Modeling temporal correlation and heterogeneity in real-time conflict rates using Bayesian Tobit models for signalized intersections.

The use of real-time traffic conflicts for safety studies provide more insight into how important dynamic signal cycle-related characteristics can affect intersection safety. However, such short-time window for data collection raises a critical issue that the observed conflicts are temporally correlated. As well, there is likely unobserved heterogeneity across different sites that exist in conflict data. The objective of this study is to develop real-time traffic conflict rates models simultaneously accommodating temporal correlation and unobserved heterogeneity across observations. Signal cycle level traffic data, including traffic conflicts, traffic and shock wave characteristics, collected from six signalized intersections were used. Three types of Tobit models: conventional Tobit model, temporal Tobit (T-Tobit) model, and temporal grouped random parameters (TGRP-Tobit) model were developed under full Bayesian framework. The results show that significant temporal correlations are found in T-Tobit models and TGRP-Tobit models, and the inclusion of temporal correlation considerably improves the goodness-of-fit of these Tobit models. The TGRP-Tobit models perform best with the lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity can further improve the model fit. The parameter estimates show that real-time traffic conflict rates are significantly associated with traffic volume, shock wave area, shock wave speed, queue length, and platoon ratio.

What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.

The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.

Safety impacts of geometric design on freeway segments with closely spaced entrance and exit ramps.

The objective of this study was to evaluate the effects of geometric design on crash risk on freeway segments with closely spaced entrance and exit ramps. Traffic flow, geometric design features and crash data from 80 segments on 14 freeways in the state of California, United States were applied. A multilevel logistic regression model with cross-level interactions was developed, where traffic variables were put on the case level, and their estimated coefficients were defined as a function of geometric design variables on the segment level. A basic logistic model and a multilevel logistic model without cross-level interactions were developed for comparison. The result shows that the one with cross-level interactions provides the best goodness of fit. The results indicate that six categories of geometric design variables are significantly associated with crash risk, i.e. lane configuration, basic number of lanes, ramp spacing, theoretical gore, inner shoulder width and speed limit. All but one (inner should width) geometric design variables have significant interaction terms with traffic flow variables. The effects of geometric design variables on crash risk are not fixed but vary with traffic conditions. The findings of this study can provide design guidance to improve road safety of freeway segments with closely spaced entrance and exit ramps.

Investigating consecutive conflicts of pedestrian crossing at unsignalized crosswalks using the bivariate logistic approach.

Pedestrians confront risky situations at unsignalized crosswalks when they are consecutively interacting with motorized vehicles and non-motorized vehicles while crossing. This study aims to investigate the safety of pedestrians with a new perspective that focuses on consecutive conflicts occurring during pedestrian crossing. Based on about 9 h video data collected by an unmanned aerial vehicle from six unsignalized crosswalks of a roundabout, consecutive conflicts were identified, and an integrated severity index that combines post encroachment time, jerk and yaw rate ratio was proposed to measure the severity of consecutive conflicts. Moreover, bivariate logistic models that account for and not account for the correlation between the pedestrian-motorized vehicle (P-MV) conflict and the pedestrian-non-motorized vehicle (P-NV) conflict of a consecutive conflict were developed, and speed-, count-, time to zebra-related factors and other factors of involved road users were considered in the models. A total of 899 consecutive conflicts were identified and on average one in six pedestrians encountered consecutive conflicts. The bivariate logistic modeling results show that the model accounting for the correlation significantly outperform its counterpart. A negative correlation is found between the severities of P-MV conflict and P-NV conflict, and the P-NV conflict is more likely to be the serious one. It is also found that speed of motorized vehicle and time to zebra for the first conflicting subject are the common factors that affect the severities of both P-NV conflicts and P-MV conflicts, while speed of pedestrian, speed of non-motorized vehicle, number of motorized vehicles, number of non-motorized vehicles, group and direction of pedestrians have significant effects on the severity of either P-MV conflicts or P-NV conflicts.

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers.

INTRODUCTION: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. METHOD: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ''middle-aged and elderly drivers with low risk of driving violations and high historical crash records," ''drivers with high risk of driving violations and high historical crash records," and ''middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.

Role of exposure in bicycle safety analysis: Effect of cycle path choice.

