roanoke_benchmark

Data for a benchmark or sandbox travel model scenario in Roanoke, Virginia

Motivation

The travel modeling community has long needed a benchmark scenario on which to implement and compare model frameworks in a quantitative manner. For example, agencies often wish to know how improvements to model sensitivity will affect model run time. This repository is intended to push us towards that goal.

The focus of this exercise is specifically on demand models, and as such we are less concerned with the highway assignment steps. But these steps are critical for the interpretation and implementation of the demand model, so they cannot be ignored completely. Researchers who implement their models in this sandbox may use whichever of the several network engines that they have available.

Contents

This repository contains several files that researchers can draw upon to generate their own scenarios. The files are compiled from and for the trip-based model developed by WSP for the Roanoke Valley Transportation Planning Organization (RVTPO), which oversees transportation planning efforts in the Roanoke, Virginia metropolitan area. The files are provided courtesy of the Virginia Department of Transportation and the RVTPO.

The first set of files are drawn from the inputs to the travel model:

• A Highway network labeled with vehicle counts as well as the trip-based model vehicle forecasts. This is stored in hwy/ as a gmns-compliant network. The source files for this network (exports from the RVTPO model) are in hwy/src, and are created with the py/network.py script.
• A Transit network with accompanying ridership measures. This is stored in transit/ as a gmns-compliant network. The source file for the GTFS routes is included in transit/gtfs, and the script to convert the GTFS to GMNS is in py/gtfs2gmns_conversion.py
• A topologically connected Bicyle network. This is included in the highway network.
• A Socioeconomic data file with employment and aggregate household characteristics by TAZ, stored in se/zones.csv.
• A Traffic Analysis Zone geojson file

A second set of files are outputs of the travel model:

• Travel time matrices
• Passenger origin-destination matrices
• Freight origin-destination matrices
• Internal/External trip matrices

These output files may be useful if researchers optionally wish to --- for example --- include freight processes in the model steps or simply include them as background traffic.

File formats

In general, tabular data is provided as plain text with comma-separated values and names in a header; a data dictionary supplied with the file provides more attributes. Geospatial data are provided as geojson files, and matrix files are provided in an OMX format. Network files are given as node / link tables but will be supplied as GMNS files in the future.

Report

If you implement the a model in this sandbox, we would kindly request for you to send us a report with the following information:

• The name of the model and the organization behind the implementation.
• The basic design of your model, including which elements are held constant with the Cache Valley model and any necessary information from the network or supply model.
• Total model run time, broken down by model step and feedback cycle.
• A report of model failure statistics
• Assigned highway volumes at counted locations in a CSV file. An example report is provided in this repository.

Using the Dockerfile/Container

Docker runs a small computer on top of your operating OS, allowing you to run a machine isolated from your computer. This machine is linux based and should have no problem running on Linux, Mac, or Window OS. A basic understanding of linux and bash commands are needed to run these programs. Here is a good place to start.

Creating an Image from the Dockerfile

docker build -t IMAGENAME .

Here is the outline code for running a docker image. Run it in the terminal. Replace IMAGENAME with your own name for the docker image. The "." designates the current folder as the place that the dockerfile reads from. This file will also be pulled into the virtual machine, so leaving the dockerfile within the downloaded file will ensure all needed code will be present.

Running Containers and Mounting Data

To run the network analysis', your image needs to be turned into a contianer and your data needs to be linked to your container. This code is an outline for running the container. Below is an explanation of each piece and what needs to be changed.

  docker run -it --name CONTAINERNAME --mount type=bind,source=/FILE/PATH/TO/DATA,target=/app/userdata CONTAINERNAME

"-d" may be added before "-it" if you don't want the container to run in your current terminal. It can still be acessed elsewhere even without this tag.

"-it" allows you to interact with your container once running.

"--name" designates the continer's name. Replace CONTAINERNAME.

"--mount type=bind" tells the continer what type of binding.

"Source=" Replace /FILE/PATH/TO/DATA with the full file path to your input data. Do not use relative path.

"target=/app/userdata" In the continer, this will be the folder where your input data can be found. If one wishes, 'userdata' may be changed to any desired name. To make navigation simpler, /app is used so that userdata will be found with the other data.

Mounted Data File

The mounted file acts as intermediaty between the host computer and the container. Files may be written to it at will. You do not need to rerun a container if you need to add or remove data from this file.

Applications

These are the current processes able to be run within the container.

Converting .dbf files to .cvs

1. Open network.py and scroll to the bottom
2. Replace input_dbf and inputn_dbf with your DBF file paths from your input data file.
3. Replace output_csv and outputn_csv with your desired CSV file paths
4. Run Code.

Converting gtfs data to gmns

Thanks to the folks at the ASU transportation lab, gtfs transit data may be converted into gmns data.

Obtaining gtfs data

Transit authorities freely offer thier routes for any who would want it. Websites such as Mobility Database, Transit Feeds, and Transit Land are good places to find this data.

Learn more about gtfs here

Conversion Steps

1. Open the gtfs2gmns_conversion.py and scroll to the bottom.
2. Modify input_path, output_path, transit_node_name, and transit_link_name as needed. time_period may also be modified if only a certain period is desired.
3. Run code.