Using smart card and GPS data for policy and planning: The case of Transantiago

Gschwender, Antonio; Munizaga, Marcela; Simonetti, C

doi:10.1016/j.retrec.2016.05.004

Cited by 46 publications

(24 citation statements)

References 5 publications

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“…Although the main purpose is to make charging and management more convenient [2], massive and continuous smart card data also can be recorded and served, which can provide lots of precious opportunities for researchers. The data can be used in various fields such as analysis of transit riders' travel patterns [3][4][5], behavior analysis [6][7][8][9], performance assessment of bus transport reform [10][11][12][13] and planning of the public transportation system [14][15][16][17]. In the study of smart card data, the spatio-temporal information on boarding and alighting is very important [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

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Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

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Section: Introductionmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

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“…Public transportation is the foundation of a sound urban public transport system, and is one of the common strategies to alleviate urban traffic congestion [51]. A well-developed public transportation system provides opportunity for the transportation departments to use information technology, such as IOT, big data, and cloud computing to collect and analyze real-time urban public transportation operations and public travel conditions [10,52,53]. In other words, relying on urban public transportation to build a smart city can promote the intelligent dispatch of urban public transportation, improve the level and efficiency of public transportation operation services; thus, be more conducive to improving urban traffic congestion.…”

Section: Research Hypothesesmentioning

confidence: 99%

Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China

2020

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More countries and regions are joining the bandwagon of smart city construction, which is an important strategy and innovative urban governance concept to solve the problem of rapid urbanization. This paper examines whether smart city innovation is able to ameliorate the traffic congestion faced by a large number of cities. Using panel data for 187 prefecture-level cities in China from 2008 to 2017, this paper tests the effect of implementation of a smart city on urban traffic congestion with the difference-in-difference method. The results show that, firstly, the construction of smart cities have significantly reduced the degree of urban traffic congestion and improved the quality and capacity of public transport. Secondly, information technology and urban innovation are the main mechanisms for smart city implementation to improve urban traffic problems. Thirdly, the improvement effect of smart city implementation on traffic management shows an increasing marginal effect over time. By overcoming the estimation bias in previous studies, this study accurately analyzes the positive role and dynamic effect of smart city construction on traffic improvement. It augments the literature of program evaluation and assessment of smart city implementation. By examining how to improve traffic congestion, it offers some insights that could inspire governments to build smarter cities with better traffic.

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“…Also, government requirements, such as by the US Federal Transit Agency, require statistical reporting that enables evaluation of other data sources. For instance, transit fare card data can be validated against traditional travel survey methods like on-board counts and surveys, in addition to understanding through interviews (Gschwender et al 2016). Combining qualitative data, such as interviews, with big quantitative data shows a mixed-methods approach to validating big data (Teddlie and Tashakkori 2010).…”

Section: Mitigating Bias In Transit Datamentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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Using smart card and GPS data for policy and planning: The case of Transantiago

Cited by 46 publications

References 5 publications

Smart Card Data Mining of Public Transport Destination: A Literature Review

Smart Card Data Mining of Public Transport Destination: A Literature Review

Could a Smart City Ameliorate Urban Traffic Congestion? A Quasi-Natural Experiment Based on a Smart City Pilot Program in China

Mitigating Bias in Big Data for Transportation

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