Weixu Wang

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.

Predicting crash likelihood and severity on freeways with real-time loop detector data

Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the models crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.

Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections.

The primary objective of this study was to identify if the VISSIM simulation model and the Surrogate Safety Assessment Model (SSAM) approach provided reasonable estimates for the traffic conflicts measured at signalized intersections. A total of 80h of traffic data and traffic conflicts data were collected at ten signalized intersections. Simulated conflicts generated by the VISSIM simulation model and identified by SSAM were compared to the traffic conflicts measured in the field. Of particular interest was to identify if the consistency between the simulated and the observed conflicts could be improved by calibrating VISSIM simulation models and adjusting threshold values used for defining simulated conflicts in SSAM. A two-stage procedure was proposed in this study to calibrate and validate the VISSIM simulation models. It was found that the two-stage calibration procedure improved the goodness-of-fit between the simulated conflicts and the real-world conflicts. Linear regression models were developed to study the relationship between the simulated conflicts and the observed conflicts. Results of data analysis showed that there was a reasonable goodness-of-fit between the simulated and the observed rear-end and total conflicts. However, it was also found that the simulated conflicts were not good indicators for the traffic conflicts generated by unexpected driving maneuvers such as illegal lane-changes in the real world. The research team further tested the prediction performance of the conflict prediction models using the simulated conflicts as independent variables. It was found that the conflict prediction models provided acceptable prediction performance for the total and the rear-end conflicts with a MAPE value of 18% and 20%, respectively. However, the prediction performance of the conflict prediction models for the crossing and the lane change conflicts was only moderate with a MAPE value of 31% and 38%, respectively.

Using support vector machine models for crash injury severity analysis

The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does. The research also investigated the potential of using the SVM model for evaluating the impacts of external factors on crash injury severities. The sensitivity analysis results show that the SVM model produced comparable results regarding the impacts of variables on crash injury severity as compared to the OP model. For several variables such as the length of the exit ramp and the shoulder width of the freeway mainline, the results of the SVM model are more reasonable than those of the OP model.

Crossing behaviors of pedestrians at signalized intersections

In China pedestrian crashes make up a large proportion of road casualties and are more likely to occur at signalized intersections. This study examines the behavioral characteristics of pedestrians at such sites and the factors affecting pedestrian behavior. Data on pedestrian behavior were obtained from video images of pedestrian movements recorded at 26 signalized intersections in three Chinese cities, and 598 pedestrians who were using crosswalks were surveyed for their perceptions about the crossing environment. The behavior data indicated that most pedestrians walked normally when they crossed roads; the rate of compliance with traffic rules for all pedestrians was 62.8%. The differences in crossing behavior by gender, age, and the type of pedestrian group were then identified. The results showed that women and middle-aged individuals were more likely to violate traffic rules. In comparison with a single pedestrian and pedestrians in a pair, pedestrians in a group tended not to look at traffic signals. Analysis of the rates of compliance with traffic rules at crosswalks also indicated that crossing distance, signal timing, the presence of traffic police, and pedestrian volume had different effects. Moreover, the major reasons for crossing on red and opinions on waiting time were obtained by analysis of the survey data. For example, the largest proportion (30.25%) of surveyed individuals indicated that they violated traffic rules to save time and for convenience. Findings from this study can help researchers and practitioners understand pedestrian behavior at crosswalks at signalized intersections and thus create a better street-crossing environment for all pedestrians.

Identifying crash-prone traffic conditions under different weather on freeways

INTRODUCTION Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. METHOD The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. RESULTS It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. IMPACT ON INDUSTRY The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather.

