MITC: An Intention-Based Model for Cooperative Resolution of Traffic Conflicts

Castaneda, Alejandro Triana; Guerrero, Enrique González

doi:10.15439/2014f491

Cited by 1 publication

(1 citation statement)

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“…of the vehicles can be specifically and cooperatively explored in the context of multiagent, so as to efficiently respond to traffic dynamics and determine optimal route guidance for all relevant vehicle agents [44]. Here is a simple scenario to justify the importance of intention exploration in multiagent-based approaches.…”

Section: Ssp Problem For Multiple Cooperative Vehiclesmentioning

confidence: 99%

Maximizing the probability of arriving on time : a stochastic shortest path problem

Cao¹

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Traffic and transportation are crucial to the sustainable development of most metropolitan cities, where the stochastic shortest path (SSP) problem for vehicle routing is a challenging topic. For this problem, optimization-based methods and multiagent-based methods are usually adopted to compute the optimal path for a single vehicle and multiple cooperative vehicles, respectively. Most of these methods take into account various uncertainties in traffic and yields route recommendations dynamically, leading to a better solution to the development of sustainable transportation systems. At the same time, various criteria for the optimal paths have been proposed for the SSP problem. Among them, the probability tail (PT) model aims to find a path that maximizes the probability of arriving on time. It is promising in that, it integrates travel time, risk (associated with the variance in the probability) and deadline. Therefore, this thesis focuses on solving this arriving on time problem, for a single vehicle and multiple cooperative vehicles. Regarding the former, an optimal path for a single vehicle is independently computed using the travel time data of the road links. Regarding the latter, the optimal paths for multiple vehicles are cooperatively computed by exploring their intentions.To circumvent the unrealistic assumptions in the current solutions to the arriving on time problem for a single vehicle, such as Gaussian distribution of travel time, independence among travel time on different road links, and relatively large deadlines, a data-driven approach is developed. More specifically, the PT model based SSP problem is first reformulated as a cardinality minimization problem by directly utilizing travel time data on each road link. Then, this minimization problem is approximately solved via relaxing the cardinality by 1 -norm and its variants, and further formulating it as a mixed integer linear programming (MILP) problem.Consequently, this arriving on time problem becomes solvable via an existing MILP solver, e.g., branch and bound (B&B) method.To improve the computation efficiency of the data-driven approach, the property of total unimodularity (TU) that exists in the equality constraint of the MILP-based arriving on time problem is further explored. This nice property can guarantee integer solutions by solving the i Abstract corresponding linear programming (LP) problem if there is no inequality constraint. In view of this, the partial Lagrange multiplier method is employed to relax the undesired inequality constraints in the MILP problem by shifting them to the objective function. After that, this relaxed problem is solved using the subgradient method in an iterative manner, and only LP problems need to be solved in each iteration, which saves computation cost significantly.To increase the accuracy of finding the real optimal path (i.e., considering that 1 -norm relaxation is an approximation to the cardinality minimization), a practical Q-learning method is developed. In particular, this Q-learning method a...

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Section: Ssp Problem For Multiple Cooperative Vehiclesmentioning

confidence: 99%

Maximizing the probability of arriving on time : a stochastic shortest path problem

Cao¹

View full text Add to dashboard Cite

show abstract

MITC: An Intention-Based Model for Cooperative Resolution of Traffic Conflicts

Cited by 1 publication

References 12 publications

Maximizing the probability of arriving on time : a stochastic shortest path problem

Maximizing the probability of arriving on time : a stochastic shortest path problem

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