@inproceedings{792c617ad58045269e1aa269fddd9a1c,
title = "Anomalous taxi route detection system based on cloud services",
abstract = "Machine learning is very popular right now. We can apply the knowledge of machine learning to deal with some problems in our daily life. Taxi service provides a convenient way of transportation, especially for those who travel to an unfamiliar place. But there can be a risk that the passenger gets overcharged on the unnecessary mileages. To help the passenger to determine whether the taxi driver has made a detour, we propose a solution which is a cloud-based system and applies machine learning algorithms to detect anomaly taxi trajectory for the passenger. This paper briefly describes the research on several state-of-art detection methods. It also demonstrates the system architecture design in detail and gives the reader a big picture on what parts of the application have been implemented.",
keywords = "Anomaly detection, Cloud service, Machine learning, Taxi route",
author = "Yu Zi and Yun Luo and Zihao Guang and Lianyong Qi and Taoran Wu and Xuyun Zhang",
year = "2020",
doi = "10.1007/978-3-030-48513-9_20",
language = "English",
isbn = "9783030485122",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer, Springer Nature",
pages = "240--254",
editor = "Xuyun Zhang and Guanfeng Liu and Meikang Qiu and Wei Xiang and Tao Huang",
booktitle = "Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications",
address = "United States",
note = "9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019 ; Conference date: 21-12-2019 Through 22-12-2019",
}