Using Mobile Phone Location Data to Develop External Trip Models

Abstract

This paper explores the use of passively collected data on the location of mobile phones in the development of external travel models, which capture trips to, from, and through an area. The data were collected 24 h a day for 1 month during May 2013 for the North Carolina Department of Transportation. The data cover the French Broad River Metropolitan Planning Organization and surrounding counties in North Carolina. This paper details the format of the data collected and the required processing and cleaning to support the development of the external trip models. The process for developing a through-trip table and estimating external trip interchanges in the region is presented, along with validation techniques for both. The results of this effort show that passively collected mobile phone data can be a good source of local information for developing external trip models.

Improving A Priori Demand Estimates Transport Models using Mobile Phone Data: A Rotterdam-Region Case

“…With a transport model distinguishing nearly 6,000 zones and OD information based on mobile phone data distinguishing 1,259 zones, there is a mismatch in zone detail. This means that we can use the data in the enriching procedure only for external trips as is seen in Huntsinger and Ward (2015). In Figure 3, the OD-data of mobile phone data for a single day is presented showing a plausible spatial distribution.…”

“…However, because counts were also used to calibrate the model (also to increase trips derived from mobile phone data to absolute levels), the validation results could have been highly influenced by the calibration process. Huntsinger and Ward (2015) developed an external trip model using mobile phone data showing promising results compared with household travel surveys. Çolak et al (2015) presented a data treatment pipeline that uses mobile phone data and population density to generate trip matrices in two metropolitan areas (i.e., Boston and Rio de Janeiro) showing comparable results with existing information reported in local travel surveys in Boston and existing origin destination matrices in Rio de Janeiro.…”

Exploring the Potential of Detailed Land Use Variables in Modelling External - Internal Trips

“…For modeling EI trips, a simpler approach has been proposed in the literature where the trips that have at least one end i.e. origin or destination in the study area are combined estimated (Huntsinger and Ward, 2015). Simply put, the study aggregated the external trips that had at least one end in the study area to total trips irrespective of separately accounting for the origin and destination location related to the study area.…”

Exploring Potentials in Mobile Phone GPS Data Collection and Analysis

“…Speed and location of data points were noted as commonly used criterion to eliminate data. The most common criteria was to remove the data points with unreasonably high speeds (Huntsinger and Ward, 2015;Wang et al, 2013;Sharman and Roorda, 2010).…”

“…There are other methods for travel mode detection. Some researchers developed a probability matrix (Huntsinger and Ward, 2015;Nitshe et al, 2012;Rojas et al, 2016;Abdi Kordan et al, 2014;Nitshe et al, 2014), and other researchers deployed fuzzy logic methods (Schüssler and Axhausen, 2008). Although these method were effective at identifying walking and cycling modes, they struggle to differentiate between motorized modes (Wolf et al, 2014).…”