Application of traffic weighted multi-map optimization strategies to traffic assignment
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60582DOI: 10.1109/ACCESS.2021.3058508
ISSN: 2169-3536
Publisher
IEEE
Date
2021-01-06Bibliographic citation
Paricio García, A. & López Carmona, M.A. 2021, "Application of traffic weighted multi-map optimization strategies to traffic assignment", IEEE Access, vol. 9, pp. 28999-29019.
Keywords
Traffic assignment
Traffic control
Traffic simulation
Vehicle routing
Multi-map routing
Traffic weighted multi-maps
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1109/ACCESS.2021.3058508Rights
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Access rights
info:eu-repo/semantics/openAccess
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
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