Designing and Operating Safe and Secure Transit Systems: Assessing Current Practices in the United States and Abroad, MTI Report 04-05

Public transit systems around the world have for decades served as a principal venue for terrorist acts. Today, transit security is widely viewed as an important public policy issue and is a high priority at most large transit systems and at smaller systems operating in large metropolitan areas. Research on transit security in the United States has mushroomed since 9/11; this study is part of that new wave of research. This study contributes to our understanding of transit security by (1) reviewing and synthesizing nearly all previously published research on transit terrorism; (2) conducting detailed case studies of transit systems in London, Madrid, New York, Paris, Tokyo, and Washington, D.C.; (3) interviewing federal officials here in the United States responsible for overseeing transit security and transit industry representatives both here and abroad to learn about efforts to coordinate and finance transit security planning; and (4) surveying 113 of the largest transit operators in the United States. Our major findings include: (1) the threat of transit terrorism is probably not universal—most major attacks in the developed world have been on the largest systems in the largest cities; (2) this asymmetry of risk does not square with fiscal politics that seek to spread security funding among many jurisdictions; (3) transit managers are struggling to balance the costs and (uncertain) benefits of increased security against the costs and (certain) benefits of attracting passengers; (4) coordination and cooperation between security and transit agencies is improving, but far from complete; (5) enlisting passengers in surveillance has benefits, but fearful passengers may stop using public transit; (6) the role of crime prevention through environmental design in security planning is waxing; and (7) given the uncertain effectiveness of antitransit terrorism efforts, the most tangible benefits of increased attention to and spending on transit security may be a reduction in transit-related person and property crimes

Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Walking and cycling in an automated future: a Dutch-Australian comparison

Technological mobility innovation is poised to accelerate, with the advent of Automated Vehicles (AVs) predicted to improve road safety, reduce transport costs, increase access to mobility, and to hasten Electric Vehicle adoption. Although AV technology is evolving rapidly, consumer preferences for AV ownership and use, as well as the potential impacts of AVs on walking and cycling are not well understood. This research compares contextual conditions, attitudes and AV adoption likelihood in two contrasting locales: car-friendly Sydney, Australia and walk/cycle-friendly The Randstad, Netherlands. The research focuses on travel behaviour for short trips, where walking and cycling have traditionally held an advantage over motor vehicles. The research uses a mixed-methods approach that uses analytical methods (a comparison of locales), qualitative methods (semi-structured interviews) and quantitative methods (discrete choice analysis). The qualitative research reveals that Dutch participants feel that all road users have the same right to use road space, and should have the same expectation of safety. In contrast, Australian participants express impatience with “other people” walking, and score the importance of safety for car occupants as higher than for non-occupants. This highlights that attitudes towards non-car modes in some localities present a risk factor for further marginalisation of walking and cycling in an AV future. The quantitative research reveals that mode choice retention is highest for those who currently cycle, and that protected bicycle infrastructure is likely to encourage bicycle use in an AV future. Walking is also encouraged by the provision of separated infrastructure and is more popular for shopping in the Netherlands, where trips are more frequent and cargo-carrying requirements are lower

Investigation of Strategic Deployment Opportunities for Unmanned Aerial Systems (UAS) at INDOT

Unmanned aerial systems (UAS) are increasingly used for a variety of applications related to INDOT’s mission including bridge inspection, traffic management, incident response, construction and roadway mapping. UAS have the potential to reduce costs and increase capabilities. Other state DOTs and transportation agencies have deployed UAS for an increasing number of applications due to technology advances that provide increased capabilities and lower costs, resulting from regulatory changes that simplified operations for small UAS under 55 pounds (aka, sUAS). This document provides an overview of UAS applications that may be appropriate for INDOT, as well as a description of the regulations that affect UAS operation as described in 14 CFR Part 107. The potential applications were prioritized using Quality Function Deployment (QFD), a methodology used in the aerospace industry that clearly communicates qualitative and ambiguous information with a transparent framework for decision making. The factors considered included technical feasibility, ease of adoption and stakeholder acceptance, activities underway at INDOT, and contribution to INDOT mission and goals. Dozens of interviews with INDOT personnel and stakeholders were held to get an accurate and varied perspective of potential for UAVs at INDOT. The initial prioritization was completed in early 2019 and identified three key areas: UAS for bridge inspection safety as a part of regular operations, UAS for construction with deliverables provided via construction contracts, and UAS for emergency management. Descriptions of current practices and opportunities for INDOT are provided for each of these applications. An estimate of the benefits and costs is identified, based on findings from other agencies as well as projections for INDOT. A benefit cost analysis for the application of UAS for bridge inspection safety suggests a benefit cost over one for the analysis period

A framework for the synergistic integration of fully autonomous ground vehicles with smart city

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This paper proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

Reinforcement learning in a multi-agent framework for pedestrian simulation

El objetivo de la tesis consiste en la utilización de Aprendizaje por refuerzo (Reinforcement Learning) para generar simulaciones plausibles de peatones en diferentes entornos. Metodología Se ha desarrollado un marco de trabajo multi-agente donde cada agente virtual que aprende un comportamiento de navegación por interacción con el mundo virtual en el que se encuentra junto con el resto de agentes. El mundo virtual es simulado con un motor físico (ODE) que está calibrado con parámetros de peatones humanos extraídos de la bibliografía de la materia. El marco de trabajo es flexible y permite utilizar diferentes algoritmos de aprendizaje (en concreto Q-Learning y Sarsa(lambda) en combinación con diferentes técnicas de generalización del espacio de estados (en concreto cuantización Vectorial y tile coding). Como herramientas de análisis de los comportamientos aprendidos se utilizan diagramas fundamentales (relación velocidad/densidad), mapas de densidad, cronogramas y rendimientos (en términos del porcentaje de agentes que consiguen llegar al objetivo). Conclusiones: Tras una batería de experimentos en diferentes escenarios (un total de 6 escenarios distintos) y los correspondientes analisis de resultados, las conclusiones son las siguientes: - Se han conseguido comportamientos plausibles de peatones -Los comportamientos son robustos al escalado y presentan capacidades de abstracción (comportamientos a niveles táctico y de planificación) -Los comportamientos aprendidos son capaces de generar comportamientos colectivos emergentes -La comparación con otro modelo de peatones estandar (Modelo de Helbing) y los análisis realizados a nivel de diagramas fundamentales, indican que la dinámica aprendida es coherente y similar a una dinámica de peatones