Traffic Safety Analysis Research Articles

Influence of familiarity with traffic regulations on delivery riders’ e-bike crashes and helmet use: Two mediator ordered logit models

Micro-mobility vehicles such as electric bicycles, or e-bikes, are becoming one of the essential transportation modes in metropolitan areas, and most deliveries in large cities are dependent on them. Due to the e-bike’s popularity and vulnerability, e-bike crash occurrence has become a major traffic safety problem in many cities across the world; finding the most important human factors affecting e-bike safety has thus been an important recent issue in traffic safety analysis. Since delivery riders are a key group of e-bike users, and since helmet use plays a crucial role in reducing the severity of a crash, this study conducted a city-wide online survey to analyze the helmet usage of 6,941 delivery riders in Shanghai, China. To determine the in-depth mechanisms influencing helmet use and e-bike crash occurrence, including the direct and indirect effects of the relevant factors, two mediator ordered logistic regression models were employed. The mediator ordered logistic model was compared with the traditional logistic regression model, and was found to be superior for modeling indirect as well as direct influencing factors. Results indicate that riders’ familiarity with traffic regulations (FTR) is an extremely important variable mediating between the independent variables of riders’ educational level and age, and the dependent variables of helmet use and e-bike crashes. Improving riders’ FTR can consequently increase helmet use and decrease crash occurrence. Authorities can apply these findings to develop appropriate countermeasures, particularly in legislation and rider training, to improve e-bike safety.

Augmented Multiple Vehicles’ Trajectories Extraction Under Occlusions With Roadside LiDAR Data

Object occlusion is a common issue in Light Detection and Ranging (LiDAR)-based vehicle tracking technology. The occlusions can cause variance in vehicle location and speed calculation. How to link the vehicle trajectories caused by occlusion issues is a challenge for traffic engineers and researchers. This paper developed an augmented vehicle tracking method under occlusions with the roadside LiDAR data. The proposed method can be divided into two parts. The first part based on the corner point is used to choose a representative vehicle tracking point. And the second part based on the GNN algorithm is employed to link the vehicles’ trajectories under two occlusion situations. The performance of the proposed method has been evaluated using roadside lidar data collected from four different scenarios. The test results showed that more than 89% of disconnected trajectories can be fixed with the proposed method, which is superior compared to the state-of-the-art method. The proposed method can benefit a lot of transportation areas, such as traffic volume count, vehicle speed tracking, and traffic safety analysis.

Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017

An understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.

Literature review on the conflict analysis of vehicle-pedestrian interactions

An alternative to traffic safety analysis based on historical crash data the use of non-crash events is becoming more popular thanks to the rapid improvement in video-based vehicle trajectory processing. By means of Surrogate Measures of Safety (SMoS) in traffic conflict studies, the most critical elements on the road network can be identified and the probability of accidents can be proactively determined. This paper aims to summarize the state-of-the-art research regarding the analysis of pedestrian-vehicle interactions at unsignalized crossings, to synthetize the previous studies using Surrogate Measures of Safety (SMoS), and to identify the research gaps.

Modeling and simulation of decision support system analysis of the road transport system in Nigeria

The complex analysis of traffic system safety in Nigeria based on qualitative and simulation modeling was performed. These models allow us to perform a preliminary assessment of measures aimed at safety improvement of the traffic system situation in Nigeria, and to make projections to improve the parameters of the transport system. In this paper, the results of the system analysis of the transport network safety in Nigeria based on qualitative and simulation modeling are presented. The qualitative model of transport network safety and the model of system dynamics is developed, the safety measures based on simulation are suggested. The obtained results could be used for support of decision making in the field of transport network management in Nigeria. The experiments were conducted on qualitative models, they showed the effect on the transport system parameters of such measures: increasing fines for violations of the road regulations and increasing of the funding of the road repairing and construction. The support organization in decision-making in the management of the urban transport system development is very important. This problem can be solved with the use of different approaches. The use of qualitative and dynamic-system modeling for complex analysis of the transport system and evaluation of measures effectiveness aimed at improving its safety is proposed in this paper.

