Utilizing mobile phone tracking data to estimate Intra-City modal mobility

A study on active mobility in two Finnish City regions

Datos Bibliográficos

ID12295293
AutoresMarton Magyar (0000-0001-9318-0212, University of Oulu, autor de correspondencia), Terhi Ala‐Hulkko (0000-0002-0884-2152, Aalto University), Terhi Ala-Hulkko, Harri Antikainen (0000-0002-0547-6441, University of Oulu), Tiina Lankila (0000-0002-2448-9643, University of Oulu), Ossi Kotavaara (0000-0002-8466-4394, University of Oulu)
Año2025
Volumen128
Páginas104326-104326
Fecha de publicación2025-06-16
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Transport Geography (JOURNAL)
Identificadores de la revistaISSN: 0966-6923 • E-ISSN: 1873-1236
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.jtrangeo.2025.104326
OpenAlexW4411339593
IdiomaEN
Referencias citadas59

Understanding sustainable and active mobility in urban areas is essential for promoting healthier, low-carbon city planning. While previous research has utilized a wide range of data sources and spatial scales, there has been minimal focus on modal detection within urban areas. This analysis utilizes mobile phone tracking data to estimate travel modes and analyse modal mobility patterns at the intra-urban scale in Finland's Greater Helsinki region and Oulu. The origin-destination matrix containing travel volumes is coupled with the accessibility network to infer estimated modes based on travel times. To account for instances of overlapping trip times or short trips that are difficult to quantify, a filter based on both trip time and length comparison is applied to the dataset. The share and spatial distribution of active mobility modes such as walking, cycling as well as public transport are analysed to explore their relationship with urban form and travel behaviour. The proposed method provides a novel way to detect transport hubs and areas with high active and public transport use, corresponding to inner urban areas. The proportion of transport modes detected in the study provides a reasonable estimate the amount reported in the National Transport Survey of the same year. These insights aim to inform sustainable urban planning, promote healthier mobility choices, and support policies that enhance carbon neutrality and climate resilience. • We use mobile phone-based mobility data and accessibility analysis to identify transport modes. • Transport hubs within cities can be identified by the mobility patterns. • The share of active transport (walking, cycling, public transport) can be detected within neighbourhoods. • The modal share of trips detected by the proposed method is close to those measured by travel surveys

Geography · GSM services · Mobile phone · Mobile phone tracking · Mobile radio · Mobile telephony · Modal · Modal shift · Public transport · Telecommunications · Tracking (education · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Materials Science · Psychology · Transportation and Mobility Innovations · Urban Transport and Accessibility