Flow Travel Research Articles

The impact of distance on international tourism demand

This study examines the impact of distance on outbound travel demand by comparing departures from 92 source markets to over 4700 discrete destination pairs. Travel to land neighbours dominates outbound travel, accounting for 53% of all departures. Share of departures for travel >5000 km was typically 3% or lower. The study also identified different travel flows depending on the origin region, with share of outbound travel a function of the number and size of receiving countries, as well as the presence of an Effective Tourism Exclusion Zone, a zone where little or no tourism activity occurs.

Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism.

Serious leisure as a predictor of travel intentions and flow in motorcycle tourism

ABSTRACTThe current study uses structural equation modelling to examine the relationships between serious leisure, the flow state, and travel intentions among motorcycle tourists. Serious leisure contrasts with casual leisure in that its participants are more committed to their pursuit, which can result in a more sustained benefit, achievement of a flow state, and enhance intention toward future travel. The findings reveal one's serious leisure orientation predicted their durable benefits, which then produced a positive relationship with flow and travel intentions. However, flow did not mediate the relationship between durable benefits and travel intentions, as hypothesised. This research provides empirical evidence of the value of using the serious leisure construct to capture the multi-faceted and complex experience that is motorcycle tourism and can augment comprehension of other high involvement tourism markets.

Discovering Frequent Movement Paths From Taxi Trajectory Data Using Spatially Embedded Networks and Association Rules

In view of the complex traffic flows, spatial interactions within a city exhibit the properties of dynamics, connectivity, and repeatability. This paper aims at mining spatial–temporal movement patterns from massive taxi trajectory data for discovering the inherent travel flow information within the urban system. Similar to the role of ocean circulation in a marine system, identifying the frequent paths and cycles of the travel flows within a city would be critical for understanding the principles behind the travel flow surfaces. Thus, we propose a multi-level method for the discovery of movement paths by incorporating the techniques of network analysis and association rules. Specifically, the proposed method begins by constructing a directed network on the subdivision of the study region, in which the node with geolocation represents the corresponding cell and the edge with weight represents the travel flow between neighboring cells. The method then adopts an extended label propagation clustering algorithm to identify frequent paths and cycles on the flow network within a specific time interval. Finally, to extract frequent paths during the whole time period, we also develop an association rules mining algorithm by modeling the edges as items and the paths in each time span as transactions. Experiment results demonstrate that our framework is able to effectively mine movement patterns in taxi trajectory data. Our results are expected to provide an avenue for further research, such as transportation planning and urban structure analysis.

Inferring dynamic origin-destination flows by transport mode using mobile phone data

Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on multimodal transport networks. In this work, we propose the first methodology to infer dynamic Origin-Destination flows by transport modes using mobile network data e.g., Call Detail Records. For this study, we pre-process 360 million trajectories for more than 2 million devices from the Greater Paris as our case study region. The model combines mobile network geolocation with transport network geospatial data, travel survey, census and travel card data. The transport modes of mobile network trajectories are identified through a two-steps semi-supervised learning algorithm. The later involves clustering of mobile network areas and Bayesian inference to generate transport probabilities for trajectories. After attributing the mode with highest probability to each trajectory, we construct Origin-Destination matrices by transport mode. Flows are up-scaled to the total population using state-of-the-art expansion factors. The model generates time variant road and rail passenger flows for the complete region. From our results, we observe different mobility patterns for road and rail modes and between Paris and its suburbs. The resulting transport flows are extensively validated against the travel survey and the travel card data for different spatial scales.

Bicyclist Route Choice: Data Exploration and Research Project Outline

Microsimulation of travel flows aims to assess the effect of decisions taken by travelers based on personal preferences, time-of-day, properties of the infrastructure and expected or perceived travel flows. Route choice represents a particular class of such decisions. Route choice prediction is an essential component of microsimulators. Specification of choice models and estimation of the corresponding parameters based on observations are required in the preparatory stage. Route choice sets need to be established for sampling in the simulation stage.This paper is part of a research project aiming to investigate how route complexity can be integrated in the choice process modeling. In particular routes for bikers collected by GPS tracking in the Dutch FietsTelWeek project in 2016 are analyzed.The data exploration stage and the research project outline are covered. Properties of the publicly available fietstelweek2016 dataset used for model training are investigated in order to assess their effect on prediction results. In order to achieve the project goal, the research project structure is briefly discussed. It is based on the observation that the number of routes recorded for each OD-pair is too small to observe a frequency distribution for complexity. Hence, complexity data are collected for sub-networks that are similar with respect to particular graph properties.

