DETERMINING THE CAPACITY OF A MAJOR HIGHWAY SEGMENT USING AN EMPIRICAL METHOD
Issued Date
2025-01-01
Resource Type
eISSN
2644108X
Scopus ID
2-s2.0-105026771863
Journal Title
Proceedings of International Structural Engineering and Construction
Volume
12
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of International Structural Engineering and Construction Vol.12 No.1 (2025)
Suggested Citation
Korbuakaew K., Phatchanakit T., Luangon C., Soe T.N., Pathomsiri S., Srisurin P. DETERMINING THE CAPACITY OF A MAJOR HIGHWAY SEGMENT USING AN EMPIRICAL METHOD. Proceedings of International Structural Engineering and Construction Vol.12 No.1 (2025). doi:10.14455/ISEC.2025.12(1).INF-03 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114014
Title
DETERMINING THE CAPACITY OF A MAJOR HIGHWAY SEGMENT USING AN EMPIRICAL METHOD
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Abstract
This study estimates the highway capacity of a segment on Borommaratchachonnani Road, an eight-lane highway in Thailand, using an empirical model based on speed– flow relationships derived from field data. Traffic flow and speed data were collected via video recordings on multiple weekdays in February 2025. A speed–flow diagram was constructed using five-minute interval data to determine the empirical capacity of the highway segment. The resulting capacity estimate was compared with two established models: the Highway Capacity Manual (HCM) 2016 and the model developed by Thailand’s Department of Highways (DOH). Results indicate that both models significantly overestimate actual capacity by 72.76% for the HCM 2016 model and 30.73% for the DOH model. The overestimation in the HCM model is attributed to differences in driver behavior and the exclusion of motorcycles, while the DOH model, though more accurate, is outdated. The findings underscore the need for localized adjustments in capacity models to reflect Thailand’s unique traffic conditions. Key variables such as motorcycle prevalence, autonomous vehicles, access point density, and terrain type should be integrated into future modeling efforts.