Analytic Methods in Accident Research最新文献

A major difficulty in assessing road traffic safety is the scarcity of historical accident data. xxThis is a common problem in contexts where a certain level of safety has been reached or where exposure is low, such as mixed traffic conditions with different levels of transport automation. Recent studies have demonstrated how severe interactions between road users and/or road users and infrastructure can be a direct measure of safety. However, limiting the investigation to only the most extreme events may lead to inconclusive results considering the lack of prediction robustness and the possible selection bias. In this context, extreme value theory (EVT) is commonly used to extrapolate crashes from road traffic interactions, even combining several indicators. The present work extends the EVT paradigm by proposing a method based on copula functions and EVT, which enables a more specific and continuous evaluation of interaction severity. Compared with pure EVT, this new approach extends the boundary to interactions of all severities while implicitly assuming that the relationship between safety-relevant events and road casualties is stochastic. This EVT-copula approach was also compared with bivariate peaks over threshold (BPOT). It was found that the two approaches yield similar prediction results for crash probabilities. Furthermore, the proposed approach applies to events not properly defined in BPOT and provides more accurate predictions for severe (and less severe) interactions compared with BPOT, when benchmarked against observations.

This paper expands the scope of geometric design inconsistency analysis from corridor scales to network-wide perspectives, exploring the impact of inconsistencies’ spatial-patterns on crashes, which remains largely under-explored. We define spatial-patterns of segment-level inconsistencies, focusing on their spread, contiguity, frequency, density, and magnitude. We devise a new method to measure inconsistency-contiguity and inconsistency-frequency based on adjacent segment-triplets within regions. Through micro–macro integrated models, we reveal the scalable influence of inconsistency which remain significant at the segment-level but gets modulated by spatial-patterns at the regional-level. The integrated models consistently outperform their non-integrated counterparts, emphasizing the importance of this integrated approach. This study highlights that regions with rare inconsistency occurrences demonstrate higher crash counts, while regions with uniform inconsistency occurrences exhibit lower crash rates, unveiling insights into the road conditions’ impact on driver behavior. Finally, we also propose a novel tool - vulnerability contours on frequency-hyperplane to map regions’ relative safety.

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

This study estimates mixed logit models taking into account heterogeneity in means and partially constrained parameters in order to explore possible shifts within parameters over time to study factors influencing bicyclist injury severity outcomes. Separate statistical models are estimated for two bicyclist helmet-wearing scenarios (helmet and non-helmet) using a comprehensive dataset from Florida covering a three-year period to assess COVID-19 effects from the 1st of January 2019 to the 31st of December 2021. This research evaluates several factors influencing helmeted and non-helmeted bicyclist injury severity, encompassing the attributes of drivers and cyclists, the environment and weather, the features of the roads and their temporal aspects, and the different types of vehicles. The performed analysis further enhances model robustness by assessing the temporal stability and transferability across different contexts through likelihood ratio tests, alongside an in-depth examination of the temporal consistency of explanatory variables via marginal effects analysis, confirming significant variations between non-helmeted and helmeted bicyclist models and revealing temporal shifts in factors affecting injury severity during the study period. Findings from the model estimations identify several significant variables with consistent parameter estimates across years. Stop signs, cycling with traffic, and dark, unlit conditions increase severe injury risk in non-helmet models, while the stop sign indicator consistently reduces severe injury risk in helmet models. Statistically significant random parameters are identified across different years and helmet-wearing scenarios, including the male driver indicator, which exhibits varying effects on injury severity. Out-of-sample prediction analysis suggests helmets reduce severe injury probability but may increase minor injuries and decrease no-injury accidents, indicating potential risk compensation behavior among helmeted bicyclists. Although helmets offer protection against severe injuries for bicyclists, it is crucial to adopt a comprehensive safety approach, particularly given the evolving demographics of bicyclists amid the COVID-19 outbreak. This entails considering factors like bicyclist and driver behavior, environmental conditions, and infrastructure enhancements. Policymakers, road safety professionals, and advocacy groups should collaborate to develop holistic strategies to address the determinants of bicycle crash severity outcomes and enhance safety measures for bicyclists across diverse road environments.

