. The ability to predict accident rates is very important to transportation planners and engineers, because it can help in identifying hazardous locations, sites which require treatment, as well as spots where deviations (either higher or lower rates) from expected (predicted) warrants further examination. The remainder of the paper is organized as follows. The next section provides some background necessary for the development of the methodology used in this paper. Following this, the data and methodology that were used, the estimation results, and examine the effects of various geometric characteristics on accident rates are presented and discussed. The final section of the paper summarizes the findings and offers some concluding remarks. 2. Background Much literature exists that addresses the problem of accident rate estimation, and the identification of the various factors affecting this rate. Joshua and Garber (1990) used multiple linear and Poisson regression to estimate truck accident rates using traffic and geometric independent variables. Jones and Whitfield (1991) used Poisson regression with data from Seattle to identify the daily characteristics (traffic, weather, etc.) that may influence the number of traffic accidents. Miaou et al. (1992) used Poisson regression on traffic data from 8779 miles of roadway from the Highway Safety Information System (HSIS) to establish quantitative relationships between truck accident rates and highway geometric characteristics. Their results indicate that surrogate measures for mean absolute curvature (for horizontal alignment) and mean absolute grade (for vertical alignment) are the most important variables for accident rate estimation. In a study of approximately seven thousand miles of roadway logs in Utah, Mohamedshah et al. (1993) used linear regression to predict truck accident involvement rate per mile per year, based on average Average Annual Daily Traffic (AADT) and truck AADT per lane, shoulder width, horizontal curvature, and vertical gradient. The results suggest that truck involvement rate increases with AADT and truck AADT, degree of curvature and gradient. Hadi et al. (1993), using data from the Florida Department of Transportation’s Roadway Characteristics Inventory (RCI) system, estimated negative binomial (NB) regression for accident rates on various types of rural and urban highways with different traffic levels. Their results suggest that higher AADT levels and the presence of intersections are associated with higher crash frequency, while wider lanes and shoulders are effective in reducing crash rates. In that paper, the authors also provide an extensive review of earlier findings relating accident rates and geometric characteristics. More recently, Ivan and O’Mara (1997), using NB regression on 1991– 1993 data from the Traffic Accident Surveillance Report of Connecticut found that annual average daily traffic was a critical accident prediction variable, while geometric design variables and speed differential measures were not found to be effective predictors of accident rates. Karlaftis and Tarko (1998), based on a county accident data set from Indiana, estimated macroscopic accident models that attempt to explicitly control for cross-section heterogeneity in NB regression that may otherwise seriously bias the resulting estimates and invalidate statistical tests. Data collected from the States of Minnesota and Washington on rural two-lane highways, estimated accident models for segments and three-legged and fourlegged intersections stop- controlled on the minor legs. Independent variables for their models included traffic, horizontal and vertical alignments, lane and shoulder widths, roadside hazard rating, channelization, and number of driveways. Results imply that segment accidents depend significantly on most of the roadway variables collected, while intersection accidents depend primarily on traffic. This brief review of some of the existing literature suggests that a variety of traffic and design elements such as AADT, cross-section design, horizontal alignment, roadside features, access control, pavement conditions, speed limit, lane width (LW), and median width, affect accident rates. And, most of these results have been based on multiple linear or Poisson and NB regression models. Much of the early work in the empirical analysis of accident data was done with the use of multiple linear regression models. As the literature has repeatedly pointed out, these models suffer from several methodological limitations and practical inconsistencies in the case of accident modelling (Lerman and Gonzales, 1980). To overcome these limitations, several authors used Poisson regression models that are a reasonable alternative for events that occur randomly and independently over time. Despite its advantages, Poisson regression assumes equality of the variance and mean of the dependent variable. This restriction (which, when violated, leads to invalid t-tests of the parameter estimates), can be overcome with the use of NB regression which allows the variance of the dependent variable to be larger than the mean. As a result, most of the recent literature has used NB