DTALite is an open-source, cross-platform, lightweight, and fast Python path engine for networks encoded in GMNS.

1. Tutorial written in Jupyter notebook with step-by-step demonstration.
2. Documentation on inputs preparation and outputs explanations.

DTALite has been published on PyPI, and can be installed using

$ pip install DTALite

The Python modules are written in Python 3.x, which is the minimum requirement to explore the most of DTALite.

Users can find the test datasets and code in test folder.

Inputs: node.csv, link.csv, demand.csv, settings.csv Outputs: link_performance.csv, od_performance.csv

The Python code for checking and sorting node.csv and link.csv:

def check_and_sort_files(node_file='node.csv', link_file='link.csv'):
    """
    Check if node_file is sorted by node_id and link_file is sorted first by
    from_node_id and then by to_node_id. If not, sort the files and save them.

    Parameters:
    - node_file: str, path to the node CSV file.
    - link_file: str, path to the link CSV file.
    """

    # Check if node.csv is sorted by node_id
    node_df = pd.read_csv(node_file)
    if not node_df['node_id'].is_monotonic_increasing:
        print(f"{node_file} is not sorted by node_id. Sorting now.")
        node_df.sort_values('node_id', inplace=True)
        node_df.to_csv(node_file, index=False)
    else:
        print(f"{node_file} is already sorted by node_id.")

    # Check if link.csv is sorted by from_node_id and to_node_id (first sort by from_node_id, then to_node_id)
    link_df = pd.read_csv(link_file)
    sorted_link_df = link_df.sort_values(by=['from_node_id', 'to_node_id']).reset_index(drop=True)
    current_link_order = link_df[['from_node_id', 'to_node_id']].reset_index(drop=True)

    if not current_link_order.equals(sorted_link_df[['from_node_id', 'to_node_id']]):
        print(f"{link_file} is not sorted by from_node_id and to_node_id. Sorting now.")
        sorted_link_df.to_csv(link_file, index=False)
    else:
        print(f"{link_file} is already sorted by from_node_id and to_node_id.")

The Python code for testing:

import DTALite as dta

# Use the function to check and sort the files, then run the assignment.
check_and_sort_files()
dta.assignment()

# Smulation if needed
## dta.simulation()

Tang, F., Zheng, H., and Zhou, X. (2025, Jan 29). DTALite. Retrieved from https://github.com/itsfangtang/DTALite_release

Welcome to join the DTALite Google Group! Any contributions are welcomed including advise new applications of DTALite, enhance documentation and docstrings in the source code, refactor and/or optimize the source code, report and/or resolve potential issues/bugs, suggest and/or add new functionalities, etc.

DTALite has a very simple workflow setup, i.e., main branch for release (on both GitHub and PyPI) and dev for development. If you would like to work directly on the source code (and probably the documentation), please make sure that the destination branch of your pull request is dev, i.e., all potential changes/updates shall go to the dev branch before merging into master for release.

1. White Paper and Application

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

Marshall, N.L. (2018). 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, 85-92.

2. NeXTA/DTALite Workshop Webinar

3. Mini-Lesson on the Algorithmic Details

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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. State-wide Transportation Modeling

Zhang, L. (2017). Maryland SHRP2 C10 Implementation Assistance – MITAMS: Maryland Integrated Analysis Modeling System, Maryland State Highway Administration.

11. Workzone Applications

Schroeder, B, et al. (2014). Work zone traffic analysis & impact assessment. FHWA/NC/2012-36. North Carolina. Dept. of Transportation. Research and Analysis Group.