Development of a Control Strategy of Variable Speed Limits to Reduce Rear-End Collision Risks Near Freeway Recurrent Bottlenecks

The primary objective of this paper was to develop a control strategy of variable speed limits (VSLs) to reduce rear-end collision risks near freeway recurrent bottlenecks. The risks of rear-end collisions were estimated using a crash risk prediction model that is specifically developed for rear-end collisions in freeway bottleneck areas. The effects of the VSL control strategy were evaluated using a cell transmission model. Several control factors were tested, including the start-up threshold of the collision likelihood, the target speed limit, the speed change rate, and the speed difference between adjacent links. A genetic algorithm was used to optimize critical control factors. For the high demand scenario, the proposed control strategy used 25% of the maximum collision likelihood for the start-up threshold, 35 mi/h for the target speed limit, 10 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. For the moderate demand scenario, the strategy used 20% of the maximum collision likelihood for the start-up threshold, 40 mi/h for the target speed limit, 15 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. The results of comparative analyses suggested that the proposed control strategy outperformed other strategies in reducing the rear-end collision risks near freeway recurrent bottlenecks. With the proposed control strategy, the VSL control reduced the rear-end crash potential by 69.84% for the high demand scenario and by 81.81% for the moderate demand scenario.

Development of a variable speed limit strategy to reduce secondary collision risks during inclement weathers

Inclement weather reduces travelers sight distance and increases vehicles stopping distance. Once a collision occurred during inclement weather and resulted in a slow traffic, approaching vehicles may not have adequate time to make emergency responses to the hazardous traffic, resulting in increased potentials of secondary collisions. The primary objective of this study is to develop a control strategy of variable speed limits (VSL) to reduce the risks of secondary collisions during inclement weathers. By analyzing the occurrence condition of secondary collision, the VSL strategy is proposed to dynamically adjust the speed limits according to the current traffic and weather conditions. A car-following model is modified to simulate the vehicle maneuvers with the VSL control. Two surrogate safety measures, based on the time-to-collision notion, are used to evaluate the control effects of VSL. Five weather scenarios are evaluated in simulation. The results show that the VSL strategy effectively reduces the risks of secondary collisions in various weather types. The time exposed time-to-collision (TET) is reduced by 41.45%-50.74%, and the time integrated time-to-collision (TIT) is reduced by 38.19%-41.19%. The safety effects are compared to those with a previous VSL strategy. The results show that in most cases our strategy outperforms the previous one. We also evaluate how drivers compliance to speed limit affects the effectiveness of VSL control.

Surrogate safety measure for evaluating rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks

This study presents a surrogate safety measure for evaluating the rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks using aggregated traffic data from ordinary loop detectors. The attributes of kinematic waves that accompany rear-end collisions and the traffic conditions at detector stations spanning the collision locations were examined to develop the rear-end collision risk index (RCRI). Together with RCRI, standard deviations in occupancy were used to develop a logistic regression model for estimating rear-end collision likelihood near freeway recurrent bottlenecks in real-time. The parameters in the logistic regression models were calibrated using collision data gathered from the 6-mile study site between 2006 and 2007. Findings indicated that an additional unit increase in RCRI results in increasing the odds of rear-end collision by 21.1%, a unit increase in standard deviation of upstream occupancy increases the odds by 19.5%, and a unit increase in standard deviation of downstream occupancy increases the odds by 18.7%. The likelihood of rear-end collisions is highest when the traffic approaching from upstream is near capacity state while downstream traffic is highly congested. The paper also reports on the findings from comparing the predicted number of rear-end collisions at the study site using the proposed model with the observed traffic collision data from 2008. The proposed models true positive rates were higher than those of existing real-time crash prediction models.

Modeling of Passing Events in Mixed Bicycle Traffic with Cellular Automata

The primary objective of this study was to use the cellular automata (CA) method to model the characteristics of bicycle passing events in mixed bicycle traffic on separated bicycle paths. The mixed traffic was composed of two types of bicycles: the conventional bicycle (c-bicycle) and the electric bicycle (e-bicycle). The number of passing events and the characteristics of mixed bicycle traffic were investigated in the field on eight physically separated bicycle paths in China. Then a CA model was calibrated with the use of the field data to simulate the passing events in the mixed bicycle traffic. The results showed that the CA model could simulate the features of bicycle passing events well. The simulation results were consistent with field observations. An increase in the ratio of e-bicycles would not significantly increase the number of passing events, but e-bicycles did contribute substantially to passing events in mixed bicycle traffic. E-bicycles were shown to have more stability than c-bicycles, especially in traffic with a high flow rate. The findings of this study could improve the performance of simulation techniques to reflect the actual characteristics of mixed bicycle traffic.