Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

INTRODUCTION

Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

METHOD

In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country.

RESULTS

The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities - Shandong Province, China, 2012-2021

Introduction

This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities.

Methods

Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson's correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities.

Results

From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers.

Conclusions

Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

Investigating injury severity interaction between urban and urban-rural fringe areas: a grouped random parameters seemingly unrelated bivariate probit approach

Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas.

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes.

Explore traffic conflict risks considering motion constraint degree in the diverging area of toll plazas

In the diverging area of toll plazas, the absence of lane markings, the gradual widening of lanes, and the crossing of vehicles with different tolling methods increase the likelihood of collisions. This study proposed a concept of motion constraint degree to investigate traffic conflict risks in the toll plaza diverging area. On the basis of the motion constraint degree, a two-step method was developed, in which all potentially influencing factors were separated into two parts. The first part was used to analyze the association between the motion constraint degree and some factors, while the remaining factors were utilized for risk regression/prediction together with the motion constraint degree. The random parameters logit model was applied for regression analysis and four prevalent machine learning models were employed for risk prediction. Results indicate that (1) the proposed approach considering motion constraint degree outperforms the conventional direct method, no matter for conflict risk regression or prediction; (2) the motion constraint degree is not monotonically correlated with the risk level of vehicles; (3) due to the layout of the toll plaza, ETC vehicles are less likely to be at risk in the diverging area; and (4) lane-changing behaviors in the restricted space increase the conflict risk.

Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways

Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways.

Can haze warning policy reduce traffic accidents: evidence from China

Haze pollution may decrease drivers' driving performance by worsening their physical and psychological states. This paper explores the effects of haze warning policy on traffic accidents for the first time. We use the daily-city traffic accident data from 2016 to 2019 in China and construct Regression Discontinuity (RD) strategies based on the warning signal thresholds of PM2.5 concentration for estimations. The results show that one yellow warning and one orange warning can reduce traffic accidents by 8.8% and 13.1% on that day respectively, while the red warning does not work significantly possibly due to the self-perceived channel rather than the warning-signal channel. We also find that the effects may vary among different groups of drivers, vehicles, and roads. Our results prove that the haze warning policy is a non-negligible tool to reduce traffic accidents, which is useful to policy-making both related to haze pollution regulation and transportation safety.

Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: The case of Scotland

This paper investigates the determinants of injury severities in pedestrian-motor vehicle accidents at signalised and unsignalised junctions, and at physically-controlled and human-controlled crossings in Scotland. The accident data were drawn from the official police crash report database of the UK spanning a period between 2010 and 2018. Correlated random parameter ordered probit models with heterogeneity in the means were developed in order to account for the multi-layered impact of unobserved heterogeneity on statistical estimation. The model estimation results showed that the severities of accident injuries are affected by roadway, location, weather, vehicle, and driver characteristics as well as temporal attributes (including time and day of the accident). Factors such as the urban context, lighting and weather conditions and road surface conditions were found to result in correlated random parameters, thus capturing the intricate, yet interactive effects of unobserved heterogeneity, and particularly the unobserved behavioural response of road users to different traffic control types at junctions and crossings. Vehicle type, driver's gender and day-of-the-week were observed to influence the random parameters' distributions. Empirically, the results showcase variations in the determinants of injury severities at signalised and unsignalised junctions, and at physically-controlled and human-controlled crossings. Even though most of these variations were related to the magnitude of impact of the determinants, differences in the directional effects on injury severities were also identified, mainly for factors related to weather conditions, hazard presence on the road, and temporal characteristics of the accidents.

Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

School travel has been a significant source of safety concerns for children, parents, and public authorities. It will continue to be a source of concerns as long as severe accidents continue to emerge during pupils’ commute to school. This study provides an empirical analysis of the factors influencing the injury severities of the accidents that occurred on trips to or from school in Scotland. Using 9-year data from the STATS19 public database, random parameter binary logit models with allowances for heterogeneity in the means were estimated in order to investigate injury severities in urban and rural areas. The results suggested that factors such as the road type, lighting conditions, vehicle type, and age of the driver or casualty constitute the common determinants of injury severities in both urban and rural areas. Single carriageways and vehicles running on heavy oil engines were found to induce opposite effects in urban and rural areas, whereas the involvement of a passenger car in the accident decomposed various layers of unobserved heterogeneity for both area types. The findings of this study can inform future policy interventions with a focus on traffic calming in the proximity of schools.

