Probabilistic spatio-temporal resource search

Guo, Qing; Wolfson, Ouri

doi:10.1007/s10707-016-0275-9

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Sweda et al (2017) propose adaptive charging and routing strategies when utilizing the public charging infrastructure, whereas Jafari & Boyles (2017) additionally model both stochastic travel time and charging consumption. Alternatively, a few papers deal with stochastic resource search problems in general settings (Guo & Wolfson 2018, Schmoll & Schubert 2018 or more specific settings, e.g., on-street parking spots (Arndt et al 2016) or stochastic taxi customer demand (Tang et al 2013). Guillet et al (2022) are the first to cover multiple variants of the stochastic charging station search problem for EVs, considering charging or waiting times at stations.…”

Section: Related Literaturementioning

confidence: 99%

Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach

Guillet¹,

Schiffer²

2022

Preprint

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Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize a driver's detour until reaching an available charging station. So far, both practical and theoretical approaches ignore driver coordination enabled by charging requests centralization or sharing of data, e.g., sharing observations of charging stations' availability or visit intentions between drivers.Against this background, we study coordinated stochastic search algorithms, which help to reduce station visit conflicts and improve the drivers' charging experience. We model a multi-agent stochastic charging station search problem as a finite-horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies. In contrast to static policies, dynamic policies account for information updates during policy planning and execution. We present a hierarchical implementation of a single-agent heuristic for decentralized decision making and a rollout algorithm for centralized decision making. Extensive numerical studies show that compared to an uncoordinated setting, a decentralized setting with visit-intentions sharing decreases the system cost by 26%, which is nearly as good as the 28% cost decrease achieved in a centralized setting, and saves up to 23% of a driver's search time while increasing her search reliability.

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Section: Related Literaturementioning

confidence: 99%

Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach

Guillet¹,

Schiffer²

2022

Preprint

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“…Besides showing the problem's NP-completeness, they proposed a brand-and-bound algorithm for small problem spaces. More generally, Guo & Wolfson (2018) studied a probabilistic spatio-temporal resource search problem, in which resources have a general usage cost but the resource seeker is not allowed to wait for an occupied resource to become available again. Contrary to Arndt et al (2016) in which resources observations are persistent during the search, stations can become available again after a defined time threshold.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Contrary to Arndt et al (2016) in which resources observations are persistent during the search, stations can become available again after a defined time threshold. Guo & Wolfson (2018) proposed a value iteration solution procedure that remains tractable by making a fast recovery assumption at the instance level, which keeps the state space small. Schmoll & Schubert (2018) studied a dynamic resource routing problem under reliable real-time information, which requires fast (re)computations as resources frequently change their occupancy state.…”

Section: Literature Reviewmentioning

confidence: 99%

Electric Vehicle Charging Station Search in Stochastic Environments

Guillet¹,

Hiermann²,

Kröller³

et al. 2020

Preprint

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Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions and CO 2 emissions, if fed by clean energy sources. However, the adoption of electric vehicles so far fell short of expectations despite significant governmental incentives. One reason for this slow adoption is the drivers' perceived range anxiety, especially for individually owned vehicles. Here, bad user-experiences, e.g., conventional cars blocking charging stations or inconsistent real-time availability data, manifest the drivers' range anxiety. Against this background, we study stochastic search algorithms, that can be readily deployed in today's navigation systems in order to minimize detours to reach an available charging station. We model such a search as a finite horizon Markov decision process and present a comprehensive framework that considers different problem variants, speed-up techniques, and three solution algorithms: an exact labeling algorithm, a heuristic labeling algorithm, and a rollout algorithm. Extensive numerical studies show that our algorithms significantly decrease the expected time to find a free charging station while increasing the solution quality robustness and the likelihood that a search is successful compared to myopic approaches.

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“…The existing work on taxi dispatch can be divided into two categories, order matching [4], [7], [8], [11], [23], [34], [36], [44], [51], [52] and route recommendation [10], [13], [15], [19], [28]- [30], [33], [35], [47]. Order matching aims to match idle taxis with passengers to maximize global revenue.…”

Section: B Taxi Dispatchmentioning

confidence: 99%

SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply

Zheng

Ming

et al. 2020

Preprint

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We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) algorithm that predicts the service requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supplydemand state. We report on extensive experiments with realworld and synthetic data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

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Probabilistic spatio-temporal resource search

Cited by 12 publications

References 14 publications

Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach

Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach

Electric Vehicle Charging Station Search in Stochastic Environments

SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply

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