Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.

Construction is among the leading industries/activities contributing the largest carbon footprint. This review paper aims to promote awareness of the sources of carbon footprint in the construction industry, from design to operation and management during manufacturing, transportation, construction, operations, maintenance and management, and end-of-life deconstruction phases. In addition, it summarizes the latest studies on carbon footprint reduction strategies in different phases of construction by the use of alternative additives in building materials, improvements in design, recycling construction waste, promoting the utility of alternative water resources, and increasing efficiencies of water technologies and other building systems. It was reported that the application of alternative additives/materials or techniques/systems can reduce up to 90% of CO₂ emissions at different stages in the construction and building operations. Therefore, this review can be beneficial at the stage of conceptualization, design, and construction to assist clients and stakeholders in selecting materials and systems; consequently, it promotes consciousness of the environmental impacts of fabrication, transportation, and operation.

This article proposes a review map for comparative designs of wireless high-power transfer (WHPT) systems using single- and double-resonance blocks. In-depth analyses and key guidelines are provided to design high-efficiency WHPT systems while keeping zero-phase-angle/unity-power-factor and constant - current/voltage supply to the load. Basic single- and double-resonance blocks based on LC- resonant circuits are analyzed. Then, resonant S- and T- blocks are recommended as the best transmission blocks for competitive designs. The proposed approach is applied to map recent developments on the wireless charging technologies for electric vehicles (EVs), especially for weak-coupling systems and dynamic-charging technologies. The proposed design approach offers a systematic and effective methodology to quickly evaluate current technologies and solutions for WHPTs in EVs applications. In extension, this article indicates different control strategies for WHPTs, especially for optimal-efficiency tracking. Design guidelines and control strategies are provided to achieve the maximum efficiency at a standard resonance frequency against variations from loads and coupling coefficients in operation which can easily map to recent research works and future research directions. Experimental verifications of dominant designs are also presented to validate the proposed approach. The map for comparative designs and control structures presented in this article aims to serve as a guideline and to ease the initial steps for other researchers in this area.

Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (ITS), it is necessary to apply appropriate forecasting methods for matching circumstances in a timely manner. This paper conducts a comprehensive review study focusing on literatures, including modern techniques proposed recently, related to travel time and traffic condition predictions that are based on ‘data-driven' approaches. Based on the underlying mechanisms and theoretical principles, different approaches are categorized as parametric (linear regression and time series) and non-parametric approaches (artificial intelligence and pattern searching). Then, the approaches are analysed for their strengths, potential weaknesses, and performances from five main perspectives that are prediction range, accuracy, efficiency, applicability, and robustness.