Integrating data-driven and simulation models to predict traffic state affected by road incidents

References

• Aimsun, 2013. “Aimsun 8 Dynamic Simulators Users’ Manual.”
   Google Scholar
• Antoniou, C., J. Barceló, M. Breen, M. Bullejos, J. Casas, E. Cipriani, B. Ciuffo, T. Djukic, S. Hoogendoorn, and V. Marzano. 2016. “Towards a Generic Benchmarking Platform for Origin–Destination Flows Estimation/Updating Algorithms: Design, Demonstration and Validation.” Transportation Research Part C: Emerging Technologies 66: 79–98. doi:https://doi.org/10.1016/j.trc.2015.08.009.
   Web of Science ®Google Scholar
• Ashok, K., and M. E. Ben-Akiva. 2000. “Alternative Approaches for Real-time Estimation and Prediction of Time-dependent Origin–Destination Flows.” Transportation Science 34 (1): 21–36. doi:https://doi.org/10.1287/trsc.34.1.21.12282.
   Web of Science ®Google Scholar
• Balakrishna, R., D. Morgan, Q. Yang, and H. Slavin, 2012. “Comparison of Simulation-based Dynamic Traffic Assignment Approaches for Planning and Operations Management.” in: Vortrag: 91st Annual Meeting of the Transportation Research Board, Washington, DC.
   Google Scholar
• Cascetta, E. 2009. Transportation Systems Analysis: Models and Applications. Springer.
   Google Scholar
• Du, B., S. Chien, J. Lee, and L. Spasovic. 2017. “Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-learning Model.” Journal of Advanced Transportation 2017: 1–8.
   Google Scholar
• Frederix, R., F. Viti, and C. M. J. Tampère. 2011. “Dynamic Origin–Destination Estimation in Congested Networks: Theoretical Findings and Implications in Practice.” Transportmetrica 9935: 1–20.
   Google Scholar
• Geron, A. 2015. Hands-on Machine Learning with Scikit-Learn & TensorFlow, Early Access Book. O’Reilly Media.
   Google Scholar
• Karim, A., and H. Adeli. 2003. “Radial Basis Function Neural Network for Work Zone Capacity and Queue Estimation.” Journal of Transportation Engineering 129 (5): 494–503. doi:https://doi.org/10.1061/(ASCE)0733-947X(2003)129:5(494).
   Web of Science ®Google Scholar
• Kim, H., D. Nam, W. Suh, and S. H. Cheon. 2018. “Origin–Destination Trip Table Estimation Based on Subarea Network OD Flow and Vehicle Trajectory Data.” Transport Planning and Technology 41 (3): 265–285. doi:https://doi.org/10.1080/03081060.2018.1435437.
   Web of Science ®Google Scholar
• Li, X., W. H. K. Lam, H. Shao, and Z. Gao. 2015. “Dynamic Modelling of Traffic Incident Impacts on Network Reliability.” Transportmetrica A: Transport Science 11: 856–872.
   Web of Science ®Google Scholar
• Liu, Y., B. Zou, A. Ni, L. Gao, and C. Zhang. 2020. “Calibrating Microscopic Traffic Simulators Using Machine Learning and Particle Swarm Optimization.” In Transportation Letters 4: 1–13.
   Google Scholar
• Morgan, C. J., and M. Veysey, 2013. “Traffic Modelling Guidelines, NSW, Roads and Maritime Services.”
   Google Scholar
• Nguyen, H., C. Cai, and F. Chen. 2017. “Automatic Classification of Traffic Incident’s Severity Using Machine Learning Approaches.” IET Intelligent Transport Systems 11 (10): 615–623. doi:https://doi.org/10.1049/iet-its.2017.0051.
   Web of Science ®Google Scholar
• Oh, S., Y. J. Byon, K. Jang, and H. Yeo. 2015. “Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach.” Transport Reviews 35 (1): 4–32. doi:https://doi.org/10.1080/01441647.2014.992496.
   Web of Science ®Google Scholar
• Oh, S., Y. J. Byon, K. Jang, and H. Yeo. 2018. “Short-term Travel-time Prediction on Highway: A Review on Model-based Approach.” KSCE Journal of Civil Engineering 22 (1): 298–310. doi:https://doi.org/10.1007/s12205-017-0535-8.
   Web of Science ®Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–2830.