regression models to evaluate accident data. But, while NB regression has been instrumental in overcoming most of the problems associated with models involving count data, it still remains a parametric procedure requiring the functional form of the model to M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 be specified in advance, it is not invariant with respect to monotone transformation of the variables, it is easily and significantly influenced by outliers, it does not handle well discrete independent variables with more than two levels, and it is adversely affected by multcollinearity among independent variables (Hadi et al., 1993; Mohamedshah et al., 1993; Tarko et al., 1996; Karlaftis and Tarko, 1998). It is likely, for example, that while the accident models have been correctly specified, multicollinearity has inflated the variance of some of the independent variables coefficient estimates, leading to lower t-statistic values and to coefficients that are not significant and/or are counter-intuitive. In this paper a methodology which attempts to recognize the existence of the above mentioned problems and develop a framework to account for them is introduced. This methodology, known as HTBR or as Binary Recursive Partitioning (BRT) (Breiman et al., 1984), can be of assistance in overcoming some of the problems associated with multiple linear and NB regression. It should be noted that besides overcoming the above, rather theoretical problems, the proposed methodology has three additional strengths. First, it allows for straightforward and quantitative assessment of the effect of various rural road geometric characteristics on accident rates; second, it allows for the quick estimation of predicted accident rates for a given rural road section; and, third, it is easily amenable to ‘if-then’ statements for incorporation in expert systems which have become increasingly popular and useful in safety management. The strengths and weaknesses of the proposed methodology are demonstrated using Indiana State Police Accident Information records and Indiana Department of Transportation’s Road Inventory database. The combined database includes five years (1991– 1995) of crashes on Indiana rural roads, along with the geometric and traffic characteristics for these roads. 3. Data and methodology 3.1. The data The data used in this paper concern rural roads and come from two sources: the Road Inventory database, from the Indiana Department of Transportation (INDOT), and the Accident Information Record form the Indiana State Police. The first database contains a list of road sections and various traffic and geometric characteristics for those sections. The second database contains a description of the location and type of accidents that occurred on Indiana’s roads. Combining these two yields a database that contains five years (1991– 1995) of accident data for Indiana along with the traffic and geometric characteristics for the location of each accident. 359 The availability of such data allows for inferences to be drawn on the effects of traffic and geometric characteristics on highway accidents. Further, to avoid the possibility of heterogeneity among roads with different number of lanes and based on previous findings in the literature (Hadi et al., 1993; Mohamedshah et al., 1993; Karlaftis and Tarko, 1998), road sections were grouped into two main categories: rural two-lane and rural multilane. The variables available for model estimation appear in Table 1. 3.2. The methodology As previously mentioned, NB regression has accounted for most of the theoretical issues in count data research. Nevertheless, there still remain a number of issues that have not been addressed (Hadi et al., 1993; Mohamedshah et al., 1993; Tarko et al., 1996; Karlaftis and Tarko, 1998). First, NB regression, much like multiple linear and Poisson regression, is a parametric procedure requiring for the functional form of the model to be known in advance. Second, it is easily and significantly affected by outliers. Third, it cannot handle missing data well. Fourth, it does not treat satisfactorily discrete variables with more than two levels. Fifth, it does not deal well with multicollinear independent variables. HTBR is a tree-structured non-parametric data analysis methodology that was first used in the 1960s in the medical and the social sciences (Morgan and Sonquist, 1963). An extensive review of the methods used to estimate the regression trees and their applications can be found in Breiman et al. (1984). HTBR is technically binary, because parent nodes are always split into exactly two child nodes, and is recursive because the process can be repeated by treating each child node as a parent. In essence, the HTBR algorithm proceeds by iteratively asking the following two questions: (i) which of the independent variables available should be selected for the model to obtain the maximum reduction in the variability of the response (dependent variable); and (ii) which value of the selected independent variable (discrete or continuous) results in the maximum reduction in the variability of the response. These two steps are repeated using a numerical search