Travel time estimation in urban road networks

2002 ◽  
Author(s):  
J. Anderson ◽  
M. Bell
Keyword(s):  
Travel Time ◽  
Road Networks ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream

scholarly journals A ROBUST METHOD FOR REAL TIME ESTIMATION OF TRAVEL TIMES FOR DENSE URBAN ROAD NETWORKS USING POINT-TO-POINT DETECTORS

Transport ◽  
2015 ◽  
Vol 30 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Evangelos Mitsakis ◽  
Josep Maria Salanova Grau ◽  
Evangelia Chrysohoou ◽  
Georgia Aifadopoulou
Keyword(s):  
Real Time ◽  
Road Networks ◽  
Central Business District ◽  
Time Estimation ◽  
Media Access Control ◽  
Travel Time Estimation ◽  
Travel Times ◽  
Error Sources ◽  
Urban Road ◽  
Point To Point

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).

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