This study presents a spatial approach for the macrolevel traffic crashes analysis based on point‐of‐interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

Roadside Units (RSUs) deployment optimization for vehicle-to-road communication is crucial in improving the performance of the vehicular and transportation networks. Current researches on RSU deployment optimization overlooks the inequal supply-demand relationship of vehicular communications caused by unbalanced vehicular density. In addition, the impact of traffic safety risks on RSU optimization is not considered. To cope with these challenges, this paper presents a data-driven and multi-factor RSU deployment strategy, which can be divided into two stages. The first stage adopts a data-driven approach to analyze vehicular density using realistic vehicle trajectory data, which helps identify preliminary deployment positions based on unbalanced vehicular density. In the second stage, an RSU deployment model is constructed that considers RSU deployment costs, geographical environment constraints, road coverage, and traffic safety risks. This model calculates the cumulative Poisson probability of simple and general traffic crashes to evaluate the traffic safety risks within the RSU coverage range. Afterward, this paper proposes an improved genetic algorithm to obtain the optimal position of RSUs within the deployment area. The algorithm employs a new method for population initialization and adopts a linear ranking-based selection mechanism. Finally, we conduct a joint simulation of transportation and communication networks by linking SUMO with OMNET $++$ through the TraCI interface and Veins framework. The results evaluate and demonstrate the performance of the proposed method in vehicle coverage, traffic safety risk coverage, and average notification time in comparison with the existing schemes.

To objectively evaluate the road traffic safety levels across different provinces in China, this study investigated the spatiotemporal heterogeneity characteristics of macro factors influencing road traffic accidents. Panel data from 31 provinces in China from 2009 to 2021 were collected, and after data preprocessing, traffic accident data were selected as the dependent variables. Population size, economic level, motorization level, highway mileage, unemployment rate, and passenger volume were selected as explanatory variables. Based on the spatiotemporal non-stationarity testing of traffic accident data, three models, namely, ordinary least squares (OLS), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR), were constructed for empirical research. The results showed that the spatiotemporal heterogeneity characterizing the macro factors of traffic accidents could not be ignored. In terms of impact effects, highway mileage, population size, motorization level and passenger volume had positive promoting effects on road traffic accidents, while economic level and unemployment rate mainly exhibited negative inhibitory effects. In terms of impact magnitude, highway mileage had the greatest impact on traffic accidents, followed by population size, motorization level, and passenger volume. Comparatively, the impact magnitude of economic level and unemployment rate was relatively small. The conclusions were aimed at contributing to the objective evaluation of road traffic safety levels in different provinces and providing a basis for the formulation of reasonable macro traffic safety planning and management decisions. The findings offer valuable insights that can be used to optimize regional traffic safety policies and strategies, thereby enhancing road safety.

Intersections are crucial and high-risk areas in urban road networks due to dense traffic and complex scenarios. The deployment of Road Side Units (RSUs) can enhances safety and efficiency by providing real-time traffic information. However, the impact of traffic accident risks on RSU deployment is largely ignored. This study introduces an innovative RSU deployment strategy that prioritizes the risk of traffic accidents at intersections. The approach begins with analyzing environmental conditions, traffic patterns, and historical accident data at target intersections to identify key risk dimensions: road dimension, accident dimension, and environmental dimension. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are used to weight the indicators to evaluate their importance in accident risk assessment. Then, construct an objective function based on the accident risk value of the intersection. In order to overcome the redundancy problem in risk assessment, this study proposes an improved 0-1 knapsack algorithm that considers the redundancy of intersection accident risk to determine the optimal deployment location of RSUs. Simulations with SUMO, TraCI, Veins, and OMNeT++ demonstrate the algorithm superiority over traditional methods in all metrics. The results show that the vehicle coverage of this strategy is on average 2.86% higher than that of the UDA algorithm, leading by about 6.63% in traffic accident coverage and 6.54% in accident risk coverage. This intersection-focused RSU deployment method ensures timely information dissemination after incidents, providing valuable insights and practical guidelines for improving urban intersection safety and efficiency.

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic–technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes.

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm.