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78
Adaptive leastexpected time paths in stochastic, timevarying transportation and data networks
 Networks
"... In congested transportation and data networks, travel (or transmission) times are timevarying quantities that are at best known a priori with uncertainty. In such stochastic, timevarying (or STV) networks, one can choose to use the a priori leastexpected time (LET) path or one can make improved r ..."
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Cited by 59 (1 self)
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In congested transportation and data networks, travel (or transmission) times are timevarying quantities that are at best known a priori with uncertainty. In such stochastic, timevarying (or STV) networks, one can choose to use the a priori leastexpected time (LET) path or one can make improved routing decisions en route as traversal times on traveled arcs are experienced and arrival times at intermediate locations are revealed. In this context, for a given origin–destination pair at a specific departure time, a single path may not provide an adequate solution, because the optimal path depends on intermediate information concerning experienced traversal times on traveled arcs. Thus, a set of strategies, referred to as hyperpaths, are generated to provide directions to the destination node conditioned upon arrival times at intermediate locations. In this paper, an efficient labelsettingbased algorithm is presented for determining the adaptive LET hyperpaths in STV networks. Such a procedure is useful in making critical routing decisions in Intelligent Transportation Systems (ITS) and data communication networks. A sidebyside comparison of this procedure with a labelcorrectingbased algorithm for solving the same problem is made. Results of extensive computational tests to assess and compare the performance of both algorithms, as well as to investigate the characteristics of the resulting hyperpaths, are presented. An illustrative example of both procedures is provided. © 2001 John Wiley & Sons, Inc.
A Simple and Effective Method for Predicting Travel Times on Freeways
"... We present a method to predict the time that will be needed to traverse a certain stretch of freeway when departure is at a certain time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that, ..."
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Cited by 54 (2 self)
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We present a method to predict the time that will be needed to traverse a certain stretch of freeway when departure is at a certain time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that,
Finding fastest paths on a road network with speed patterns
 In Proc. Int. Conf. on Data Engineering (ICDE’06
, 2006
"... This paper proposes and solves the TimeInterval All Fastest Path (allFP) query. Given a userdefined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each subinterval of I. Note that the query algorithm should fin ..."
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Cited by 45 (0 self)
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This paper proposes and solves the TimeInterval All Fastest Path (allFP) query. Given a userdefined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each subinterval of I. Note that the query algorithm should find a partitioning of I into subintervals. Existing methods can only be used to solve a very special case of the problem, when the leaving time is a single time instant. A straightforward solution to the allFP query is to run existing methods many times, once for every time instant in I. This paper proposes a solution based on novel extensions to the A * algorithm. Instead of expanding the network many times, we expand once. The travel time on a path is kept as a function of leaving time. Methods to combine traveltime functions are provided to expand a path. A novel lowerbound estimator for travel time is proposed. Performance results reveal that our method is more efficient and more accurate than the discretetime approach. 1
A fictitious play approach to largescale optimization.
 Operations Research,
, 2005
"... In this paper we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max{u(y) : y = (y ..."
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Cited by 37 (7 self)
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In this paper we investigate the properties of the sampled version of the fictitious play algorithm, familiar from game theory, for games with identical payoffs, and propose a heuristic based on fictitious play as a solution procedure for discrete optimization problems of the form max{u(y) : y = (y 1 , . . . , y n ) ∈ Y 1 × · · · × Y n }, i.e., in which the feasible region is a Cartesian product of finite sets The contributions of this paper are twofold. In the first part of the paper we broaden the existing results on convergence properties of the fictitious play algorithm on games with identical payoffs to include an approximate fictitious play algorithm which allows for errors in players' best replies. Moreover, we introduce samplingbased approximate fictitious play which possesses the above convergence properties, and at the same time provides a computationally efficient method for implementing fictitious play. In the second part of the paper we motivate the use of algorithms based on sampled fictitious play to solve optimization problems in the above form with particular focus on the problems in which the objective function u(·) comes from a "black box," such as a simulation model, where significant computational effort is required for each function evaluation.
Parallel implementation of the TRANSIMS microsimulation
, 2001
"... This paper describes the parallel implementation of the TRANSIMS traffic microsimulation. ..."
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Cited by 36 (10 self)
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This paper describes the parallel implementation of the TRANSIMS traffic microsimulation.
Finding timedependent shortestpaths over large graphs,” in
 Proc. 11th EDBT,
, 2008
"... ABSTRACT The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamical ..."
