scholarly journals Travel Time Prediction System Based on Data Clustering for Waste Collection Vehicles

2019 ◽  
Vol E102.D (7) ◽  
pp. 1374-1383 ◽  
Author(s):  
Chi-Hua CHEN ◽  
Feng-Jang HWANG ◽  
Hsu-Yang KUNG
Keyword(s):  
Travel Time ◽  
Data Clustering ◽  
Prediction System ◽  
Waste Collection ◽  
Travel Time Prediction ◽  
Time Prediction

scholarly journals Travel Time Prediction Based on Data Feature Selection and Data Clustering Methods

2018 ◽  
Author(s):  
Chi-Hua Chen
Keyword(s):  
Feature Selection ◽  
Travel Time ◽  
Data Clustering ◽  
Arrival Time ◽  
Intelligent Transportation System ◽  
Previous Method ◽  
Fleet Management ◽  
Clustering Methods ◽  
Travel Time Prediction ◽  
Time Prediction

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

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.

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