Motorcyclists’ at-fault status is an important factor influencing crash injury severity in that intrinsically unsafe riders tend to be at fault and are the ones likely to be involved in severe crashes. However, this endogeneity issue and its influence on model estimations have seldom been investigated with regard to motorcyclist crash severity analysis. This study proposes a simultaneous model system to account for the endogenous effects of at-fault status in the motorcyclists’ injury severity analysis. Four Bayesian simultaneous models were developed using motorcyclist crash injury data from Queensland, Australia, from the year 2017 through 2018, including an independent binary and independent ordered Probit model, a simultaneous binary-ordered Probit model without recursive structure, a simultaneous binary-ordered Probit model with a recursive structure, and a simultaneous random-parameters binary-ordered Probit model with a recursive structure. The results of all simultaneous models indicate the existence of endogeneity associated with at-fault status in the injury outcome analysis. In particular, the endogenous relationship is detected by the significant cross-equation correlations in the simultaneous models. The model comparison by Deviance Information Criteria highlights the superiority of the simultaneous random-parameters model with a recursive structure. It was found that exogenous variables such as traffic sign-controlled measures, posted speed limits of 100–110 km/h, the presence of vertical grades, rider age 16–24 years, and unlicensed influenced injury severity indirectly through at-fault status, and ignoring these indirect influences could result in biased estimates. The presence of random parameters, such as collisions with heavy vehicles and riders over 59 years, highlights the importance of considering heterogeneity. The simultaneous random-parameters model with a recursive structure model revealed that the critical factors contributing to riders’ at-fault status included unlicensed riders and posted speed limits of 100–110 km/h, and the crucial factors influencing riders’ injury levels included head-on crashes, collisions with heavy vehicles, darkness-unlighted, and riders over 59 years old. The proposed model system demonstrates the importance of considering both endogeneity and heterogeneity for modeling the injury severity of motorcyclists.

Overturned crashes are associated with a disproportionate number of severe injuries and fatalities, while hit-fixed-object crashes are acknowledged as the most frequent single-vehicle crashes. To investigate the temporal stability and differences of contributing factors determining different injury severity levels in overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving, this paper estimates two groups of correlated random parameters logit models with heterogeneity in the means (one group relating to overturned crashes and the other relating to hit-fixed-object crashes). Three injury-severity categories are determined as outcome variables: severe injury, minor injury and no injury, while multiple factors are investigated as explanatory variables including driver, vehicle, roadway, environmental, and crash characteristics. The overall temporal instability and non-transferability between overturned and hit-fixed-object crashes are captured through likelihood ratio tests. Marginal effects are adopted to further exhibit temporal variations of the explanatory variables. Despite the overall temporal instability, some variables still present relative temporal stability such as alcohol, truck, aggressive driving, vehicle age (>14 years old), and speed limit (<45 mph). This non-transferability between overturned and hit-fixed-object crashes provides insights into developing differentiated strategies targeted at mitigating and preventing different types of crashes. Besides, out-of-sample prediction is undertaken given the captured temporal instability and non-transferability of overturned and hit-fixed-object crash observations. More studies can be conducted to accommodate the spatial instability, under-reporting of severe-injury crashes, the trade-off between model predictive capability, inference capability, and selectivity bias.

This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles during the car-following process is characterized by how an instantaneous crash-risk measure - rear crash risk index (RCRI) - changes by time. A spectral clustering analysis is first conducted to classify the driving risk evolution of the observed car-following maneuvers, showing the existence of five distinctive risk evolution patterns in the car-following processes. In order to investigate the relationship between the identified driving risk evolution clusters and their contributing factors, a regression analysis employing a random parameter multinomial logit model with heterogeneity in means and variances is followed, revealing several significant contributing factors to the car-following risk evolution patterns, such as congestion level, driver’s ability to maintain stable headways, and vehicle deceleration. This study has provided important insights into driving risk from the new perspective of risk evolution patterns, which is expected to have significant implications for the future development of advanced traffic management and traveler information systems (ATMS/ATIS) strategies, advanced driver assistance systems (ADAS), and connected and autonomous vehicles (CAV).

