Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms

Stolfi, Daniel H.; Alba, Enrique

doi:10.1016/j.asoc.2014.07.014

Cited by 27 publications

(10 citation statements)

References 16 publications

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“…Our approach may also be the subject of integration in smart mobility solutions by addressing the routing phenomenon with uncertain information for ATIS. e WAZE project by Google, the Red Swarm by the Networking, and Emerging Optimization research group at the University of Malaga [43] and the work of [44] are among smart mobility solutions based on a category of technological infrastructure that makes our solution perfectly fit in them. us, plans are currently in place to test the approach within those kinds of systems with a real dataset and as validation of results.…”

Section: Numerical Resultsmentioning

confidence: 99%

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Benalla

Achchab

Hrimech

2020

Journal of Advanced Transportation

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Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.

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Section: Numerical Resultsmentioning

confidence: 99%

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Benalla

Achchab

Hrimech

2020

Journal of Advanced Transportation

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“…In [13] a new architecture to reduce vehicles' travel times as well as greenhouse gas emissions and fuel consumption, is presented. In the article, the authors use a series of spots distributed throughout the city, normally installed in traffic lights, to suggest alternative routes to drivers by using WiFi communications.…”

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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“…Firstly, ITS can be used to monitor the transport system for accidents and other obstacles and adjust the system to address these issues. This can have a positive effect on the safety of vehicles maneuvering around these obstacles, and the safety of vehicles and individuals involved or in close proximity to accidents [22] [36] [88]. Furthermore, the realtime distribution of traffic and transport system data can ensure the efficient distribution of information related to transport incidents to the closest and most suitable respondent-i.e.…”

Section: A Safetymentioning

confidence: 99%

Smart Urban Mobility Innovations: A Comprehensive Review and Evaluation

2020

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Recent smart urban mobility innovations such as intelligent transportation systems, electric vehicles, autonomous vehicles, demand-responsive transportation, shared transportation, and mobility-as-aservice are consistently identified as the panacea to many of the economic, social and environmental effects associated with private vehicles-including road congestion, urban sprawl, social exclusion, increased costs, crashes, emissions, and environmental degradation. One of the strategies of many smart urban mobility policies is to view the transportation system from a holistic perspective to seek reduction of negative effects and an improve performance. Nonetheless, given the rapid technological advances in the transportation sector, there is a need to identify and evaluate primary smart mobility innovations from a sustainability perspective. This paper presents a thorough technology review and evaluation of the main smart mobility innovations identified in the literature. The study has identified and categorized six main smart mobility innovations most commonly discussed within the literature including: (a) intelligent transport systems; (b) alternative fuel systems; (c) driving automation systems; (d) shared mobility services; (e) demand responsive transport; and (f) integrated mobility systems. Furthermore, this paper includes a brief description of their characteristics, applications, and also evaluates their sustainability according to their proposed impacts on transport safety, road congestion, energy consumption, the environment, and accessibility.

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Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms

Cited by 27 publications

References 16 publications

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach

Smart Mobility Policies with Evolutionary Algorithms

Smart Urban Mobility Innovations: A Comprehensive Review and Evaluation

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