Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

The imbalance of traffic resources is one of the important reasons that restrict the healthy development of the urban traffic system. Based on the concept of “sponge city”, this paper puts forward a kind of road traffic system, which can explore the flexible space of transportation infrastructure. The Fuzzy Analytic Hierarchy Process is established to comprehensively evaluate the system performance, deeply explore the flexible space of urban transportation facilities, and improve the performance of the urban transportation system. The sponge-type road traffic system evaluation model proposed in this paper can provide a reference for other regions to tap the flexible space of regional traffic infrastructure resources.