Pedestrians represent a vulnerable road user group at signalised intersections. As such, properly estimating pedestrian crash risk at discrete short intervals is important for real-time safety management. This study proposes a novel real-time vehicle-pedestrian crash risk modelling framework for signalised intersections. At the core of this framework, a Bayesian Generalised Extreme Value modelling approach is employed to estimate crash risk in real-time from traffic conflicts captured by post encroachment time. A Block Maxima sampling approach, corresponding to a Generalised Extreme Value distribution, is used to identify pedestrian conflicts at the traffic signal cycle level. Several signal-level covariates are used to capture the time-varying heterogeneity of traffic extremes, and the crash risk of different signal cycles is also addressed within the Bayesian framework. The proposed framework is operationalised using a total of 144 hours of traffic movement video data from three signalised intersections in Queensland, Australia. To obtain signal cycle-level covariates, an automated covariate extraction algorithm is used that fuses three data sources (trajectory database from the video feed, traffic conflict database, and signal timing database) to obtain various covariates to explain time-varying crash risk across different cycles. Results show that the model provides a reasonable estimate of historical crash records at the study sites. Utilising the fitted generalised extreme value distribution, the proposed model provides real-time crash estimates at a signal cycle level and can differentiate between safe and risky signal cycles. The real-time crash risk model also helps understand the differential crash risk of pedestrians at a signalised intersection across different periods of the day. The findings of this study demonstrate the potential for the proposed real-time framework in estimating the vehicle-pedestrian crash risk at the signal cycle level, allowing proactive safety management and the development of real-time risk mitigation strategies for pedestrians.

A novel and effective approach to safety management requires evaluating the safety of locations over short time periods (e.g. minutes). Unlike traditional methods that are based on aggregate crash records over a few years, crash proneness in this approach reflects short-time durations and is related to dynamic traffic changes and dangerous driving events. This paper proposes a new approach to dynamically assess the crash proneness of traffic conditions within a very short time (e.g., signal cycle length) and to dynamically identify high-risk locations. Using a Bayesian hierarchal Extreme Value Theory (EVT) model, the short-term crash risk metrics, risk of crash (ROC), and return level (RL), are calculated using traffic conflict data. A short-term hazardous location identification and ranking framework is developed based on crash-risk threshold exceedances for every short-term analysis period. By further investigating the variation in short-term crash risk, longer-term hazardous location identification and ranking metrics such as the longer-term crash risk index (LTCRI) and the percent of time exceeding (PTE) were developed. Using these metrics, a framework is proposed by which hazardous intersections can be dynamically classified and ranked in both the short-term and the longer-term. This ranking may be dynamically updated as more data becomes available. The proposed framework was applied to a trajectory dataset consisting of 47 signalized intersections obtained from a UAV-based dataset. Conflicts were identified from vehicle trajectories and were used to compute the proposed short-term and longer-term metrics. The intersections within the network were then ranked based on the proposed framework. This study demonstrates the importance of investigating short-term fluctuations in crash risk that may otherwise be lost to averaging in longer-term analysis and proposes a simple and practical solution.

Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.

Crashes at highway-rail grade crossings often result in higher proportion of injury and fatality of the vehicle occupants as compared to other crash types, necessitating in-depth investigation to identify their causal factors. In this study, injury-severity outcomes from highway-rail grade crossing crashes are analyzed using crash data from Texas and California, which are the most vulnerable states in the United States, in terms of highway-rail grade crossing crash occurrences. The data are collected from the Federal Railroad Administration’s (FRA) Office of Safety Analysis, covering a period between 2012 and 2020. Such data often suffer from out-of-date or missing information due to cost and available resources limitations, which inevitably may lead to unobserved characteristics varying systematically across various aspects of the data. Unobserved heterogeneity is an important misspecification issue, that in turn introduces modeling bias. To address these limitations, the random parameters multinomial logit modeling framework with heterogeneity in the means and variances is employed for the econometric analysis in this paper, which effectively accounts for multilayered unobserved heterogeneity. Spatial instability of the factors affecting different injury-severity levels is investigated as well. The results indicate that the factors are not spatially stable across Texas and California, leading to the estimation of two separate state-specific models. The estimation results of the two state-specific models help identify several vehicle-, train-, vehicle driver-, weather- and crossing-specific factors affecting different injury severity outcomes. Moreover, the results also demonstrate the varying magnitude of the identified factors on injury-severity across the two states, indicating the presence of spatial instability. The findings of this study highlight the importance of accounting for unobserved heterogeneity and spatial instability to avert critical methodological issues and misleading inferences from the simple aggregation used in most econometric analysis of highway-rail grade crossing crashes.

