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This research estimated the spatial-temporal distribution of humans in buildings through image sensing. Inputs were the in-building network, image sequences recording the movement of human,...

A methodology for optimizing variable pedestrian evacuation guidance in buildings with convex polygonal interior spaces is proposed. The optimization of variable guidance is a bi-level problem. The calculation of variable guidance based on the prediction of congestion and hazards is the upper-level problem. The prediction of congestion provided the variable guidance is the lower-level problem. A local search procedure is developed to solve the problem. The proposed methodology has three major contributions. First, a logistic regression model for guidance compliance behavior is calibrated using a virtual reality experiment and the critical factors for the behavior are identified. Second, the guidance compliance and following behaviors are considered in the lower-level problem. Third, benchmarks are calculated to evaluate the performance of optimized variable guidance, including the lower bound of the maximum evacuation time and the maximum evacuation time under a fixed guidance. Finally, the proposed methodology is validated with numerical examples. Results show that the method has the potential to reduce evacuation time in emergencies.

The mixed traffic flow has complex dynamics by nature. The kinematic differences between automobiles and motorcycles result to distinct driving behaviors. Traditional automobile-based traffic flow theory is not always suitable for mixed traffic streams. The purpose of this study is to observe from actual data a clearance boundary, called Safety Space, drivers maintain from other vehicles, and use it as a spatial filter to determine conflicts in mixed traffic flows. Image data are collected from an Unmanned Aerial Vehicle (UAV), and microscopic characteristics such as vehicle type, position, velocity, and trajectory are extracted through computer vision techniques. The Histogram of Oriented Gradients (HOG) feature and the Support Vector Machine (SVM) classifier are utilized for the vehicle detection, while the Kalman Filter is employed for the derivation of vehicle trajectories. The Safety Space is then determined based on those trajectories. Validation data are collected at intersections in Taipei, Taiwan; Bangkok, Thailand; and Mumbai, India. The vehicle detection and tracking are satisfactory, and the Safety Space surrogate reveals risk zones caused by spatial proximity between vehicles.

Routing decisions for rescue and evacuation are critical for emergency-response missions. For infrastructures with large and complex geometric spaces, routing decisions could be facilitated with automated approaches for higher efficiency and accuracy. This research aims to respond to emergencies in buildings. In this work, network analysis is combined with building information modeling (BIM) models. Building geometry is retrieved from the BIM model for the construction of a graph, which is an essential element in network modeling. Rescue routes based on a graph can be determined depending on actual building layouts and conditions. In this paper, medial axis transform (MAT) is utilized for the graph construction from BIM models. A time-dependent vehicle routing problem (TDVRP) model is formed for the planning of rescue routes. Risk based on building materials and congestion based on pedestrian flow are to be adapted for network edge costs to enable a lowest utility route finding in the future, and ...

After a disaster hits an urban area, well-organized response operations are required to efficiently mitigate the chaotic situation. Efficient distribution of resources, such as heavy construction equipment, is critical to urban search and rescue operations. The efficiency of these lifesaving operations depends on the delivery of required resources. Challenges within resource allocation operations for disaster response have been identified, which include the convergence of resources to the disaster-affected areas, lack of detailed information of available resources, and inefficient decision making for resource deployment. These challenges are obstacles to efficient search-and-rescue operations. This paper presents an innovative approach that facilitates immediate equipment distribution in response to disasters. The equipment control structure, inspired by the behavior control structure of honeybees’ foraging, enables decentralized and collective decision-making for equipment prioritization and distribution...

This paper presents an information-technology-based collaboration framework that facilitates disaster response operations. The collaboration framework incorporates a web collaboration service, radio frequency identification (RFID) tags, a building blackbox system (BBS), a geo-database, and a geographic information system (GIS). Through the integration of these technologies, the framework provides a collaboration medium for first responders, including civil engineers, to cohesively respond to disasters. Access to critical building information, such as construction documents, through the BBS supports assessments of building integrity during disaster response. Building assessment information is stored on RFID tags, which are accessible to first responders through digital devices via a wireless ad hoc network. With on-site assessment information shown on a digital map, decision makers locate, collect, and distribute critical resources through the GIS to first responders. In addition, the decision makers at distributed locations evaluate the incident through discussion sessions, hosted by the web collaboration environment, for integrated decision making. Test-bed simulations for the framework have been carried out with encouraging results at the training ground of the Illinois Fire Service Institute.

The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis’ definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice.

The performance of an emergency medical service (EMS) depends on the existing infrastructure and allocation of medical resources. It is challenging to implement an efficient EMS owing to the spatial complexity of the population and geographical layout within urban areas. Management of ambulance headquarters and hospitals, which are part of the physical infrastructure, should be assessed to ensure a timely response. The objective of this research is to propose a general assessment process for a prehospital EMS by evaluating the physical infrastructure. A geographic information system is adapted to visualize and analyze the EMS of a region EMS from various perspectives. In the assessment process, the EMS is evaluated with incident data and the transportation network is evaluated through spatial and temporal analyses such as service area, clustering, and spatial interpolation. As a case study, the process is applied to New Taipei City for the assessment of its prehospital EMS. The results have shown ...

During emergencies, the efficiency of first response deployment is critical. Once the assignments are decided for the distribution of first responders, the deployment efficiency for the tea...

Civil engineers always face the challenge of uncertainty in planning, building, and maintaining infrastructure. These works rely heavily on a variety of surveying and monitoring techniques. Unmanned aerial vehicles (UAVs) are an effective approach to obtain information from an additional view, and potentially bring significant benefits to civil engineering. This paper gives an overview of the state of UAV developments and their possible applications in civil engineering. The paper begins with an introduction to UAV hardware, software, and control methodologies. It also reviews the latest developments in technologies related to UAVs, such as control theories, navigation methods, and image processing. Finally, the paper concludes with a summary of the potential applications of UAV to seismic risk assessment, transportation, disaster response, construction management, surveying and mapping, and flood monitoring and assessment.