bstabler/TransportationNetworks: Transportation Networks for Research

Transportation Networks is a networks repository for transportation research.

If you are developing algorithms in this field, you probably asked yourself
more than once: where can I get good data? The purpose of this site is to
provide an answer for this question! This site currently contains several examples
for the traffic assignment problem. Suggestions and additional data are always welcome.

Many of these networks are for studying the Traffic Assignment Problem, which is one of the most
basic problems in transportation research. Theoretical background can be found in
“The Traffic Assignment Problem – Models and Methods” by Michael Patriksson, VSP 1994,
as well as in many other references.

This repository is an update to Dr. Hillel Bar-Gera’s TNTP.
As of May 1, 2016, data updates will be made only here, and not in the original website.

Each individual network and related files is stored in a separate folder. There
are a number of ways to download the networks and related files:

  • Click on a file, click view as Raw, and then save the file
  • Clone the repository to your computer using the repository’s clone URL. This is done with a Git
    tool such as TortoiseGit. Cloning will download the
    entire repository to your computer.

There are two ways to add a network:

  • Fork the repo
    • Create a GitHub account if needed
    • Fork (copy) the repo to your account
    • Make changes such as adding a new folder and committing your data
    • Issue a pull request for us to review the changes and to merge your changes into the master
  • Create an issue, which will notify us. We will then reply to coordinate adding your network to the site.

Make sure to create a README in Markdown for your
addition as well. Take a look at some of the existing README files in the existing network folders to see what
is expected.

All data is currently donated. Data sets are for academic research purposes only.
Users are fully responsible for any results or conclusions obtained by using these data sets.
Users must indicate the source of any dataset they are using in any publication that relies
on any of the datasets provided in this web site. The Transportation Networks for Research team is not
responsible for the content of the data sets. Agencies, organizations, institutions and
individuals acknowledged in this web site for their contribution to the datasets are not
responsible for the content or the correctness of the datasets.

Transportation Networks for Research Core Team. Transportation Networks for Research. https://github.com/bstabler/TransportationNetworks. Accessed Month, Day, Year.

This repository is maintained by the Transportation Networks for Research Core Team. The current members are:

This effort is also associated with the TRB Network Modeling Committee. If you are interested in contributing in a more significant role, please get in touch. Thanks!

Any documented text-based format is acceptable. Please include a README.MD that describes the files,
conventions, fields names, etc. It is best to use formats that can be easily read in with technologies
like R, Python, etc. Many of the datasets on TransportationNetworks are in TNTP format.

TNTP Data format

TNTP is tab delimited text files, with each row terminated by a semicolon. The files have the following format:

  • First lines are metadata; each item has a description. An important one is the <FIRST THRU NODE>.
    In the some networks (like Sioux-Falls) it is equal to 1, indicating
    that traffic can move through all nodes, including zones. In other networks when traffic is not
    allow to go through zones, the zones are numbered 1 to n and the <FIRST THRU NODE> is set to n+1.
  • Comment lines start with ‘~’.
  • Network files (must be named <network>_net.tntp) – one line per link; links are directional, going from “init node” to “term node”.
    • Link travel time = free flow time * ( 1 + B * (flow/capacity)^Power ).
    • Link generalized cost = Link travel time + toll_factor * toll + distance_factor * distance
    • The network files also contain a “speed” value for each link. In some cases the “speed” values
      are consistent with the free flow times, in other cases they represent posted speed limits, and
      in some cases there is no clear knowledge about their meaning. All of the results reported below
      are based only on free flow travel times as described by the functions above, and do not use the speed values.
    • The standard order of the fields in the network files is:
      • Init node
      • Term node
      • Capacity
      • Length
      • Free Flow Time
      • B
      • Power
      • Speed limit
      • Toll
      • Link Type
  • Trip tables (must be named <network>_trips.tntp) – An Origin label and then Origin node number, followed by Destination node numbers and OD flow
Origin origin#
destination# , OD flow ; …..

Import scripts

The networks’ formatting has been harmonized to facilitate programatic imports, and import scripts are provided inside the folder _scripts:

Language Format Networks Trip matrix
Python Jupyter Notebook Instructions on using Pandas Code to import into OMX
Julia Jupyter Notebook Using Julia package Using Julia package
Network Zones Links Nodes Compatible with provided scripts
Anaheim 38 914 416 Yes
Austin 7388 18961 7388 Yes
Barcelona 110 2522 1020 Yes
Berlin-Center 865 28376 12981 Yes
Berlin-Friedrichshain 23 523 224 Yes
Berlin-Mitte-Center 36 871 398 Yes
Berlin-Mitte-Prenzlauerberg-Friedrichshain-Center 98 2184 975 Yes
Berlin-Prenzlauerberg-Center 38 749 352 Yes
Berlin-Tiergarten 26 766 361 Yes
Birmingham-England 898 33937 14639 Yes
Braess-Example 2 5 4 Yes
chicago-regional 1790 39018 12982 Yes
Chicago-Sketch 387 2950 933 Yes
Eastern-Massachusetts 74 258 74 Yes
GoldCoast, Australia 1068 11140 4807 Yes
Hessen-Asymmetric 245 6674 4660 Yes
Philadelphia 1525 40003 13389 Yes
SiouxFalls 24 76 24 Yes
Sydney, Australia 3264 75379 33837 Yes
Symmetrica Transportation Electrification N.A. 624 169 No. Not in the TNTP format
Terrassa-Asymmetric 55 3264 1609 Yes
Winnipeg 147 2836 1052 Yes
Winnipeg-Asymmetric 154 2535 1057 Yes

A partial list of publications where datasets from this repository have been used. All website users are kindly requested to add their publications to this list.

