Despite the growing popularity of autonomous vehicles (AVs), public acceptance of AV technologies remains uncertain. This study aims to explore how user demographics, human-related factors, and environmental factors influence people's decisions to adopt three distinct AV types: general AVs, shared AVs, and AVs with a human-shaped dummy driver. 765 valid responses were gathered via a questionnaire survey conducted in China. A random parameter univariate probit model with heterogeneity in means and a random parameter bivariate model with heterogeneity in means were employed. Findings suggest that gender, occupation, age, trust, self-efficacy, behavioral intentions, perceived safety risks as well as social and traditional media influences are the prominent factors affecting individuals' decision to adopt these AVs. Furthermore, this study reveals that significant factors vary depending on the type of AVs considered. These results are expected to offer insights for policymakers, promoters of AVs and transportation authority’s seeking to enhance public acceptance.

Smart intersection monitoring systems face critical limitations when relying on single-sensor modalities, with cameras degrading under poor visibility and radar struggling with sparse point clouds. This work presents a novel multi-modal sensor fusion framework that integrates radar and camera detections through adaptive range-dependent strategies for robust vehicle detection across diverse operational conditions. We develop a radar detection pipeline that leverages drone-supervised learning to transform sparse radar points into precise 2D bounding boxes, achieving 93.0% precision through feature consistency filtering and motion-aware association. A geometry-aware camera-to-bird’s-eye-view transformation architecture combines homography priors with residual neural networks to enable direct spatial comparison between modalities, achieving 0.494 average IoU with lane-aware geometric correction. The fusion framework implements four distinct combination strategies—weighted IoU, range-adaptive, envelope, and intersection fusion—evaluated against 19,612 ground truth bounding boxes across six range bins. Results demonstrate universal fusion superiority with IoU improvements ranging from 6.1% to 28.3% across all operational ranges, achieving 30.4% overall ground truth win rate and 22.6% average IoU enhancement. Range-based analysis reveals distinct operational zones: camera dominance at close range (0-40m), fusion optimization in transition zones (40-80m), and progressive radar emergence through fusion support (80-100m), leading to radar effectiveness beyond 100m. The range-adaptive fusion strategy achieves peak IoU of 0.708 at 60–80m, highlighting intelligent sensor integration as key for intersection safety and guiding deployment strategies for next-generation transportation systems.

Vulnerable road users (VRUs) such as pedestrians and cyclists frequently engage in distracted behaviors (e.g., phone or headphone use) that elevate crash risk. General-purpose vision–language models (VLMs) struggle to capture these subtle, small, and context-dependent cues, and most camera pipelines still rely on frame-based analyses that ignore temporal context. We present IntersectionPAR, a lightweight vision–language framework that couples an Interactive Attribute Encoder with region-level captioning to recover safety-relevant semantic attributes (gender, phone/headphone use, mobility mode and aids) together with kinematic cues in a bird’s-eye view. We further introduce a temporal risk module that computes a continuous Situational Awareness Score (from phone, pose, and gaze proxies) and a Dynamic Risk Score that fuses awareness with spatial context (safe vs. unsafe zones), aligning system output with the specific moments practitioners need to act. Our dataset contains 12,273 annotated images drawn from~200 intersection-crossing scenarios under varied lighting and weather. Under a standardized zero-shot protocol against a broad suite of modern VLMs, IntersectionPAR attains keyword-based accuracy of 0.6123 with balanced precision/recall (0.7936/0.7864) and an F1-score of 0.7719 while requiring only ~9 GB of VRAM(>40 GB for several baselines). Qualitative analyses show strong behavior on safety-critical cues (e.g., cane and mobility-aid detection) and illustrate how the temporal risk module elevates risk precisely when low-awareness VRUs enter the roadway. Statistical analyses of 200 crossing events further reveal behavioral impacts of phone use (e.g., longer waiting time and increased path deviation). By pairing accurate, attribute-aware perception with low computational cost and an actionable temporal risk signal, IntersectionPAR supports real-time intersection safety monitoring and proactive interventions to mitigate VRU risk.

