Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions

doi:10.1016/j.future.2020.08.017

Future Generation Computer Systems

Volume 114, January 2021, Pages 649-660

https://doi.org/10.1016/j.future.2020.08.017 Get rights and content

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Highlights

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Traffic Flow reconstruction a key technology for many smart city applications.
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GPU based parallel computing for traffic flow reconstruction.
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Solving Indeterminacy on road junctions.

Abstract

The knowledge of the real time traffic flow status in each segment of a whole road network in a city or area is becoming fundamental for a large number of smart services such as: routing, planning, dynamic tuning services, healthy walk, etc. Rescue teams, police department, and ambulances need to know with high precision the status of the network in real time. On the other hand, the costs to obtain this information either with direct measures meant to add instruments on the whole network or acquiring data from international providers such as Google, TomTom, etc. is very high. The traditional modeling and computing approaches are not satisfactory since they are based on many assumptions that typically are doomed to change over time, as it occurs with traffic distribution at junctions; in short they cannot cover the whole network with the needed precision. In this paper, the above problem has been addressed providing a solution granting any traffic flow reconstruction with high precision and solving the indeterminacy of traffic distribution at junctions for large networks. The identified solution can be classified as a stochastic relaxation technique and resulted affordable on a parallel architecture based on GPU. The result has been obtained in the framework of the Sii-Mobility national project on smart city transport systems in Italy, a very large research project, and it is at present exploited in a number of cities/regions across Europe and by a number of research projects (Snap4City, TRAFAIR) of the European Commission.

Keywords

Smart city

Reconstruction algorithm

Traffic flow

Parallel computing approach

GPUs

Cited by (0)

Stefano Bilotta was born in Arezzo, Italy, in 1983. He received the Master degree in mathematics from the University of Siena, Italy, in 2009 and the Ph.D. degree in computer engineering and automation from the University of Florence, Italy, in 2013. From 2013 to 2016, he was a postdoctoral researcher at the Department of mathematics and computer science of the University of Florence. Since 2017, he has been a postdoctoral researcher of the DISIT Lab at the Department of information engineering of the University of Florence. His research interests include traffic flow reconstruction algorithms, parallel solution, dynamic systems, languages and coding theory. He has been involved in projects such as: Sii-Mobility and Trafair.

Paolo Nesi is a full professor at the University of Florence, Department of Information Engineering, chief of the Distributed Systems and Internet Technology, DISIT lab, and research group. His research interests include massive parallel and distributed systems, physical models, IOT, mobility, big data analytic, semantic computing, formal model, machine learning. He has been the general Chair of IEEE SC2, IEEE ICSM, IEEE ICECCS, DMS, etc., international conferences and program chair of several others. He is and has been the coordinator of several R&D multipartner international R&D projects of the European Commission such as Snap4City, RESOLUTE, ECLAP, AXMEDIS, WEDELMUSIC, MUSICNETWORK, MOODS and he has been involved in many other large projects on mobility and transport, smart city, such as Km4City, Sii-Mobility, Herit-Data, MobiMart, Trafair, Mosaic, REPLICATE, Weee, etc.

¹: https://www.disit.org, https://www.km4city.org, https://www.snap4city.org.