Open AccessRegularity analysis on bus networks and route directions by automatic vehicle location raw data
Author(s) -
Barabino Benedetto,
Di Francesco Massimo,
Mozzoni Sara
Publication year - 2013
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2012.0182
Subject(s) - raw data , computer science , measure (data warehouse) , key (lock) , transit (satellite) , service (business) , automatic vehicle location , service quality , task (project management) , bus rapid transit , public transport , operator (biology) , data mining , database , transport engineering , engineering , telecommunications , computer security , systems engineering , economy , global positioning system , economics , programming language , biochemistry , chemistry , repressor , transcription factor , gene
Bus regularity is a key element for high‐frequency transportation systems: it represents a measure of service quality for both users and transit agencies. Therefore, evaluating the regularity is highly important, but may also be a complex task in medium‐size cities, because of the huge amount of data, which must be collected and processed effectively. Automatic vehicle location (AVL) technologies can address the data collection problem, but they involve several challenges such as correcting anomalies in gathered raw data and processing information efficiently. In this study, the authors propose a methodology to handle AVL raw data in order to measure the level of service of bus regularity in each route direction of a transit network, as well as in every bus stop and time period. The results are represented by easy‐to‐read control dashboards. The authors discuss the experimentation of this methodology to provide a detailed characterisation of bus regularity. The methodology is applied to about 800 000 data records of the bus operator CTM in Cagliari (Italy).