Long-term forecasts of statewide travel demand patterns using large-scale mobile phone GPS data: A case study of Indiana

TL;DR

This study demonstrates how large-scale mobile phone GPS data can be used to forecast statewide travel demand patterns over the long term, providing insights into regional growth and trip behaviors for Indiana.

Contribution

It introduces a data-driven trip generation and distribution model using GPS data, enhancing traditional travel demand models with long-term forecasts for Indiana.

Findings

Travel demand growth concentrated in suburban regions.

Major urban corridors will see increased trip flows.

Trip counts forecasted with 5-15% difference from existing models.

Abstract

The growth in availability of large-scale GPS mobility data from mobile devices has the potential to aid traditional travel demand models (TDMs) such as the four-step planning model, but those processing methods are not commonly used in practice. In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana. This involves extracting trip segments from the data and inferring the phone users' home locations, adjusting for data representativeness, and using a data-driven travel time-based cost function for the trip distribution model. The trip generation and interchange patterns in the state are modeled for 2025, 2035, and 2045. Employment sectors like industry and retail are observed to influence trip making behavior more than other sectors. The travel growth is predicted to be mostly concentrated in the suburban regions, with a small decline in the urban cores. Further, although the majority of the growth in trip flows over the years is expected to come from the corridors between the major urban centers of the state, relative interzonal trip flow growth will likely be uniformly spread throughout the state. We also validate our results with the forecasts of two travel demand models, finding a difference of 5-15% in overall trip counts. Our GPS data-based demand model will contribute towards augmenting the conventional statewide travel demand model developed by the state and regional planning agencies.