Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Dumbliauskas, Vytautas; Barauskas, Abhilasha

doi:10.7307/ptt.v29i6.2369

Cited by 25 publications

(13 citation statements)

References 19 publications

(22 reference statements)

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“…Private vehicle travel times are based on tracks’ historical data combined with real-time traffic patterns from mobile phone records. Using Google API services, researchers can apply this data, computing OD travel time matrices for different times and days of the week (Dumbliauskas et al 2017 ), which allows us to analyse the impacts of congestion in different temporal scenarios (García-Albertos et al 2019 ). Also, thanks to GTFS files, API services allow interested parties to analyse the level of coverage of public transport networks, average speeds, and line overlaps (Hadas 2013 ), as well as to compute travel time matrices according to time slots, which can be used in dynamic accessibility studies (Boisjoly and El-Geneidy 2016 ; Fransen et al 2015 ; Pritchard et al 2019 ; Stępniak et al 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

et al. 2021

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This paper aims to evaluate the impacts of the economic context on traffic congestion and its consequential effects on private vehicle accessibility. We conduct a long-term analysis of spatiotemporal traffic congestion patterns in Madrid (Spain), comparing two urban realms: the 2008 economic crisis and the following post-crisis situation. We apply TomTom Speed Profiles data to assess daily variations in traffic congestion and their changes between both periods, and Twitter data to capture spatial patterns of the daily pulse of the city. Increased traffic, a by-product of economic recovery, resulted in higher congestion, particularly during peak hours. Nevertheless, these changes are spatially uneven. In the city core, an increase in congestion is relatively temporally homogeneous, while in the peripheral suburban zones, there has been only a marginal increase in travel times. On the other hand, in the urban outskirts, increased traffic congestion is more severe but visibly different between north and south. These differences have strong social connotations: over 40% of the population experienced a dramatic increase in travel times (more than 25%) during peak hours. Moreover, low-income groups are more likely to live in the more affected southern districts, suffering most the negative consequences of increased congestion.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

et al. 2021

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“…Previous studies adopting web-based data sources in transport research (e.g. Dumbliauskas, Grigonis, and Barauskas 2017;Fuellhart et al 2015;Grubesic and Zook 2007) reveal that there are three predominant approaches through which consumeroriented, web-based travel data may be extracted, and which largely depend on the resources available.…”

Section: Approaches To Web-based Travel Data Collectionmentioning

confidence: 99%

“…Through the implementation of an API in a programming script, large volumes of data can be efficiently extracted and processed. Examples of overland travel APIs include, among others, those provided through the Google Maps Platform that allow accessing Google's anonymised and aggregated travel data, collected from smartphone users (Dumbliauskas, Grigonis, and Barauskas 2017;García-Albertos et al 2018, in press).…”

Section: Approaches To Web-based Travel Data Collectionmentioning

confidence: 99%

“…Related to the this, the reliability of web-based travel data, and in this study Google-based data in particular, should be evaluated. Since Google does not provide any metadata on the sample size used to calculate the average (aggregated) car travel times provided by the Google Maps Distance Matrix API, Google's data reliability is uncertain (Dumbliauskas, Grigonis, and Barauskas 2017) and therefore entails caution and careful deliberation when adopting these data. This lack of data transparency regarding Google's road network has also been addressed in Wang and Xu (2011), although others (e.g.…”

Section: The Use Of Apis In Transport Research: Strengths and Weaknessesmentioning

confidence: 99%

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A bimodal accessibility analysis of Australia through application programming interfaces

Meire

Derudder

2020

Transportmetrica A: Transport Science

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In this paper we discuss some of the major strengths and limitations of an application programming interfaces (API)-based approach to studying accessibility in transport networks. To this end, we present a framework for assessing the bimodal accessibility of locations by using publicly available webbased resources that can be accessed via APIs. By way of empirical example, we query the Google Maps Distance Matrix and Google QPX Express APIs to collect travel time for both land-and airside travel, and process the extracted data using Python. We then apply our analytical strategy to the case of Australian statistical units to illustrate our data collection and processing framework, and in the process try to improve our understanding of the Australian urbangeographical landscape in terms of its bimodal accessibility. In addition to a largely-expected accessibility pattern centred on major agglomerations, we also reveal a number of idiosyncratic and perhaps less-expected geographical patterns.

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“…Trip distance estimation. As soon as the locations have been identified properly the search of the shortest path was carried out with the help of Google Maps Distance Matrix Application Programming Interface following the procedure set out by Dumbliauskas et al [23] and Wang et al [24]. Within this step, an explicit assumption that travelers have chosen the shortest route was made, which is not necessarily true in all the observations.…”

mentioning

confidence: 99%

An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City

Dumbliauskas

2020

Sustainability

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The approach defines the process of conducting an empirical research of the travel behavior patterns of residents of Vilnius city. It defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments during the weekday. This empirical research is based on a travel diary survey that was planned and executed in cooperation with Vilnius Municipality during preparation of sustainable mobility plan. The following work describes the research object, the questionnaire design, sampling strategy and the analysis of results based on characteristics of respondents. An innovative activity sequence-focused travel behavior research approach designed to collect data for a tour-based travel demand model.

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Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Abstract: ABSTRACT

Cited by 25 publications

References 19 publications

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

A bimodal accessibility analysis of Australia through application programming interfaces

An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City

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