Title |
On-Street Parking Guidance with Real-Time Sensing Data for Smart Cities |
ID_Doc |
43243 |
Authors |
Liu, KS; Gao, J; Wu, XB; Lin, S |
Title |
On-Street Parking Guidance with Real-Time Sensing Data for Smart Cities |
Year |
2018 |
Published |
|
DOI |
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Abstract |
On-street parking is an essential component of parking infrastructure for smart cities, which allows users to park near their destinations for short term. However, due to limited capacity, saturated on-street parking becomes a serious and widespread problem for urban transportation systems. Greedily searching for an on-street parking spot in a saturated area is often a frustrating task for drivers, and cruising for vacant parking spots results in additional delays and impaired local circulation. With the recent development of networked smart parking meter, real-time city-wide on-street parking information becomes available for more efficient parking management. In this paper, we design an online parking guidance system that recommends parking spots in real-time based on the parking availability prediction. With a receding horizon optimization framework, our solution minimizes the user's driving and walking cost by adapting the spatiotemporally dynamic supply and demand in the local area, significantly reducing parking competitions in a timely manner. We implement and evaluate our solution with a dataset of 13,503,655 parking records collected from 5228 in-ground sensors distributed in the Australian city Melbourne. The evaluation results show that our approach achieves up to 63.8% delay reduction compared with existing solutions. |
Author Keywords |
on-street parking; smart parking; smart city |
Index Keywords |
Index Keywords |
Document Type |
Other |
Open Access |
Open Access |
Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
EID |
WOS:000468651200018 |
WoS Category |
Computer Science, Hardware & Architecture; Remote Sensing; Telecommunications |
Research Area |
Computer Science; Remote Sensing; Telecommunications |
PDF |
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