Real-world Cities Research Articles

URBAN DESIGN ANALYSIS OF NEW YORK CITY’S VIRTUAL MODEL - THE CASE OF TOM CLANCY’S THE DIVISION

People have started spending time with digital tools and virtual worlds to escape reality's horrors. However, designed spaces are more than the players' needs, especially those digital games that their stories involve urban environments. This inefficiency causes spending futile efforts both in time and cost for the digital games' productions; The urban environments in these digital games are replicas of real-world cities. Some companies use some techniques for downgrading replicas. Therefore, this study aims to uncover the used techniques for designing Tom Clancy's The Division (2016). By using reverse engineering methodology and qualitative comparative analysis, the in-game map compared with the real-world map. Based on the results, the used techniques allowed the designers to scale down the game environment to be 2.5 times smaller than the actual city. Rather, verisimilitude is achieved by combining sufficiently accurate elements to give the impression of complete accuracy. By implementing the results of this research, designers can develop smaller replicas to be perceived as more extensive.

Generative methods for Urban design and rapid solution space exploration

Rapid population growth and climate change drive urban renewal and urbanization at massive scales. New computational methods are needed to better support urban designers in developing sustainable, resilient, and livable urban environments. Urban design space exploration and multi-objective optimization of masterplans can be used to expedite planning while achieving better design outcomes by incorporating generative parametric modeling considering different stakeholder requirements and simulation-based performance feedback. However, a lack of generalizable and integrative methods for urban form generation that can be coupled with simulation and various design performance analysis constrains the extensibility of workflows. This research introduces an implementation of a tensor-field–based generative urban modeling toolkit that facilitates rapid design space exploration and multi-objective optimization by integrating with Rhino/Grasshopper ecosystem and its urban analysis and environmental performance simulation tools. Our tensor-field modeling method provides users with a generalized way to encode contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs such as network directionality, desired densities of streets, amenities, buildings, and people as forces that modelers can weigh. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs. We present a case study to demonstrate the proposed framework's flexibility and applicability and show how modelers can identify design and environmental performance synergies that would be hard to find otherwise.

Automatic Itinerary Planning Using Triple-Agent Deep Reinforcement Learning

Automatic itinerary planning that provides an epic journey for each traveler is a fundamental yet inefficient task. Most existing planning methods apply heuristic guidelines for certain objective, and thereby favor popular preferred point of interests (POIs) with high probability, which ignore the intrinsic correlation between the POIs exploration, traveler’s preferences, and distinctive attractions. To tackle the itinerary planning problem, this paper explores the connections of these three objectives in probabilistic manner based on a Bayesian model and proposes a triple-agent deep reinforcement learning approach, which generates 4-way direction, 4-way distance, and 3-way selection strategy for iteratively determining next POI to visit in the itinerary. Experiments on five real-world cities demonstrate that our triple-agent deep reinforcement learning approach can provide better planning results in comparison with state-of-the-art multiobjective optimization methods.

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

AbstractSpatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

Modeling Urban Morphology by Unifying Diffusion-Limited Aggregation and Stochastic Gravitation

More than 30 years ago, Diffusion-Limited Aggregation (DLA) has been studied as mechanism to generate structures sharing similarities with real-world cities. Recently, a stochastic gravitation model (SGM) has been proposed for the same purpose but representing a completely different mechanism. Both approaches have advantages and disadvantages, while e.g. the dendrites emerging via DLA are visually very different from real-world cities, the SGM does not preserve undeveloped locations close to the city center. Here we propose a unification of both mechanisms, i.e. a particle moves randomly and turns into an urban site with a probability that depends on the proximity to already developed sites. We study the cluster size distributions of the structures generated by both models and find that SGM generates more balanced distributions. We also propose a way to assess to which extent the largest cluster is a primate city and find that in both models, beyond certain parameter value, the size of the largest cluster becomes inconsistent with being drawn from the same distribution of remaining clusters.

