The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach

Abstract

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.

Built Environment Features and Pedestrian Accidents: An Italian Retrospective Study

“…The influence of road network patterns on safety was recently explored by Guo et al. [28] who found the grid pattern network to be the least safe, compared to the irregular street layout, due to its inherent high level of integrations and connectivity, and consequently, higher potential for pedestrian-vehicle conflict points. The influence of road network structure on traffic volumes and vehicular speed has been widely demonstrated [29][30][31].…”

Rethinking safety in numbers: are intersections with more crossing pedestrians really safer?

“…We estimated the random parameters,29 measurement errors30 and spatial models18 for comparison. The estimated coefficients and deviance information criterion of the three models were very similar to those derived from the Poisson-lognormal model, indicating that our data were fairly robust to the model configuration.…”

“…A vague normal prior, $true0\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\left(0,1000\right)$ , was applied to $true0{\beta }_0$ , $true0{\beta }_1$ , $true0{\beta }_2$ and $true0{\beta }_k$ 18. The variance parameter $true0{\sigma }_u$ was assigned as a uniform distribution, $true0\mathrm{U}\mathrm{n}\mathrm{i}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{m}\left(0,10\right)$ 27 28…”

“…To account for biases due to unobserved heterogeneity, measurement errors in the estimates of vehicle and pedestrian volumes, and spatial dependence between neighbouring intersections, in addition to the basic Poisson-lognormal model, we developed random parameters,29 measurement errors30 and spatial models 18. To avoid highly correlated variables included in a model, we computed the variance inflation factor31 to check for multicollinearity.…”

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

“…In the study of Cai et al, spatial spillover effects were considered in the dual-state models [38]. They used zero-inflated negative binomial [39]. The model results reflected that the greater global integration was positively related to the higher frequency of pedestrian crashes, and the irregular road network was much safer than the grid pattern.…”