Time-Variant Travel Time Prediction Model and Its Application in a Cooperative Traffic Control System

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.

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Travel Time Prediction Model for Urban Road Network based on Multi-source Data

Procedia - Social and Behavioral Sciences ◽

10.1016/j.sbspro.2014.07.230 ◽

2014 ◽

Vol 138 ◽

pp. 811-818 ◽

Cited By ~ 5

Author(s):

Zhou Jiang ◽

Cunbao Zhang ◽

Yinxia Xia

Keyword(s):

Prediction Model ◽

Travel Time ◽

Road Network ◽

Travel Time Prediction ◽

Urban Road Network ◽

Urban Road ◽

Time Prediction ◽

Source Data

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Travel Time Prediction Model of Freeway Corridor Based on Real-Time Safety Reliability

Journal of Advanced Transportation ◽

10.1155/2020/8882011 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Huazhi Yuan ◽

Zhaoguo Huang ◽

Hongying Zhang

Keyword(s):

Prediction Model ◽

Travel Time ◽

Real Time ◽

Traffic Safety ◽

Management Unit ◽

Travel Time Prediction ◽

Time Prediction ◽

Relative Safety ◽

Driving Risk ◽

Freeway Corridor

By considering the feature of vehicle driving on the event management unit of the freeway corridor, according to the system target, a method to divide the management unit of the road network was put forward. The relative safety braking deceleration was taken as the evaluation index of single-vehicle driving risk. The reliability graph relationship and structure-function between the management unit and subunit were analyzed. Then, dynamic safety reliability and real-time safety reliability were determined on the basis of driving risk. In addition, the queuing and dissipating characteristics of the management unit under traffic incidents were analyzed based on the wave theory. The incident duration and dissipation time were also calculated. At the same time, the travel time prediction model of the incident management unit was set up when the real-time safety reliability was taken as a road resistance function. Finally, an improved travel time prediction model established in this paper is of great significance to improve traffic safety and efficiency, and the research results will provide an important theoretical foundation in the freeway corridor route decision.

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