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.

The speeding violation has become a key concern in the traffic safety management, as it increases the risk of traffic crashes, as well as the severity of these crashes. This uncivilized phenomenon is prominent and presents an increasing trend in Wujiang in recent years, which severely endangers the road traffic safety. This study is approved by the Traffic Police Brigade of Wujiang Public Security Bureau and aims to explore the characteristic of the speeding violation behaviour and attempt to make an effective prediction about it. This study proposes a speeding violation type (including type 1 and type 2) prediction method using electronic law enforcement data obtained from the public security administration of Wujiang. Before the prediction, a speeding violation influence factor analysis based on the binary logical regression model is proposed. The binary logical regression analysis identifies that the license plate, season, speeding area, position, and rainfall are the influence factors of Wujiang’s speeding violation. Then a decision tree method is used to predict the speeding violation type according to the influence factors, and from which the speeding violation situations can be determined. The prediction results demonstrate that under the hypothetical conditions, the high speeding violation level (i.e., type 2) tends to occur under high rainfall environment, and the foreign license plate and autumn present a larger probability of high speeding violation level than the local license plate and other seasons (i.e., spring, summer, and winter), respectively. Finally, a model comparison between the proposed method and other tree-based approaches is conducted. The comparison results show that the decision tree method outperforms other methods in prediction performance (including accuracy, precision, recall, and classification error), runtime, and ROC curve, which indicates that the decision tree method is feasible in predicting the speeding violation type of Wujiang. Based on the findings, the traffic managers can macroscopically grasp the speeding violation situation of the whole road networks, which can be referred for making the related polices and taking intervention measures.

Intoxicated driving is a threat to both drivers and other road users. Exploring the association between intoxicated driving factors and traffic crashes is essential for taking effective countermeasures. Most previous works have studied the relation between intoxicated driving and traffic crash based on some large-sized cities. The current study aims to evaluate the effect of driving factors on traffic crashes among intoxicated drivers in a small-sized city in China. Descriptive statistics and binary logistic regression analysis are performed to guide the study, and the data (N=1010) for the period 2016–2017 in Wujiang (i.e., a small-sized city in China) are employed as the target samples. The results demonstrate age, years of driving experience, road position, week, hour and blood alcohol concentration (BAC) are associated with traffic crashes in Wujiang. Specifically, the age of “18–25”, the years of driving experience of “≤2”, the “road intersection”, the “weekend”, the period of “0:00–6:59” and the BAC of “above 150 mg/100 mL” are more likely to cause traffic crashes among intoxicated drivers. The findings can be referred to make some targeted policies or measures to relieve Wujiang’s intoxicated driving situation and reduce the number of crashes caused by intoxicated driving.

Red-light violations of pedestrians crossing at signal intersections is one of the key factors in pedestrian traffic accidents. Even though there are various studies on pedestrian behavior and pedestrian traffic conflicts, few focus on the risk of different crosswalks for the violating pedestrian group. Due to the spatio-temporal nature of violation risk, this study proposes a geographical and temporal risk evaluation method for pedestrian red-light violations, which combines actual survey and video acquisition. First, in the geographical-based risk evaluation, the pedestrian violation rate at signal intersections is investigated by Pearson correlation analysis to extract the significant influencing factors from traffic conditions, built environment, and crosswalk facilities. Second, in the temporal-based risk evaluation, the survival analysis method is developed to quantify the risk of pedestrian violation in different scenarios as time passes by. Finally, this study selects 16 typical signalized intersections in Suzhou, China, with 881 pedestrian crosswalk violations from a total size of 4586 pedestrians as survey cases. Results indicate that crossing distance, traffic volume on the crosswalk, red-light time, and crosswalk-type variables all contribute to the effect of pedestrian violation from a geographical perspective, and the installation of waiting refuge islands has the most significant impact. From the temporal perspective, the increases in red-light time, number of lanes, and traffic volume have a mitigating effect on the violations with pedestrian waiting time increases. This study aims to provide a development-oriented path by proposing an analytical framework that reconsiders geographical and temporal risk factors of violation. The findings could help transport planners understand the effect of pedestrian violation-related traffic risk and develop operational measures and crosswalk design schemes for controlling pedestrian violations occurring in local communities.

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