Abstract
This paper addresses the search for a run-based dynamic optimal travel strategy, to be supplied through mobile devices (apps) to travelers on a stochastic multiservice transit network, which includes a system forecasting of bus travel times and bus arrival times at stops. The run-based optimal strategy is obtained as a heuristic solution to a Markovian decision problem. The hallmarks of this paper are the proposals to use only traveler state spaces and estimates of dispersion of forecast bus arrival times at stops in order to determine transition probabilities. The first part of the paper analyses some existing line-based and run-based optimal strategy search methods. In the second part, some aspects of dynamic transition probability computation in intelligent transit systems are presented, and a new method for dynamic run-based optimal strategy search is proposed and applied.
Highlights
Dynamic Optimal Travel Strategies inThe search for the best path on stochastic multiservice transit networks someonly path is classified stochastic multiservice network (SMSTN) is no simple task compared with the case of regular service networks
A heuristic search method was proposed in the context of a run-based optimal stra search for stochastic multiservice transit networks
The method determines the opt strategy as a solution of a Markovian Decision Problem and, in order to reduce the co of dimensionality, explicitly considered only the traveler states
Summary
The search for the best path on stochastic multiservice transit networks SMSTN (defined below) is no simple task compared with the case of regular service networks. This paper intends to contribute to solving this problem in the case of intelligent transit systems (ITS), with automated vehicle location (AVL) and at-stop bus arrival time forecasting, where apps (trip planners) are available on mobile devices, advising travelers of the best path to their destination [1,2,3,4]. The need to equip users/travelers with mobile transit route planners that provide personal information [14,15] on origin–destination routes rather than to real-time information at stops or pre-. It envisions the creation of a better transportation experience.
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