Bus travel time prediction with real-time traffic information

2019 ◽  
Vol 105 ◽  
pp. 536-549 ◽  
Author(s):  
Jiaman Ma ◽  
Jeffrey Chan ◽  
Goce Ristanoski ◽  
Sutharshan Rajasegarar ◽  
Christopher Leckie
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Information ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Bus Travel Time

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 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.

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

scholarly journals Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

2018 ◽  
Vol 2672 (42) ◽  
pp. 180-191 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Elham Sharifi ◽  
Stanley Young
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Ground Truth ◽  
Traffic Information ◽  
Incident Detection ◽  
Dynamic Message Signs ◽  
Real Time Traffic ◽  
Outsourced Data ◽  
Message Signs

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

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