Deep reinforcement learning (DRL) algorithms have been widely explored to enhance connected and automated vehicle (CAV) decision-making in complex driving environments such as freeway weaving segments, with various methods being developed to model vehicle interactions. However, different vehicle interaction characteristics caused by heterogenous driving intention and varying relative positions, were not modeled in existing DRL algorithms. In this research, we present an innovative approach that incorporates different interaction types between CAVs and their neighboring vehicles into the decision-making process. Using Relational Graph Attention Networks (R-GAT), this method captures the heterogeneous interaction dynamics by applying a novel interaction type-specific transformation to vehicle interaction features (e.g., relative time headway) and modified graph attention mechanism. Afterward, the R-GAT is integrated with a Deep Q-network (DQN) to make driving decisions for CAVs. Experimental results demonstrated that the proposed model outperforms traditional Graph Attention Networks (GAT) and DQN in various traffic scenarios with higher mean rewards, reducing 30%-48% conflicts in dense traffic. The proposed model is also evaluated by comparing the model-controlled driving behavior with field trajectories, and it was proven that the algorithm is capable of completing the driving tasks at weaving segments with a more stable speed profile than the human driver trajectories. The model can be transferred to various driving tasks at different locations by modifying the graph features and DRL settings, highlighting its potential for broader applications in CAV decision-making problems.

We introduce a real-time crash safety system capable of predicting crashes 5-10 minutes in advance, using advanced machine learning techniques. The system integrates primary, severity, and secondary crash predictions into a unified framework, employing a multi-layered prediction strategy that enhances proactive traffic safety management by providing robust and actionable alerts. It uses a comprehensive segment-specific approach, utilizing well-established machine learning models suited to the unique characteristics of different road segment types, such as basic, merge, diverge, and ramp. Additionally, route-specific models capture regional variations in traffic patterns and environmental factors, ensuring that the system adapts to the unique characteristics of different routes. Performance evaluations demonstrate that the system achieves high sensitivity (0.798-0.918) and low false alarm rates (0.088-0.208), depending on the segment type. The use of the continuity concept ensures the consistency of high crash likelihood situations, which improves the reliability of predictions. The results show that the system can accurately predict crashes and provide proper warnings. It can make traffic operators to take proactive actions such as adjusting traffic management measures. By predicting both the occurrence and severity of crashes, as well as the likelihood of secondary crashes, the system enhances the overall efficiency and effectiveness of traffic crash management. This comprehensive approach to crash prediction represents a significant advancement in proactive traffic management and road safety enhancement.

Signalized intersections are a critical component of road safety, yet rear-end crashes due to the yellow and red clearance phases remain a persistent problem. Traditional methods for setting these phases rely on kinematic models and video-based trajectory analysis, which suffer from high costs, weather/lighting limitations, and an inability to isolate signal timing impacts from confounding factors. This study introduces a novel framework for optimizing yellow and red clearance durations using Automated Traffic Signal Performance Measures (ATSPM). Analyzing over 446,000 traffic signal cycles across multiple intersections, the study examines rear-end conflicts during these phases and estimates the impact of signal timing adjustments using a causal forest. The results show that even small changes in yellow or red clearance durations can significantly affect rear-end conflict rates, with variations depending on intersection characteristics, traffic direction, and time of day. Unlike current standards, the proposed framework identifies context-specific optima, demonstrating how adaptive signal timing strategies can enhance safety without compromising traffic flow. This research expands the application of ATSPM beyond mobility optimization, offering a scalable and data-driven framework to improve intersection safety.

Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles’ continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and drones, are able to collect high-resolution trajectory data, which enable trajectory-based risk analysis. This study investigates a new vehicle group (VG) based risk analysis method and explores risk evolution mechanisms considering VG features. An impact-based vehicle grouping method is proposed to cluster vehicles into VGs by evaluating their responses to the erratic behaviors of nearby vehicles. The risk of a VG is aggregated based on the risk between each vehicle pair in the VG, measured by inverse Time-to-Collision (iTTC). Logistic Regression and a Graph Neural Network (GNN) are used to predict VG risks based on both aggregated and disaggregated VG information. Both methods achieve excellent performance with AUC values exceeding 0.93. For the GNN model, GNNExplainer with feature perturbation is applied to identify critical individual vehicle features and their directional impact on VG risks. Overall, this research contributes a new perspective for identifying, predicting, and interpreting traffic risks.

