Passenger counting in minivan-taxis using crowd-sourcing and hierarchical clustering

Abstract

A novel method for estimating the passenger densities of minivan taxis popularly known as Trotros in Ghana is proposed. A smartphone is used to collect time, location and velocity data from groups of passengers travelling in parts of the Kumasi Metropolitan Assembly, Ghana. Passengers are clustered by four different combinations of their location, time and average acceleration data using the agglomerative hierarchical clustering algorithm. A classification method was then used to externally validate the results by comparing the cluster labels to an initial class labelling which had been assigned during data collection called the group code. The count of the group code represented the estimated number of passengers aboard the vehicle. Results from the various clustering combinations performed indicated that using the time and location variables only for classification gave the highest accuracy of about 89.2% as compared to the other combinations. The proposed method of counting passengers in moving vehicles is particularly useful in the Ghanaian context due to the fact that trotros do not have to be retro-fitted with expensive devices for data collection and thus can be implemented without financially burdening the privately-owned trotro industry. Also, counting passengers in trotros adds to the growing pool of trotro research data which is beneficial for improvements in the trotro industry and also for future research.

Keywords

• intelligent transport
• Passenger counting
• Minivan taxi
• Trotro
• Crowdsourcing
• Hierarchical clustering algorithm