A simulation-based empirical bayes approach: Incorporating unobserved heterogeneity in the before-after evaluation of engineering treatments

The Empirical Bayes approach for before-after evaluation methodology utilizing the negative binomial model does not account well for unobserved heterogeneity. Building on the Empirical Bayes approach, the objective of this study was to propose a framework to accommodate unobserved heterogeneity in before-after countermeasure evaluation. In particular, this study has proposed a simulation-based Empirical Bayes approach by applying the panel random parameters negative binomial model with parameterized overdispersion (PRNB-PO) to evaluate the effectiveness of engineering treatments. The proposed framework has been tested for the wide centerline treatment (WCLT) on rural two-lane two-way highways in Australia. The empirical analysis included 511 km of WCLT treated highways in a before-after evaluation within a time period of 2010 - 2018 and 430 km of reference sites in Queensland, Australia. The PRNB-PO models outperformed the traditional negative binomial models in terms of goodness-of-fit and prediction performance for total injury crashes, and fatal and serious injury (FSI) crashes. The simulation-based Empirical Bayes approach using the PRNB-PO model resulted in more precise estimates of crash modification factors than the standard Empirical Bayes approach. The WCLT is found to result in significant reductions in total injury crashes by 28.21% (95% confidence interval (CI) = 22.92 - 33.50%), FSI crashes by 13.90% (95% CI = 6.99 - 20.81%), and head-on crashes by 25.45% (95% CI = 14.87 - 36.03%). Overall, WCLT is an effective engineering treatment and should be considered a low-cost countermeasure on rural two-lane two-way highways.

Impact of High-Speed Rail on Social Equity—Insights from a Stated Preference Survey in Vietnam

This study investigated the impact of high-speed rail (HSR) on social equity, utilizing information from a stated preference survey conducted in Vietnam. Social equity was examined across the population of four cities representing the northern, central, and southern areas of Vietnam. In general, the high price of HSR is one of the barriers to using HSR over inter-city buses and conventional trains. Low-income groups (less than VND 6 million per month) have 4.894 and 4.725 times the likelihoods, compared to higher income groups, of retaining the use of an inter-city bus or conventional train, respectively, after introducing HSR. Our findings reveal the fact that social inequity may occur, with the low-income group being especially vulnerable, due to the existence of HSR in the future. Furthermore, our results indicate that the interest of people towards inter-city buses and conventional trains varied among the four cities before and after the presence of HSR. More specifically, low-income groups in Vinh and Nha Trang were observed to have a higher feeling of staying away from HSR, as they prefer to use inter-city buses. The findings of this study suggest that planners and policymakers need to consider various components of HSR ticket planning, in order to achieve sustainable evolution of the passenger rail system.

Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means

In Thailand in 2016, more than 70% of all deaths due to road accidents were motorcyclist deaths. This study uses a correlated random parameters ordered probit model with heterogeneity in means (CRPOPHM) to obtain insight into differences in the significant factors determining the severity of motorcyclist injury between motorcycle crashes in urban and rural roadways, using data on motorcycle crashes in Thailand from 2016 to 2019. Using a rating system for injury severity level from minor injury to severe injury and to fatal injury, a wide range of potential risk factors are considered, including rider characteristics and actions, roadway characteristics, environmental and temporal characteristics, and crash characteristics. The findings indicate that, although some factors are significant in both urban and rural models, factors such as male rider, illegally overtaking, drowsiness, four-lane or wider highway, flush or depressed median, road on slope, weekend, nighttime with light, crash with van or minibus, and rear-ending or sideswiping crash, are significant only in the rural model, whereas the factors barrier median, occurring between 18:00 and 23:59, and striking a passenger car are statistically significant in only the urban model. These findings further suggests that difference in effect of unobserved characteristics could be seen in different crash locations, and splitting the model estimation between both location types could be done to develop effective guidance for policies to mitigate the severity of motorcyclist injuries. In addition, practical policy-related recommendations drawn from the results of the analysis are provided. With respect to methodology, the proposed CRPOPHM method outperforms lower-ordered models in terms of statistical fit and captures unobserved heterogeneity to a greater extent.