Electric vehicle charging point placement optimisation by exploiting spatial statistics and maximal coverage location models

Electric vehicles (EVs) are increasingly considered as a promising solution to tackle climate change impacts, improve air quality, and enhance growth sustainability. This paper proposes a two-step approach for optimally deploying charging points (CPs) by bringing together spatial statistics and maximal coverage location models. CP locations are conceptualised as a spatial point pattern, driven by an underlying stochastic process, and are investigated by using a Bayesian spatial log-Gaussian Cox process model. The spatial distribution of charging demand is approximated by the predicted process intensity surface of CP locations, upon which a maximal coverage location model is formulated and solved to identify optimal CP locations. Drawing upon the large-scale urban point of interest (POI) data and other data sources, the developed method is demonstrated by exploring the deployment of CPs in London. The results show that EV charging demand is statistically significantly associated with workplace population density, travel flows, and densities of three POI categories (transport, retail and commercial). The robustness of model estimation results is assessed by running spatial point process models with a series of random subsets of the full data. Results from a policy scenario analysis suggest that with increasing numbers of charging stations to be planned, optimal CP locations gradually expand to the suburban areas of London and the marginal gains in charging demand covered decrease rapidly.

Bayesian inference of channelized section spillover via Markov Chain Monte Carlo sampling

Channelized section spillover (CSS) is usually referred to the phenomenon of a traffic flow being blocked upstream and not being able to enter the downstream channelized section. CSS leads to extra delays, longer queues, and a biased detection of the flow rate. An estimation of CSS, including its occurrence and duration, is helpful for analysis of the state of traffic flow, as a basis for traffic evaluation and management. This has not been studied or reported in prior literature. A Bayesian model is developed through this research to estimate CSS, with its occurrence and duration formulated as a posterior distribution of given travel time and flow rate data. Basic properties of CSS are discussed initially, followed by a macroscopic model that explicitly models the CSS and encapsulates first-in-first-out (FIFO) behavior at an upstream section, with a goal of generating the prior distribution of CSS duration. Posterior distribution is then constructed using the detected flow rate and travel time vehicles samples. The Markov Chain Monte Carlo (MCMC) sampling method is used to solve this Bayesian model. The proposed model is implemented and tested in a channelized intersection and its modeling results are compared with Vissim simulation outputs, which demonstrated satisfactory results.

Crossing Borders: Control of Geographical Mobility in Early China

AbstractQin and Han subjects were required by law to register residency with local governments. Their mobility was thus subject to government scrutiny. This study explores (a) how such control extended to both official and private travel and (b) the various reasons that led people to travel. To facilitate surveillance, the Qin and Han governments both demarcated their territory through administrative units and checkpoints, issued transit permits, and enforced strict laws controlling the flow of travelers. Such control meant that people required permission to travel on private business to manage family matters, seek employment, and perform various other tasks. Although control was tight, the people in the early empire were still free to move from place to place.

Assessing the Impact of App-Based Ride Share Systems in an Urban Context: Findings from Austin

App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners have released little information about riders’ trip-making characteristics. This lack of data prevents planners and modelers from understanding and quantifying the impact of these new modes on regional travel patterns. In 2016, RideAustin, a not-for-profit company, was established to provide mobility-on-demand services in the Austin region. RideAustin released its dataset of over one million trips to researchers to support transportation planning through a better understanding of urban travel flows. This paper presents findings from an in-depth analysis of this dataset and summarizes key aspects of interest to the transportation research community such as the number of riders, drivers, and trips; total vehicle miles including deadhead miles; and terminal times. The paper also presents findings from two case studies that show the competitiveness of RideAustin versus transit and the utilization of the RideAustin system during the South by Southwest festival. While some of the metrics cannot be readily transferred to other regions, several findings can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks. We also believe that some of the research findings may provide insights into a future system of autonomous and shared vehicles.

Predicting travel flows with spatially explicit aggregate models: On the benefits of including spatial dependence in travel demand modeling

The prediction of travel demand is a key step in transport planning and is a topic of intense discussion of the literature. This paper adds to the debate about the accuracy of travel demand prediction by addressing the ‘technical’ problem of spatial autocorrelation. This paper aims to systematically assess the predictive performance of spatially explicit models that take spatial autocorrelation into account vis-à-vis more conventional models. We compare the performance of both types of models in predicting the transit passenger flows for alternative transit network designs in the region of Arnhem-Nijmegen, the Netherlands. We find that models taking spatial dependence into account outperform the conventional models in nearly all respects: model fit, parameters of variables, and the quality and stability of the predictions. Results show that taking spatial autocorrelation into account is not only important for the analysis of spatial interactions, but also result in different and more accurate predictions of the impact of interventions. We conclude that travel demand models should account for spatial dependence in order to avoid overprediction of the impact of transport system changes. We end with a discussion about the relevance of our findings for the debate about the causes for the observed systematic overestimation of travel demand in the practice of transport planning.

New technical framework for assessing the spatial pattern of land development in Yunnan Province, China: A “production-life-ecology” perspective

With the global accelerated pace of industrialization and urbanization, the state of disorder in land development has become the primary problem affecting sustainable and coordinated development in China. This study proposes a new technical framework to support the assessment of the spatial pattern of land development from a “production-life-ecology” perspective. The objective of our study was to develop a comprehensive assessment methodology by clarifying the functions of land and establishing a comprehensive index system that includes a total of 29 indicators. This framework can produce realistic and reliable patterns of land development because it synthetically takes formative environmental, social, and economic indicators into account while also considering the impact of human behaviors such as travel flow and economic flow on spatial land patterns. This research conducts a case study on the spatial pattern of land development in Yunnan Province of China in 2015, finding that the land development occurs in a scattered production-function pattern of one core, five groups, and coupled pairs of urban collaboration, a polycentric life-function pattern represented by radial concentric-ringed zones, and an aggregated ecology-function pattern with high values in the west and low values in the east. The proposed technical framework of pattern assessment can provide an effective reference for governments to improve the planning and development of land resources.

Responding to yellow fever outbreaks in West and Central Africa: Rapid prioritization assessment for the pre-emptive vaccination campaigns

Introduction Recent yellow fever (YF) outbreaks in Africa, such as 2016 in Angola, or 2017 in Nigeria, have demonstrated the ongoing threat of large-scale urban YF outbreaks. Moreover, outbreaks of other mosquito-borne diseases (Zika, chikungunya) have increased awareness of the potential for rapid, international spread of arboviruses. Swift outbreak response, for YF primarily in the form of vaccination, is therefore essential to prevent local and international spread. However, limited global stockpiles means that the prioritization of geographic areas for pre-emptive vaccination campaigns is required (i.e. vaccination campaigns targeting areas that are at risk of disease introduction and spread but as yet unaffected). Conducting a rapid prioritization assessment based on the risk of disease spread is thus highly valuable to inform decisions on vaccination activities in a context of emergency. Objective To develop a method for rapid risk assessment of YF spread to prioritize sub-national administrative units (hereafter called province) for pre-emptive campaigns. Specific requirements for this method are: –speed of implementation in the context of emergency response; –transparent methodology to allow discussions with and feedback from decision-makers. Method We developed a heuristic method to quantify the risk of YF spread by integrating multiple data streams: population sizes, estimates of existing vaccine-induced population-level immunity, recent incidence of yellow fever cases in the province and travel flows between provinces. The resulting risk score primarily reflects the expected absolute number of yellow cases in the respective province, accounting for local cases and the risk of case importation. Based on their risk score, provinces are ranked according to priority for vaccination and target population sizes are estimated. This baseline, quick-to-compute, risk assessment can be refined by integrating additional elements. For example, the presence and population size of large urban centres at the province level may be relevant to characterize the risk of urban outbreaks, and of international spread in the case of highly connected urban centres. Similarly, independent estimates of the local transmission potential of the disease produced by a mathematical model can be combined into this risk metric. Results This heuristic method has been used in collaboration with the World Health Organization in the context of urgent response to yellow fever outbreaks that affected Angola and the Democratic Republic of Congo (DRC) in 2016 and Nigeria in 2017. Results were provided in a form that allowed decision-makers to easily and interactively adjust the relative weights of different factors and visualize the effect on the results. Moreover, the transparency of the method allowed decision-makers to provide feedback and to request the integration of additional elements considered as relevant for outbreak control (for instance: trans-border movements in Angola-DRC). This risk assessment contributed to inform decision for mass vaccination campaigns conducted in the DRC in 2017 and for campaigns (still under consideration) in Nigeria in 2018. Conclusion By integrating different, mostly publicly available, data streams, we developed a risk assessment method that can be quickly implemented in the context of yellow fever outbreak response. The transparency and flexibility of the method enhanced interactions with decision-makers to refine estimates.

