Road traffic queue length estimation with artificial intelligence (AI) methods
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Abstract
Sustainable monitoring of traffic has always been a significant problem for engineers, queue length being one of the most important metrics required for the performance assessment of signalized intersections. The authors of the present study propose a novel approach to estimating cycle-by-cycle queue lengths at a given signalized intersection. Focusing on the examination of shock wave phenomena and the traffic model, this study first elucidates the definitions and assumptions it employs. Subsequently, it delves into the creation of the queuing model, alongside the utilization of a machine-learning (ML) based Kalman Filter (KF) algorithm for estimation. The information contained within the output files is visualized on distinct graphs, along with the velocities at various time intervals derived from virtual simulations involving a queue of 12 vehicles. This graphical representation serves as a conclusive validation, demonstrating a strong correlation between the simulation and the estimation achieved through the KF approach. The method presented yielded dependable and resilient estimates for the simulated queue lengths, even in the presence of noisy measurements.
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References
Akçelik, R. (2001). HCM 2000 Back of Queue Model for Signalized Intersections. Akçelik & Associates Pty. Ltd., Melbourne, Australia.
Cai, Q., Wang, Z., Zheng, L., Wu, B. Wang, Y. (2014). Shock wave approach for estimating queue length at signalised intersections by fusing data from point and mobile sensors. Transportation Research Record: Journal of the Transportation Research Board.2422(2), 79–87. DOI: https://doi.org/bkphhn
Cao, H.,Zöldy, M. (2020). An investigation of autonomous vehicle roundabout situation. Periodica Polytechnica Transportation Engineering. 48(3), 236–241. DOI: https://doi.org/hg4m
Cetin, M. (2012). Estimating queue dynamics at signalised intersections from probe vehicle data: Methodology based on kinematic wave model. Transportation Research Record. 2315(1), 164–172. DOI: https://doi.org/gjh7th
Ferencz, Cs., Zöldy, M. (2021). driving cycle modeling, simulation and validation on 1: 10 scale vehicle model platform [In Hungarian: Autonóm menetciklus-modellezés,-szimuláció és-validálás 1: 10-es méretarányú járműmodell-platformon: Autonomous]. 29th International Conference on Mechanical Engineering. 29, 208–212.
Fuliang, L., Keshuang, T., Jiarong, Y. Keping L. (2017). Real-Time Queue Length Estimation for Signalized Intersections Using Vehicle Trajectory Data. Transportation Research Record: Journal of the Transportation Research Board. 2623, 49–59. DOI: https://doi.org/gdjcrs
Goodall, N. J., Smith, B. L. and Park, B. (2013). Traffic Signal Control with Connected Vehicles. Transportation Research Record: Journal of the Transportation Research Board. 2381, 65–72. DOI: https://doi.org/gcvj4f
Henrickson, K., Zou, Y., Wang, Y. (2015). Flexible and robust method for missing loop detector data imputation. Transportation Research Record: Journal of the Transportation Research Board. 2527, 29–36. DOI: https://doi.org/gqcghh
Horváth, M., Tettamanti T. (2021). Robust vehicle count estimation on urban signalised links. Measurement. 181, 109581. DOI: https://doi.org/kvf8
Ibrahim, A., Cicic, M., Goswami, D., Basten, T., Johansson, K. (2019). Control of Platooned Vehicles in Presence of Traffic Shock Waves, 2019 IEEE Intelligent Transportation Systems Conference (ITSC). 1727–1734. DOI: https://doi.org/gh95kw
Ishihara, J. Y., Terra, M. H., Campos, J. C. T. (2006). Robust Kalman Filter for Descriptor Systems. IEEE Transactions on Automatic Control. 51(8), 1354. DOI: https://doi.org/bm5w3g
Lee, S., Wong, S. C., Li, Y. C. (2015). Real-Time Estimation of Lane-Based Queue Lengths at Isolated Signalized Junctions. Transportation Research Part C: Emerging Technologies. 56, 1–17. DOI: https://doi.org/f7hr8w
Liu, H., Wu, X., Ma, W., Hu, H. (2019). Real-time queue length estimation for congested signalised intersections. Transportation Research Part C: Emerging Technologies. 17(4), 412–427. DOI: https://doi.org/dprtn9
Road Traffic Control Laboratory BME (2021). Simple example code with MATLAB. URL: http://kjit.bme.hu/index.php/en/kutatas-traffic-en/sumotraci-en
Road Traffic Control Laboratory BME (2021). The beginning steps of SUMO TRACI COM programming with MATLAB. URL: http://kjit.bme.hu/images/trafficlab/Research/SUMOTRACI/MATLAB_TraCI.pdf
Schrank, D., Eisele, B., Lomax, T (2015). 2014 Urban Mobility Scorecard. Texas A&M Transportation Institute, College Station, 2015.3.
Sharma, A., Bullock, D. M., Bonneson, J. A. (2007). Input-Output and Hybrid Techniques for Real-Time Prediction of Delay and Maximum Queue Length at Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board. 2035, 69–80. DOI: https://doi.org/fw429q
SUMO User Documentation (2021). TraCI overview. URL: https://sumo.dlr.de/docs/TraCI.html#traci_commands
Terra, M. H., Cerri, J. P., Ishihara, J. Y. (2014). Optimal Robust Linear Quadratic Regulator for Systems Subject to Uncertainties. IEEE Transactions on Automatic Control. 59(9), 2586–2591. DOI: https://doi.org/kvf9
Tettamanti, T. (2021). Fundamental Diagram of Traffic Flow. Master Program Course Autonomous Vehicle Control Engineer. Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest. INTFIN Course Material supported by EMMI 32708-2/2017. (Downloaded: 2 May 2023 12:43)
Tettamanti, T., Luspay, T., Varga, I. (2019). Road Traffic Modeling and Simulation. Akadémiai Kiadó, Budapest.
Varga, B., Tettamanti, T. (2023). Jam Propagation Analysis With Mesoscopic Traffic Simulation. IEEE Transactions on Intelligent Transportation Systems. pp. 1-12. DOI: https://doi.org/kwgg
Viti, F., Van Zuylen, H. J. (2010). Probabilistic Models for Queues at Fixed Control Signals. Transportation Research Part B: Methodological. 44(1), 120–135. DOI: https://doi.org/dv7t7s
Wang, S., Huang, W., Lo, H. (2019). Traffic parameters estimation for signalised intersections based on combined shockwave analysis and Bayesian network. Transportation Research Part C: Emerging Technologies. 104, 22–37. DOI: https://doi.org/gf5t9q
Wu, A., Yang, X. (2013). Real-time queue length estimation of signalised intersections based on RFID data. Procedia – Social and Behavioral Sciences. 96, 1477–1484. DOI: https://doi.org/kvgb
Wu, X., Liu, H. (2011). A Shock Wave Profile Model for Traffic Flow on Congested Urban Arterials. Transportation Research Part B: Methodological, 45, No. 10, 1768–1786. DOI: https://doi.org/dpjqxq
Yao, J., Tang, K. (2019). Cycle-based queue length estimation considering spillover conditions based on low-resolution point detector data. Transportation Research Part C: Emerging Technologies. 109, 1–18. DOI: https://doi.org/kvgc
Yin, J., Sun, J., Tang, K. (2018). A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board. DOI: https://doi.org/gffh6j
Zöldy, M., Baranyi, P. (2021). Cognitive Mobility – CogMob. 12th IEEE International Conference on Cognitive Infocommunications – CogInfoCom 2021. Proceedings IEEE, 921–925. URL: https://www.researchgate.net/publication/361435417_Cognitive_Mobility_-_CogMob