Personalised assistance for fuel-efficient driving

Gilman, Ekaterina; Keskinarkaus, Anja; Tamminen, Satu; Pirttikangas, Susanna; Röning, Juha; Riekki, Jukka

doi:10.1016/j.trc.2015.02.007

Cited by 67 publications

(72 citation statements)

References 30 publications

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“…A context‐aware driving assist system was prototyped by Gilman et al . Zhao et al . developed eco‐driving feedback system and reported idling for a long time as one of the contributing factors for non‐eco‐driving.…”

Section: Related Literaturementioning

confidence: 99%

“…Jamson et al[46] used a driving simulator to investigate the rate of learning of eco-driving, where steady driving is important for fuel-efficient driving. A context-aware driving assist system was prototyped by Gilman et al [47] Zhao et al[48] developed eco-driving feedback system and reported idling for a long time as one of the contributing factors for non-eco-driving. Car navigation system is also evolving as a driver assist system for reduction in fuel consumption.…”

mentioning

confidence: 99%

“…[46] used a driving simulator to investigate the rate of learning of eco-driving, where steady driving is important for fuel-efficient driving. A context-aware driving assist system was prototyped by Gilman et al [47] Zhao et al…”

mentioning

confidence: 99%

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Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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Summary Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Literaturementioning

confidence: 99%

mentioning

confidence: 99%

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Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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“…Many phone applications already provide their users with information about, for instance, the travel time to home at the end of a working day, route conditions, or traffic jams. Secondly, predictions may help to save energy on the societal level, for instance, by fuel savings via carpooling or real time route optimisation . Thirdly, predictions may be used for providing personalized content, for instance, via location‐based social networks .…”

Section: Introductionmentioning

confidence: 99%

A survey of evaluation methods for personal route and destination prediction from mobility traces

Stegmann

Žliobaitė

Tolvanen

et al. 2017

WIREs Data Min & Knowl

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Personal mobility data can nowadays be easily collected by personal mobile phones and used for analytical modeling. To assist in such an analysis, a variety of computational approaches have been developed. The goal is to extract mobility patterns in order to provide traveling assistance, information, recommendations or on‐demand services. While various computational techniques are being developed, research literature on destination and route prediction lacks consistency in evaluation methods for such approaches. This study presents a review and categorization of evaluation criteria and terminology used in assessing the performance of such methods. The review is complemented by experimental analysis of selected evaluation criteria, to highlight the nuances existing between the evaluation measures. The experimental study uses previously unpublished mobility data of 15 users collected over a period of 6 months in Helsinki metropolitan area in Finland. The article is primarily intended for researchers developing approaches for personalized mobility analysis, as well as a guideline for practitioners to select criteria when assessing and selecting between computational approaches. Our main recommendation is to consider user‐specific accuracy measures in addition to averaged aggregates, as well as to take into consideration that for many users accuracy does not saturate fast and the performance keeps evolving over time. Therefore, we recommend using time‐weighted measures. WIREs Data Mining Knowl Discov 2018, 8:e1237. doi: 10.1002/widm.1237 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Society and Culture Application Areas > Industry Specific Applications

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“…These systems have evolved over time and some exploit the recent advances in CV technology [20,22,23]. Some recent works on driver assistant systems that focus on vehicle fuel economy are available in [24][25][26][27]. Authors [24] have provided optimal velocity and acceleration information to the driver by minimizing an approximate fuel consumption model.…”

Section: Introductionmentioning

confidence: 99%

A fuel economic model predictive control strategy for a group of connected vehicles in urban roads

HomChaudhuri

Vahidi

Pisu

2015

2015 American Control Conference (ACC)

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The advancements in communication, sensing, and computing has enabled the development of connected vehicle systems where improved decision and control strategies are enabled with the aid of information exchange within the vehicular system. In this paper, we consider a connected vehicle system and develop fuel economic control strategies for a group of vehicles in congested urban road conditions. We exploit the Signal Phase and Timing (SPAT) information from the traffic lights and utilize model predictive control with a modified cost to reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance for our proposed method.

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Personalised assistance for fuel-efficient driving

Cited by 67 publications

References 30 publications

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

A survey of evaluation methods for personal route and destination prediction from mobility traces

A fuel economic model predictive control strategy for a group of connected vehicles in urban roads

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