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This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels. [ABSTRACT FROM AUTHOR]

Given the potential difference in drivers' route familiarity, this study aims to investigate the respective factors contributing to local-vehicle and non-local-vehicle crashes on freeway. Crash data from Kaiyang Freeway in Guangdong Province, China in 2014 are collected for the investigation, where the crashes with all involved vehicles registered in the province are labeled as local-vehicle crashes while others are labeled as non-local-vehicle crashes. Bayesian spatial models are developed to build local-vehicle and non-local-vehicle crash frequencies' relationships with traffic characteristics, roadway design, and weather condition. The parameter estimation results indicate that the factors including daily vehicle kilometers traveled, vertical grade, air temperature, and precipitation, impact local-vehicle and non-local-vehicle crash frequencies in the same directions. The proportion of vehicles in Class 1 (e.g., passenger car) impacts the two type crash frequencies in different directions. The proportion of vehicles in Class 3 (e.g., medium-sized bus and truck) and horizontal curvature have significant effects on local-vehicle crashes only. The results are indicative of developing countermeasures from the perspectives of roadway geometry design and driving assistance apps, which aims to reduce freeway crash occurrence. [ABSTRACT FROM AUTHOR]

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors. [ABSTRACT FROM AUTHOR]

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers. [ABSTRACT FROM AUTHOR]

The vehicular edge computing is considered an enabling technology for intelligent and connected vehicles since the optimization of communication and computing on edge has a significant impact on driving safety and efficiency. In this paper, with the road traffic assignment to “proactively” reshape the spatiotemporal distribution of resource demands, we investigate the joint service migration and mobility optimization problem for vehicular edge computing. The goal is to meet the service delay requirements of vehicular edge computing with minimum migration cost and travel time. As service migration and mobility optimization are coupled, the joint scheduling problem suffers from the curse of dimensionality, which cannot be solved in real time by centralized algorithms. To this end, a multi-agent deep reinforcement learning (MADRL) algorithm is proposed to maximize the composite utility of communication, computing, and route planning in a distributed way. In the MADRL algorithm, a two-branch convolution based deep Q-network is constructed to coordinate migration action and routing action. Extensive experimental results show that the proposed algorithm is scalable and substantially reduces service delay, migration cost and travel time as compared with the existing baselines. [ABSTRACT FROM AUTHOR]

Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways. [ABSTRACT FROM AUTHOR]

Problem, research strategy, and findings: Recent research reveals growing spatial disparities in warehousing-related environmental externalities, including air pollution and traffic safety concerns, across municipalities. The existing research, however, fails to present how institutional factors contribute to spatial variations. In this study, I explore how variations in planning practices contribute to the different trajectories of warehousing development. I interviewed planners, local residents, warehousing developers, and regional agency staff to identify local planning practices and policy elements that affect the location choice of warehousing facilities. My results show land use policies (land use permission, industrial zoning, and land parcel division schemes), job-related policies (job creation initiatives and job density requirements), financial incentives (tax rates and financial incentives), and environmental regulations (building design, land use buffering, and landscaping) are the major planning elements that affect warehousing development. Relative to brownfield redevelopment in the municipalities close to the urban core of a metropolitan area, developing greenfield warehousing facilities in suburban cities is likely to cause more environmental concerns in the near future. However, unmeasured factors could be responsible for some of the warehousing development patterns I find in the data. Takeaway for practice: Knowledge, communication, and collaboration are needed to cope with the rapid growth and, in particular, the disproportionate concentration of warehousing-related environmental externalities in certain municipalities. In this study I also provide planning strategies to regulate excessive warehousing development, including land use- and job-related policies, financial incentives, and environmental regulations. With these strategies, planners in warehousing-intensive cities can determine the best way to reduce the impacts of environmental externalities on local communities in the long term. [ABSTRACT FROM AUTHOR]

Objective: E-commerce has increased freight volumes and shifted freight vehicles to local streets and arterials. These changes have caused increasing concern over traffic safety in urban areas, but there has been limited investigation of road safety impacts. To advance understanding of this issue, we examined recent trends in urban freight-related safety in the United States and characteristics of these crashes. Methods: We estimated annual rates of fatal and nonfatal urban freight-related injuries from 2005 through 2015 using data from the NHTSA's Fatal Analysis Reporting System and National Automotive Sampling System General Estimates System. We compared trends in fatal and nonfatal urban freight-related injuries, stratified by road type (interstate vs. not), to overall crash injury trends. We used piecewise linear regression models to test trend changes. We examined and compared specific crash and injury characteristics associated with urban freight-involved crashes, compared to other types of crashes, for the most recent year of data analyzed. Results: Though freight and overall crash fatality rates per vehicle mile traveled declined between 2005 and 2009, increases were observed between 2009 and 2015. Fatality rates increased 3% for all vehicle crashes, 17% for urban freight-involved crashes, and 15% for urban freight-involved crashes not occurring on interstates. Nonfatal injury rates for urban freight-involved crashes rose even more rapidly between 2009 and 2015. Nonfatal injury rates for urban freight-involved crashes increased 45%, rates for urban freight-involved crashes not occurring on interstates increased 40%, and overall nonfatal injury rates for all vehicle crashes increased 3% during this time. Of all urban freight-related crashes resulting in nonfatal injury, the proportion occurring on non-interstate roads increased from 17% to 25% between 2005 and 2015, and these crashes were more likely to occur on weekdays between 6 a.m. and 4 p.m., when freight demand is high, than weekends or evenings. Conclusions: Freight-involved injury and fatality rates are rising more rapidly than overall road traffic-related rates, both in all areas and in urban areas. These crashes are also increasingly occurring on local roads and arterials as opposed to interstates. These findings can help policymakers better understand the changing patterns of freight-related safety issues. As freight volumes increase in commercial and residential areas, planners must increasingly consider freight needs and ensure that space is allocated to this function. Additionally, changes to surveillance systems are necessary to better track burden and risk factors associated with these crashes and inform crash prevention efforts. [ABSTRACT FROM AUTHOR]

