Network Travel Research Articles

Observing the dynamic community structure of urban travel networks based on navigation data

Dynamic community division is crucial for comprehending the interactive structure and mechanism between residents and space. Currently, there is insufficient research on dynamic community changes in travel networks regarding travel distance. This study utilizes residents’ travel trajectories to construct a multi-layer network based on travel purposes. The study establishes methods for identifying travel network communities and analyzing community evolution influence. Dynamic characteristics of travel network communities at different distances are extracted. Results show that within 3–7 km travel distance, different travel network communities exhibit continuous spatial aggregation, whereas beyond 11 km, heterogeneity is observed. Moreover, communities within 3-7 km experience drastic dynamic evolution, whereas beyond 7 km, they tend to stabilize, forming a large-scale and fixed travel mode. This study enriches the dynamic interaction research of functional space from the perspective of distance, and the experimental results provide an effective reference for urban planning and management.

Routes of importation and spatial dynamics of SARS-CoV-2 variants during localised interventions in Chile

Abstract Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale, and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of non-pharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymised human mobility data from mobile phones and official records of international travellers entering the country to characterise the routes of importation of different variants, the relative contributions of airport and land border importations and the real-time impact of the country’s mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighbouring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travellers, and the strength of connectivity between comunas (Chile’s lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel, and on the consequences of disrupting regular movement patterns at lower spatial scales.

Beyond half-mile circle: Measuring the impact of subway expansion on home-based travels in Beijing, China

Subways play an important role in supporting travel needs, yet whether the benefits of subway expansion can justify the substantial investments remains a much-debated question. Existing studies have arbitrarily defined the catchment area of a subway station using walking distance and have disproportionately focused on commuting trips, potentially underestimating broader subway effects. To address these limitations, this study employed the distance-band approach to accurately define the treatment area of subway stations while incorporating both local and network effects. Based on longitudinal mobile signaling data, we used the difference-in-differences approach to estimate the average treatment effects of 32 new subway stations on different types of home-based travels in Beijing, China. We found that the opening of new subway stations has led to significant increases in subway usage, trip duration, and trip distance within a 2000-metre radius, and that commuting trips have been more affected than home-based non-commuting trips, except during the COVID-19 pandemic. Moreover, the new subway stations also enhanced the connectivity of the city-wide network, influencing the travel behavior of residents in the vicinity of existing subway stations. We further found that subway expansion is particularly beneficial for women, younger residents, and residents of low-rent neighborhoods. According to these findings, planning new subway stations in Beijing and other densely populated megacities requires an integrated approach that considers the relationships between the existing transport network and diverse travel demand.

A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

COVID-19 전후 우리나라 지역 간 이동 네트워크의 특성 분석

The purpose of this study is to analyze the characteristics of the interregional mobility network in South Korea before and after COVID-19. South Korea has been experiencing a decline in population not only in the overall population, but also by region, which has become a major social problem. Under these circumstances, interregional travel can contribute to revitalizing local economies, and local governments are exploring various policies to increase the number of visitors. This study uses social, economic, tourism infrastructure, and public relations variables as factors that can explain interregional travel networks in South Korea, and utilizes social network analysis methods. In addition, to consider the COVID-19 pandemic as a variable that has had a significant impact on interregional travel networks in recent years, the study compares the pre- and post-COVID-19 periods in 2019 and 2021. We find that the use of social media before and after the COVID-19 outbreak is very useful in explaining interregional travel networks in South Korea. In particular, we find that social media is more useful than traditional tourism infrastructure in explaining intraregional visitor flows. While it is important for local governments to expand traditional tourism infrastructure, they need to better utilize social media as a medium to communicate this effectively.

A simulation‐based impact assessment of autonomous vehicles in urban networks

AbstractThe behavioural differences between autonomous vehicles (AVs) and human‐driven vehicles (HDVs) can significantly impact traffic efficiency, safety, and emissions. Simulation‐based impact assessments using microscopic traffic models often modify car‐following (CF) and lane‐changing (LC) configurations to differentiate AVs from HDVs. Typically, researchers adjust CF model parameters to replicate AV driving behaviour, but these assumptions can lead to varying conclusions on AV impacts. The scope of each study (e.g., freeways, highways, urban links, intersections) also influences the outcomes. This research conducts an impact assessment utilizing optimized AV driving behavior rather than assumptions on a city network level (Munich) using a simulation‐based platform. The particle swarm optimization (PSO) algorithm is used to calibrate the base model and run simulation experiments under various penetration rates (PRs) and demand scenarios. Results show significant safety improvements throughout the network under higher PRs, while lower PRs might lead to deteriorating safety. At 100% AV PR, the total number of conflicts decreased by around 25% compared to a fully HDV environment. Considering AVs' sensing capabilities, additional safety improvements are found in almost any AV PR. However, AVs might not improve traffic efficiency; in some cases, they may slightly increase average network travel time, though this change is minimal.

