01660513

Component

Searching for parking has been a problem faced by many drivers, especially in urban areas. With an increasing public demand for parking information and services, as well as the proliferation of advanced smartphones, a range of smartphone-based parking management services began to emerge. Funded by the National Science Foundation, this research aims to explore the potential of smartphone-based parking management services as a solution to parking problems, to deepen the understandings of travelers’ parking behaviors, and to further advance the analytical foundations and methodologies for modeling and assessing parking solutions. This paper summarizes progress and results from the authors' research projects on smartphone-based parking management, including parking availability information prediction, parking searching strategy, the development of a mobile parking application, and next steps to learn and discover new knowledge from its deployment. To predict future parking occupancy, the authors proposed a practical framework that integrates machine-learning techniques with a model-based core approach that explicitly models the stochastic parking process. The framework is able to predict future parking occupancy from historical occupancy data alone, and can handle complex arrival and departure patterns in real-world case studies, including special event. With the predicted probabilistic availability information, a cost-minimizing parking searching strategy is developed. The parking searching problem for an individual user is a stochastic Markov decision process and is formalized as a dynamic programming problem. The cost-minimizing parking searching strategy is solved by value iteration. The simulated experiments showed that cost-minimizing strategy has the lowest expected cost but tends to direct a user to visit more parking facilities compared with two greedy strategies. Currently, the authors are working on implementing the predictive framework and the searching algorithm in a mobile phone application. The authors are working closely with Arizona State University (ASU) Parking and Transit Services to implement a three-stage pilot deployment of the prototype application around the ASU main campus. In the first stage, the application will provide real-time information and the authors will incorporate availability prediction and searching guidance in the second and third stages. Once the mobile application is deployed, it will provide unique opportunities to collect data on parking search behaviors, discover emerging scenarios of smartphone-based parking management services, and assess the impacts of such systems.

This paper was sponsored by TRB committee AHB15 Standing Committee on Intelligent Transportation Systems.

18-03805

English

15p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Algorithms; Costs; Machine learning; Mathematical prediction; Mobile applications; Parking; Parking guidance systems; Simulation; Smartphones; Stochastic processes; Travel behavior

Highways; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2018 Paper #18-03805

TRIS, TRB, ATRI

Jan 8 2018 10:57AM