Individual Travel Research Articles

DOES THE ECONOMIC VALUE OF THE PRAMBANAN TEMPLE REFLECT ITS HERITAGE VALUE?

<p>This study aims to estimate the economic value of the Prambanan Temple tourist attraction using the Travel Cost Method (TCM). The visitor willingness to pay for entrance tickets is used to reflect the economic value of the Prambanan Temple's cultural heritage, assessed with the Individual Travel Cost Method (ITCM) approach. The analysis utilizes Ordinary Least Squares (OLS) regression. The study included 186 respondents. Total visitor willingness to pay for the Prambanan Temple is Rp 10,747,976 ($688.57), with an average visitor willingness to pay of Rp 57,753 ($3.70) per visit. The current entrance ticket price is Rp 50,000 ($3.22). This suggests that the value of the Prambanan Temple's cultural heritage to visitors is higher than the current ticket price. The consumer surplus generated by the 186 respondents is Rp 1,399,358 ($89.65), with a consumer surplus per individual of Rp 7,492 ($0.48). </p>

On alert for Ebola: public health risk assessment of travellers from Uganda to the USA during the 2022 outbreak.

On 20 September 2022, the Ugandan Ministry of Health declared an outbreak of Ebola disease caused by Sudan ebolavirus. From 6 October 2022 to 10 January 2023, Centers for Disease Control and Prevention (CDC) staff conducted public health assessments at five US ports of entry for travellers identified as having been in Uganda in the past 21days. CDC also recommended that state, local and territorial health departments ('health departments') conduct post-arrival monitoring of these travellers. CDC provided traveller contact information, daily to 58 health departments, and collected health department data regarding monitoring outcomes. Among 11 583 travellers screened, 132 (1%) required additional assessment due to potential exposures or symptoms of concern. Fifty-three (91%) health departments reported receiving traveller data from CDC for 10 114 (87%) travellers, of whom 8499 (84%) were contacted for monitoring, 1547 (15%) could not be contacted and 68 (1%) had no reported outcomes. No travellers with high-risk exposures or Ebola disease were identified. Entry risk assessment and post-arrival monitoring of travellers are resource-intensive activities that had low demonstrated yield during this and previous outbreaks. The efficiency of future responses could be improved by incorporating an assessment of risk of importation of disease, accounting for individual travellers' potential for exposure, and expanded use of methods that reduce burden to federal agencies, health departments, and travellers.

Providing real-time en-route suggestions to CAVs for congestion mitigation: A two-way deep reinforcement learning approach

This research investigates the effectiveness of information provision for congestion reduction in Connected Autonomous Vehicle (CAV) systems. The inherent advantages of CAVs, such as vehicle-to-everything communication, advanced vehicle autonomy, and reduced human involvement, make them conducive to achieving Correlated Equilibrium (CE). Leveraging these advantages, this research proposes a reinforcement learning framework involving CAVs and an information provider, where CAVs conduct real-time learning to minimize their individual travel time, while the information provider offers real-time route suggestions aiming to minimize the system’s total travel time. The en-route routing problem of the CAVs is formulated as a Markov game and the information provision problem is formulated as a single-agent Markov decision process. Then, this research develops a customized two-way deep reinforcement learning approach to solve the interrelated problems, accounting for their unique characteristics. Moreover, CE has been formulated within the proposed framework. Theoretical analysis rigorously proves the realization of CE and that the proposed framework can effectively mitigate congestion without compromising individual user optimality. Numerical results demonstrate the effectiveness of this approach. Our research contributes to the advancement of congestion reduction strategies in CAV systems with the mitigation of the conflict between system-level and individual-level goals using CE as a theoretical foundation. The results highlight the potential of information provision in fostering coordination and correlation among CAVs, thereby enhancing traffic efficiency and achieving system-level goals in smart transportation.

Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council’s open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables.

