Estimating the Mobility Benefits of Adaptive Signal Control Technology Using a Bayesian Switch-Point Regression Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 5
Abstract
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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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This project was funded by the Florida Department of Transportation (FDOT) and conducted as a cooperative effort by the University of North Florida (UNF) and Florida International University (FIU). The opinions, results, and findings expressed in this manuscript are those of the authors and do not necessarily represent the views of FDOT, FIU, or UNF.
References
Ali, M. D., A. E. Kitali, J. H. Kodi, P. Alluri, and T. Sando. 2021a. “Safety effects of transit signal priority using the full Bayesian approach.” In Proc., Transportation Research Board 100th Annual Meeting. Washington, DC: Transportation Research Board.
Ali, M. S., A. E. Kitali, J. Kodi, P. Alluri, and T. Sando. 2022. “Quantifying the safety benefits of transit signal priority using full Bayes before–after study.” J. Transp. Eng. Part A: Syst. 148 (1): 1–9. https://doi.org/10.1061/jtepbs.0000620.
Ali, M. S., J. Kodi, P. Alluri, and T. Sando. 2021b. “Mobility benefits of transit signal priority (TSP) in adaptive signal control technology (ASCT) environment.” In Proc., Transportation Research Board 100th Annual Meeting. Washington, DC: Transportation Research Board.
Ali, S., A. E. Kitali, J. H. Kodi, and P. Alluri. 2021c. “Safety impacts of transit signal priority using a full Bayesian approach.” Transp. Res. Rec. 2675 (11): 1189–1204. https://doi.org/10.1177/03611981211025285.
Alluri, P., T. Sando, C. Kadeha, H. Haule, J. H. Salum, M. S. Ali, J. H. Kodi, and A. E. Kitali. 2020. “Developing Florida-specific mobility enhancement factors (MEFs) and crash modification factors (CMFs) for TSM & O strategies final report.” Accessed January 5, 2022. https://fdotwww.blob.core.windows.net/sitefinity/docs/default-source/research/reports/fdot-bdv29-977-46-rpt.pdf.
Bhagat, A., J.-D. Saphores, R. Jayakrishnan, and T. R. Board. 2017. “Detecting changes in accident rates using a hierarchical bayesian approach: An application to the I-710 and the implementation of the PierPASS program.” Accessed August 1, 2020. https://trid.trb.org/view/1439686.
Chen, X., L. Li, and Q. Shi. 2015. Stochastic evolutions of dynamic traffic flow. Berlin: Springer.
Day, C. M., J. M. Ernst, T. M. Brennan, C. S. Chou, A. M. Hainen, S. M. Remias, A. Nichols, B. D. Griggs, and D. M. Bullock. 2012. “Performance measures for adaptive signal control: Case study of system-in-the-loop simulation.” Transp. Res. Rec. 2311 (1): 1–15. https://doi.org/10.3141/2311-01.
Dutta, U., S. Bodke, and B. Dara. 2010. Safety evaluation of the SCATS control system. Detroit, MI: Univ. of Detroit Mercy.
Fehon, K., M. Krueger, J. Peters, R. Denney, P. Olson, and E. Curtis. 2012. Model systems engineering documents for adaptive signal control technology systems – Guidance document. Rep. No. FHWA-HOP-11-027. Washington, DC: Federal Highway Administration.
FHWA (Federal Highway Administration). 2017. “Center for accelerating innovation.” Accessed January 5, 2022. https://www.fhwa.dot.gov/innovation/everydaycounts/edc-1/asct.cfm.
Fontaine, M. D., J. Ma, and J. Hu. 2015. “Evaluation of the Virginia department of transportation adaptive signal control technology pilot project.” Accessed January 3, 2022. http://www.virginiadot.org/vtrc/main/online_reports/pdf/15-r24.pdf.
Gord Associates. 2007. ATMS evaluation white paper. Montreal: Gord & Associates.
Hu, J., M. D. Fontaine, B. B. Park, and J. Ma. 2016. “Field evaluations of an adaptive traffic signal—Using private-sector probe data.” J. Transp. Eng. 142 (1): 04015033. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000806.
Hunter, M. P., S. K. Wu, H. K. Kim, and W. Suh. 2012. “A probe-vehicle-based evaluation of adaptive traffic signal control.” IEEE Trans. Intell. Transp. Syst. 13 (2): 704–713. https://doi.org/10.1109/TITS.2011.2178404.
Hutton, J., C. Bokenkroger, and M. Meyer. 2010. “Evaluation of an adaptive traffic signal system: Route 291 in Lee’s Summit, Missouri.” Accessed March 1, 2010. http://trid.trb.org/view.aspx?id=916407.
