Results 251 to 260 of about 63,289 (291)

Dynamic Travel Time Prediction with Real-Time and Historic Data

Journal of Transportation Engineering, 2003
Travel time prediction has been an interesting research area for decades during which various prediction models have been developed. This paper discusses the results and accuracy generated by different prediction models developed in this study.
Steven I-Jy Chien
exaly   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Travel Time Functions Prediction for Time-Dependent Networks

Cognitive Computation, 2018
The studies on the TDN (time-dependent network), in which the travel time of the same road segment varies depending on the time of the day, have attracted much attention of researchers, but there is little work focusing on the travel time functions prediction problem.
Jiajia Li 0003, Xiufeng Xia, Xiangyu Liu, Liang Zhao 0004, Botao Wang   +4 more
openaire   +1 more source

Travel Time Prediction for Trams in Warsaw

2017
The paper presents a comparison between different prediction methods for trams time travels in Warsaw. Predictions are constructed based on historical trams GPS positions. Three different prediction approaches were implemented and compared with the official timetables and real time travels.
Adam Zychowski, Konstanty Junosza-Szaniawski, Aleksander Kosicki   +2 more
openaire   +1 more source

Real-Time Freeway Travel-Time Prediction

Engineering & Technology Reference, 2015
This article develops a framework that includes three major categories of methodologies for real-time freeway travel-time prediction. The proposed methodologies include traffic modelling, pattern recognition and recursive probabilistic algorithms. Each developed method attempts to predict travel times for different prediction horizons.
Hao Chen, Hesham Rakha
openaire   +1 more source

A Personalised Online Travel Time Prediction Model

2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013
Congestion slows road traffic. This has become a prominent urban road traffic problem. For commuters about to travel, or on route, accurate travel forecasts enable them to choose the right routes in a timely manner to avoid travel delays. In this paper, a personalised online travel time prediction model is proposed. The novelty of the work is threefold.
Zhenchen Wang, Stefan Poslad
openaire   +1 more source

Travel time prediction with LSTM neural network

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016
Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural network model, for travel time prediction.
Yanjie Duan, Yisheng Lv, Fei-Yue Wang 0001   +2 more
openaire   +1 more source

Bus Travel-Time Prediction with a Forgetting Factor

Journal of Computing in Civil Engineering, 2014
AbstractBus travel-time prediction has drawn a lot of research interests in previous literature.
Bin Yu, Ting Ye, Xiaomei Tian, Guobao Ning, Shiquan Zhong   +4 more
openaire   +1 more source

Online travel time prediction based on boosting

2009 12th International IEEE Conference on Intelligent Transportation Systems, 2009
Travel time prediction is a very important problem in intelligent transportation system research. We examine the use of boosting, a machine learning technique in travel time prediction, and combine boosting and neural network models to increase prediction accuracy.
Ying Li 0010, Richard M. Fujimoto, Michael P. Hunter   +2 more
openaire   +1 more source

Experienced travel time prediction for freeway systems

2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012
Travel time is considered as one of the most important performance measures for roadway systems, and dissemination of travel time information can help travelers to make reliable travel decisions such as route choice or time departure. Since the traffic data collected in real time reflects the past or the current conditions on the roadway, a predictive ...
Mehmet Yildirimoglu, Nikolas Geroliminis
openaire   +2 more sources

On the Limitations of Linear Models in Predicting Travel Times

2007 IEEE Intelligent Transportation Systems Conference, 2007
Traffic congestion is growing in major cities, and, consequently, delays are becoming more frequent. Route guidance systems can significantly reduce delays by assisting drivers in finding alternative routes. Due to simplicity and scalability, the linear predictors have been an essential part of route guidance systems in predicting the future travel ...
Erick J. Schmitt, Hossein Jula
openaire   +1 more source