Adaptive routing for road traffic

Fawcett, John K.; Robinson, P.

doi:10.1109/38.844372

Cited by 58 publications

(37 citation statements)

References 6 publications

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“…Whereas an exhaustive search would find the guaranteed shortest path, the time required to calculate this route would increase dramatically for each increment in the size of the underlying transport data [13]. A secondary benefit of this search algorithm is that the addition of any new modes of transport to the iTransIT framework is not expected to effect the performance, or the computational cost incurred by the STIS platform.…”

Section: Generating Journey Routesmentioning

confidence: 99%

STIS: Smart Travel Planning Across Multiple Modes of Transportation

Brennan

Meier

2007

2007 IEEE Intelligent Transportation Systems Conference

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Abstract-Travellers require information on individual transport systems when planning a journey. Many transportrich urban environments contain numerous underlying transport infrastructures, offering a traveller various ways to complete the journey. This paper presents the Smart Traveller Information Service, a system designed to offer travellers an easy to use and efficient means of planning journeys in an otherwise complex multi-modal transport environment. The Smart Traveller Information Service bridges the coordination gap between the available transport systems (both public and private), and hides the complexity of the travel planning process from the user. This allows travellers to construct detailed journey plans without concerning themselves with the often heterogeneous and disjoint nature of the available transport facilities.

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Section: Generating Journey Routesmentioning

confidence: 99%

STIS: Smart Travel Planning Across Multiple Modes of Transportation

Brennan

Meier

2007

2007 IEEE Intelligent Transportation Systems Conference

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“…In CTP, the goal is to find the minimum expected length path over a finite graph whose edges are marked with their respective probabilities of being traversable and each edge's status can be discovered dynamically when encountered. SOSP and CTP have practical applications in important probabilistic path-planning environments such as robot navigation in stochastic domains (Blei and Kaelbling 1999, Ferguson et al 2004, Likhachev et al 2005, minefield countermeasures (Smith 1995, Witherspoon et al 1995, and adaptive traffic routing (Fawcett andRobinson 2000, Gao andChabini 2006). In fact, both problems as well as closely related ones have gained considerable attention recentlysee, e.g., Nikolova and Karger (2008), Eyerich et al (2009), Likhachev and Stentz (2009), Xu et al (2009), Aksakalli and Ceyhan (2012).…”

Section: Introductionmentioning

confidence: 99%

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

Aksakallı¹,

Arı²

2014

INFORMS Journal on Computing

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W e consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a generalized framework encompassing these algorithms that uses penalty functions to guide the navigation in real time. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set.Data, as supplemental material, are available at http://dx

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“…[2]) or the least congested route (e.g. [3]) is chosen, however, some studies (e.g. [4]) showed that the road geometry and the condition of the road infrastructure can significantly affect fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning for fuel consumption modelling of trucks

Perrotta

Parry

Neves

2017

2017 IEEE International Conference on Big Data (Big Data)

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-This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R2, and lower error.

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Adaptive routing for road traffic

Abstract: An integrated system uses road congestion information to guide routing. GPS tracks vehicles, and the GSM short message service maintains communications with a central planning service.

Cited by 58 publications

References 6 publications

STIS: Smart Travel Planning Across Multiple Modes of Transportation

STIS: Smart Travel Planning Across Multiple Modes of Transportation

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

Application of machine learning for fuel consumption modelling of trucks

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