Toll plaza is a designated area of controlled-access roads like expressway, bridge, and tunnel for toll collection. A number of toll booths are often placed at the toll plaza accommodating high passing traffic and multiple payment methods. Traffic and safety characteristics of toll plazas are different from that of other road entities. Different conflict risk indicators, which are usually longitudinal, have been adopted for real-time safety assessment. In this study, correlated grouped random parameter logit models with heterogeneity in the means are established to capture the unobserved heterogeneity, with additional flexibility, at road user level for the association between conflict risk and influencing factors. In addition, modified conflict risk indicator is developed to assess the safety of diverging, merging, and weaving movements of traffic, with which vehicles’ dimensions (width and length), and longitudinal and angular movements are considered. Also, prevalence and severity of both rear-end and sideswipe conflicts are assessed. Results indicate that toll collection type, vehicle’s location, average longitudinal speed, angular speed, acceleration, and vehicle class all affect the risk of traffic conflicts. Furthermore, there are significant correlation among the random parameters of severe traffic conflicts. Proposed analytic method can accommodate the conflict risk analysis for different conflict types and account for the correlation of unobserved heterogeneity. Findings should shed light on appropriate remedial measures like traffic signs, road markings, and advanced traffic management system that can improve the safety at tunnel toll plazas.

Driving at nighttime may make drivers more likely to be involved in fatal crashes. To investigate the temporal instability and age differences of contributors determining different injury severity levels in nighttime crashes, this paper estimates three groups of random parameters logit models with heterogeneity in the means and variances (young/middle-age/old groups). Nighttime single-vehicle crashes in this study are gathered over four years in California, from January 1, 2014, to December 31, 2017, provided by Highway Safety Information System, including single-vehicle crashes occurring under dark, dawn, and dusk lighting conditions. Simultaneously, to investigate the temporal instability and transferability of nighttime crash severity relating to drivers of different ages, three disaggregate groups are defined: young drivers (15–29 years old), middle-age drivers (30–49 years old), old drivers (over 49 years old). Three injury-severity categories are determined as outcome variables: severe injury, minor injury, and no injury, while multiple factors are investigated as explanatory variables, including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics, and temporal characteristics. Two series of likelihood ratio tests are undertaken to unveil the contributors determining nighttime crash injury severities varying among drivers of different ages over time. Besides, the current study also compares the differences between out-of-sample and within-sample predictions. The results indicate the unstable direction of predictions across different age groups over time and underscore the necessity to adequately accommodate the temporal instability and age differences in accident prediction. More studies can be conducted to accommodate the self-selectivity issue and the out-of-sample prediction differences between using the parametric models and non-parametric models.

To minimize non-recurrent congestion, a better understanding of the factors that affect accident clearance time is crucial, in order to optimize incident management strategies. A number of methods have been developed to predict incident clearance duration, but few of those have considered the time-varying nature of certain observed factors. In addressing this gap in the literature, this study developed a grouped random parameters hazard-based duration model with time-varying covariates, while accounting for unobserved heterogeneity. Data on accidents, traffic, road inventory, and real-time weather condition were compiled for the Kaiyang freeway in 2014. Comparison of candidate models shows that the proposed model with Weibull distribution exhibits the best fit performance. The results suggest that the effects of rear-end accident, involvements of trucks or other vehicles, evening hours, and shoulder blockage on the hazard function are heterogeneous across observations. Other variables such as angle accident, injury severity, traffic volume and composition, morning or pre-dawn hours, and blockage of overtaking lane were also found to have significant but homogenous effects on accident clearance time. More importantly, the results also reveal the significant effects of the time-varying covariates (wind speed, temperature, and humidity). Accordingly, the viability and superiority of the proposed model in analyzing accident clearance time are confirmed. Overall, the results of this study are expected not only to improve traffic incident management by allowing government agencies to better understand factors affecting accident clearance times, but also to facilitate incident clearance through the recognition of time-varying pattern.

