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.

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a prevalent issue in extensive indoor environments. The study explores various scenarios, comparing the performance of the base CNN and meta-learning models. The meta-learning approach involves few-shot learning tasks, such as three-way N-shot, five-way N-shot, etc., to enhance the model’s ability to generalize from limited data. The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. The scenarios included both base classes and novel classes, with and without meta-learning. The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. With 20 samples per class, the base CNN achieved an accuracy of 0.80 during the pre-training stage, while meta-learning (three-way one-shot) achieved an accuracy of 0.78 on a new small dataset with novel classes.

This paper is focused on calibration of an intelligent network simulation model (INSIM) with real-life transportation network to analyse the INSIM’s feasibility in simulating commuters’ travel choice behaviour under the influence of real-time integrated multimodal traveller information (IMTI). A transportation network model for the central and western areas of Singapore was simulated in PARAMICS and integrated with INSIM expert system by means of an application programming interface to form the INSIM. Upon calibration, INSIM was able to realistically present complicated scenarios in which real-time IMTI was provided to commuters and the network performance measures being recorded.