Spatial Interaction Analysis of Infectious Disease Import and Export between Regions

Human travel plays a crucial role in the spread of infectious disease between regions. Travel of infected individuals from one region to another can transport a virus to places that were previously unaffected or may accelerate the spread of disease in places where the disease is not yet well established. We develop and apply models and metrics to analyze the role of inter-regional travel relative to the spread of disease, drawing from data on COVID-19 in the United States. To better understand how transportation affects disease transmission, we established a multi-regional time-varying compartmental disease model with spatial interaction. The compartmental model was integrated with statistical estimates of travel between regions. From the integrated model, we derived a transmission import index to assess the risk of COVID-19 transmission between states. Based on the index, we determined states with high risk for disease spreading to other states at the scale of months, and we analyzed how the index changed over time during 2020. Our model provides a tool for policymakers to evaluate the influence of travel between regions on disease transmission in support of strategies for epidemic control.

Machine learning-based prediction model for disc cutter life in TBM excavation through hard rock formations

Replacing damaged or worn disc cutters is costly and time-consuming, which can significantly reduce the utilization and advance rate of tunnel boring machines (TBMs). Accurate prediction of disc cutter life is crucial for optimizing TBM operation in tunneling projects. This study introduces a machine-learning-based prediction model designed to forecast disc cutter wear, incorporating the analysis of each cutter’s travel length and the intervals between cutterhead interventions (CHI). The principle underlying the developed machine learning approach involves usage of multiple learning algorithms, ensemble learning methods, and a sophisticated analysis of these factors to identify patterns and relationships essential for effectively predicting wear rates. Employing CHI report data from the Daegok–Sosa tunneling project’s hard rock excavation, the model evaluates 15 wear-influencing factors, providing precise wear predictions. This study proposes the ensemble machine learning (ML) methods, namely Random Forest (RF) and Extreme Gradient Boosting (XGB), for accurate wear rate predictions and discern influential factors. The proposed model demonstrated exceptional prediction accuracy, as evidenced by a root mean square error of 0.049 mm for a single-ring excavation length. Notably, this model innovatively accounts for variable wear rates of cutters based on the individual cutter travel lengths as well as other geological and operational parameters. Furthermore, the predicted cutter consumption rate showed reasonable correspondence when compared to the actual CHI records. The proposed model is expected to improve existing disc cutter life prediction methods and reduce the cost and time for replacing damaged or worn disc cutters.

A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data

A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data

Heterogeneous choice of personalized Mobility-as-a-Service bundles and its impact on sustainable transportation

The Mobility-as-a-service (MaaS) framework has the potential to induce changes in individual travel behavior towards more sustainable transportation alternatives by integrating multimodal travel services. However, insights into the behavioral responses of specific groups of people to MaaS, thus promoting green travel, are currently lacking. This paper intends to investigate the intergroup heterogeneous preferences on the choice of MaaS bundles and evaluate their contributions to eco-friendly travel. A stated choice experiment is designed, incorporating personalized MaaS service bundles complemented by different green travel-oriented ticketing strategies. The experiment caters to different groups of users related to public transport, cars, and other modes of transport. The results indicate a substantial improvement in the goodness-of-fit of the behavioral model after user classification, revealing significant heterogeneity in preferences among users regarding MaaS bundle choices. Notably, the impact of sub-tickets on nudging toward green transportation is more pronounced than that of monthly and discount tickets.

Intercity Traffic Travel Mode Identification Method Based on Mobile Signalling Data

With the popularity of mobile devices, the signalling data generated by them provides significant opportunities for studying intercity travel behaviour in terms of data scale and information continuity. However, due to the low quality of the data in spatial accuracy, temporal frequency, and traffic semantics, the accuracy of identifying individual travel modes is low and it is difficult to extend to complex traffic scenarios. In this paper, we propose a new framework for identifying individual intercity travel modes based on mobile signalling data. The framework includes components for data pre-processing, geo-information mapping, feature and attribute extraction, and travel mode recognition. We utilize a comprehensive detection model to identify users’ multimodal intercity transport behaviour. Using two modules, Random Forest Embedding (RFE) and Bidirectional Long Short-Term Memory (Bi-LSTM), the model can capture the spatiotemporal characteristics and complex multi-stage associations in intercity travel chains. A large-scale mobile phone dataset from Jiangsu Province, China, was used for verification. The results showed that, on average, the method was able to detect travel mode with 92% accuracy. This study provides valuable support for further research on individual travel behaviour and the enhancement of transportation planning.

