Locational Intelligence Using GPS Trajectory Records of Courier Motorcycles

Abstract

In recent years, the use of motorcycles has witnessed a remarkable surge in urban areas, paralleled by a growing demand for research and development in the motorcycle industry. Furthermore, the widespread adoption of GPS-enabled devices over the last few decades has opened up exciting possibilities, particularly in the realm of data analysis, where motorcycle GPS data has emerged as a valuable resource for various applications. This article presents a novel methodology for estimating the travel duration of powered two-wheelers (PTWs) in heterogeneous traffic using GPS data generated by motorcycles on urban road networks. The proposed methodology has the potential to offer valuable insights into the behavior of PTWs in heterogeneous traffic environments. By analyzing Big Data generated by GPS-based trajectory data, researchers can identify areas with high motorcycle density and pinpoint potential bottlenecks that impact travel times. Temporal data storing with bearing information in hexagonal shards called “bubbles” enables researchers to utilize Big Data more efficiently. Spatial transformation, Kalman filtering, and map-matching of the trajectory data significantly enhance the quality of the data. In this study, the 10-minute interval is performed as optimal for estimating travel time with 4.3% MAPE. Furthermore, combining historical bubble data with a 0.35 scale factor improves MAPE by 9.6%. Despite the limitations, not only is the transferability of this methodology noteworthy, but it is also opening the door to broader applications in diverse urban settings.