Assessing driving behavior in Macau public transportation through mobile crowd sensing

This thesis introduces, implements and evaluates an innovative concept for assessing driving behavior in public transportation through Mobile Crowd Sensing (MCS), under the field of Advanced Public Transportation System (APTS) - a sub-group of Intelligent Transportation Systems (ITS). Aggressive driving behavior is known to be a cause of avoidable accidents and to increase fuel consumption. In public transportations, it is also a case for costumers� dissatisfaction. Monitoring the quality of driving behavior is a key element to overcome this issue and to improve road safety and customer satisfaction. In this research project, a software application (app) for mobile devices was developed as an experimental tool / proof-of-concept, to monitor aggressive driving behavior in bus drivers, collecting data coming from mobile device�s accelerometer and passengers� qualitative evaluation. The experimental procedure took place in public transportation in Macau (bus only) and consisted of data collection of drivers� aggressive driving behavior using the developed application. The analysis of collected data suggests that MCS is a viable way to assess drivers� behavior in public transportation, thus contributing to the improvement of the service and increase of road safety. Although the methodology has been tailor-made for Macau public transportation, it is believed that the same concept can be applied to other cities, leading them towards the goal of becoming smarter cities. Keywords: driving behavior; mobile crowd sensing; crowdsourcing; smart city; advanced public transportation system; intelligent transportation system; road safety; mobile device accelerometer