Spatiotemporal analysis of within-country imported malaria in Brazilian municipalities, 2004-2022.

Human mobility has challenged malaria elimination efforts and remains difficult to routinely track. In Brazil, administrative records from the Ministry of Health allow monitoring of mobility locally and internationally. Although most imported malaria cases are between municipalities in Brazil, detailed knowledge of patterns of mobility is limited. Here, we address this gap by quantifying and describing patterns of malaria-infected individuals across the Amazon. We used network analysis, spatial clustering, and linear models to quantify and characterize the movement of malaria cases in Brazil between 2004 and 2022. We identified sources and sinks of malaria within and between states. We found that between-state movement of cases has become proportionally more important than within-state, that source clusters persisted longer than sink clusters, that movement of cases into sinks was seasonal while movement out of sources was not, and that importation is an impediment for subnational elimination in many municipalities. We elucidate the vast travel networks of malaria infected individuals that characterize the Amazon region. Uncovering patterns of malaria case mobility is vital for effective microstratification within Brazil. Our results have implications for intervention stratification across Brazil in line with the country's goal of malaria elimination by 2035.

Segmenting social media users in international tourism: Insights integrated from multi‐homing theory and international visitor survey data

AbstractThis study utilizes multi‐homing theory to assess international travelers' usage of social media platforms, revealing insights into their gender, country of origin, generation cohorts, and travel purposes. These insights can guide the development of effective social media marketing strategies for national destination management organizations (DMOs). Data from South Korea's 2019 International Visitor Survey (with 12,194 participants) was analyzed using chi‐square and correspondence analysis techniques. The results revealed distinctive preferences of international visitors among generational cohorts with different travel purposes across 19 countries in the usage of online travel communities, social network platforms, video‐sharing platforms, and travel blogs. Understanding the dynamics of social media platforms helps national DMOs enhance their social media marketing strategies for international visitors, based on the multi‐sided platform usage paradigm. Nevertheless, acknowledging the study's limitations points towards potential avenues for future research.

Integrated real-time signal control and routing optimization: A two-stage rolling horizon framework with decentralized solution

This paper presents an integrated framework for optimizing signal control and vehicle routing. An important feature of the proposed framework is the ability to simultaneously determine signal states and individual vehicle routes in real time. The general objective is to minimize the network travel time, which can be represented as a trade-off between the total route length of all vehicles and traffic conditions at signalized intersections. A two-stage rolling horizon framework is proposed to explicitly describe the relationship between individual vehicle routes and predicted traffic flow dynamics at signalized intersections. The first stage involves a signal optimization problem, while the second stage optimizes a joint signal control and vehicle routing problem. Both stages are formulated as mixed integer linear programming problems. The optimization procedure is decentralized, and the effects of vehicle routing on control performance is considered by incorporating the route length cost into the objective function. Simulation experiments validate the advantages of the proposed framework over advanced signal control strategies and dynamic user-optimal routing strategies in various scenarios. The effectiveness in improving network capacity, alleviating spillback, and decreasing congestion dissipation time under over-saturation conditions is discussed. The results of vehicle routing suggest that the total travel time can be reduced at a low rerouting cost. Sensitivity analyses demonstrate the network control performance under different compliance rates and model coefficients. Moreover, the computational feasibility of the framework is verified.

Spatiotemporal characteristics of residents' intercity travel in China under the impact of COVID-19 pandemic

Investigating the spatiotemporal characteristics of residents' intercity travel affected by the coronavirus disease (COVID-19) pandemic is of great significance to understanding and extending the impact of major public health affairs on intercity travelers, urban management policies, and intercity cooperative interactions. Taking 366 cities in China as an example, this study proposed a novel research framework for analyzing the spatiotemporal characteristics of intercity population flows. Using AutoNavi population migration big data sets from web crawling, the spatiotemporal structure patterns of intercity travel in China from January 1st to June 30th, 2020 were extracted by using the singular value decomposition (SVD) method. Then the spatial organization patterns of intercity travel networks in China and its five major urban agglomerations were identified by the weighted stochastic block model (WSBM). The results showed that the total volume of intercity travel in China and its five major urban agglomerations experienced a process of rapid decline followed by a slow recovery, presenting a “V-shaped” distribution since the outbreak of the COVID-19 pandemic. Three distinctive intercity travel patterns, namely the stable, holiday, and weekday-weekend intercity trip patterns, were extracted from the spatiotemporal OD matrix of intercity travel. Strong intercity travels were mainly distributed in eastern and central China, forming an intercity travel pattern in which core cities pushed and pulled each other within the national urban agglomerations. However, intercity migration flows were dominated by weak-intensity flows. Intercity trips during the holidays had a phenomenon of “escaping from the metropolis”. Meanwhile, it had significant spatial proximity and directionality. The daily intercity trips were characterized by a remarkable periodicity with seven days as a repeat. A more interesting finding is that when weekends overlap with holidays, intercity travel on the 1–2 days before the vacations has presented an effect of “suppression travel”. Furthermore, at the national scale, the stable intercity travel network has formed a polycentric core-periphery structure of “Three Cores-Six Semi-peripheries-Five Peripheries”. At the urban agglomeration scale, they formed respectively distinguished monocentric, bicentric, and polycentric core-periphery structures.

Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit

A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study.

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Coupling graph neural networks and travel mode choice for human mobility prediction

Travel mode serves as the link for communication among people, between people and objects, and between people and places. The human mobility is closely related to travel mode. Scientifically predicting human mobility can help alleviate traffic congestion and provide flexible travel choices. However, in current predictions, only the travel demand or human mobility is taken into account, while the influence of residents on the travel mode choice is neglected. Therefore, taking Wuhan City as an example, this paper proposes a new method for predicting human mobility by employing graph neural network techniques and travel mode choice behavior. The prediction model established in this study utilizes network analysis to measure the accessibility time and road network distance of traffic travel mode. The graph neural network method is employed to capture the dynamic temporal and spatial relationships underlying human mobility. Furthermore, the performance of the prediction model is evaluated. The results indicate that at a fine spatial scale, the new method can more accurately reveal the spatial patterns of changes in human mobility, significantly improving the accuracy of predicting human mobility.

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Global transboundary transmission path and risk of Mpox revealed with Least Cost Path model

ObjectivesThe recent surge of Mpox outbreaks in multiple countries has garnered global attention. As of July 12, 2023, there have been 88,288 reported cases of Mpox worldwide. Although genetic variation was not found to be the cause of the epidemic outbreak, the reasons for its rapid spread remain unclear. MethodsUsing the niche method, this study identified high-risk regions for Mpox and determined that human factors are the primary contributors to global risks. To further investigate, a travel network resistance surface was created based on various modes of transportation and was combined with sea, airline, highway, and railway routes to construct the least cost path for human travel networks in different risk areas. ResultsThe results indicated that high-risk regions for Mpox are mainly concentrated in Europe and the United States, with large risk ranges and high-risk values. The least cost path revealed three primary transmission paths rely on developed transportation networks, including internal transmission in North America, Europe-Africa, and Europe-Asia-Africa. These findings suggest that human activities, facilitated by developed travel networks, remain the main contributing factor to the spread. ConclusionsIn summary, based on the Mpox epidemic report, this study conducted risk prediction and driving factor analysis on Mpox. The research results indicate that human use of transportation for long-distance activities is a key factor leading to the rapid spread of the virus. Subsequently, we focused on studying the global transmission pathways of Mpox and revealed several transmission pathways with high global population migration rates by constructing the LCPs between different high-risk areas. This study also emphasizes the importance of applying early monitoring data of Mpox to model risk prediction in controlling emerging infectious diseases, providing a new perspective for controlling Mpox and similar diseases.

An integrated geospatial data model for active travel infrastructure

Active travel has received increased investment and interest in many countries both due to COVID-19 and to policies which promote a shift in mobility behaviours to support a wide range of public and individual goods. However, while there has often been substantial investment in the physical infrastructure that can help facilitate active travel, there has not so far always been commensurate investment in the data infrastructure which can help enable people to shift trips to active modes. Current fragmented data and data models and a lack of data standards pose a barrier to the development of the applications which are needed to support planners, users and journey planning. There is therefore a need for a more integrated, better-connected and more richly attributed active travel geospatial network model. This paper describes the development of such a data model, and its initial application to a case study of Great Britain. It demonstrates how the development, population and maintenance of such a data model could facilitate a range of novel applications, such as the personalisation of active travel journey planning to address different user needs and capabilities. If the potential societal benefits from investment in physical active travel infrastructure are to be fully realised, this needs to be supported by the availability of a robust spatial data infrastructure capable of providing the information required by walkers, wheeled users and cyclists to make effective use of the active travel network.

