2019
DOI: 10.1007/s41745-019-00125-9
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Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Abstract: 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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Cited by 66 publications
(29 citation statements)
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“…Beside origin-destination matrices, multiple studies are dealing with the application of telecom big data in public transport planning [24][25][26][27], urban mobility planning [28,29], transport mode detection [27,[30][31][32], urban mobility estimation [33], traffic flow analysis [34][35][36][37] and the reconstruction of human mobility in general [38][39][40][41][42].…”
Section: Overview Of Previous Researchmentioning
confidence: 99%
“…Beside origin-destination matrices, multiple studies are dealing with the application of telecom big data in public transport planning [24][25][26][27], urban mobility planning [28,29], transport mode detection [27,[30][31][32], urban mobility estimation [33], traffic flow analysis [34][35][36][37] and the reconstruction of human mobility in general [38][39][40][41][42].…”
Section: Overview Of Previous Researchmentioning
confidence: 99%
“…Cellular network data and smart card data can be used to obtain large samples of observations, which we can use to fit the parameters of a model more exactly (Jánošíková et al, 2014). The large sample size of observations could also allow making travel behaviour models more detailed and include more parameters such as dynamic events and the influence of weather as in Zannat and Choudhury (2019). In particular, for agent-based traffic models, which in detail model individual travel patterns, large-scale observations of real travel patterns make it possible to calibrate and validate the models in detail.…”
Section: Usage Of the Extracted Travel Patternsmentioning
confidence: 99%
“…Types of big data in PT are: (1) smart card data, (2) mobile phone data, (3) GPS data, and (4) Automatic Vehicle Location (AVL). Only a quality mix of different data collection can gain feasible results for integrated public transport system [30].…”
Section: Planning Public Passenger Transport Servicesmentioning
confidence: 99%