Hasil untuk "City planning"

Tuba Bakici, E. Almirall, J. Wareham

J. Carter, G. Cavan, Angela Connelly et al.

The significant shifts in climate variables projected for the 21st century, coupled with the observed impacts of ongoing extreme weather and climate events, ensures that adaptation to climate change is set to remain a pressing issue for urban areas over the coming decades. This volume of Progress in Planning seeks to contribute to the widening debate about how the transformation of cities to respond to the changing climate is being understood, managed and achieved. We focus particularly on spatial planning, and building the capacity of this key mechanism for responding to the adaptation imperative in urban areas. The core focus is the outcomes of a collaborative research project, EcoCities, undertaken at the University of Manchester's School of Environment and Development. EcoCities drew upon inter-disciplinary research on climate science, environmental planning and urban design working within a socio-technical framework to investigate climate change hazards, vulnerabilities and adaptation responses in the conurbation of Greater Manchester, UK. Emerging transferable learning with potential relevance for adaptation planning in other cities and urban areas is drawn out to inform this rapidly emerging international agenda. Approaches to build adaptive capacity challenge traditional approaches to environmental and spatial planning, and the role of researchers in this process, raising questions over whether appropriate governance structures are in place to develop effective responses. The cross-cutting nature of the adaptation agenda exposes the silo based approaches that drive many organisations. The development of a collaborative, sociotechnical agenda is vital if we are to meet the climate change adaptation challenge in cities.

V. Watson

C. Calvillo, A. Sánchez-Miralles, J. Villar

T. Crainic, N. Ricciardi, Giovanni Storchi

Fengchao Chen, Tingmin Wu, Van Nguyen et al.

Large Language Models (LLMs) have enabled agents to move beyond conversation toward end-to-end task execution and become more helpful. However, this helpfulness introduces new security risks stem less from direct interface abuse than from acting on user-provided content. Existing studies on agent security largely focus on model-internal vulnerabilities or adversarial access to agent interfaces, overlooking attacks that exploit users as unintended conduits. In this paper, we study user-mediated attacks, where benign users are tricked into relaying untrusted or attacker-controlled content to agents, and analyze how commercial LLM agents respond under such conditions. We conduct a systematic evaluation of 12 commercial agents in a sandboxed environment, covering 6 trip-planning agents and 6 web-use agents, and compare agent behavior across scenarios with no, soft, and hard user-requested safety checks. Our results show that agents are too helpful to be safe by default. Without explicit safety requests, trip-planning agents bypass safety constraints in over 92% of cases, converting unverified content into confident booking guidance. Web-use agents exhibit near-deterministic execution of risky actions, with 9 out of 17 supported tests reaching a 100% bypass rate. Even when users express soft or hard safety intent, constraint bypass remains substantial, reaching up to 54.7% and 7% for trip-planning agents, respectively. These findings reveal that the primary issue is not a lack of safety capability, but its prioritization. Agents invoke safety checks only conditionally when explicitly prompted, and otherwise default to goal-driven execution. Moreover, agents lack clear task boundaries and stopping rules, frequently over-executing workflows in ways that lead to unnecessary data disclosure and real-world harm.

Helge Ritter, Otthein Herzog, Kurt Rothermel et al.

We attempt to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes defining a city's overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major "traditional" urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multi-modal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from Terabytes of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a "social AI in a city ecosystem" that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures for human culture, city resilience and sustainability.

Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz et al.

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage, and (b) provide high quality-of-service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves much lower peak resource usage compared to manual scheduling without compromising on the quality-of-service.

Matthew Lai, Keegan Go, Zhibin Li et al.

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatio-temporal constraints remain computationally intractable for classical methods at real-world scales. Existing multi-arm systems deployed in the industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally-generated environments with diverse obstacle layouts, robot configurations, and task distributions. It employs a graph representation of scenes and a graph policy neural network trained through reinforcement learning to generate trajectories of multiple robots, jointly solving the sub-problems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to new capabilities such as fault-tolerant planning and online perception-based re-planning, where rapid adaptation to dynamic task sets is required.

Muhammad Imran Zaman, Usama Ijaz Bajwa, Gulshan Saleem et al.