Paganism in Ukraine as a potential for the development of religious tourism

An artificial term “pagan” is used to denote someone who believes in his/her authentic religion different from Abrahamic religions: Judaism, Christianity, and Islam. There are 400–500 millions of pagans in the world. They are divided into such groups: 1) aboriginal or autochthonous cults are widespread mostly among indigenous peoples of Asia, Africa, America, Australia and Oceania, and have not only deep historical roots, but also have kept the polytheistic religious worldview of their ethnos; 2) representatives of “vernacular” paganism, which combines some elements of ancient beliefs (magic, cult of nature, cult of ancestors, fortune telling etc.) with a specific Abrahamic religion; 3) groups of supporters of healthy lifestyle and living well in the harmony with nature, pagan religions characterized by substantial syncretism with environment-oriented teachings; 4) religious denominations, which combine an autochthonous religion of its people with its current political aims. Overall paganism is one of the least aggressive religions in the world. Ukrainian pagans (Ridnovirs, rodovirs (Slavic Native Faith)) have approximately 120 communities in all regions of Ukraine. The biggest amount of them is concentrated in Vinnytska, Khmelnytska, Zaporizka, Dnipropetrovska, Poltavska, Lvivska oblasts and the city of Kyiv. There are no foreigners among all clerics-pagans, which is unusual for most Ukrainian denominations. Paganism is an indigenous religion of the Ukrainians, which stands up for the authenticity of our society and country. According to our calculations, there are more than 100 pagan sacred places in Ukraine: ancient and functional, more or less preserved and managed, attractive for tourists. Podillia, Podniprovia, the Carpathians, Pollissia are characterized by the greatest concentration of pagan sacred sites - places of worship of anthropogenic and natural origin: sanctuaries, temples, sacrificial altars, caves, cliffs, megaliths (dolmens, cromlechs, menhirs), petroglyphs, burial mounds, trees, idols, springs, pantheons etc. However, most of these places are not widely known or they are known only as natural or historical and cultural objects. In order to make those facts well known, it is necessary to organize an advertising campaign in support of this issue and to stop an adversary, biased attitude of the whole society or some representatives of separate religions towards paganism. In addition, it is important to stop destroying pagan sacred places and to turn them into touristic spots. Multiple highlights of the ethnical religious and cultural significance of Ukrainian pagan sacred sites will augment already known information about them and will add religious tourists and pilgrims to the general flow of travellers. Key words: paganism, Abrahamic religions, organization of religious tourism, paganism in Ukraine, organization of pilgrimage, religious tourism in Ukraine.

A consistent reliability-based user-equilibrium problem with risk-averse users and endogenous travel time correlations: Formulation and solution algorithm

The traditional traffic assignment model has been extended in the literature to incorporate uncertainty and travelers who are sensitive to it. However, the proposed formulations that model risk-averse users lack (i) a consistent representation of the various stochastic quantities involved and (ii) a general solution approach. In this context, this study formulates a consistent, reliability-based user-equilibrium problem with risk-averse travelers. In the proposed formulation, various stochastic quantities are consistent with each other: the stochastic origin–destination demands result in stochastic path-flows and in turn stochastic link-flows, which result in stochastic link travel times, and travelers make decisions based on the stochasticity of travel times. The formulation also incorporates (i) endogenous link-flow variances and covariances and (ii) flow-dependent and endogenous link travel time correlations. At equilibrium – which is defined over the distributions of travel times and flows – no user can improve her travel time reliability by unilaterally changing route. The conditions for existence of a solution to the formulated problem are presented. A gradient projection based algorithm is proposed to solve the formulated problem, which is demonstrated to be correct and efficient through computational experiments. We also show that ignoring consistency in the formulation can lead to potentially erroneous conclusions. Experiments also demonstrate that the solution can be sensitive to the demand levels, stochasticity in demand, and users’ degree of risk-aversion. This work has the potential to make existing reliability-based user-equilibrium formulations more general and operational. It also has important applications in reliability-based traffic planning and management.