The impacts of COVID-19 pandemic on bus transit demand: A 30-month Naturalistic Observation in Jiading, Shanghai, China

The COVID-19 pandemic and subsequent governmental responses have had unprecedented effects on public transit (PT) demand. This paper presents a naturalistic observation of the Jiading bus transit system in Shanghai, China, spanning from April 2021 to October 2023 and covering different stages under various extreme policy response combinations. We employ Prais-Winsten regression to quantitatively assess the pandemic’s impact on bus demand and explore demand recovery patterns at both aggregated and individual levels in the post-pandemic era. Our findings reveal a strong negative correlation between bus demand and the stringency of containment policies, consistent across both levels of analysis. In the post-pandemic period, ridership has only rebounded to 77% of the pre-Omicron near-normal level, with notable spatial and temporal disparities across different regions. While the temporal distribution of ridership has largely normalized, the recovery of travel demand between zones outpaces that of travel within zones. Moreover, a persistent decline in individual travel frequency has been observed, which has not reverted in the post-pandemic period. The insights from this study can help policymakers better respond to potential future crises and improve PT services in the post-pandemic era.

Ruralease-Stay,Travel,Savor

In a rapidly evolving travel landscape, characterized by a growing desire for more personalized and immersive experiences, the emergence of innovative solutions becomes paramount. Addressing this demand, the RURALEASE Android application stands as a beacon, bridging the divide between hosts and guests, ushering in a new era of seamless connectivity in the realm of homestay accommodations. Homestays, a burgeoning trend in the hospitality sector, offer travellers a unique opportunity to eschew traditional hotel confines and instead embed themselves within local communities, fostering authentic cultural encounters and deepened travel experiences. The RURALEASE application, tailored specifically for Android devices, redefines the landscape of homestay bookings with its intuitive interface and comprehensive features. Through RURALEASE, users navigate a user-friendly platform, effortlessly securing accommodations that resonate with their individual preferences and travel aspirations. By facilitating direct communication between hosts and guests, RURALEASE fosters a sense of trust and transparency, enriching the booking experience for both parties involved. Homestay applications like RURALEASE represent more than just a technological innovation; they signify a fundamental shift in how travelers engage with destinations. By transcending traditional lodging paradigms, these applications empower travelers to forge meaningful connections with local communities, unlocking a tapestry of enriching experiences that transcend the confines of conventional tourism.This research paper endeavors to delve into the profound impact of homestay applications on the modern travel landscape. Through a meticulous examination of their functionalities, user experiences, and broader implications for hosts and guests alike, this study seeks to shed light on the transformative potential of homestay accommodations in shaping authentic travel experiences. Keywords: Homestay, Application, Android

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

Second-generation South Asian [im]migrants: a new frontier of diaspora tourism

ABSTRACT Diaspora tourism refers to the travel and tourism activities of individuals or communities of a particular ethnicity or culture to their ancestral or historical destinations. The study of diaspora tourism remains a challenging endeavour, mainly due to the apparent inadequacy of previous studies. Diaspora tourism is often viewed as a form of ‘homecoming.’ I argue that tourism in the traditional sense is different from the tourism rganized by the second-generation diaspora because these generations do not simply go to their ancestor’s places to explore the connections. Hence, this research seeks to understand the lived experiences of second-generation immigrants better when they visit their ancestral lands and the influence of second-generation transnationalism on their experiences of diaspora tourism. This study will contribute to the literature on diaspora tourism by comparing first and second-generation immigrants and highlighting the differences in their tourism experiences in the South Asian diaspora.

Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm

Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro systems.