procedure until a desirable end-condition is met. In mathematical terms, deviance D is initially defined as2 2 In this section only the essential parts of the HTBR methodology formulation that may be of interest to the reader are presented. Readers interested in the details of the formulation are encouraged to refer to Breiman et al. (1984) for an in-depth treatment, or Washington and Wolf (1996) and Washington et al. (1996) for a presentation of the methodology in the context of engineering applications. The discussion of HTBR presented in this paper is based on Washington and Wolf (1996). M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 360 L Da = % (yia −x̄a )2, (1) l=1 where Da is the total deviance of a variable y at node a, or the sum of squared error (SSE) at the node, yia is the observation on dependent variable y in node a and is the mean of L observations in node a. A split of the observations can be found at node a on a value of an independent variable x1 that results in two branches and corresponding nodes b and c, each containing M and N of the original L observation (M+ N = L). The goal of HTBR is to find the variable x1 at its optimum split (i ) so that the reduction in deviance is maximized, or more formally when Z(Öx) = maximum. (2) The maximum reduction occurs at some x1(i ) (independent variable x1 at value i ). When the data are split at this value of x, the remaining two samples have much smaller variance of y than the original data set. Numerical search procedures are employed to maximize Eq. (2). The HTBR methodology has several attractive technical properties: it is non-parametric and does not require specification of a functional form; it does not require variables to be selected in advance since it uses a stepwise method to determine optimal splitting rules; its results are invariant with respect to monotone transformations of the independent variables; it can handle data sets with complex (non-homegeneous) structure; it is extremely robust to the effects of outliers; it can use any combination of categorical and qualitative (discrete) variables; and, it is not affected by multicollinearity between the independent variables. Further, and as it pertains to this research, HTBR can straightforwardly yield predictions for the ‘dependent’ variable (y), incorporating the optimal splitting rules in an ‘if-then’ series of statements, making the incorporation of the results in an expert system rather simple. 4. HTBR model estimation and interpretation As previously mentioned, HTBR partitions the data into relatively homogeneous (low standard deviation) terminal nodes, and it takes the mean value observed in each node as its predicted value. In general, HTBR models can be fairly complex and detailed, and therefore difficult to illustrate mathematically. Nevertheless, the methodology lends itself to graphical ‘tree’ like representations well. The model shown in Fig. 1 is the result of the HTBR methodology applied to crashes on rural two-lane roads. Interpreting the tree, both for explanatory and predictive purposes, is rather straightforward. The top of the tree, or root node, shows that the first optimal split for crashes on rural two-lane roads occurs on Table 1 Independent variables available for model estimation Variable Symbol Type Description Section length Number of lanes Lane widths Shoulder widths Median width Shoulder type Pavement type L NoL Continuous Count Length of the road section were an accident occurred Number of moving traffic lanes in the section LW SW MW ST PT Continuous Continuous Continuous Qualitative Binary Concrete pavement Median type CP Binary MT Qualitative TL NoC Binary Count Widths of the northbound, southbound, and average lane widths The widths of the left, right, inside, and outside shoulders Width of the median (or 0 if median not available) Dummy variables for type of shoulder (paved, earth, stabilized) The variables takes the value of 1 if the road surface is bituminous concrete, sheet or rock asphalt, and 0 otherwise The variable takes the value of 1 if the road surface is Portland concrete cement and 0 otherwise The variable takes the value of 0 if there is no median, 1 for grass or sod, 2 for bituminous concrete, and 3 for non-mountable barrier median These variables indicate the presence of left, right, left and right, and continuous turn lanes The number of curbs on the road section (0, 1, 2) NoPL Count The number of parking lanes on the section (0, 1, 2) FR Continuous Coefficient of wet sliding (skidding) FR at 40 mph between a wet pavement surface and a standard tire Takes the value of 0 for dirt and gravel roads, 1 for very poor, 2 for deteriorated, 3 for fair, 4 for good, and 5 for very good pavements Turn lanes Number of curbs Number of park lanes Friction Pavement Serviceability Index Access control SI Qualitative A Qualitative AADT AADT Continuous Takes the value of 1 for no access control, 2 for partial access control, and 3 for full access control Annual Average Daily Traffic M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 Fig. 1. Regression tree for accidents and geometric characteristics on rural two-lane roads. 