Improving Public Transportation

Benton Harbor, Michigan, USA

Many Benton Harbor residents depend on public transportation to get to work and school, but the service’s reliability is questionable and its route coverage is limited. Assistant Professor Tierra Bills is collecting travel data to help city officials improve and streamline travel options for residents, with the goal of increased employment participation and retention.

Mobility Theme Social Theme

Funding Source

Urban Collaboratory

About the Project

By tracking residents’ transportation activity, Tierra Bills hopes to help city officials improve and streamline the services offered to the Benton Harbor population.

“There’s a direct connection between public transportation accessibility and employment outcomes and health-related outcomes,” Bills said.

The project goals are:

  •  Collect representative sample of travel activity data, using a mixture of traditional and electronic modes (i.e. paper and online survey, wearable GPS trackers, and smart phone tracking app)
  •  Develop fundamental travel demand models for assessing the affecting of proposed transit improvement scenarios
  •  Deliver to Benton Harbor government, recommendations for transit improvements that will best support greater employment accessibility

The study specifically targets traditionally difficult-to-reach populations, with the goal of determining the extent to which city-wide transit accessibly can be enhanced by paying special attention to the transportation needs of the most disadvantaged travelers.


Project Phases

The Data Collection phase will deliver a rich panel dataset of individual and household travel behaviors, over two weeklong survey periods. One survey period will be conducted in the summer season and another survey period in the winter season, in order to capture weather variation in travel behaviors. An initial baseline survey will be conducted to gather detailed household information for a representative sample of Benton Harbor residents.  Each weeklong survey period will involve GPS tracking of a sample of transit and auto users. The tracking will be done using 1) a smartphone-based mobility tracking application for participants with smartphones, and 2) external GPS tracking devices for participants without smartphones. This smartphone application has previously been developed by Dr. Pascal Van Hentenryck’s research group.


The Travel Modeling phase involves the development (estimation and testing) of a small scale activity-based travel model. The choice dimensions will include the following:

  •  Employment Choice – calculates the probability of being employed and the employment duration.
  •  Destination Choice – calculates the probability of traveling to any work destination in the region (trip distribution).
  •  Activity Choices – calculates to probability of choosing from among various tour patterns, number of stops along each tour, and travel scheduling alternatives.
  •  Mode choice – calculates the probability of traveling by all available travel modes.

The innovations here are 1) the inclusion of an employment model for connecting transportation improvements to employment outcomes, and 2) the design of destination and activity and mode choice models that are conditioned on an employment model.


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