Travel time prediction and departure time adjustment behavior dynamics in a congested traffic system

1988 ◽  
Vol 22 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Gang-Len Chang ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Travel Time Prediction ◽  
Traffic System ◽  
Congested Traffic ◽  
Time Prediction ◽  
Behavior Dynamics ◽  
Departure Time

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.

Real-Time Travel Time Prediction Framework for Departure Time and Route Advice

2015 ◽  
Vol 2490 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Simeon C. Calvert ◽  
Maaike Snelder ◽  
Taoufik Bakri ◽  
Bjorn Heijligers ◽  
Victor L. Knoop
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Travel ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Departure Time

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

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

scholarly journals Multi-output bus travel time prediction with convolutional LSTM neural network

2019 ◽  
Vol 120 ◽  
pp. 426-435 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time ◽  
Convolutional Lstm
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