361 362 M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 AADT, sending cases (road sections) with less than or equal to 8020 to the left and all others to the right. In other words, the single best variable to explain the variability in total crashes on rural two-lane roads is AADT. Assume for the moment the interest is in rural roads with AADT larger than 8020. Conditional on this, the next best explanatory variable is LW. For LW less than or equal to 12.5 ft the road sections go to the left, where for LW larger than 12.5 ft the road sections go to the right forming what is called a terminal node, or leaf of the tree. For these road sections the tree predicts an average of 32 accidents (normalized on section length). The remaining splits, for the road sections with LW less than or equal to 12.0 ft, are made on Friction (FR) and Serviceability Index (SI). In general, an estimate on the number of accidents is obtained by continuing down the branches of the tree in similar fashion until a terminal node is reached. Recall that the estimate provided at terminal nodes is the mean of the sample at the node. This means that, since there is a number of observations that fall within the characteristics of a terminal node, the expected number of accidents is the mean of those observations. For example, there are 37 observations with AADT\ 8020 and LW \12.5, and their mean is 32 accidents. More importantly, since transportation planners are very frequently interested in predicting accident rates for given highway sections, the profile of a road section can be examined, and the tree can be used to determine the prediction. For example, assume a planner wants to predict the expected number of accidents for a rural two-lane road section with AADT of 4000, FR of 0.5, and LW of 12.0 ft. Beginning at the root node (top of the tree), we branch left (AADT0 8020), right (AADT \2751), left (FR0 0.5), right (LW\ 11.5), to get the estimate of 17.2 crashes for that highway section. It should be noted that, for the tree-structure, a X 2-test was used to evaluate the ‘accuracy’ of the predictions. Using a ‘hold-out’ sample of 120 randomly selected observations, the tree structure was estimated on the remaining 334 observations (for the rural twolane road case). Then, using the ‘if-then’ rules yielded by the estimated tree, the accident rates for the 120 hold-out observations were estimated. At the 90% significance level for the X 2-test, the null hypothesis that the difference between the actual and predicted rates was zero could not be rejected. Nevertheless, it should be noted that while the ‘holdout sample’ method is a rather popular approach to validating the estimates yielded by the tree approach, it does have a shortcoming. Because both the estimation and validation samples are from the same general area (the State of Indiana), it is not surprising that their patterns are similar and hence the results of the model validation process are good. As such, it would be interesting to cross-validate the estimation results with data from a different area (but from rural roads nonetheless). In general of course, the process of randomly selecting a subsample for validation is the most frequently used technique. Looking closer at Fig. 1, it is clear that for lower flows the parameter that seems to be more important is the FR coefficient, while for higher flows LW seems to have the greatest importance. This seems to be justified by the fact that lower flows are related to higher speeds, which render the slippery of the roads a critical parameter. However, when flows are high the risk for an accident seems to stem mainly from the interaction of vehicles travelling at the same or opposite direction, rendering LW the more important factor. Fig. 2 shows the results of the HTBR methodology applied to crashes on multilane rural roads. Interestingly, using again the X 2-test, the methodology yielded a ‘simpler’ tree. Its first optimal split occurs on AADT. Thus, it seems that AADT is the best variable to explain crash variability in multilane roads as well. What may also be noted is that for lower flows the existence of a median is an important factor while when it comes to higher flows the existence of access control seems to be the more important factor safety wise. Thus, vehicle interaction and vehicle maneuvering arrangements prove again to be important factors when traffic demand increases. For predicti6e purposes, the profile of a multilane road section can be examined similar to that of a two-lane road section and the tree can be used to determine the prediction. For example, assume a prediction is needed for the number of accidents on a rural multilane road section with AADT of 8300, and No Access Control. Beginning at the root node (top of the tree), we branch right (AADT\6851), right (AADT\ 8075), left (A= 1), to get the estimate of 27.1 crashes for that highway section. It is interesting to note that some variables are selected more than once in the estimation process. For instance, taking all the left branches to the terminal node (leaf), AADT appears three times. Since one of the goals of HTBR is to develop a simple tree structure for data, relatively few variables will appear