scholarly journals Feature Selection and Model Fusion Approach for Predicting Urban Macro Travel Time

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
D. D. Li ◽  
D. X. Yu ◽  
Z. J. Qu ◽  
S. H. Yu
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Fusion Model ◽  
Travel Time Prediction ◽  
Data Set ◽  
Time Prediction ◽  
Traffic Guidance ◽  
Control And Management ◽  
Fusion Approach

With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction.

scholarly journals A Freeway Travel Time Prediction Method Based on An XGBoost Model

2021 ◽  
Vol 13 (15) ◽  
pp. 8577
Author(s):  
Zhen Chen ◽  
Wei Fan
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Model Performance ◽  
Route Planning ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Vehicle Data ◽  
Comparison Results

Travel time prediction plays a significant role in the traffic data analysis field as it helps in route planning and reducing traffic congestion. In this study, an XGBoost model is employed to predict freeway travel time using probe vehicle data. The effects of different parameters on model performance are investigated and discussed. The optimized model outputs are then compared with another well-known model (i.e., Gradient Boosting model). The comparison results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency. The developed model and analysis results can greatly help the decision makers plan, operate, and manage a more efficient highway system.

scholarly journals Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

2021 ◽  
Author(s):  
Osama Osman ◽  
Hesham Rakha ◽  
Archak Mittal
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Short Term Memory ◽  
Temporal Correlation ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Time Prediction ◽  
Long Short Term Memory ◽  
Control Devices

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

scholarly journals Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiandong Zhao ◽  
Yuan Gao ◽  
Jinjin Tang ◽  
Lingxi Zhu ◽  
Jiaqi Ma
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Sensor Nodes ◽  
Tensor Completion ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Travel Time Prediction ◽  
Data Sparseness ◽  
Time Prediction

Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

scholarly journals Modelling travel time uncertainty in urban networks based on floating taxi data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dietmar Bauer ◽  
Mirsad Tulic ◽  
Wolfgang Scherrer
Keyword(s):  
Travel Time ◽  
Prediction Errors ◽  
Travel Times ◽  
Model Errors ◽  
Travel Time Prediction ◽  
Data Set ◽  
Time Uncertainty ◽  
Time Prediction ◽  
Travel Time Uncertainty ◽  
Link Travel Time

Abstract The prediction of the uncertainty of route travel time predictions for all possible routes in an urban road network is of importance for example for logistics. Such predictions need to take the essential features of the data set as well as the underlying traffic dynamics into account.In this paper a large floating taxi data set is used in order to derive predictions of route travel time uncertainty based on link travel time uncertainty predictions. Prediction errors, that is actual travel times minus predicted travel times, are differentiated from model errors, that is measured travel times minus predicted travel times. These two errors are related, but not identical, as model errors contain measurement noise while the prediction errors do not. Detailed models for the variance of the link travel time prediction errors as well as the correlation between the model errors for different links are derived. The models are validated in depth using two different validation data sets.Estimates for the variance of prediction errors are obtained. The standardized model error distributions show a remarkable stability, such that modelling the variance appears to be sufficient for quantifying the uncertainty of the model errors.Furthermore we show that the model errors for adjacent links are highly correlated but correlations fade with increasing distance. Additionally usage of the road network plays a role with high correlation for links along common routes and low correlations for links along seldom used routes. We assume identical features for the prediction errors which is partly validated based on additional data.The paper provides a way to estimate the complete distribution of route travel time prediction errors for any given route in the street network.

Research on Travel Time Prediction under the Condition of Urban Rainstorm in Overpass Area

2014 ◽  
Vol 989-994 ◽  
pp. 5565-5570 ◽  
Author(s):  
Song Bi ◽  
Zhong Cheng Zhao ◽  
Guan Wang ◽  
Lin Kong ◽  
Qi Diao ◽  
...  
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Travel Time Prediction ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Time Prediction ◽  
Traffic Capacity ◽  
Water Logging ◽  
Traffic Guidance

Overpass is an important hub for urban road network facility, its traffic capacity severely restricts that of the entire road network. Since overpass area is easy to gather water in urban road network, rain water under the overpass is an important incentive for traffic jams. In this paper, a reliable and easily maintainable method is discussed to detect the depth of the road surface water, which designs and implements a monitoring system of urban road network ponding depth. Based on this, technique of predicting travel time has been researched about overpass area under water-logging condition. Through a real example, the technique discussed in this paper has been proved to be highly effective and veracious, and can be used to provide basic data for traffic guidance to plan out sound routes.

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