A correlated random parameters approach to investigate large truck rollover crashes on mountainous interstates

Rollover risk on mountainous interstates is a major concern for transportation agencies due to the combined mixed effects of adverse weather conditions and complex topography. Such crashes incur hazardous consequences on road users' lives. Therefore, a correlated random parameters logit modeling framework was employed to investigate the influences of crash precursors on rollover risk to identify effective safety countermeasures. This approach was selected to account for both the crash contributing factors' unobserved heterogeneity effects and the correlations among those effects. The data, used in this study, were those of single-truck crashes on Wyoming's interstate curved sections. The traditional logit and uncorrelated random parameters, or mixed, logit models were attempted as well. With that, the analysis results indicated that the fit of the correlated random parameters logit model was superior to those of the others. It also revealed insights regarding correlations among random parameters that were obscure in the other models. According to its results, on average, veering off the road, overcorrections and severe winds raised the risk of single-truck rollover crashes. On the other hand, median barriers, roadside guardrails, tight horizontal curves, icy road surfaces, wet surfaces and surfaces covered by loose material, in general, reduced the likelihood of rollovers. Correlations, such as those between severe winds and icy surfaces and those between roadside guardrails and icy surfaces, were inferred as well. This study's results will assist transportation officials in efficiently identifying appropriate countermeasures to mitigate the impact of truck rollovers particularly during adverse weather conditions.

Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability

This study employs the correlated mixed logit models with heterogeneity in means by accounting for temporal instability to estimate both injury severity and vehicle damage. Two years of intersection crash data from Connecticut were analyzed based on driver characteristics, highway and traffic attributes, environmental variables, vehicle and crash types. These elements were used as independent variables to explore the contributing factors to crash outcome. The likelihood ratio test highlights that the temporal instability exists in both injury severity and vehicle damage models. The model estimation results illustrate that the means of some random parameters are different among crashes. The correlation coefficients of random parameters verify that these random parameters are not always independent, and their correlations should be considered and accounted for in crash severity estimation models. The investigation and comparison between injury severity models and vehicle damage models verify that the injury severity and vehicle damage are highly correlated, and the effects of contributing factors on vehicle damage are consistent with the results of injury severity models. This finding demonstrates that vehicle damage can be used as a potential surrogate measure to injury severity when suffering from a low sample of severe injury crashes in crash severity prediction models. It is anticipated that this study can shed light on selecting appropriate statistical models in crash severity estimation, identifying intersection crash contributing factors, and help develop effective countermeasures to improve intersection safety.

A mixed logit model with mean-variance heterogeneity to investigate factors of crash occurrence

This study thoroughly investigated factors affecting crash occurrence using detailed data of crash, traffic condition and freeway geometries. To fully account for heterogeneity induced by unobserved characteristics of crash factors, a mixed logit model with mean-variance heterogeneity was estimated as an alternative to the commonly used mixed logit model and the fixed parameters logit model. Results indicate that the mixed logit model with mean-variance heterogeneity could improve the goodness-of-fit and was more flexible in accounting for unobserved heterogeneity compared with its counterparts. Additionally, by allowing means and variances of random parameters to be estimable functions of explanatory variables, the safety effect of interactions among multiple factors was concluded, for example: (1) sharp curves resulted in an increasing risk of crash and the rate of increase was positively correlated with the distance travelled by vehicles along a steep downgrade; (2) the adverse safety effect of steep downgrade increased with the distance covered by vehicles, especially for segments with high proportion of heavy trucks; (3) downhill segments with steep slopes were particularly dangerous. Findings from this study are expected to provide an insightful knowledge to the mechanism of crash occurrence and should be beneficial to design and manage safer freeways.

