This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.

Unproductive time spent in transportation not only frustrates passengers but also incurs significant economic costs globally. The rise of autonomous transportation systems has intensified the need for adaptive traffic signal control (ATSC) methods capable of responding to dynamic traffic conditions. In the field of traffic signal control, multi-agent reinforcement learning (MARL) methods are gaining interest, treating each traffic signal as an intelligent agent offers a promising solution to adaptive traffic signal control (ATSC) in complex urban environments, yet its real-world deployment remains challenging due to complex spatial-temporal dependencies and inter-agent coordination. Existing deep learning-based ATSC approaches often rely on historical or current traffic states, resulting in a one-step temporal lag that limits real-time responsiveness. To address this, we propose a multi-agent traffic signal control framework that integrates a sequence-to-sequence (seq2seq) time-series prediction module with a value-decomposition network (VDN). The seq2seq model predicts future traffic states based on historical data, enabling proactive signal control. To further enhance temporal representation and decision stability, we incorporate a gated recurrent unit (GRU) into the VDN architecture. Experimental evaluations on two real-world traffic networks, Jinan12 and Hangzhou16, demonstrate that our method outperforms baseline models including independent deep Q-learning with GRU (IDQN-GRU) and standard VDN in Hangzhou16 datasets and achieve comparable performance with VDN in Jinan12 dataset.