Inferring dynamic origin-destination flows by transport mode using mobile phone data

Autor: Ghazaleh Khodabandelou, Mounim El Yacoubi, Vincent Gauthier, Jakob Puchinger, Danya Bachir
Přispěvatelé: IRT SystemX (IRT SystemX), Réseaux, Systèmes, Services, Sécurité (R3S-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Théorique de la Matière Condensée (LPTMC), Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Département Réseaux et Services de Télécommunications (RST), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Laboratoire Génie Industriel - EA 2606 (LGI), CentraleSupélec
Rok vydání: 2019
Předmět:
Multimodal transport
Computer science
Real-time computing
Transport network
Transportation
02 engineering and technology
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Machine learning
0502 economics and business
11. Sustainability
0202 electrical engineering
electronic engineering
information engineering
Origin destination matrix
Travel flows
Urban mobility
Civil and Structural Engineering
050210 logistics & transportation
Transportation planning
Transport mode
05 social sciences
Mode (statistics)
Mobile phone data
020206 networking & telecommunications
Computer Science Applications
[SPI.GCIV]Engineering Sciences [physics]/Civil Engineering
Geolocation
Travel survey
Mobile phone
Automotive Engineering
Cellular network
[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis
Statistics and Probability [physics.data-an]
Zdroj: Transportation research. Part C, Emerging technologies
Transportation research. Part C, Emerging technologies, Elsevier, 2019, 101, pp.254-275. ⟨10.1016/j.trc.2019.02.013⟩
ISSN: 0968-090X
1879-2359
DOI: 10.1016/j.trc.2019.02.013
Popis: International audience; Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on multimodal transport networks. In this work, we propose the first methodology to infer dynamic Origin-Destination flows by transport modes using mobile network data e.g., Call Detail Records. For this study, we pre-process 360 million trajectories for more than 2 million devices from the Greater Paris as our case study region. The model combines mobile network geolocation with transport network geospatial data, travel survey, census and travel card data. The transport modes of mobile network trajectories are identified through a two-steps semi-supervised learning algorithm. The later involves clustering of mobile network areas and Bayesian inference to generate transport probabilities for trajectories. After attributing the mode with highest probability to each trajectory, we construct Origin-Destination matrices by transport mode. Flows are up-scaled to the total population using state-of-the-art expansion factors. The model generates time variant road and rail passenger flows for the complete region. From our results, we observe different mobility patterns for road and rail modes and between Paris and its suburbs. The resulting transport flows are extensively validated against the travel survey and the travel card data for different spatial scales
Databáze: OpenAIRE
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