Individual Travel Research Articles

Detecting Pattern Changes in Individual Travel Behavior Based on a Bayesian Method

Detecting Pattern Changes in Individual Travel Behavior Based on a Bayesian Method

State-of-the-art of Longitudinal Travel Surveys – A Comparison of the MOP and MPN

Longitudinal travel surveys are needed to capture individual travel behaviour changes. Only two longitudinal tavel surveys of national relevance are currently in operation, the German Mobility Panel (MOP) since 1994 and the Netherlands Mobility Panel (MPN) since 2013. This paper provides an overview of both panels' differences and similarities in design and data collection. Furthermore, representativeness, diary fatigue and non-random attrition are assessed in both panels to show the challenges panel surveys have to deal with. Overall, this paper shows important aspects of a panel survey that should be considered when designing a new longitudinal travel survey.

Studying Travel Networks Using Establishment Covisit Networks in Online Review Data

Whenever someone posts an online review of a restaurant, museum, or barbershop, they also leave a trace of where they traveled. The author visualizes travel patterns in 11 North American metropolitan areas using geolocated review data. The data are based on approximately 7 million online Yelp.com reviews posted by 2 million reviewers between 2005 and 2020. First, the author demonstrates how individual travel patterns can be mapped using the review data and discusses the potential applications of such individual-level data. The author then turns to aggregate-level maps, creating establishment covisit networks in which two establishments are linked if multiple reviewers visit both. Maps of establishment covisits reveal various intriguing patterns related to consumption and geography, such as the connections between neighborhoods and the centralization and segregation within a metropolitan area. Establishment covisit maps can also inform researchers about the diffusion of ideas and practices, trends in crime, and gentrification.

Using insights on potential users’ behavior and transport requirements to inform the development of a more sustainable vehicle concept as part of a mobility transition

Current mobility systems put a burden on people and the environment, e.g., through greenhouse gas emissions, and depletion of natural resources. Therefore, a mobility transition including changes in legislation, infrastructure, and behavior as well as socio-technical innovations is required. Against this backdrop, the inter- and transdisciplinary project presented in this article aims at developing a new vehicle for individual everyday travel. Target characteristics of the vehicle are: resource-efficient, low in harmful substances in its components, and low on greenhouse gas emissions while driving. To inform the vehicle’s development, n = 730 citizens of a medium-sized university city and its surrounding areas in central Germany took part in a survey on their mobility behavior, intention to switch to the new vehicle, their preferences for possible product features, payment models, and road use. Regression analyses revealed that socioeconomic factors (income, children in the household), individual travel characteristics (travel distance, motorization of vehicle), and psychological factors (innovativeness, weather resistance, travel habit strength, attitudes towards cars and bicycles) explained significant portions of the observed variance in participants’ intention to switch from current travel modes to the new vehicle. Furthermore, our results suggest that the new vehicle may be particularly attractive to potential users if it combines advantageous characteristics of bicycles and cars. Compared to sustainability-relevant aspects of vehicle usage, participants ascribed less importance to aspects of vehicle production and disposal. These learnings provide valuable insights for actors in industry, politics, design, and research striving to promote socio-technical innovations for a transition towards more sustainable mobility.

Chinese outbound tourism: between state control and autonomous individual travel. The case of honeymoons in France

Chinese outbound tourism: between state control and autonomous individual travel. The case of honeymoons in France

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

Nilai Ekonomi Objek Taman Wisata Sungai Mudal Menggunakan Metode Individual Travel Cost Method

The Sungai Mudal Tourism Park is situated in Banyunganti Hamlet, Jatimulyo Village, Girimulyo District, Kulon Progo Regency. This destination showcases ecotourism, emphasizing the natural beauty of the Menoreh mountains and water-based activities as its primary attractions. This study aims to analyze tourist characteristics and determine the potential economic value of Mudal River's natural allure. The potential economic value is calculated via consumer surplus using the Individual Travel Cost Method (ITCM). Tourist characteristics, including age, gender, occupation, income, and distance, are analyzed based on primary data gathered through interviews, observations, and questionnaires, supplemented by secondary data from literature. The findings reveal that Mudal River Tourism Park mostly attracts male visitors (50,9%), drawn to its adventurous offerings. The majority are students aged 15-25 with income levels mostly below Rp1.000.000. Most visitors reside within a distance of 0-50 km, primarily originating from Yogyakarta City. The calculated economic potential value of Sungai Mudal Tourism Park reaches IDR Rp 23.257.628.649,27 per year, averaging IDR 346.634,25 per individual annually in consumer surplus. However, the actual economic value recorded in 2022 stands at IDR 7.052.230.000 which represents 30% of the total potential economic value. This indicates that tourists visiting Sungai Mudal Tourism Park obtain environmental service benefits surpassing the incurred costs. Yet, this potential economic value is non-monetary. Effective management strategies for Sungai Mudal Tourism Park are necessary to enhance the actual economic value of tourism.

