Impact of traffic states on freeway crash involvement rates

Freeway traffic accidents are complicated events that are influenced by multiple factors including roadway geometry, drivers’ behavior, traffic conditions and environmental factors. Among the various factors, crash occurrence on freeways is supposed to be strongly influenced by the traffic states representing driving situations that are changed by road geometry and cause the change of drivers’ behavior. This paper proposes a methodology to investigate the relationship between traffic states and crash involvements on the freeway. First, we defined section-based traffic states: free flow (FF), back of queue (BQ), bottleneck front (BN) and congestion (CT) according to their distinctive patterns; and traffic states of each freeway section are determined based on actual measurements of traffic data from upstream and downstream ends of the section. Next, freeway crash data are integrated with the traffic states of a freeway section using upstream and downstream traffic measurements. As an illustrative study to show the applicability, we applied the proposed method on a 32-mile section of I-880 freeway. By integrating freeway crash occurrence and traffic data over a three-year period, we obtained the crash involvement rate for each traffic state. The results show that crash involvement rate in BN, BQ, and CT states are approximately 5 times higher than the one in FF. The proposed method shows promise to be used for various safety performance measurement including hot spot identification and prediction of the number of crash involvements on freeway sections.     

Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge.     

Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data

In order to improve traffic safety on expressways, it is important to develop proactive safety management strategies with consideration for segment types and traffic flow states because crash mechanisms have some differences by each condition. The primary objective of this study is to develop real-time crash risk prediction models for different segment types and traffic flow states on expressways. The mainline of expressways is divided into basic segment and ramp vicinity, and the traffic flow states are classified into uncongested and congested conditions. Also, Korean expressways have irregular intervals between loop detector stations. Therefore, we investigated on the effect and application of the detector stations at irregular intervals for the crash risk prediction on expressways. The most significant traffic variables were selected by conditional logistic regression analysis which could control confounding factors. Based on the selected traffic variables, separate models to predict crash risk were developed using genetic programming technique. The model estimation results showed that the traffic flow characteristics leading to crashes are differed by segment type and traffic flow state. Especially, the variables related to the intervals between detector stations had a significant influence on crash risk prediction under the uncongested condition. Finally, compared with the single model for all crashes and the logistic models used in previous studies, the proposed models showed higher prediction performance. The results of this study can be applied to develop more effective proactive safety management strategies for different segment types and traffic flow states on expressways with loop detector stations at irregular intervals.     

Matched-pair cohort methods in traffic crash research

Standard analysis of matched-pair cohort data requires information only from pairs in which at least one had the study outcome. This can be useful in traffic fatality studies of characteristics that can vary among vehicle occupants, such as seat belt use, as crash databases often lack information about vehicles in which all survived. However, matching crash victims who were in the same vehicle does not necessarily eliminate confounding by vehicle or crash related factors, because the matched occupants must be in different seat positions. This paper reviews three methods for estimating relative risks in matched-pair crash data. The first, Mantel-Haenszel stratified methods, may produce biased estimates if seat position is associated with the outcome. The second, the double-pair comparison method, was designed to deal with confounding by seat position. If the effects of seat position vary according to some vehicle or crash characteristic which is associated with the study exposure, adjustment for this characteristic may be needed to produce unbiased estimates. Third, conditional Poisson regression and Cox proportional hazards regression can produce unbiased estimates, but may require model interaction terms between seat position and vehicle or crash characteristics. This paper reviews some of the strengths and limitations of each of these methods, and illustrates their use in simulated and real crash data.     

