Abstract | ||
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AbstractSpot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art. |
Year | DOI | Venue |
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2021 | 10.1145/3460200 | ACM Transactions on Sensor Networks |
Keywords | DocType | Volume |
Mobile sensing, parking availability, crowdsourcing, truth discovery | Journal | 17 |
Issue | ISSN | Citations |
4 | 1550-4859 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yi Zhu | 1 | 0 | 0.34 |
Abhishek Gupta | 2 | 0 | 1.01 |
Shaohan Hu | 3 | 0 | 0.34 |
Weida Zhong | 4 | 7 | 3.14 |
lu su | 5 | 1118 | 66.61 |
Chunming Qiao | 6 | 3971 | 400.49 |