11 research outputs found

    Multi-dimensional geometric complexity in urban transportation systems

    Get PDF
    Transportation networks serve as windows into the complex world of urban systems. By properly characterizing a road network, one can better understand its encompassing urban system. This study offers a geometrical approach toward capturing inherent properties of urban road networks. It offers a robust and efficient methodology toward defining and extracting three relevant indicators of road networks—area, line, and point thresholds—through measures of their grid equivalents. By applying the methodology to 50 U.S. urban systems, one can successfully observe differences between eastern versus western, coastal versus inland, and old versus young cities. Moreover, we show that many socioeconomic characteristics, as well as travel patterns, within urban systems are directly correlated with their corresponding area, line, and point thresholds

    Exploration of the Complex Similarity of Urban System Components

    Get PDF
    Similar to countless natural phenomena, cities have inherent orders that can be properly captured and expressed through a complex analysis of their components. Using Geographic Information Systems (GIS), this work offers a ring-buffer fractal approach to analyze the spatial characteristics of the components of an urban system. This approach was applied to road length, number of intersections, population+employment, and building gross floor area for the city of Chicago. The complex nature of these four components manifested itself in power-law relationships and represented by their fractal dimensions. Results showed that road length and number of intersections were closely related, albeit their fractal patterns followed slightly different trends. Additionally, population+employment and building gross floor area are significantly similar and one can explain the other. Moreover, the method developed in this study was able to identify the boundary of the old city of Chicago, highlighting its ability to capture hidden characteristics of an urban system. The proposed method could further be used to correlate complex properties of urban transportation systems to other relevant measures, including connectivity, accessibility, and mobility to name a few

    Geometric Complexity of Urban Road Networks

    No full text
    An urban system has a starting point when it has been founded, and from where it has spread into its current form. Previous research has suggested that no matter how an urban system has evolved, from a larger perspective it has inherent order and organization. As a result, cities are considered as complex systems consisting of many inter-related components and features. And similar to complex living organisms, they exhibit orderly characteristics that are lying beneath their physical forms. In order to better understand the complex nature of an urban system, studies have been focused on the characterization of its components. A transportation network provides a window into the complex world of its encompassing urban system, because they have followed the same path during their evolution. This work focuses on a better understanding of the complex geometric characteristics of urban road networks. It tries to develop novel methodologies to study and characterize them, which can in turn lead to a better understanding of their corresponding urban systems. Along that line, this study develops new methodologies to characterize the complex geometry of urban road networks and develops new indicators representing their unique multi-dimensional characteristics. The study also succeeds in uncovering the coupled geometric complexity of road networks and offers a novel approach towards their characterization

    Multi-Step Heuristic Method for Bus Terminal Location Problem

    No full text
    Bus terminals are one of the main facilities that have a key role in the collection and distribution of passengers within an urban transportation network. The location of a bus terminal strongly affects its performance. Owing to the increasing demand, it is sometimes necessary to add a new bus terminal to the existing urban bus network. Finding a proper location, however, is a challenge that is influenced by different transportation and socio-economic considerations, which in turn affects the surrounding land-use and traffic patterns. In this paper, a new multi-step heuristic method is proposed for the bus terminal location problem to identify a new bus terminal location based on the existing network as well as other transportation considerations and constraints. This is achieved by identifying the existing bus stops that have the greatest potential to be turned into a bus terminal. Other factors taken into consideration are the locations of the existing bus terminals, adjacent land-use, construction costs, node connectivity, and system accessibility. Owing to the multi-objective nature of the problem, a goal programming approach is used to formulate the objective function. To evaluate the proposed model, it was applied to the city of Shiraz in Iran. The results show that the model can provide acceptable and reliable outcomes

    Map of Road Density in Lyon, Chicago, Kolkata and Singapore

    No full text
    <p>This figure is part of the article "Intersections of Jane Jacobs’ Conditions for Diversity and Low-Carbon Urban Systems: Global Look at Four Cities" published in the Journal of Urban Planning and Development [full citation to follow once the article is published]. The figure shows road density of the Grand Lyon (France), Chicago (Cook County – United States), Kolkata (India), and Singapore (Singapore). Darker grid shades relate to higher densities. The black circles represent an area of 500 km2 so as to be able to compare cities with one another. Because data for Kolkata is incomplete, we only show the part of the city within the 500km2 buffer, but the city naturally expands much further. The data was collected from OpenStreetMap in February 2015. In line with OpenStreetMap license agreement, any figure made using OpenStreetMap data has to be publicly available. Since the published article is not open access, we decided to make the figure publicly available here. </p
    corecore