1 research outputs found
Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models
The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable
source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different
investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and
gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However,
figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor
data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still,
walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to
public transport stops based on a random forest model