Individual Travel Research Articles

Economic Valuation of Marine Tourism Based on Bio-Ecoregion (Case Study in Batukaras Beach, Pangandaran District, West Java Province, Indonesia)

The COVID-19 pandemic has an impact on all economic sectors and the order of life in society, including the marine tourism sector. As in the Batukaras Beach tourism area, West Java Province. This research aims to analyze the factors that influence the level of tourist visits marine tourism area of Batukaras Beach and the economic value of marine tourism. The research conducted in May 2021 – June 2022. The data used primary and secondary data, using purposive sampling as many as 106 respondents using multiple linear regression and travel cost method. Based on the results of the research, the factors that influence marine tourism visits are variables X1 natural conditions of marine tourism, X2 types of marine tourism attractions, X3 distance traveled, X4 length of time for tourism activities, X5 level of education, X6 income level, X7 age, X8 travel cost. Based on this research it was obtained economic value of the marine tourism environment based on travel expenses in Batukaras Beach is IDR 79,795,000. Consumer surplus or tourists' willingness to pay based on the individual travel cost method is IDR 2,975.08/individual/visiting and economic value of marine tourism based on willingness to pay in Batukaras Beach IDR 1,039,130,027.

Camili Biyosfer Rezerv Alanının rekreasyonel amaçlı kullanımının ekonomik değerinin tahmini

Bu çalışmada, ülkemizin ilk ve tek biyosfer rezerv alanı olan Camili Biyosfer Rezerv Alanı örneğinden hareketle, ormanların işlevlerden biri olan rekreasyon işlevinin ekonomik değerinin tahmini amaçlanmıştır. Araştırmada seyahat maliyeti yöntemi ve koşullu değer belirleme yönteminden birlikte istifade edilmiştir. Alanda rekreasyon etkinliği gerçekleştiren 96 ziyaretçi ile 2016 yılında anket çalışması gerçekleştirilmiştir. Seyahat maliyeti yöntemi bulgularına göre; ziyaretçilerin ortalama bireysel seyahat maliyeti ise 201.57 TL, bireysel tüketici rantı 697.16 TL ve toplam tüketici rantı ise 12200300 TL olarak tahmin edilmiştir. Koşullu değer belirleme yöntemi kapsamında ise ziyaretçilere rekreasyon hizmetlerine yönelik bir projeyi konu alan kuramsal senaryo sunulmuş ve buna bağlı olarak ziyaretçilerin ortalama ve toplam ödeme eğilim değerleri belirlenmeye çalışılmıştır. Buna göre alanda yürütülen rekreasyon hizmetlerinin korunması ve geliştirilmesine yönelik ortalama ödeme eğilimi 186.48 TL, toplam ödeme eğilimi ise 3263400 TL olarak tahmin edilmiştir. Sonuç olarak, kişilerin yaptıkları harcamadan daha az ödeme eğilimine sahip oldukları söylenebilir. Ayrıca alanın tanınırlığın artırılması ve yöre halkının rekreasyon faaliyetlerini çeşitlendirilebilmesi sayesinde ziyaretçi sayısı ve ziyaretçilerin alanda yaptıkları harcamalar artırılabilir ve alanın rekreasyon değeri daha da yüksek çıkabilir.

Real-time traffic congestion prediction using big data and machine learning techniques

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

Valuing nature-based recreation in forest areas in Italy: An application of Travel Cost Method (TCM)

Nature-based recreation provides benefits facilitating social interactions and cohesion, creating job opportunities, and stimulating the local economy. The aim of this study was to assess the recreational economic value of different forest areas considering two aspects: stand features and proximity of the forest to urban areas. An individual Travel Cost Method (TCM) was applied in three study areas in Italy involving 603 visitors in the survey. The results show that in all three case studies the most important forest-based recreational activity carried out by visitors is hiking, followed by picnicking and mountain biking. The TCM estimates a consumer surplus between €7.33 and €17.37 per visit. The key points highlighted by the study are that: managed natural forests have a higher recreational economic value than unmanaged artificial forests; the peri-urban forests are characterized by a higher number of visitors but a lower value per visit than mountain forests.

