Estimating the Mobility Benefits of Adaptive Signal Control Technology Using a Bayesian Switch-Point Regression Model
The adaptive signal control technology (ASCT) is a traffic management strategy that adjusts signal timing parameters to optimize corridor performance based on actual traffic demand. This study used a Bayesian switch-point regression model (BSR) to estimate the mobility benefits of the ASCT. A 5.3-km (3.3-mi) corridor of Mayport Road in Jacksonville, Florida, was used as the case study. The results indicated that the ASCT improved travel speeds by 4% on midweekdays (Tuesday, Wednesday, and Thursday) in the northbound direction. However, in the southbound direction, mixed results were observed that may be attributed to higher driveway density and congestion. Moreover, the BSR model results revealed that there is a significant difference in the operating characteristics between with and without ASCT scenarios. Transportation agencies could use the findings of this study to justify and plan the future deployment of the ASCT.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
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Supplemental Notes:
- © 2022 American Society of Civil Engineers.
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Authors:
- Kodi, John
- Kidando, Emmanuel
- Sando, Thobias
- Alluri, Priyanka
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 04022015
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Serial:
- Journal of Transportation Engineering, Part A: Systems
- Volume: 148
- Issue Number: 5
- Publisher: American Society of Civil Engineers
- ISSN: 2473-2907
- EISSN: 2473-2893
- Serial URL: http://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Adaptive control; Bayes' theorem; Mobility; Regression analysis; Traffic signal control systems; Traffic speed
- Geographic Terms: Jacksonville (Florida)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01840453
- Record Type: Publication
- Files: TRIS, ASCE
- Created Date: Mar 28 2022 10:27AM