Economic Assessment for the Recreation Value of Al-Sunut Forest Reserve in Khartoum State, Sudan

This study aims to present a methodological framework for estimating the recreational value as part of the ecosystem’s services provided by the Sudanese forests. The number of visitors ready to pay for the forest’s services has been analyzed using the individual travel cost method (ITCM). The data were collected using questionnaires with 640 visitors randomly participating at the forest site, and respondents’ results were analyzed using SPSS software v21. Further analysis of ITCM was performed using analysis of moment structure. The linear regression model is used to estimate the effects of variables, like socioeconomic variables, on the frequency of the visits to assess the recreational value of the forest site. The results showed that the consumer’s excess for each visitor was 21,500 Sudanese pounds (SDG), and travel costs, age, income, distance, and family size of visitors affect the recreational use of the site. Most of the visitors were students, with the majority of their ages ranging between 21 and 30. An additional discovery indicated that higher-income visitors were more willing to travel. These encouraging findings are a helpful guide for planning the future management of forests for recreational uses. This meant that forests offer great recreational value, which might help the Forestry Office ensure that natural forests are planned for and used sustainably.

Privacy-preserving adaptive traffic signal control in a connected vehicle environment

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy-preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using partially connected vehicle data. Specifically, we proposed a privacy-preserving mechanism to protect CV data against three types of attacks: CV collusion attacks, database attacks, and inference attacks. The mechanism leverages secure Multi-Party Computation and differential privacy to aggregate individual-level CV data to calculate key traffic parameters without compromising the privacy of CV users. For seamless integration with the privacy-preserving mechanism, we develop a traffic signal optimization model and an arrival rate estimator relying only on aggregated CV data, being applied to both undersaturated and oversaturated traffic conditions. The optimization model is further extended to a stochastic programming problem to explicitly handle the noises added by the privacy-preserving mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.

A metro smart card data-based analysis of group travel behaviour in Shanghai, China

Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy.

Designing Neighbourhoods to Facilitate Intercultural Encounters: Negotiating Between Self, Society and Place

Globalisation, and the consequent migratory processes, have radically transformed many countries across the world. A greater number of people with diverse backgrounds have been travelling to more places for numerous reasons. Consequently, immigrants have become an intrinsic part of most societies across the globe. When an individual travels from one place to another, they carry unique cultural information about specific areas. As such, immigrants inadvertently influence the spatial environment they interact with. Consequently, the built context of destination areas can be interpreted as the physical manifestation of accumulated information over time. Thus, immigrants effectively serve as catalysts for increasing levels of cultural flow between the place they have come from and the place they choose to resettle.

How do adolescents experience the decision-making process on travel mode choice?

Active travel can contribute to multiple health benefits in youth. Previous research has identified several factors influencing travel behavior. This study investigates how adolescents process these factors during their decision-making process on travel mode choice. Semi-structured interviews were conducted with 13 adolescents (11-14 years) and analysed using deductive-inductive thematic analysis. Four themes were generated from which the decision-making process on mode choice was conceptualized according to adolescents. The step-by-step process in which travel mode options were gradually reduced, was dependent on the context (Theme 1), the perceived availability of travel mode choice options, which was influenced by factors beyond the individual's perceived control (Theme 2), and on adolescents' pros/cons assessment to identify the most convenient option. Adolescents' habitually used travel mode influenced the decision at several stages throughout the process (Theme 4). To promote healthy travel behaviors, interventions should consider contextual circumstances, balance between enabling autonomous choices and promoting advantages of active travel modes, address individual travel modes and include practices that foster conscious processing of decision-making.

Hide Your Distance: Privacy Risks and Protection in Spatial Accessibility Analysis.

Measuring spatial accessibility to healthcare resources and facilities has long been an important problem in public health. For example, during disease outbreaks, sharing spatial accessibility data such as individual travel distances to health facilities is vital to policy making and designing effective interventions. However, sharing these data may raise privacy concerns, as information about individual data contributors (e.g., health status and residential address) may be disclosed. In this work, we investigate those unintended information leakage in spatial accessibility analysis. Specifically, we are interested in understanding whether sharing data for spatial accessibility computations may disclose individual participation (i.e., membership inference) and personal identifiable information (i.e., address inference). Furthermore, we propose two provably private algorithms that mitigate those privacy risks. The evaluation is conducted with real population and healthcare facilities data from Mecklenburg county, NC and Nashville, TN. Compared to state-of-the-art privacy practices, our methods effectively reduce the risks of membership and address disclosure, while providing useful data for spatial accessibility analysis.

