We study the topological and geographic structure of the national road networks of the United States, England and Denmark. By transforming these networks into their dual representation, where roads are vertices and an edge connects two vertices if the corresponding roads ever intersect, we show that they exhibit both topological and geographic scale invariance. That is, we show that for sufficiently large geographic areas, the dual degree distribution follows a power law with exponent 2.2 < alpha < 2.4, and that journeys, regardless of their length, have a largely identical structure. To explain these properties, we introduce and analyze a simple fractal model of road placement that reproduces the observed structure, and suggests a testable connection between the scaling exponent alpha and the fractal dimensions governing the placement of roads and intersections.
Urban road networks are typical complex systems, which are crucial to our society and economy. In this study, topological characteristics of a number of urban road networks based on purely physical roads rather than routes of vehicles or buses are investigated in order to discover underlying unique structural features, particularly compared to other types of transport networks. Based on these topological indices, correlations between topological indices and small-worldness of urban road networks are also explored. The finding shows that there is no significant small-worldness for urban road ne…
We propose a novel self-organized traffic management strategy for incidents in traffic-light-controlled road networks. During incidents, it regulates the inflow into already congested road segments by restricting or skipping green times. Simultaneously, the remaining green times are used for the yet free turning directions. In this way, drivers can utilize unused road capacities and circumvent congested areas by revising their originally chosen routes. Consequently, the network has "healed itself" as soon as all affected traffic flows have been redistributed along remaining road capacities. Dr…
Understanding the dynamics of road networks has theoretical implications for urban science and practical applications for sustainable long-term planning. Various generative models to explain road network growth have been introduced in the literature. We propose in this paper a systematic benchmark of such models integrating different paradigms (spatial interactions, cost-benefit compromises, biological network growth), focusing on the feasible space of generated network measures. We find a quantitatively high complementarity between the different models. This confirms the necessity of a plural…
In this paper, we introduce and test our algorithm to create a road network representation for city-scale active transportation simulation models. The algorithm relies on open and universal data to ensure applicability for different cities around the world. In addition to the major roads, their geometries and the road attributes typically used in transport modelling (e.g., speed limit, number of lanes, permitted travel modes), the algorithm also captures minor roads usually favoured by pedestrians and cyclists, along with road attributes such as bicycle-specific infrastructure, traffic signals…
In the field of urban planning, road network system planning is often the first step and the main purpose of urban planning is to create a spatial configuration of different functions such as residence, education, business, etc. Generally speaking, the more mixed the functions of an area has, the more possible its vitality may be. Therefore, in this article, we propose a new framework to study the specific spatial influence patterns of the overall structure and different sub-structures of road networks on the mixed functions. Taking road segment as the basic unit, we characterize mixed functio…
Urban Resilience.AI Lab（城市韧性人工智能实验室）；Texas A&M University（德克萨斯A&M大学）；Department of Computer Science and Engineering（计算机科学与工程系）
The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to roa…
Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods are more concerned with the number of connections between nodes, and lack of consideration for interactions. So, this leads to the well-known node paradox problem, and their ability of characterizing the local and…
In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of flood waters in urban road networks. A network of urban roads resilient to flooding events is essential for provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of flood waters in urban road networks. The evolution of f…
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Minnesota（明尼苏达大学）；Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of Southern California（南加州大学）
Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integra…
In order to more accurately predict the pedestrian flow and understand the interactive relationship between tourist space and pedestrians, this paper uses spatial syntax and neural network methods to construct an evaluation model of tourist road congestion. This model makes full use of the advantages of neural network method and spatial syntax. For example, neural network method can objectively and dynamically assign the weight of attractions, and it can estimate the weight of other attractions through training. Analysis, we can clearly understand the connection relationship between roads; the…
Studies of the effect of network structure on performance have, thus far, been restricted to examining ensembles of synthetic networks generated by canonical models from the Network Science literature, which do not plausibly represent real road networks. Furthermore disparate ranges of parameter settings for demand and supply structure are often used, making it difficult to compare one study with another. To address these deficiencies, this paper proposes an experimental framework for the design of numerical investigations to study how network performance varies with respect to the structural …
Road networks represent a key component of human settlements, such as cities, towns, and villages, that mediate pollution and congestion, as well as economic development. However, little is known about the long-term development trajectories of road networks in rural and urban settings. We leverage novel spatial data sources to reconstruct and analyze road networks in more than 850 US cities and over 2,500 US counties since 1900. Our analysis reveals significant variations in the structure of roads both within cities and across the conterminous US. Despite differences in the evolution of these …
This article studies the interregional Greek road network (GRN) by applying complex network analysis (CNA) and an empirical approach. The study aims to extract the socioeconomic information immanent to the GRN's topology and to interpret the way in which this road network serves and promotes the regional development. The analysis shows that the topology of the GRN is submitted to spatial constraints, having lattice-like characteristics. Also, the GRN's structure is described by a gravity pattern, where places of higher population enjoy greater functionality, and its interpretation in regional …
During the last decade, road network vulnerability assessment has received an increasing attention. On one hand, it is due to the significant advances in Network Science and the potentialities that its tools offer. On the other hand, it is due to its utility for urban planning and emergency response. Despite these facts and the increasingly available data, related work is still sparse in Latin America, even more so in Ecuador. Due to its geographical, historical, and social characteristics, the city of Quito is considered as a case study. At first, the spatial distributions of several topologi…
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；Carnegie Mellon University（卡内基梅隆大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e. second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e. the Cauchy problems with constant initial data on each incident road. The l…
Quantifying the topological similarities of different parts of urban road networks (URNs) enables us to understand the urban growth patterns. While conventional statistics provide useful information about characteristics of either a single node's direct neighbors or the entire network, such metrics fail to measure the similarities of subnetworks considering local indirect neighborhood relationships. In this study, we propose a graph-based machine-learning method to quantify the spatial homogeneity of subnetworks. We apply the method to 11,790 urban road networks across 30 cities worldwide to m…
