Freeway Short-Term Travel Time Prediction Based on Dynamic Tensor Completion

2015 ◽  
Vol 2489 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Huachun Tan ◽  
Qin Li ◽  
Yuankai Wu ◽  
Wuhong Wang ◽  
Bin Ran
Keyword(s):  
Travel Time ◽  
Tensor Completion ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction

A space–time diurnal method for short-term freeway travel time prediction

2014 ◽  
Vol 43 ◽  
pp. 33-49 ◽  
Author(s):  
Yajie Zou ◽  
Xinxin Zhu ◽  
Yunlong Zhang ◽  
Xiaosi Zeng
Keyword(s):  
Travel Time ◽  
Space Time ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction

scholarly journals A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

2018 ◽  
Vol 45 (2) ◽  
pp. 77-86 ◽  
Author(s):  
Hang Yang ◽  
Yajie Zou ◽  
Zhongyu Wang ◽  
Bing Wu
Keyword(s):  
Markov Chain ◽  
Travel Time ◽  
Prediction Accuracy ◽  
Travel Time Prediction ◽  
Time Data ◽  
Short Term ◽  
Time Prediction ◽  
Travel Time Data ◽  
Forecasting Performance ◽  
Fitting Problem

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

scholarly journals Short-Term Urban Link Travel Time Prediction Using Dynamic Time Warping With Disaggregate Probe Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 98959-98970
Author(s):  
Ruotian Tang ◽  
Ryo Kanamori ◽  
Toshiyuki Yamamoto
Keyword(s):  
Travel Time ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction ◽  
Dynamic Time ◽  
Link Travel Time

Short-Term Travel Time Prediction considering the Effects of Weather

2012 ◽  
Vol 2308 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Wenxin Qiao ◽  
Ali Haghani ◽  
Masoud Hamedi
Keyword(s):  
Travel Time ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction

scholarly journals Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3354 ◽  
Author(s):  
Jianqing Wu ◽  
Qiang Wu ◽  
Jun Shen ◽  
Chen Cai
Keyword(s):  
Information Systems ◽  
Travel Time ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Series Data ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Time Prediction ◽  
Long Short Term Memory

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

Short-Term Travel Time Prediction for a Car Dynamic Navigation System

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Yuxi He ◽  
Renjie Du ◽  
Tong Zou ◽  
Nian Zhang ◽  
Xunfei Gao
Keyword(s):  
Travel Time ◽  
Navigation System ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction ◽  
Dynamic Navigation
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