Latest revision as of 11:55, 15 February 2021

Abstract

Purpose Traffic congestion is a large-scale problem in urban areas all over the world that can lead to substantial costs for travellers and business operations. This paper focus on how to measure the way in which congestion selectively affects different traffic streams, with special emphasis on light duty vehicles travelling around a city. Methods The idea is to integrate a dataset collecting Global Positioning System (GPS) vehicle traces with road side data sources related to traffic conditions in a road network, which on the other hand usually lack focus on specific traffic streams. The core of the data integration method is the creation of a specific indicator focusing on the time lost in congestion. This is a Key Performance Indicator (KPI) of an urban network that is of paramount importance as a decision support tool for policy makers, also because it has an impact on other key issues such as air pollution, noise emissions, energy efficiency and health problems. Then, a method is proposed to quantify the congestion KPI in a highly disaggregated fashion (each single vehicle travelling on each single link or street segment). Results This KPI can be used to inform a wide range of policy actions within the transport sector, both from the viewpoint of a city and from that of an individual actor of the transport system, such as the operator of a fleet of vehicles for urban freight deliveries. Some preliminary examples of how the aggregation of the KPI at different scales can provide insights into the transport system are presented.

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• http://dx.doi.org/10.1186/s12544-019-0378-0 under the license https://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1186/s12544-019-0378-0 under the license https://creativecommons.org/licenses/by/4.0

• http://hdl.handle.net/11583/2752638 under the license https://creativecommons.org/licenses/by/4.0

• https://etrr.springeropen.com/track/pdf/10.1186/s12544-019-0378-0

• http://link.springer.com/article/10.1186/s12544-019-0378-0,

https://doaj.org/toc/1867-0717,

https://doaj.org/toc/1866-8887 under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s12544-019-0378-0.pdf,

http://link.springer.com/article/10.1186/s12544-019-0378-0/fulltext.html,

http://dx.doi.org/10.1186/s12544-019-0378-0

• https://etrr.springeropen.com/articles/10.1186/s12544-019-0378-0,

https://etrr.springeropen.com/track/pdf/10.1186/s12544-019-0378-0,

https://link.springer.com/article/10.1186/s12544-019-0378-0,

https://academic.microsoft.com/#/detail/2969552073 under the license https://creativecommons.org/licenses/by/4.0