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

Abstract

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.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…The results show that this method can predict the demand of the taxi destination better [4]. Bachir D used mobile network data, combined with transportation network geospatial data, tourism survey, census and tourism card data, to infer the user's dynamic departure destination [5]. Mungthanya W proposed a new method to construct a dynamic OD matrix of taxi in space and time by using taxi trajectory data, and analyzed the demand mode of taxi travel [6].…”

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

“…It is challenging, on the contrary, to use cellular network data to obtain additional metadata about the travel patterns besides their description as flows in time and space. Alexander et al [11] and Widhalm et al [15] have made some attempts to classify trip purposes and activities, and Bachir et al [18] and Graells-Garrido et al [13] have investigated possibilities to infer travel demand for each travel mode. Socioeconomic data for individuals are not available in cellular network data for privacy reasons.…”

“…By the nature of the cellular network, one base station typically hosts three antennas at the same position with antenna each covering different angles. Bachir et al [18] have proposed a method to improve the Voronoi tessellation when having three sectors per base station. For this dataset, we use a simple approach to improve the representation of sectors.…”

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

“…Therefore, information collected from the smart card can be used for PT planning other than merely the fare collection 57 . (b) Mobile phone data At present, most individuals carry mobile phone almost everywhere, which results in mobile phone datathe largest human mobility data source 8 . There are broadly two sources of mobile phone data-cellular network-based data and smartphone sensor-based data 69 .…”