explicitly in the splitting criteria, and some highly important variables will appear more than once (such as AADT in this tree structure). While this could be taken to mean that the other variables are not important in understanding or predicting the dependent variable, an independent variable could be considered highly important even if it never appears as a primary node splitter. The software used in this paper (CART 1995) keeps track of surrogate splits in the tree-growing process, evaluating the contribution a variable makes in prediction by both primary and surrogate splits. That is, while the treestructure can be used, as previously shown, for predictive purposes, a different measure called variable M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 Fig. 2. Regression tree for accidents and geometric characteristics on rural multilane roads. 363 364 M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 Table 2 Independent variable importance for crash rates (crashes normalized on highway section length) Rural two-lane Rural multilane Variable Relative importance (%) Variable Relative importance (%) AADT Lane width Serviceability index Friction Pavement type Access control 100 72 59 32 30 14 AADT Median width Access control Friction Lane width Serviceability index Pavement type 100 63 59 25 24 21 11 importance score should be used to estimate the importance of the effect of various geometric characteristics on accident rates. To calculate a variable importance score, the software looks at the improvement measure attributable to each variable in its role as a surrogate to the primary split. The values of these improvements are summed over each node and totalled, and are then scaled relative to the ‘best’ performing variable. As a result, the variable with the highest sum of improvements is scored 100, and all other variables will have lower scores ranging downwards towards zero. The relative importance of the independent variables in explaining crash rates on various types of roadways appear in Table 2 (for crashes normalized on highway section length), and Table 3 (for crashes normalized on highway section length and AADT). It is interesting to note the differences in the variables that ‘explain’ crashes on the two types of roadways. While AADT is overall the most important variable when crashes are normalized on section length (Table 2), the characteristics of subsequent importance vary for the two types of roadway. For the rural two-lane case, LW is the variable with the higher importance after AADT. It is obvious that the proximity of the opposing traffic streams renders the width of the lane an important factor for safety. The next more important variables – SI, FR and pavement type – are related to the road pavement conditions. However, when it comes to multilane rural roads the variables with the higher importance after AADT are the existence of median width and of access control. The importance of these two factors seems to be justified mainly by the increased speeds on multilane rural roads. This fact renders the above two factors more important than LW and pavement condition variables, FR, SI and pavement type, which follow in importance (Table 2). Furthermore, it should be noted that when crashes are normalized on section length and AADT (Table 3), the variables of importance are similar to those of Table 2 (normalization on section length), the only new variable being the existence of a left turn lane, for both two-lane and multilane rural roads. 5. Discussion and conclusions Much interest exists in the area of accident rate estimation, and the identification of the various factors affecting this rate. Much of the literature in this area has concentrated in identifying the factors affecting accident occurrence (accident rates), and secondarily in predicting them. The ability to predict accident rates is very important to transportation planners and engineers, because it can help in identifying hazardous locations, sites which require treatment, as well as spots where deviations (either higher or lower rates) from expected (predicted) levels warrants further examination. The aim of this paper was twofold. First, it developed a methodology that quantitatively assesses the effects of various highway characteristics on accident rates. Second, it provided a straightforward, yet fundamentally and mathematically sound way of predicting accident rates. The methodology used in this paper, known as HTBR, has a number of both theoretical and applied advantages over multiple linear and NB regression that have been commonly used in accident rate research. It allows for the quantitative assessment of the effect of various geometric characteristics on accident rates. It allows for the quick estimation of predicted accident rates for a given highway section. Finally, it is easily amenable to ‘if-then’ statements for incorporation in expert systems, which have become increasingly popular and useful in safety management. The methodology was demonstrated using data from the Indiana State Police Accident Information records and the INDOT’s Road Inventory database. The results of the investigation of the roadway characteristics