Ye, Qianqian (Zhejiang Universit), Cai, Zhengyi (Zhejiang University), Hu, Simon (Zhejiang University)

Optimizing Urban Parking Pricing with a Dual Dynamic Evolution Model for Multimodal Networks

Scheduled for presentation during the Poster Session "Modeling and Optimization of Mobility and Transport Systems " (ThBT14), Thursday, September 26, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Parking pricing is crucial for managing travel demand and promoting a more equitable distribution of resources. However, few studies involve multimodal traffic networks, and parking pricing is rarely optimized based on within-day and day-by-day traffic evolution. In this study, a bi-level programming model is introduced to optimize urban parking pricing: the upper level addresses parking pricing issues and the lower level focuses on dual dynamic evolution to optimize dynamic parking pricing in multimodal networks. We extend the foundational principles of the dual dynamic evolution model by integrating path-level dynamics into its framework. The model is tested on multimodal networks in Jiangning District, Nanjing, China, using mobile signaling data. The analysis focuses on the impact of dynamic parking pricing strategy on traveler behavior choices and the status of the multimodal traffic network. Compared with the traditional planning algorithm, this model is more robust and can better tackle the parking pricing problem under complex traffic dynamics based on real-world data.