A data-driven framework of urban mobility estimation using large-scale mobile phone location data

Keywords: Origin-destination matrix; App-based data; Travel survey; Multi-sourced data
Session Type: Virtual Poster Abstract
Day: Monday
Session Start / End Time: 2/28/2022 02:00 PM (Eastern Time (US & Canada)) - 2/28/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 32

Abstract

The mobility demand estimation is a fundamental step in transportation modelling and planning that results typically from traditional data capture tools like travel surveys. The standard tools are usually prohibitively expensive to install and time-consuming to extract the results. However, we require easy-access and (near) real-time mobility data, especially in crisis periods like the Covid-19 pandemic as profound changes in mobility patterns happened. The new form of data such as mobile phone location data is emerging as an up to date and large-scale data source for urban modelling in the new data-driven world. This study represents applications performed with two big app-based mobile phone datasets collected by two well-known data companies, Tamoco and Huq, from 2019 to 2021 in Glasgow Region, UK. Trips are extracted from the passive collected location-based services (LBS) using distance and time threshold hypothesis to define activities. Matches between the Scottish Household Survey (SHS) results in the trip distance distribution and TomTom dataset in daily profile calibrated the algorithm. Trips are then aggregated in an origin-destination (OD) matrix at the various geographical levels, compared with the SHS as a typical travel survey in this area. The results show a somewhat similar origin-destination matrix when comparing the percentage of trips from the two different large scale mobile phone datasets and Scottish household data.