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Macro-level Safety Analysis of Pedestrian Crashes in Shanghai, China

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Xuesong Wang, Junguang Yang, Chris Lee, Shikai You, Zhuoran Ji

Pedestrian safety has become one of the most important issues in the field of traffic safety nowadays. With the rapid urbanization and motorization, pedestrian crashes in China surged in recent years. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, traffic, and land use features. The relationship was modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai – the largest city in China. Since spatial correlation existed among the zonal-level crashes, Bayesian Conditional Autoregressive (CAR) models with six different spatial proximity structures were developed to characterize the spatial correlations among TAZs. Model results indicated that the 0-1 adjacency matrix outperformed the other spatial proximity structures. Two land use weight matrixes also provided good model fit and prediction accuracy. More pedestrian crashes were associated with higher road density and shorter intersection spacing since they increase vehicle-pedestrian interactions. Car trip generation used as the surrogate pedestrian exposure variable had positive effect on pedestrian crashes. Pedestrian crash frequency were higher in TAZs with medium land use intensity than TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with median land use intensity to improve the pedestrian safety.

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