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.

Mode Choice Latent Class Estimation on Mobile Network Data

“…Out of the 22 studies found by Huang et al (2019) only four studies separate bus trips from car trips, where Danafar et al (2017), Kalatian and Shafahi (2016) and Phithakkitnukoon et al (2017) mainly focus on short distance trips based either on proximity to route or travel speed, and Wang et al (2010) only consider an example origin-destination pair (OD pair) where there is a clear difference in travel time between car and mass transit. Yang et al (2022) confirm the difficulty of distinguishing bus and car trips based on geospatial information, even in the case of location-based services data (which derived from a combination of sensors such as Wi-Fi, Bluetooth, cellular tower, and GPS information whenever a mobile application updates the phone's location). In their study bus trips had the least prediction accuracy of all considered modes, probably due to the similarity between bus and car trips.…”

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

“…Then, a travel mode imputation model is further applied to infer four travel modes—namely, air, drive, rail, and nonmotorized modes—based on heuristic rules and a random forest model. Detailed descriptions of the trip end identification algorithm and the travel mode imputation model can be found in the following references ( 12 , 51 ).…”

“…Since the early 2000s, along with technological advancement in mobile sensors and mobile networks, the quantity of mobile device location data (MDLD) has been growing dramatically in coverage and size, with broader spatiotemporal coverage of the population and its mobility. A series of research studies has demonstrated the usefulness of MDLD for enhancing the traditional travel survey and revealed its potential to substitute surveys ( 11 , 12 ). At the same time, obtaining travel statistics solely based on MDLD is also worth investigating to reduce human labor and cost.…”

National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

“…The context-based method considers the surroundings, such as land use and nearby points of interest (POIs), and infers the activity types with empirical rules (36,37). Next, the trip-level information, including trip ends (38,39), travel mode (40)(41)(42)(43)(44)(45)(46), and trip purpose (47)(48)(49)(50)(51)(52)(53)(54)(55)(56), is identified.…”