Optimal routing policy problems in stochastic time-dependent networks

Gao, Song; Chabini, Ismaïl

doi:10.1016/j.trb.2005.02.001

Cited by 194 publications

(93 citation statements)

References 19 publications

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“…Specifically, this paper builds on the state-of-the-art stochastic time-dependent network model introduced by Gao and Chabini [11], [12]. Similar to our work, they model the routing problem on a road network with vertices and edges where travel times over the edges are stochastic, and where their distributions depend on the time of day.…”

Section: Related Workmentioning

confidence: 99%

Intention-Aware Routing of Electric Vehicles

Weerdt

Stein

Gerding

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin-destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.

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Section: Related Workmentioning

confidence: 99%

Intention-Aware Routing of Electric Vehicles

Weerdt

Stein

Gerding

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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“…At the same time, the real situation shows that the travel time is continuously changing and depends on many factors such as time of day, weather conditions, traffic situation, etc. In models with stochastic travel time [4,5,6], the time is represented by a random variable with a time-dependent distribution function. In either case, the optimality condition for routing can be determined in different ways depending on the used objective function.…”

Section: Introductionmentioning

confidence: 99%

“…The following types of objective functions can be used: 1) minimization of the least expected travel time [4 -7]; 2) maximization of the probability of arriving at the destination at a predetermined time interval [8,9]; 3) maximization of the probability that travel time is less than a given threshold [10,11]; 4) minimization of the worst possible travel time [12]; 5) minimization of the travel time to guarantee a given likelihood of arriving on-time in a stochastic network [13]. These types of objective functions can be divided into two groups: the least expected time (LET) (1) and the reliable shortest path (RSP) problem (2)(3)(4)(5). The LET problem has been well studied, there are many effective algorithms for different types of the problem [6,7].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

Abdulganiev¹,

Agafonov²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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In this paper, we propose a modification of the reliable routing algorithm in a stochastic time-dependent network. We consider a stochastic ontime arrival problem. Reliability means maximization the probability of arrival at a destination within a given period of time. Modification of the shortest-path algorithm is aimed to decrease the computation time of the algorithm. The base idea of the proposed modification is to select a certain subset of nodes and links of the graph which can be used for calculating the shortest path. We propose two methods for selecting subset of nodes: based on a bounding box and based on the k-shortest path algorithm. Experimental studies of the base and modified algorithms are carried out on the transport network of Samara, Russia.

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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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Optimal routing policy problems in stochastic time-dependent networks

Cited by 194 publications

References 19 publications

Intention-Aware Routing of Electric Vehicles

Intention-Aware Routing of Electric Vehicles

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

Penalty-Based Algorithms for the Stochastic Obstacle Scene Problem

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