Abstract
Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.
Original document
The different versions of the original document can be found in:
- https://zenodo.org/record/1483377 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
- https://zenodo.org/record/1483377 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
- https://zenodo.org/record/1483377 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
DOIS: 10.5281/zenodo.1483376 10.5281/zenodo.1483377
Document information
Published on 01/01/2018
Volume 2018, 2018
DOI: 10.5281/zenodo.1483376
Licence: Other
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