Rapid advancements in crash modeling have yet to fully integrate multiple behavior-driven crash types and their severity outcomes within a single, scalable framework, especially in a way that captures and quantifies the interdependencies among these crash types, where areas prone to one risky-driving crash type also tend to be prone to another. This study proposes an integrated multivariate econometric framework to jointly model crash frequency and severity outcomes for three major behaviorally driven crash types: alcohol-related, distraction-related, and aggressive-driving-related crashes. Specifically, using Oregon's 2022 census block group-level crash data, we propose an Integrated Multivariate Negative Binomial–Generalized Ordered Probit Fractional Split (IMNB–GOPFS) model to analyze these dimensions simultaneously while accounting for the cross-equation associations across the behavioral crash types and multiple levels of correlated unobserved heterogeneity across crash counts and severity proportions. A comparison in terms of model fit and predictive performance reveals the superior performance of the proposed framework over traditional non-integrated approaches, highlighting the importance of capturing such interdependencies among behaviorally driven crashes. An elasticity assessment further highlights how several variables are associated with crash outcomes through both direct and indirect components. Further, we apply the model results to identify high-risk zones and find that census block groups with increased truck exposure, greater rural and freeway road coverage, and larger variations in speed limits are consistently associated with elevated crash occurrences across multiple behavioral crash types. Overall, this research moves beyond purely correlation-based dependence by explicitly modeling and quantifying cross-equation associations across crash types, thereby revealing interrelationships among risky driving behavior related crashes. By focusing on the full spectrum of behavior-driven crash types and their severity outcomes, this study offers a comprehensive framework to support data-driven safety planning and proactive crash risk assessment at the planning level.
Conflict-based safety assessments have increasingly incorporated unobserved heterogeneity in crash risk estimation; however, the explicit integration of evasive actions into rear-end crash risk estimation remains limited. While ignoring unobserved heterogeneity can bias parameter estimates, overlooking evasive maneuvers may undermine the behavioral validity of conflict-based safety assessments. This study proposes a non-stationary bivariate random parameters extreme value model that integrates conflict measures conditioned on evasive actions to estimate rear-end crash frequencies by severity at signalized intersections. Video data were collected from five signalized intersections in Queensland, Australia. The proposed model was developed using Modified Time-to-Collision and Deceleration Rate to Avoid a Crash as crash proximity measures and the expected post-collision velocity difference (Delta-V) as a severity measure. The random parameters bivariate extreme value model has been found to outperform the fixed parameters model in terms of goodness-of-fit statistics and out-of-sample predictions. The results indicate that the rear-end crash risk is positively associated with the frequency of rear-end conflicts per signal cycle, while the proportion of right-turning vehicles influences both crash risk and injury severity. Models developed using evasive-action-based conflicts produced crash frequency estimates that more closely matched the observed total and non-severe crash frequencies than those developed using all conflicts. The proposed framework enhances the behavioral realism of conflict-based safety evaluations and supports the development of time-sensitive countermeasures at signalized intersections.
This study investigates public attitudes toward 30 km/h (20mph) speed limits in urban areas and their liveability dimensions. It specifically examines two key aspects: (i) the public acceptance of the policy; and (ii) perceived impacts on city-centre liveability. A two-stage integrated modelling framework was developed and adopted. First, latent attitudinal factors underlying public perceptions were extracted through Exploratory Factor Analysis (EFA). Second, these latent factors, alongside observed socio-demographic and travel characteristics, were incorporated into advanced random parameter models with heterogeneity in the means: an ordered probit model for policy acceptance, and a bivariate probit model for jointly analysing city-centre attractiveness for living and working. Focusing on Greece, where a default 30 km/h speed limit was recently introduced, the empirical results showed that latent factors relating to safety, sustainable travel, driving behavior, and urban accessibility, alongside socio-demographic characteristics and travel habits, shape policy acceptance. Expectations for enhanced road safety, sustainable mobility, and improved neighbourhood accessibility are strong determinants of public support. Conversely, perceived implementation barriers, low-speed driving frustration, and environmental scepticism reduce acceptance. Socio-demographic characteristics, including education, age, gender, and residential context, are found to mediate the impact of attitudinal factors on public support. The bivariate probit model reveals distinct determinants for perceived city-centre attractiveness for living versus working. While safety and sustainable travel consistently matter, environmental attitudes, household composition, and employment sector exert differential effects. Overall, public attitudes surrounding the 30 km/h policy are driven by road safety considerations alongside broader expectations related to quality of life, urban functionality, and liveability.
Advisory systems tailored for the yellow light dilemmas have been implemented in connected vehicles (CVs) to enhance intersection safety. Evaluations of such systems have focused solely on drivers' stop/go decisions, neglecting the subsequent control responses (i.e., maximum decelerations or accelerations) that determine actual safety outcomes. Thus, the efficacy of CVs remains partially understood. To overcome this gap, this study develops a bivariate copula framework with unobserved heterogeneity for understanding stop/go decisions at signalized intersections. A C-V2X-enabled stop/go advisory system was developed and implemented in a high-fidelity co-simulation driving simulator combining Unity3D and VISSIM, with 62 licensed drivers completing the experiments under baseline and advisory conditions. The results indicate that the advisory system elicited high driver trust and reduced mental workload, with compliance high overall but varying by advisory type. Further, the copula model reveals significant dependence structure between decision and control behavior, whereby drivers who decide to run yellow lights exhibit lower maximum acceleration, while those who stop apply smooth braking. Further, the advisory system increases the probability of proceeding without inducing risky accelerations and reduces harsh deceleration when stopping. The model further uncovers the unobserved heterogeneities across driver characteristics and driving history. The findings present methodological and empirical insights that can support smoother intersection operations and inform safety-oriented advisory-system design in future connected traffic environments.
Forecasting crash risk at the network level over future horizons is fundamental to proactive safety management. Existing conflict-based extreme value theory (EVT) models, however, largely ignore spatial correlations in extreme traffic conflicts and remain restricted to one-step-ahead crash risk forecasts. This study addresses both limitations by proposing a score-driven peak-over-threshold (SPOT) model within a marked point process framework, combined with conditional autoregressive spatial priors in a Bayesian hierarchical structure to account for spatial dependence across the network. Probabilistic forecasts of two crash risk measures, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), are generated at multiple future horizons via posterior simulation of the marked point process. The framework is applied to an urban network of 50 intersections from the pNEUMA drone trajectory dataset in Athens, Greece. In-sample results reveal meaningful spatial correlation, and model comparison shows that covariate specification has a substantially larger impact on predictive performance than spatial prior choice. Out-of-sample, crash risk forecasts are most reliable within 20 s. Forecast quality degrades at longer horizons due to growing simulation errors and parameter reversion toward baseline levels, becoming inadequate beyond 60 s. The practical value of the framework is demonstrated through a forecast-based safest-route finding application, which yields routes that are on average 7% safer than those based on static crash risk estimates with comparable travel times.
