Agile urban parking recommendation service for intelligent vehicular guiding system

Eric Hsiao Kuang Wu, Jagruti Sahoo, Chi Yun Liu, Ming Hui Jin, Shu Hui Lin

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Nowadays, Intelligent Transportation Systems (ITS) technologies are exploring a wide range of services such as freeway management, crash prevention & safety, driver assistance, and infotainment of drivers and/or passengers. In this paper, an agile urban parking recommendation service for vehicular intelligent guiding system is designed to facilitate city citizens with fully efficient, real-time and precise parking lot guiding suggestions for the sustainability of the future green city. The system offers drivers a friendly parking lot recommendation sequence and saves drivers? time circling around by the accurate prediction of the successful parking probability in each parking lot. The proposed cost model constructs an optimal recommendation sequence considering successful parking probability and time to reach the parking lot. Through the collection and analysis of realistic records from parking lots in Taipei city, a prediction algorithm is developed to estimate the successful parking probability by using current available space counts. Extensive experiments are conducted to demonstrate the effectiveness of the prediction algorithm.

Original languageEnglish
Article number6717140
Pages (from-to)35-49
Number of pages15
JournalIEEE Intelligent Transportation Systems Magazine
Issue number1
StatePublished - 2014


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