Development of Penambang Boat Driving Cycle to Evaluate Energy Consumption and Emissions

Penambang Boat is a service that has been developed in the Kuala Terengganu River to boost the public transport network in Terengganu. As far as real-world cities are concerned, the Penambang Boat process is important for each running waterway to increase energy efficiency and reduce pollution. This research study is to define and validate the parameters of this driving cycle and to develop the Penambang Boat driving cycle on the Kuala Terengganu River throughout its operation path, to analyze fuel economy and emissions using Advance Vehicle Simulator (ADVISOR) software. Through the analysis of energy consumption and emissions, the last two of the micro trips were chosen and the real driving cycles are developed. The results show that the fuel consumption is 8.7 l/100km, the CO emission is 3.479 g/km, the HC emission is 3.718 g/km and lastly the NOx emission is 0.003 g/km. Penambang Boat has been shown the needs to reduce the power consumption and thus minimize the impacts on the environment and economy.

Decentralized Optimization of Vehicle Route Planning—A Cross-City Comparative Study

The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing: Knowing the origin and destination of each vehicle in the network can allow for coordinated real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being “altruistic,” i.e., willing to accept alternative less-preferred routes. We conduct a study to compare different levels of agent altruism in decentralized vehicles coordination and the effect on the network-level traffic performance. This work introduces novel load-balancing scenarios of traffic flow in real-world cities for varied levels of agent altruism. We show evidence that the new decentralized optimization router is more effective with networks of high load.

Optimizing Transportation Dynamics at a City-Scale Using a Reinforcement Learning Framework

Urban planners, authorities, and numerous additional players have to deal with challenges related to the rapid urbanization process and its effect on human mobility and transport dynamics. Hence, optimize transportation systems represents a unique occasion for municipalities. Indeed, the quality of transport is linked to economic growth, and by decreasing traffic congestion, the life quality of the inhabitants is drastically enhanced. Most state-of-the-art solutions optimize traffic in specific and small zones of cities (e.g., single intersections) and cannot be used to gather insights for an entire city. Moreover, evaluating such optimized policies in a realistic way that is convincing for policy-makers can be extremely expensive. In our work, we propose a reinforcement learning frameworks to overtake these two limitations. In particular, we use human mobility data to optimize the transport dynamics of three real-world cities (i.e., Berlin, Santiago de Chile, Dakar) and a synthesized one (i.e., SynthTown). To this end, we transform the transportation dynamics’ simulator MATSim into a realistic reinforcement learning environment able to optimize and evaluate transportation policies using agents that perform realistic daily activities and trips. In this way, we can assess transportation policies in a manner that is convincing for policy-makers. Finally, we develop a model-based reinforcement learning algorithm that approximates MATSim dynamics with a Partially Observable Discrete Event Decision Process (PODEDP) and, with respect to other state-of-art policy optimization techniques, can scale on big transportation data and find optimal policies also on a city-scale.

A Task Assignment Method for Sweep Coverage Optimization Based on Crowdsensing

One of the keys for the success of sweep coverage is to organize the participants to patrol effectively in large-scale target areas in order to satisfy the quality requirements of the sweep tasks. In this article, we first analyze the possibility of applying crowdsensing technology for sweep coverage and propose a framework to solve the problem of arranging participants to sweep large-scale target areas when the quality requirements change dynamically over time. First, this problem is formulated as a task assignment problem with the goal of maximizing social welfare. Then, we establish a sweep coverage quality model for the area, which is a different approach from conventional methods that focus on the point of interest (PoI), and we propose a participant incentive model that considers the sustainability of the crowdsensing platform. Since determining the optimal assignment solution is an NP-hard problem, we design two approximate algorithms to arrange participants with the goal of maximizing social welfare; these are the participant-task oriented search (PTOS) algorithm based on a two-stage greed search and the bipartite graph-based participant search (BGPS) algorithm. We evaluate our methods using a population density map dataset from real-world cities as the large-scale target area and create dynamic task requirements for the sweep coverage. The experimental results show that the performance of our two algorithms is significantly better than the performance of the baseline algorithm.