Vision sensors are becoming more important in Intelligent Transportation Systems (ITS) for traffic monitoring, management, and optimization as the number of network cameras continues to rise. However, manual object tracking and matching across multiple non-overlapping cameras pose significant challenges in city-scale urban traffic scenarios. These challenges include handling diverse vehicle attributes, occlusions, illumination variations, shadows, and varying video resolutions. To address these issues, we propose an efficient and cost-effective deep learning-based framework for Multi-Object Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for object detection and employs Non-Maximum Suppression (NMS) to select target objects from overlapping detections. Transfer learning is employed for re-identification, enabling the association and generation of vehicle tracklets across multiple cameras. Moreover, we leverage appropriate loss functions and distance measures to handle occlusion, illumination, and shadow challenges. The final solution identification module performs feature extraction using ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed framework is evaluated on the 5th AI City Challenge dataset (Track 3), comprising 46 camera feeds. Among these 46 camera streams, 40 are used for model training and validation, while the remaining six are utilized for model testing. The proposed framework achieves competitive performance with an IDF1 score of 0.8289, and precision and recall scores of 0.9026 and 0.8527 respectively, demonstrating its effectiveness in robust and accurate vehicle tracking.

Leonardo Rosa Amado, Adriano Vogel, Dalvan Griebler et al.

Data pipeline frameworks provide abstractions for implementing sequences of data-intensive transformation operators, automating the deployment and execution of such transformations in a cluster. Deploying a data pipeline, however, requires computing resources to be allocated in a data center, ideally minimizing the overhead for communicating data and executing operators in the pipeline while considering each operator's execution requirements. In this paper, we model the problem of optimal data pipeline deployment as planning with action costs, where we propose heuristics aiming to minimize total execution time. Experimental results indicate that the heuristics can outperform the baseline deployment and that a heuristic based on connections outperforms other strategies.

Gaëtan Laziou, Rémi Lemoy

A good understanding of cities is crucial to implement urban planning policies leading to social and economic sustainability and an efficient use of resources. While urban concentration has been associated with both positive and negative effects, echoing debates on compact cities, few studies have documented how density evolves with city size. We fill this gap by investigating how the population density radial structure changes across the urban hierarchy. Our results uncover strong regularities in urban settlements. In terms of density, cities can be seen as exponential cones which evolve homothetically with city population. This rather simple but universal geometric structure of cities provides a new spatial scaling law, which is an important step forward in understanding how cities work and grow. Some deviations can be observed, which mainly oppose dense cities in the developing world and sprawled cities in high-income countries, associated with high energy use per capita. This suggests that urban lifestyle in wealthiest countries has come at the price of negative impacts on environmental outcomes. This research has a broad range of applications as it provides a powerful tool to compare cities of different sizes.

Wayne S Singh

This paper investigates the relationship between smart city initiatives and evolving urbanization trends in the United States. The research addresses the critical issue of rapid urban growth in the U.S. and explores how innovations within the smart city paradigm influence urban development. Utilizing principles from Urban Complexity Theory, this study identifies four key variables relevant to smart cities and their impact on urbanization: smart city technology, government policy, environmental sustainability, and socioeconomic factors. A mixed-method approach, combining quantitative and qualitative methodologies, was employed. A web-based survey (n=50) utilizing a five-point Likert scale was conducted among residents of Manhattan, New York, and Capitol Hill, Seattle. Results indicate that the implementation of smart city technologies is significantly associated with shifts in population density, land use diversification, and enhanced infrastructure dynamics. Additionally, residents demonstrated preferences for smart cities based on efficient urban mobility, environmental sustainability, and personal socioeconomic improvements. The findings highlight essential considerations for urban planners, policymakers, and employers. This study concludes that incorporating the identified influential factors into strategic urban planning optimizes city development to better accommodate growing urban populations.

Ramesh Anguluri, P. Narayanan

Kamiba I. Kabuya, Olasupo O. Ajayi, Anotine B. Bagula

The "Smart City" (SC) concept has been around for decades with deployment scenarios revealed in major cities of developed countries. However, while SC has enhanced the living conditions of city dwellers in the developed world, the concept is still either missing or poorly deployed in the developing world. This paper presents a review of the SC concept from the perspective of its application to cities in developing nations, the opportunities it avails, and challenges related to its applicability to these cities. Building upon a systematic review of literature, this paper shows that there are neither canonical definitions, models or frameworks of references for the SC concept. This paper also aims to bridge the gap between the "smart city" and "smart village" concepts, with the expectation of providing a holistic approach to solving common issues in cities around the world. Drawing inspiration from other authors, we propose a conceptual model for a SC initiative in Africa and demonstrate the need to prioritize research and capacity development. We also discuss the potential opportunities for such SC implementations in sub-Saharan Africa. As a case study, we consider the city of Lubumbashi in the Democratic Republic of Congo and discuss ways of making it a smart city by building around successful smart city initiatives. It is our belief that for Lubumbashi, as with any other city in Sub-Saharan Africa, the first step to developing a smart city is to build knowledge and create an intellectual capital.

