References
- Ahmed, K., Al-Zoubi, K., Siddiqui, M. A., & Anas, M. (2016). Evaluation of the effectiveness of portable variable message signs in work zones in United Arab Emirates. IET Intelligent Transport Systems, 10(2), 114–121. https://doi.org/10.1049/iet-its.2015.0007
- Alabama Department of Transportation (ALDOT). (n.d.-a). Algo traffic. https://algotraffic.com
- Alabama Department of Transportation (ALDOT). (n.d.-b). Alabama traffic data. https://aldotgis.dot.state.al.us/TDMPublic/
- Bai, Y., Huang, Y., Schrock, S. D., Li, Y. (2011). Determining the effectiveness of graphic-aided dynamic message signs in work zone. University of Kansas Center for Research. https://kuscholarworks.ku.edu/handle/1808/19837
- Banerjee, S., Jeihani, M., Khadem, N. K., & Brown, D. D. (2019). Units of information on dynamic message signs: A speed pattern analysis. European Transport Research Review, 11(1), 1–9. https://doi.org/10.1186/s12544-019-0355-7
- Basso, F., Cifuentes, A., Pezoa, R., & Varas, M. (2021). A vehicle-by-vehicle approach to assess the impact of variable message signs on driving behavior. Transportation Research Part C: Emerging Technologies, 125, 103015. https://doi.org/10.1016/j.trc.2021.103015
- Bham, G. H., & Leu, M. C. (2018). A driving simulator study to analyze the effects of portable changeable message signs on mean speeds of drivers. Journal of Transportation Safety & Security, 10(1-2), 45–71. https://doi.org/10.1080/19439962.2017.1314398
- Fu, X., Nie, Q., Liu, J., Khattak, A., Hainen, A., & Nambisan, S. (2022). Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks. Journal of Intelligent Transportation Systems, 26(5), 572–585. https://doi.org/10.1080/15472450.2021.1944133
- Guler, S., Kersavage, K., & Pietrucha, M. T. (2018). Evaluation of colored VMS boards (no. PA-2018-007-511601 WO 004). Dept. of Transportation.
- Hassan, H. M., Abdel-Aty, M. A., Choi, K., & Algadhi, S. A. (2012). Driver behavior and preferences for changeable message signs and variable speed limits in reduced visibility conditions. Journal of Intelligent Transportation Systems, 16(3), 132–146. https://doi.org/10.1080/15472450.2012.691842
- Huang, Y., & Bai, Y. (2014). Effectiveness of graphic-aided portable changeable message signs in reducing vehicle speeds in highway work zones. Transportation Research Part C: Emerging Technologies, 48, 311–321. https://doi.org/10.1016/j.trc.2014.09.007
- Jafarnejad, A., Gambatese, J., & Hernandez, S. (2017). Influence of truck-mounted radar speed signs in controlling vehicle speed for mobile maintenance operations: Oregon case study. Transportation Research Record: Journal of the Transportation Research Board, 2617(1), 19–26. https://doi.org/10.3141/2617-03
- Jeihani, M., Banerjee, S., Ahangari, S., & Brown, D. D. (2018). The potential effects of composition and structure of dynamic message sign messages on driver behavior using a driving simulator (no. MD-18-SHA/MSU/4-14).
- Jeihani, M., NarooieNezhad, S., & Kelarestaghi, K. B. (2017). Integration of a driving simulator and a traffic simulator case study: Exploring drivers’ behavior in response to variable message signs. IATSS Research, 41(4), 164–171. https://doi.org/10.1016/j.iatssr.2017.03.001
- Karimi, G. (2021). Introduction to YOLO algorithm for object detection. https://www.section.io/engineering-education/introduction-to-yolo-algorithm-for-object-detection/
- Lee, C., Ran, B., Yang, F., & Loh, W. Y. (2010). A hybrid tree approach to modeling alternate route choice behavior with online information. Journal of Intelligent Transportation Systems, 14(4), 209–219. https://doi.org/10.1080/15472450.2010.516229
- Liu, J. (2021). yolov5-deepsort-vehicle-tracking-master. GitHub. https://github.com/John1liu/YOLOV5-DeepSORT-Vehicle-Tracking-Master
- Liu, J., Hainen, A., Li, X., Nie, Q., & Nambisan, S. (2019). Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach. Accident; Analysis and Prevention, 132, 105272. https://doi.org/10.1016/j.aap.2019.105272
- Liu, J., & Khattak, A. (2020). Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. Journal of Intelligent Transportation Systems, 24(1), 11–23. https://doi.org/10.1080/15472450.2019.1699076
- Liu, J., Khattak, A. J., Li, X., Nie, Q., & Ling, Z. (2020). Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates. Journal of Safety Research, 73, 25–35. https://doi.org/10.1016/j.jsr.2020.02.006
- Liu, J., Khattak, A. J., Richards, S. H., & Nambisan, S. (2015a). What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors. Accident; Analysis and Prevention, 85, 157–169. https://doi.org/10.1016/j.aap.2015.09.004
- Liu, J., Khattak, A., & Wang, X. (2015b). The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel. Transportation Research Part C: Emerging Technologies, 55, 379–392. https://doi.org/10.1016/j.trc.2015.01.028
- Liu, J., Khattak, A., & Wang, X. (2017). A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities. International Journal of Sustainable Transportation, 11(3), 170–185. https://doi.org/10.1080/15568318.2016.1230803
- Liu, J., Khattak, A., & Zhang, M. (2016). What role do precrash driver actions play in work zone crashes?: Application of hierarchical models to crash data. Transportation Research Record: Journal of the Transportation Research Board, 2555(1), 1–11. https://doi.org/10.3141/2555-01
- Liu, J., Penmetsa, P., Yang, C., Hainen, A., & Barnett, T. (2023). Protecting roadside workers: field evaluation of a vehicle-mounted variable message sign and examination of worker perceptions and use of countermeasures (Technical report). AAA Foundation for Traffic Safety.
