A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data

ABSTRACT While benefiting people's daily life in so many ways, smartphones and their location-based services are generating massive mobile device location data that has great potential to help us understand travel demand patterns and make transportation planning for the future. While recent studies have analyzed human travel behavior using such new data sources, limited research has been done to extract multimodal travel demand patterns out of them. This paper presents a datadriven analytical framework to bridge the gap. To be able to successfully detect travel modes using the passively collected location information, we conduct a smartphone-based GPS survey to collect ground truth observations. Then a jointly trained single-layer model and deep neural network for travel mode imputation is developed. Being "wide" and "deep" at the same time, this model combines the advantages of both types of models. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines and therefore enhance the imputation accuracy. To showcase the applications of the introduced framework in answering real-world planning needs, a separate mobile device location data is processed through trip end identification and attribute generation, in a way that the travel mode imputation can be directly applied. The estimated multimodal travel demand patterns are then validated against typical household travel surveys in the same Washington D.C. and Baltimore Metropolitan Regions. BACKGROUND Thanks to the rapidly evolving smartphone industry and mobile computing technology, mobile device location data has never been so readily available before. According to the Pew Research Center, the United States has around 223 million smartphone users in 2017 (Mobile Fact Sheet). More than three-quarters of Americans (77%) now own a smartphone, with lower-income Americans and senior citizens above the age of 50 exhibiting a sharp uptick in ownership over the past years. These devices are generating a massive amount of location data continuously through the widespread use of location-based service (LBS) via Wi-Fi hotspots, cellular towers, Global Positioning System (GPS)-based technologies, and GPSenabled applications on these smartphone devices. This ubiquitous LBS data provides an opportunity to innovatively and accurately observe individuals' travel behavior and model the overall travel demand patterns for a region, a state, and even an entire country.