Observational-based study to explore pedestrian crossing behaviors at signalized and unsignalized crosswalks

Despite many studies on exploring the behaviors of pedestrians crossing the road, there is a need for comprehensive studies that identify the factors that may influence pedestrians crossing behavior at signalized and unsignalized intersections. This study aims to comprehensively examine the influence of gender, age group, group-crossing, technological devices and carrying items on pedestrians crossing behaviors at signalized and unsignalized crosswalks simultaneously. Observational data of 552 pedestrians at two signalized and two unsignalized crosswalks in Tehran were collected. Temporal and spatial violations, conflict experience and collision avoidance, situational awareness, and pedestrians crossing speed were used as pedestrians crossing behaviors indicators. To model crossing behaviors, linear mixed models (LMMs) and Generalized linear mixed models (GLMMs) with fixed-effect approach were applied for the continuous outcome (pedestrians' crossing speed) and binary outcomes, respectively. Phi and Cramer's V coefficients were used to avoid multicollinearity. Results showed that traffic checks before and while crossing showed a high positive correlation with crossing at "don't walk" and flashing "don't walk" signals and conflict experience at signalized crosswalks. As compared to females, males started their crossing more on flashing "don't walk" signal and crossed the crosswalk faster. Older pedestrians exhibited more cautious behaviors at signalized intersections but less in unsignalized intersections. Alone pedestrians behaved more cautiously than groups and crossed the crosswalk more quickly. Using technological devices, regardless of their types, caused pedestrians to not exhibit one or more safe crossing indicators considered in this study. Pedestrians talking on their phones had the least cautious behaviors. Pedestrians listening to music mostly looked at the ground or straight direction rather than looking left-right for traffic. The findings from this study are a valuable resource to road authorities and policy makers to develop appropriate targeted strategies to prevent pedestrians' injuries and fatalities and improve crosswalks safety.

Analysis of factors influencing delivery e-bikes' red-light running behavior: A correlated mixed binary logit approach

The red-light running (RLR) behavior of delivery e-bike (DEB) riders in cities has become the primary cause of traffic accidents associated with this group at signalized intersections. This study aimed to explore the influencing factors of red light running behavior and identify the differences between the DEB riders and the ordinary e-bike (OEB) riders to aid the development of countermeasures. In this study, the mixed (random parameter) binary logistic model was employed to capture the effects of unobserved heterogeneity. With this approach, factors including individual characteristics, behavioral variables, characteristics of signalized intersections, and the traffic environment were examined. Additionally, to account for the combined influence on the RLR occurrence, mixed logit framework was developed to reveal the correlations among the random parameters. The data of e-bike riders' crossing behaviors at four signalized intersections in Xi'an, China were collected, and 3335 samples were recorded. The results indicated showed that DEB riders are more likely to run red lights than OEB riders. Factors that affect RLR behaviors of the two groups are different. Factors associated with the unobserved heterogeneity include red-light stage, observation time, age and waiting position of the rider. The joint influence among random parameters further illustrates the complexity of the contributing factors of riders' crossing behavior. Results from the models provide insights into the development of intervention systems to improve the traffic safety of e-bike riders at intersections.

Injury risk assessment based on pre-crash variables: The role of closing velocity and impact eccentricity

