A Field Study of Internet of Things-Based Solutions for Automatic Passenger Counting

Abstract

The planning of public transport operations is an essential component of urban transport management systems that aims to provide the most efficient, safe and effective way to support movement of people. Improving the customer journey experience is a key focus, as cities grow and sustainable public transport becomes more critical. This has led to an increased interest in Automatic Passenger Counting (APC) technologies that provide real-time estimates of occupancy in order to support better planning and customer information. The proliferation of sensors and power-efficient miniaturized computing capabilities offer a range of low-cost and versatile APC choices. However, it is important to understand the various design and implementation considerations and trade-offs of the APC technologies in the context of transport operation scenarios they are deployed in. In this paper, we present outcomes of a field study that evaluated the four APC solutions video, floor-based sensing, WiFi and Infrared sensing. We present an evaluations methodology that authentically captures operating conditions while providing a robust way to assess APC solutions. While most technologies achieve over 70% accuracy in some settings, the differences between weekend trips with longer legs and weekday services with short distances between stops lead to stark variations in the performances.

Keywords

• Passenger counting
• computer vision
• image processing
• floor-based sensing
• sensor mat
• WiFi sensing