Jabari, S. E., J. Zheng, and H. X. Liu. 2014. “A probabilistic stationary speed-density relation based on Newell’s simplified car-following model.” Transp. Res. Part B: Methodol. 68 (Oct): 205–223. https://doi.org/10.1016/j.trb.2014.06.006.
Kergaye, C., A. Stevanovic, and P. T. Martin. 2009. “Comparison of before-after versus off-on adaptive traffic control evaluations: Park City, Utah, sase study.” Transp. Res. Rec. 2128 (1): 192–201. https://doi.org/10.3141/2128-20.
Khattak, Z. H., M. D. Fontaine, and R. A. Boateng. 2018. “Evaluating the impact of adaptive signal control technology on driver stress and behavior using real-world experimental data.” Transp. Res. Part F: Psychol. Behav. 58 (Oct): 133–144. https://doi.org/10.1016/j.trf.2018.06.006.
Khattak, Z. H., M. J. Magalotti, and M. D. Fontaine. 2020. “Operational performance evaluation of adaptive traffic control systems: A Bayesian modeling approach using real-world GPS and private sector PROBE data.” J. Intell. Transp. Syst. Technol. Plann. Oper. 24 (2): 156–170. https://doi.org/10.1080/15472450.2019.1614445.
Kidando, E., R. Moses, E. E. Ozguven, and T. Sando. 2017a. “Bayesian nonparametric model for estimating multistate travel time distribution.” J. Adv. Transp. 2017 (Jan): 1–9. https://doi.org/10.1155/2017/5069824.
Kidando, E., R. Moses, E. E. Ozguven, and T. Sando. 2017b. “Evaluating traffic congestion using the traffic occupancy and speed distribution relationship: An application of bayesian Dirichlet process mixtures of generalized linear model.” J. Transp. Technol. 7 (3): 318–335. https://doi.org/10.4236/jtts.2017.73021.
Kidando, E., R. Moses, and T. Sando. 2019. “Bayesian regression approach to estimate speed threshold under uncertainty for traffic breakdown event identification.” J. Transp. Eng. Part A: Syst. 145 (5): 04019013. https://doi.org/10.1061/JTEPBS.0000217.
Kitali, A. E., E. Kidando, P. Martz, P. Alluri, T. Sando, R. Moses, and R. Lentz. 2018. “Evaluating factors influencing the severity of three-plus multiple-vehicle crashes using real-time traffic data.” Transp. Res. Rec. 2672 (38): 128–137. https://doi.org/10.1177/0361198118788207.
Kodi, J. H. 2019. “Evaluating the mobility and safety benefits of adaptive signal control technology (ASCT).” Ph.D. thesis, Dept. of Engineering, Univ. of North Florida Graduate.
Kodi, J. H., E. Kidando, T. Sando, and P. Alluri. 2021a. “Quantifying the mobility benefits of adaptive signal control technology.” In Proc., Transportation Research Board 100th Annual Meeting. Washington, DC: Transportation Research Board.
Kodi, J. H., A. E. Kitali, M. S. Ali, and P. Alluri. 2021b. “Estimating safety impacts of adaptive signal control technology using a full Bayesian approach.” Transp. Res. Rec. 2675 (11): 1168–1179. https://doi.org/10.1177/03611981211025281.
Kodi, J. H., A. E. Kitali, M. S. Ali, P. Alluri, and T. Sando. 2021c. “Estimating safety effects of adaptive signal control technology using the full Bayesian approach (No. TRBAM-21-03255).” In Proc., Transportation Research Board 100th Annual Meeting. Washington, DC: Transportation Research Board.
Kodi, J. H., A. E. Kitali, T. Sando, P. Alluri, and R. Ponnaluri. 2022. “Safety evaluation of an adaptive signal control technology using an empirical Bayes approach.” J. Transp. Eng. Part A: Syst. 148 (4): 04022008. https://doi.org/10.1061/JTEPBS.0000652.
Kruschke, J. K. 2010. “Bayesian data analysis.” Wiley Interdiscip. Rev. Cognit. Sci. 1 (5): 658–676. https://doi.org/10.1002/wcs.72.
Kruschke, J. K. 2013. “Bayesian estimation supersedes the test.” J. Exp. Psychol.: Gen. 142 (2): 573–603. https://doi.org/10.1037/a0029146.
Kruschke, J. K., and T. M. Liddell. 2018. “The bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.” Psychonomic Bull. Rev. 25 (1): 178–206. https://doi.org/10.3758/s13423-016-1221-4.
Lidbe, A. D., E. G. Tedla, A. M. Hainen, and S. L. Jones. 2017. “Analytical techniques for evaluating the implementation of adaptive traffic signal control systems.” J. Transp. Eng. Part A: Syst. 143 (5): 1–10. https://doi.org/10.1061/JTEPBS.0000034.
