Road safety has long been considered as one of the most important issues. Numerous studies have been conducted to investigate crashes with significant progress, whereas most of the work concentrates on the lifespan period of roadways and safety influencing factors. This paper undertakes a systematic literature review from the crash procedure to identify the state-of-the-art knowledge, advantages and disadvantages of crash risk, crash prediction, crash prevention and safety of connected and autonomous vehicles (CAVs). As a result of this literature review, substantive issues in general, data source and modeling selection are discussed, and the outcome of this study aims to provide the summary of crash knowledge with potential insight into both traditional and emerging aspects, and guide the future research direction in safety.
Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy. In order to provide persuasive passenger flow forecast data for ITS, a deep learning model considering the influencing factors is proposed in this paper. In view of the lack of objective analysis on the selection of influencing factors by predecessors, this paper uses analytic hierarchy processes (AHP) and one-way ANOVA analysis to scientifically select the factor of time characteristics, which classifies and gives weight to the hourly passenger flow through Duncan test. Then, combining the time weight, BILSTM based model considering the hourly travel characteristics factors is proposed. The model performance is verified through the inbound passenger flow of Ningbo rail transit. The proposed model is compared with many current mainstream deep learning algorithms, the effectiveness of the BILSTM model considering influencing factors is validated. Through comparison and analysis with various evaluation indicators and other deep learning models, the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968, and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.
With the development of intelligent vehicles and autonomous driving technology, the safety of vulnerable road user (VRU) in traffic has been more guaranteed, and many research achievements have been made in the key area of collision avoidance decision-making methods. In this paper, the knowledge mapping method is used to mine the available literature in depth, and it is found that the research focus has shifted from the traditional accident cause analysis to emerging deep learning and virtual reality technology. This paper summarizes research on the three core dimensions of environmental perception, behavior cognition and collision avoidance decision-making in intelligent vehicle systems. In terms of perception, accurate identification of pedestrians and cyclists in complex environments is a major demand for VRU perception; in terms of behavior cognition, the coupling of VRU intention identification and motion trajectory prediction and other multiple factors needs further research; in terms of decision-making, the intention identification and trajectory prediction of collision objects are not included in the risk assessment model, and there is a lack of exploration specifically for cyclists' collision risk. On this basis, this paper provides guidance for the improvement of traffic safety of contemporary VRU under the conditions of intelligent and connected transportation.
Rail transit plays a key role in mitigating transportation system carbon emissions. Accurate measurement of urban rail transit carbon emission can help quantify the contribution of urban rail transit towards urban transportation carbon emission reduction. Since the whole life cycle of urban rail transit carbon emission measurement involves a wide range of aspects, a systematic framework model is required for analysis. This research reviews the existing studies on carbon emission of urban rail transit. First, the characteristics of urban rail transit carbon emission were determined and the complexity of carbon emission measurement was analyzed. Then, the urban rail transit carbon emission measurement models were compared and analyzed in terms of the selection of research boundaries, the types of greenhouse gas (GHG) emissions calculation, and the accuracy of the measurement. Following that, an intelligent station was introduced to analyze the practical application of digital collaboration technology and energy-saving and carbon-reducing system platforms for rail transit. Finally, the urgent problems and future research directions at this stage were discussed. This research presents the necessity of establishing a dynamic carbon emission factor library and the important development trend of system integration of carbon emission measurement and digital system technology.
To explore the safety of highway traffic operations, the vehicle state and guardrail deformation during highway guardrail collisions are simulated and analyzed. The vehicle-guardrail collision is simulated by finite element software such as LS-DYNA and HyperMesh. The vehicle speed settings are 60, 80, 100 and 120 km/h, and the collision angles are 5°, 10°, 15° and 20°. The guardrail deformation, vehicle acceleration and energy changes under different collision speeds and angles are studied. The research results show that at the same collision speed, an increase in the collision angle causes more serious damage to the vehicle, a greater transverse displacement of the guardrail, and a greater range of car acceleration fluctuations. When the collision angle is the same, an increase in the collision speed causes greater lateral displacement of the guardrail, a greater vehicle acceleration fluctuation range, and more serious vehicle damage. The results of the study can provide a reference for demonstrating highway guardrail safety.
Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards. This paper first systematically compares the research in Chinese and foreign literature, and proposes three types of identification indicators, namely absolute, relative and comprehensive, according to different reference standards. According to the evaluation indicators and modelling methods, the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics, cluster analysis, machine learning and conflict technology. The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management, while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation. Driven by big data, the identification of traffic accident-prone points has been further developed at the meso-micro scale. Morphological image processing methods are widely used, combined with GIS platforms, to accurately mine the spatial attributes and correlations of accidents. Also, considering the spatial and temporal distribution of accidents, the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.
