TY - GEN
T1 - Estimation of Travel Time by Vehicle Type on Unobserved and Observed Links with Limited Detector Data from Different Sources
AU - Shi, Chaoyang
AU - Lam, William H.K.
AU - Ma, Wei
AU - Tam, Mei Lam
AU - Ho, H. W.
AU - Li, Qingquan
AU - Wong, S.C.
AU - Chow, Andy H.F.
N1 - Funding Information:
* Research supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU R5029-18), the National Natural Science Foundation of China (No. 41901392), and the Natural Science Foundation of Hubei Province (No, 2019CFB098).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11
Y1 - 2022/11
N2 - Estimating travel time by vehicle type is challenging, particularly for an expressway corridor with limited data on different vehicle types. In practice, path travel time is collected by automatic vehicle identification (AVI) detectors, which can differentiate vehicle types and is installed on expressways with comparative longer spacing. Additionally, while link travel times for all vehicles may be available for links equipped with point detectors, in reality, the number of observed links is limited. Most current methods focus on estimating the travel times of site-specific links or paths without classifying vehicle type. Few attempts have been made to estimate travel times by vehicle type on observed links in a long study corridor or path with very limited detector data on observed links from different sources. Therefore, this paper proposes a quadratic optimization model that makes full use of both AVI and point detectors and estimates the travel time by vehicle type on both the observed and unobserved links along the expressway concerned. A case study using real-world data from Hong Kong is presented to show the merits of the proposed modeling approach. The estimation results are satisfactory and robust in different scenarios.
AB - Estimating travel time by vehicle type is challenging, particularly for an expressway corridor with limited data on different vehicle types. In practice, path travel time is collected by automatic vehicle identification (AVI) detectors, which can differentiate vehicle types and is installed on expressways with comparative longer spacing. Additionally, while link travel times for all vehicles may be available for links equipped with point detectors, in reality, the number of observed links is limited. Most current methods focus on estimating the travel times of site-specific links or paths without classifying vehicle type. Few attempts have been made to estimate travel times by vehicle type on observed links in a long study corridor or path with very limited detector data on observed links from different sources. Therefore, this paper proposes a quadratic optimization model that makes full use of both AVI and point detectors and estimates the travel time by vehicle type on both the observed and unobserved links along the expressway concerned. A case study using real-world data from Hong Kong is presented to show the merits of the proposed modeling approach. The estimation results are satisfactory and robust in different scenarios.
UR - http://www.scopus.com/inward/record.url?scp=85141842442&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9921834
DO - 10.1109/ITSC55140.2022.9921834
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141842442
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 562
EP - 567
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Y2 - 8 October 2022 through 12 October 2022
ER -