Zhou, Xuesong, and Jeffrey Taylor. "DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration." Cogent Engineering 1.1 (2014): 961345. https://www.tandfonline.com/doi/full/10.1080/23311916.2014.961345
Application of DTALite: Marshall, Norman L. "Forecasting the impossible: The status quo of estimating traffic flows with static traffic assignment and the future of dynamic traffic assignment." Research in Transportation Business & Management 29 (2018): 85-92. https://www.sciencedirect.com/science/article/pii/S2210539517301232
NeXTA/DTALite Workshop Webinar by Jeff Taylor https://www.youtube.com/channel/UCUHlqojCQ4f7VvqroUhbaFA
https://youtu.be/rorZAhNNOf0 (What is the best way to learn dynamic traffic simulation and network assignment for a beginner? Do you want to integrate a powerful traffic simulator in your deep learning framework? We would like to offer a collaborative learning experience through 500 lines of python codes and real-life data sets. This is part of our mini-lessons through teaching dialog.)
Python source codes: https://github.com/asu-trans-ai-lab/DTALite/blob/main/src/python/dtalite_s.py
C++ source codes: https://github.com/asu-trans-ai-lab/DTALite/blob/main/src/v2/Exe_src/AgentLite/main_api.cpp
Python DLL testing environment: https://github.com/asu-trans-ai-lab/DTALite/tree/main/DTALite_DLL_test
(1)Parallel computing algorithms: Qu, Y., & Zhou, X. (2017). Large-scale dynamic transportation network simulation: A space-time-event parallel computing approach. Transportation research part c: Emerging technologies, 75, 1-16.
(2)OD demand estimation: Lu, C. C., Zhou, X., & Zhang, K. (2013). Dynamic origin–destination demand flow estimation under congested traffic conditions. Transportation Research Part C: Emerging Technologies, 34, 16-37.
(3)Simplified emission estimation model: Zhou, X., Tanvir, S., Lei, H., Taylor, J., Liu, B., Rouphail, N. M., & Frey, H. C. (2015). Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies. Transportation Research Part D: Transport and Environment, 37, 123-136.
(4)Eco-system optimal time-dependent flow assignment: Lu, C. C., Liu, J., Qu, Y., Peeta, S., Rouphail, N. M., & Zhou, X. (2016). Eco-system optimal time-dependent flow assignment in a congested network. Transportation Research Part B: Methodological, 94, 217-239.
(5)Transportation-induced population exposure assessment: Vallamsundar, S., Lin, J., Konduri, K., Zhou, X., & Pendyala, R. M. (2016). A comprehensive modeling framework for transportation-induced population exposure assessment. Transportation Research Part D: Transport and Environment, 46, 94-113.
(6)Integrated ABM and DTA: Xiong, C., Shahabi, M., Zhao, J., Yin, Y., Zhou, X., & Zhang, L. (2020). An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems. Transportation Research Part C: Emerging Technologies, 113, 57-73.
(7)State-wide transportation modeling: Zhang. L. (2017) Maryland SHRP2 C10 Implementation Assistance – MITAMS: Maryland Integrated Analysis Modeling System, Maryland State Highway Administration
(8)Workzone applications: Schroeder, B, et al. Work zone traffic analysis & impact assessment. (2014) FHWA/NC/2012-36. North Carolina. Dept. of Transportation. Research and Analysis Group.