Transportation Network Analysis Research Articles

Public health infrastructure and COVID-19 spread: An air transportation network analysis

This study examines the effectiveness of public health infrastructure, specifically hospital and Intensive Care Unit (ICU) capacities, in mitigating the spread of COVID-19 in various US counties. We use the air transportation network to quantify the risk of importing COVID-19-infected passengers to a given county in the United States. Utilizing a dataset that encompasses COVID-19 case rates, healthcare resources, and socioeconomic and transportation-related variables across multiple destination counties, we found that increased availability of hospital and ICU beds is statistically significantly associated with a reduced risk of COVID-19 spread, after controlling for origin county infection rates, travel patterns, and socioeconomic factors. These findings underline the critical role of robust healthcare infrastructure in public health crisis management and suggest targeted policy interventions to bolster healthcare capacity as a viable strategy for mitigating pandemic impacts.

A topological analysis of Milan historical surface transport networks from 1856 to the present

This research examines the transport networks operating in Milan, Italy, in seven years, from 1856 to 2016. The networks are transformed into graphs and analysed with several techniques from graph theory, including a new centrality index called Icentr. With the help of this index, we identify the nodes, to be considered primary in the transportation network, that are better related both in terms of topology and more interconnected in terms of distance. The findings provide a detailed picture of how the primary nodes have shifted from the city’s inner core to outer areas over the course of the investigated decades, both for the topological case and when the graphs are weighted based on the distance between nodes. Particularly, the effect of distance is more evident in those historical phases when the city expands, creating longer connections between nodes, in contrast to phases when the city becomes denser. The proposed method of analysis appears suitable to be applied to other cities, both for simplicity and effectiveness.

Resilience analysis of maritime transportation networks: A systematic review

Abstract As supply chains in today's world become more complex and fragile, enhancing the resilience of maritime transport is increasingly imperative. The COVID-19 epidemic in 2020 exposed the vulnerability of existing supply chains, causing substantial impacts such as supply shortages, procurement constraints, logistics delays, and port congestion, highlighting the need to build resilient maritime transportation networks (MTN) and reigniting research on the resilience of maritime transport. Based on science mapping, we quantitatively analyzed the domain of resilience of MTN. We mainly study the resilience of MTN from the following aspects: construction of MTN and their topological characterization, vulnerability-oriented resilience analysis of MTN, recovery-oriented resilience analysis of MTN, investment decision-oriented resilience analysis of MTN, climate change-oriented resilience analysis of MTN and pandemic-oriented resilience analysis of MTN. This study reviews recent advances in MTN resilience research, highlighting research topics, shortcomings, and future research agenda.

Transportation network analysis and hub identification for exporting agricultural products

Agriculture products are one of the main income sources of developing countries. This study analyzes the road transport system (network) to reveal the network’s structural properties and identifies hubs that consolidate agricultural products to export to China via the China-Laos railway. Analysis of 20 provinces in the Northeast of Thailand has found that the network has 340 districts connected by 1,015 transport routes. The network is sparse, in which all districts cannot be connected to each other. The network has low connectivity efficiency but has high intra-connectivity among districts in the same province. In addition, the network has a modularity structure that can develop the communities. Hubs consolidating agricultural products of the region are Na Khu, Kuchinaria, Mueng Chiyaphum, Nam Phong, Na Wa, Mueang Mukdahan, Prasat and Rasi Salai. The findings of the study draw implications for the government, sector and exporters to design and improve their operations and services.

An evaluation tool for backbone extraction techniques in weighted complex networks

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

Spatial clustering and spillover pathways analysis of O3, NO2, and CO in eastern China during 2017–2021

The eastern China presented the most serious O3 pollution and increasingly prominent regional characteristics. To understand the transport characteristics of O3 and its precursors and identify their potential relationships are of great guiding significance for interregional joint prevention and control. In this study, the annual and seasonal transport networks of O3 and its precursors (NO2 and CO) during 2017–2021 were constructed by applying the complex network method to air quality observations. And the key spatial clusters, the spillover paths and the potential links among pollutants were comprehensively analyzed based on the topological characteristic analysis of the established air pollutant transport networks. As the results showed, O3 pollution in the eastern China was affected by active regional transports of O3 and its precursors. Regional transports of O3, NO2, and CO were more prominent in autumn and showed high synchronization. The regional transport of precursors, especially NOx, was an important cause of regional O3 pollution. Air pollutant transport characteristics varied with seasons and regions, which demonstrating the importance of regulating seasonal and regional differentiated joint prevention and control strategies, especially for NOx. The results of this study can provide science-based guidance for the regional cooperative control of O3 pollution in the eastern China, and the application of complex networks can also provide a new methodological perspective for the study of air pollution transmission.

