ADVERTISEMENT

Using Computational Models To Improve Street Planning

Nowadays, we are continually facing mobility issues caused by the high number of circulating automobiles and people. These issues are more familiar to large cities, such as metropolis and megalopolis, but we can already note locomotion issues to a lesser extent in countryside cities.

Lack of mobility is a problem that usually causes several other sub-problems, such as the lack of street mobility, increasing of prices related to the transportation of people and goods, as well as reducing the efficiency of time-sensitive services. The latter refers to the case of the displacement of ambulances, fire engines, and police-related vehicles. We have a few possibilities to solve (or lessen) such problems, for instance, through the better planning of neighborhoods and cities, allocation of facilities (hospitals, schools, and police stations) in places of easy access to other regions of a city, or even avoiding less access-friendly sites.

ADVERTISEMENT

For such tasks, computer-based techniques showed promising features in both urban design and street planning. In the last decade, complex networks, which are the basis of our methods, have been used to model real and synthetic systems, from neural connections and power grids to street networks and sewage systems. Complex networks are a powerful toolset to model data focusing on the relations between the entities found in a given data set. These networks, as mathematical models, stand out due to their algebraic properties and computing potential, with analytic applicability to brace cognitive processes of knowledge discovery and decision-making. In the case of cities, the entities of the network are street crossings and the relationships are streets connecting two entities. This way, the resulting network preserves not only the mobility information of cities’ streets but also their geometry and distance.

Through street meshes modeled as complex networks, this research contributes with a toolset to track and reduce intrinsic problems in the urban design of cities caused by the misallocation of points of interest. Such problems are referred to as urban inconsistencies. The toolset tracks inconsistencies regarding displacements of both pedestrians and automobile vehicles. At the same time, it suggests relocations of points of interest to reduce the number of inconsistencies. Our results were validated over the Brazilian city of Sao Carlos, showing how the techniques behave in a real-world example using both quantitative and qualitative analysis.

More specifically, the toolset assumes that street networks should provide the shortest routes among different destinations of the same kind (hospitals, police stations, and schools); when considering a site that is closer to a point of interest by geodesic distance and closer to another by shortest (directed or undirected) path distance, the site is regarded as an inconsistency. To the task of reducing urban inconsistencies, the toolset tracks different sites in the street network that are proper to bear a point of interest. In other words, the algorithm greedily searches for places that reduce rather than eliminate inconsistencies. These places are the output of the algorithm, and they support tasks of urban data-driven decision-making. It is noteworthy mentioning that this process is suitable both for enhancing the location of existing cities’ points of interest and also for planning entire cities and neighborhoods from scratch.

Although we focus on pedestrians and automobiles, the proposal is suitable for any variety of on-surface transportation (e.g., bicycles, and buses, to name a few), as they follow the paths of street networks. On the whole, concerning other domains, our proposal can track routing problems of computer networks, aid the topological design of electronic circuits, enhance the location of facilities in transportation networks, and much more.

ADVERTISEMENT

These findings are described in the article entitled Detecting multi-scale distance-based inconsistencies in cities through complex-networks, recently published in the Journal of Computational Science.

Comments

READ THIS NEXT

Pharmacognosy In The Digital Era

Human life has always depended on natural resources. Nature provides our housing, food, fragrances, and oils, and is our pharmacy. […]

2018 Spending Bill Drastically Increases Funding For Scientific Research

Scientists and researchers around the United States may have reason to rejoice today, as the US has just passed it’s […]

A Simple And Accurate Optical Instrument For Characterizing Optical Properties Of A Material

Light is made of electromagnetic waves, which are composed of a time-varying electric field and time-varying magnetic field. The time-varying […]

Valve-In-Valve As An Alternative To Redo Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR) is a percutaneous procedure that emerged relatively recently as a substitute for the invasive open […]

Sympathy Of Nanoparticles With Primary Astrocytes Leads To Neurotoxicity

Nanoscience and nanotechnology offer extremely optimistic possibilities to be applied in various disciplines of science and technology. Nobel laureate Richard […]

Should You Stop Having Children? The Science, Climate, and Statistics of Population Growth

When the editors of the popular United Kingdom-based publication The Guardian decided to publish an article with the title, “Want […]

Why Are There More Species Packed In Some Places Than Others, And Why Does It Matter?

THE QUESTION Biodiversity  – the richness of species found at a given location – is critical to maintaining ecological processes […]

Science Trends is a popular source of science news and education around the world. We cover everything from solar power cell technology to climate change to cancer research. We help hundreds of thousands of people every month learn about the world we live in and the latest scientific breakthroughs. Want to know more?