Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/22983
Title: Exploring crowdsourcing information to predict traffic-related impacts
Author: Tafidis, Pavlos
Teixeira, João
Bahmankhah, Behnam
Macedo, Eloísa
Coelho, Margarida C.
Bandeira, Jorge
Keywords: Crowdsourcing
Traffic impacts
Emissions
Air pollutants
Issue Date: Jun-2017
Publisher: IEEE
Abstract: Due to the increased public awareness on global climate change and other environmental problems, advanced strategies and tools are being developed and used to reduce the environmental impact of transport. The main objective of this paper is to explore the potential of using crowdsourcing information as an alternative or complementary source data to predict traffic-related impacts. Three main road connections to two important commercial areas in the city of Aveiro in Portugal, are examined. Driving patterns over different periods were collected using a probe vehicle equipped with a GNSS data logger and volumes of traffic were counted during different days of the week. The emissions estimation was based on the concept of Vehicle Specific Power (VSP), which has the capability to predict emissions during a trip often-according recorded second-by-second vehicle dynamics. Various tests were conducted in order to explore the potential correlations between these data sets and the information of a certain place’s busy times that are provided by Google Maps. The findings of the study prove the potential of crowdsourcing information and shows that ICT technologies can be used to estimate environmental and traffic-related impacts.
Peer review: yes
URI: http://hdl.handle.net/10773/22983
DOI: 10.1109/EEEIC.2017.7977595
Appears in Collections:DEM - Comunicações



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