Energy-efficient routing based on vehicular consumption predictions of a mesoscopic learning model

Masikos, Michail; Demestichas, Konstantinos; Adamopoulou, Evgenia; Theologou, M.

doi:10.1016/j.asoc.2014.11.054

Cited by 23 publications

(10 citation statements)

References 22 publications

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“…Mostly, a driver would choose a route with either the shortest travel distance or the fastest travel time. However, the shortest or fastest route is not always the best choice in terms of fuel consumption and emissions [68][69][70]. A Swedish study found that 46% trips of the drivers' spontaneous choices were not the most fuel-efficient routes and 8.2% of fuel could be saved by using a fuel-optimised navigation system [71].…”

Section: Route Choicementioning

confidence: 99%

Eco-driving technology for sustainable road transport: A review

Huang

Zhou

et al. 2018

Renewable and Sustainable Energy Reviews

290

128

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Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated.

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Section: Route Choicementioning

confidence: 99%

Eco-driving technology for sustainable road transport: A review

Huang

Zhou

et al. 2018

Renewable and Sustainable Energy Reviews

290

128

View full text Add to dashboard Cite

show abstract

“…In this study, Method A is based on the use of neural networks [26][27][28] for the estimation of the energy consumption of alternative routes to the desired destination. Once the energy cost of every road segment toward the selected destination is estimated by the neural networks (a properly trained neural network is used for each segment of the road network), the route, i.e., the sequence of adjacent road segments, that is expected to lead to the lowest energy consumption is computed and suggested to the driver.…”

Section: Description Of the Experiments And The Collected Datasetmentioning

confidence: 99%

Statistical Validation of Energy Efficiency Improvements through Analysis of Experimental Field Data: A Guide to Good Practice

Demestichas

Adamopoulou

2020

Vehicles

Self Cite

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Often in the area of road transport solutions and intelligent transport systems, two or more alternative solutions or methods compete in terms of energy gains, time efficiency, or other aspects. Measurements collected from field trials are used to make a comparative assessment but are usually limited because of resource constraints. The present paper describes how statistical inference techniques can be used in a systematic way, in order to validate the superior performance of one method over the other. We adopt such an approach to study the performance of two alternative routing methods in terms of achievable energy savings, although the same methodology can be widely applied to other use cases as well. We specifically employ and describe three different techniques to achieve the intended comparison, namely paired sample tests, statistical testing of mean value in a normal population, and two-sample tests in normal populations with unknown yet equal variances. We reach conclusions on whether claims of outperformance of one routing method over the other can be supported by our collected experimental data and to what extent.

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“…Vehicular Ad-Hoc Networks are made of a group of, mostly, mobile vehicles. Contextual awareness has been used in vehicle ad-hoc networks for a variety of applications including route planning and learning [53]- [55], real time perception of traffic congestion [56], collision avoidance [57] and finding vacant carparks [58]. In the maritime domain contextual awareness systems have even been used to detect anomalous behaviour in order to predict security threats [59].…”

Section: A Contextual Awareness In Vehicular Ad-hoc Networkmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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Energy-efficient routing based on vehicular consumption predictions of a mesoscopic learning model

Cited by 23 publications

References 22 publications

Eco-driving technology for sustainable road transport: A review

Eco-driving technology for sustainable road transport: A review

Statistical Validation of Energy Efficiency Improvements through Analysis of Experimental Field Data: A Guide to Good Practice

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

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