Singapore has a sophisticated and efficient system of land transport to serve a growing demand for transportation. Constrained by limited space, a comprehensive set of land transport policies has been in place to balance the growth in transport demand and the effectiveness and efficiency of the land transport system. A multi-pronged approach has been used to achieve the objective of a world-class transportation system. These include integration of urban and transport planning, expansion of the road network and improvement of the transport infrastructure, harnessing the latest technology in network and traffic management, managing vehicle ownership and usage, and improvement and regulations of public transport (Ministry of Transport (MOT) (2003) Policy and Regulations, Land Transport, Available: www.mot.gov.sg, Date of Access: 15 September 2003). Singapore was the first country in the world to introduce various new techniques, notably the Area License Scheme (ALS) in 1975 and the Vehicle Quota System (VQS) in 1990. An Electronic Road Pricing (ERP) system replaced the ALS in 1998 to take the role of congestion management, the experience of which has also drawn particular attention from many large cities in the world. In 2003, the world's first and only fully automatic heavy rail Mass Rapid Transit system was opened to the public, marking a new chapter in Singapore's innovative approach to solving its land transport problem. This paper reviews the land transport policy implemented in Singapore and pays special emphasis to its public transportation systems.
Short-term prediction of traffic flow is essential for the deployment of intelligent transportation systems. In this paper we present an efficient method for short-term traffic flow prediction using a Support Vector Machine (SVM) in comparison with baseline methods, including the historical average, the Current Time Based, and the Double Exponential Smoothing predictors. To demonstrate the efficiency and accuracy of the SVM method, we used one-month time-series traffic flow data on a segment of the Pan Island Expressway in Singapore for training and testing the model. The results show that the SVM method significantly outperforms the baseline methods for most prediction intervals, and under various traffic conditions, for the rolling horizon of 30 min. In investigating the effect of the input-data dimension on prediction accuracy, we found that the rolling horizon has a clear effect on the SVM’s prediction accuracy: for the rolling horizon of 30–60 min, the longer the rolling horizon, the more accurate the SVM prediction is. To look for a solution for improvement of the SVM’s training performance, we investigate the application of k-Nearest Neighbor method for SVM training using both actual data and simulated incident data. The results show that the k- Nearest Neighbor method facilitates a substantial reduction of SVM training size to accelerate the training without compromising predictive performance.
This paper presents an investigation into the performance of support vector machine (SVM) in short-term travel-time prediction in comparison with baseline methods, including the historical mean, current time based, and time varying coefficient predictors. To demonstrate the SVM performance, 1-month time-series speed data on a section of Pan-Island Expressway in Singapore were used to estimate the travel time for training and testing the SVM model. The results show that the SVM method significantly outperforms the baseline methods in both normal and recurring congestion over a wide range of prediction intervals. In studying SVM prediction behavior under incident situations, the results show that all the predictors are not responsive enough using 15-minute aggregated field data, but the SVM predicted outcome follows the test data profile closely for 2-minute aggregated simulated data. Finally, to improve the prediction performance, an empirical k-nearest neighbor method is introduced to retrieve patterns closest to the test vector for SVM training. The results show that k-Nearest Neighbor is an attractive tool for SVM travel-time prediction. In retrieving the most similar patterns for SVM training, k-nearest neighbor allows dramatic reduction of training size to accelerate the training task while maintaining prediction accuracy.
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