Campus commute mode choice in a college town: An application of the integrated choice and latent variable (ICLV) model

This study examined commute mode choice in a small college town by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables, such as pro-car, pro-bus, and pro-environmental attitudes. The campus commute data (n = 554) came from an individual travel survey of the faculty and staff at the University of Illinois at Urbana-Champaign. The ICLV model provided a richer and more nuanced understanding of the commute mode choice of campus members than a traditional multinomial logit (MNL) model by illustrating how various sociodemographic characteristics influence mode decisions through mode preference and environmental attitudes. Results of a scenario analysis show that policy efforts that aim to shift commuter attitudes toward sustainable modes can be as effective as measures that improve actual transportation systems.

Travel mode choice prediction based on personalized recommendation model

AbstractThe accurate prediction of individual travel mode in the city is necessary for the development of urban traffic intelligence. Through deep analysis on distribution of travel modes, policy makers can make proper policies to improve traffic conditions. With the development of sensing techniques and telecommunication technologies, it is easy to collect travel data of individuals and use some popular models to detect travel modes. However, existing methods are limited to aggregation analysis and these models suggest that travelers' preference mainly depend on the characteristics of travelers, little is known considering to extract individual travel preference from individual historical trips. Due to collect the user information is expensive and time‐consuming, one of the difficult tasks facing the prediction of travel mode by traditional methods is the lack of user's profile. What is more, data of different travel modes are always imbalanced. Accordingly, the paper proposes a model Balance Multi Travel Mode Deep Learning Prediction (BMTM‐DLP) for individual travel mode prediction inspired by the idea of recommender system. For the first time, the theory of recommender system is introduced into the field of individual travel mode choice prediction and we take advantage of the model to extract individual travel preference for travel mode prediction. Further, the focal loss function module is introduced into the model to reduce the impact of unbalanced categories. Experiments show that the model performs well on both datasets LPMC and XSCR, achieving 92.2% and 82.8% prediction accuracy in respective. It showed that the proposed model performed well in individual travel mode prediction.

Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic Prediction

The provincial highway transportation system supports substantial cross-city transitions of people and logistics, where the prediction tasks in terms of station/road traffic, urban transitions, and individual traveling are crucial for boosting data intelligence. However, to achieve efficient prediction model design, the predictability analytics with data is the basis, but has not been sufficiently investigated in the existing literature yet. To bridge this gap, in this paper, we study one large-scale dataset collected from one provincial highway transportation system, which contains totally 21,685,765 vehicles and 351,766,743 transaction records, and conduct a comprehensive mobility analytics on its predictable performance. We first investigate the station traffic by mining its spatio-temporal correlations, then examine the multi-type urban transition flows (i.e., people flows and logistics) by demystifying the difference and similarity between the two types of behaviors, and finally analyze the uncertainty of individual traveling behaviors in terms of the destination and arriving time. After that, in accordance with the analytical findings, we cast a case study of data-driven model design for station traffic prediction. Specifically, a novel learning model is devised, named STAR, i.e., Spatio-Temporal Attention based pRediction model, which consists of station outflow/inflow temporal embedding components and spatio-temporal attention blocks to push the limit of prediction capability. Extensive experiments corroborate the efficacy of the proposed STAR.

관광지 만족도를 평가하는 관광지 선택속성에 관한 연구

The purpose of this study is to add to our understanding of actual traveler interest in the tourism market by investigating the causal conditions of tourist destination attributes on tourist satisfaction. Prior to proceeding, natural tourist destinations that were recommended 5 times in a row in South Korea's top 100 must-see tourist destinations were chosen as the study's target. Seoraksan National Park, Hallasan National Park, Suncheonman Wetland (Suncheonman National Garden), Nami Island, and Udo are among them. For an analysis of the online reviews of these five well-known tourist destinations, topic modeling was used, and the latent dirichlet allocation (LDA) technique with TF-IDF weight was used to create a comprehensive list of the topics posted by Tripadvisor reviewers. Making scientific inferences about nature tourism phenomena, such as Fuzzy-set Qualitative Comparative Analysis (FsQCA), to better predict and explain real-world nature tourism phenomena using a configurational approach, is becoming more popular. The purpose of this article is to identify the key contribution and knowledge structure of tourist destination selection attributes as well as their overall satisfaction research using complexity theory and FsQCA. The primary goal of this study is to investigate not only the influence of tourist destination selection attributes as a single variable in online reviews, but also how these selection attributes influence tourist satisfaction with various combinations of causal conditions. These findings are useful in understanding individual travelers' perceptions of tourist destinations as well as their overall satisfaction.

