Disparities in travel times between car and transit: Spatiotemporal patterns in cities

Yuan Liao,Sonia Yeh,Vilhelm Verendel,Jorge Gil,Rafael H M Pereira

doi:10.1038/s41598-020-61077-0

Yuan Liao, Sonia Yeh + Show 3 more

Open Access

https://doi.org/10.1038/s41598-020-61077-0

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Abstract

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.

Highlights

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
In São Paulo, close to 80% of the grid is accessible by PT
This is a result of the polycentric urban structure with PT provision concentrated along corridors in Amsterdam (Fig. 2)

Summary

Introduction

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. 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. Growth in PT ridership is constrained by many factors, such as fixed schedules and routes, low population density, and travellers’ attitudes[8] Among these constraints, travel time and the built environment are considered the most critical factors in the choice of transport mode[6,9,10]. Recent studies show how transit travel times can vary greatly by route and time of day[17,18], while a growing body of research using GPS and mobile phone data to record travel speed shows that overall transport performance varies significantly by time of day due to congestion in cities[19,20,21]. Open data standards such as General Transit Feed Specification (GTFS)[31] and crowdsourcing initiatives such as OpenStreetMap (OSM)[32] provide data that can be used to estimate PT travel times with the most up-to-date schedules[33]

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