The study of geographical systems as graphs, and networks has gained significant momentum in the academic literature as these systems possess measurable and relevant network properties. Crowd-based sources of data such as OpenStreetMaps (OSM) have created a wealth of worldwide geographic information including on transportation systems (e.g., road networks). In this work, we offer a Geographic Information Systems (GIS) protocol to transfer polyline data into a workable network format in the form of; a node layer, an edge layer, and a list of nodes/edges with relevant geographic information (e.g., length). Moreover, we have developed an ArcGIS tool to perform this protocol on OSM data, which we have applied to 80 urban areas in the world and made the results freely available. The tool accounts for crossover roads such as ramps and bridges. A separate tool is also made available for planar data and can be applied to any line features in ArcGIS.
Climate change will impact urban infrastructure networks by changing precipitation patterns in a region. This study presents a novel vulnerability assessment framework for infrastructure networks against extreme rainfall-induced flash floods, with a specific application to transportation. The framework combines climate models, network science, geographical information systems (GIS), and stochastic modeling to compile a vulnerability surface (VS). Daily precipitation simulations for 2006–2100 from the Community Climate System Model, version 4 (CCSM4), are used to produce a stochastic simulation of extreme flash flood events in five U.S. cities—that is, Boston, Massachusetts; Houston, Texas; Miami, Florida; Oklahoma City, Oklahoma; and Philadelphia, Pennsylvania—under two different climate scenarios (RCP4.5 and RCP8.5). To assess the impact of these events, percentage drops in static (i.e., overall properties and robustness topological indicators) and dynamic (i.e., GIS accessibility and travel demand metrics) network properties are measured before and after simulated extreme events. The results of these metrics are inputs on a radar diagram to form a VS. Overall, the results show that changes in flash flood frequency due to climate change can have a significant impact on road networks, as was demonstrated recently in Houston, Texas. The magnitude of these impacts is chiefly associated with the geographic location of the cities and the size of the networks. The proposed framework can be reproduced in any city around the world, and researchers can use the results as guidelines for infrastructure design and planning purposes. Moreover, sensitivity analysis to varying greenhouse gas concentration trajectories can help local and national authorities to prioritize strategies for adaptation to climate change in more vulnerable regions.
Many commuters find themselves stranded during natural disasters like typhoons. In the Tokai region in Japan, many road sections become heavily congested during typhoons, with some commuters reporting homebound trips taking more than four times longer than usual because of road flooding at several locations. Although large typhoons are considered extreme events (in terms of magnitude), they occur frequently (i.e., several times per year), substantiating the need for better preparedness. Nonetheless, it is impossible to predict exactly which roads are going to be flooded during a typhoon. As a result, in this study, a stochastic modeling approach was used that assigns a failure probability to each road segment based on climate model outputs for the region. Using this stochastic model, the travel time reliability between any given origin–destination pair can be determined. By applying this model to the road network of the Tokai region, two major measures were identified that could be implemented to reduce severe congestion during a typhoon. First, targeted infrastructure management measures can be implemented to strengthen heavily used roads, thus reducing the failure probability of major roads. Second, travel demand management measures can be implemented, such as asking commuters to leave their workplace or school one or two hours after their normal departure time. Overall, it was found that strengthening heavily used roads has a bigger impact in relieving congestion than delaying departure time, but that combining both strategies achieves the best results.
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