Crash risk evaluation studies mainly established the relationship between the macro traffic status and crashes. However, the impact of risky driving behavior, a significant factor in crashes, has not been thoroughly investigated due to the data collection limitations of fixed detectors. In this study, the risky driving behavior data generated by Connected Vehicle (CV) techniques was introduced along with traffic flow data to develop the crash risk evaluation model. An LSTM + Transformer approach was developed, in which the Transformer could extract the non-aggregated spatial-temporal features of risky driving behaviors and LSTM learn the temporal patterns of traffic flow. An ensemble layer was proposed to integrate the macro traffic status features and micro driving behavior, and automatically fit their weights to optimize crash risk evaluation performance. Data from a Chinese freeway was used for empirical analysis. The results show that the proposed LSTM + Transformer model achieved high model accuracy (77.7%), recall (68.6%), and AUC (0.785), with average improvement of between 5.34%, 15.69%, and 5.97%, respectively, compared to existing LSTM, XGBoost, SVM and Logistic Regression (LR) models. Moreover, utilizing risky driving behavior data by incorporating the macro traffic status has proved to capture the pre-crash traffic flow turbulence more precisely. The model results explained by SHapley Additive exPlanations (SHAP) reveal that higher frequency, longer duration and greater acceleration of risky braking behavior increase the number of road vehicles affected, thereby heightening the crash risks. These findings could help the deployment of proactive traffic management and target CV control strategies to reduce crashes.

Large-scale real-time crash prediction is critical to traffic operation and management, but very challenging, even for machine learning models because the observation data are not independent and identically distributed (non-IID) due to its spatial heterogeneity. Data discontinuity, computational inefficiency, and higher false alarm rates are some other challenges in real-world and real-time crash prediction. To address these issues, we propose a generic framework, which blends spatial ensemble learning and knowledge distillation. Spatial ensemble learning leverages individual segment-level modeling to alleviate the non-IID issue for more accurate crash prediction, while knowledge distillation reduces the model size and improves false alarm rate. We justify the effectiveness of the proposed method using real-world and real-time scenario and comparing the results with the up-to-date benchmark models. Our method successfully improves sensitivity and false alarm rate to 90.35% and 24.21%. With this more accurate prediction model, we analyze the prediction variability across this region. We found that: 1) across segments, false alarm rate exhibits more heterogenous pattern than sensitivity; 2) rear-end crashes are more accurately predicted compared to sideswipe/angle crashes; 3) urban segments show better prediction performance compared to rural segments. 4) developing models with desired accuracy requires special attention in higher traffic fluctuation segments. These observations are very useful to develop more accurate prediction model and traffic safety decision making. To the best of our knowledge, this is one of the pioneering studies to integrate spatial ensemble learning and knowledge distillation to predict large-scale real-time crashes and apply it to analyze crash prediction variability.

Traffic estimation is imperative for conducting fundamental transportation engineering tasks such as transportation planning and traffic safety studies. Additionally, traffic prediction is vital for many data-driven intelligent transportation system applications. Most traffic estimation and prediction methods rely on infrastructure-based sensors to collect traffic parameters. However, infrastructure-based data collection can be costly and time consuming to set up and maintain. Additionally, the spatial distribution of the collected traffic data is limited by the location of the deployed hardware sensors. Probe vehicle data can be used to overcome these limitations. Traffic modeling for estimation and prediction is a complex task due to the stochastic nonlinear spatiotemporal dependencies exhibited by traffic parameters. In this paper, a deep learning-based sequence-to-sequence architecture called Seq2seq GCN-LSTM was proposed to estimate and predict network-wide traffic volume and speed. The proposed method utilizes short-term historical traffic data collected from a low-penetration rate probe vehicle fleet to estimate and predict traffic parameters up to 60-minutes ahead. The method utilizes Graph Convolutional Networks for spatial dependency extraction and Long Short-Term Memory networks to model temporal dependencies. The proposed method generated superior traffic results compared to the baseline models. Additionally, the model demonstrated robustness against perturbations caused by the low rate. Furthermore, the probe vehicle penetration rate was varied to test its effect on the proposed method’s modeling capability. The model was able to maintain traffic volume and speed estimation and prediction performance within a reasonable margin of error using penetration rates as low as 1.5% and 0.5%, respectively.