Rainfall effect on single-vehicle crash severities using polychotomous response models

As part of the Wisconsin road weather safety initiative, the objective of this study is to assess the effects of rainfall on the severity of single-vehicle crashes on Wisconsin interstate highways utilizing polychotomous response models.Weather-related factors considered in this study include estimated rainfall intensity for 15 min prior to a crash occurrence, water film depth, temperature, wind speed/direction, stopping sight distance and deficiency of car-following distance at the crash moment. For locations with unknown weather information, data were interpolated using the inverse squared distance method. Non-weather factors such as road geometrics, traffic conditions, collision types, vehicle types, and driver and temporal attributes were also considered. Two types of polychotomous response models were compared: ordinal logistic and sequential logistic regressions. The sequential logistic regression was tested with forward and backward formats. Comparative models were also developed for single vehicle crash severity during clear weather.In conclusion, the backward sequential logistic regression model produced the best results for predicting crash severities in rainy weather where rainfall intensity, wind speed, roadway terrain, driver's gender, and safety belt were found to be statistically significant. Our study also found that the seasonal factor was significant in clear weather. The seasonal factor is a predictor suggesting that inclement weather may affect crash severity. These findings can be used to determine the probabilities of single vehicle crash severity in rainy weather and provide quantitative support on improving road weather safety via weather warning systems, highway facility improvements, and speed limit management.     

A simultaneous equations model of crash frequency by severity level for freeway sections

This paper presents a simultaneous equations model of crash frequencies by severity level for freeway sections using five-year crash severity frequency data for 275 multilane freeway segments in the State of Washington. Crash severity is a subject of much interest in the context of freeway safety due to higher speeds of travel on freeways and the desire of transportation professionals to implement measures that could potentially reduce crash severity on such facilities. This paper applies a joint Poisson regression model with multivariate normal heterogeneities using the method of Maximum Simulated Likelihood Estimation (MSLE). MSLE serves as a computationally viable alternative to the Bayesian approach that has been adopted in the literature for estimating multivariate simultaneous equations models of crash frequencies. The empirical results presented in this paper suggest the presence of statistically significant error correlations across crash frequencies by severity level. The significant error correlations point to the presence of common unobserved factors related to driver behavior and roadway, traffic and environmental characteristics that influence crash frequencies of different severity levels. It is found that the joint Poisson regression model can improve the efficiency of most model coefficient estimators by reducing their standard deviations. In addition, the empirical results show that observed factors generally do not have the same impact on crash frequencies at different levels of severity.     

Evaluation of the impacts of traffic states on crash risks on freeways

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states.     

Understanding crash mechanism on urban expressways using high-resolution traffic data

Urban expressways play a vital role in the modern mega cities by serving peak hour traffic alongside reducing travel time for moderate to long distance intra-city trips. Thus, ensuring safety on these roads holds high priority. Little knowledge has been acquired till date regarding crash mechanism on these roads. This study uses high-resolution traffic data collected from the detectors to identify factors influencing crash. It also identifies traffic patterns associated with different types of crashes and explains crash phenomena thereby. Unlike most of the previous studies on conventional expressways, the research separately investigates the basic freeway segments (BFS) and the ramp areas. The study employs random multinomial logit, a random forest of logit models, to rank the variables; expectation maximization clustering algorithm to identify crash prone traffic patterns and classification and regression trees to explain crash phenomena. As accentuated by the study outcome, crash mechanism is not generic throughout the expressway and it varies from the BFS to the ramp vicinities. The level of congestion and speed difference between upstream and downstream traffic best explains crashes and their types for the BFS, whereas, the ramp flow has the highest influence in determining the types of crashes within the ramp vicinities. The paper also discusses about the applicability of different countermeasures, such as, variable speed limits, temporary restriction on lane changing, posting warnings, etc., to attenuate different patterns of hazardous traffic conditions. The study outcome can be utilized in designing location and traffic condition specific proactive road safety management systems for urban expressways.     

