Abstract
Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and over 93% tenfold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a random forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.
Highlights
Accurate measurement of travel behavior can help agencies understand how travel demand evolves and better allocate resources in support of transportation planning processes
Another two Location-based Service (LBS) datasets are obtained from one of the leading data vendors in the U.S Similar to the description provided in the literature, the data is passively collected through mobile device applications
The differences observed between the nationwide LBS estimates and NHTS 2017 are even smaller than the statewide comparison with 2007/2008 Transportation Planning Board (TPB)-Baltimore Metropolitan Council (BMC) HHTS, resulting in a higher Pearson correlation value (0.99) (Fig. 15b) across 50 states and D.C
Summary
Accurate measurement of travel behavior can help agencies understand how travel demand evolves and better allocate resources in support of transportation planning processes. Researchers and practitioners design and conduct travel surveys to obtain household- and individual-level travel behavior information, including trip origins and destinations, trip distance, trip time, trip purposes, travel modes, etc. Methods to conduct travel surveys usually require respondents to record their daily trips with the original paper-and-pencil interview (PAPI), computerassisted telephone interview (CATI), and computer-assisted-self-interview (CASI) (Wolf et al 2001; Wolf 2006) These methods are prone to several well-known biases, such as under-reported short trips, inaccurate travel times, and travel distances (Stopher et al 2007; McGowen and McNally 2007). Results suggest that the proposed framework can be readily applied in many regions with low cost to obtain travel mode share estimates and study travel trends, which can help decision-makers prioritize multimodal travel needs. “Conclusions and discussions” section concludes this paper and discusses the limitations and future research directions
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