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DTALite Classic Edition

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DTALite-Classic-Edition (This version uses input_node, and input_link format before adopting GMNS standard QVDF functions in 2020)

The stable version using GMNS file format can be found at https://github.com/asu-trans-ai-lab/DTALite

The active development version using a multi-resolution modeling framework can be found at https://github.com/asu-trans-ai-lab/DLSim-MRM.

DTALite

This site mains the source code and Windows-based release for DTALite+NeXTA package. DTALite is an open-source AMS library for efficiently macroscopic and mesoscopic traffic assignment based on General Modeling Network Specification (GMNS) format. NeXTA as a visualization tool for transportation Analysis, Modeling, and Simulation (AMS), developed through the support of a FHWA study.

Remarks:

  1. For the Python version of DTALite and Path4GMNS package portable on Windows, Linux and MacOS, please go to https://github.com/jdlph/Path4GMNS.

  2. Please download latest DTALite-NeXTA package, which follows calendar versioning. Here are the user guides for NeXTA and NeXTA+QGIS using GMNS data format. We have prepared a number of self-learning documents too.

Step 1: White Paper and Application:

Zhou, Xuesong, and Jeffrey Taylor. "DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration." Cogent Engineering 1.1 (2014): 961345.

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.

Step 2: Youtube Teaching Videos on Use of DTALite/NEXTA Packages

NeXTA/DTALite Workshop Webinar by Jeff Taylor

8 lessons by Xuesong (Simon) Zhou at ASU https://www.youtube.com/channel/UCpwXRD0kEkR5iQ77iCXCNuQ/videos

Step 3: Mini-Lesson on the Internal Algorithmic Details

Mini-lessson : 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.

C++ source codes

Python source code

References:

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.

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