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.

Transport resilience is an important area of research in the global effort to adapt to climate change. This paper introduces and applies a stochastic modeling methodology to assess the impact of multihazard events. Most cities are exposed to multiple types of extreme events, sometimes simultaneously, and focusing on single events may lead to inadequate design recommendations. By assigning failure probabilities of road segments and by estimating road failure through Monte Carlo simulation, roads and areas that are particularly vulnerable to multihazard events can be detected. The performance of the large-scale road network of the Tokai region in Japan (prone to both typhoons and earthquakes) is analyzed by considering three scenarios of hazards: flash flood, earthquake, and the combination of both hazards. The model considers two key traffic performance characteristics: postdisaster reduced road capacity and hourly variations in travel demand. Overall, several areas in the region are found to be currently severely at risk, thus providing direct information that can help authorities test the effectiveness of future road infrastructure projects.

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.

This article presents a method to uncover universal patterns and similarities in the urban road networks of the 80 most populated cities in the world. To that end, we used degree distribution, link length distribution, and intersection angle distribution as topological and geometric properties of road networks. Moreover, we used ISOMAP, a nonlinear dimension reduction technique, to better express variations across cities, and we used K-means to cluster cities. Overall, we uncovered one universal pattern between the number of nodes and links across all cities and identified five classes of cities. Gridiron Cities tend to have many 90° angles. Long Link Cities have a disproportionately high number of long links and include mostly Chinese cities that developed towards the end of the 20th century. Organic Cities tend to have short links and more non-90 and 180° angles; they also include relatively more historical cities. Hybrid Cities tend to have both short and long links; they include cities that evolved both historically and recently. Finally, Mixed Cities exhibit features from all other classes. These findings can help transport planners and policymakers identify peer cities that share similar characteristics and use their characteristics to craft tailored transport policies.

The trucking industry, a vital part of the economic structure, faces numerous challenges such as greenhouse gas emissions, labor-related issues, fluctuating fuel costs, and safety concerns. These challenges intensify as the industry expands to meet growing demand. The advent of artificial intelligence has led to the development of autonomous trucks, which are seen as a promising solution to these ongoing issues. This study is the first comprehensive review of literature on autonomous trucks, organized by theme and research method. Studies are initially categorized based on the timeline of the issues investigated, divided into two main subcategories: foundational aspects of autonomous truck implementation and practical implementation and economic analysis of autonomous trucks. Research on the foundational aspects of autonomous trucks is further divided into four categories: (1) Acceptance surveys, (2) Identification of barriers, (3) Core technologies for autonomous trucks implementation, and (4) Predictions of adoption rates. Research on practical and economical aspects of autonomous trucks falls into three subcategories: (1) Infrastructure, (2) Systemic performance optimization, and (3) Cost estimation. To enhance the accuracy of this review, a more detailed classification was conducted on two specific subcategories: core technologies for autonomous truck implementation and systemic performance optimization. Additionally, the studies were also categorized based on their research methods and assumptions, which include accurate descriptions of autonomous technology, data collection methods, assumptions about the study environment, the fuel type of autonomous trucks, and approach to analysis: simultaneous or separate. This comprehensive review of the literature offers a roadmap for researchers, aiding them in identifying unique and novel research topics, thereby propelling the advancement of autonomous trucks as a viable solution to numerous challenges in the trucking industry.