A method for real-time trajectory monitoring to improve taxi service using GPS big data

Abstract

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.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

“…Processes of decision-making, value creation [148], innovation, and supply chain [149] were significantly enhanced using big data analytics techniques. Zhou et al investigated a trajectory detection method to improve taxi services using big data from GPS [150]. Applications of big data are also found in creating competitive advantages by troubleshooting, personalization, and detection of areas that require improvement [151].…”

Proposal of big data route selection methods for autonomous vehicles

“…In 2012, big data has become a research focus in academic, industry, and government agencies for gaining competitive advantage. Typically, in January 2012, big data was a key theme of the World Economic Forum, and is highlighted in a report titled “Big Data, Big Impact: New Possibilities for International Development” . Spatial big data possesses the characteristics that traditional big data does which normally features at least one of the 3 or 5 V's: volume, velocity, variety, veracity, and value .…”

“…As for this moment (year 2017), not much work has been done in terms of stream analytics in transportation field in general. Instead of a few existing literature positions taking into account real‐time stream processing, , most of them focus only on batch analysis of historical data , . It seems rather surprising considering the fact that autonomous vehicles' development is progressing really fast.…”

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

“…In the field of traffic safety, traffic events are detected by using video detection data in urban traffic automatically, and the relationship between events and conflict is analyzed [7][8][9]. For GNSS trajectory data, they have two areas of application: one is to discover anomalous traffic [10,11], and the other is to infer anomalous trajectories [12][13][14][15]. Our research focuses on the detection of anomalous trajectories.…”

“…In recent years, anomalous trajectory detection has included the distance-based method [16,17], the clustering-based method [12,18,19], the classification-based method [20,21], and the grid-based method [13][14][15]. The distance-based method divides the whole trajectory into sub-trajectories that include certain trajectory points and calculates the Hausdorff distance between sub-trajectories.…”