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

The impacts of transit-oriented development (TOD) on travel behavior have been extensively studied, with a predominant focus on cross-sectional analyses that provide a static evaluation at a specific point in time by comparing TODs and non-TODs. Longitudinal assessments that capture changes in behavior over time remain relatively uncommon, and the literature tends to overlook differences in evaluating TOD effects across cross-sectional and longitudinal analyses. Additionally, the role of trip purpose as a significant but unexplored variable influencing the degree of TOD effects is often disregarded. To address these gaps, this systematic review examines 48 quantitative studies, comparing the effects of TOD on travel behavior from cross-sectional and longitudinal perspectives, restructuring indicators of effects into transit use, non-motorized travel, vehicle dependence, and vehicle ownership, and differentiating the effects by trip purpose. A metric has been introduced to quantitatively assess the impact of TOD on travel behavior. The pooled results indicate that private vehicle usage remains high in TOD areas, particularly for non-commuting trips, and that the longitudinal effects of TOD are limited and potentially influenced by individual travel attitudes, residential self-selection, and long-term travel habit change. Furthermore, the methodological differences between cross-sectional and longitudinal studies may lead to divergent conclusions regarding the effects of TOD on travel behavior. Our analysis sheds light on the importance of carefully selecting an appropriate method for a given research question to maximize the accuracy and relevance of the findings. Combining TOD and shared mobility can create a more efficient multi-model transport network that meets the diverse needs of city residents and improves accessibility for all. Overall, this review provides new insights into the impacts of TOD on travel behavior and supports the potential for a paradigm shift toward multimodal transport through the integration of TOD and shared mobility.

Orthopedics in Space Travel: Developing Procedures to Evaluate the Safety of Implants Amidst the Rise of Commercial Space Tourism

With the rise of commercial space tourism, the barrier of entry into space lowers. Therefore, passengers with more complex medical conditions are predicted to enter space. This report aims to initiate the development of procedures assessing the safety of space travel for individuals with orthopedic implants. In preparation for the 2023 sounding rocket launch by McGill Rocket Team, the Payload subteam developed a bone model, a human model, a finite element analysis model, and a testing model for determining the safety of orthopedic implants under the harsh conditions of spaceflight. Measuring the dynamic forces of the MRT's Portho's rocket in flight yielded vibrations in the 300-2750 Hz range, which is valuable for creating better models of the loading conditions on orthopedic implants in silico. Three point bending testing revealed high precision but low accuracy in measuring the mechanical strength of the models. Ultimately, the study recommends adjusting the human, bone, and testing models to prevent oversimplification. Further, future work should analyze bone screw interfaces on a microscopic level to detect small changes in implant stresses. By implementing these changes, procedures can accurately describe the safety of spaceflight for those with orthopedic implants.

The Effect of Criminal Activity on Tourism

The global economy of the 21st century has witnessed a significant rise in tourism, emerging as a key driver of economic growth and development. However, alongside its growth, the tourism industry faces numerous challenges, with crime emerging as a prominent concern. Crime can have detrimental effects on tourist destinations and the experiences of individual travelers, impacting the industry's overall sustainability and growth. This review article aims to examine the multifaceted impact of crime on tourism, focusing on both macro and micro levels. By analyzing existing literature and case studies, this review seeks to provide insights into the dynamics of crime in the tourism industry and offer recommendations for mitigating its adverse effects. Keywords: Tourism, criminal activity in tourism.

