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.

Travel time prediction in transport and logistics

2019 ◽  
Vol 49 (3) ◽  
pp. 277-306 ◽  
Author(s):  
Xia Li ◽  
Ruibin Bai ◽  
Peer-Olaf Siebers ◽  
Christian Wagner
Keyword(s):  
Travel Time ◽  
Model Fitting ◽  
Real Life ◽  
Gps Data ◽  
Travel Time Prediction ◽  
Time Data ◽  
Content Type ◽  
Time Prediction ◽  
Travel Time Data ◽  
Real Life Data

Purpose Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions. Design/methodology/approach The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case. Findings The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper. Research limitations/implications The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances. Practical implications The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions. Originality/value This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

scholarly journals Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

2019 ◽  
Vol 48 (3) ◽  
pp. 276-289
Author(s):  
Akhilesh Jayan ◽  
Sasidharan Premakumari Anusha
Keyword(s):  
Travel Time ◽  
Performance Measure ◽  
Mixed Traffic ◽  
Travel Time Prediction ◽  
Time Data ◽  
Traffic Conditions ◽  
Time Prediction ◽  
Match Rate ◽  
Travel Time Data ◽  
Time Information

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations.

scholarly journals Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 265
Author(s):  
Ruotian Tang ◽  
Ryo Kanamori ◽  
Toshiyuki Yamamoto
Keyword(s):  
Travel Time ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Penetration Rate ◽  
Coverage Rate ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction ◽  
Proposed Model ◽  
Link Travel Time

Short-term travel time prediction is an important consideration in modern traffic control and management systems. As probe data technology has developed, research interest has moved from highways to urban roads. Most research has only focused on improving the prediction accuracy on urban roads because it is the key index of evaluating a model. However, the low penetration rate of probe vehicles at urban networks may result in the low coverage rate which restricts prediction models from practical applications. This research proposed a non-parametric model based on Bayes’ theorem and a resampling process to predict short-term urban link travel time, which can enhance the coverage rate while maintaining the prediction accuracy. The proposed model used data from vehicles in both the target link and its crossing direction, so its coverage rate can be expanded, especially when the data penetration rate is low. In addition, the utilization of relationships between vehicles in both directions can reflect the influence of signal timing. The proposed model was evaluated in a computer simulation to test its robustness and reliability under different data penetration rates. The results implied that the proposed model has a high coverage rate, demonstrating stable and acceptable performance at different penetration rates.

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
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