In the past decades, many cities have adopted transit-oriented development approach for urban planning. Studies have explored the effects of built environment, street network and accessibility on the perception and behaviour of pedestrians. However, the relationship between pedestrian safety and walking accessibility is less studied. In this study, influences of land use, socio-demographics, pedestrian network, and transport facilities on pedestrian crash frequencies in the areas around metro stations would be evaluated. Additionally, walking accessibility for individuals with and without physical disabilities would be accounted for. Since data at different spatial scales, i.e., zone level (individual) versus catchment area level (group), are used, the hierarchical approach is adopted for the crash frequency model. Furthermore, some zones are nested within the catchment areas of more than one metro station, the multiple membership approach should be adopted, accounting for the possible correlation. Different from the conventional multiple membership multilevel model, multiple membership weights would be assigned in accordance with the walking distances between zones and stations. Last but not least, temporal instability in the parameter estimation is also explored. Results indicate that pedestrian crash frequencies increase with population density, working population, traffic volume, walking trip, footpath density, node density, barrier-free facilities, bus stop, residential area, commercial area, and government and utility area. In contrast, pedestrian crash frequencies decrease with average gradient and walking accessibility. Findings should shed light on the street design that can enhance walking accessibility and public transport use, without compromising pedestrian safety. Moreover, issues of spatial crash analysis, including hierarchical data structure, and between- and within-group variances, are addressed.

Pedestrians, particularly susceptible to road traffic crashes, experience varying injury severities influenced by age and time shifts. This research aims to investigate the differences and temporal shifts in factors influencing pedestrian injury severities across different age groups. To achieve this, three random parameters binary logit models with heterogeneity in the means (and variances) were employed. Four years of pedestrian crash data in Hong Kong were utilized in this study. According to United Nations’ definitions of the young and elderly, pedestrians were categorized into three groups: young (under 25 years old), middle-aged (25–65 years old), and elderly (over 65 years old). Initial likelihood ratio tests indicated temporal stability in the young group between 2019 and 2021, with further tests confirming age transferability and overall temporal stability after integrating the three years of young data. The partially constrained temporal stability approach was then developed to further capture the temporal stability of individual variables and simplify model results. Model results identified factors impacting pedestrian injury severities, encompassing pedestrian, driver, vehicle, temporal, and light condition characteristics. Some contributing variables exhibit age-transferability or temporal stability, such as controlled crossing, near controlled crossing, inattentive driver and private car. However, the significance of most contributors varies across age groups and years, with certain factors being age-specific or year-specific. Out-of-sample predictions underscore the cumulative likelihood of fatal or severe injuries with advancing age, and the middle-aged models showed the highest level of temporal stability regarding the risk of injury severity compared to the other two age models. Moreover, middle-aged pedestrians in Hong Kong faced the highest risk of fatal or severe injuries during the first year of the COVID-19 lockdown (2020), but the risk significantly declined for pedestrians of all age groups in the subsequent year. Based on these findings, targeted preventive measures that take into account age differences have been proposed to effectively enhance pedestrian safety.

Multivariate models are widely employed for crash frequency analysis in traffic safety literature. In the context of analyzing data for multiple instances (such as years), it becomes essential to evaluate the stability of parameters over time. The current research proposes a novel approach, labelled the mixed spline indicator pooled model, that offers significant enhancement relative to current approaches employed for capturing temporal instability. The proposed approach entails carefully creating independent variables that allow us to measure parameter slope changes over time and can be easily integrated into existing methodological frameworks. The current research effort compares four multivariate model systems: year specific negative binomial model, year indicator pooled model, spline indicator pooled model, and mixed spline indicator pooled model. The model performance is compared using log-likelihood and Bayesian Information Criterion. The empirical analysis is conducted using the Traffic Analysis Zone (TAZ) level crash severity records from Central Florida for the years from 2011 to 2019. The comparison results indicate that the proposed mixed spline indicator pooled model outperforms the other models providing superior data fit while optimizing the number of parameters. The proposed mixed spline model can allow a piece-wise linear functional form for the parameter and is suitable to forecast crashes for future years as illustrated in our predictive performance analysis.

