A Travel Time Prediction Method for Urban Road Traffic Sensors Data

2015 ◽  
Author(s):  
Guangyu Zhu ◽  
Kang Song ◽  
Peng Zhang
Keyword(s):  
Travel Time ◽  
Road Traffic ◽  
Prediction Method ◽  
Travel Time Prediction ◽  
Urban Road ◽  
Traffic Sensors ◽  
Time Prediction

scholarly journals Travel time prediction of urban road based on BP neural network

2020 ◽  
Vol 1651 ◽  
pp. 012190
Author(s):  
Fangyi Deng ◽  
Pei Su ◽  
Bingxue Luo ◽  
Peng Wu ◽  
Yan Guo
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Travel Time Prediction ◽  
Urban Road ◽  
Time Prediction

A Multistep Corridor Travel-Time Prediction Method Using Presence-Type Vehicle Detector Data

2011 ◽  
Vol 15 (2) ◽  
pp. 104-113 ◽  
Author(s):  
Jingxin Xia ◽  
Mei Chen ◽  
Wei Huang
Keyword(s):  
Travel Time ◽  
Prediction Method ◽  
Travel Time Prediction ◽  
Time Prediction

Travel Time Prediction in Urban Road Networks

1997 ◽  
Vol 30 (8) ◽  
pp. 1109-1114
Author(s):  
Jessica Anderson
Keyword(s):  
Travel Time ◽  
Road Networks ◽  
Travel Time Prediction ◽  
Urban Road ◽  
Time Prediction

Analytical Approach to Identify the Optimum Inputs for a Bus Travel Time Prediction Method

2015 ◽  
Vol 2535 (1) ◽  
pp. 25-34 ◽  
Author(s):  
B. Anil Kumar ◽  
Snigdha Mothukuri ◽  
Lelitha Vanajakshi ◽  
Shankar C. Subramanian
Keyword(s):  
Travel Time ◽  
Analytical Approach ◽  
Prediction Method ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time

Urban Road Travel Time Prediction Based on Taxi GPS Data

ICTIS 2013 ◽  
2013 ◽  
Author(s):  
Yanguo Huang ◽  
Lunhui Xu ◽  
Xianyan Kuang
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Travel Time Prediction ◽  
Urban Road ◽  
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 Research on Expressway Travel Time Prediction Based on Exclusive Disjunctive Soft Set

2020 ◽  
Vol 308 ◽  
pp. 02005
Author(s):  
Qingqing Wang ◽  
Huamin Li ◽  
Weixin Xiong
Keyword(s):  
Travel Time ◽  
Set Theory ◽  
Road Traffic ◽  
Influence Factors ◽  
Soft Set ◽  
Travel Time Prediction ◽  
Time Model ◽  
Traffic System ◽  
The Road ◽  
Time Prediction

In order to study the prediction problem of expressway travel time, due to the ambiguity and uncertainty in the road traffic system, the travel time prediction model is established based on the exclusive disjunctive soft set theory. Through the parameter reduction theory of soft set, the main influence factors are extracted, and the mapping relationship between the influence factors and the travel time is obtained through the exclusive disjunctive soft set decision system. The travel time model is established based on the soft set theory, and the travel time is calculated through the mapping relationship. The experimental results show that, compared with the BPR function model, the travel time model based on the exclusive disjunctive soft set theory reduces the prediction error and effectively improves the calculation accuracy of the travel time.

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