Travel Time Prediction for Trams in Warsaw

2017 ◽  
pp. 53-62 ◽  
Author(s):  
Adam Zychowski ◽  
Konstanty Junosza-Szaniawski ◽  
Aleksander Kosicki
Keyword(s):  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction

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

Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data

2000 ◽  
Vol 1717 (1) ◽  
pp. 120-129 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Benjamin Coifman ◽  
Peter Bickel
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Prediction Errors ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Loop Detectors ◽  
Stepwise Variable Selection ◽  
Planning Models ◽  
Current Implementation

An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.

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