Optimization Approach of Dynamic Parking Guidance Information for Special Events

Lv, Nengchao; Yan, Xinping; Wu, Chaozhong

doi:10.1061/41177(415)110

Cited by 2 publications

(1 citation statement)

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“…Traffic management problems describing the interaction between PGI and travelers can be modeled by bilevel programming. Lv et al proposed bilevel programming for PGI optimization during special events (2). This previous work indicates the feasibility of bilevel programming in modeling traffic network optimization problems.…”

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confidence: 90%

Optimization of Dynamic Parking Guidance Information for Special Events

Yan

et al. 2012

Transportation Research Record: Journal of the Transportation R

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Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

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confidence: 90%