Towards a realistic optimization of urban traffic flows

Angius, Fabio; Reineri, Massimo; Chiasserini, Carla-Fabiana; Gerla, Mário; Pau, Giovanni

doi:10.1109/itsc.2012.6338902

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…Another method for optimizing urban traffic is presented in [1]. It consists of the analysis of each junction topology by using basic heuristics and computationally efficient simulations in order to understand the outcomes they present with different traffic patterns and intensity.…”

Section: Related Workmentioning

confidence: 99%

Smart Mobility Policies with Evolutionary Algorithms

Stolfi

Alba

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels.

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Section: Related Workmentioning

confidence: 99%

Smart Mobility Policies with Evolutionary Algorithms

Stolfi

Alba

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…These maps can later be used to optimize traffic light cycles [12], to study the placement of LED Panels throughout the city [14], to reroute vehicles with the aim of reducing travel times [13], and for many other uses in smart mobility [1,5,6,9].…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Algorithm to Generate Real Urban Traffic Flows

Stolfi

Alba

2015

Advances in Artificial Intelligence

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Abstract. In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.

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“…In relation to the traffic optimization problem, [20] studies different kinds of vehicular route optimizations that utilize traffic information gathered from vehicles to estimate travel times and to find optimal routes. The authors study the difference between proactive and flow-based methods.…”

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confidence: 99%

Traffic routing in urban environments: The impact of partial information

Codecà

Frank

Engel

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-There are many studies concerning the problem of traffic congestion in cities. One of the best accepted solutions to relieving congestion involves optimization of resources already available, by means of balancing traffic flows to minimize travel delays. To achieve this optimization, it is necessary to collect and process Floating Car Data (FCD) from vehicles. In this paper we evaluate the repercussions of partial information on the overall traffic view, and consequently on the outcome of the optimization. Our study focuses on the role of the user participation rate and the availability of Road Side Units to collect the FCD. By means of simulation we quantify the impact of partially-available information on the computation of route optimization, and how it impedes traffic flows. Our results show that even minor uncertainties can significantly impact routing strategies and lead to deterioration in the overall traffic situation.

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Towards a realistic optimization of urban traffic flows

Cited by 8 publications

References 16 publications

Smart Mobility Policies with Evolutionary Algorithms

Smart Mobility Policies with Evolutionary Algorithms

An Evolutionary Algorithm to Generate Real Urban Traffic Flows

Traffic routing in urban environments: The impact of partial information

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