This study explores the differences between day and night pedestrian-injury severities in vehicle–pedestrian crashes over a five-year period using data from the state of Kansas. Separate statistical models (random parameters logit models with possible heterogeneity in the means and variances of the random parameters) were estimated for day and night crashes to examine different pedestrian injury severity outcomes (no visible injury, moderate injury, and severe injury). Likelihood ratio tests were conducted to explore the temporal stability of the model estimations over different times of day and years. Many variables affecting injury severities were considered in model estimation including time and location of accidents, in addition to information on environmental, roadway, crash, vehicle, driver, and pedestrian characteristics. The findings indicate that the factors affecting pedestrian injury severities did change over time but that there is a clear day-night difference in the resulting injury severities of pedestrians, with nighttime crashes consistently resulting in more severe injuries overtime. This suggests policies and technologies that seek to essentially replicate daytime conditions (improved illumination, infrared pedestrian detection in vehicles, etc.) in nighttime conditions could have considerable safety benefits. Using the estimated random parameters models, extensive out-of-sample prediction simulations are used to provide estimates of the potential benefits of such nighttime mitigation policies and technologies, as well as how daytime/nighttime pedestrian injury severity probabilities have been changing over time.

With proactive safety assessment gaining significant attention in the literature, the relationship between traffic conflicts (which form the underpinnings of proactive safety measures) and observed crashes remains a critical research need. Such a need will grow significantly with the ongoing introduction of connected and autonomous vehicles where software and hardware improvements are likely to be determined from observed traffic conflict data as opposed to data from accumulated crashes. Extreme value theory has been applied for over two decades to study the relationship between traffic conflicts and crashes. While several advancements have been made in extreme value theory models over time, the need to continually evaluate the strengths and weaknesses of these models remains, particularly considering their likely use in improving the safety–critical elements of connected and autonomous vehicles. This paper seeks to comprehensively review studies on extreme value theory applications in traffic conflict/crash contexts by providing an in-depth assessment of alternate modelling methodologies and associated issues. Critical research needs relating to the further development of extreme value theory models are identified and include identifying efficient techniques for sampling extremes, determining optimal sample size, assessing and selecting appropriate traffic conflict measures, incorporating covariates, accounting for unobserved heterogeneity, and addressing issues associated with real-time estimations.

In this study, a speed-route joint choice model considering traveler’s safety concerns is proposed to concurrently model traveler’s safety-oriented travel speed and route choice behavior. Specifically, the safe-speed choice behavior is modeled as a trade-off process between perceived traffic safety and efficiency using a disutility function. The safe-route choice behavior is described by the proposed Mean-excess Crash Risk Cost model, where the route safety is modeled as a random variable following a specific distribution, and traveler’s concerns about both reliability and unreliability aspects of safety variability are considered. The model is accommodative to account for the random nature and the traveler’s perception of traffic safety. Also, the travel time cost is considered, which is depicted as a parallel criterion of travel safety in the route choice model. Moreover, the heterogeneities of travelers’ safety concerns in both the choices of speed and route are considered in the proposed joint model. Then, the study formulated the equilibrium problem with the two behavior elements (speed and route) and two choice criteria (safety and time), based on the assumption that all travelers tend to maximize their disutility when choosing speed while minimizing their travel safety variability and travel time. To illustrate the model, Nguyen and Dupuis, Sioux falls, and Changsha arterial networks are conducted as numerical studies. The result demonstrates the model’s capability in depicting travelers’ trade-off between safety and time when selecting the optimal travel speed. Considering the impact of route safety unreliability makes the model sensible to describe travelers’ safety-concerned route choice behavior. The model is also flexible to account for travelers’ crash risk aversion heterogeneity.

