A data-driven travel mode share estimation framework based on mobile device location data

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.

Keywords: Machine learning; Mobile device location data; Travel mode share; Travel surveys.

Fig. 1

The data-driven travel mode share estimation framework

Fig. 2

Typical daily travel pattern of an individual

Fig. 3

incenTrip trip trajectories for a drive; b bus; c rail; d non-motorized

Fig. 4

Raw location point distribution for a Dataset I; b Dataset II

Fig. 5

Multimodal transportation networks: drive (grey), rail (green), bus (blue). (Color figure online)

Fig. 6

Trip end identification results

Fig. 7

a Spatial accuracy distribution for the three LBS datasets; b location recording interval distribution for the two case studies’ LBS datasets

Fig. 8

Comparison with 2007/2008 TPB-BMC HHTS on: a trip distance distribution; b trip time distribution; c trip rate distribution; d time of day distribution

Fig. 9

a Statewide travel mode share comparison; b county-level travel mode share correlation; c county-level travel mode share comparison

Fig. 10

Census tract-level mode share illustration for a and b: rail mode share in D.C. and Baltimore City; c and d: Bus mode share in D.C. and Baltimore City

Fig. 11

Comparison with NHTS 2017 on: a Trip distance distribution; b trip time distribution; c trip rate distribution

Fig. 12

Day of week variation

Fig. 13

Nationwide-level air trip origins

Fig. 14

a Nationwide travel mode share comparison; b state-level travel mode share correlation; c state-level travel mode share comparison

Fig. 15

CBSA-level illustration of a rail travel mode share; b bus travel mode share

Fig. 16

Feature importance of the random forest classifier

Fig. 17

Observed duration distribution for: a number of unique hours (Dataset I); b unique hour bins (Dataset II); (3) number of unique days (Dataset II)

Fig. 18

Comparison with 2007/2008 TPB-BMC HHTS on: a short-distance trip distance distribution; b long-distance trip distance distribution

Fig. 19

Comparison with NHTS 2017 on: a short-distance trip distance distribution; b long-distance trip distance distribution

Fig. 20

County-level travel mode share comparison between Dataset I and NHTS 2017

Fig. 21

State-level travel mode share comparison between Dataset II and NHTS 2017