Methods to rank traffic rule violations resulting in crashes for allocation of funds

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks - A case study of New York

INTRODUCTION

Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.

METHOD

This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City.

RESULTS

Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors.

CONCLUSIONS

Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them.

PRACTICAL APPLICATIONS

The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

Identification and screening of key traffic violations: based on the perspective of expressing driver's accident risk

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

Effects of traffic enforcement cameras on macro-level traffic safety: A spatial modeling analysis considering interactions with roadway and Land use characteristics

Nowadays, intelligent transportation system (ITS) planning has been often integrated into transportation planning stage. As a component of ITS, traffic enforcement cameras have been found to reduce dangerous behaviors, such as red-light running and speeding. However, with limited resource, it is important to understand the effects of enforcement cameras on macro-level safety, so that traffic policy-makers can better allocate those resources to improve traffic safety from the planning stage. In this paper, we examined the effects of various traffic enforcement cameras on regional traffic crash risk, considering their interactions with roadway and land use characteristics. The Kunshan city in Suzhou, China was selected in this study and a spatial modeling analysis was applied. According to the modeling results, several conclusions can be drawn: 1. Interaction effects on regional injury/PDO crash risk were found between traffic enforcement cameras and roadway/land use factors; 2. Traffic enforcement cameras were found to be associated with decreased regional crash risk. Among them, red-light running and speeding cameras were associated with the reduction of injury/PDO crash frequency, which can be further enhanced when being installed in certain area (e.g. industrial, commercial, residential land use) and on certain roadways (e.g. major arterials, local roads). Illegal lane changing cameras were associated with the decrease in PDO crash frequency, while such effect on reducing injury crashes was only found as significant on major arterials; 3. The main effects of certain land use and roadway factors appeared to be mediated by traffic enforcement interaction terms. For example, the main effect of industrialized land use was found as insignificant, while the interaction term between industrial area and speeding cameras showed a significant effect of reducing injury/PDO crash frequency. Based on those findings, traffic enforcement cameras, as one of the major components of ITS, need to be carefully considered at the transportation planning stage. In general, this study provides valuable information for policy-makers and transportation planners to improve regional traffic safety, by properly allocating traffic enforcement resources.

A new approach for resource allocation for black spot treatment (case study: The road network of Iran)

Currently, spatial and temporal distribution of safety resources in Iran is entirely based on expert opinions, regardless of network priorities. Considering the lack of resources for implementing safety treatments, prioritizing unsafe points is an important and complicated issue where the effectiveness of each safety treatment option should be thoroughly investigated. The political, social, and environmental aspects should also be taken into consideration, including social and political pressures and officials talks on less important topics. Obviously, this inappropriate resource allocation poses a serious challenge to the expected goals. In this study, a methodology based on economic and social issues is proposed to optimize the annual budget allocation for eliminating or reducing the risk of accident-prone points. In this methodology, the spatial and temporal distribution of budget is determined using a mathematical model aimed to maximize the benefits of reducing the accidents after deducting the costs of implementing the safety countermeasures. The outputs of this model include the safety countermeasure alternatives and a five-year time schedule for implementing them, or the alternative of no action with regard to budget, social, and judicial constraints. In order to evaluate the proposed method, it is applied to the road network of Iran and the results are compared with those of the conventional method that is currently used for resource allocation in this country. The results show that the proposed method leads to 15% higher benefits compared to the conventional method. Moreover, this method makes 641 safe points, which is about 17% more than the safe points resulted from the existing method. Therefore, the proposed method brings about a safer network as a result of the optimal allocation of available resources.

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively.

Modeling and comparing injury severity of at-fault and not at-fault drivers in crashes

This paper examines and compares the effect of selected variables on driver injury severity of, both, at-fault and not at-fault drivers. Data from the Highway Safety Information System (HSIS) for the state of North Carolina was used for analysis and modeling. A partial proportional odds model was developed to examine the effect of each variable on injury severity of at-fault driver and not at-fault driver, and, to examine how each variable affects these two drivers' injury severity differently. Road characteristics, weather condition, and geometric characteristics were observed to have a similar effect on injury severity in a crash to at-fault and not at-fault drivers. Age of the driver, physical condition, gender, vehicle type, and, the number and type of traffic rule violations were observed to play a significant role in the injury severity of not at-fault drivers when compared to at-fault drivers in the crash. Moreover, motorcyclists and drivers 70 years or older are observed to be the most vulnerable road users.