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

With increasing traffic demand in urban areas of metropolises, many tunnels have been constructed to improve road capacity and traffic mobility. The distance between two consecutive tunnels is relatively short which usually forms a weaving section, leading to considerable traffic conflicts. The objective of this study is to evaluate the safety performance of such inter-tunnel sections. Conflict prediction models based on negative binomial regression were developed to identify influential factors. Field data were collected at ten selected sites in Nanjing, China, and used for calibrating and validating the proposed models. Two types of inter-tunnel weaving sections (type 1 and type 2) were found in the field with distinct lane markings and operation rules. The unique lane markings in type 1 weaving sections are designed to isolate weaving traffic flows and thus reduce conflicts, but in practice, contradictory to its design intention, lead to more traffic conflicts compared with type 2 weaving sections. In addition, the length of the diverging section, merging section, and whole weaving section are found to be significant influencing factors on the conflict occurrence. The findings in the present study are expected to help engineer better design inter-tunnel sections.

The Multi-State Maritime Transportation System Risk Assessment and Safety Analysis

Maritime transportation has a pivotal role in the foreign trade and hence, the world’s economic growth. It augments the realization of “Maritime Silk Road” strategy. However, the catastrophic nature of the maritime accidents has posed a serious threat to life, property, and environment. Maritime transportation safety is a complex system and is prone to human, equipment, and environment-based risks. In the existing literature, the risk assessment studies aimed at the analysis of maritime traffic safety usually consider the state of system as two ultimate states—one is the normal state and the other is the complete failure state. In contrast to the conventional approaches, this study incorporates a multistate criterion for system state giving consideration to the near or partial failures also. A Markov Chain-based methodology was adopted to determine the variations in state system and define the instant at which a low probability incident transforms into a high-risk intolerable event. The analysis imparts critical time nodes that could be utilized to reduce the risk and evade accidents. This study holds practical vitality for the concerned departments to circumvent the potential dangers and devise systematic preemptive procedures before the accident takes place. The results of this study could be employed to augment safety and sustainability of maritime traffic and decrease the associated pollution.

Analyzing Factors that Influence Expressway Traffic Crashes Based on Association Rules: Using the Shaoyang–Xinhuang Section of the Shanghai–Kunming Expressway as an Example

Analyzing the influencing factors in expressway traffic crashes is an important aspect of traffic safety analysis. This study examines detailed data from 217 crashes that took place from 2015 to 2016 on the Shaoyang–Xinhuang section of the Shanghai–Kunming Expressway. The data were recorded by the Expressway Administration of Hunan Province, China, which is the only officially available and reliable source of traffic accident data. Descriptive statistics for crash characteristics, vehicle conditions, and road environmental condition data are provided. This paper studies the characteristics of expressway traffic accidents and their influencing factors via the association rule data mining method. The 21 rules obtained from the association rules were analyzed, the accident characteristics of various types of vehicles were studied, and the causes of injury/fatal accidents in various situations were identified. The results of this study will contribute to the targeted enforcement of traffic regulations and the improvement of road facilities to reduce traffic casualties and promote road safety in other areas.

Analysis of road traffic safety in the Piła poviat

Analysis of road traffic safety in the Piła poviat

Left turn crash risk analysis: Development of a microsimulation modeling approach

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection.Safety impacts for 750 unique combinations of intersection geometry, traffic, and signal timing parameters were simulated and the number of left-turn conflicts per hour noted. Results of the simulation analyses were used to develop statistical models that expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn phasing mode and prevailing conditions during that hour. The study was motivated by the recent widespread application of the flashing yellow arrow (FYA) which provides the opportunity to vary left-turn phasing mode by time of day—potentially leading to more efficient traffic operations at signalized intersections. In this regard, the study addresses a basic need for tools that predict how the risk for left-turn crashes might vary at a more disaggregated level than that provided by existing crash prediction models, which typically predict yearly totals of left-turn crashes, often based on annual average daily traffic volumes. Potential application of the model to the implementation of a time-variable safety-based left-turn phasing selection scheme using FYA was successfully demonstrated.