Inclement weather conditions, particularly heavy rain and the consequent wet road surface, have an unfavorable effect on driving conditions, traffic infrastructure, and operational plans. To mitigate the potentially detrimental ramifications of turbulent weather, it must be continuously monitored in real time and with high geospatial granularity. Traditionally, road weather conditions are monitored using weather forecasts or Roadside Weather Information Systems (RWIS). However, these methods are either ill-equipped or too expensive to provide the required fine-grained observations. Alternatively, roadside traffic CCTV cameras are ubiquitously deployed on US freeways and can serve as inexpensive sensors to surveil weather. In this paper, a novel vision-based methodology is proposed to detect rain and road surface conditions from roadside traffic cameras. Vision Transformers were utilized for image-based classification. They demonstrated superior results compared to convolution-based approaches. Furthermore, the geographical distribution of roadside cameras was leveraged to add spatial context awareness to the detection model. A Spatial Self-Attention network was proposed to model the relationship between the detection results of adjacent images as a sequence-to-sequence detection task. The results indicate that the addition of the sequential detection module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score. The boost in performance enhanced the F1-scores of the stand-alone Vision Transformer by 5.61% and 5.97% for the rain and road surface condition detection tasks, respectively, raising the total F1-score to 96.71% and 98.07%.

Speed prediction is a crucial yet complicated task for intelligent transportation systems. The challenge derives from the complex spatiotemporal dependencies of traffic parameters. In the past few years, deep neural networks have achieved the best traffic speed prediction performance. However, most models depend on short-term input sequences to predict short/long-term traffic speed (e.g., predicting speed for the next hour using data from the past hour). These models fail to consider the daily and weekly periodic behavior of traffic. Another problem posed by neural networks is the lack of interpretability as they often operate as “black boxes”. In this paper, an attention-based multi-encoder-decoder (Att-MED) model is proposed to predict traffic speed. The model uses convolutional-LSTMs to capture the spatiotemporal relationship of multiple input sequences, namely short-term, daily and weekly traffic patterns. The model also employs an LSTM to model the output predictions sequentially. Furthermore, attention mechanism is used to weigh the contribution of each traffic sequence towards the output predictions. The proposed network architecture, when trained end-to-end, results in a superior prediction accuracy compared to baseline models. In addition to contributing towards performance, the attention mechanism creates weight values, which when visualized, provide insights into the decision-making process of the neural network, and consequently produce explainable outputs. Att-MED’s extracted attention weights highlight the contribution of daily and weekly periodic input towards speed prediction.

Identifying vehicle maneuvers in the context of Connected Vehicles (CV) system brings huge potentials to enhance traffic safety. However, this process requires various advanced sensors, which are either available for luxury vehicles or expensive to install. Differently, smartphone is a more feasible choice with high penetration rate and various built-in sensors. Among the existing studies of applying the smartphone to detect vehicle maneuvers, most treated the detection as a classification problem without considering the real-world application. For example, the smartphone was fixed. Too many descriptive features were generated from the sensor data. To alleviate these problems, this paper developed a vehicle maneuvers detection system using a common smartphone with GPS, gyroscope, accelerometer, and magnetometer sensors. We first released the constraints on the smartphone’s position through a coordination system reorientation method. Then, simply filtered sensor data were directly used. A stacked-LSTM model was built to detect the vehicle maneuvers considering the time-dependency of the sensor data. This paper compared the performance of the proposed system with previous studies and various machine learning methods, including LightGBM, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. Extensive experimental results indicated that the proposed system accurately detected different vehicle maneuvers with an average F1-score of 0.98, precision of 0.97, and recall of 0.98, which outperformed the counterparts. Moreover, the model can be easily transferred to different drivers and locations. The system is robust and suitable for the real-time application as it requires simple processing of smartphone sensor data.

Pedestrians’ red-light crossing can present a threat to traffic safety. Among all the existing work related to pedestrian’s red-light crossing, there are few studies using trajectory data in time sequence. This paper uses pose estimation (keypoint detection) to generate pedestrians’ variables from CCTV videos. Four machine learning models are used to predict pedestrians’ crossing intention at intersections’ red-light. The best model achieves an accuracy of 0.920 and AUC value of 0.849, with data from three intersections. Different prediction horizons (up to 4 sec) are used. With longer prediction horizons, the sample size gets smaller, which partially leads to worse model performance. However, the performance with prediction horizon up to 2 sec is still good (AUC value as 0.841). It is found that keypoint variables such as the angles between ankle and knee (left side) and elbow and shoulder (right side) are important. This model can be further implemented in the Infrastructure-to-Vehicle (I2V) applications and thus prevent accidents due to pedestrians’ red-light crossing by issuing warnings to drivers.