A geographic analysis of hosts' irritation levels towards mainland Chinese cross-border day-trippers

There is a proliferation of research on Chinese tourists' unpleasant behaviour in host destinations, but analyses on cross-border day-trippers remain scarce. This paper offers a geographic analysis of hosts' attitude towards mainland Chinese visitors in Sheung Shui, a border town in Hong Kong. It utilises a novel adaptation of G.V. Doxey's Irridex Model together with Geographical Information System to spatially assess attitudes of over 280 shop owners. The spatial differentiation of antagonism levels, and correlations between irritation and factors like business type and distance from major transport hubs were discussed. Results revealed businesses related to ‘Fashion and beauty’ and ‘Supermarkets and convenient stores’ had greater resentment. There were also statistically significant differences between irritation levels and the shortest distances to various transport hubs (p < 0.001). The findings will have implications on the planning and management of the flow and circulation of mainland Chinese travelers in Hong Kong.

Neurocysticercosis and Epilepsy: A Review

Neurocysticercosis is an infection of the central nervous system caused by the larval form of the tapeworm, Taenia solium. It is an endemic disease, especially in developing countries of sub-Saharan Africa, Asia and Latin America. The flow of travelers and immigrants from these places to the developed countries has contributed to increase the prevalence also in Europe and North America. It is known that in addition to genetic and structural causes, infectious and inflammatory causes can also be common causes of epilepsy. Some studies points neurocysticercosis as one of the leading causes of epilepsy in the world. The objective of this review is to highlight the main epidemiological, pathophysiological and clinical aspects that correlate neurocysticercosis with epilepsy.

The possibility of increasing the intensity of use of transport infrastructure, which are monuments of history and architecture, in the field of regional tourism

In the present paper the problems of development of regional tourism and cultural and educational activities with the help of program-target approach are considered. For the development of cultural and educational, event, environmental, business, cruise types of tourism it is proposed to use the objects and buildings of transport infrastructure, built more than a hundred years ago and are historical and architectural monuments of regional or local level, such objects include railway stations and station buildings, which, on the one hand, are subject to conservation, and on the other often need repair and restoration work. An important aspect in planning the further stages of the life cycle of this complex of objects is to clarify the possibility of their use in the changed conditions for the modern needs of citizens. One of the regions of the Central Federal district – the Voronezh region-is used as the object of research. Taking into account the growth of the flow of independent travelers in Russia, as well as the increase in people living in district centers and spending their leisure time outside the home, the paper proposes a model for the development of regional tourism, combining cultural and educational tourism with cruise tourism using rail transport. To calculate the areas that can be involved in additional activities in the development of cultural and educational leisure and regional tourism, take into account the requirements of standards that specify the parameters of passenger traffic and the area of premises to provide the basic functionality of the object. The proposed model does not require additional capital investments, and the profitability of the buildings will increase significantly, will attract the attention of guests and residents of the region to the history of the region and the railway.

Understanding Human Mobility Flows from Aggregated Mobile Phone Data

In this paper we deal with the study of travel flows and patterns of people in large populated areas. Information about the movements of people is extracted from coarse-grained aggregated cellular network data without tracking mobile devices individually Mobile phone data are provided by the Italian telecommunication company TIM and consist of density profiles (i.e. the spatial distribution) of people in a given area at various instants of time. By computing a suitable approximation of the Wasserstein distance between two consecutive density profiles, we are able to extract the main directions followed by people, i.e. to understand how the mass of people distribute in space and time. The main applications of the proposed technique are the monitoring of daily flows of commuters, the organization of large events, and, more in general, the traffic management and control.

A Hybrid Algorithm for Estimating Origin-Destination Flows

With the development of intelligent transportation systems, the estimation of traffic flow in urban areas has attracted a great attention of researchers. The timely and accurate travel information of urban residents could assist users in planning their travel strategies and improve the operational efficiency of intelligent transportation systems. Currently, the origin-destination (OD) flows of urban residents are formulated as an OD matrix, which is used to denote the travel patterns of urban residents. In this paper, a simple and effective model, called NMF-AR, is proposed for predicting the OD matrices through combining the nonnegative matrix factorization (NMF) algorithm and the Autoregressive (AR) model. The basic characteristics of travel flows are first revealed based on the NMF algorithm. Then, the nonlinear time series coefficient matrix, extracted from the NMF algorithm, is estimated based on the AR model. Finally, we predict OD matrices based on the estimated coefficient matrix and the basis matrix of NMF. Extensive experiments have been implemented, in collected real data about taxi GPS information in Beijing, for comparing our proposed algorithm with some known methods, such as different kinds of $K$ -nearest neighbor algorithms, neural network algorithms and classification algorithms. The results show that our proposed NMF-AR algorithm have a more effective capability in predicting OD matrices than other models.