A zonal inference model based on observed smart-card transactions for Santiago de Chile

Tamblay, Sebastián; Galilea, Patricia; Iglesias, Paula; Raveau, Sebastián; Muñoz, Juan Carlos

doi:10.1016/j.tra.2015.10.007

Cited by 21 publications

(25 citation statements)

References 10 publications

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“…Using the proposed algorithms by Barry et al 9 , O-D trip tables were created for short-term and long-term demand estimation. Besides disaggregate O-D estimation, a study conducted by Tamblay et al 62 also attempted to infer zonal O-D matrix form smart card information to reflect the city-wise travel demand. Since PT journeys often comprise multiple transfers, smart card data were also used in the contemporary studies to understand linked trips to derive more complete information of PT trip.…”

Section: Estimation Of Origin and Destination (O-d)mentioning

confidence: 99%

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat

Choudhury

2019

J Indian Inst Sci

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Introduction Rapid urbanization and associated increase in population are resulting in a higher growth of motorized traffic flow in urban areas. As a consequence, cities are experiencing different problems such as air pollution, road accidents, and congestions. In response to these problems, public transportation (PT) could help to reduce air pollution, road congestion and travel time, and dependency on non-renewable energy, which benefit both

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Section: Estimation Of Origin and Destination (O-d)mentioning

confidence: 99%

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat

Choudhury

2019

J Indian Inst Sci

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“…Smartcard data can be used to derive the origin-destination matrix, which is a vital input to public transport planning and modelling (Alsger et al, 2015). They can be used to identify activity purpose, duration, route choice, segmentation, volume and location of transit riders (Tao et al, 2014b;Ma et al, 2012;Janosikova et al, 2014;Kim et al, 2014;He and Trepanier, 2015;Kieu et al, 2015;van der Hurk et al, 2015;van Oort et al, 2015;Tamblay et al, 2016). They can also be employed to evaluate the performance of metropolitan passenger railways with respect to on-schedule rates, occupancy and operational speeds (Eom et al, 2015).…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Monitoring transit-served areas with smartcard data: A Brisbane case study

Zhou

Sipe

et al. 2019

Journal of Transport Geography

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A city can be divided into areas that are served by transit and those that are not. In this study, the former is referred to as "transit-served areas (TSAs)". To quantify, monitor and visualise the TSAs of the Southeast Queensland (SEQ), this study analyses half-year smartcard data between 2012 and 2013 from TransLink, the transit agency for SEQR For scenarios are prescribed and four corresponding metrics (the minimum, actual, random and maximum travels) are calculated, which reflect transit riders' different levels of elasticity of distance travelled (EDT) relative to the cost of travel within or between TSAs and how transit riders could possibly travel as EDT varies. The total trips generated by or attracted to TSA and the temporal and spatial variations of these metrics across days are used to monitor TSAs, especially transit trips within or between them. The results indicate that transit trips attracted to, and generated by TSA and transit trips between TSAs vary significantly over time and across space. Across the scenarios, the temporal variance tends to be larger as EDT becomes more inelastic. The above results provide useful references for decision-makers to understand better the ranges of transit demand (by TSA) across the space and time when EDT is a variable.

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“…Different authors confirm this finding in different urban contexts and suggest some methods to deal with it. Nonetheless, when it comes to transit, the definition of transit connectors has received limited attention (Tamblay et al, 2016). To our knowledge, the impact of transit connectors on assignment results has not yet been demonstrated nor evaluated.…”

Section: Introductionmentioning

confidence: 99%

Spatial Aggregation Issues in Traffic Assignment Models

Manout

2020

Netw Spat Econ

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This thesis owes its existence to Patrick Bonnel. From the first day when I had expressed my will to undergo a PhD thesis, Patrick has always been of great support and help to me and to this work. His invaluable scientific supervision, commitment, and kindness have made of this 40-month PhD a valuable experience, rich of scientific learning and discoveries. I am also grateful to Louafi Bouzouina for being of advice to me. In his company, I have learnt a great deal on the academic macrocosm. This PhD would not have seen the day without the goodwill and the support of ForCity. A special thanks to Frédéric Derkx, Hakim Ouaras, and François Pacull for their engagement with this thesis. Without the backing and the arrangements made by Frédéric, this PhD would not be as it is today. The help of François Pacull and the supervision of Hakim Ouaras have been gratefully appreciated.

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A zonal inference model based on observed smart-card transactions for Santiago de Chile

Cited by 21 publications

References 10 publications

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Monitoring transit-served areas with smartcard data: A Brisbane case study

Spatial Aggregation Issues in Traffic Assignment Models

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