Procedural city generation beyond game development

The common trend in the scientific inquiry of urban areas and their populations is to use real-world geographic and population data to understand, explain, and predict urban phenomena. We argue that this trend limits our understanding of urban areas as dealing with arbitrarily collected geographic data requires technical expertise to process; moreover, population data is often aggregated, sparsified, or anonymized for privacy reasons. We believe synthetic urban areas generated via procedural city generation, which is a technique mostly used in the gaming area, could help improve the state-of-the-art in many disciplines which study urban areas. In this paper, we describe a selection of research areas that could benefit from such synthetic urban data and show that the current research in procedurally generated cities needs to address specific issues (e.g., plausibility) to sufficiently capture real-world cities and thus take such data beyond gaming.

Characterizing Aggregated Exposure to Primary Particulate Matter: Recommended Intake Fractions for Indoor and Outdoor Sources.

Exposure to fine particulate matter (PM2.5) from indoor and outdoor sources is a leading environmental contributor to global disease burden. In response, we established under the auspices of the UNEP/SETAC Life Cycle Initiative a coupled indoor-outdoor emission-to-exposure framework to provide a set of consistent primary PM2.5 aggregated exposure factors. We followed a matrix-based mass balance approach for quantifying exposure from indoor and ground-level urban and rural outdoor sources using an effective indoor-outdoor population intake fraction and a system of archetypes to represent different levels of spatial detail. Emission-to-exposure archetypes range from global indoor and outdoor averages, via archetypal urban and indoor settings, to 3646 real-world cities in 16 parametrized subcontinental regions. Population intake fractions from urban and rural outdoor sources are lowest in Northern regions and Oceania and highest in Southeast Asia with population-weighted means across 3646 cities and 16 subcontinental regions of, respectively, 39 ppm (95% confidence interval: 4.3-160 ppm) and 2 ppm (95% confidence interval: 0.2-6.3 ppm). Intake fractions from residential and occupational indoor sources range from 470 ppm to 62 000 ppm, mainly as a function of air exchange rate and occupancy. Indoor exposure typically contributes 80-90% to overall exposure from outdoor sources. Our framework facilitates improvements in air pollution reduction strategies and life cycle impact assessments.

City development strategy: theoretical background, themes, and building blocks

ABSTRACTCity development strategy (CDS) has transformed numerous cities in developing countries to cope with the unprecedented challenges of the twenty-first century. However, the CDS literature indicates that the needs, culture, and planning background of a city determine its CDS themes and methodologies. Drawing upon various CDS experiences, this paper provides an overview of the theory underpinning CDS, its themes, and building blocks to elucidate a more comprehensive understanding of CDS as it applies to real-world cities that have employed CDS and those who plan to do so in the future to confront their challenges and achieve their goals. This paper conducts a comprehensive systematic review, inclusive of journal articles and international CDS implementation literature as previously reported by international agencies and local governments. From this review, a number of recommendations have emerged for cities wishing to apply CDS. This paper makes a significant contribution to the CDS literature by clarifying the theoretical background, themes, and building blocks that are features of successful CDS implementation.

A GIS-based urban simulation model for environmental health analysis

This paper introduces an urban simulation model for environmental health analysis (SIENA). SIENA is a novel tool to explore urban interactions and processes with regard to exposure assessments. It is based on urban structures and relationships observed in real-world cities making it a realistic representation of a functioning city. The development of SIENA involved identifying and quantifying fundamental processes and similarities in urban areas in Great Britain and using those to guide the building of SIENA within a GIS. An internal validation confirmed SIENA's realism. Its generality, achieved through the pooling of information from different real-world cities, makes it particularly useful for developing and testing spatial epidemiological concepts and models; simulating processes and interactions relating to environmental exposure; and exploring theoretical and methodological problems in the spatial analysis of environmental health. SIENA ultimately provides a much needed tool in the form of a controlled, simplified urban simulation model.

