In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named “trash doesn't touch the ground”, which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users' MDs and relevant data from WCVs' OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.

During the detection of maritime targets, the jitter of the shipborne camera usually causes the video instability and the false or missed detection of targets. Aimed at tackling this problem, a novel algorithm for maritime target detection based on the electronic image stabilization technology is proposed in this study. The algorithm mainly includes three models, namely the points line model (PLM), the points classification model (PCM), and the image classification model (ICM). The feature points (FPs) are firstly classified by the PLM, and stable videos as well as target contours are obtained by the PCM. Then the smallest bounding rectangles of the target contours generated as the candidate bounding boxes (bboxes) are sent to the ICM for classification. In the experiments, the ICM, which is constructed based on the convolutional neural network (CNN), is trained and its effectiveness is verified. Our experimental results demonstrate that the proposed algorithm outperformed the benchmark models in all the common metrics including the mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean average precision (mAP) by at least -47.87%, 8.66%, 6.94%, and 5.75%, respectively. The proposed algorithm is superior to the state-of-the-art techniques in both the image stabilization and target ship detection, which provides reliable technical support for the visual development of unmanned ships.

Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.