Spatio-temporal analysis on online designated driving based on empirical data

In recent years, mobility-on-demand services have experienced rapid growth and become an integral part of the urban transport system. As one of the prevalent mobility-on-demand services, online designated driving allows car-owners booking professional drivers via mobile apps to deliver private cars (frequently with the car-owners inside) to a designated location for a fee. Online designated driving can play a crucial role in promoting road safety by providing a responsible alternative for those who cannot drive themselves due to drinking or fatigue, but there is still a lack of empirical study on its basic travel patterns. Using massive trip records of online designated driving, this study investigates the collective and individual spatio-temporal travel patterns, and reveals the spatio-temporal impact mechanism of various land-use types on online designated driving demand. Take Beijing as a case study, the results show that unlike ride-hailing trips that primarily occur during the daytime, 93.07 % of online designated driving trips occurs between 18:00 and 7:00 the next day, with the longest peak observed on Friday nights. Meanwhile, the average distance of online designated driving trips is approximately twice as long as that of ride-hailing trips (16.58 km vs. 8.48 km). The collective travel patterns of online designated driving exhibit a shrinking trend in the form of “surface-line-point” over time. Regarding individual patterns, we rigorously demonstrate that 81.55 % of high-frequency users exhibit a “fixed-destination” phenomenon, indicating a clear centripetal tendency. Furthermore, online designated driving is primarily associated with limited land-use types, revealing that it may serve specific target audiences, like travelers returning from entertainment and catering activities after consuming alcohol, or those in need of transportation for business and automotive services. These findings can contribute to refining operational practices and enhancing regulatory measures for online designated driving.

Economic Evaluation of Ewanrigg Botanical Gardens in Zimbabwe Using an Individual Travel Cost Approach

Economists face an environmental goods and services valuation dilemma as natural resources are considered to be common pool resources with no markets. If markets exist they are mostly imperfect which results in undervaluation or overvaluation of natural resources. This study estimated the imputed recreational use value of Ewanrigg Botanical Gardens in Zimbabwe from 2015‑2017 using the Individual Travel Cost Method (ITC) which elicits the consumer surplus and deduces an optimum entry fee. It investigated key socioeconomic factors which influence visits to Ewanrigg Botanical Gardens. A questionnaire survey was conducted on 54 visitors to Ewanrigg Botanical Gardens during the period of the research to solicit for primary data. The primary data were analysed using the Ordinary Least Squares model to obtain a predictive model of visits to Ewanrigg Botanical Gardens. Using the ITC Method the total consumer surplus which equates to the recreational use value of Ewanrigg Botanical Gardens was USD19 142.16/peak period/year. Travel costs, income and employment status were the major factors driving visitation rates to Ewanrigg Botanical Gardens at an optimum entry fee of USD7.81. To maximise profits for self-sustenance of Ewanrigg Botanical Gardens, the Zimbabwe Parks and Wildlife Authority should concentrate on marketing efforts to individuals of higher income and solid employment status and those proximal to the botanical gardens as these incur less travel costs and can afford entry fees. For posterity, the use of ITC Method to ascertain economic value allows for the true valuation of natural resources and is justifiable for the allocation of funds by central government for the management and maintenance of artificial heritage sites such as Ewanrigg Botanical Gardens in developing African countries.

PERCEPTION AND ECONOMIC VALUATION OF WAY BELERANG TOURISM IN THE WAY PISANG FOREST MANAGEMENT UNIT (KPH) USING THE TRAVEL COST METHOD

Visitor perceptions and the economic value of tourism greatly influence sustainable tourism development. The aim of the research is to identify visitor perceptions and calculate the economic value of Way Belerang tourism in KPH Way Pisang. Data collection was carried out using interviews, literature studies and documentation. The data that has been collected is then analyzed using a Likert scale, Zonal Travel Cost Method (ZTCM), Individual Travel Cost Method (ITCM) and Multiple Linear Regression. The research results show that the assessment of visitors' perceptions of the facilities is good. Existing facilities are adequate, but repairs need to be made to the ceiling in the rinse area. Promotion and information are not very good because there is not yet complete media available. The tourist attractions are good, the natural attractions and the tourist atmosphere are very supportive. Security and comfort are good, the parking area is large and the guard posts are manned by officers. The average cost to travel to Way Belerang Tourism is IDR 58,876/person/visit, so the economic value of Way Belerang Tourism is IDR 939,725,328/year. Comparison of travel costs based on the visitor's zone of origin within the district Rp. 23,083/zone/visit times, outside the district Rp. 55,195/zone/visit times and outside the province Rp. 114,545/zone/visit times. Supporting promotion and information on Way Belerang requires adequate tools for promoting tourist attractions so that the tourist attractions are more visited by visitors and improving the facilities available at Way Belerang.