Estimation of spatiotemporal travel speed based on probe vehicles in mixed traffic flow

Jongho Kim; Woosuk Kim; Eunjeong Ko; Yong-Shin Kang; Hyungjoo Kim; Jongho Kim; Woosuk Kim; Eunjeong Ko; Yong-Shin Kang; Hyungjoo Kim

doi:10.3934/era.2024015

Electronic Research Archive

2024, Volume 32, Issue 1: 317-331. doi: 10.3934/era.2024015

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Estimation of spatiotemporal travel speed based on probe vehicles in mixed traffic flow

1.
Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology CCS Graduate School of Mobility, Yuseonggu, Daejeon 193, Republic of Korea
2.
Department of Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon-si, Gyeonggi-do 16229, Republic of Korea
3.
Industrial Intelligence Laboratory, Advanced Institute of Convergence Technology, Suwon-si, Gyeonggi-do 16229, Republic of Korea

Received: 20 October 2023 Revised: 21 November 2023 Accepted: 03 December 2023 Published: 25 December 2023

Conventional fixed traffic detectors are limited to their installed locations and are unable to collect general traffic information or monitor microscopic traffic flows. Mobile detectors overcome spatial constraints by allowing the vehicle to act as a detector and can observe microscopic traffic flows by collecting high-resolution trajectory data from individual vehicles. The objective of this study is to estimate spatiotemporal traffic information based on the autonomous driving sensor headway distance and to calculate the appropriate spatiotemporal interval according to the sampling rate. First, individual vehicle trajectory data was collected, and a traffic information estimation was established. Travel speed was calculated based on generalized definitions, and its estimation and errors were analyzed. In addition, the appropriate spatiotemporal interval according to cell size, time interval, and sampling rate was analyzed. The analysis demonstrated that the estimation accuracy was improved by cell size, time interval, and sampling rate. Based on this, the appropriate time and space to minimize the error rate were calculated considering the sampling rate. When the sampling rate was 40% or more, the error rate was 5% or less in all time and space; however, error rate differences occurred in several cases at sampling rates below 40%. These results are anticipated for efficient management of collecting, processing and providing traffic information.
- spatiotemporal travel speed,
- probe vehicle,
- NGSIM,
- mixed traffic flow
Citation: Jongho Kim, Woosuk Kim, Eunjeong Ko, Yong-Shin Kang, Hyungjoo Kim. Estimation of spatiotemporal travel speed based on probe vehicles in mixed traffic flow[J]. Electronic Research Archive, 2024, 32(1): 317-331. doi: 10.3934/era.2024015

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Abstract

Conventional fixed traffic detectors are limited to their installed locations and are unable to collect general traffic information or monitor microscopic traffic flows. Mobile detectors overcome spatial constraints by allowing the vehicle to act as a detector and can observe microscopic traffic flows by collecting high-resolution trajectory data from individual vehicles. The objective of this study is to estimate spatiotemporal traffic information based on the autonomous driving sensor headway distance and to calculate the appropriate spatiotemporal interval according to the sampling rate. First, individual vehicle trajectory data was collected, and a traffic information estimation was established. Travel speed was calculated based on generalized definitions, and its estimation and errors were analyzed. In addition, the appropriate spatiotemporal interval according to cell size, time interval, and sampling rate was analyzed. The analysis demonstrated that the estimation accuracy was improved by cell size, time interval, and sampling rate. Based on this, the appropriate time and space to minimize the error rate were calculated considering the sampling rate. When the sampling rate was 40% or more, the error rate was 5% or less in all time and space; however, error rate differences occurred in several cases at sampling rates below 40%. These results are anticipated for efficient management of collecting, processing and providing traffic information.

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