Impact of distracted driving on safety and traffic flow

Studies have documented a link between distracted driving and diminished safety; however, an association between distracted driving and traffic congestion has not been investigated in depth. The present study examined the behavior of teens and young adults operating a driving simulator while engaged in various distractions (i.e., cell phone, texting, and undistracted) and driving conditions (i.e., free flow, stable flow, and oversaturation). Seventy five participants 16–25 years of age (split into 2 groups: novice drivers and young adults) drove a STISIM simulator three times, each time with one of three randomly presented distractions. Each drive was designed to represent daytime scenery on a 4 lane divided roadway and included three equal roadway portions representing Levels of Service (LOS) A, C, and E as defined in the 2000 Highway Capacity Manual. Participants also completed questionnaires documenting demographics and driving history. Both safety and traffic flow related driving outcomes were considered. A Repeated Measures Multivariate Analysis of Variance was employed to analyze continuous outcome variables and a Generalized Estimate Equation (GEE) Poisson model was used to analyze count variables. Results revealed that, in general more lane deviations and crashes occurred during texting. Distraction (in most cases, text messaging) had a significantly negative impact on traffic flow, such that participants exhibited greater fluctuation in speed, changed lanes significantly fewer times, and took longer to complete the scenario. In turn, more simulated vehicles passed the participant drivers while they were texting or talking on a cell phone than while undistracted. The results indicate that distracted driving, particularly texting, may lead to reduced safety and traffic flow, thus having a negative impact on traffic operations. No significant differences were detected between age groups, suggesting that all drivers, regardless of age, may drive in a manner that impacts safety and traffic flow negatively when distracted.     

Impact of pavement conditions on crash severity

Pavement condition has been known as a key factor related to ride quality, but it is less clear how exactly pavement conditions are related to traffic crashes. The researchers used Geographic Information System (GIS) to link Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) data and Pavement Management Information System (PMIS) data, which provided an opportunity to examine the impact of pavement conditions on traffic crashes in depth. The study analyzed the correlation between several key pavement condition ratings or scores and crash severity based on a large number of crashes in Texas between 2008 and 2009. The results in general suggested that poor pavement condition scores and ratings were associated with proportionally more severe crashes, but very poor pavement conditions were actually associated with less severe crashes. Very good pavement conditions might induce speeding behaviors and therefore could have caused more severe crashes, especially on non-freeway arterials and during favorable driving conditions. In addition, the results showed that the effects of pavement conditions on crash severity were more evident for passenger vehicles than for commercial vehicles. These results provide insights on how pavement conditions may have contributed to crashes, which may be valuable for safety improvement during pavement design and maintenance. Readers should notice that, although the study found statistically significant effects of pavement variables on crash severity, the effects were rather minor in reality as suggested by frequency analyses.     

Motorcycles entering from access points and merging with traffic on primary roads in Malaysia: Behavioral and road environment influence on the occurrence of traffic conflicts

This paper uses data from an observational study, conducted at access points in straight sections of primary roads in Malaysia in 2012, to investigate the effects of motorcyclists’ behavior and road environment attributes on the occurrence of serious traffic conflicts involving motorcyclists entering primary roads via access points. In order to handle the unobserved heterogeneity in the small sample data size, this study applies mixed effects logistic regression with multilevel bootstrapping. Two statistically significant models (Model 2 and Model 3) are produced, with 2 levels of random effect parameters, i.e. motorcyclists’ attributes and behavior at Level 1, and road environment attributes at Level 2. Among all the road environment attributes tested, the traffic volume and the speed limit are found to be statistically significant, only contributing to 26–29% of the variations affecting the traffic conflict outcome. The implication is that 71–74% of the unmeasured or undescribed attributes and behavior of motorcyclists still have an importance in predicting the outcome: a serious traffic conflict. As for the fixed effect parameters, both models show that the risk of motorcyclists being involved in a serious traffic conflict is 2–4 times more likely if they accept a shorter gap to a single approaching vehicle (time lag <4 s) and in between two vehicles (time gap <4 s) when entering the primary road from the access point. A road environment factor, such as a narrow lane (seen in Model 2), and a behavioral factor, such as stopping at the stop line (seen in Model 3), also influence the occurrence of a serious traffic conflict compared to those entering into a wider lane road and without stopping at the stop line, respectively. A discussion of the possible reasons for this seemingly strange result, including a recommendation for further research, concludes the paper.     

