دانلود رایگان مقاله روشی برای نظارت بر مسیر زمان واقعی با کلان داده GPS

Abstract

As taxi service is supervised by certain electronic equipment (e.g., global positioning system (GPS) equipment) and network technique (e.g., cab reservation through Uber in USA or DIDI in China), taxi business is a typical electronic commerce mode. For a long time, taxi service is facing a typical challenge, that is, passengers may be detoured and overcharged by some unethical taxi drivers, especially when traveling in unfamiliar cities. As a result, it is important to detect taxi drivers’ misbehavior through taxi’s GPS big data analysis in a real-time manner for enhancing the quality of taxi services. In view of this challenge, an online anomalous trajectory detection method, named OnATrade (pronounced “on a trade,” which means activities in a taxi trade on the fly), is investigated in this paper for improving taxi service using GPS big data. The method mainly consists of two steps: route recommendation and online detection. In the first step, route candidates are generated by using a route recommendation algorithm. In the second step, an online anomalous trajectory detection approach is presented to find taxis that have driving anomalies. Experiments evaluate the validity of our method on large-scale, real-world taxi GPS trajectories. Finally, several value-added applications benefiting from big data analysis over taxi’s GPS data sets are discussed for potential commercial applications.

7. Conclusions and future work

As taxi service is supervised by certain electronic equipment (e.g., GPS equipment) and network technique (e.g., cab reservation through Uber in USA or DIDI in China), taxi business is a typical electronic commerce mode. Mobile computing technology over GPS big data from GPS-equipped taxis makes it possible to obtain potential knowledge in understanding the behavior of urban commerce, the rule of social activities and road network dynamics. In this paper, we have proposed a real-time taxi trajectory monitoring method to detect taxi anomalous driving activities online and in real time. Technically, first the road network modeling is investigated. Based on the road networking model, an online anomalous trajectory detection method, named OnATrade, has been presented to analyze the online driving behaviors of taxi drivers. This method is validated based on a large data set for realworld GPS traces. In the future, the method could be perfected for demonstrating its advantage in social behavior analysis. Moreover, more real-world applications will be developed to validate our method, such as mobile APP supporting smart travel, real-time path recommendation and navigation service in smart city development, and so on. We believe that these value-added applications could benefit from our big data analysis method over taxi’s GPS data sets.