Pedestrian mobility has become an increasingly important concern in transportation system analysis because of its positive impacts on the environment and healthy lifestyles. However, pedestrian safety in a vehicle-dominated transportation network remains a concern and potential barrier to pedestrian mobility, with pedestrian intersection safety being of particular concern. In addition, it is important to understand how pedestrian safety has been affected by the COVID-19 pandemic, perhaps permanently shifting pedestrian injury risks. This research seeks to provide insight into how pedestrian injury risks at intersections have changed as a result of the pandemic by estimating a series pedestrian injury severity models. To do so, unconstrained and partially constrained random parameters multinomial logit models with heterogeneity in the means of random parameters were estimated. Using Florida data, two one-year periods (one year before and one year after the COVID-19 pandemic) were defined based on vehicle miles traveled. The sample includes 3,780 single pedestrian-single vehicle crashes (2,348 from daytime and 1,432 from nighttime). A broad range of variables was considered to assess how the parameters may have shifted between the before and after periods. A series of likelihood ratio tests were conducted to examine the stability of model parameter estimates across the predefined time periods as well as to determine the differences between the daytime and nighttime crash injury severity outcomes. The results show that the nighttime crashes experienced more temporal shifts relative to daytime crashes. The findings also showed that both pedestrian and driver behavior played key temporally-shifting roles before and after the COVID-19 pandemic period. Finally, the out-of-sample simulations suggest that pedestrian injuries have become more severe after the pandemic.

Crash risk measures (CRMs) are widely used in safety analysis to complement crash reports. However, none of the existing CRMs are specifically developed for modeling the risk of single-vehicle run-off-road (SVROR) crashes, especially those on horizontal curves. This paper proposes a novel crash risk measure for modeling SVROR crash risk using connected vehicle data. The proposed SVROR crash risk measure (SVROR-CRM) is based on the concept of tetraquark in particle physics. It utilizes the adjusted position deviation risk force ($F_{\mathrm{posi}}^{\mathrm{risk}}$) and adjusted attitude deviation risk moment (${\Gamma }_{\mathrm{atti}}^{\mathrm{risk}}$) to quantify SVROR crash risk. The SVROR crash risk is then estimated by the joint probability of $F_{\mathrm{posi}}^{\mathrm{risk}}$ and ${\Gamma }_{\mathrm{atti}}^{\mathrm{risk}}$ using a peak-over threshold approach. The risk threshold is automatically determined via a mean absolute error function. The SVROR-CRM is validated using connected vehicle and crash data from sixteen curves on Interstate 80 in Wyoming. The results suggest that the estimated SVROR crash risks well match historical crash records. Also, it is found that attitude deviation poses a higher risk of SVROR crash than position deviation on horizontal curves. Therefore, it is critical for drivers to steer properly on curves to minimize SVROR crash risks. The proposed approach bridges an important gap in crash risk measure research and can be used to estimate SVROR crash risk and identify unsafe trajectories and high-crash locations and/or periods on highway horizontal curves.

Recently released autonomous vehicle datasets like Waymo can provide rich information (and unprecedented opportunities) to investigate lane-changing behaviour of autonomous vehicles, requiring data from multiple drivers and lanes with different objectives. As such, the study investigates the discretionary lane-changing execution behaviour of autonomous vehicles and compares its behaviour with human-driven vehicles from Waymo and Next Generation Simulation (NGSIM) datasets. Several behavioural factors are statistically analysed and compared, whereas the discretionary lane-changing execution time (or duration) is modelled by a random parameters hazard-based duration modelling approach, which accounts for unobserved heterogeneity. Descriptive analyses suggest that autonomous vehicles maintain larger lead and lag gaps, longer discretionary lane-changing execution time, and lower acceleration variation than human-driven vehicles. The random parameters duration model reveals heterogeneity in discretionary lane-changing execution behaviour, which is higher in human-driven vehicles but decreases significantly for autonomous vehicles. Whilst contradictory to a general hypothesis in the literature that autonomous vehicles will eliminate heterogeneity, our finding indicates that heterogeneous behaviour also exists in autonomous vehicles (although to a lesser extent than in human-driven vehicles), which can be contextual to prevailing traffic conditions. Overall, autonomous vehicles show safer discretionary lane-changing behaviour compared to human-driven vehicles.