Utilizing mobile phone tracking data to estimate Intra-City modal mobility: A study on active mobility in two Finnish City regions

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.

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Publication Details

Journal
Journal of Transport Geography
Published
2025-06-16
DOI
https://doi.org/10.1016/j.jtrangeo.2025.104326
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Utilizing mobile phone tracking data to estimate Intra-City modal mobility: A study on active mobility in two Finnish City regions

Marton Magyar, Tiina Lankila, Harri Antikainen, Ossi Kotavaara et al.
Journal of Transport Geography
Human Mobility and Location-Based Analysis
article

Utilizing mobile phone tracking data to estimate Intra-City modal mobility: A study on active mobility in two Finnish City regions

Marton Magyar, Tiina Lankila, Harri Antikainen, Ossi Kotavaara, Terhi Ala‐Hulkko
article en

Abstract

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.

Journal of Transport GeographyVol. 128
Oulu Deaconess Institute (FI), University of Oulu (FI), Aalto University (FI)
Academy of Finland, Strategic Research Council
Sustainable cities and communities
Openalex Percentile: Top 85%
Human Mobility and Location-Based Analysis
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