Disparities in travel times between car and transit: Spatiotemporal patterns in cities
Journal article, 2020

Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

big data

social media data

public transit

transport mode

travel time



Yuan Liao

Chalmers, Space, Earth and Environment, Physical Resource Theory, Physical Resource Theory 2

Jorge Gil

Chalmers, Architecture and Civil Engineering, Urban Design and Planning

Rafael H. M. Pereira

Institute for Applied Economic Research (Ipea)

Sonia Yeh

Chalmers, Space, Earth and Environment, Physical Resource Theory

Vilhelm Verendel

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd, Data Science Research Engineers

Scientific Reports

2045-2322 (ISSN)

Vol. 10 1 4056

Sustainable cities: the use of large amounts of data to understand and handle movement patterns and congestion

Formas, 2017-01-01 -- 2019-12-31.

Areas of Advance

Information and Communication Technology



Driving Forces

Sustainable development

Subject Categories

Civil Engineering

Environmental Engineering

Electrical Engineering, Electronic Engineering, Information Engineering





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