Abstract
Traffic Assignment Problem (TAP) is a critical issue for transportation and mobility models that deals mainly with the calculus and delivery of best-cost routes for the trips in a traffic network. It is a computationally complex problem focused on finding user equilibrium (UE) and system optimum (SO). The Traffic Weighted Multi-Maps (TWM) technique offers a new perspective for TAP calculus, based on routing decisions using different traffic network views. These TWM are complementary cost maps that combine physical traffic networks, traffic occupation data, and routing policies. This paper shows how evolutionary algorithms can find optimal cost maps that solve TAP from the SO perspective, minimizing total travel time and providing the best-cost routes to vehicles. Several strategies are compared: a baseline algorithm that optimizes the whole network and two algorithms based on extended k-shortest path mappings. Algorithms are analyzed following a simulation-optimization methodology over synthetic and real traffic networks. Obtained results show that TWM algorithms generate solutions close to the static UE traffic assignment methods at a reasonable computational cost. A crucial aspect of TWM is its good performance in terms of optimal routing at the system level, avoiding the need for continuous route calculus based on traffic status data streaming.
Highlights
Traffic Assignment is a key concern of urban mobility systems, where transportation demand formed by origin/destination vehicle trips, needs to be allocated to the traffic network
EVALUATION The main objective of our simulation-based evaluation is to compare the performance of the different optimization strategies for Traffic Assignment Problem (TAP) related to other classical (CAM, MSA, WEI-LP and random):
For the experimental process over a given urban traffic network, traffic demands are loaded to the Traffic Weighted Multi-Maps (TWM) generation software that produces several TWM optimized configurations
Summary
Traffic Assignment is a key concern of urban mobility systems, where transportation demand formed by origin/destination vehicle trips, needs to be allocated to the traffic network. Our work relates to these previous studies in the floworiented and GA approaches used to solve the optimization process for SO, though it presents important differences: a) it decouples optimal map generation and distribution from the route calculation problem, enabling feasible implementations over existing systems and methods; b) flow-paths are defined as an abstraction of the KSP based routing enabling optimization strategies; c) there is not a central routing-server assumption that computes and distributes routes: they can be managed in a centralized or distributed way; d) proposed GA algorithms are simpler as they do not use a per-vehicle optimization but a per flow-path basis: only traffic demand matrices are required instead of knowing individual trips in advance. The k-shortest paths of an O/D pair is formed by the set of its k minimum cost routes [18], where the cost function generally depend on: a) network topology; b) traffic policies (for instance, speed limits); c) network usage and status (congestion, density, blocking); d) the multi-objectives considered; and e) driver’s subjective perception
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