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement. [ABSTRACT FROM AUTHOR]

Drivers tend to have more range anxiety compared with driving traditional fuel vehicles if they are driving battery electric vehicle (BEV) with a long trip. Range anxiety could potentially have negative effect on driver’s emotions and behaviors. In order to understand this behavior and improve the related safety issues, this paper will focus on BEV drivers’ study in China. A survey on BEV drivers’ actual range anxiety as well as the effect of range anxiety on drivers’ behaviors is conducted in this research. Levels of feelings and attitudes of the interviewees are quantized with Likert scales using mathematical tools of the relationship. Safety buffer is defined as a measurement of the period given range anxiety starting to significantly intervene in driver’s operation. The research reveals the proportional quotative relationship between BEV drivers’ safety buffer and the mileage of trip. Factors, including driving experience, satisfactory level of recharge accessibility, and resistibility to emotions, are found to be significant contributing factors to influence the perceived range anxiety level of BEV drivers. This research will provide implications to the future study on the interface design of BEV. [ABSTRACT FROM AUTHOR]

Single-vehicle collision is a typical traffic collision, which usually causes heavy casualties and property losses. This study uses crash data during the last five years to obtain the characteristics of injury severity and contributing factors, and further provides some important insights for crash prevention and vehicle safety. Based on the investigation and in-depth data analysis of traffic collisions, 100 serious single-passenger vehicle collisions in Beijing, China, from 2009 to 2013 are collected. Main characteristics of occupant, vehicle, fixed objects, road, environment and their correlations with severity are analysed, respectively. The results demonstrate that time of occurrence, type of date, number of occupants and speed of vehicles are the significant factors related to the severity. And for the total crashes, chi-square test reveals that night time travelling, common weekday, single occupant in vehicle and over 80 km/h vehicle speed are significant related to nonfatal/fatal crashes. A logic regression model shows that driving solo and impacting trees increased the crash severity. Further, this paper discusses the main causation and related safety countermeasures on the serious single-vehicle collisions. [ABSTRACT FROM PUBLISHER]

The significant growth in freight traffic and relevant crashes has aroused increasing concerns about road safety threats in local communities. We use data from the Minneapolis-St. Paul Metropolitan Area and examine the spatial relationship between freight-related crashes and neighborhoods with low-income and minority populations. We find that both household income and percentage of minority population are significantly correlated with the density of both freight-related crashes and freight-related crashes causing severe injuries and fatalities. The results indicate that freight-related crashes are subject to a spatial inequity problem. The findings underscore the importance of incorporating freight-related safety improvement within these low-income and minority neighborhoods. • The relationship between freight-related crashes and socioeconomics of neighborhoods is examined. • Lower-income and minority neighborhoods have a higher risk of freightrelated crashes. • A coverage validity of the results is revealed in both the census tract and block group level analyses. • Policy implications on local traffic safety improvements and freight planning are provided based on the findings. [ABSTRACT FROM AUTHOR]

• Spatial distribution of freight truck related crashes differs by injury severity. • In contrast with ZIP model, XGBoost model is used to test nonlinear relationships. • Demographics, land uses and road network matter in occurrence of those crashes. • SHAP method is employed to display details of nonlinearity in XGBoost model. • Impacts of built environment vary across contexts due to nonlinear relationships. Due to the burgeoning demand for freight movement, freight related road safety threats have been growing substantially. In spite of some research on the factors influencing freight truck-related crashes in major cities, the literature offers limited evidence about the effects of the built environment on the occurrence of those crashes by injury severity. This article uses data from the Los Angeles region in 2010–2019 to explore the relationships between the built environment factors and the spatial distribution of freight truck-related crashes using XGBoost and SHAP methods. Results from the XGBoost model show that variables related to the built environment, in particular demographics, land uses and road network, are highly correlated to freight truck related crashes of all three injury types. The SHAP value plots further indicate the important nonlinear relationships between independent variables and dependent variables. This study also emphasizes the differences in modeling mechanisms between the XGBoost model and traditional statistical models. The findings will help transport planners develop operational measures for resolving the emerging freight truck related traffic safety problems in local communities. [ABSTRACT FROM AUTHOR]

Due to the burgeoning demand for freight movement in the era of e-commerce, freight related road safety threats have been growing in both urban and suburban areas, despite the improved general traffic safety over the past decades. The empirical evidence on how freight trucks related crashes are distributed across neighborhoods and correlated to spatially varying factors is, however, highly limited. This article uses data from the Los Angeles region in 2018 to analyze the spatial patterns of freight trucks related traffic crashes and examines the major factors that contribute to those patterns using spatial econometric models. Maps show that freight trucks related crashes are highly associated with major freight generators but less clustered than the overall traffic crashes. Results from the spatial Durbin model indicate that access to freight generators, economic attributes, land uses, road infrastructure, and road network variables all contribute to the spatial distribution of freight trucks related crashes. The findings could help transport planners understand the dynamics of freight trucks related traffic safety and develop operational measures for mitigating the impacts of growing goods movement on local communities. [ABSTRACT FROM AUTHOR]