A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
| Autor: | Lei Zhang, Yixuan Pan, Aref Darzi, Mofeng Yang, Chenfeng Xiong, Sepehr Ghader |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Computer science Mobile device location data Population Travel surveys FOS: Physical sciences Transportation Physics and Society (physics.soc-ph) Travel mode share Development computer.software_genre Article Data-driven Computer Science - Computers and Society Classifier (linguistics) Computers and Society (cs.CY) Machine learning education Civil and Structural Engineering Ground truth education.field_of_study Random forest Travel behavior Data mining Noise (video) Mobile device computer |
| Zdroj: | Transportation |
| Popis: | 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 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 tends to study the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from the MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) 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 95% accuracy in identifying trip ends and 93% accuracy in imputing 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. Transportation (2021) |
| Databáze: | OpenAIRE |
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