Despite the recognized environmental and health benefits of cycling, bicyclists are vulnerable to severe injuries and mortalities in the road crashes. Therefore, it is of paramount importance to identify the possible factors that may affect the bicycle crash risk. However, reliable estimates of bicycle exposure are often not available for the safety risk evaluation of different entities. The objective of this study is to advance the estimation of exposure in the bicycle safety analysis, using the detailed origin-destination data of each trip of the London public bicycle rental system. Two approaches including shortest path method (SPM) and weighted shortest path method (WSPM) are proposed to model the bicycle path choice and to estimate the bicycle distance traveled (BDT). Then, the bicycle crash frequency models that adopt BDTs as the exposure estimated using SPM and three WSPMs are developed. Three exposure measures including bicycle trips, bicycle time traveled (BTT), and BDT are assessed. Results indicate that the bicycle crash frequency models that incorporate the BDTs using WSPM have superior model fit. Moreover, the bicycle crash frequency model that incorporate the BDTs as the exposure outperforms those that incorporate the bicycle trips and BTT as the exposures. Findings of current study are indicative to the development of bicycle crash frequency model. Moreover, it should enhance the understanding on the roles of environmental, traffic and bicyclist factors in bicycle crash risk, based on appropriate estimates of bicycle exposures. Therefore, it should be useful to the transport planners and engineers for the development of bicycle infrastructures that can improve the overall bicycle safety in the long run.

A hierarchical bayesian peak over threshold approach for conflict-based before-after safety evaluation of leading pedestrian intervals.

A hierarchical Bayesian peak over threshold (POT) approach is proposed for conflict-based before-after safety evaluation of Leading Pedestrian Intervals (LPI). The approach combines traffic conflicts of different sites and periods to develop a uniform generalized Pareto distribution (GPD) model for the treatment effect estimation. The hierarchical structure has three levels, a data level that consists of modeling the traffic conflict extremes through the POT approach, a latent process level that relates GPD parameters of the data level to certain covariates, and a prior level with prior distributions to characterize the latent process. The approach was applied to a before-after (BA) safety evaluation of leading pedestrian interval (LPI) in Vancouver, BC. Pedestrian-vehicle traffic conflicts were collected from treatment and control sites during the before and after periods using an automated computer vision analysis technique. The treatment effect was measured by the best fitted GPD model with the calculation of the odds ratio (OR). The overall treatment effect varies from 18.1%-20.9% in terms of reduction in the estimated extreme-serious conflicts. The treatment effect indicates a considerable improvement in pedestrian safety after the implementation of the LPI, and the consistent results demonstrate a reliable BA safety evaluation. As such, the proposed approach is recommended as a promising tool for BA safety studies, particularly in cases where collision data is limited.

Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections.

The safety of signalized intersections has traditionally been evaluated at an aggregate level by relating historical collision records for several years to the annual traffic volume and the geometric characteristics of the intersection. This is a reactive and macroscopic approach that gives little insight into how important dynamic signal cycle-related variables can affect intersection safety such as the arrival type and the shock wave characteristics. The objective of this study is to develop traffic conflict-based real-time safety models for signalized intersections using several state-of-the-art techniques. Traffic conflicts were measured by multiple indicators including time-to-collision (TTC), modified time-to-collision (MTTC), and deceleration rate to avoid collision (DRAC). Traffic conflict rate was employed as independent variable while traffic volume, queue length, shock wave area, shock wave speed, and platoon ratio of each cycle were used as covariates in the safety models. Four candidate Tobit models were developed and compared under the Bayesian framework: conventional Tobit model, grouped random parameters Tobit (GRP-Tobit) model, random intercept Tobit (RI-Tobit) model, and random parameters Tobit (RP-Tobit) model. The results showed that the GRP-Tobit model performs best with lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity across sites can significantly improve the model fit. The model estimation results showed that higher conflict rates were associated with various shock wave characteristics (positive sign for shock wave area, shock wave speed, and queue length) and higher traffic volume. Lower conflict rates were related with higher platoon ratio (favorable arrival patterns). The developed models can have potential applications in real-time safety evaluation, real-time optimization of signal control, and connected and autonomous vehicles (CAV) trajectories planning.

Roles of infrastructure and land use in bicycle crash exposure and frequency: A case study using Greater London bike sharing data.

Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012-2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.

