This study proposes a short-term traffic flow prediction framework. The vector autoregression (VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model based on deep learning are employed in the analysis. An intrinsic association among traffic variables is first evaluated using the VAR model, and the predictable relationship of these variables is determined. Then the multi-features speed prediction for one spatial location using the CNN-LSTM hybrid neural network model is conducted, the prediction results prove that prediction with multi-feature is better than that with a single feature. Subsequently, several popular deep learning models and other shallow predicted models are proposed to be compared with the constructed CNN-LSTM network model, and the comparison illustrates that the model performance of the developed CNN-LSTM network model is superior to other models in forecasting the short-term traffic flow. Then the multi-feature speed predictions for a group of spatial locations are further conducted using the CNN-LSTM model. The result demonstrates the predictive accuracies are associated with the spatial correlation of traffic flow. Finally, a heatmap is produced to visualize the predicted speed, from which the spatial-temporal traffic condition can be presented clearly. The research results have the potential to be applied to the travel information releasing and traffic congestion management.

Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.

With the continuous development of intelligent networks and autonomous driving technologies, heterogeneous traffic flow represented by conventional vehicles (CV), autonomous vehicles (AV), and connected and autonomous vehicles (CAV) have emerged, and consequently, the driving safety risk issues in this mixed driving environment have become increasingly complex. In a mixed and connected environment, different traffic streams in complex driving scenarios are intertwined with each other, and driving behaviours such as steering and lane-changing between various traffic streams are frequent, thus increasing the crash risk of vehicles. To understand the research methods, theoretical models, and system architectures in the field of driving safety analysis in this mixed-connected environment, this article reviews the driving safety risk progress from four major aspects: driving safety risk perception and identification, driving safety risk prediction, driving safety risk quantification, and driving safety risk early warning. By summarizing the existing research, it can be found that academics have achieved many achievements in urban driving safety risk evaluation in mixed-connected environments. Still, there are several problems and challenges that need to be solved, such as the stability and reliability of collaborative sensing systems of AV and CAV in complex traffic environments, the accuracy of object detection, the vehicle information security, the limitations of a single factor analysis used in driving safety risk assessment, and the accuracy of trajectory prediction for both the CV, AV, and CAV. By analyzing the limitations of the existing research, this article proposes a future research direction, which provides a reference for the development of driving safety risk research.

Proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.

Pedestrian evacuation risk of subway stations is an important concern in city management, as it not only endangers public safety but also affects the efficiency of urban subway transportation. Determination of how to effectively evaluate the pedestrian evacuation risk of subway stations is of great significance to improve pedestrian safety. Previous studies about the pedestrian evacuation of subway station were primarily focused on pedestrian moving behaviors and the evacuation modeling, and the evacuation scenario is the regular subway operation. There is a dearth of studies to quantify the pedestrian evacuation risk in the evacuation process, especially the pedestrian evacuation risk quantitative characterization of subway station in large-scale sport activity. The current study develops a quantitative pedestrian evacuation risk assessment model that integrates pedestrian stampede probability and pedestrian casualty. Then several different simulation scenarios based on the social force model (SFM) are simulated to evaluate the pedestrian evacuation risk of the “Olympic Park Station” in Beijing, China. The results demonstrate that the pedestrian evacuation method, pedestrian stampede location, and distance from the stampede location to the ticket gate have a large impact on pedestrian evacuation risk. Then, the pedestrian evacuation scenarios with the lowest and highest risk for the “Olympic Park Station” in large-scale sport activity are determined. The findings have potential applications in pedestrian safety protection of subway station during large-scale sports activity.