scholarly journals Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

2020 ◽  
Author(s):  
Homa Taghipour ◽  
Amir Bahador Parsa ◽  
Abolfazl Mohammadian
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Travel Time ◽  
Data Sources ◽  
Travel Time Prediction ◽  
Short Term ◽  
Average Speed ◽  
Time Prediction ◽  
Multiple Data ◽  
Harmonic Average

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time.

scholarly journals Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

2021 ◽  
Vol 13 (13) ◽  
pp. 7454
Author(s):  
Bo Qiu ◽  
Wei (David) Fan
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Numerical Results ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Short Term ◽  
Time Prediction ◽  
Extreme Gradient Boosting

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

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

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.

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