scholarly journals The Topography Change Prediction Model that considered a Flow caused by Wind in the Niigata West Coast

2008 ◽  
Vol 55 ◽  
pp. 586-590
Author(s):  
Hiroshi YOSHINAGA ◽  
Hideki YOSHIDA ◽  
Tadashi YOSHIDA ◽  
Tomonori IBE ◽  
Keiji KUROKI
Keyword(s):  
Prediction Model ◽  
West Coast ◽  
Change Prediction

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

A Bayesian inference based adaptive lane change prediction model

2021 ◽  
Vol 132 ◽  
pp. 103363
Author(s):  
Jinghua Wang ◽  
Zhao Zhang ◽  
Guangquan Lu
Keyword(s):  
Bayesian Inference ◽  
Prediction Model ◽  
Lane Change ◽  
Change Prediction

Annual Rut Depth Change Prediction Model for National Highway Asphalt Pavements

2021 ◽  
Vol 23 (5) ◽  
pp. 11-19
Author(s):  
Jae Hoon Lee ◽  
Jae Kyu Lim ◽  
Moon Gyu Choi ◽  
Jin Hoon Jeong
Keyword(s):  
Prediction Model ◽  
Asphalt Pavements ◽  
National Highway ◽  
Change Prediction
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