A New Traffic Conflict Measure for Electric Bicycles at Intersections

As electric bicycles (e-bikes) are becoming popular in China, concerns have been raised about their safety conditions. A traffic conflict technique is commonly used in traffic safety analysis, and there are many conflict measures designed for cars. However, e-bikes have high flexibility to change speed and trajectories, which is different from cars, so the conflict measures defined for e-bikes need to be independently explored. Based on e-bike driving characteristics, this paper proposes a new measure, the Integrated Conflict Intensity (ICI), for traffic conflicts involving e-bikes at intersections. It measures the degree of dangerousness of a conflict process, with consideration of both conflict risk and conflict severity. Time to collision is used to measure the conflict risk. Relative kinetic energy is used to measure the conflict severity. ICI can be calculated based on video analysis. The method of determining ICI thresholds for three conflict levels (serious, less serious, and slight) and two conflict types (conflicts between two e-bikes, and conflicts between an e-bike and a car) is put forward based on the questionnaires about safety perception of e-bike riders, which is regarded as the criterion of e-bike safety conditions at intersections. The video recording and a questionnaire survey about conflicts involving e-bikes at intersections have been conducted, and the unified thresholds applicable to different intersections have been determined. It is verified that ICI and its thresholds meet the criterion of e-bike safety conditions. This work is expected to be used in the selection of intersections for safety improvement of e-bike traffic.

Improving the transferability of the crash prediction model using the TrAdaBoost.R2 algorithm

The crash prediction model is a useful tool for traffic administrators to identify significant risk factors, estimate crash frequency, and screen hazardous locations, but some jurisdictions interested in traffic safety analysis can collect only limited or low-quality data. Existing crash prediction models can be transferred if calibrated, but the current aggregate calibration method limits prediction accuracy and the disaggregate method is resource-consuming. Transfer learning is another approach to calibration that acquires knowledge from old data domains to solve problems in new data domains. An instance-based transfer learning technique, TrAdaBoost.R2, is adopted in this study since it meets the requirement of site-based crash prediction model transfer. TrAdaBoost.R2 was compared with AdaBoost.R2 using a simply pooled data set to examine the efficiency in extracting knowledge from a spatially outdated source data domain (old data domain). The target data domain (new data domain) was sampled to test the technique’s adaptability to small sample size. The calibration factor method based on a negative binomial model was employed to compare its predictive performance with that of the transfer learning technique. Mean square error was calculated to evaluate the prediction accuracy. Two cities in China, Shanghai and Guangzhou, were taken mutually as source data domain and target data domain. Results showed that the models constructed with TrAdaBoost.R2 had better prediction accuracy than the conventional calibration method. The TrAdaBoost.R2 is recommended due to its predictive performance and adaptability to small sample size. Crash prediction models are proposed to construct for peak and off-peak hours separately.

City-Level China Traffic Safety Analysis via Multi-Output and Clustering-Based Regression Models

In the field of macro-level safety studies, road traffic safety is significantly related to socioeconomic factors, such as population, number of vehicles, and Gross Domestic Product (GDP). Due to different levels of economic and urbanization, the influence of the predictive factors on traffic safety measurements can differ between cities (or regions). However, such region-level or city-level heterogeneities have not been adequately concerned in previous studies. The objective of this paper is to adopt a novel approach for traffic safety analysis with a dataset containing multiple target variables and samples from different subpopulations. Based on a dataset with annual traffic safety and socioeconomic measurements from 36 major cities in China, we estimate single-output regression models, multi-output regression models, and clustering-based regression models. The results indicate that the 36 cities can be clustered into a metropolitan city class and a non-metropolitan city class, and the class-specified models can notably improve the goodness-of-fit and the interpretability of city-level heterogeneities. Specifically, we note that the effect of primary and secondary industrial GDP on traffic safety is opposite to that of tertiary industrial GDP in the metropolitan city class, while the effects of the two decomposed GDP on traffic safety are consistent in the non-metropolitan city class. We also note that the population has a positive effect on the number of fatalities and the number of injures in metropolitan cities but has no significant influence on traffic safety in non-metropolitan cities.

Risk Assessment Research of Urban Road Traffic Safety Based on Extension Matter Element Model

In order to improve the urban road traffic safety early warning ability and traffic safety situation, this paper expounds the relationship between the main factors affecting traffic safety and traffic safety from macro and micro aspects. Then the evaluation index system of urban road traffic safety is constructed by selecting four factors including pedestrian, vehicular, road and environment. And the index weight are determined by the entropy weight method, the traffic safety evaluation model is established by extension theory, so it can make a qualitative and quantitative analysis of road traffic safety. Finally, Wuhan City is used as an example to verify the established index system and evaluation model, after determining the evaluation level according to the actual situation of urban road traffic safety, the constructed matter element model is applied to the evaluation of urban road traffic safety status in Wuhan. It is concluded that the safety level of the actual roads in Wuhan is three levels, and the safety is moderate and needs to be improved.

