Journal of safety research · 2026
Spatiotemporal trends in pedestrian crashes: Socio-demographic influence and built environment impacts.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Joshi Manmohan, Ivan John N, Poudel Ashok, Shirazi Mohammadali
Аннотация
BACKGROUND: Despite significant advancements in motor-vehicle safety, the number of pedestrian fatalities from road crashes has increased in recent years. In addition, these crashes are disproportionately distributed across sociodemographic and economic categories. Further, the emergence of COVID-19 added a layer of complexity through its direct and indirect influence on user behavior, traffic patterns, and socio-economy.
METHODS: A Bayesian spatiotemporal model incorporating Conditional Autoregressive (CAR) spatial and Autoregressive (AR) temporal components are used to analyze crash patterns across merged census tracts in Connecticut. Models are estimated sequentially to study the crash risk disparity based on ethnic and socioeconomic characteristics: first using ethnicity variables, then socioeconomic variables, and finally temporal interactions to assess evolution of disparity over time.
RESULTS: Global Moran's I indicates significant spatial correlation in pedestrian crashes. The Ethnicity Models show that higher proportions of minority populations are associated with increased pedestrian crash rates; However, these ethnic effects diminish and become statistically insignificant when socioeconomic variables are introduced, suggesting that observed ethnic disparities are largely mediated by correlated socioeconomic disadvantages rather than ethnicity per se. The Temporal Interaction Model reveals moderate spatial and temporal correlations that align with the identified clustering patterns.
CONCLUSION: The findings reveal that pedestrian crashes disproportionately affect analysis units with higher proportions of Hispanic and Black residents, who are concentrated in areas with greater poverty, unemployment, and lower educational attainment. The relative risks of pedestrian crashes in the most socioeconomically vulnerable areas decreased during the COVID-19 pandemic but resurged to levels exceeding pre-pandemic disparities, indicating that disproportionate risks persist and can rapidly re-emerge without sustained, targeted interventions.
PRACTICAL APPLICATION: Pedestrian crashes disproportionately affect socioeconomically vulnerable neighborhoods, reflecting differences in infrastructure. These findings underscore the need to prioritize these communities when allocating safety investments in pedestrian infrastructure, speed management, and education.
METHODS: A Bayesian spatiotemporal model incorporating Conditional Autoregressive (CAR) spatial and Autoregressive (AR) temporal components are used to analyze crash patterns across merged census tracts in Connecticut. Models are estimated sequentially to study the crash risk disparity based on ethnic and socioeconomic characteristics: first using ethnicity variables, then socioeconomic variables, and finally temporal interactions to assess evolution of disparity over time.
RESULTS: Global Moran's I indicates significant spatial correlation in pedestrian crashes. The Ethnicity Models show that higher proportions of minority populations are associated with increased pedestrian crash rates; However, these ethnic effects diminish and become statistically insignificant when socioeconomic variables are introduced, suggesting that observed ethnic disparities are largely mediated by correlated socioeconomic disadvantages rather than ethnicity per se. The Temporal Interaction Model reveals moderate spatial and temporal correlations that align with the identified clustering patterns.
CONCLUSION: The findings reveal that pedestrian crashes disproportionately affect analysis units with higher proportions of Hispanic and Black residents, who are concentrated in areas with greater poverty, unemployment, and lower educational attainment. The relative risks of pedestrian crashes in the most socioeconomically vulnerable areas decreased during the COVID-19 pandemic but resurged to levels exceeding pre-pandemic disparities, indicating that disproportionate risks persist and can rapidly re-emerge without sustained, targeted interventions.
PRACTICAL APPLICATION: Pedestrian crashes disproportionately affect socioeconomically vulnerable neighborhoods, reflecting differences in infrastructure. These findings underscore the need to prioritize these communities when allocating safety investments in pedestrian infrastructure, speed management, and education.