A GIS toolset for automated partitioning of urban lands

Urban growth models operating at finer spatial scale usually incorporate a subdivision module that carries out automated partitioning of the lands selected for future development. In this paper, we describe the development and implementation of Parcel-Divider – a GIS toolset for automated subdivision of land parcels. In addition to automating the process of generating urban layouts such as city blocks, streets and cadastral lots, the toolset is capable of extending roads to new subdivisions. Researchers can integrate the toolset into their modeling while planners can use it as a standalone application to visualize scenarios of infrastructure arrangements in growing areas of the city. Our tool-generated subdivision configurations closely match the subdivision styles observed in real-world cities when compared visually as well as statistically.

Approach-and-Roam (AaR): A Geographic Routing Scheme for Delay/Disruption Tolerant Networks

Considering that delay/disruption-tolerant networks (DTNs) suffer from a large variation of network topology, geographic routing is an alternative scheme that utilizes real-time geographic information instead of network topology information. However, the real-time geographic information of mobile destination is likely unavailable due to sparse network density. With this in mind, we propose a geographic routing scheme by relying on historical geographic information to estimate the movement range of destination. The idea is to make efficient message replication toward this estimated range via the proposed Approach Phase to reduce routing overhead. Meanwhile, the effective message replication within this range is guaranteed via the proposed Roam Phase to increase the message delivery ratio. We further propose a novel scheme to handle the local maximum problem for geographic routing in sparse networks. Simulation results obtained assuming the maps of three real world cities, namely, Helsinki, Finland; Karlsruhe, Germany; and Manhattan, New York City, USA, show an advantage of the proposed Approach-and-Roam (AaR) over the compared algorithms in terms of delivery ratio, average delivery latency, and overhead ratio.

Exploring the patterns and evolution of self-organized urban street networks through modeling

As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

Interactive example-based urban layout synthesis

We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.

Customizing Visualization in Three-Dimensional Urban GIS via Web-Based Interaction

This paper presents our research results of visualizing three-dimensional 3D city models in a web explorer environment under the supports of the digital building model, the digital terrain model, large-scale true urban orthoimage, and actual building sidewall pictures. A system has been designed and developed under a client/server architecture, which integrates the Internet Geographic Infor- mation System with multitier web applications for creation of a 3D city model. The front-end client provides a user interface for setting the properties of the 3D city model, and the back-end server processes these parameters and generates a 3D model, which is then sent back to the Virtual Reality Modeling Language-enabled World Wide Web browser for display and navigation. The technology for display and navigation of the 3D model is implemented using Java Server Pages/Servlet technology built under multitier web architecture. The technology for data accessing and processing is implemented through Java programming language. The displayer is linked to the database, where the required GIS spatial data are stored. The results from our experiments demonstrated that this developed system is able to display and visualize 3D city data in a web environment for public accesses. Using the sidewall pictures taken from real city buildings, we refill the white walls of urban buildings into a real appearance as seen in our real world cities. The player provides several click buttons to enhance the effect and position of visualization, including rotation, zoom, pan, seek, etc. The users can easily view the details of a specified building or/and the entire city in a moving 3D manner, and locate a specific building.

Alphaworld: The urban design of a digital city

This paper describes the urban design of the digital city called AlphaWorld. AlphaWorld is a digital community created in 1995 and accessed daily through the Internet by thousands of users. Unlike other digital communities, AlphaWorld's users may also settle land and construct objects within the world. The design and content of these constructions are determined autonomously, and the cumulative result of this settlement is an organically evolved digital city. AlphaWorld's urban form differs substantially from real-world cities in the absence of many of the constraints which govern the design of those cities: permanent population, land cost, zoning and transportation. The paper examines AlphaWorld's urban design at both the regional and neighbourhood scale. At the regional scale, settlement in AlphaWorld is highly concentrated at its centre but highly deconcentrated elsewhere because of transportation constraints. At the neighbourhood scale, AlphaWorld lacks organizing design features and is very low-density because land has no cost and is settled in an ad hoc fashion. The resulting urban fabric is visually chaotic and difficult to navigate. The freedom of settlement action offered by AlphaWorld allows for individual creativity at the cost of larger-scale urban order. The paper concludes by placing AlphaWorld in the context of real-world urban places. Three urban theories--ideal morphologies, heterotopia and self-organizing systems--show that AlphaWorld is unlike any real-world city but reflects many elements of real-world urban design.