Implementation of a Danish activity-based model

Accurately modeling individual travel demand and behavior is essential for forecasting the impact ofalternative policies both at the disaggregated and aggregated levels. In the last decades, the state-ofthe-art of travel demand models advanced from traditional four-step models, passing through tourbasedmodels, to activity-based models. The latter models postulate that the demand for travel isderived from the demand for performing activities, i.e., travels are only undertaken when the utility ofan activity and its associated travel exceeds the utility of activities involving no travel [1]. Furthermore, itconsiders that daily travels are temporally and spatially constrained, and individuals start their day athome and return to a home base at the end of the day

Consumer surplus analysis using the Travel Cost Method (TCM) at the Petrus Kafiar Beach tourist attraction, Manokwari Regency, West Papua

This study aims to determine the economic value of tourism objects and whether there is a consumer surplus at the Petrus Kafiar beach based on the Travel Cost Method (TCM). The consumer surplus value is obtained by using the Individual Travel Cost Method (ITCM) approach and willingness to pay to calculate the willingness to pay of tourists visiting Petrus Kafiar Beach. The value of WTP obtained is Rp. 23,358.21 per person per visit. Meanwhile, the average WTP (willingness to pay) per year for one visit is only Rp. 5,491.23. The results of the integral calculation to find the consumer surplus of visitors to the Petrus beach tourism object, the consumer surplus is ranged from Rp. 5,201.5 to Rp. 10,403.91 per individual/visit.

Influence of environmental variables on functional capacity in patients with lung disease

Air pollution is related to negative impacts mainly on people with Chronic Respiratory Diseases (CRD), reflecting on respiratory health, reduced physical performance and so on. The objective was to investigate the influence of meteorological and air quality variables on the functional capacity of patients with CRD. This was a descriptive, retrospective and cross-sectional study of information collection in a database from 2016 to 2019. For the analysis of environmental variables, three different databases were used, one from the automatic air quality monitoring station in Novo Hamburgo, and the weather stations of Novo Hamburgo and Campo Bom (RS). The monitored data were: NO2, CO, O3, PM10, PM2,5, average temperature, minimum temperature, maximum temperature, precipitation, average wind speed and relative humidity. The sample consisted of 85 individuals from the Pulmonary Rehabilitation Program (PRP), where the variables spirometry and six-minute walk test (6MWT) were collected. It was observed that the higher the concentration of PM10 and the higher the minimum temperature, the lower the spirometric results. Furthermore, the greater the concentration of PM2.5, the shorter the distance that the individual travels. A relationship was found between environmental data and functional tests, where individuals with CRD are more sensitive to high levels of air pollutants, as well as to lower temperatures.

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering work is often required to extract effective features. Additionally, features and models are mostly calibrated for individual trajectories with regular daily travel routines and patterns, and therefore suffer from poor generalizability when applied to new trajectories with more irregular patterns. Meanwhile, most existing models cannot extract features to explicitly represent regular travel activity sequences. Therefore, this paper proposes a graph-based representation of spatiotemporal trajectories and point-of-interest (POI) data for travel activity type identification, defined as Gstp2Vec. Specifically, a weighted directed graph is constructed by connecting regular activity areas (i.e., zones) detected via clustering individual daily travel trajectories as graph nodes, with edges denoting trips between pairs of zones. Statistics of trajectories (e.g., visit frequency, activity duration) and POI distributions (e.g., percentage of restaurants) at each activity zone are encoded as node features. Next, trip frequency, average trip duration, and average trip distance are encoded as edge weights. Then a series of feedforward neural networks are trained to generate low-dimensional embeddings for activity nodes through sampling and aggregating spatiotemporal and POI features from their multihop neighborhoods. Activity type labels collected via travel surveys are used as ground truth for backpropagation. The experiment results with real-world GPS trajectories show that Gstp2Vec significantly reduces feature engineering efforts by automatically learning feature embeddings from raw trajectories with minimal prepossessing efforts. It not only enhances model generalizability to receive higher identification accuracy on test individual trajectories with diverse travel patterns, but also obtains better efficiency and robustness. In particular, our identification of the most common daily travel activities (e.g., Dwelling and Work) for people with diverse travel patterns outperforms state-of-the-art classification models.

