115 research outputs found

    TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

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    Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.Comment: 13 pages, 12 figure

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Metropolitan Research

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    Metropolitan research requires multidisciplinary perspectives in order to do justice to the complexities of metropolitan regions. This volume provides a scholarly and accessible overview of key methods and approaches in metropolitan research from a uniquely broad range of disciplines including architectural history, art history, heritage conservation, literary and cultural studies, spatial planning and planning theory, geoinformatics, urban sociology, economic geography, operations research, technology studies, transport planning, aquatic ecosystems research and urban epidemiology. It is this scope of disciplinary - and increasingly also interdisciplinary - approaches that allows metropolitan research to address recent societal challenges of urban life, such as mobility, health, diversity or sustainability

    Metropolitan Research: Methods and Approaches

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    Metropolitan research requires multidisciplinary perspectives in order to do justice to the complexities of metropolitan regions. This volume provides a scholarly and accessible overview of key methods and approaches in metropolitan research from a uniquely broad range of disciplines including architectural history, art history, heritage conservation, literary and cultural studies, spatial planning and planning theory, geoinformatics, urban sociology, economic geography, operations research, technology studies, transport planning, aquatic ecosystems research and urban epidemiology. It is this scope of disciplinary - and increasingly also interdisciplinary - approaches that allows metropolitan research to address recent societal challenges of urban life, such as mobility, health, diversity or sustainability

    Evidence-based stragegies to inform urban design decision-making: the case of pedestrian movement behaviour.

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    Walking is an essential mode of transportation, and pedestrian movement is a major influencing parameter in city design. Due to the complexity of pedestrian behaviour, new insights concerning the significance of factors affecting walking are challenging to obtain without the use of technology. Furthermore, despite the impact of decision-making in the design of buildings and places, there is currently a limited understanding concerning how urban design decisions are best made. This research aims to “assess the adoption of, and opportunities deriving from, data-driven innovation techniques in the design of urban spaces, by the analysis of pedestrian movement patterns in urban environments, and to evaluate how the integration of evidence-based strategies can be established in supporting decision-making in relation to future urban designs”. The research focuses on two groups of stakeholders: Decision-makers in designing buildings and places and End-users undertaking walking activities within urban space. In addressing the aim, a range of research methodologies has been developed and trialled. The work centres on an extended case study concerning a retail high-street locale in London, UK. This study makes several contributions to the immediate field of urban design research. Firstly, the findings advance the research methods applied to study pedestrian movement in urban environments. Secondly, the results offer real impact in practice by demonstrating the value and importance of adopting data-driven innovation techniques in decision-making processes in urban design via the adoption of a quantitative data- driven, evidence-based methodological framework. Thirdly, the findings support decision-making by presenting a novel methodological framework to assess pedestrian routing in urban environments utilising the classification of pedestrian behaviours and spatial visibility interactions. Finally, this study raises awareness of the critical challenges and opportunities, priorities, and potential development areas for applying evidence- based strategies in informing building and urban design decisions. The research presents a series of recommendations for enhancing data-driven innovation techniques in urban design decision-making processes.Natural Environmental Research (NERC)PhD in Environment and Agrifoo

    Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

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    Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents

    Constructing pedestrian-centric street mobility: Observation and simulation for design

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    There are three principal components to the research presented in this thesis: a video-observation study of pedestrian behaviours and interactions with traffic, leading to the development of an agent-based digital simulation, and demonstrating the potential of this simulation for designing pedestrian-centric interventions in the streetscape. The long-term objective is to devise streetscapes that responsively adapt to the needs of pedestrians. Since the advent of car culture in the late 1930s, the approaches to street design have prioritised efficient motorised traffic flow, restricting walking and neglecting the pedestrian point of view. In recent years, however, a growing interest in making urban spaces more pedestrian-friendly has emerged, popularising concepts such as walkability, shared space, and traffic calming. These approaches aim to promote active travel and reduce car dependency in order to mitigate congestion, pollution, accidents and other harms. Urban studies have concentrated primarily on pedestrian-only zones and utilised spatial features as a way to reach pedestrian-friendly streets. Meanwhile, transport studies have tended to approach the street from a throughput and vehicle-oriented stance. Despite these endeavours, pedestrian-oriented approaches appear to lack systematic consideration of pedestrian behaviours as they interact with motor vehicles and street infrastructure. My PhD research differs from prior studies by focusing on these behaviours and interactions to support a pedestrian-oriented street mobility system. The current design of streets communicates to pedestrians via its structures and signs, such as barriers, crossings, and lights, while its capacity to respond and adapt is minimal. In contrast, this thesis argues that, since the street environment is inherently dynamic, we should analyse its dynamics and design the street to be responsive. Through responsiveness, my aim is to increase the convenience of pedestrian movement whilst creating a safe experience. This PhD asks the question 'how to design a pedestrian-centric street system that dynamically manages street mobility?'. The research takes a practice-based and reflective approach, designing agent-based simulations based on a qualitative observational study. Designing a simulation accomplishes two things: 1) it creates a space for implementing and evaluating possible design interventions, and 2) it prompts new insights into the behavioural processes of pedestrians. My research has followed an iterative cycle in line with second-order cybernetics: in two feedback loops, the first study informed the second study while the second informed the first. The video observation of street behaviours particularly explored pedestrian decision and interaction processes, identifying pedestrians’ own observational strategies and their varying levels of risk-taking. These aspects are reflected in the simulation. The first chapter introduces the pedestrian issues on the street and sets out the key concepts in pedestrian-centric street design. The second chapter examines the literature and existing practice that addresses pedestrian and vehicle interactions on the street. Chapter three sets out the theoretical framework and the following chapter describes the methodology. The three subsequent chapters present the following studies: (1) understanding the context by conducting qualitative video observation in a real street environment to observe and document the relations between streets, pedestrians and vehicles; (2) creating an artificial pedestrian society for simulation purposes, using agent-based modelling, both to refine the understanding developed through video analysis and to create a platform for experimentation; (3) design and implementation of prototype responsive interventions within the simulation, focusing on localised changes in the environment to empower pedestrians. The last chapter reflects on these projects by discussing the research contributions in terms of methods, techniques, and practices. The methodological innovation includes combining qualitative and computational tools as well as the use of simulation and video analysis in an iterative and reflexive cycle. Theoretical contributions include evaluating streets through pedestrian dynamics, creating a taxonomy of existing pedestrian interventions according to their spatial and temporal impacts, and rethinking the street as a responsive environment. The practical component advances the technical state of the art by expanding the capabilities of pedestrian agents when negotiating with vehicles and making crossing decisions and demonstrates the potential for designing novel interventions in the streetscape, including those that respond to pedestrian behaviour. The last chapter, also, emphasises the role of reflective design practice and the place of simulation within it
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