Analysis of Intercity Transportation Network Efficiency Using Flow-Weighted Time Circuity: A Case Study of Seven Major City Clusters in China

Enhancing the efficiency of intercity transportation networks is crucial for sustainable regional transport development, significantly impacting travel behaviors and energy consumption. The transportation infrastructure within the city cluster is rapidly developing to accommodate the increasing traffic demand, necessitating substantial investments. It is imperative to investigate the effectiveness of intercity traffic within urban clusters, to evaluate the influence of transportation infrastructure enhancements on regional traffic efficiency. Circuity is a conventional metric used to assess the efficiency of transportation networks, primarily emphasizing distance, while overlooking factors such as travel time and traffic flow. In this study, the concept of circuity has been redefined in terms of travel time and has been referred to as the transportation network travel speed. Subsequently, the amalgamation of travel speed within the transportation network and traffic flow culminates in the proposition of Flow-Weighted Time Circuity (FWTC). Real-time intercity navigation data, offering accurate travel time estimations, are utilized to analyze the spatial distribution of intercity transport efficiency in the seven major city clusters of China, via both automobile and train modes of transportation. The results indicate that (1) as the travel distance extends, the speed of transportation within the network typically increases, albeit with increasing fluctuations, especially in the case of intercity train travel; (2) concerning the efficiency of intercity automobile travel, most city clusters demonstrate satisfactory performance, with the exception of the Guanzhong Plain. The Yangtze River Delta and Beijing–Tianjin–Heibei regions stand out for their superior performance. In terms of intercity train efficiency, the Yangtze River Delta, Beijing–Tianjin–Heibei, and Mid-Yangtze River regions exhibit higher levels of efficiency in intercity train transportation, while the Guanzhong Plain city cluster falls behind in this aspect. On the whole, the efficiency of intercity travel using automobiles surpasses that of train travel, indicating a pressing need for improvement in the latter.

A scoping review on cycling network connectivity and its effects on cycling

ABSTRACT There is growing recognition of the importance of a well-connected cycling infrastructure. Common connectivity measures apply aggregate area-based metrics, such as the number of streets per transportation analysis zone or census area. However, such measures capture cyclist movement insufficiently, as cyclist movement easily extends beyond areas, and variations in the sizes and shapes of areas lead to inconsistent outcomes. In contrast to area-based connectivity, network connectivity considers the connectivity of the entire cycling network, capturing the continuity or discontinuity of routes. Certain measures can account for other important aspects, such as topology and the relative importance of individual links in large networks. Although previous reviews have covered several aspects of cycling infrastructure, no review has been dedicated to the measures, methods and models applied to assess network connectivity or the impact of increased network connectivity on cycling behaviour. We conduct a scoping review of measures, methods, models, and data sources to assess cycling network connectivity and review associations between network connectivity and travel behaviour. The findings suggest an increase in the number of publications on network connectivity up to 2019, followed by a plateau in the number of studies but with more complex methods. However, that complexity may constitute a barrier to practical implementation. We conclude that empirical verifications regarding the effects of network connectivity on travel behaviour remain a research gap, even in high-cycling countries, with evidence further limited by limited link-level travel data. Future research should also focus on developing more consistent and validated metrics.

Analysis of the impact of the COVID-19 pandemic on the development of the international tourism market

The COVID-19 pandemic has dealt a significant blow to the international tourism market, resulting in job losses, reduced incomes, and rising unemployment. Therefore, studying the impact of the pandemic on the development of the international tourism market is relevant and important. This article focuses on analyzing the impact of the COVID-19 pandemic on the international tourism industry in the context of globalization. It highlights key aspects and trends that emerged during the crisis period and influenced the development of the tourism market. The authors specifically concentrate on the pandemic's effects on the global travel network, encompassing changes in demand, restrictions on international travel, and the industry's adaptation to new global challenges. The COVID-19 pandemic has been identified as the cause of the most significant drop in international tourism in human history. It led to a sharp decline in the number of international tourist arrivals, export earnings from tourism, and the economic contribution of tourism to the world economy. The impact of the pandemic on international tourism varied across regions, with the Asia-Pacific region being the most affected, experiencing an 84% decrease in international tourist arrivals. The authors pinpoint the main challenges facing the industry after the pandemic, such as changes in demand, restrictions on international travel, and the reorganization of tourism services and infrastructure. The study also explores possibilities for recovery and adaptation of the tourism industry to new realities post-pandemic, with particular attention given to strategies and innovations that can restore interest in international travel and enhance the competitiveness of countries in the global tourism market. It was found that the recovery of international tourism is progressing slowly. In 2022, international tourist arrivals were still 34% lower than in 2019. The study determined that the development of tourism in the post-pandemic period will be influenced by trend changes such as the prioritization of health and safety, shifts in consumer behavior, a desire for unique emotional experiences, new forms of employment, digital transformation, and the emphasis on ecotourism. Taking these trends into account will contribute to the recovery of the tourism industry and ensure its sustainable development in the future.

On the dynamic vulnerability of an urban rail transit system and the impact of human mobility

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system.