Thorough evaluations on injury risk (IR) are fundamental for guiding interventions toward the enhancement of both the road infrastructure and the active/passive safety of vehicles. Well-established estimates are currently based on IR functions modeled on post-crash variables, such as velocity change sustained by the vehicle (ΔV); thence, these analyses do not directly suggest how pre-crash conditions can be modified to allow for IR reduction. Nevertheless, ΔV can be disaggregated into two contributions which enable its apriori calculation, based only on the information available at the impact instant: the Crash Momentum Index (CMI), representing impact eccentricity at collision, and the closing velocity at collision (V_r). By extensively employing the CMI indicator, this work assesses the overall influence of impact eccentricity and closing velocity on the risk for occupants to sustain a serious injury. As CMI synthesizes indications regarding ΔV, its use can be disjointed from the ΔV itself for the derivation of high-quality IR models. This feature distinguishes CMI from the other eccentricity indicators available at the state-of-the-art, allowing for the contribution of eccentricity on IR to be completely isolated. Because of this element of originality, special attention is given to the CMI variable throughout the present work. Based on data extracted from the NASS/CDS database, the influence of the CMI and V_r variables on IR is specifically highlighted and analyzed from several perspectives. The feature ranking algorithm ReliefF, whose use is unprecedented in the accident analysis field, is first employed to assess importance of such impact-related variables in determining the injury outcome: if compared to vehicle-related and occupant-related variables (as category and age, respectively), the higher influence of CMI and V_r is initially highlighted. Secondly, the relevance of CMI and V_r is confirmed by fitting different predictive models: the fitted models which include the CMI predictor perform better than models which neglect the CMI, in terms of classical evaluation metrics. As a whole, considering the high predictive power of the proposed CMI-based models, this work provides valuable tools for the apriori assessment of IR.

Assessing the impact of traffic signal performance on crash frequency for signalized intersections along urban arterials: A random parameter modeling approach

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

Assessing the explanatory and predictive performance of a random parameters count model with heterogeneity in means and variances

Random parameters model has been demonstrated to be an effective method to account for unobserved heterogeneity that commonly exists in highway crash data. However, the predefined single distribution for each random parameter may limit how the unobserved heterogeneity is captured. A more flexible approach is to develop a random parameters model with heterogeneity in means and variances by allowing the mean and variance of potential each random parameter to be an estimable function of explanatory variables. This burgeoning technique for modelling unobserved heterogeneity has been increasingly applied to various safety evaluation scenarios recently. However, the predictive performance of this emerging method, which determines the practicability of the model for a specific circumstance, has never been investigated as far as our knowledge. In addition, the explanatory power by including heterogeneous means and variances of random parameters need to be further investigated to confirm the potential merits of this method in crash data analysis. In this paper, a random parameters negative binomial with heterogeneity in means and variances (RPNBHMV) model, a standard random parameters negative binomial (RPNB) model and a traditional fixed parameters negative binomial (NB) model were estimated using the same dataset. The explanatory and predictive performance of the three models were thoroughly evaluated and compared. Results showed that: 1) the RPNB model fitted the data significantly better than the NB model, and the RPNBHMV model further improved the statistical fit of the RPNB model but the improvement was slight; 2) more insights into interactions of safety factors were inferred from the RPNBHMV model, which demonstrates the explanatory benefit of this model; 3) the RPNBHMV and RPNB models had both advantages (e.g., produced overall better prediction accuracy) and disadvantages (e.g., provided reduced prediction accuracy across the range of explanatory variables) when applied to in-sample observations (i.e., observations used to estimate the model); 4) the RPNBHMV and RPNB models might be less precise than the NB model when applied to out-of-sample observations. These findings indicate that the RPNBHMV model offers more insights and may be used for explanatory safety analysis for sites where reliable data can be collected. However, the simple NB model is more reliable - at least with the dataset used in this study - than its random parameters model counterparts for other sites where the data are unavailable or unreliable, which is a common safety evaluation scenario in practice.

Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.

