Skip to Main content Skip to Navigation
New interface
Journal articles

Dual-objective optimization for lane reservation with residual capacity and budget constraints

Abstract : With the increase of transport demands, more pressureand challenges are being imparted into efficient transportation.As a conventional and direct congestion alleviation strategy,constructing new roads and lanes is increasingly restricted bylimited land resources and high costs, making full use of existingtransport network via appropriate management is thus critical torealize the sustainable development of transportation systems. Asa flexible management strategy, lane reservation strategy has beenwidely adopted in real life. The reserved lanes can improve theefficiency of special transports, while they bring negative impactsuch as travel delay for general-purpose transports. In addition,the setting and operating of reserved lanes require a certainamount of cost. This paper proposes a new dual-objective integerlinear programming model for optimally determining reservedlanes on a network for time-guaranteed special transports inorder to simultaneously maximize the benefits and minimize thenegative impact brought by reserved lanes, which incorporatesroad residual capacity and limited budget to the actual decision.Moreover, an iterative weighted sum based algorithm is proposedto solve it, in which a new relax-and-optimize algorithm is developedto exactly solve the single-objective optimization problems.Results of extensive numerical experiments show the effectivenessand efficiency of the proposed model and approach.
Document type :
Journal articles
Complete list of metadata
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Sunday, February 25, 2018 - 4:36:03 PM
Last modification on : Monday, December 13, 2021 - 9:17:13 AM



Peng Wu, Feng Chu, Ada Che, Yongxiang Zhao. Dual-objective optimization for lane reservation with residual capacity and budget constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50 (6), pp.2187--2197. ⟨10.1109/TSMC.2018.2810114⟩. ⟨hal-01716962⟩



Record views