Simulation of pedestrian evacuation route choice using social force model in large-scale public space: Comparison of five evacuation strategies.

The primary objective of this study is to compare pedestrian evacuation strategies in the large-scale public space (LPS) using microscopic model. Data were collected by video recording from Tian-yi square for 36 hours in city of Ningbo, China. A pedestrian evacuation simulation model was developed based on the social force model (SFM). The simulation model parameters, such as reaction time, elasticity coefficient, sliding coefficient, et al, were calibrated using the real data extracted from the video. Five evacuation strategies, strategy 1 (S1) to strategy 5 (S5) involving distance, density and capacity factors were simulated and compared by indicators of evacuation time and channel utilization rate, as well as the evacuation efficiency. The simulation model parameters calibration results showed that a) the pedestrians walking speed is 1.0 ~ 1.5m/s; b) the pedestrians walking diameter is 0.3 ~ 0.4m; c) the frequency of pedestrian arrival and departure followed multi-normal distribution. The simulation results showed that, (a) in terms of total evacuation time, the performance of S4 and S5 which considering the capacity and density factors were best in all evacuation scenarios, the performance of S3 which only considering the density factor was the worst, relatively, and S1 and S2 which considering the distance factor were in the middle. (b) the utilization rate of channels under S5 strategy was better than other strategies, which performs best in the balance of evacuation. S3 strategy was the worst, and S1, S2 and S4 were in the middle. (c) in terms of the evacuation efficiency, when the number of evacuees is within 2, 500 peds, the S1 and S2 strategy which considering the distance factor have best evacuation efficiency than other strategies. And when the number of evacuees is above 2, 500 peds, the S4 and S5 strategy which considering the capacity factor are better than others.

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model.

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.

Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model.

Crashes present different collision types. There usually exist unobserved risk factors which could jointly affect crash rates of different types, resulting in correlation and heterogeneity issues across observations. The primary objective of the study is to propose a novel random parameters multivariate Tobit (RPMV-Tobit) model for evaluating risk factors on crash rates of different collision types. Crash data from 367 freeway diverge areas in a three-year period were obtained for modeling. Three major types of collisions including rear-end, sideswipe, and angle collisions were considered. The RPMV-Tobit model was structured to simultaneously accommodate correlations between crash rates across collision types and unobserved heterogeneity across observations. The RPMV-Tobit model was compared with a multivariate Tobit (MV-Tobit) model, a random effect multivariate Tobit (REMV-Tobit) model, and independent univariate Tobit (IU-Tobit) models under the Bayesian framework. The results showed that MV-Tobit model outperforms the IU-Tobit models on fitting crash rates, indicating that accounting for the correlation between crash types can improve model fit. The RPMV-Tobit model and REMV-Tobit model perform better than the MV-Tobit model, suggesting that accounting for the unobserved heterogeneous can further improve model fit. The improvement of model performance with the RPMV-Tobit model is higher than that with the REMV-Tobit model. The impacts of each risk factor on crash rates were estimated and some differences were found across different collision types. The lane-balanced design, number of lanes on mainline, speed limit, and speed difference present significant heterogeneous effects on crash rates. Findings suggest that the RPMV-Tobit model is a superior approach for comprehensive crash rates modeling and traffic safety evaluation purposes.

Identifying the crash characteristics on freeway segments based on different ramp influence areas.

Objective: This study aimed to explore the relationship between crash types and different freeway segments and identify the factors contributing to crashes on different freeway segments. Unlike most of the previous studies on freeway segments, this study separately investigates basic freeway segments, single ramp influence segments, and multiple ramp influence segments. Methods: Nonlinear canonical correlation analysis (NLCCA) and proportionality test were used to identify the relationship between crash types and different freeway segments. The data sets for the different freeway segments accumulated for this study consist of 9,867 crash samples with complete information on all 22 chosen variables. A multinomial logit model (MNL) was used to estimate the influence of crash factors on different freeway segments. Results: The results show that weaving and diverge overlap influence segments (WD) are more likely to have injury or fatal crashes; diverge and diverge overlap influence segments (DD) are more likely to have property damage-only (PDO) crashes; merge and merge overlap influence segments (MM) are more likely to have sideswipe crashes; and WD have non-sideswipe crashes; WD and weaving overlap influence segments (MW) are more likely to have rear end crashes; and MM segments are less likely to have hit object crashes. The contributing factors are identified by MNL and the results show that different traffic variables, environmental variables, vehicle variables, driver variables, and geometric variables significantly affected the likelihood of crashes on different freeway segments. Conclusions: Investigation of crash types and factors contributing to crashes on different freeway segments is based on multiple ramp influence segments, which can promote a better understanding of the safety performance of various freeway segments.

