Public transport occupancy estimation using WLAN probing

Prediction of availability of physical services can be a valuable addition to transportation systems operation. In this paper we are focusing on estimation of public transport occupancy (PTO), or more specifically, on estimating bus passenger load, i.e., the number of people on the bus. This information can be used by bus operators as input to the analysis of bus routes' efficiency, or to provide an app indicating passenger load. PTO estimation based on collecting WiFi probes emitted by WiFi enabled devices is cheap and easy to install. This paper presents a prototype implementation of this method, analysis of the collected data and of the estimation algorithm accuracy. Analysis of passenger load in a bus has indicated that there are two main challenges of the estimation using WiFi probes. The algorithm provides overestimation due to inclusion of WiFi devices that are outside the bus and underestimation due to exclusion of people without an active WiFi enabled device or by missing out probes in the detection algorithm from devices carried on board. We have shown how by fine-tuning parameters of the algorithm the probes received from people outside the bus can be filtered out thereby reducing the severity of the underestimation problem. The typical approach to combat the overestimation problem is to make the adjustments based on a statistical ratio of people possessing a WiFi enabled smart device over the whole population.