This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.

With the popularization of autonomous vehicle (AV) technology, mixed traffic flows that consist of AVs and human-driven vehicles (HDVs) will appear in the real world. Although many studies of the features of mixed traffic flow have been carefully evaluated, few studies have focused on the effect of aggressive driving performance on mixed traffic flow. This study aims to develop an approach to evaluate the effects of aggressive driving on the stability and capacity performance under the conditions of AV and HDV mixed traffic flow. First, since a car-following model can describe the relationship between vehicles, we calibrate a car-following model for aggressive driving and nonaggressive driving behaviors based on real traffic data and previous research results. Then, in a mixed traffic flow environment, a basic linear stability formula and capacity calculation expression are developed that consider the effects of vehicle order on the capacity. Finally, because the proportion of aggressive driving and aggressive driving parameters may change, nine combinations of three aggressive driving proportions and three driving parameter cases are used for the sensitivity analysis. The results indicate that the effect of aggressive driving on mixed traffic flow is complex. When the proportion of aggressive driving is less than 35%, the increase in the proportion of aggressive driving increases the traffic capacity and reduces the unstable part. However, when the proportion of aggressive driving is greater than 35%, the increase in the proportion of aggressive driving increases the unstable part. When the penetration rate of AVs exceeds 0.490, mixed traffic flow remains stable at all aggressive driving proportions. In addition, the capacity of a mixed traffic flow may be improved as the penetration rate of AVs increases. To a certain extent, these conclusions provide a theoretical basis for formulating different management modes of AVs and HDVs.

With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in real-time EVCM is the handling of missing data caused by unexpected events, which can impair both real-time monitoring and its downstream applications. To address this vital yet underexplored issue, we propose a curriculum guided multi-feature fusion transformer (CurriFusFormer) learning framework – a novel approach designed to enhance the resilience of EVCM systems against real-time information omissions. Our framework integrates curriculum learning with a multi-feature fusion transformer model, capable of handling various patterns and rates of missing data, ranging from random to block omissions. This innovative approach leverages spatial, temporal, and static features to generate accurate real-time estimations for missing values in diverse scenarios. Extensive experiments on a real-world EVCM dataset demonstrate that CurriFusFormer can perform well with $R^2$ ranging from 0.92 to 0.83 given the rising missing rate from 30-90%, outperforming seven popular and state-of-the-art methods, especially in scenarios with high missing rates and complex patterns, such as, at 90% missing rate, kNN ( $R^2$ $=$ 0.65), XGBoost ( $R^2$ $=$ 0.78), BRITS ( $R^2$ $=$ 0.79), TFT ( $R^2$ $=$ 0.80), and GRIN ( $R^2$ $=$ 0.82). All results suggest that the proposed framework could be a promising solution for developing future resilient EVCM networks.

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

In the realm of traffic safety analysis, the inherent imbalance in crash datasets, particularly in terms of injury severity, poses a significant challenge for machine learning-based classification models. This study delves into the efficacy of Generative Adversarial Networks (GANs), with a specific focus on Conditional Tabular GAN (CTGAN), for synthesizing minority crash data to address this imbalance. Utilizing traffic crash data from Chicago spanning 2020 to 2022, the research evaluates the capabilities of CTGAN against three traditional data resampling methods, as well as an additional cost-sensitive learning approach. These methods are evaluated across various injury severity classification scenarios (2-class, 3-class, and 4-class) using five commonly applied injury severity classification models. The study's dual evaluation approach encompasses both the quality of synthetic data and the enhancement of classification model performance. The pivotal findings reveal that: 1) CTGAN markedly outperforms other data resampling techniques in generating superior quality synthetic data, particularly for the least represented injury severity category; 2) While CTGAN demonstrates substantial improvements over traditional data resampling methods in classification model performance, this advantage diminishes as the number of injury categories increases; 3) Surprisingly, CTGAN's superior data quality does not result in better classification performance compared to cost-sensitive learning, especially in more complex classification scenarios. Cost-sensitive learning combined with LightGBM achieves the best classification performance across all scenarios. Given the significantly lower computational resources required by cost-sensitive learning, this approach is recommended for handling imbalanced injury severity data.