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Cited by 36 (1 self)
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ABSTRACT The spatial and temporal databases have been studied widely and intensively over years. In this paper, we study how to answer queries of finding the best departure time that minimizes the total travel time from a place to another, over a road network, where the traffic conditions dynamically change from time to time. We study a generalized form of this problem, called the timedependent shortestpath problem. A timedependent graph GT is a graph that has an edgedelay function, wi,j(t), associated with each edge (vi, v j ), to be stored in a database. The edgedelay function w i,j (t) specifies how much time it takes to travel from node v i to node v j , if it departs from v i at time t. A userspecified query is to ask the minimumtraveltime path, from a source node, vs, to a destination node, ve, over the timedependent graph, GT , with the best departure time to be selected from a time interval T . We denote this user query as LTT(v s , v e , T ) over G T . The challenge of this problem is the added complexity due to the time dependency in the timedependent graph. That is, edge delays are not constants, and can vary from time to time. In this paper, we propose a novel algorithm to find the minimumtraveltime path with the best departure time for a LTT(v s , v e , T ) query over a large graph G T . Our approach outperforms existing algorithms in terms of both time complexity in theory and efficiency in practice. We will discuss the design of our algorithm, together with its correctness and complexity. We conducted extensive experimental studies over large graphs and will report our findings.
Spatiotemporal Network Databases and Routing Algorithms: A Summary of Results
 Proceedings of International Symposium on Spatial and Temporal Databases (SSTD’07
, 2007
"... Transportation. The content of this work does not necessarily reflect the position or policy of the government and no official ..."
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Cited by 32 (11 self)
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Transportation. The content of this work does not necessarily reflect the position or policy of the government and no official
ContinuousTime Dynamic Shortest Path Algorithms
, 1999
"... We consider the problem of computing shortest paths through a dynamic network – a network with timevarying characteristics, such as arc travel times and costs, which are known for all values of time. Many types of networks, most notably transportation networks, exhibit such predictable dynamic beha ..."
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Cited by 26 (1 self)
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We consider the problem of computing shortest paths through a dynamic network – a network with timevarying characteristics, such as arc travel times and costs, which are known for all values of time. Many types of networks, most notably transportation networks, exhibit such predictable dynamic behavior over the course of time. Dynamic shortest path problems are currently solved in practice by algorithms which operate within a discretetime framework. In this thesis, we introduce a new set of algorithms for computing shortest paths in continuoustime dynamic networks, and demonstrate for the first time in the literature the feasibility and the advantages of solving dynamic shortest path problems in continuous time. We assume that all timedependent network data functions are given as piecewise linear functions of time, a representation capable of easily modeling most common dynamic problems. Additionally, this form of
Dynamic Shortest Paths Minimizing Travel Times and Costs
 Networks
, 2001
"... In this paper, we study dynamic shortest path problems, which is to determine a shortest path from a specified source node to every other node in the network where arc travel times change dynamically. We consider two problems: the minimum time walk problem (which is to find a walk with the minimum t ..."
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Cited by 23 (0 self)
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In this paper, we study dynamic shortest path problems, which is to determine a shortest path from a specified source node to every other node in the network where arc travel times change dynamically. We consider two problems: the minimum time walk problem (which is to find a walk with the minimum travel time) and the minimum cost walk problem (which is to find a walk with the minimum travel cost). The minimum time walk problem is known to be polynomially solvable for a class of networks called FIFO networks. This paper makes the following contributions: (i) we show that the minimum cost walk problem is an NPcomplete problem; (ii) we develop a pseudopolynomialtime algorithm to solve the minimum cost walk problem (for integer travel times); and (iii) we develop a polynomialtime algorithm for the minimum time walk problem arising in road networks with traffic lights.
LargeScale Traffic Simulations for Transportation Planning
, 2002
"... An agentbased approach to simulation for transportation planning applications offers a lot of conceptual flexibility. Many millions of agents plus many hundreds of thousands of elements of transportation infrastructure need to be represented. For transportation planning applications, the demand ..."
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Cited by 22 (1 self)
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An agentbased approach to simulation for transportation planning applications offers a lot of conceptual flexibility. Many millions of agents plus many hundreds of thousands of elements of transportation infrastructure need to be represented. For transportation planning applications, the demand needs to be sensitive to changes in supply, which implies that besides the realistic representation of the transportation system it is also necessaxy to represent people's decisionmaking process leading to the demand.