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. For a long time, taxi service is facing a typical challenge, i.e., passengers may be detours 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 datasets are discussed for potential commercial applications.
With the growing volume of the airport passengers, public transit is needed for healthy and sustainable city development, in which airport shuttle buses play a key role in satisfying the demand. In this paper, a two-phase airport shuttle bus stop planning method is proposed based on taxi GPS data. It aims at providing convenient public transit to the airport by identifying optimal airport shuttle bus stop. In our method the first phase focuses on filtering the irrelevant "dirty" records. Then the remained data set is divided into two parts consisting of origin dataset and destination dataset. In the second phase, the kmeans clustering algorithm is employed to identify representative points as candidate airport shuttle bus stops based on these two datasets. After that, taking advantage of traffic model and rules defined in traffic engineering, the candidate stops set can be further optimized. Finally, extensive experiments are conducted on a large-scale real-world taxi GPS data set to verify the practicality of our method on an In-memory database platform, HANA.
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