that affect accident rates are of interest. It is clear that for both rural two-lane and multilane roadways AADT is the most important variable. However, the factors of subsequent importance vary for each M.G. Karlaftis, I. Golias / Accident Analysis and Pre6ention 34 (2002) 357–365 365 Table 3 Independent variable importance for crash rates (crashes normalized on highway section length and AADT) Rural two-lane Rural multilane Variable Relative importance (%) Variable Relative importance (%) Lane width Serviceability index Pavement type Friction Left turn 100 89 62 22 16 Median width Access control Friction Lane width Serviceability index Left turn 100 73 55 25 19 16 case. Looking closely at the results of accident rates normalized on AADT (which cancels out the effect of AADT), it can be generally inferred that LW and pavement condition factors – SI, pavement type and FR – are the most important variables affecting crash rates for the two-lane case. The importance of LW seems to increase with higher flows. On the contrary, the importance of pavement condition factors seems to increase with lower flows due to higher speeds. For rural multilane roads, with the effect of AADT cancelled out, median width and access control are the most important factors followed by pavement condition factors. It is worth mentioning that the importance of access control seems to increase with heavier traffic that renders vehicle maneuvering arrangements critical, while the existence of a median becomes more important in low flow conditions. Although the importance of isolated variables differs for the two roadway types it is obvious that ‘geometric design’ captured through LW and access control and ‘pavement condition’ captured through FR, SI and pavement type are, as expected, the two most important factors affecting accident rates. The methodology used in this paper also allows the explicit prediction of accident rates for given highway sections. As soon as the profile of a road section is given, predictions regarding the expected accident rates can be obtained. In essence, when the AADT, LW, SI and FR of a road section are known, predictions can be obtained. Further, the ‘if-then’ rules for obtaining these predictions can be easily incorporated in an expert system that can automate the accident rate prediction effort. The work presented in this paper is part of the larger effort to tackle the problem of accident occurrence on the world’s roadways. The extremely high cost of highway accidents paid by societies makes highway safety improvement maybe the most important objective of transportation engineering. This effort’s eventual goal is to reduce injuries and fatalities due to highway design and maintenance deficiencies. References Breiman, L., Friedman, J., Olshen, R., Stone, C., 1984. Classification and Regression Trees, Wadsworth International Group, Belmont, CA. Hadi, M.A., Aruldhas, J., Chow, L.F., Wattleworth, J.A., 1993. Estimating Safety Effects of Cross-Section Design for Various Highway Types Using Negative Binomial Regression. Transportation Research Record, 1500, TRB, National Research Council, 169 – 177. Jones, I.S., Whitfield, R.A., 1991. Predicting injury risk with new car assessment program crashworthiness ratings. Accident Analysis and Prevention 6 (20), 411 – 419. Joshua, S.C., Garber, N.J., 1990. Estimating truck accident rate and involvement using linear and poisson regression models. Transportation Planning and Technology 15, 41 – 58. Karlaftis, M.G., Tarko, A., 1998. Heterogeneity considerations in accident modeling. Accident Analysis and Prevention 30 (4), 425 – 433. Lerman, S.R., Gonzales, S.L., 1980. Poisson regression analysis under alternate sampling strategies. Transportation Science 14 (4), 346 – 364. Miaou, S.P., Hu, P.S., Wright, T., Rathi, A.K., Davis, S.C., 1992. Relationship between truck accidents and highway geometric design: a poisson regression approach. Transportation Research Record 1376, 10 – 18. Mohamedshah, Y.M., Paniati, J.F., Hobeika, A.G., 1993. Truck accident models for interstate and two-lane rural roads. Transportation Research Record 1407, 35 – 41. Morgan, J.N., Sonquist, J.A., 1963. Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association 58, 415 – 434. Ivan, J.N., O’Mara, P.J., 1997. Prediction of Traffic Accident Rates Using Poisson Regression, Presented in the 1997. Transportation Research Board Meeting, Washington, DC. Tarko, A.P., Sinha, K.C., Farooq, O., 1996. A Methodology for Identifying Highway Safety Problem Areas, Presented in the 1997. Transportation Research Board Meeting, Washington, DC. US DOT, 1969. National Highway Functional Classification Study Manual. Federal Highway Administration, Washington, DC. Washington, S., Wolf, J., 1996. Hierarchical Tree-Based versus Ordinary Least Squares Linear Regression Models: Theory and Example Applied to Trip Generation. Presented in the 1996. Transportation Research Board Annual Meeting, Washington, D.C. Washington, S., Wolf, J., Guensler, R., 1996. A Binary Recursive Partitioning Method for Modeling Hot-Stabilized Emissions from Motor Vehicles. Presented in the 1996. Transportation Research Board Annual Meeting, Washington, DC. International