Open AccessA ROBUST METHOD FOR REAL TIME ESTIMATION OF TRAVEL TIMES FOR DENSE URBAN ROAD NETWORKS USING POINT-TO-POINT DETECTORS
Author(s) -
Evangelos Mitsakis,
Josep Maria Salanova Grau,
Evangelia Chrysohoou,
Georgia Aifadopoulou
Publication year - 2015
Publication title -
transport
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 31
eISSN - 1648-4142
pISSN - 1648-3480
DOI - 10.3846/16484142.2015.1078845
Subject(s) - outlier , detector , computer science , point (geometry) , estimation , transport engineering , intersection (aeronautics) , key (lock) , set (abstract data type) , real time computing , data mining , bluetooth , telecommunications , engineering , computer security , mathematics , artificial intelligence , geometry , systems engineering , wireless , programming language
Data collection for the provision of real time traveller information services is a key issue, both for the travellers as well as for traffic managers. This paper presents a methodology for estimating travel times in dense urban road networks using point-to-point detectors. The aim is to fill in the gap of existing travel time estimation methodologies, which are based on point-to-point detection devices. Bluetooth (BT) is considered as one of the less expensive technologies for estimating travel times. Data filtering and data correction require rigorous methodologies, which if not correctly applied may result in inaccurate results as compared to other methods. The main difficulty of data processing is to identify the correct set of Media Access Control (MAC) addresses for estimating travel times, especially in dense urban road networks, where three main error sources exist: the co-existence of various transport modes (private vehicles, buses, pedestrians, bicycles etc.), the existence of more than one possible paths between two BT detectors and the existence of stops or trips ending between two BT detectors. These error sources create outliers that need to be identified and taken into account. The results of the proposed methodology confirm that outliers are eliminated, as shown by a case study with 10 BT detectors installed at major intersections of Thessaloniki’s Central Business District (CBD).