Credit charge-cum-reward scheme for green multi-modal mobility

To promote green mobility and alleviate congestion and emissions, this study proposes a credit charge-cum-reward (CCR) scheme where the government determines mode-specific credit charging and rewarding rates together with charging and redemption prices to achieve regulation objectives. Over a CCR scheme period, travelers choose either driving by consuming credits or taking transit to accumulate credits to minimize individual travel costs. At the end of the period, the individual credit balance will be settled by the government at a charging (redemption) price for credit deficits (surpluses). From the viewpoint of travelers, we consider their heterogeneity in value of time and characterize their periodic mode usage equilibrium by formulating a decision optimization problem. From the viewpoint of the government, we discuss the design of CCR schemes without and with revenue constraints. We also investigate the Pareto-improvement and revenue-neutrality of CCR schemes. We prove theoretically and numerically that the proposed CCR scheme flexibly facilitates multi-modal traffic demand management by regulating travelers’ periodic mode usage behavior via charging and rewarding rates and differentiated charging and redemption prices.

Comparison of Discrete Choice and Machine Learning Models for Simultaneous Modeling of Mobility Tool Ownership in Agent-Based Travel Demand Models

Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results.

Travel Patterns and Motivations of the Hungarian Gay and Lesbian Community

ABSTRACT Tourism plays a key role for gay people, as it is not only a way to escape from work, but also a source of liberation and self-embracement. This role can be particularly valued in societies and political environments that are perceived as hostile toward this group. The present study examines the travel motivations and habits of gay and lesbian people, based on a questionnaire sample of 1473 respondents. The results show that for this group, but especially for women, acceptance and an open culture are of paramount importance when choosing a destination. In contrast, services and events specifically targeted at this sector, which are popular in the Western world, do not typically influence decision-making (they tend to be more attractive to women). Sex per se is not a travel motivator for this group, but is an important feature of travel for men. In addition to gender, the services and activities that an individual engages in while traveling are influenced by the degree of their sexual embracement, the place of residence and the company with which the individual travels.

Evaluating geospatial context information for travel mode detection

Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model. Here, we identify context representations from related work and propose an analytical pipeline to assess the contribution of geospatial context information for travel mode detection based on a random forest model and the SHapley Additive exPlanation (SHAP) method. Through experiments on a large-scale GNSS tracking dataset, we report that features describing relationships with infrastructure networks, such as the distance to the railway or road network, significantly contribute to the model's prediction. Moreover, features related to the geospatial point entities help identify public transport travel, but most land-use and land-cover features barely contribute to the task. We finally reveal that geospatial contexts have distinct contributions in identifying different travel modes, providing insights into selecting appropriate context information and modeling approaches. The results from this study enhance our understanding of the relationship between movement and geospatial context and guide the implementation of effective and efficient transport mode detection models.

Predicting modal choice for urban transport using an algebraic equation

Demand estimation and forecasting is an essential step in urban passenger transport planning. Relating the factors that influence the modal choice behavior of individuals facilitates demand estimation. In this study, we develop machine learning models that consider individuals' demographic, socioeconomic, and travel characteristics to justify their mode choice. Two datasets are used to train and validate the models. We use logistic regression and multilayer perceptron models to classify public or private transportation trips. It was observed that a multilayer perceptron model with a low number of parameters could predict modal selection with an accuracy exceeding 90%. We derive an algebraic equation from this result to perform modal selection prediction. Our results show that the models can effectively predict the mode of transportation of individuals based on their demographic and travel characteristics.

Risk culture as a blessing in tourism development: Long-run effects of epidemic disasters

Using a unique dataset from China, this paper shows that the intensity of epidemic disasters during the country's last feudal period (1644–1895) promoted tourism businesses' contemporary performance. The results remain robust when considering geography, institutions, culture, confounding factors, and potential endogeneity bias. Three sources of tourism performance are referenced: business census data from individual travel agencies, visit data for national parks, and tourism revenue data from Chinese prefectures. To explain this complex but apparently uniform finding, we perform causal mediation analysis to demonstrate the importance of promotional effects by encouraging corporate risk-taking, with robustness to cultural fusion. Ultimately, we show the power of risk culture to justify the path dependence of tourism development on historical epidemics.

블로그 코퍼스 기반 여행지 다차원 이미지 자동 측정 연구

The aim of this study is to propose an exploratory methodology for automatically estimating multi-dimensional images of travel destinations based on unsupervised learning using a large-scale travel blog corpus so that it can be used for longitudinal and cross-sectional studies. First literature reviews on the construal level theory conducted to clarify the relationship between the travel experiencer's level of travel destination interpretation and potential travelers' travel destination selection decision-making and instead of surveys, the value of travel blogs as message information on the travel experiencer's travel destination interpretation are useful data resource for automatically estimating multi-dimensional images was discussed. Next, by applying web crawling, natural language processing technology and the unsupervised learning-based machine learning algorithm principal component analysis, the frequency of co-occurrence of the travel destination named entity-subjective adjective pair is vectorized to estimate the multi-dimensional images of travel destinations, and visualize the image property structure of individual travel destinations through network centrality analysis by referring to the destination image attributes of Echtner and Ritchier (2003). Finally, the validity of this methodology was demonstrated by applying to the case of Changwon. The results of this study can contribute to gaining insight in making demand-oriented regional tourism vitalization policy decision-making and can be used for large-scale travel blogs corpus-based convergence follow-up studies.