Journal of Civil Engineering andOF Technology ISSN 0976 – 6308 (Print), INTERNATIONAL JOURNAL CIVIL(IJCIET), ENGINEERING AND ISSN 0976 – 6316(Online) Volume 4, Issue 4, July-August (2013), © IAEME TECHNOLOGY (IJCIET) ISSN 0976 – 6308 (Print) ISSN 0976 – 6316(Online) Volume 4, Issue 4, July-August (2013), pp. 146-162 © IAEME: www.iaeme.com/ijciet.asp Journal Impact Factor (2013): 5.3277 (Calculated by GISI) www.jifactor.com IJCIET © IAEME THE INFLUENCE OF ROAD GEOMETRIC DESIGN ELEMENTS ON HIGHWAY SAFETY HameedAswad Mohammed Civil Engineering Department – Anbar University- Iraq ABSTRACT Road safety is an issue of prime importance in all motorized countries. The road accident results a serious social and economic problems. Studies focused on geometric design and safety aim to improve highway design and to eliminate hazardous locations. The effects of design elements such as horizontal and vertical curves, lane width, shoulder width, superelevation, median width, curve radius, sight distance, etc. on safety have been studied. The relationship between geometric design elements and accident rates is complex and not fully understood. Relatively little information is available on relationships between geometric design elements and accident rates. Although it has been clearly shown that very restrictive geometric elements such as very short sight distances or sharp horizontal curve result a considerably higher accident rates and that certain combinations of elements cause an unusually severe accident problem. In this paper, road geometric design elements and characteristics are taken into consideration, and explanations are given on how to which extent they affect highway safety. The relationship between safety and road geometric design are examined through results of studies mad in different countries and it compares the results of studies in different countries and summarizes current international knowledge of relationship between safety and the principal non-intersection geometric design parameters. In general, there is broad international agreement on these relationships. Key words: Highway Safety, Geometric Design, Traffic Accident 1. INTRODUCTION Geometric design elements play an important role in defining the traffic operational efficiency of any roadway. Key geometric design elements that influence traffic operations include number and width of lanes, the presence and widths of shoulders and highway medians, and the horizontal and vertical alignment of the highway [1]. Generally speaking, any evaluation of road safety, such as in the driving dynamic field, has been conducted more or less qualitatively. It is safe to say, from a traffic safety point view, that no one is able to say with great certainty, or prove by measure or number, where traffic accidents could occur or where accident black spots could develop 146 International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 4, July-August (2013), © IAEME [2].However, everyone agrees that there exists a relationship between traffic safety and geometric design consistency. By all means, alignment consistency represents a key issue in modern highway geometric design. A consistency alignment would allow most drivers to operate safety at their desired speed along the entire alignment. However, existing design speed-based alignment policies permit the selection of a design speed that is less than the desired speeds of majority of drivers [2]. Much of the research in highway safety has focused on different factors which affect roadway safety. The factors are categorized as traffic characteristics, road geometrics, road surface condition, weather and human factors. Previous research has shown that geometric design inconsistencies, operations (traffic mix, volume, and speed), environment, and driver behavior are the common causes of accidents. Most of the studies have shown the influence of various geometric design variables on the occurrence of accidents and have concluded that not all variables have the same level of influence in all places [3]. From the relation of factor mentioned above, different researchers have developed the relationship of roadway safety in terms of crash frequency and crash rates, fatality and injury rates and the road elements, traffic characteristics, and pavement conditions. Many of these previous studies investigated the relationship of crash rates or frequency in terms number of lanes, lane width, presence of median, median width, type of median, shoulder width, access density, speed limit, vertical grade, horizontal curvature, weather condition. The relationship between safety on the highway and factors mentioned above is the primary focus in crash reduction and predictions[3]. 