Research on The Influence of Social Network Information Quality On Tourism Willingness

Social networks have become an important way for people to obtain information, socialize and show their lifestyles. They have also become an important platform for cloud tourism and cloud exhibitions against the backdrop of COVID-19. Will photos or videos on social networks have an impact on individual travel intentions? Based on these questions, this study explores the measurement of information quality of social networks and its impact on individual travel decisions. Based on the social impact theory, this study constructed THE SOR theoretical model from the perspective of informational social impact. Through questionnaires, 696 sample data were obtained, and AMOS was used to analyze the Structural equation model. It is found that the quality of social network information can be composed of five dimensions. The tourism information published by others in social network has a significant positive influence on tourism decision making. The higher the information quality is, the stronger the individual will be to travel.

Path-Based Approach for Expanding Rail Transit Network in a Metropolitan Area

Rail transit network design is an important strategic problem in determining the layout of infrastructure and improving operating performance. A core transit network with multiclass rail transit systems has been constructed in many metropolitan areas worldwide. In this study, we aimed to expand an existing network to shorten travel time and improve service quality under the restriction of limited transport supply. We formulate the studied problem as a mixed-integer linear model to obtain optimal construction links, the number of trains required on each link, and the path selected by each traveler such that the weighted sum of total costs from the perspective of travelers, operators, and investors is minimized. The formulated model is path-based, where feasible paths for each traveler are generated to describe the full door-to-door journey, including the first/last mile, transfers, and multiclass transit modes. Owing to the complexity of the network design problem and because it is impractical to enumerate all feasible paths for each traveler in real-size problems, we propose a column generation-based algorithm to find both tight lower bounds and good-quality solutions efficiently by considering only a subset of feasible paths. We prove that the pricing subproblem in column generation can be decomposed into multiple shortest path problems, which can be solved efficiently and separately, based on O/D pairs instead of individual travelers. A rail transit network along a metropolitan corridor was studied as an example. Multiple computational experiments were conducted, and the results illustrate the validity and practicality of the proposed methodology for solving the problem.

Gastronomik Deneyimsel Değerlerin Tekrar Ziyaret Niyetine Etkisi: Eskişehir Örneği

The aim of this research is to try to determine the effect of gastronomic experiential values of tourists visiting Eskişehir on their intention to revisit. In this context, the survey technique, which is one of the quantitative research methods, was used in the research. The data were collected from 399 domestic tourists visiting Eskişehir by survey technique. The collected data were subjected to demographic analysis, validity and reliability, explanatory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM). The results of the analysis revealed that local tourists reach information about Eskişehir through social media, the places they visit the most are street food, local restaurants and fast food restaurants, and they prefer individual travel. Considering the findings related to the hypotheses, the effect of epistemic value, health value and price value on revisit intention was supported, but it was determined that emotional value, interaction value, taste-quality and prestige values were not supported.

Evaluating the flooding level impacts on urban metro networks and travel demand: behavioral analyses, agent-based simulation, and large-scale case study

