scholarly journals Rule-based transit signal priority control method using a real-time transit travel time prediction model

2013 ◽  
Vol 40 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Jinwoo Lee ◽  
Amer Shalaby
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Real Time ◽  
Control Method ◽  
Transit Signal Priority ◽  
Travel Time Prediction ◽  
Rule Based ◽  
Time Prediction

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

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.

Real time bus travel time prediction using k-NN classifier

2017 ◽  
Vol 11 (7) ◽  
pp. 362-372 ◽  
Author(s):  
B. Anil Kumar ◽  
R. Jairam ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time

Prediction Model of Bus Arrival Time for Real-Time Applications

2005 ◽  
Vol 1927 (1) ◽  
pp. 195-204 ◽  
Author(s):  
Ranhee Jeong ◽  
Laurence R. Rilett
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Real Time ◽  
Arrival Time ◽  
Historical Data ◽  
Transportation Systems ◽  
Test Bed ◽  
Travel Time Prediction ◽  
Ann Models ◽  
Real Time Applications

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications.

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