2. GEOMETRIC PARAMETERS AFFECTING ROAD SAFETY An accident is always characterized by multiple causes. The alignment of road is an important influence factor: dimension of radii, ratio of consecutive curves, dimension of vertical curves and sight distance conditions. In many evaluation studies of safety effects of road design elements it turns out the present poor capacity to explain accidentality phenomenon; in fact the main causes of accident is behavior of driver, which is mainly influenced by his personality, skills, and experience. Furthermore external impacts like weather conditions, road conditions, time of day, or light conditions influence the driver behavior as well. It is out of question that analyzing accidents and their dependence on technical values or human factors has always to consider these interactions. The relation between accidents (all, property damage only, slight injuries, severe injuries, fatalies) and road geometry is proved but it is also a question of the driving behavior, especially of the velocity. Again and again investigations show that comparable curves (similar geometry) are characterized by different accident occurrence. One reason could be a different driving behavior: lower speeds are less critical than higher speeds in curves. Several studies, oriented to create relationships between accidentality and independent variables, were obtained in a particular context; so, in every other different conditions, weather conditions, user behavior, etc.) the influence of these factors should be considered, e.g. calibration procedure. Summarized, accidents do not depend on only one factor; accidents are caused rather by a combination of several factors [4]. 3. THE RELATIONSHIP BETWEEN SPEED AND SAFETY Speed is one of the major parameters in geometric design and safety is synonymous with accident studies [5]. For example, Finch et al. [6] recently concluded that a reduction of 1.6 km/hr (1 mph) in the average speed reduces the incidence of injuries by about 5%. Also it is generally accepted that there are substantial safety benefits from lower speed limits. For example, reducing rural speed limits from 100 km/hr to 90 km/hr has been predicted to reduce casualties by about 11% [7]. It is interesting to note that the relationship between the design speed and the speed limit is not referred to in the geometric design standards of many countries [8]. 147 International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 4, July-August (2013), © IAEME 4. EFFECTS OF CHARACTERISTICS OF ROAD GEOMETRICAL DESIGN ELEMENTS ON TRAFFIC ACCIDENTS Some of the primary geometric design elements that can affect on highway safety are carriageway, grade, horizontal curvature, shoulder, median, vertical curve [9]. The relationship between some characteristics of these elements and traffic accidents, including studies made in different countries are classified into groups: Cross-section effects and Alignment effects. 4.1 Cross-Section Effects The widths of the various cross section elements affect the capability of driver to perform evasive maneuvers and determine the lateral clearances both between vehicles and between vehicles and other road users [5]. In the existing literature are mentioned especially the following parameters: 4.1.1 Lane Width Wider lanes are traditionally associated with higher operating speeds and increased safety. The Highway Capacity Manual (HCM) documents that wider lanes for multilane highways result in higher free-flow speeds [10]. On the other hand, very little has been found on the safety implications of wider lanes. It is reasonable to assume that wider lanes may provide additional space to the driver to correct potential mistakes and thus avoid crashes. However, a driver could be expected to adapt to the available space, and the positive safety effects from the wider lanes may be offset by the higher speeds [10].Generally, most studies agree that lower accident rates are attributed to wider lanes. But it seems that there is an optimal lane width around 3.5m. Studies have also noted that approaches should base on more parameters of the cross section, at least also on traffic volume [4]. However, Hearne's results [11] suggested that there was a marginal increase in accident occurrence with an increase in carriageway width. Hedman [12] noted that some results indicated a rather steep decrease in accidents with increased width of 4m to 7m, but that little additional benefit is gained by widening the carriageway beyond 7m. Zeger [13], Zeger/Council [14], and Mclean [15] have shown that width of 3.4 3.7m show the lowest accident rates. This is supported by the NCHRP Repot 197 [16] conclusion that there is little difference between the accident rate for 3.35m and a 3.65m lane width. However, studies on low volume rural roads indicate that accidents continue to reduce for widths greater than 3.65m, although at a lower rate [17]. TRB [18] pointed out lanes wider than 3.70m do not contribute to a higher safety because they may result in unsafe maneuvers such as over taking despite of oncoming traffic. Another reason is the higher speed on wider lanes which leads to more accidents. Yagar and VanAerdo [19] found that the passage of a vehicle requires a minimum lane width and that any additional width beyond this minimum allows one to drive faster and /or with a greater measure and preception of safety. For lane widths from 3.3m to 3.8m, they reported that the operating speed is decreased by approximately 5.7 km/hr for each 1m reduction in width of the road [5]. Lamm et al.[20] found a significant d...
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