In the context of pro-active traffic management, real-time crash risk evaluation is one of the most critical components. Signalized intersections are well-known high-risk locations because of the variety of traffic movements, modes, and their interactions. Unlike access-controlled freeways, the traffic flow at signalized intersections presents cyclical characteristics, which are temporally separated by traffic signals. Therefore, the data preparation for real-time crash risk prediction at signalized intersections should be based on the signal cycle rather than a predefined fixed time interval (e.g., 5 minutes). In this research, the corresponding signal cycles where crashes occurred were verified based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). Six types of real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather. Two undersampling strategies, matched case-control and random undersampling, were utilized to develop conditional logistic and binary logistic models, respectively. Model results indicate that the random undersampling performs better than the matched case-control method. It was found that higher cycle volume, overall average flow ratio across lanes, arrivals on yellow ratio, traffic volatility across approach sections, as well as longer cycle length and lower green ratio could significantly increase the odds of crash occurrence at signalized intersections. Moreover, longer maximum queue length, bigger shockwave, and higher absolute queuing shockwave speed tend to increase the odds of crash occurrence.

This research proposes a method to estimate the practical effectiveness of 6 advanced driver assistance systems (ADAS) in avoiding crashes on different roadway facilities (locations). The systems include blind spot warning/lane change warning (BSW/LCW), forward collision warning (FCW), intersection movement assist (IMA), pedestrian crash avoidance and mitigation (PCAM), lane departure warning (LDW) and left turn assist (LTA) systems. The method involves the use of meta-analysis and quasi-induced exposure methodologies to estimate the distribution of effectiveness factors across the driver population. These factors include system operating conditions, system adoption rates and system usage features. Then a Monte-Carlo procedure is applied to sample these factors for simulating the practical ADAS effectiveness. The research shows that the practical effectiveness could be as much as 48.25% lower than the ideal effectiveness proposed by previous research. It is also found that BSW/FCW, PCAM, FCW and LDW systems exhibited the highest effectiveness when operating on urban expressways/freeways. On rural multilane highways, such systems are demonstrated to have the least effectiveness. Furthermore, LTA systems are found to perform better at rural intersections than at urban intersections while the conclusion is contrary regarding IMA systems. Among the six systems, BSW/LCW has the most consistent performance among facilities, while LTA is most likely to be affected by facility type. The integrated system of six ADAS systems could prevent at most 29.50% of total crashes on urban multi-lane highways, while its lowest practical effectiveness is 13.72% on rural two-lane highways. Finally, technical and policy implications of the results are discussed.

As users' needs are changing and new technologies are developing at a faster rate than ever before, it is important to evaluate and update the design and mechanism of specific products based on various points and factors. This paper assessed the effectiveness of in-vehicle warning and assistance information design under reduced visibility driving conditions using the Kansei engineering methods. Various warning and assistance information design scenarios through in-vehicle head-up display (HUD) under the connected vehicle environments were collected and devised for the analysis. In order to determine the connection between the information features and design elements and the desired emotional needs of drivers in low visibility conditions, the Kansei engineering quantification Type I theory with two-level hierarchy structure and the partial least square-based Kansei engineering approaches were utilized. By analyzing the emotion dimensions and design category factors, the optimized in-vehicle HUD information designs were identified for different driver groups (i.e., all, male, and female participants) based on three driving scenarios considering changes of weather, real-time crash risk, and traffic conditions. On the basis of understanding the importance of collecting drivers' needs and perceptions for different combinations of information technologies, the optimized information features and design elements could be appreciated.