A new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.

Transferability of safety performance functions and hotspot identification for freeways of the United States and China

Safety performance function (SPF) has been a vital tool in traffic safety evaluation including finding contributing factors to crashes, identifying hotspots, and assessing safety effects of countermeasures. In the United States (U.S.), the Highway Safety Manual provides a number of SPFs for a variety of road facilities. Due to the limited availability of traffic data in many regions, the transferability of SPFs has been an important topic in traffic safety analysis and has been evaluated by several studies. Nevertheless, the international transferability of freeway SPFs and the applicability of transferred SPFs on hotspot identification has been rarely investigated. Based on data from two Chinese cities, Shanghai and Suzhou, and three U.S. states, Texas, New York and Florida, this study analyzes the transferability of freeway SPFs between Chinese and U.S. regions. These SPFs are then transferred to the other country and their performance on hotspot identification is investigated. SPFs were developed in the frameworks of Poisson, Poisson-lognormal and negative binomial regressions for the five localities separately, and were calibrated using the calibration functions before being transferred. Without calibration, the poor model transferability was found between the two countries, while after calibration, the transferred SPFs between Shanghai/Suzhou and Texas/New York showed satisfactory performance on both model fitting and hotspot identification. However, the transferability of SPFs between Florida and the Chinese cities turned out to be unsatisfactory regardless of whether being calibrated or not, which was attributable to the considerable difference in traffic flow. The findings of this study are expected to be a good reference for researchers and practitioners who want to understand the transferability and applicability of SPFs in the international context.

Quantitative Traffic Safety Analysis for India by Japanese Experience

Quantitative Traffic Safety Analysis for India by Japanese Experience

Measuring rutting dimension and lateral position using 3D line scanning laser and inertial measuring unit

Pavement rutting caused by repeated traffic loads is a common type of structural damage to pavement. Rutting dimensions and lateral position are strongly related to traffic safety and ride comfort. However, in the field, rutting depth is usually measured by traditional methods, and rutting lateral position is ignored due to the limitations of data sources and algorithms. This study provides an automatic method for rutting dimension and lateral position measurement using a 3D line scanning laser system and Inertial Measuring Unit (IMU). Rutting dimensions (e.g., width, depth, depression area, wall slope and spacing between the inner and outer rutting) and locations (e.g., lateral position and rutting deviation away from lane center line) are measured by fusing multivariate data (e.g., 2D pavement images, transverse profiles and IMU data). In this study, two straight road sections located in Fuzhou, China, are chosen to validate the reliability and robustness of the proposed algorithm. Findings indicate that the measurement results from the new algorithm are highly correlated with the ground truths manually collected in the field. This study would benefit transportation agencies in traffic safety analysis, ride comfort evaluation and structural damage prediction based on the continuous pavement rutting dimension and its lateral positions.

Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation

Underreporting and spatial correlations are two important issues in traffic safety analysis. To deal with them simultaneously, this study proposes a Bayesian underreporting conditional autoregressive (CAR) model for analyzing crash frequency. In the formulation of the proposed model, a latent reporting process is incorporated into the crash counting process, and residual terms with CAR priors are added into the two processes to account for their respective spatial correlations. The seasonal crash data collected from Kaiyang Freeway, China in 2014 are used to verify the performance of the proposed model. It is estimated and compared with a traditional CAR model via Bayesian methods. The superiority of the underreporting model is indicated by its better model fit, more reasonable estimation results, and statistical significance of the spatial terms in the counting and reporting processes. Estimation results show that more crashes are expected to occur on longer freeway segments with larger traffic volume, smaller proportion of large truck/bus, greater horizontal curvature, and higher vertical grade. It is also shown that light traffic, traffic with more medium truck/bus or less large truck/bus, smaller horizontal curvature, bridge, and segments without ramps tend to increase the likelihood of crash reporting. These results are generally consistent with the findings in existing literature and engineering experience, which further support the proposed model as a good alternative for crash frequency analyzing.