Lin, J. G., J. Chen, and Y. Li. 2012. “Bayesian analysis of student linear regression with unknown change-point and application to stock data analysis.” Comput. Econ. 40 (3): 203–217. https://doi.org/10.1007/s10614-011-9305-8.
Liu, Z., and L. Qian. 2010. “Changepoint estimation in a segmented linear regression via empirical likelihood.” Commun. Stat.- Simul. Comput. 39 (1): 85–100. https://doi.org/10.1080/03610910903312193.
Lupton, R. C., and J. M. Allwood. 2018. “Incremental material flow analysis with Bayesian inference.” J. Ind. Ecol. 22 (6): 1352–1364. https://doi.org/10.1111/jiec.12698.
Martin, P. T., and A. Stevanovic. 2008. “Adaptive signal control- SCATS evaluation in Park City, Utah.” Accessed July 2, 2020. http://pinellas.gov/PublicWorks/pdf/smart_tracs_assessment.pdf.
McElreath, R. 2016. In Vol. 1 of Rethinking: An R package for fitting and manipulating bayesian models, 1–26. Boca Raton, FL: CRC Press.
Petrella, M., and J. Lappin. 2007. Measuring driver satisfaction with an urban arterial before and after deployment of an adaptive timing signal system. Cambridge, MA: Volpe National Transportation Systems Center.
Qu, X., S. Wang, and J. Zhang. 2015. “On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models.” Transp. Res. Part B: Methodol. 73 (Mar): 91–102. https://doi.org/10.1016/j.trb.2015.01.001.
Salvatier, J., T. V. Wiecki, and C. Fonnesbeck. 2016. “Probabilistic programming in Python using PyMC3.” PeerJ Comput. Sci. 2 (4): e55. https://doi.org/10.7717/peerj-cs.55.
Schrank, D., B. Eisele, and L. Tim. 2019. “Urban mobility report 2019.” Accessed May 7, 2021. https://static.tti.tamu.edu/tti.tamu.edu/documents/mobility-report-2019.pdf.
Sharma, A., N. Hawkins, S. Knickerbocker, S. Poddar, and J. Shaw. 2018. “Performance-based operations assessment of adaptive control implementation in Des Moines, Iowa.” Accessed January 5, 2022. https://rosap.ntl.bts.gov/view/dot/36845.
Slavin, C., W. Feng, M. Figliozzi, and P. Koonce. 2013. “Statistical study of the impact of adaptive traffic signal control on traffic and transit performance.” Transp. Res. Rec. 2356 (1): 117–126. https://doi.org/10.1177/0361198113235600114.
Sopasakis, A. 2004. “Stochastic noise approach to traffic flow modeling.” Physica A 342 (3–4): 741–754. https://doi.org/10.1016/j.physa.2004.05.040.
Sprague, D. 2012. “Adaptive signal timing comparison between the InSync and QuicTrac adaptive signal systems installed in Colorado.” Accessed January 5, 2022. http://www.coloradodot.info/programs/research/pdfs.
Stevanovic, A. 2010. Adaptive traffic control systems: Domestic and foreign state of practice. Washington, DC: National Academies Press.
Tian, Z., F. Ohene, and P. Hu. 2011. “Arterial performance evaluation on an adaptive traffic signal control system.” Procedia Social Behav. Sci. 16 (Jan): 230–239. https://doi.org/10.1016/j.sbspro.2011.04.445.
Trafficware. 2012. “SynchroGreen real-time adaptive control system.” Accessed January 5, 2022. https://www.trafficware.com/synchrogreen.html.
Watanabe, S. 2010. “Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory.” J. Mach. Learn. Res. 11 (12): 3571–3594.
Wolshon, B., and W. C. Taylor. 1999. “Analysis of intersection delay under real-time adaptive signal control.” Transp. Res. Part C: Emerging Technol. 7 (1): 53–72. https://doi.org/10.1016/S0968-090X(99)00011-X.
Zhao, Y., and Z. Tian. 2012. “An overview of the usage of adaptive signal control system in the United States of America.” Appl. Mech. Mater. 178–181 (May): 2591–2598. https://doi.org/10.4028/www.scientific.net/AMM.178-181.2591.
Zheng, Y., P. Manjunatha, L. Elefteriadou, and R. Ponnaluri. 2017. “Empirical assessment of adaptive signal control technologies in Florida.” In Proc., Transportation Research Board, 96th Annual Meeting, 1–15. Washington, DC: Transportation Research Board.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 5 • May 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: May 10, 2021
Accepted: Jan 6, 2022
Published online: Feb 24, 2022
Published in print: May 1, 2022
Discussion open until: Jul 24, 2022
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