Computing shortest paths is one of the most researched topics in algorithm engineering. Currently available algorithms compute shortest paths in mere fractions of a second on continental sized road networks. In the presence of unreliability, however, current algorithms fail to achieve results as impressive as for the static setting. In contrast to speed-up techniques for static route planning, current implementations for the stochastic on-time arrival problem require the computationally expensive step of solving convolution products. Running times can reach hours when considering large scale networks. We present a novel approach to reduce this immense computational effort of stochastic routing based on existing techniques for alternative routes. In an extensive experimental study, we show that the process of stochastic route planning can be speed-up immensely, without sacrificing much in terms of accuracy.
One approach to unreliable travel times is to take into account the inherent variability in the travel time distributions and compute robust routing strategies. A commonly used method in this setting is to maximize the probability of reaching the destination within a prespecified time budget. There are two different approaches that have been developed to solve this problem. The first approach is to find the optimal path that maximizes the probability of on-time arrival and is known as the shortest path with on-time arrival reliability (SPOTAR) problem. Nie and Wu @cite_5 proposed a general solution to the problem based on first-order stochastic dominance of paths, but the solution has worst-case exponential running times. They also present a pseudo-polynomial algorithm that gives an approximate solution to the problem and performs well in practice. @cite_7 showed how to solve the problem in @math time when the arc travel time distributions are Gaussian.
@cite_5 @cite_7
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This paper studies the problem of finding a priori shortest paths to guarantee a given likelihood of arriving on-time in a stochastic network. Such "reliable" paths help travelers better plan their trips to prepare for the risk of running late in the face of stochastic travel times. Optimal solutions to the problem can be obtained from local-reliable paths, which are a set of non-dominated paths under first-order stochastic dominance. We show that Bellman's principle of optimality can be applied to construct local-reliable paths. Acyclicity of local-reliable paths is established and used for proving finite convergence of solution procedures. The connection between the a priori path problem and the corresponding adaptive routing problem is also revealed. A label-correcting algorithm is proposed and its complexity is analyzed. A pseudo-polynomial approximation is proposed based on extreme-dominance. An extension that allows travel time distribution functions to vary over time is also discussed. We show that the time-dependent problem is decomposable with respect to arrival times and therefore can be solved as easily as its static counterpart. Numerical results are provided using typical transportation networks. We consider the problem of computing shortest paths through a dynamic network  a network with time-varying 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 discrete-time framework. In this thesis, we introduce a new set of algorithms for computing shortest paths in continuous-time 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 time-dependent network data functions are given as piece-wise linear functions of time, a representation capable of easily modeling most common dynamic problems. Additionally, this form of representation and the solution algorithms developed in this thesis are well suited for many augmented static problems such as time-constrained minimum-cost shortest path problems and shortest path problems with time windows. We discuss the classification, formulation, and mathematical properties of all common variants of the continuous-time dynamic shortest path problem. Two classes of solution algorithms are introduced, both of which are shown to solve all variants of the problem. In problems where arc travel time functions exhibit First-In-First-Out (FIFO) behavior, we show that these algorithms have polynomial running time; although the general problem is NP-hard, we argue that the average-case running time for many common problems should be quite reasonable. Computa