- Lu, W., Liu, J., Fu, X., Yang, J., & Jones, S. (2022). Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes. Accident; Analysis and Prevention, 168, 106622. https://doi.org/10.1016/j.aap.2022.106622
- Ma, J., Smith, B. L., & Fontaine, M. D. (2016). Comparison of in-vehicle auditory public traffic information with roadside dynamic message signs. Journal of Intelligent Transportation Systems, 20(3), 244–254. https://doi.org/10.1080/15472450.2015.1062729
- Matowicki, M., & Pribyl, O. (2022). A driving simulation study on drivers speed compliance with respect to variable message signs. Journal of Intelligent Transportation Systems, 26(5), 613–623. https://doi.org/10.1080/15472450.2021.1926247
- New York State Thruway Authority. (2011). Guidelines for use of variable message signs (VMS) (AP-633). New York State Tollway Authority. https://www.thruway.ny.gov/commercial/forms/tap633.pdf
- Nygårdhs, S. (2011). Literature review on variable message signs (VMS) 2006-2009. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A669230&dswid=-8853
- Nygårdhs, S., Helmers, G. (2007). VMS—Variable message signs: A literature review. http://www.vti.se/EPiBrowser/Publikationer/R570ASwe.pdf
- Park, J.-H., Song, T.-J., & O, J.-T. (2009). Analysis of user preferences for traffic safety warning information using portable variable message signs (PVMS). Journal of Korean Society of Transportation, 27(5), 51–62.
- Qing, Z., Zhang, S., Brown, H., & Sun, C. (2019). Evaluation of truck-mounted automated flagger assistance devices in Missouri: Case study. Journal of Transportation Engineering, Part A: Systems, 145(12), 05019006. https://doi.org/10.1061/JTEPBS.0000271
- Ronchi, E., Nilsson, D., Modig, H., & Walter, A. L. (2016). Variable message signs for road tunnel emergency evacuations. Applied Ergonomics, 52, 253–264. https://doi.org/10.1016/j.apergo.2015.07.025
- Sato, H., Iida, K., Takahashi, H., Yamamoto, T., & Oneyama, H. (2017). Readability impact of symbols displayed on variable message sign. Journal of the Eastern Asia Society for Transportation Studies, 12, 2181–2197.
- Song, T. J., Oh, C., Kim, T., & Yeon, J. Y. (2010). Estimation of legibility distance for portable variable message signs. Journal of the Eastern Asia Society for Transportation Studies, 8, 1609–1620.
- Tan, B. (2020). Guide to car detection using YOLO. https://towardsdatascience.com/guide-to-car-detection-using-yolo-48caac8e4ded
- Tsirimpa, A., Polydoropoulou, A., & Antoniou, C. (2007). Development of a mixed multi-nomial logit model to capture the impact of information systems on travelers’ switching behavior. Journal of Intelligent Transportation Systems, 11(2), 79–89. https://doi.org/10.1080/15472450701293882
- Ullman, B. R., Trout, N. D., & Sun, D. (2012). Truck-mounted changeable message signs with symbols for work zone operations. Transportation Research Record: Journal of the Transportation Research Board, 2272(1), 78–86. https://doi.org/10.3141/2272-09
- Ullman, B. R., Trout, N. D., & Ullman, G. L. (2009). Recommended messages for truck-mounted changeable message signs during mobile operations (no. FHWA-WY-09/07F). Department of Transportation.
- Ullman, B. R., Ullman, G. L., & Trout, N. D. (2011). Driver comprehension of messages on truck-mounted changeable message signs during mobile maintenance operations. Transportation Research Record: Journal of the Transportation Research Board, 2258(1), 49–56. https://doi.org/10.3141/2258-06
- Wagner, D., Vercruyssen, M., & Hancock, P. (1997). A computer-based methodology for evaluating the content of variable message signage. ITS Journal - Intelligent Transportation Systems Journal, 3(4), 353–373. https://doi.org/10.1080/10248079708903730
- Wang, J. H., & Cao, Y. (2005). Assessing message display formats of portable variable message signs. Transportation Research Record: Journal of the Transportation Research Board, 1937(1), 113–119. https://doi.org/10.1177/0361198105193700116
- Wang, X., Khattak, A. J., Liu, J., Masghati-Amoli, G., & Son, S. (2015). What is the level of volatility in instantaneous driving decisions? Transportation Research Part C: Emerging Technologies, 58, 413–427. https://doi.org/10.1016/j.trc.2014.12.014