With urban residents’ increasing reliance on metro systems for commuting and other daily activities, extreme weather events such as heavy rainfall and flooding impacting the metro system services are becoming increasingly of concern. Plans for such emergency interruptions require a thorough understanding of the potential outcomes on both the system and individual component scales. However, due to the complex dynamics, constraints, and interactions of the elements involved (e.g., disaster, infrastructure, service operation, and travel behavior), there is still no framework that comprehensively evaluates the system performance across different spatiotemporal scales and is flexible enough to handle increasingly detailed travel behavior, transit service, and disaster information data. Built on an agent-based model (ABM) framework, this study adopts a data-driven ABM simulation approach informed by actual metro operation and travel demand data to investigate the impact of flood-induced station closures on travelers as well as the overall system response. A before-after comparison is conducted where the traveler behaviors in disaster scenarios are obtained from a discrete choice model of alternative stations and routes. A case study of the Shanghai Metro is used to demonstrate the ability of the proposed approach in evaluating the impacts of flood-induced station closures on individual traveler behavior under normal operation and a series of water level rise scenarios of up to 5m. It was found that, when the flood-induced station closures only affect a few river-side stations in the city center, the travelers experience only minor disruptions to their trips due to the availability of unaffected stations nearby as a backup. However, as the water level increases and more stations (mainly in the suburban area) are affected, up to 25% of trips are no longer being fulfilled due to the loss of entrances, exits, or transfer links. The system experiences overall less crowdedness in terms of passenger volume and platform waiting time with a few exceptions of increased passenger load due to concentrations of passenger flows to alternative stations under flooding-induced station closures. The proposed approach can be adapted to other disaster scenarios to reveal the disaster impacts on both aggregated and disaggregated levels and guide the design of more spatio- and temporally-targeted emergency plans for metro systems.

Individual Low-Cost Travel as a Route to Tourism Sustainability

The article discusses the issues of individual low-cost and sustainable tourism and its main goal is to confirm that individual low-cost travel is in fact a form of tourism within which the principles of sustainable tourism are implemented. It is a theoretical article based on desk research involving literature analysis. The study has confirmed that individual low-cost tourism and various other types related to it show relevant relationships with the ideas of sustainable development. These affinities are visible concerning economic, sociocultural, and environmentally balanced growth. However, the research results presented in the article are theoretical and need further empirical verification. Therefore, the authors treat this study as a work provoking discussion on the issue of the relationship between individual low-cost and sustainable tourism.

Non-Endemic Leishmaniases Reported Globally in Humans between 2000 and 2021-A Comprehensive Review.

Leishmaniases are human and animal parasitic diseases transmitted by phlebotomine sand flies. Globalization is an important driver of the burden and in the current dynamics of these diseases. A systematic review of articles published between 2000 and 2021 was conducted using the PubMed search engine to identify the epidemiology and clinical management of imported human leishmaniases as a fundamental step to better manage individual cases and traveler and migrant health from a global perspective. A total of 275 articles were selected, representing 10,341 human imported cases. Identified drivers of changing patterns in epidemiology include conflict and war, as well as host factors, such as immunosuppression, natural and iatrogenic. Leishmania species diversity associated with different clinical presentations implies diagnostic and treatment strategies often complex to select and apply, especially in non-endemic settings. Thus, diagnostic and management algorithms for medical clinical decision support are proposed. Increased surveillance of non-endemic cases, whether in vulnerable populations such as refugees/migrants and immunocompromised individuals or travelers, could improve individual health and mitigate the public health risk of introducing Leishmania species into new areas.

A Federated Mixed Logit Model for Personal Mobility Service in Autonomous Transportation Systems

Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS.

An Adaptive Staying Point Recognition Algorithm Based on Spatiotemporal Characteristics Using Cellular Signaling Data

Cellular signaling data (CSD) have attracted unprecedented attention due to their large size, long observation period, and high followability. Before applying CSD, a series of data processing steps are indispensable; among those steps, staying point recognition is the basis for recognizing individual travel states and thus the influence of further application of CSD. Previous work indicates that the existing staying point recognition algorithms have two common aspects. One is the requirement of a fixed spatiotemporal threshold to analyze the user’s travel characteristics. The other is the insufficiency of accuracy assessment, which indicates that further studies are expected owing to the lack of ground truth data in CSD. In this work, a “spatiotemporal window”-based algorithm is proposed to recognize individual staying and moving states. First, an iterative-learning-based model is designed to cluster individual trajectory points without predefined spatiotemporal thresholds. Then, rules to distinguish the staying or moving cluster are made from individual travel characteristics. Moreover, verification work is carried out by collecting volunteers’ ground truth data using our developed smartphone application, which achieves an accuracy of 91.3%. Finally, the results demonstrate the effectiveness and robustness of the algorithm through the performance of comparison and sensitivity analyses.