Bus Travel Time Predictions Using Additive Models

Transportmetrica A: Transport Science, 2020

Sustainability

The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.

2022

— Land Transportation Sector is one of the key sectors in the Philippine economy particularly in Metro Manila. With the rapid urbanization of the Philippines, the urban transport infrastructure is expected to experience pressures posing a major risk of urban transport degradation resulting into longer travel times, economic and productivity losses. In light of this, the Land Transportation Franchising and Regulatory Board (LTFRB) along with DOST-ASTI has initiated a project on implementing a bus management system for Public Utility Vehicles utilizing real time GPS location data. This study takes on establishing a travel time prediction for the buses given a specific route. The travel time estimation was performed using Extremely Randomize Trees, a supervised machine learning algorithm. The resulting prediction set had a correlation of determination score indicative of a good predictive performance for travel time prediction.

Lecture Notes in Computer Science

Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, 2018

Proceedings of the AAAI Conference on Artificial Intelligence

Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles. There is only a limited literature on methods using data input from probe vehicles. The spatio-temporal dependencies captured by existing data driven approaches are either too detailed or very simplistic. We strike a balance of the existing data driven approaches to account for varying degrees of influence a given road may experience from its neighbors, while controlling the number of parameters to be learnt. Specifically, we use a NoisyOR conditional probability distribution (CPD) in conjunction with a dynamic Bayesian network (DBN) to model state transitions of various roads. We propose an efficient algorithm to learn model parameters. We also propose an algorithm for predicting travel times on trips of arbitrary durations. ...

International Journal of Intelligent Transportation Systems Research, 2018

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.

Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005., 2005

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently