scholarly journals A New Travel Time Prediction Method for Intelligent Transportation System

2014 ◽  
Vol 16 (3) ◽  
pp. 24-30 ◽  
Author(s):  
Lutfun Nahar ◽  
◽  
Zinnia Sultana
Keyword(s):  
Travel Time ◽  
Intelligent Transportation System ◽  
Prediction Method ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Travel Time Prediction ◽  
Time Prediction

Simulation of Efficient Vehicle Dispatching and Routing for the Application in Intelligent Transportation System

2014 ◽  
Vol 598 ◽  
pp. 709-713
Author(s):  
Yen Wen Chen ◽  
Chang Wu Chen ◽  
Xin Chang Chen ◽  
Addison Y.S. Su
Keyword(s):  
Travel Time ◽  
Intelligent Transportation System ◽  
Transportation Cost ◽  
Specific Area ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Vehicle Dispatching ◽  
Simulation Results ◽  
Safety Considerations

This paper provides the simulation for a designed vehicle dispatching and routing system for safe and efficient vehicle transportation. The objective of the simulation is to verify the performance of the cooperative dispatching and routing decisions for vehicles within a specific area consisting of streets and several intersections. In addition to computing a specific routing path for the arrival vehicle based on its destination and safety considerations, the proposed scheme provides a detour mechanism under the transportation cost constraint. This paper also points out the existence of hidden conflicting path problems in the dispatching approach and proposes a backtracking approach to solve this problem at the route decision phase so that the travel time can be guaranteed. The simulation results show that the proposed scheme can effectively assign the vehicles with proper routes and without any collisions with good performance.

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.

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