Less is more: the influence of traffic count on drinking and driving behaviour

Drinking and driving road checks are often organized with either a clear prevention or repression objective in mind. The objective of a prevention strategy is to make as many people as possible believe that police officers are enforcing drinking and driving laws and that drinking drivers will most likely be caught. As such, targeting high traffic count road sites with high-visibility road checks is a priority because it serves to increase awareness of the enforcement activity. An alternative to this prevention approach is the “repression” approach that involves targeting times and places where the highest number of drinking drivers are to be expected. Rather than attempting to affect the subjective chance of getting caught, this approach seeks to increase the objective likelihood of getting caught; the aim is to apprehend as many drinking drivers as possible. Regardless of the chosen strategy, there is a need to understand how traffic count influences drinking and driving behaviour as traffic count may play a role in a police officer's choice of sites for a road check. The objective of this paper is to shed some light on this relationship between drinking and driving behaviour and traffic count. In this paper, data from a roadside survey, carried out in British Columbia in 2003, are used. A two-level logistic regression analysis was carried out with data from 2627 drivers coming from 48 different road sites to replicate a model that was previously obtained with comparable data from a Belgian roadside survey, also carried out in 2003. The present study successfully replicated the findings of the Belgian model, substantiating that the probability for drivers to be drinking and driving significantly decreases with an increasing level of traffic count. This supports the suggestion that drinking drivers avoid high traffic count road sites. The relevance of these findings with respect to organizing preventive or repressive road checks and possible confounding variables are discussed at the end of this paper.     

An assessment of the skid resistance effect on traffic safety under wet-pavement conditions

Pavement-tire friction provides the grip that is required for maintaining vehicle control and for stopping in emergency situations. Statistically significant negative correlations of skid resistance values and wet-pavement accident rates have been found in previous research. Skid resistance measured with SCRIM and crash data from over 1750 km of two-lane rural roads in the Spanish National Road System were analyzed to determine the influence of pavement conditions on safety and to assess the effects of improving pavement friction on safety. Both wet- and dry-pavement crash rates presented a decreasing trend as skid resistance values increased. Thresholds in SCRIM coefficient values associated with significant decreases in wet-pavement crash rates were determined. Pavement friction improvement schemes were found to yield significant reductions in wet-pavement crash rates averaging 68%. The results confirm the importance of maintaining adequate levels of pavement friction to safeguard traffic safety as well as the potential of pavement friction improvement schemes to achieve significant crash reductions.     

The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

Driving simulator sickness: Impact on driving performance,influence of blood alcohol concentration,and effect of repeated simulator exposures

Simulator sickness is a major obstacle to the use of driving simulators for research, training and driver assessment purposes. The purpose of the present study was to investigate the possible influence of simulator sickness on driving performance measures such as standard deviation of lateral position (SDLP), and the effect of alcohol or repeated simulator exposure on the degree of simulator sickness. Twenty healthy male volunteers underwent three simulated driving trials of 1 h’s duration with a curvy rural road scenario, and rated their degree of simulator sickness after each trial. Subjects drove sober and with blood alcohol concentrations (BAC) of approx. 0.5 g/L and 0.9 g/L in a randomized order. Simulator sickness score (SSS) did not influence the primary outcome measure SDLP. Higher SSS significantly predicted lower average speed and frequency of steering wheel reversals. These effects seemed to be mitigated by alcohol. Higher BAC significantly predicted lower SSS, suggesting that alcohol inebriation alleviates simulator sickness. The negative relation between the number of previous exposures to the simulator and SSS was not statistically significant, but is consistent with habituation to the sickness-inducing effects, as shown in other studies. Overall, the results suggest no influence of simulator sickness on SDLP or several other driving performance measures. However, simulator sickness seems to cause test subjects to drive more carefully, with lower average speed and fewer steering wheel reversals, hampering the interpretation of these outcomes as measures of driving impairment and safety. BAC and repeated simulator exposures may act as confounding variables by influencing the degree of simulator sickness in experimental studies.