The present paper introduces the time between the start of a snowfall and the occurrence of a motor vehicle accident as a novel measure for evaluating motor vehicle safety during snowfalls. Detailed information of accidents that occurred during snowfalls between 2017 and 2020 in the state of New York are used to explore the accelerating or delaying effect of different factors on the time between the start of a snowfall and the occurrence of an accident. To that end, the hazard-based duration modeling framework is employed, and to account for multiple layers of unobserved heterogeneity, a random parameters with heterogeneity in means and variances approach is introduced – for this first time, to the authors’ knowledge. The temporal stability of the factors across the study period is investigated through conducting a series of systematic likelihood ratio tests, and the factors are not found to be temporally stable across the study years. Hence, separate year-specific models are estimated. The results show that a number of factors affect the time between the start of a snowfall and the occurrence of a motor vehicle accident such as: visibility conditions; concrete road sections; road sections with high Pavement Condition Index (PCI); roads with more than 4 lanes in both directions; locations in close proximity to bus stations; the period during the cold winter months (specifically February); the amount of accumulated snow on the ground before snowfall; the presence of ramps; and longtime intervals between snowfalls (especially for heavy snow conditions and adverse visibility conditions). The findings from this paper are anticipated to offer insights to winter maintenance teams, transportation system operators, and users regarding accident-prone periods and locations during snowfalls.

Traffic barrier crashes have been a major concern in many prior studies in traffic safety literature, especially in the crash-prone sections of mountainous regions. However, the effect of factors affecting the injury-severities resulting from crashes involving different types of traffic barriers may be different. This paper provides an empirical assessment of the performance of ordered and unordered discrete outcome models for examining the impact of exogenous factors determining the driver injury-severity of crashes involving two types of traffic barriers in mountainous regions: w-beam barriers and cable barriers. For the ordered framework, the alternative modeling approaches include: the generalized ordered logit (GOL) and the random thresholds random parameters generalized ordered logit model (RTRPGOL). Whereas, for the unordered framework, the alternative modeling approaches include: the multinomial logit (MNL), the random parameters multinormal logit (RPL), and the random parameters multinormal logit model with heterogeneity in the means and variances (RPLHMV). Using injury-severity data from 2016 to 2019 for mountainous regions in Guiyang City, China, three injury-severity categories are determined as outcome variables: severe injury (SI), minor injury (MI), and no injury (NI), while the potential influencing factors including drivers-, vehicles-, road-, and environment-specific characteristics are statistically analyzed. The model estimation results show: (a) that the MNL model statistically outperforms the GOL model in terms of goodness-of-fit measures; (b) the RTRPGOL model is statistically superior to the MNL and RPL models; and (c) the RPLHMV model is statistically superior to the RTRPGOL model, and therefore the preferred option among the model alternatives. To that end, the RPLHMV model is leveraged to quantitatively describe the impact of explanatory variables on the driver injury-severity and explore how these factors change over the years (between 2016–2017 and 2018–2019). The results further show that the factors affecting driver injury severities and the effects of significant factors on injury severity probabilities change across traffic barrier crash models and across years. In addition, the results of the temporal effects analysis show that some variables present relative temporal stability, which is important for formulating long-term strategies to enhance traffic safety on mountainous roads. Most importantly, the effects of the explanatory factors that exhibit relative temporal stability are found to vary across traffic barrier crashes. For example, trucks, daylight, curved section segments, and high-speed limit (greater than 55 mph) are some of the factors that have opposite effects between traffic barrier crash models. The findings from this paper are expected to help policy makers to take necessary measures in reducing traffic barrier crashes in mountainous regions by forming appropriate strategies, and by allocating properly their available resources at the pre-planning phase.

