1,721,087 research outputs found
Air Temperature and Humidity Sensor Data on McGill University’s Campus
No full textThe CSV files contain data about air temperature and humidity readings taken from sensors located on McGill Campus between 1 June to 31 Dec 2022
“Eyes on the Street”: Estimating Natural Surveillance Along Amsterdam’s City Streets Using Street-Level Imagery
No full textNeighborhood safety and its perception are important determinants of citizens’ health and well-being. Contemporary urban design guidelines often advocate urban forms that encourage natural surveillance or “eyes on the street” to promote community safety. However, assessing a neighborhood’s level of natural surveillance is challenging due to its subjective nature and a lack of relevant data. We propose a method for measuring natural surveillance at scale by employing a combination of street-level imagery and computer vision techniques. We detect windows on building facades and calculate sightlines from the street level and surrounding buildings across forty neighborhoods in Amsterdam, the Netherlands. By correlating our measurements with the city’s Safety Index, we also validate how our method can be used as an estimator of neighborhood safety. We show how perceived safety varies with window level and building distance from the street, and we find a non-linear relationship between natural surveillance and (perceived) safety.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Human-Centred Artificial IntelligenceInternet of Thing
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Multi-Modal Multi-GPU based Parallel Micro Simulation: Bringing Scalability to Regional Transportation Simulation
No full textThis dissertation introduces a comprehensive framework for large-scale, multi-modal transportation simulations leveraging multi-GPU parallel computing to achieve unprecedented scalability in regional traffic modeling. With the rising complexities in urban mobility, traditional microscopic traffic simulation tools struggle to efficiently process the vast datasets required for modern regional analyses. This research addresses these challenges by developing LPSim, a multi-GPU-based simulation framework that utilizes graph partitioning techniques and vectorized data processing to facilitate efficient simulation across multiple GPUs.LPSim's ability to scale up to handle millions of Origin-Destination (OD) trips in metropolitan areas, such as the San Francisco Bay Area, is demonstrated through extensive case studies. These studies highlight the simulator's capability to accelerate traffic assignment and propagation processes by over 100 times compared to traditional CPU-based approaches. Additionally, this framework incorporates the Intelligent Driver Model (IDM) and supports integration with other traffic propagation models, ensuring adaptability for various research needs.The dissertation also explores the application of this framework in evaluating the impact of Urban Air Mobility (UAM) integration on existing ground transportation networks. By simulating multi-modal interactions between air and ground traffic, this research provides insights into the potential of UAM to alleviate congestion and reduce travel times in densely populated urban areas. Moreover, the simulation results underscore the importance of optimizing vertiport locations and designing efficient air corridors to maximize the benefits of UAM services. The simulator is also calibrated with the proposed DRBO calibration framework.This work advances the field of transportation simulation by offering a scalable, high-performance tool that enables researchers and policymakers to explore complex, regional-scale traffic dynamics with greater accuracy and efficiency. The proposed framework not only addresses current limitations in traffic simulation but also sets the stage for future innovations in urban transportation planning and management
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Technologies for Mobile ITS Applications and Safer Driving
Full text linkTechnologies for Mobile ITS Applications and Safer DrivingbyChristian Georges ManassehDoctor of Philosophy in Engineering - Civil and Environmental EngineeringUniversity of California, BerkeleyProfessor Raja Sengupta, ChairThis thesis presents a user application for safer driving. The technology to enable such an application is discussed and evaluated for the more general use in ITS mobile applications. Two main problems present themselves when building user-centric ITS mobile applications: the first has to do with knowing the state of the environment variables as captured by relevant sensing hardware, and the second has to do with relaying the right amount of information to the user to achieve the purpose of the application. For the first, we present a middleware architecture that abstracts sensor data into an HTTP interface to allow data consumers to discover and bind to data producers. For the second we present user preference adaptive services that would enable the application to learn from past user experience and adapt its user interaction to meet user preferences. Both research efforts are combined to field test a Smartphone traffic safety application system that is enabled by the middleware and that leverages the user preference adaptive services to enhance user experience and improve driver safety. The middleware is implemented and tested on three types of traffic mobility and safety applications to measure