Chengcheng Xu

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.

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.

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.

A Genetic Programming Model for Real-Time Crash Prediction on Freeways

This paper aimed at evaluating the application of the genetic programming (GP) model for real-time crash prediction on freeways. Traffic, weather, and crash data used in this paper were obtained from the I-880N freeway in California, United States. The random forest (RF) technique was conducted to select the variables that affect crash risk under uncongested and congested traffic conditions. The GP model was developed for each traffic state based on the candidate variables that were selected by the RF technique. The traffic flow characteristics that contribute to crash risk were found to be quite different between congested and uncongested traffic conditions. This paper applied the receiver operating characteristic (ROC) curve to evaluate the prediction performance of the developed GP model for each traffic state. The validation results showed that the prediction performance of the GP models were satisfactory. The binary logit model was also developed for each traffic state using the same training data set. The authors compared the ROC curve of the GP model and the binary logit model for each traffic state. The GP model produced better prediction performance than did the binary logit model for each traffic state. The GP model was found to increase the crash prediction accuracy under uncongested traffic conditions by an average of 8.2% and to increase the crash prediction accuracy under congested traffic conditions by an average of 4.9%.

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.

Ultraviolet electroluminescence from horizontal ZnO microrods/GaN heterojunction light-emitting diode array

ZnO microrods were assembled on p-GaN substrate to form a heterostructural light-emitting diode (LED) array. Ultraviolet (UV) emission was obtained under a low forward bias of 3.5 V. The investigation on the electroluminescence, photoluminescence demonstrated three distinct electron-hole recombination processes. The relative intensity of these three emission bands changed with increase of the forward bias, and resulted in blue shift and spectral narrowing of electroluminescence. The present work provides a facile technique for micro-/nano-devices fabrication besides obtaining UV LED arrays.

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.

Effects of transverse rumble strips on safety of pedestrian crosswalks on rural roads in China

The primary objective of this study is to evaluate the impacts of transverse rumble strips in reducing crashes and vehicle speeds at pedestrian crosswalks on rural roads in China. Using crash data reported at 366 sites, the research team conducted an observational before-after study using a comparison group and the Empirical Bayesian (EB) method to evaluate the effectiveness of transverse rumble strips in reducing crashes at pedestrian crosswalks. It was found that transverse rumble strips may reduce expected crash frequency at pedestrian crosswalks by 25%. The research team collected more than 15,000 speed observations at 12 sites. The speed data analysis results show that transverse rumble strips significantly reduce vehicle speeds in vicinity of pedestrian crosswalks on rural roads with posted speed limits of 60 km/h and 80 km/h. On average, the mean speed at pedestrian crosswalks declined 9.2 km/h on roads with a speed limit of 60 km/h; and 11.9 km/h on roads with a speed limit of 80 km/h. The 85th percentile speed declined 9.1 km/h on roads with a speed limit of 60 km/h; and 12.0 km/h on roads with a speed limit of 80 km/h. However, the speed reduction impacts were not found to be statistically significant for the pedestrian crosswalk on the road with a speed limit of 40 km/h. The study also looked extensively at the influence area of transverse rumble strips on rural roads. Speed profiles developed in this study show that the influence area of transverse rumble strips is generally less than 0.3 km.

Analyzing Travelers’ Intention to Accept Travel Information: Structural Equation Modeling

Advanced traveler information systems (ATIS) cannot improve the traffic environment if travelers do not accept the travel information provided by the system. To understand better why travelers accept or refuse travel information and to explain, predict, and increase travelers’ acceptance of travel information, a research framework based on the technology acceptance model is developed to establish the relationship between travelers’ intention to accept travel information, trust in travel information, perceived usefulness of travel information, perceived ease of its use, and other related variables. Then structural equation modeling is used to examine and analyze the relationship among these variables. The results show that the factors that significantly determine travelers’ intention to accept travel information are trust in travel information, its perceived usefulness, its perceived ease of use, and information attributes. Through an examination of the direct, indirect, and total effects in the model system, it is discovered that perceived ease of use has the largest total effect on intention to accept by a standardized coefficient of 0.522, followed by trust in information (0.348), perceived usefulness (0.199), and information attributes (0.079). These results indicate the practical value of the estimated model for guiding recommendations aimed at increasing travelers’ intention to accept travel information and at improving the service quality of travel information in China.