This paper examines the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019. While also considering the temporal stability assessment of significant factors, this study adopted a random parameters logit model with possible heterogeneity in means and variances to account for unobserved heterogeneity. Three levels of motorcyclist injury severity were considered including minor injury, severe injury, and fatal injury. Two series of likelihood ratio tests clearly indicated nontransferability between weekday, weekend, and holiday crashes and substantial temporal instability over the four-year study period. Findings also revealed many statistically significant factors that affect motorcyclist injury severity probabilities in various time-of-year and yearly models. In addition, the prediction comparison results (using out-of-sample prediction simulation) clearly illustrated substantial differences between weekday, weekend, and holiday motorcyclist injury severity probabilities, and substantial changes in each injury predicted probabilities over time. This paper highlights the importance of accounting for day-of-week and holiday transferability and temporal instability with unobserved effects in the determinants that affect motorcyclist injury severity. Through the observed nontransferability and temporal instability, the findings provide valuable knowledge for practitioners, researchers, institutions, and decision-makers to enhance highway safety, specifically motorcyclist safety, and facilitate the development of more effective motorcycle crash injury mitigation policies.

Many intersections belong to more than one zone, but most research has not considered the effects of multiple zones in intersection crash analysis. This issue is known as a boundary problem. Unobserved heterogeneity between zones can lead to model misspecification which can result in biased parameter estimates and poor model fitting performance. This study investigated the issue using five years of intersection crash data from the City of Regina, Saskatchewan, Canada. The study developed three multiple membership multilevel negative binomial models to reduce unobserved zonal-level heterogeneity. Each multiple membership multilevel model used a different weight strategy. When the fitting performance of the three multiple membership multilevel models was compared with two additional models, a traditional single level model and a conventional multilevel model, all three multiple membership multilevel models had a better fitting performance. Five individual-level and seven group-level variables were statistically significant (90% confidence level) in all the models with five of the individual-level and four of the group-level variables statistically significant at the 99% confidence level. The multiple membership multilevel models also helped to prevent the underestimation of group-level variance and type I statistical errors that tend to occur with single level models and conventional multilevel models. In particular, the three multiple membership multilevel models produced more accurate results for intersections with a large AADT. As intersections with a large AADT are known to have more crashes, multiple membership multilevel models are likely to be more useful than single level models and conventional multilevel models when selecting intersections for safety improvement. The study recommends the adoption of a multiple membership multilevel model to improve fitting performance and reduce the boundary problem for intersections affected by more than one zone.

Real-time safety analysis and optimization using surrogate safety measures such as traffic conflicts and techniques such extreme value theory (EVT) models is an emerging research topic in the context of proactive traffic safety management. However, the predictive performance and temporal transferability of the existing real-time safety analysis EVT models are subject to the assumption of invariant model parameters, which do not account for the temporal variability and is not suitable for real-time traffic data analysis. This study proposes a Bayesian dynamic extreme value modeling approach for conflict-based real-time safety analysis which integrates a Bayesian dynamic linear model with the extreme value distribution. The proposed approach has several unique advantages as it: 1) allows the model parameters to be time-varying; 2) integrates the newer data with prior information to recursively update the model parameters and account for state-space changes and react to sudden trend changes; 3) accounts for temporal variability and non-stationarity in conflict extremes; and 4) quantitatively evaluates the real-time safety levels of a road facility. The proposed approach is applied for cycle-by-cycle safety analysis at four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts are characterized by the modified time to collision indicator. Three traffic parameters (traffic volume, shock wave area, and platoon ratio) at the signal cycle level are considered as covariates to account for non-stationarity. Several Bayesian dynamic and static extreme value models are developed and two safety indices, namely risk of crash (RC) and return level (RL), are generated to quantitatively represent the cycle-level safety. The RC directly reflects whether a cycle is risky while the RL can evaluate the safety levels of individual cycles. The results show that the dynamic model can identify more crash-risk cycles with either a positive RC or a positive RL than the static model and is more capable of differentiating the safety levels for individual cycles in terms of RL. Overall, the dynamic model outperforms the static model in terms of the statistical fit and aggregate crash estimation accuracy.

With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.