Vehicle Travel Time Prediction Algorithm Based on Historical Data and Shared Location

2009 ◽  
Author(s):  
Peng Chen ◽  
Zhao Lu ◽  
Junzhong Gu
Keyword(s):  
Travel Time ◽  
Historical Data ◽  
Prediction Algorithm ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Vehicle Travel Time ◽  
Shared Location

scholarly journals Predicting the travel time of arterial traffic using particle filter with speed matrix

2018 ◽  
Vol 189 ◽  
pp. 10004
Author(s):  
Qiangrong Yang ◽  
Qi Peng
Keyword(s):  
Particle Filter ◽  
Travel Time ◽  
Historical Data ◽  
Prediction Algorithm ◽  
Prediction Methods ◽  
Travel Time Prediction ◽  
High Coverage ◽  
Traffic Pattern ◽  
Time Prediction ◽  
Arterial Traffic

Travel time prediction is an essential part of intelligent transportation system applications. However, the existing travel time prediction methods mainly focus on the freeway due to its simplicity and the high coverage of sensors and few researches have been conducted for the urban arterial road. Consequently, a travel time prediction algorithm based on particle filter is proposed in this paper to predict short-term travel time of the arterial traffic with historical floating car data and the concept of speed matrix is developed to illustrate the spatiotemporal properties of the arterial traffic. Unlike previous travel time prediction methods, the proposed algorithm uses particles with corresponding weights to model the traffic trend in the historical data instead of state-transition function and the weight for each particle are calculated with similarities between the speed matrix of the particle and the current traffic pattern. Moreover, a resampling process is developed to solve the degeneracy problem of the particles by replacing the low-weight particles with historical data. A real floating car dataset of 10357 taxis over a period of 3 months within Beijing is utilized to evaluate the performances of the algorithms. The proposed algorithm has the least errors by comparing with other three algorithms.

scholarly journals Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 106
Author(s):  
Irfan Ahmed ◽  
Indika Kumara ◽  
Vahideh Reshadat ◽  
A. S. M. Kayes ◽  
Willem-Jan van den Heuvel ◽  
...  
Keyword(s):  
Travel Time ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Temporal Features ◽  
Box Models ◽  
Challenging Tasks ◽  
Spatio Temporal ◽  
Accident Detection

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence 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.

Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes

2005 ◽  
Vol 1935 (1) ◽  
pp. 147-153 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Karl Petty
Keyword(s):  
Regression Model ◽  
Travel Time ◽  
Traditional Approach ◽  
Computational Cost ◽  
Computation Time ◽  
Prediction Algorithm ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Freeway Networks

A travel time prediction algorithm scalable to large freeway networks with many nodes with arbitrary travel routes is proposed. Instead of constructing separate predictors for individual routes, it first predicts the whole future space–time field of travel times and then traverses the required subsection of the predicted travel time field to compute the travel time estimate for the requested route. Compared with the traditional approach that offers the same flexibility, the proposed method substantially reduces storage and computation time requirements at the relatively small computational cost at the time of actual prediction. It is first established that travel times computed by traversing travel time fields are compatible with more direct measurements of travel times from a vehicle reidentification technique based on electronic toll collection tags. This provides a conceptual justification of the proposed approach. When applied to the loop data from an 8.7-mi section of the I-80 freeway, the proposed approach with a time-varying coefficient (TVC) linear regression model as the component predictor not only improves the baseline historical travel time predictor substantially, with a 40% to 60% reduction in the prediction error, but also improves the traditional whole-route predictor based on the same TVC regression model by 6% to 9%. The result suggests that the proposed algorithm not only achieves the scalability but also improves prediction accuracy, both of which are critical for successful deployment of the advanced traveler information system for large freeway networks.

Travel Time Prediction of Multi-Source Historical Data Fusion

2011 ◽  
Vol 474-476 ◽  
pp. 777-781
Author(s):  
Wen Ting Liu ◽  
Zhi Jian Wang ◽  
Qin Yan
Keyword(s):  
Data Mining ◽  
Data Fusion ◽  
Travel Time ◽  
Navigation System ◽  
Historical Data ◽  
Traffic Patterns ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Loop Detector ◽  
Traffic Rules

For improving the travel time predication accuracy, a travel time predication model based multi-source historical is proposed. The model analyzes the different features between the loop detector data and the probing vehicles data, and creates traffic rules based on traffic patterns through data mining. Finally, the experiment of the navigation system based on multi-source historical data fusion is given. The results show the effectiveness of the model performs well.

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