Abstract
A multi-objective optimization model is proposed to allocate the location of VMSs by maximizing the average traffic guidance utility of VMSs and the number of benefited links, while minimizing information redundancy. The traffic guidance utility is defined to quantitatively measure the value of an installed VMS, which is calculated from passively collected GPS data and the physical topology of road network. The number of benefited links is to measure how many links are covered by upstream VMS to disseminate information. Information redundancy is introduced to quantify the mutual impairing between any two VMSs. A heuristic search algorithm is developed to solve the optimization model, which can calculate the saturated number of VMS for a road network and optimize the project schedule of VMS installation process based on the proposed objectives. A real-world case study is conducted in Beijing to illustrate the validity of the proposed approach, where taxis are used as probe vehicles to provide GPS data. The results show the effectiveness of the proposed multi-objective optimization model and it is promising to use the emerging GPS data to help agencies to allocate the locations of VMSs on both urban roads and highway networks, instead of relying on the subjective judgment from practitioners.
Highlights
Advanced Intelligent Transport System (AITS) technology is an important way to balance the traffic in road network and to mitigate traffic congestion
Guidance utility of Variable Message Sign (VMS) located on a link is defined as the level of utilities drivers can get from the traffic information provided by VMS, which is derived from the physical topology of the road network and GPS probe data
This paper proposes a multi-objective optimization model to allocate the locations of VMS by maximizing average guidance utility of VMSs and number of benefited links while minimizing information redundancy
Summary
Advanced Intelligent Transport System (AITS) technology is an important way to balance the traffic in road network and to mitigate traffic congestion. A multi-objective optimization model is proposed to maximize the average traffic guidance utility and the number of benefited links while minimizing information redundancy.
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