The network effects and spatial spillovers of transport accessibility on intra-metropolitan employment growth: Evidence from the Beijing metropolitan area, China

ABSTRACT The network effects and spatial spillovers of transport accessibility on local employment growth highlight the benefits of transport improvements that extend beyond the areas with newly invested transport infrastructure, which can be important for a better understanding of the land use effects of urban transport. Previous studies have largely ignored transport network effects and spillovers on intra-metropolitan employment growth. In this paper, we incorporate transport network analysis and spatial econometric techniques to examine the impact of transport accessibility on local employment growth in the Beijing metropolitan area from 1996 to 2018. Our results indicate significant positive transport network effects and the existence of spatial spillovers of transport accessibility on intra-metropolitan employment growth. As for the spatial spillovers, we argue that whether the spillovers of transport accessibility on nearby areas are positive or negative depends on the spatial scale and the types of economic activities and transport infrastructure. Our results show that the spillovers of highways are negative at the relatively small spatial scale and mainly for sectors with intensive demand for transport services, while rail transit imposes positive spillovers on the economy of regions around rail transit at the larger spatial scale and mainly for higher-order services.

Frequent Itemset-Driven Search for Finding Minimal Node Separators and its Application to Air Transportation Network Analysis

The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$</tex-math> </inline-formula> -separator problem ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$</tex-math> </inline-formula> -SP) consists of finding the minimum set of vertices whose removal separates the network into multiple different connected components with fewer than a limited number of vertices in each component, which belongs to the family of critical node detection problems. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$</tex-math> </inline-formula> -SP problem is an important NP-hard problem with various real-world applications. In this paper, we propose a frequent itemset-driven search (FIS) algorithm to solve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$</tex-math> </inline-formula> -SP, which integrates the concept of frequent itemset into the well-known memetic search framework. Starting from a high-quality population built by population construction and population repair, FIS then iteratively employs a frequent itemset recombination operator (to generate promising offspring solution), a tabu-based simulated annealing (to find local optima), a population repair procedure, and a population management strategy (to guarantee healthy/diverse population). Extensive evaluations on 50 benchmark instances show that FIS significantly outperforms the state-of-the-art algorithms. In particular, it discovers 29 new upper bounds and matches 18 previous best-known bounds. Finally, we experimentally analyze the importance of each key algorithmic component, and perform a case study on an air transportation network for understanding its network structure and identifying its influential airports.

Snowed in? An analysis of social and transportation networks in the 2015 Boston Snowmageddon

AbstractContinuous subzero temperatures and an unrelenting Polar vortex turned the 2014–2015 Boston winter into a compound disaster, causing deaths and roof collapses, shutting down businesses, and paralyzing transportation networks. Little is known about the circumstances, which enhanced community resilience and reduced impacts during this extended disaster. Using original surveys of more than 140 affected community members alongside demographic data from census and ESRI sources and GIS data on bus and rail networks, we investigate the factors that allowed vulnerable communities to overcome the crisis. Through mixed‐methods approaches, including GIS spatial analysis, multiple regression, average treatment effects, and propensity score matching, we illuminate the factors that helped urban residents mitigate the impact of the Boston snowstorms. Controlling for a number of potential confounding variables, including demographic and economic factors and transportation access, we find that bonding social ties served as a significant mitigating force during the crisis. These findings bring with them important public policy findings for residents, NGOs, and decision‐makers alike as we struggle to adapt to extreme weather events amidst increasing urbanization.

Application of GIS technology in cross-border tourism cooperation planning. Taking the analysis of the spatial pattern of cross-border eco-tourism cooperation in the Altai Mountains region as an example

Since the 1960s, the application of GIS technology in the field of geography has led geography to the devel-opment path of informatization and digitization. GIS technology, which focuses on spatial information analysis and spatial data management, has become an important spatial system for geographic exploration. GIS technology has extremely important advantages in collecting, processing, simulating, analyzing and expressing geospatial data. Object: This paper takes the spatial pattern of cross-border tourism cooperation in the Altai Mountains region as the research object. Methods: The research methods are: the terrain analysis, three-dimensional scene simulation, data statistics, suitability analysis, visual domain analysis, transportation network analysis, and project site selection of GIS technology in tourism planning. Results: The presented research is analytical in nature. The specific application of GIS technology in tourism planning is discussed. Also, 7 types of GIS applications were considered in the paper. In summary, GIS is mainly composed of data input system, data management system, spatial analysis system and data output system. It has very important application value in tourism management and development.

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain.

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

Important subgraph discovery using non-dominance criterion

Graph mining techniques have received a lot of attention to discover important subgraphs based on certain criteria. These techniques have become increasingly important due to the growing number of applications that rely on graph-based data. Some examples are: (i) microarray data analysis in bioinformatics, (ii) transportation network analysis, (iii) social network analysis. In this study, we propose a graph decomposition algorithm using the non-dominance criterion to identify important subgraphs based on two characteristics: edge connectivity and diameter. The proposed method uses a multi-objective optimization approach to maximize the edge connectivity and minimize the diameter. In a similar vein, identifying communities within a network can improve our comprehension of the network's characteristics and properties. Therefore, the detection of community structures in networks has been extensively studied. As a result, in this paper an innovative community detection method is presented based on our approach. The performance of the proposed technique is examined on both real-life and synthetically generated data sets.

