In China pedestrian crashes make up a large proportion of road casualties and are more likely to occur at signalized intersections. This study examines the behavioral characteristics of pedestrians at such sites and the factors affecting pedestrian behavior. Data on pedestrian behavior were obtained from video images of pedestrian movements recorded at 26 signalized intersections in three Chinese cities, and 598 pedestrians who were using crosswalks were surveyed for their perceptions about the crossing environment. The behavior data indicated that most pedestrians walked normally when they crossed roads; the rate of compliance with traffic rules for all pedestrians was 62.8%. The differences in crossing behavior by gender, age, and the type of pedestrian group were then identified. The results showed that women and middle-aged individuals were more likely to violate traffic rules. In comparison with a single pedestrian and pedestrians in a pair, pedestrians in a group tended not to look at traffic signals. Analysis of the rates of compliance with traffic rules at crosswalks also indicated that crossing distance, signal timing, the presence of traffic police, and pedestrian volume had different effects. Moreover, the major reasons for crossing on red and opinions on waiting time were obtained by analysis of the survey data. For example, the largest proportion (30.25%) of surveyed individuals indicated that they violated traffic rules to save time and for convenience. Findings from this study can help researchers and practitioners understand pedestrian behavior at crosswalks at signalized intersections and thus create a better street-crossing environment for all pedestrians.

The importance of modeling pedestrian behavior has been well recognized in both fields of safety and capacity assessment. Although various theories have been adopted to develop reliable pedestrian walking models, limited effort has been made to shed light on the case at signalized crosswalk, especially during the pedestrian flashing green (PFG) interval. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis. First, considering the impact of PFG signal on pedestrian crossing behavior, a three-step framework is proposed to describe pedestrian decision-making process in detail. The desired velocity of pedestrians at crosswalk in PFG phase is defined according to the process. Then, based on this framework, social force model is embedded in a new crossing behavior model, and the parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Furthermore, through simulation experiments, the model performance is verified and some key points for setting criterion of PFG are discussed. Finally, the implication significance of this study and recommendations for future research have also been provided to improve pedestrian safety.

Nowadays phone distraction has started to become an increasingly recognized phenomenon. This article aims to examine the influences of phone use on pedestrian crossing behavior at signalized intersections in China. Using video recording and manual counting, pedestrian crossing behavior, age, gender, phone use, and waiting time are obtained at four signalized intersections. Totally, 4196 pedestrians are observed in four peak hours. Among them, 328 pedestrians (7.82%) are using their mobile phones, including 162 male pedestrians and 166 female pedestrians. The average phone use rate in different age groups are presented as follows: children (4.49%), youth (10.69%), middle-aged (6.87%), and elderly (1.15%). In terms of the phone using behavior on the crosswalk, age is a significant factor, while gender is not. For the type of violation behavior, the results show that pedestrians who use mobile phones are most likely to be late starters. In addition, some other important results are found: mobile phone use can raise the violation of pedestrian crossing behavior; pedestrians using their phones are more likely to cross on red; and pedestrians using their phones while crossing walk more slowly. Finally, the application significance of this study and some recommendations are provided to improve pedestrian safety.

Road accidents impose serious problems on society. Possible collisions between vehicles and pedestrians must be detected before they occur so that a timely warning may be issued. By using the vision‐based approach, this study presents an effective and efficient algorithm to estimate the vehicle–pedestrian collision probability at intersections. The real‐time trajectories and movement parameters (position, speed, acceleration or direction) of vehicles and pedestrians are obtained based on state‐of‐the‐art detection and tracking algorithm which include background subtraction method, faster regions with convolutional neural networks and optical flow method. To find the appropriate time to identify the latent collision risk for calculating the collision probability, this study defines the critical time based on different collision patterns of perception‐reaction failure and evasive action failure. In addition, based on discrete acceleration and discrete angle, the authors get different extended trajectories which can include most situation when the conflict happened. Trajectories generation probability are given by the discrete choice probability model based on the Logit model to get the accurate collision probability. Real‐world video data is implemented to demonstrate the approach. This proposed collision prediction method can provide some important results for designing the intelligent pedestrian signal timing schemes at intersections.

Accurate pedestrian crossing intention prediction is critical for autonomous vehicles and advanced driver assistance systems. In multi-pedestrian and multi-vehicle interaction scenes, the social interaction between pedestrians and other traffic participants is ubiquitous, which affects pedestrian crossing decisions and the accuracy of prediction. However, previous studies on pedestrian crossing intention lack comprehensive consideration and mathematical modeling of the social interaction. We propose a “social interaction force” (SIF) to identify and quantify social interaction behaviors and combine the hidden Markov model (HMM) to predict pedestrian crossing intentions 1.0 s ahead. Firstly, a large dataset of pedestrian-vehicle interaction samples is collected from two views, and high-dimensional features are extracted for pedestrian intention prediction. Next, the concept of SIF is proposed to quantitatively characterize the influence of other pedestrians and vehicles on pedestrian crossing decisions, including “pedestrian interaction force” and “pedestrian-vehicle interaction force.” Finally, SIF, pedestrian features, and road structure features are input into HMM. Sliding time windows are applied to the HMM to achieve dynamic prediction of pedestrian intention sequences. Experimental results show that the recognition accuracy of the proposed model is 0.976, and the accuracy of 1.0 s ahead prediction is 0.932 with guaranteed prediction speed. The proposed model performance is superior to that of the most prevalent models developed thus far.

This study provides a methodology to identify travellers’ transportation modes by tracking the mobile phone data, which aims to obtain the accurate mode split rate for providing decision support in urban traffic planning. First, the effective mobile phone singling data and GPS data are collected from the communication operators and a mobile phone app, respectively. Considering the differences in velocity and acceleration of different trip modes, a trip mode characteristic description model is built based on wave characteristics and moving average method. Compared with the wave characteristics, the moving average method shows a better accuracy of 90%. Then training samples are drawn by two data selection methods including probability proportional to size sampling and equal amount sampling. Furthermore, the classifier method for mode choice prediction is developed by support vector machines (SVMs) and back propagation neutral network. Finally, the results of the case study show that using a 30‐point moving average training data set can improve the prediction accuracy largely, and the SVM method gets a better accuracy of 82%. The potential of using the mobile phone data to build a new mode choice prediction method in the field of transportation is shown.