Efficient People Movement through Optimal Facility Configuration and Operation

There are a variety of circumstances in which large numbers of people gather and must disperse. These include, for example, carnivals, parades, and other situations involving entrance to or exit from complex buildings, sport stadiums, commercial malls, and other type of facilities. Under these situations, people move on foot, commonly, in groups. Other circumstances related to large crowds involve high volumes of people waiting at transportation stations, airports, and other types of high traffic generation points. In these cases, a myriad of people need to be transported by bus, train, or other vehicles. The phenomenon of moving in groups also arises in these vehicular traffic scenarios. For example, groups may travel together by carpooling or ridesharing as a cost-saving measure. The movement of significant numbers of people by automobile also occurs in emergency situations, such as transporting large numbers of carless and mobility-impaired persons from the impacted area to shelters during evacuation of an urban area. This dissertation addresses four optimization problems on the design of facilities and/or operations to support efficient movement of large numbers of people who travel in groups. A variety of modeling approaches, including bi-level and nonlinear programming are applied to formulate the identified problems. These formulations capture the complexity and diverse characteristics that arise from, for example, grouping behavior, interactions in decisions by the system and its users, inconvenience constraints for passengers, and interdependence of strategic and operational decisions. These models aim to provide: (1) estimates of how individuals and groups distribute themselves over the network in crowd situations; (2) an optimal configuration of the physical layout to support large crowd movement; (3) an efficient fleet resource management tool for ridesharing services; and (4) tools for effective regional disaster planning. A variety of solution algorithms, including a meta-heuristic scheme seeking a pure-strategy Nash equilibrium, a multi-start tabu search with sequential quadratic programming procedure, and constraint programming based column generation are developed to solve the formulated problems. All developed models and solution methodologies were employed on real-world or carefully created fictitious examples to demonstrate their effectiveness

Large-scale Evacuation Routing and Scheduling Optimization with Uninterrupted Traffic Flow

In many emergency management operations, an efficient evacuation strategy is of great importance because if it is successful, it has the ability to significantly reduce the loss of property and human life. This thesis develops a routing and scheduling optimization framework for large-scale vehicular evacuation. To guarantee high optimization efficiency, we consider the routing and scheduling optimization as a two-stage problem instead of optimizing them as a whole (i.e. using time-space network). In the first phase, a multiple-objective binary programming model, with the objectives of minimizing the network clearance time and total in-network time is proposed to find an optimal routing plan. In the second phase, a simulation-based scheduling Heuristic is proposed to dynamically generate the time-dependent departure rates. A real-world evacuation scenario in Eastern Shore of Maryland is studied by using the proposed optimization model. The calculation results indicate a good optimization capability and flexibility of the proposed model

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Stations as Nodes

At the main point of intersection between the railway and the city, stations are key elements in the organization of the intermodal transport as well as catalysts of urban developments in metropolises, medium and small cities. The focus of this publication is to explore the enrichment of a renewed approach of railway stations as intermodal nodes, therefore acting as breeding grounds for both urban and social developments. This book has been initiated and built upon several activities currently running at the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute), Delft University of Technology (DIMI, Delft Deltas Infrastructure Mobility Initiative and Department of Architecture of the Faculty of Architecture and the Built Environment) and University of Paris-Est (l’École d’Urbanisme de Paris). These activities have been framed within the context of two rapidly developing metropolitan areas: Randstad in the Netherlands and Métropole du Grand Paris in the Ile de France. This volume forms the basis for a research on the ‘role of stations in future metropolitan areas’ with the ambition to link the two countries, learning from their different cities and distinct geographical context through comparable mobility challenges on the levels of the inner city, suburban and peripheral areas. In line with these considerations, in 2018 AMS Institute, TU Delft/ DIMI and the Dutch Embassy in Paris with Atelier Néerlandais organized a successful workshop: ‘Stations of the Future’, in collaboration with La Fabrique de la Cité. Together with Dutch and French planning entities, involving mass transit operators and railway companies, this workshop focused on several case studies in both metropolitan areas to understand the role of station hubs as intermodal nodes. During this joint French-Dutch event that took place in Paris, we spoke on topics like Station as intermodal node, Station as destination and Station as data center, including a debate on the relation between public space and architecture, densification and programming of station areas, pedestrian flows management and the integration of data. Following the Paris workshop, the summer school ‘Integrated Mobility Challenges in Future Metropolitan Areas’ was organised by AMS Institute and Delft University of Technology/DIMI with the collaboration of the ARENA architectural research network, University of Paris-Est and the City of Amsterdam. This 8-day workshop extended the debate among international young professionals, academics and master students by looking at an important rail-metro node in the metropolitan area of the city Amsterdam: Sloterdijk Station – a crucial hub in a bigger urban area for mobility and exchange, and for urban growth. The main question was: which approaches and scenarios can be tested and applied to these intermodal nodes, particularly when dealing with lack of space and growing number of users? The results were four very different plans to improve the Sloterdijk Station area and to make the station a ‘future proof’ intermodal hub. In this publication, invited experts from practice and knowledge institutes in France and the Netherlands share their common experience and draw on specific aspects and problems of conception, management and development of stations. A brief overview of the results of the two initiatives ‘Stations of the Future’ and the summer school ‘Integrated Mobility Challenges in Future Metropolitan Areas’ is here illustrated, accompanied by photo reportages of both events and by a curated reportage of the Amsterdam Sloterdijk station area

A self-learning intersection control system for connected and automated vehicles

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the chain impact of taking random actions in the training course, the trained model can deal with unprecedented volume circumstances, the main challenge in intersection management. Application of the model to a single-lane intersection with no turning movement as a proof-of-concept test reveals noticeable improvements in traffic measures compared to three other intersection control systems. A Spring Mass Damper (SMD) model is developed to control platooning behavior of CAVs. In the SMD model, each vehicle is assumed as a mass, coupled with its preceding vehicle with a spring and a damper. The spring constant and damper coefficient control the interaction between vehicles. Limitations on communication range and the number of vehicles in each platoon are applied in this model, and the SMD model controls intra-platoon and inter-platoon interactions. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 29% and 63% under 50% and 100% market penetration rate of CAVs. A merging section with different volume combinations on the main section and merging section and different market penetration rates of CAVs is also modeled to test inter-platoon spacing performance in accommodating merging vehicles. Noticeable travel time reduction is observed in both mainline and merging lanes and under all volume combinations in 80% and higher MPR of CAVs. For a more reliable assessment of the DSCLS, the model is applied to a more realistic intersection, including three approaching lanes in each direction and turning movements. The proposed algorithm decreases delay by 58%, 19%, and 13% in moderate, high, and extreme volume regimes, improving travel time accordingly. Comparison of safety measures reveals 28% improvement in Post Encroachment Time (PET) in the extreme volume regime and minor improvements in high and moderate volume regimes. Due to the limited acceleration and deceleration rates, the proposed model does not show a better performance in environmental measures, including fuel consumption and CO2 emission, compared to the conventional control systems. However, the DSCLS noticeably outperforms the other pixel-reservation counterpart control system, with limited acceleration and deceleration rates. The application of the model to a corridor of four interactions shows the same trends in traffic, safety, and environmental measures as the single intersection experiment. An automated intersection control system for platooning CAVs is developed by combining the two proposed models, which remarkably improves traffic and safety measures, specifically in extreme volume regimes compared to the regular DSCLS model