A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates

Numerous studies have previously used a variety of count-data models to investigate factors that affect the number of crashes over a certain period of time on roadway segments. Unlike past studies which deal with crash frequency, this study views the crash rates directly as a continuous variable left-censored at zero and explores the application of an alternate approach based on tobit regression. To thoroughly investigate the factors affecting freeway crash rates and the potentially temporal instability in the effects of crash factors involving traffic volume, freeway geometries and pavement conditions, a classic uncorrelated random parameters tobit (URPT) model and a correlated random parameters tobit (CRPT) model were estimated, along with a conventional fixed parameters tobit (FPT) model. The analysis revealed a large number of safety factors, including several appealing and interesting factors rarely studied in the past, such as the safety effects of climbing lanes and distance along composite descending grade. The results also showed that the CRPT model was not only able to reflect the heterogeneous effects of various factors, but also able to estimate the underlying interactions among unobserved characteristics, and therefore provide better statistical fit and offer more insights into factors contributing to freeway crashes than its model counterparts. Additionally, the results showed significant temporal instability in CRPT models across the studied time periods indicating that crash factors (including unobserved characteristics and the underlying interactions among them) and their effects on crash rates varied over time, and more attentions should be paid when interpreting crash data-analysis findings and making safety policies. The modeling technique in this study demonstrates the potential of CRPT model as an effective approach to gain new insights into safety factors, particularly when the heterogeneous effects of factors on safety are interactive. Additionally, findings from this study are also expected to assist in developing more effective countermeasures by better understanding the safety effects of factors associated with freeway design characteristics and pavement conditions.

Macro-level accident modeling in Novi Sad: A spatial regression approach

In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.

Examining correlations between motorcyclist's conspicuity, apparel related factors and injury severity score: Evidence from new motorcycle crash causation study

Motorcyclists are vulnerable road users at a particularly high risk of serious injury or death when involved in a crash. In order to evaluate key risk factors in motorcycle crashes, this study quantifies how different "policy-sensitive" factors correlate with injury severity, while controlling for rider and crash specific factors as well as other observed/unobserved factors. The study analyzes data from 321 motorcycle injury crashes from a comprehensive US DOT FHWA's Motorcycle Crash Causation Study (MCCS). These were all non-fatal injury crashes that are representative of the vast majority (82%) of motorcycle crashes. An anatomical injury severity scoring system, termed as Injury Severity Score (ISS), is analyzed providing an overall score by accounting for the possibility of multiple injuries to different body parts of a rider. An ISS ranges from 1 to 75, averaging at 10.32 for this sample (above 9 is considered serious injury), with a spike at 1 (very minor injury). Preliminary cross-tabulation analysis mapped ISS to the Abbreviated Injury Scale (AIS) injury classification and examined the strength of associations between the two. While the study finds a strong correlation between AIS and ISS classification (Kendall's tau of 0.911), significant contrasts are observed in that, when compared to ISS, AIS tends to underestimate the severity of an injury sustained by a rider. For modeling, fixed and random parameter Tobit modeling frameworks were used in a corner-solution setting to account for the left-tail spike in the distribution of ISS and to account for unobserved heterogeneity. The developed random parameters Tobit framework additionally accounts for the interactive effects of key risk factors, allowing for possible correlations among random parameters. A correlated random parameter Tobit model significantly out-performed the uncorrelated random parameter Tobit and fixed parameter Tobit models. While controlling for various other factors, we found that motorcycle-specific shoes and retroreflective upper body clothing correlate with lower ISS on-average by 5.94 and 1.88 units respectively. Riders with only partial helmet coverage on-average sustained more severe injuries, whereas, riders with acceptable helmet fit had lower ISS Methodologically, not only do the individual effects of several key risk factors vary significantly across observations in the form of random parameters, but the interactions between unobserved factors characterizing random parameters significantly influence the injury severity score as well. The implications of the findings are discussed.

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.

Time-varying mixed logit model for vehicle merging behavior in work zone merging areas

This study aims to develop a time-varying mixed logit model for the vehicle merging behavior in work zone merging areas during the merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. From the safety perspective, vehicle crash probability and severity between the merging vehicle and its surrounding vehicles are regarded as major factors influencing vehicle merging decisions. Model results show that the model with the use of vehicle crash risk probability and severity could provide higher prediction accuracy than previous models with the use of vehicle speeds and gap sizes. It is found that lead vehicle type, through lead vehicle type, through lag vehicle type, crash probability of the merging vehicle with respect to the through lag vehicle, crash severities of the merging vehicle with respect to the through lead and lag vehicles could exhibit time-varying effects on the merging behavior. One important finding is that the merging vehicle could become more and more aggressive in order to complete the merging maneuver as quickly as possible over the elapsed time, even if it has high vehicle crash risk with respect to the through lead and lag vehicles.