its performance. The performance measurements show the middleware to be efficient enough for road safety and congestion relief applications by limiting the overhead on the system to the order of 100 msec.A field test consisting of 9 drivers in the San Francisco Bay Area is conducted using a Smartphone traffic safety application built on the middleware. Results from the field test show that driver safety on the highway can be improved through a soft-safety warning of slow traffic 1-mile ahead on the driver's route. Data from the field test is then fed to a couple of user preference adaptive services to improve user experience of the Smartphone application. In the first service we construct a Support Vector Machine learning engine for each driver and predict to 75% accuracy their classification of favorable vs. non-favorable alerts. In the second service, we construct a Decision Tree model for the driver's previous destinations and predict with 96% accuracy their destination given their current location on the road, time of day, and day of week
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Mobile Reactive Systems over Bigraphical Machines - A Programming Model and its Implementation
Full text linkIn this dissertation we address the problem of bridging reactive programs and mobile computing machinery embedded in physical spaces with dynamic structure. We propose the BigActor Model as a bridging model between programs and logical-space models. The BigActor model [1] combines Hewitt and Agha's Actor model [2] for specifying concurrent reactive programs with Robin Milner's Bigraphical Model [3] for specifying the location and connectivity of the computing machines. The BigActor Model makes location and connectivity first-class citizens in distributed machines. This is analogous to another bridging model, the von Neumann machine, which makes first-class citizens of memory, instructions, and their sequentiality. The BigActor Programming Language (BAL) is an implementation of the BigActor Model. It has a runtime system named the BigActor Runtime System (BARS).The BARS targets an abstract machine (bigraphs). The abstract machine has to be realized on a physical space of mobile and distributed computing machines. The realization is produced by the Logical-Space Execution Engine (LSEE), which bridges bigraphs with the physical space. The Logical-Space Runtime System (LSRS) extends BARS with LSEE so that programs written in BAL can seamlessly execute over physical spaces.The second part of this dissertation is concerned with the formalization and implementation of the interactions between logical spaces and physical spaces. First, we approach this problem formally, by introducing the logical-space computing semantics. In logical-space computing, spatial agents operate over logical-space models while the runtime system is in charge of interacting with the physical space. We presented an implementation that follows the logical-space computing semantics. The LSRS uses the LSEE to generate logical-space models using bigraphs. The physical space is modelled using polygons defined using GPS coordinates. The spatial agents are bigActors. Our implementation programs robots and sensors in logical-space to execute an oil-spill monitoring exercise in the Atlantic. BigActor programs execute over BARS, which interacts with physical spaces through the LSEE. LSEE executes over the Robot Operating System (ROS) - an open-source middleware for robotics.The physical machinery used in the demonstration consisted of one Air Force UAV, three ground control stations, four drifters that broadcast their position using AIS, and one Navy vessel equipped with a small speedboat. The Portuguese Navy emulated the oil-spill by releasing 100kg of popcorn in the ocean
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Experimental Methods in Transportation Pricing: Applications to Employee Parking
Full text linkIn this dissertation, we develop two experimental methods for the problem of pricing or incentivizing use of a transportation service and apply them to the pricing of employee parking at the University of California, Berkeley. The University of California, Berkeley, with 23,962 employees is the largest employer in the eastern half of the San Francisco Bay Area and has a problem with employee parking. The university wants to explore a daily parking cash-out program, named the FlexPass, to make employees more mindful of their parking consumption. We use a Randomized Controlled Trial(RCT) to reveal the causal power of the cash-out. The RCT is applied to 392 employees, representing 10% of the university employees driving alone and parking, over three months using an IT system able to collect daily parking consumption, weekly commute mode reports and location data. The FlexPass treatment reduced consumption by 6.1% with high significance. Our second experiment is focused on measuring an incentive response curve. We use a repeated 2nd price reverse auction, in which 215 parking permit holders participate for 61 working days. Our method measures the incentive response curve for our subjects and we estimate the curve for the employee population using a quantile regression. We find the known and heavy overhead of repeated bidding can be removed by a lightweight IT system compressed of apps on iPhone and Android and a server in the cloud.Finally, we build a two-stage signaling game and design a variable-rate daily incentive scheme, where the incentive changes based on weekday and weather. The variable-rate daily incentive outperforms the fixed-rate daily incentive on both parking cruising times and leftover parking spaces
