28,658 research outputs found

    Route Planning in Transportation Networks

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    We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

    Stochastic on-time arrival problem in transit networks

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    This article considers the stochastic on-time arrival problem in transit networks where both the travel time and the waiting time for transit services are stochastic. A specific challenge of this problem is the combinatorial solution space due to the unknown ordering of transit line arrivals. We propose a network structure appropriate to the online decision-making of a passenger, including boarding, waiting and transferring. In this framework, we design a dynamic programming algorithm that is pseudo-polynomial in the number of transit stations and travel time budget, and exponential in the number of transit lines at a station, which is a small number in practice. To reduce the search space, we propose a definition of transit line dominance, and techniques to identify dominance, which decrease the computation time by up to 90% in numerical experiments. Extensive numerical experiments are conducted on both a synthetic network and the Chicago transit network.Comment: 29 pages; 12 figures. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0

    Timely Data Delivery in a Realistic Bus Network

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    Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I

    Data-driven optimization of bus schedules under uncertainties

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    Plusieurs sous-problĂšmes d’optimisation se posent lors de la planification des transports publics. Le problĂšme d’itinĂ©raires de vĂ©hicule (PIV) est l’un d’entre eux et consiste Ă  minimiser les coĂ»ts opĂ©rationnels tout en assignant exactement un autobus par trajet planifiĂ© de sorte que le nombre d’autobus entreposĂ© par dĂ©pĂŽt ne dĂ©passe pas la capacitĂ© maximale disponible. Bien que les transports publics soient sujets Ă  plusieurs sources d’incertitude (Ă  la fois endogĂšnes et exogĂšnes) pouvant engendrer des variations des temps de trajet et de la consommation d’énergie, le PIV et ses variantes sont la plupart du temps rĂ©solus de façon dĂ©terministe pour des raisons de rĂ©solubilitĂ©. Toutefois, cette hypothĂšse peut compromettre le respect de l’horaire Ă©tabli lorsque les temps des trajets considĂ©rĂ©s sont fixes (c.-Ă -d. dĂ©terministes) et peut produire des solutions impliquant des politiques de gestion des batteries inadĂ©quates lorsque la consommation d’énergie est aussi considĂ©rĂ©e comme fixe. Dans cette thĂšse, nous proposons une mĂ©thodologie pour mesurer la fiabilitĂ© (ou le respect de l’horaire Ă©tabli) d’un service de transport public ainsi que des modĂšles mathĂ©matiques stochastiques et orientĂ©s donnĂ©es et des algorithmes de branch-and-price pour deux variantes de ce problĂšme, Ă  savoir le problĂšme d’itinĂ©raires de vĂ©hicule avec dĂ©pĂŽts multiples (PIVDM) et le problĂšme d’itinĂ©raires de vĂ©hicule Ă©lectrique (PIV-E). Afin d’évaluer la fiabilitĂ©, c.-Ă -d. la tolĂ©rance aux dĂ©lais, de certains itinĂ©raires de vĂ©hicule, nous prĂ©disons d’abord la distribution des temps de trajet des autobus. Pour ce faire, nous comparons plusieurs modĂšles probabilistes selon leur capacitĂ© Ă  prĂ©dire correctement la fonction de densitĂ© des temps de trajet des autobus sur le long terme. Ensuite, nous estimons Ă  l'aide d'une simulation de Monte-Carlo la fiabilitĂ© des horaires d’autobus en gĂ©nĂ©rant des temps de trajet alĂ©atoires Ă  chaque itĂ©ration. Nous intĂ©grons alors le modĂšle probabiliste le plus appropriĂ©, celui qui est capable de prĂ©dire avec prĂ©cision Ă  la fois la vĂ©ritable fonction de densitĂ© conditionnelle des temps de trajet et les retards secondaires espĂ©rĂ©s, dans nos modĂšles d'optimisation basĂ©s sur les donnĂ©es. DeuxiĂšmement, nous introduisons un modĂšle pour PIVDM fiable avec des temps de trajet stochastiques. Ce problĂšme d’optimisation bi-objectif vise Ă  minimiser les coĂ»ts opĂ©rationnels et les pĂ©nalitĂ©s associĂ©es aux retards. Un algorithme heuristique basĂ© sur la gĂ©nĂ©ration de colonnes avec des sous-problĂšmes stochastiques est proposĂ© pour rĂ©soudre ce problĂšme. Cet algorithme calcule de maniĂšre dynamique les retards secondaires espĂ©rĂ©s Ă  mesure que de nouvelles colonnes sont gĂ©nĂ©rĂ©es. TroisiĂšmement, nous proposons un nouveau programme stochastique Ă  deux Ă©tapes avec recours pour le PIVDM Ă©lectrique avec des temps de trajet et des consommations d’énergie stochastiques. La politique de recours est conçue pour rĂ©tablir la faisabilitĂ© Ă©nergĂ©tique lorsque les itinĂ©raires de vĂ©hicule produits a priori se rĂ©vĂšlent non rĂ©alisables. Toutefois, cette flexibilitĂ© vient au prix de potentiels retards induits. Une adaptation d’un algorithme de branch-and-price est dĂ©veloppĂ© pour Ă©valuer la pertinence de cette approche pour deux types d'autobus Ă©lectriques Ă  batterie disponibles sur le marchĂ©. Enfin, nous prĂ©sentons un premier modĂšle stochastique pour le PIV-E avec dĂ©gradation de la batterie. Le modĂšle sous contrainte en probabilitĂ© proposĂ© tient compte de l’incertitude de la consommation d’énergie, permettant ainsi un contrĂŽle efficace de la dĂ©gradation de la batterie grĂące au contrĂŽle effectif de l’état de charge (EdC) moyen et l’écart de EdC. Ce modĂšle, combinĂ© Ă  l’algorithme de branch-and-price, sert d’outil pour balancer les coĂ»ts opĂ©rationnels et la dĂ©gradation de la batterie.The vehicle scheduling problem (VSP) is one of the sub-problems of public transport planning. It aims to minimize operational costs while assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Even thought public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel time and energy consumption, the VSP and its variants are usually solved deterministically to address tractability issues. However, considering deterministic travel time in the VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may result in solutions with inadequate battery management. In this thesis, we propose a methodology for measuring the reliability (or schedule adherence) of public transport, along with stochastic and data-driven mathematical models and branch-and-price algorithms for two variations of this problem, namely the multi-depot vehicle scheduling problem (MDVSP) and the E-VSP. To assess the reliability of vehicle schedules in terms of their tolerance to delays, we first predict the distribution of bus travel times. We compare numerous probabilistic models for the long-term prediction of bus travel time density. Using a Monte Carlo simulation, we then estimate the reliability of bus schedules by generating random travel times at each iteration. Subsequently, we integrate the most suitable probabilistic model, capable of accurately predicting both the true conditional density function of the travel time and the expected secondary delays, into the data-driven optimization models. Second, we introduce a model for the reliable MDVSP with stochastic travel time minimizing both the operational costs and penalties associated with delays. To effectively tackle this problem, we propose a heuristic column generation-based algorithm, which incorporates stochastic pricing problems. This algorithm dynamically computes the expected secondary delays as new columns are generated. Third, we propose a new two-stage stochastic program with recourse for the electric MDVSP with stochastic travel time and energy consumption. The recourse policy aims to restore energy feasibility when a priori vehicle schedules are unfeasible, which may lead to delays. An adapted algorithm based on column generation is developed to assess the relevance of this approach for two types of commercially available battery electric buses. Finally, we present the first stochastic model for the E-VSP with battery degradation. The proposed chance-constraint model incorporates energy consumption uncertainty, allowing for effective control of battery degradation by regulating the average state-of-charge (SOC) and SoC deviation in each discharging and charging cycle. This model, in combination with a tailored branch-and-price algorithm, serves as a tool to strike a balance between operational costs and battery degradation

    Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks

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    We study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.Comment: Submitted to IEEE Transactions on Control of Network Systems on June 1st 201

    Smart Cities and M<sup>3</sup>: Rapid Research, Meaningful Metrics and Co-Design

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    The research described in this paper is undertaken under the banner of the smart city, a concept that captures the way urban spaces are re-made by the incursion of new technology. Much of smart is centred on converting everyday activities into data, and using this data to generate knowledge mediated by technology. Ordinary citizens, those that may have their lives impacted by the technology, usually are not properly involved in the ‘smartification’ process. Their perceptions, concerns and expectations should inform the conception and development of smart technologies at the same extent. How to engage general public with smart cities research is the central challenge for the Making Metrics Meaningful (MMM) project. Applying a rapid participatory method, ‘Imagine’ over a five-month period (March – July) the research sought to gain insights from the general public into novel forms of information system innovation. This brief paper describes the nature of the accelerated research undertaken and explores some of the themes which emerged in the analysis. Generic themes, beyond the remit of an explicit transport focus, are developed and pointers towards further research directions are discussed. Participatory methods, including engaging with self- selected transport users actively through both picture creation and programmatically specific musical ‘signatures’ as well as group discussion, were found to be effective in eliciting users’ own concerns, needs and ideas for novel information systems
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