   Web of Science ®Google Scholar
• Polson, N. G., and V. O. Sokolov. 2017. “Deep Learning for Short-term Traffic Flow Prediction.” Transportation Research Part C: Emerging Technologies 79: 1–17. doi:https://doi.org/10.1016/j.trc.2017.02.024.
   Web of Science ®Google Scholar
• Rao, W., Y.-J. Wu, J. Xia, J. Ou, and R. Kluger. 2018. “Origin–Destination Pattern Estimation Based on Trajectory Reconstruction Using Automatic License Plate Recognition Data.” Transportation Research Part C: Emerging Technologies 95: 29–46. doi:https://doi.org/10.1016/j.trc.2018.07.002.
   Web of Science ®Google Scholar
• Shafiei, M., M. Nazemi, and S. Seyedabrishami. 2015. “Estimating Time-dependent Origin–Destination Demand from Traffic Counts: Extended Gradient Method.” Transportation Letters 7 (4): 210–218. doi:https://doi.org/10.1179/1942787514Y.0000000048.
   Web of Science ®Google Scholar
• Shafiei, S., A.-S. Mihăiţă, H. Nguyen, C. Bentley, and C. Cai, 2020. “Short-term Traffic Prediction under Non-recurrent Incident Conditions Integrating Datadriven Models and Traffic Simulation.” in: 99th Annual Meeting of Transportation Research Board, Washington, D.C.
   Google Scholar
• Shafiei, S., M. Saberi, A. Zockaie, and M. Sarvi. 2017. “Sensitivity-based Linear Approximation Method to Estimate Time-dependent Origin–Destination Demand in Congested Networks.” Transportation Research Record: Journal of the Transportation Research Board 2669 (1): 72–79. doi:https://doi.org/10.3141/2669-08.
   Google Scholar
• Tedjopurnomo, D. A., Z. Bao, B. Zheng, F. Choudhury, and A. K. Qin. 2020. “A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges.” IEEE Transactions on Knowledge and Data Engineering 1: 13–14.
   Google Scholar
• Tercan, E., E. Beşdok, and S. Tapkın. 2020. “Heuristic Modelling of Traffic Accident Characteristics.” In Transportation Letters 3: 1–9.
   Google Scholar
• TfL, 2010. “Traffic Modelling Guidelines TfL Traffic Manager and Network Performance Best Practice Version.” 3. 184.
   Google Scholar
• Verbas, I. Ö., H. S. Mahmassani, and K. Zhang. 2011. “Time-dependent Origin–Destination Demand Estimation.” Transportation Research Record: Journal of the Transportation Research Board 2263 (1): 45–56. doi:https://doi.org/10.3141/2263-06.
   Web of Science ®Google Scholar
• Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. 2014. “Short-term Traffic Forecasting: Where We Are and Where We’re Going.” Transportation Research Part C: Emerging Technologies 43: 3–19.
   Web of Science ®Google Scholar
• Wang, D., F. Fu, X. Luo, S. Jin, and D. Ma. 2016. “Travel Time Estimation Method for Urban Road Based on Traffic Stream Directions.” Transportmetrica A: Transport Science 12: 479–503.
   Web of Science ®Google Scholar
• Wen, Y., R. Balakrishna, M. Ben-Akiva, and S. Smith, 2006. “Online Deployment of Dynamic Traffic Assignment: Architecture and Run-time Management.” in: IEE Proceedings-Intelligent Transport Systems. IET, 12, pp. 76–84.
   Google Scholar
• Wu, C.H., Ho, J.M. and Lee, D.T., 2004. Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5(4), pp.276-281.
   Google Scholar
• Xu, D., Y. Wang, P. Peng, S. Beilun, Z. Deng, and H. Guo. 2018. “Real-time Road Traffic State Prediction Based on Kernel-KNN.” Transportmetrica A: Transport Science 9935: 1–15.
   Google Scholar
• Yao, B., C. Chen, Q. Cao, L. Jin, M. Zhang, H. Zhu, and B. Yu. 2017. “Short-term Traffic Speed Prediction for an Urban Corridor.” Computer-Aided Civil and Infrastructure Engineering 32: 154–169.
   Web of Science ®Google Scholar
• Zhou, X., and H. S. Mahmassani. 2007. “A Structural State Space Model for Real-time Traffic Origin–Destination Demand Estimation and Prediction in a Day-to-day Learning Framework.” Transportation Research Part B: Methodological 41: 823–840.
   Web of Science ®Google Scholar
• Ziliaskopoulos, A. K. 2001. “Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future.” Networks and Spatial Economics 1: 233–265.
   Google Scholar