  • Bar-Gera, H.(2002), Origin-based algorithm for the traffic assignment problem, Transportation Science 36(4), 398-417.
    Bar-Gera, H. & Boyce, D. (2003), Origin-based algorithms for combined travel forecasting models, Transportation Research Part B – Methodological 37 (5), 405-422.
  • Boyce, D. & Bar-Gera, H. (2003), Validation of urban travel forecasting models combining origin-destination, mode and route choices, Journal of Regional Science, 43, 517-540.
  • Boyce, D., Ralevic-Dekic, B. & Bar-Gera, H. (2004), Convergence of Traffic Assignments: How Much Is Enough? The Delaware Valley Region Case Study, ASCE Journal of Transportation Engineering, 130 (1), 49-55.
  • Boyce, D. & Bar-Gera, H. (2004), Multiclass Combined Models for Urban Travel Forecasting, Networks and Spatial Economics, 4 (1), 115-124.
  • Bar-Gera, H. & Boyce D. (2006), Solving a non-convex combined travel forecasting model by the Method of Successive Averages with constant step sizes, Transportation Research Part B – Methodological, 40 (5), 351-367.
  • Bar-Gera, H. (2006), Primal Method for Determining the Most Likely Route Flows in Large Road Networks, Transportation Science, 40 (3), 269-286.
  • Bar-Gera, H., Mirchandani, P.B. & Wu, F.ST (2006), Evaluating the assumption of independent turning probabilities, Transportation Research Part B – Methodological, 40 (10), 903-916.
  • Bar-Gera, H. & Luzon, A. (2007), Differences among route flow solutions for the user-equilibrium traffic assignment problem, ASCE Journal of Transportation Engineering, 133 (4), 232-239.
  • Bar-Gera, H. & Luzon, A. (2007), Non-unique route flow solutions for user-equilibrium assignments. Traffic Engineering and Control, 48 (9), 408-412.
  • Bar-Gera, H. (2010), Traffic assignment by paired alternative segments, Transportation Research Part B – Methodological, 44 (8-9), 1022-1046.
  • Bar-Gera, H., Boyce, D. & Nie, Y. (2012), User-equilibrium route flows and the condition of proportionality. Transportation Research Part B – Methodological 46 (3), 440–462.
  • Bar-Gera, H., Hellman, F. & Patriksson, M. (2013), Efficient design and pricing of equilibrium traffic networks precise calculations of equilibria and sensitivities. Transportation Research Part B – Methodological, 57, 485-500.
  • Rey, D.PI, Bar-Gera, H.PI, Dixit, V.PI, Waller, S.T.PI (2019). A Branch and Price Algorithm for the Work-zone Scheduling Problem. Accepted for publication in Transportation Science.
  • TRB Network Modeling Committee
  • InverseVIsTraffic is an open-source repository that implements some inverse Variational Inequality (VI) formulations proposed for both single-class and multi-class transportation networks. The package also implements algorithms to evaluate the Price of Anarchy in real road networks. Currently, the package is maintained by Jing Zhang.
  • Frank-Wolfe algorithm that demonstrates how to read these
    data formats and runs a FW assignment. The header file “stdafx.h” is for Microsoft Visual C (MSVC) compiler. On
    Unix and other compilers it can be simply omitted.
  • seSue is an open source tool to aid research on static path-based
    Stochastic User Equilibrium (SUE) models. It is designed to carry out experiments to analyze the effects of
    (1) different path-based SUE models associated with different underlying discrete choice models
    (as well as hybrid models), and (2) different route choice set generation algorithms on the route choice
    probabilities and equilibrium link flows. For additional information, contact Ugur Arikan
  • TrafficAssignment.jl is an open-source, Julia package that implements some traffic assignment algorithms. It also loads the transportation network test problem data in vector/matrix forms. The packages is maintained by Changhyun Kwon.
  • DTALite-S – Simplified Version of DTALite for Education and Research
  • NeXTA open-source GUI for visualizing static/dynamic traffic assignment results
  • Transit Network Design Instances – transit network design instances for research repository
  • Fast-Trips – open source dynamic transit assignment software, data standards, and research project
  • AMS Data Hub is an FHWA research project to develop a prototype data hub and data schema for transportation simulation models
  • GTFS-PLUS – GTFS-based data transit network data standard suitable for dynamic transit modeling
  • Open matrix – Open matrix standard for binary matrix data management that is supported by the major commercial travel demand modeling packages and includes code for R, Python, Java, C#, and C++.
  • AequilibraE – Python package for transportation modeling
  • General Modeling Network Specification – GMNS defines a common human and machine readable format for sharing routable road network files. It is designed to be used in multi-modal static and dynamic transportation planning and operations models.

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