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Improving Traveler Information and Collecting Behavior Data with Smartphones
Full text linkThe recent growth of smartphones along with cheap, scalable cloud computing infrastructure has allowed for a plethora of new applications to be built. In transportation, two main efforts have been greatly impacted by this, delivering better traveler information to users, and collecting travel behavior information for researchers. This thesis describes 4 major efforts along these two themes; the development of a real-time transit trip planner, the evaluation of the value of real- time data, the development of a smartphone based automated travel diary system, and the design and evaluation of a behavior change experiment using the travel diary system. The first half of the thesis describes the technical development of the real-time transit trip planner along with experiments showing the positive impacts to travelers. Prior to this work, transit trip planners primarily used schedule data to route people through a transit network. This work is the first solution to the K- shortest paths problem to use real-time transit arrival data retrieved from a third party API. The algorithm then was implemented in an application called BayTripper, which serves over 1,000 users in the San Francisco Bay Area. The second half of the thesis describes the technical development of the automated travel diary system, which consists of battery efficient smartphone applications, server infrastructure to process data with trip determination algorithms, and web tools used to evaluate the accuracy of the system. The contribution to the literature is a catalogue of problems and related algorithmic solutions to building an end-to-end, battery efficient, automated travel diary. A behavior change experiment was designed and run using the automated travel diary system, which showed the potential for changes in users' awareness of their travel behavior, intentions to change, and short-term behavior change. This experiment represents a large scale test of the automated travel diary system, as well as a demonstration of using behavior change techniques, feedback and comparison, to promote sustainable travel behavior
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Urban Air Mobility: Deconstructing the Next Revolution in Urban Transportation - Feasibility, Capacity and Productivity
Full text linkOwing to a century of innovation in aircraft design, for the first time in history, air transport presents a potential competitive alternative to road, for hub-to-door and door-to-door urban services. In this dissertation, we first study the feasibility of uncongested air transport, for moving people and goods in an urban area, based on three metrics - enroute travel time, fuel cost and carbon dioxide (CO2) emissions. We estimate the metrics from emission standards and operational assumptions on vehicles based on current market data and compare electric air travel of near future to predominantly gasoline road travel of today. For passenger movement, air is faster than road for all distances. It fares better on fuel cost and emissions for longer distances (specific transition distances are stated in the main text). For consolidated movement of goods, air is at par or better than road dependent on the type of aircraft used. Finally, for movement of unconsolidated goods, air far outperforms road on all three metrics. To enable the feasible air-based services, a typical metropolitan region's airspace needs to accommodate traffic orders of magnitude higher than the manned airspace of today, while staying uncongested to deliver the afore-mentioned benefits. Hence we also develop methods to study the urban airspace capacity. We use our methods to evaluate the airspace capacity for a specific use case of goods movement under 400 feet (low altitude airspace) and find that with today's technologies at least 10,000 free routed small Unmanned Aircraft Systems (sUAS) flights per day can be safely enabled in the San Francisco Bay area. Better onboard technologies would only improve this number. Furthermore, our methods can be extended to evaluate the metropolitan airspace capacity to accommodate other use cases including movement of passengers and goods in a much wider band of airspace.Finally, we look at the energy efficiency, travel time and throughput trade-off between speed and direction control. We find that while maintaining a similar decent throughput, direction control is more energy efficient for enroute tactical resolution unless aircraft can be built with very high hover energy efficiency. However, speed control has a lower impact on travel time extension. Hovering capability additionally offers high flexibility for the type of operations that can be enabled in an urban airspace. Hence, the findings of this dissertation also have policy implications for the aircraft design industry for enabling Urban Air Mobility (UAM). It is quite noteworthy that all our results are based on a road-friendly urban design. Changes in design that facilitate easier access to air-based hub-to-door and door-to-door services, would only make the case stronger for UAM as the next revolution in urban transportation
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