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Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite the promising results that indicate the superiority of deep RL in terms of the quality of solution and optimality over fixed time signal control, the real-world multi-modal traffic flows, especially pedestrians, are not properly considered nor sufficiently investigated. This study presents a novel deep RL-based adaptive traffic signal model to control the vehicles and pedestrian flows by allocating an equitable green time to each, aiming at minimizing “total user delays” as opposed to “total vehicle delays” dominantly being used in the literature. Our proposed intelligent vehicle pedestrian light (IVPL) method can perform in the absence or presence of pedestrians, especially when there is jaywalking at the intersection, interrupting vehicle flows. To this end, an extended reward function is designed to capture delays due to vehicle-to-vehicle, vehicle-to-pedestrian, and pedestrian-to-pedestrian interactions, as well as red-light delays for vehicles and pedestrians. To evaluate the performance of IVPL, a microsimulation model of an intersection in city of Melbourne is used as a case-study. The real traffic signal parameters of an existing operation system (SCATS) are employed, and the simulation is calibrated using video-based camera data and loop detectors data collected at intersection. The experimental results demonstrate the superiority of the proposed model over fully actuated traffic signal, not only in terms of the quality of optimal solution, but also considering the fact that the proposed model can minimize the “total user delays”.

This paper presents a conceptual hybrid model that embodies a synergistic approach by integrating principles from operations research (OR) with steps from the design science research methodology (DSRM). The model is applied to develop an evacuation support system for a group of hospitals located in Western Sydney, Australia, exposed to the risk of floods induced by climate change. By combining these two approaches, the conceptual hybrid model effectively overcomes the limitations associated with traditional DSRM and provides a comprehensive research framework for addressing OR problems. The research findings hold significant implications for academia, professionals, and policy makers engaged in the field of disaster risk management. Moreover, this study offers valuable insights into enhancing the effectiveness and efficiency of OR-based solutions in practical scenarios, thus contributing to the advancement of knowledge in this area.

Near future safety risk evaluation is a critical step towards adaptive traffic safe operation at a smart intersection. This paper proposes a data-driven model that can quickly evaluate simulated safety risks for use in adaptive operational interventions. A traffic micro-simulation model was utilised to generate conflicts-based data for developing the machine learning model. Conflict indicators including time to collision, TTC, and post encroachment time, PET, were used to identify safety risk. Supervise learning models such as linear regression and machine learning models including random forest and extreme Gradient Boosting (XGBoost) were employed to evaluate risk indices for adaptive operations. In total, 9 models were trained, and XGBoost were found to outperform the other algorithms with 0.87 of the overall accuracy. The findings of this study contribute to the development of edge computing traffic operation system accounting safety.

Extreme weather events such as floods are predicted to become increasingly common and severe as the climate changes. Effectively functioning hospitals are critical to a community's resilience to the adverse health impacts of such events. Yet many hospitals have not been designed with extreme weather risks in mind and are built in flood-prone areas, raising concerns about their ability to support community healthcare needs when they eventuate. While considerable research has been conducted on developing disaster responses to maintain healthcare services in the face of floods, there is a paucity of research on hospital evacuating planning. Addressing this critical gap in research, this paper explores current state-of-the-art hospital evacuation transportation models to identify key factors for developing more effective evacuation planning for future flooding risks. A new modelling framework is proposed, which addresses the limitations of existing hospital evacuation models and proposes factors that should be incorporated into future models to enhance community resilience to growing flood risks due to climate change.

The frequency and severity of floods is increasing in many countries, and hospitals play an essential role in protecting the community from their potentially devastating impacts. However, many hospitals are vulnerable to flooding having been designed and located without these risks in mind. In such situations, evacuation of vulnerable patients to safer alternative healthcare facilities might be the most reasonable response. However, there has been little research into hospital evacuation, and to address this research gap, this paper presents an innovative hospital evacuation model that can facilitate the optimal scheduling and sequencing of hospital evacuation under indeterministic flooding conditions. The proposed model combines a flood simulator and a mathematical model. Assuming the high levels of complexity of a realistic hospital evacuation system, a BendersDecomposition approach is developed to cope with large size problems. A case study in New South Wales (NSW), Australia, is used to illustrate the proposed model, and results confirm that the model can generate effective plans to evacuate hospitals in the shortest time possible in flood emergency conditions. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about disaster response strategies in hospitals and have implications for health service management and health policymakers in countries where health infrastructures are vulnerable to increasing climate change risks.

[EN] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.

The frequency and intensity of both human-made and natural disasters are predicted to increase, and hospitals play a critically important role in reducing injury and mortality rates. However, there is increasing evidence that many hospitals are vulnerable to disasters, and more effective strategies are needed to enable the safe evacuation of patients to alternative healthcare centres. Transport planning is central to this process, but there has been no systematic and critical review of the research on this critically important challenge. This means there is no collective synthesis of this literature for hospital managers, policymakers, and researchers to refer to in addressing these important vulnerabilities. This paper reports the findings of a critical literature review to contribute practical insights for health facilities planning and management decision-making in a context where both the likelihood and consequences of natural disasters are increasing in many countries.

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.