Hotspot Identification Through Pick-Up and Drop-Off Analysis of Ride-Hailing Transport Service

It is important to extract hotspots in urban traffic networks to improve driver route efficiency. This research aims to identify hotspot pick-up and drop-off (PUDO) areas in ride-hailing transportation services using a clustering approach. However, there are challenges in applying clustering algorithms to trajectory data in the coordinates of the Global Positioning System (GPS). So this research proposes modifications to the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm by considering the radius from the center of the cluster to determine the presence of amenities around the cluster. We used a dataset containing 55,988 trip trajectories of Grab drivers over a two-week period in Jakarta. A preliminary statistical analysis was carried out to understand the distribution of trips. Next, we identify the PUDO point of each trip for use in the clustering analysis. The research explores the various parameters and settings of the clustering method and their impact on the results. The study found that the results obtained from the clustering method are sensitive to parameter selection, including epsilon radius and minimum number of points needed to form a cluster. The optimal cluster with the best parameters (eps: 0.25, minpts: 100) in the pick-up (PU) location analysis produced 17 clusters with the silhouette coefficient of 0.752, while in the drop-off (DO) location there are 18 clusters with a silhouette coefficient of 0.694. Overall, the research highlights the potential of the clustering analysis method for ride-hailing transportation.