Journal ArticleDOI
Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses
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TLDR
One of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions is presented, using global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India.Abstract:
Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.read more
Citations
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A review of travel time estimation and forecasting for Advanced Traveller Information Systems
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Bus travel time prediction using a time-space discretization approach
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Real-Time Bus Arrival Time Prediction: Case Study for Jinan, China
TL;DR: This study proposes two artificial neural network models to predict the real-time bus arrivals, based on historical global positioning system (GPS) data and automatic fare collection (AFC) system data, and reveals that both proposed ANN models can outperform the Kalman filter model.
References
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Travel-time prediction with support vector regression
TL;DR: The feasibility of applying SVR in travel-time prediction is demonstrated and it is proved that SVR is applicable and performs well for traffic data analysis.
Journal ArticleDOI
Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data:
TL;DR: 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 and linear regression, with the stepwise-variable-selection method and more advanced tree-based methods.
Journal ArticleDOI
Performance evaluation of short-term time-series traffic prediction model
Sherif Ishak,Haitham Al-Deek +1 more
TL;DR: One of the key functions of an effective Advanced Traveler and Management Information System is the ability to model short-term predictions of traffic conditions on major freeways and arterials with reasonable accuracy.
Proceedings ArticleDOI
A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed
TL;DR: The present paper proposes the application of a recently developed pattern classification and regression technique called support vector machines (SVM) for the short-term prediction of traffic speed and an ANN model is developed and a comparison of the performance of both these techniques is carried out.