Prediction of pedestrians’ wait-or-go decision using trajectory data based on gradient boosting decision tree

Abstract

The road-crossing of pedestrians at unsignalized crosswalks is a major concern for road safety. Previous studies focused on explaining of the mechanism underlying this behavior, but a framework of prediction is missing. To predict this behavior, only variables measured before the decision is made should be considered. To explore whether historical data is able to predict the behavior, this paper investigates pedestrians’ wait-or-go (WOG) behavior based on trajectory data and a machine learning method, both of which have been rarely applied by previous studies. The use of trajectory data enables the analysis of several influential factors related to moving characteristics, which are critical for pedestrians’ decision making. The framework based on machine learning, combined with trajectory data, achieves good explanatory power and predictability of pedestrians’ WOG behavior. Moreover, a possible application of this study is the prediction of pedestrian road-crossing intention in the context of autonomous cars.

KEYWORDS:

• Pedestrians’ road-crossing behaviour
• wait-or-go
• trajectory data
• ensemble learning

Acknowledgements

We are grateful for the financial support from the National Natural Science Foundation of China (72010107004, 71701146, 72171168), and Funding: The First Batch of 2020 MOE of PRC Industry-University Collaborative Education Program (Program No. 202001SX04, Kingfar-CES ‘Human Factors and Ergonomics’ Program).

CRediT authorship contribution statement

Xiuying Xin contributed to Conceptualization, Methodology, Formal analysis, Writing - original draft. Ning Jia contibuted to Conceptualization. Shuai Ling contributed to Methodology, Writing - review & editing, Supervision. Zhengbing He contributed to Resources.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 $t_{p0}$ is the time point a pedestrian trajectory starts. If a pedestrian appears in the walking area (Figure 2(a)) and arrive at the start line of trajectory collection (Figure 1(c)), the first point is collected and a trajectory starts.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71701146]; National Natural Science Foundation of China [grant number 72010107004].