1,721,129 research outputs found
Conclusion : reflections and lessons from the pandemic
No full textThis concluding chapter presents a summary of the research findings in the previous chapters, along with some reflections for each of the five themes of the book and a discussion of necessary future responses (post-pandemic or in the event of a new pandemic) and topics that require further exploration. The pandemic brought into sharp relief pre-existing social disparities and affected vulnerable populations the most. The economic impacts of the pandemic were diverse and varied by geography, but again certain geographies and economic sectors were more buffered from negative outcomes than others. A lesson and a challenge for policymakers is to find ways to understand and reduce these disparities, instead of pushing them under the rug. The impacts on mobility and travel were dramatic as total trips decreased, transit usage fell dramatically, and telecommuting and active modes of transportation increased. Some positive impacts included an improved air quality, a reduced number of traffic crashes, and a proliferation of walking and biking in some neighbourhoods. As cities are slowly recovering from the pandemic, the challenge is to keep the positive impacts but also find ways to help the transit industry rebound from its plunge. Long-term impacts of the pandemic in terms of changing patterns of work and work arrangements, shopping, recreation, and other human activities that will affect travel need additional time and more research to discern
Adaptive averaging in accelerated descent dynamics
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We study accelerated descent dynamics for constrained convex optimization. This dynamics can be described naturally as a coupling of a dual variable accumulating gradients at a given rate , and a primal variable obtained as the weighted average of the mirrored dual trajectory, with weights . Using a Lyapunov argument, we give sufficient conditions on and to achieve a desired convergence rate. As an example, we show that the replicator dynamics (an example of mirror descent on the simplex) can be accelerated using a simple averaging scheme. We then propose an adaptive averaging heuristic which adaptively computes the weights to speed up the decrease of the Lyapunov function. We provide guarantees on adaptive averaging in continuous-time, prove that it preserves the quadratic convergence rate of accelerated first-order methods in discrete-time, and give numerical experiments to compare it with existing heuristics, such as adaptive restarting. The experiments indicate that adaptive averaging performs at least as well as adaptive restarting, with significant improvements in some cases
Understanding Network Traffic States using Transfer Learning
No full textLarge-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow to exploit pretrained models and apply them to new domains. In this paper, we encode traffic states as images and use a pretrained deep convolutional neural network as a feature extractor. Experimental results show how the extracted feature vectors cluster naturally into meaningful network traffic states and illustrate how these network states can be used for traffic state prediction.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.Transport and Plannin
Design of Haptic Feedback Control for Steer-by-Wire
No full textThis paper illustrates a comparison of different haptic feedback control strategies; primarily focusing on open and closed-loop methods for a Force-Feedback Steer-by-Wire system. Due to shortcomings caused by the feedback motor impedance in the open loop architecture, the tracking performance is deteriorated. Consequently it is shown that the closed-loop solutions provide an improved response within the desired steering excitation range. The closed-loop possibilities, torque and position control, are designed and objectively compared in terms of performance and stability. The controller objectives are inertia compensation and reference tracking. For a given reference, the stability constraint between the controller gains responsible for the two objectives is contrasting in both the methods. Higher bandwidth is achieved for torque controller, whereas the driver arm inertia limits the position control performance. The linear system analysis is supported by the experimental results
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From Simulation to the Real World: Deep Reinforcement Learning for Training Robust, Wave-Smoothing Policies for Autonomous Vehicles
Get PDFWith the advent of autonomous vehicles (AVs) comes a broad array of possibilities for control. Looking beyond the immediate wave of research that is focused on training models that can drive safely, this work looks into the future, and aims to develop models for AVs that can achieve more than safe driving. Traffic dynamics are notoriously difficult to model and capture on the micro-level, with behaviors ranging from human-observable to ones we are not aware of, happening every second. Reinforcement learning (RL) is a method which is effective in capturing structure from highly complex, heavy, behavioral data. In this work, we use RL and leverage its ability to understand complex human-driver and traffic dynamics in order to develop policies that are able to not only drive, but drive in a way that can smooth traffic.With the goal of taking these traffic-smoothing algorithms to the real world, the aim of this work takes a path through three parts, from work done purely in simulation to a eventual 100-AV road test. We first explore the concept of using RL as a means of control for wave-smoothing policy control by examining experiments across a variety of traffic scenarios that demonstrate its effectiveness. This portion happens purely in simulation and explores various components of RL design, from environment design to reward shaping. With the goal of deployment always in mind, we also conduct research on how to develop RL policies that are robust enough to survive the transfer from simulation to the real world while sacrificing minimal performance. Lastly, the work comes together to explore the development and deployment of the MegaVanderTest, the deployment of 100 RL-enabled AVs, and to our knowledge, the largest test of AVs designed to smooth traffic
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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Routing strategies for the reliable and efficient utilization of road networks
Get PDFThe research presented in this dissertation aims to develop computationally tractable models and algorithms for the reliable and efficient utilization of capacity restricted transportation networks via route selection and demand redistribution, motivated by the fact that traffic congestion in road networks is a major problem in urban communities. Three related topics are considered, 1) route planning with reliability guarantees, 2) system optimal dynamic traffic assignment, and 3) controlling user equilibrium departure times.Route planning can in many practical settings require finding a route that is both fast and reliable. However, in most operational settings, only deterministic shortest paths are considered, and even when the link travel-times are known to be stochastic the common approach is to simply minimize the expected travel-time. This approach does not account for the variance of the travel-time and gives no reliability guarantees. In many cases, travelers have hard deadlines or are willing to sacrifice some extra travel-time for increased travel-time reliability, such as in commercial routing applications where delivery guarantees need to be met and perishables need to be delivered on time. The research presented in this dissertation develops fast computation techniques for the reliable routing problem known as the stochastic on-time arrival (SOTA) problem, which provides a routing strategy that maximizes the probability of arriving at the destination within a fixed time budget. Selfish user optimal routing strategies can, however, lead to very inefficient traffic equilibria in congested traffic networks. This "Price of Anarchy" can be mitigated using system optimal coordinated routing algorithms. The dissertation considers the system optimal dynamic traffic assignment problem when only a subset of the network agents can be centrally coordinated. A road traffic dynamics model is developed based on the Lighthill-Williams-Richards partial differential equation and a corresponding multi-commodity junction solver. Full Lagrangian paths are assumed to be known for the controllable agents, while only the aggregate split ratios are required for the non-controllable (selfish) agents. The resulting non-linear optimal control problem is solved efficiently using the discrete adjoint method. Spill-back from under-capacitated off-ramps is one of the major causes of congestion during the morning commute. This spill-back induces a capacity drop on the freeway, which then creates a bottleneck for the mainline traffic that is passing by the off-ramp. Therefore, influencing the flow distribution of the vehicles that exit the freeway at the off-ramp can improve the throughput of freeway vehicles that pass this junction. The dissertation studies the generalized morning commute problem where vehicles exiting the freeway at the under-capacitated off-ramp have a fixed desired arrival time and a corresponding equilibrium departure time schedule, and presents strategies to manipulated this equilibrium to maximize throughput on the freeway via incentives or tolls
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