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The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

Severe traffic congestion has promoted the development of the Intelligent Transportation System (ITS). Accurately analyzing and predicting the traffic states of the urban road networks has important theoretical significance and practical value for improving traffic efficiency and formulating ITS scheme according to local conditions. This study aims to identify and predict the traffic operation status in the road network within the Third Ring Road in Xi'an and explore spatiotemporal patterns of traffic congestion. In this paper, firstly, we discriminated the traffic status of the urban road network used the GPS data of floating vehicles (e.g., taxis and buses) in Xi'an by the Travel Time Index (TTI). Secondly, we used the emerging hot spot analysis method to locate different hot spot patterns. The time series clustering method was used to divide the whole road network's locations into distinct clusters with similar spatiotemporal characteristics. Thirdly, we applied three different time series forecasting models, including Curve Fit Forecast (CFF), Exponential Smoothing Forecast (ESF), Forest-based Forecast (FBF), to predict the traffic operation status. Finally, we summarized the spatiotemporal characteristics of the whole-network congestion. The results of this study can contribute some helpful insights for alleviating traffic congestion. For instance, it is essential to speed up the construction of urban traffic microcirculation and increase the road network density. Moreover, it is crucial to adhere to the urban public transport priority development strategy and increase public transportation travel sharing.INDEX TERMS Urban traffic congestion, spatiotemporal pattern, short-term prediction, taxi trajectory, road traffic performance index.

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