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

Abstract

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.

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

“…By their capabilities of maintaining state between predictions, Kalman filters (KF) have been the topic of several studies either as an independent model (Chen & Chien, 2001;Shalaby & Farhan, 2004), or in combination with other models (Bai, Peng, Lu, & Sun, 2015;Yu, Yang, Chen, & Yu, 2010). In all cases, the applied filters are traditional linear KFs, and applied independently to each link.…”

“…This has sparked the interest in studying composite or hybrid models. Bai et al (2015) use a two-stage approach by combining an offline ANN model with an adaptable/online Kalman filter to yield a dynamic model. The advantage is the balance between computational complexity and the ability to adapt to smaller deviations quickly.…”

“…Some recent research recognizes that several routes can benefit from each other's predictions if they share some partial route segment, e.g. (Bai et al, 2015;Gal, Mandelbaum, Schnitzler, Senderovich, & Weidlich, 2017;Yu, Lam, & Tam, 2011). However, none of these approaches consider cross-temporal correlations between different route segments, and they only use a small window for correlation with upstream links (e.g.…”

A Study on the Behavior of Clustering Techniques for Modeling Travel Time in Road-Based Mass Transit Systems

“…Moreira-Matias et al [2] conduct a comprehensive review of techniques used for this type of prediction. Focusing on Machine Learning techniques, taking into account the type of technique used and the references number, we highlight Yu et al [3] who proposed models based on support vector machines, Bai et al [4] who proposed a combined model based on support vector machine and Kalman filters, Gurmu et al [5] who presented a prediction model based on artificial neuronal networks, Chang et al [6] who proposed the technique k-nearest neighbors, Gal et al [7] that used decision tree regression and finally, the work of Lee et al [8] that proposed clustering techniques, specifically K-means and V-means. All these short-term TT forecasting models use, as input data, a set of TT observed at different points in the transport network in certain instants in time.…”

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

“…Support Vector Machine (SVM) [4] is widely used in this task. In the proposed methods, SVM is combined with a Genetic Algorithm [5], Kalman filter [6], and artificial neural network (ANN) [7], respectively. In addition to Kalman filtering and SVM, there are other time series prediction methods, such as road segment average travel time [8], the Relevance Vector Machine Regression [9], clustering [10], Queueing Theory combined with Machine Learning [11], and Random Forests [12].…”