Including equity considerations in resilient transport network planning and analysis: A flood impact perspective

Transportation plays a pivotal role in society in the accessibility of socio-economic functions, such as education and health services. At the same time these transport networks are put under pressure due to increasing demands and the often-increasing occurrence of climate-induced events. To increase resilience of the transportation network to disruptions, network criticality has been used to prioritise segments of the network for interventions. Here we present how equity principles can be applied in the context of decision making for resilient infrastructure. This is done for both a data-rich (The Hague, The Netherlands) and data-poor (Pontianak, Indonesia) environment. The results show that depending on the underlying equity principle different intervention locations are prioritized and changes the impact for different socio-economic groups and the general population.

Spatial Analysis of Transportation Networks for Urban Planning

Spatial Analysis of Transportation Networks for Urban Planning

Urban Road Transport Network Analysis: Machine Learning and Social Network Approaches

Traffic congestion is one of the most significant problems in urban transportation. It has been increasing, especially in regions close to intersections. Several methods have been developed to reduce the traffic congestion. One of the analysis methods is social network analysis (SNA). This method, which has increased use in transportation, can quickly identify the most central intersections in transportation networks. Improvements to central intersections, identified in a road network structure, speed up the traffic flow across the entire network structure. In this study, the Istanbul highway transportation network has been examined and values for a series of network centrality measures have been calculated using the SNA. The accuracy and error scales of the centrality values were compared using a machine learning algorithm. The Bonacich power centrality has been the best performance. Based on the study results the most central intersections in Istanbul have been determined.

Accessibility-based vulnerability analysis of multi-modal transportation networks with weibit choice models

This study proposes utility-based accessibility measures and a weibit-based analysis method for analyzing multi-modal transportation network vulnerability. To account for the multi-dimensional travel choice behavior in multi-modal transportation networks, an advanced weibit-based combined modal split and traffic assignment model is adopted to derive the accessibility measures based on the weibit-expected travel disutility from both route choice and mode choice dimensions. The accessibility at the modal split level is derived from the nested weibit model, while that at the route choice level is obtained based on the path-size weibit model. The proposed weibit-based vulnerability measures are inherently suitable for assessing the relative degradation in network performance and can measure the vulnerability of networks with heterogeneous scales without overestimating the importance of modes or routes sharing similar features. Using a simplified multi-modal transportation network of Hong Kong, numerical experiments are conducted to demonstrate the properties of the proposed multi-modal vulnerability analysis. The results indicate that the proposed analysis can effectively address similarity and heterogeneity issues in the analysis of choice behavior and network vulnerability, which are often ignored by the traditional logit-based measures.

General stochastic ridesharing user equilibrium problem with elastic demand

In this study, we propose a general stochastic ridesharing user equilibrium (SRUE) problem with elastic demand (ED) for urban transportation network analysis with ridesharing activities by extending the deterministic ridesharing user equilibrium (DRUE) problem, which is referred to as the general SRUE-ED problem. We develop a novel variational inequality (VI) model in terms of the Lagrange multipliers corresponding to the ride-matching constraints for the general SRUE-ED problem. The existence and uniqueness of the SRUE-ED solution are demonstrated under mild conditions. We proceed to put up a global convergent method to solve the developed VI model by incorporating the weighted cost averaging (WCA) method. The purpose of WCA method is to solve a parametric fixed-point model formulated for the general SRUE-ED problem without the ride-matching constraints. Finally, we use three numerical examples to provide practical insights into the operation and management of the ridesharing systems and to examine the effectiveness of the proposed model and solution method.

Evaluation and prevention and control measures of urban public transport exposure risk under the influence of COVID-19-Taking Wuhan as an example.

BackgroundSince December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry.MethodBased on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites.ResultsThe overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated.DiscussionThis paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures.

Contingency analysis for gas transport networks with hydrogen injection

Novel methods for contingency analysis of gas transport networks are presented. They are motivated by the transition of our energy system where hydrogen plays a growing role. The novel methods are based on a specific method for topological reduction and so-called supernodes. Stationary Euler equations with advanced compressor thermodynamics and a gas law allowing for gas compositions with up to 100% hydrogen are used. Several measures and plots support an intuitive comparison and analysis of the results. In particular, it is shown that the newly developed methods can estimate locations and magnitudes of additional capacities (injection, buffering, storage etc.) with a reasonable performance for networks of relevant composition and size.

Guest Editorial Special Issue on Modeling Dynamic Transportation Networks in the Age of Connectivity, Autonomy and Data

The recent emergence of new technologies and systems such as connected and automated vehicles (CAVs), novel incentive and routing platforms, and shared mobility services is making a significant impact on traffic flow in road networks. The rapid development of these innovations, powered by new capabilities in data collection, communication, and vehicle autonomy raises both great opportunities and new challenges for managing and controlling the transportation network efficiently. It is thus imperative to integrate the emerging systems into a dynamic transportation network analysis, and to develop new methodologies, which coherently integrate dynamic traffic models with increasingly available data, and methods for large-scale computation. Consequently, they call for new theories, models, computational methods, and application scenarios to study dynamic transportation networks with the emerging technologies as essential components.