Identifying Irregularity Sources by Automated Location Vehicle Data

Irregularity is unavoidable in high frequency transit services due to the stochastic environment where bus services are operated. Therefore, identifying irregularity sources provides an opportunity to maintain planned headways. Previous research examined the irregularity sources by using scheduled and actual arrival (or departure) times at bus stops. However, as far as the authors’ know, no studies analyzed the irregularity sources by comparing arrivals and departures headways between two consecutive bus stops. This analysis is relevant when buses run with short headways and it is difficult to maintain the planned timetable. This gap is addressed by an offline framework which characterizes the regularity over all bus stops and time periods and discloses systematic irregularity sources from collected Automated Vehicle Location (AVL) data by inferring information on headways only. Moreover, this framework selects preventive strategies, accordingly. This framework is tested on the real case study of a bus route, using about 15,000 AVL data records provided by the bus operator, CTM in Cagliari (Italy), whose vehicles are all equipped with AVL technologies. The experimentation shows that transit managers could adopt this framework for accurate regularity analysis and service revision