A Correlational Study on Cerebral Microbleeds and Carotid Atherosclerosis in Patients with Ischemic Stroke.

PURPOSE: This study aimed to investigate the correlation between cerebral microbleeds and carotid atherosclerosis in patients with ischemic stroke. SUBJECTS AND METHODS: Patients with ischemic stroke treated in a hospital in China from 2016 to 2017 were enrolled in the study. Based on the results from susceptibility-weighted imaging, the patients were divided into cerebral microbleed and noncerebral microbleed groups. The degree of carotid atherosclerosis was assessed with carotid intima-media thickness (CIMB) and Crouse score of carotid plaque. The details of patients' demographic information, cerebrovascular disease-related risk factors, carotid atherosclerosis indices, cerebral microbleed distribution, and grading were recorded, compared, and analyzed. RESULTS: Logistic regression analysis of the 198 patients showed that CIMB and Crouse score were significantly correlated with the occurrence of cerebral microbleeds. The CIMB thickening group (P = .03) and the plaque group (P = .01) were more susceptible to cerebral microbleeds. In the distribution of cerebral microbleed sites, Crouse scores were the highest in the mixed group and showed a statistically significant difference (P < .01). As the degree of carotid atherosclerosis increased, the average number of cerebral microbleeds also increased (P < .01). The receiver operating characteristic curve analysis of the carotid atherosclerosis indices showed a statistically significant difference. The CIMB value combined with the Crouse score was the best indicator (P < .01). CONCLUSION: In patients with ischemic stroke, cerebral microbleeds are closely related to carotid atherosclerosis. Active control of carotid atherosclerosis is important to prevent cerebral microbleeds in patients with ischemic stroke.

Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities.

Bicyclists running the red light at crossing facilities increase the potential of colliding with motor vehicles. Exploring the contributing factors could improve the prediction of running red-light probability and develop countermeasures to reduce such behaviors. However, individuals could have unobserved heterogeneities in running a red light, which make the accurate prediction more challenging. Traditional models assume that factor parameters are fixed and cannot capture the varying impacts on red-light running behaviors. In this study, we employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects. Two types of crossing facilities were considered which were the signalized intersection crosswalks and the road segment crosswalks. Electric and conventional bikes were distinguished in the modeling. Data were collected from 16 crosswalks in urban area of Nanjing, China. Factors such as individual characteristics, road geometric design, environmental features, and traffic variables were examined. Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model. More red-light runners are predicted at signalized intersection crosswalks than at road segment crosswalks. Factors affecting red-light running behaviors are gender, age, bike type, road width, presence of raised median, separation width, signal type, green ratio, bike and vehicle volume, and average vehicle speed. Factors associated with the unobserved heterogeneity are gender, bike type, signal type, separation width, and bike volume.

A cross-comparison of different techniques for modeling macro-level cyclist crashes.

Despite the recognized benefits of cycling as a sustainable mode of transportation, cyclists are considered vulnerable road users and there are concerns about their safety. Therefore, it is essential to investigate the factors affecting cyclist safety. The goal of this study is to evaluate and compare different approaches of modeling macro-level cyclist safety as well as investigating factors that contribute to cyclist crashes using a comprehensive list of covariates. Data from 134 traffic analysis zones (TAZs) in the City of Vancouver were used to develop macro-level crash models (CM) incorporating variables related to actual traffic exposure, socio-economics, land use, built environment, and bike network. Four types of CMs were developed under a full Bayesian framework: Poisson lognormal model (PLN), random intercepts PLN model (RIPLN), random parameters PLN model (RPPLN), and spatial PLN model (SPLN). The SPLN model had the best goodness of fit, and the results highlighted the significant effects of spatial correlation. The models showed that the cyclist crashes were positively associated with bike and vehicle exposure measures, households, commercial area density, and signal density. On the other hand, negative associations were found between cyclist crashes and some bike network indicators such as average edge length, average zonal slope, and off-street bike links.

Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China.

The boom in bike-sharing is receiving growing attention as societies become more aware of the importance of active non-motorized traffic modes. However, the low usage of this transport mode in China raises concerns. The primary objective of this study is to explore factors affecting bike-sharing usage and satisfaction degree of bike-sharing among the bike-sharing user population in China. Data were collected by a questionnaire survey in Ningbo. A bivariate ordered probit (BOP) model was developed to examine simultaneously those factors associated with both bike-sharing usage and satisfaction degree of bike-sharing among users. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results showed that the BOP model can account for commonly shared unobserved characteristics within usage and satisfaction of bike-sharing. The BOP model results showed that the usage of bike-sharing was affected by gender, household bicycle/e-bike ownership, trip model, travel time, bike-sharing stations location, and users' perception of bike-sharing. The satisfaction degree of bike-sharing was affected by household income, bike-sharing stations location, and users' perception of bike-sharing. It is also found that bike-sharing usage and satisfaction degree are strongly correlated and positive in direction. The results can enhance our comprehension of the factors that affect usage and satisfaction degree of bike-sharing. Based on the results, some suggestions regarding planning, engineering, and public advocacy were discussed to increase the usage of bike-sharing in Ningbo, China.

Effects of parallelogram-shaped pavement markings on vehicle speed and safety of pedestrian crosswalks on urban roads in China.

The primary objective of this study was to evaluate the effects of parallelogram-shaped pavement markings on vehicle speed and crashes in the vicinity of urban pedestrian crosswalks. The research team measured speed data at twelve sites, and crash data at eleven sites. Observational cross-sectional studies were conducted to identify if the effects of parallelogram-shaped pavement markings on vehicle speeds and speed violations were statistically significant. The results showed that parallelogram-shaped pavement markings significantly reduced vehicle speeds and speed violations in the vicinity of pedestrian crosswalks. More specifically, the speed reduction effects varied from 1.89km/h to 4.41km/h with an average of 3.79km/h. The reduction in the 85th percentile speed varied from 0.81km/h to 5.34km/h with an average of 4.19km/h. Odds ratios (OR) showed that the parallelogram-shaped pavement markings had effects of a 7.1% reduction in the mean speed and a 6.9% reduction in the 85th percentile speed at the pedestrian crosswalks. The reduction of proportion of drivers exceeding the speed limit varied from 8.64% to 14.15% with an average of 11.03%. The results of the crash data analysis suggested that the use of parallelogram-shaped pavement markings reduced both the frequency and severity of crashes at pedestrian crosswalks. The parallelogram-shaped pavement markings had a significant effect on reducing the vehicle-pedestrian crashes. Two crash prediction models were developed for vehicle-pedestrian crashes and rear-end crashes. According to the crash models, the presence of parallelogram-shaped pavement markings reduced vehicle-pedestrian crashes at pedestrian crosswalks by 24.87% with a 95% confidence interval of [10.06-30.78%]. However, the model results also showed that the presence of parallelogram-shaped pavement markings increased rear-end crashes at pedestrian crosswalks by 5.4% with a 95% confidence interval of [0-11.2%].

Comparative analysis of risky behaviors of electric bicycles at signalized intersections.

OBJECTIVE: The primary objective of this study was to compare the risky behaviors of e-bike, e-scooter, and bicycle riders as they were crossing signalized intersections. METHODS: Pearson's chi-square test was used to identify whether there were significant differences in the risky behaviors among e-bike, e-scooter, and bicycle riders. Binary logit models were developed to evaluate how various variables affected the behaviors of 2-wheeled vehicle riders at signalized intersections. Field data collection was conducted at 13 signalized intersections in 2 cities (Nanjing and Kunming) in China. RESULTS: Three different types of risky behaviors were identified, including stop beyond the stop line, riding in motorized lanes, and riding against traffic. Two-wheeled vehicle riders' gender and age and traffic conditions were significantly associated with the behaviors of 2-wheeled vehicle riders at the selected signalized intersections. CONCLUSIONS: Compared to e-bike and bicycle riders, e-scooter riders are more likely to take risky behaviors. More specifically, they are more likely to ride in motorized lanes and ride against traffic.