Laura J. Martin

This position paper distinguishes restoration from rewilding and argues for the establishment of a slow restoration movement. Repair takes time. Restoration is an active and ongoing process that unites insights and methods from ecology and landscape architecture and design. Slow restoration acknowledges that repair is a never-ending process, one in which people care for other beings and attempt to undo the harms caused by centuries of colonialism, consumption, and death.

Victoria Fernandez-Añez

El concepto de Ciudad Inteligente ha evolucionado desde marcos sectoriales específicos a otros más holísticos que enfatizan la gobernanza y la participación de las partes interesadas, pero existe una brecha crítica entre las estrategias y la ejecución de proyectos en el mundo real. Se explora la dinámica entre los discursos que rodean a las Ciudades Inteligentes y su implementación tangible, con un enfoque específico en la Iniciativa de Ciudad Inteligente de Milán. Mediante el uso de un modelo conceptual validado, esta investigación identifica (a) a las partes interesadas clave en las iniciativas de Ciudad Inteligente, (b) los proyectos ejecutados y (c) los desafíos encontrados en el camino. El modelo se aplica para el análisis de una visión integrada de los Proyectos de Ciudad Inteligente dentro de una ciudad y, por otro lado, la síntesis de la diversidad de puntos de vista de las partes interesadas sobre la. Opone y compara estos dos enfoques para comprender la brecha entre la visión de las partes interesadas y la implementación de la Estrategia de Ciudad Inteligente. Al emplear un enfoque de estudio de caso centrado en Milán, este artículo no solo aclara las características de una ciudad inteligente del consumidor sino que también aborda las implicaciones más amplias del modelo se gobernanza.

Marit L. Wilkerson, M. Mitchell, Danielle F. Shanahan et al.

How green spaces in cities benefit urban residents depends critically on the interaction between biophysical and socio-economic factors. Urban ecosystem services are affected by both ecosystem characteristics and the social and economic attributes of city dwellers. Yet, there remains little synthesis of the interactions between ecosystem services, urban green spaces, and socio-economic factors. Articulating these linkages is key to their incorporation into ecosystem service planning and management in cities and to ensuring equitable outcomes for city inhabitants. We present a conceptual model of these linkages, describe three major interaction pathways, and explore how to operationalize the model. First, socio-economic factors shape the quantity and quality of green spaces and their ability to supply services by influencing management and planning decisions. Second, variation in socio-economic factors across a city alters people’s desires and needs and thus demands for different ecosystem services. Third, socio-economic factors alter the type and amount of benefit for human wellbeing that a service provides. Integrating these concepts into green space policy, planning, and management would be a considerable improvement on ‘standards-based’ urban green space planning. We highlight the implications of this for facilitating tailored planning solutions to improve ecosystem service benefits across the socio-economic spectrum in cities.

Robert Klar, Anna Fredriksson, Vangelis Angelakis

Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the port's digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.

Mamdouh Alenezi

In today's world, many cities are embracing cutting-edge technology and transforming into "smart cities". These emerging innovations are revolutionizing the standard of living for people, and as a result, smart city infrastructure development has become a major focus for city planners and policymakers worldwide. The goal is to create more livable, sustainable, and efficient urban environments, and software engineering plays a crucial role in achieving this. In this article, we will delve into what makes a city "smart" and what it means for the future. We will explore the software engineering roadmap for smart city infrastructure development, highlighting the goals and challenges that come with this innovative approach to urban planning. Our aim is to provide valuable insights into the importance of software engineering in achieving successful smart city infrastructure development. As cities continue to grow and evolve, it is essential to adopt new technologies that can help us build smarter, more sustainable communities. Smart city initiatives are paving the way for a brighter future, and software engineering is at the forefront of this movement. By understanding the software engineering roadmap for smart city infrastructure development, we can work towards creating more livable, efficient, and sustainable urban environments for generations to come.