Estimating pedestrian delay at signalized intersections using high-resolution event-based data: a finite mixture modeling method

Abstract

It has been widely shown that pedestrians’ level of frustration grows with the increase of pedestrian delay, and may cause pedestrians to violate the signals. However, for agencies seeking to use multimodal signal performances for signal operations, the pedestrian delay is not always readily available. To tackle this issue, this study proposed a finite mixture modeling method to estimate pedestrian delay using high-resolution event-based data collected from the smart sensors. The proposed method was used to estimate pedestrian delay at four signalized intersections on a major arterial corridor in Pima County, Arizona. The results showed the proposed method was able to capture and track the actual pedestrian delay fluctuations during the day at all the study intersections with average errors of 10 s and 13 s for mean-absolute-error and root-mean-square-error, respectively. In addition, the proposed model was compared with three conventional methods (HCM 2010, Virkler, Dunn) and the comparison results showed that the proposed method outperforms all the other methods in terms of both mean-absolute-error and root-mean-square-error. Furthermore, it was found that the proposed method is transferable and can be used as a network-wide delay estimation model for intersections with similar traffic patterns. The application of the proposed method could provide agencies with a more reliable, robust, and yet accurate approach for estimating pedestrian delay at signalized intersections where the pedestrian data are not readily available. In addition, it will allow system operators to quantitatively assess existing delays and enact changes to incorporate the better serve pedestrian needs.

Keywords:

• Finite mixture modeling
• network-wide estimation
• pedestrian delay
• transferability test pedestrian delay

Acknowledgments

The authors would also like to thank the Pima County Department of Transportation for data support.

Additional information

Funding

This project was funded by the National Institute for Transportation and Communities (NITC; grant number 1298) a U.S. DOT University Transportation Center.