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Facing various uncertainties in real world traffic, navigation services are typically formulated as a certain stochastic shortest path problem (SSPP). In the past several years, many stochastic objectives in SSPP has been proposed, among which maximizing the probability of arrival on time draws certain attentions from researchers as it takes, besides absolute travel time, an extra dimension of information...
Transit signal priority (TSP) strategy gives transit vehicles preferential treatments to move through an intersectionwith minimum delay. To produce a good TSP timing, advance planning with enough look-ahead time is the key. This,however, means added uncertainty about bus arrival time at stop bar. In this paper, we proposed a stochasticmixed-integer nonlinear program (SMINP) model as the core component...
In the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly derived by means of an optimization framework, which tries to maximize the fit between real and simulated...
Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain...
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