This paper investigates the effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types for a freeway. The I-70 Freeway was chosen for this paper since automatic vehicle identification (AVI) and weather detection systems are implemented along this corridor. A main objective of this paper is to expand the purpose of the existing intelligent transportation system to incorporate traffic safety improvement and suggest active traffic management (ATM) strategies by identifying the real-time crash patterns. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristic data were utilized as explanatory variables in this paper. First, binary logistic regression models were estimated to compare single- with multivehicle crashes and sideswipe with rear-end crashes. Then, a hierarchical logistic regression model that simultaneously fits two conditional logistic regression models for the three crash types has been developed. Results from the models indicate that single-vehicle crashes are more likely to occur in snowy seasons, at moderate slopes, three-lane segments, and under free-flow conditions, whereas the sideswipe crash occurrence differs from rear-end crashes with the visibility situation, segment number of lanes, grades, and their directions (up or down). Furthermore, the innovative way of estimating two conditional logistic regression models simultaneously in the Bayesian framework fits the correlated data structure well. Conclusions from this paper imply that different ATM strategies should be designed for three- and two-lane roadway sections and are also considering the seasonal effects.

Real-time crash prediction research attempted the use of data from inductive loop detectors; however, no safety analysis has been carried out using traffic data from one of the most growing nonintrusive surveillance systems, i.e., the tag readers on toll roads known as automatic vehicle identification (AVI) systems. In this paper, for the first time, the identification of freeway locations with high crash potential has been examined using real-time speed data collected from AVI. Travel time and space mean speed data collected by AVI systems and crash data of a total of 78 mi on the expressway network in Orlando in 2008 were collected. Utilizing a random forest technique for significant variable selection and stratified matched case-control to account for the confounding effects of location, time, and season, the log odds of crash occurrence were calculated. The length of the AVI segment was found to be a crucial factor that affects the usefulness of the AVI traffic data. While the results showed that the likelihood of a crash is statistically related to speed data obtained from AVI segments within an average length of 1.5 mi and crashes can be classified with about 70% accuracy, all speed parameters obtained from AVI systems spaced at 3 mi or more apart were found to be statistically insignificant to identify crash-prone conditions. The findings of this study illustrate a promising real-time safety application for one of the most widely used and already present intelligent transportation systems, with many possible advances in the context of advanced traffic management.

The objective of this paper is to collect and analyze data that can be used to model mode- and different route-choice paradigms using same subjects and same experiment. In this paper, the authors estimated five models to address drivers' diversion, compliance, and route choice. In addition, the effect of advanced traveler information systems (ATIS) on the mode choice is also considered. A travel simulator was used as a dynamic data collection tool. The simulator uses a realistic network, two modes of travel, real historical volumes, and different weather conditions. It provides five different levels of traffic information/advice, one at a time, and collects dynamic mode choices and pretrip (long-term) and en-route (short-term) route choices. The binomial and multinomial generalized extreme equations (BGEE and MGEE) were used to account for the correlation between repeated choices made by the same subject. In addition, MGEE accounts for the correlation between alternatives in multidimensional route-choice models. The modeling results showed that travel time and familiarity with the device that provides the information had significant effects on the first four models. It is shown that developing in-vehicle information devices may lead to a less transit usage in some cases; this indicates a potential drawback of this technology. Expressway users are shown as the most travel-time savers who would divert if they are guided to a less-travel-time alternative. The number of traffic signals on the normal and advised routes affects the diversion from the normal route and compliance with the pretrip advised route. This paper underlines the importance of modeling correlation, if it exists, in mode/route-choice data.

Predicting a crash occurrence is the key to traffic safety. Real-time identification of freeway segments with high crash potential is addressed in this paper. For this study, historical crashes and corresponding traffic-surveillance data from loop detectors were gathered from a 36-mi corridor of Interstate 4 for 4 years. Following an exploratory analysis, two types of logistic-regression models (i.e., simple and multivariate) were developed. It was observed that, although the simple models have the advantage of being tolerant in their data requirements, their classification accuracy was inferior to that of the final multivariate model. Hence, the simple models were used to deduce time-space patterns of variation in crash risk while the multivariate model was chosen for final classification of traffic patterns. As a suggested application for the simple models, their output may be used for the preliminary assessment of the crash risk. If there is an indication of high crash risk, then the multivariate model may be employed to explicitly classify the data patterns as leading or not leading to a crash occurrence. A demonstration of this two-stage real-time application strategy, based on simple and multivariate models, is provided in the paper. The output from these model-processing real-time loop-detector data may be utilized by traffic-management authorities for developing proactive traffic-management strategies

Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.