Large-scale Travel Research Articles

Revealing the Complex Dynamics of Social Disparities in Personal Transit Availability Considering Human Mobility and Neighborhood Effect Averaging

Research on inequalities in transit availability typically focuses on fixed residential neighborhoods, ignoring the dynamic exposure caused by people’s daily mobility. In addition, the neighborhood effect averaging problem (NEAP), as a major methodological issue in mobility-dependent exposures, could lead to erroneous conclusions on the assessments of personal transit availability and social disparities. To address these limitations, this study uses large-scale travel survey data of 26,626 participants in the Chicago Metropolitan Area to evaluate personal transit availability in people’s daily lives and examine the dynamic social disparities across varying geographic contexts by considering human mobility and the NEAP. For the first time, we observe the presence of the NEAP in personal transit availability: Individual transit availability tends toward the average level of the study area when people’s daily mobility is considered. We also find that personal transit availability experienced by different social groups might exhibit varying extents of neighborhood effect averaging owing to differences in their mobility levels. This further contributes to the complex dynamics of social disparities in transit availability during their daily mobility, with some residential disparities being exacerbated (e.g., between gender groups, African American and White individuals), some maintained (e.g., between age groups, low- and middle-income groups, employed and unemployed individuals, Asian and White individuals), and some changing direction (e.g., between high- and middle-income groups). Our findings emphasize the importance of considering human mobility and the NEAP in mobility-dependent environmental inequality research and the formulation of related policies.

Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid

Shared autonomous electric vehicles (SAEVs) are predicted to become a significant solution to reduce global emissions and energy consumption resulting from urban transportation. The centralized operation of SAEVs not only allows large-scale travel demand response but also can provide essential ancillary services to the smart grid through the concept of vehicle-to-grid (V2G). With V2G technology, unused electric vehicles can work as a distributed energy storage facility for the electricity grid to smoothen the intermittent demand. Designing and operating a V2G-enabled SAEV system is challenging. This problem involves complicated planning and operational decisions, as well as time-varying electric tariffs. In this work, a flow-based Integer Linear Programming (ILP) model is formulated for determining the optimal configurations (charging infrastructure and fleet size) and daily operation strategies (serving passengers, relocation, charging/discharging). The developed mathematical model allows for maximizing the total profit, comprising investment cost, revenue from serving passengers, and V2G profit through charging/discharging schedules. A two-stage Benders decomposition-based algorithm is proposed to address the sophisticated ILP problem. Via testing instances in the Manhattan network based on real-world and synthetic data, we have demonstrated the feasibility of our approaches and studied the benefits of integrating V2G in the SAEV system.

Does it matter if you like it? exploring the relationship between travel mode choice, preference, and satisfaction

Understanding the level of dissonance between travel mode and preference and its relationship with satisfaction can help develop transport strategies that encourage the use of sustainable modes. We study the difference in satisfaction levels for work and school trips of consonant travellers and dissonant travellers. The research uses a large-scale (N = 1,865) travel survey administered in Montreal, Canada. A binary logistic regression model reveals that both consonant and dissonant commuters have a high probability of satisfaction with their commute, except for dissonant car users. We find that consonant pedestrians have the highest probability of satisfaction when compared to all other groups, and that dissonant car users have the lowest probability of satisfaction. We further investigate the reasons preventing the use of preferred modes for dissonant car and transit users. Findings from this research help inform researchers and practitioners aiming to make sustainable mode choices the preferred one among travellers.

Remora optimization algorithm-based adaptive fusion via ant colony optimization for traveling salesman problem

The traditional ant colony optimization (ACO) is easy to fall into local optimal when solving large-scale traveling salesman problem (TSP), and the convergence speed is slow. In order to enhance the local search ability of ACO, speed up the efficiency of ACO and avoid the premature problem, this paper proposes a novel remora optimization algorithm-based adaptive fusion via ant colony optimization for solving TSP. Firstly, an improved K-means clustering method is used to obtain the best clustering results and the optimal solutions of each class quickly by adaptive clustering strategy based on the maximum and minimum distance and class density. By using an improved Remora optimization algorithm, adjacent classes are fused to effectively improve the accuracy of the initial solution. In addition, the initial solution is optimized by the k-opt strategy. Finally, the random recombination strategy is used to recombine the pheromone and random excitation to make the algorithm jump out of the local optimal as far as possible and improve the accuracy of the algorithm. The experimental results show that the proposed algorithm not only guarantees the accuracy of solution, but also improves the stability when solving large-scale TSP.

Response bias in Likert-style psychological items – an example from a large-scale travel survey in China

This paper addresses the challenge of ensuring response quality when using item sets with Likert scales in travel surveys. Particularly for capturing psychology, such item sets play an important role in travel behavior research. A challenge with this kind of data is the identification of response bias. An example is straightlining, which describes selecting the same response category for each item. Since there is no universal indicator in the literature to identify unusual or strategic response patterns, we apply various indicators, compare the results and develop a new indicator based on correlations which encounters plausible straightlining.

The Influence of the COVID-19 Pandemic on Travel Behaviour in the Greater Copenhagen Area

The COVID-19 pandemic drastically changed the way of living for billions of people and severe restrictions were implemented by governments around the world, affecting the travel patterns of all citizens. This article investigates how travel patterns changed in the Greater Copenhagen area of Denmark during the full two-year period covering 2020 and 2021, thus allowing for an analysis of both the short-term and medium-term impacts as society gradually reopened and restrictions were lifted. The analysis covers large-scale travel survey data as well as a segmentation clustering analysis of public transport smart card data. The results showed that impacts were strongly linked to changes in trip purpose and were thus not uniformly distributed throughout the public transport system. User segmentation analysis revealed that most users changed to less intense travel use of public transport. The results highlight important policy implications in terms of how to adapt service provision within a public transport network more efficiently.

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.

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

The ant colony algorithm faces dimensional catastrophe problems when solving the large-scale traveling salesman problem, which leads to unsatisfactory solution quality and convergence speed. To solve this problem, an adaptive ant colony optimization for large-scale traveling salesman problem (AACO-LST) is proposed. First, AACO-LST improves the state transfer rule to make it adaptively adjust with the population evolution, thus accelerating its convergence speed; then, the 2-opt operator is used to locally optimize the part of better ant paths to further optimize the solution quality of the proposed algorithm. Finally, the constructed adaptive pheromone update rules can significantly improve the search efficiency and prevent the algorithm from falling into local optimal solutions or premature stagnation. The simulation based on 45 traveling salesman problem instances shows that AACO-LST improves the solution quality by 79% compared to the ant colony system (ACS), and in comparison with other algorithms, the PE of AACO-LST is not more than 1% and the Err is not more than 2%, which indicates that AACO-LST can find high-quality solutions with high stability. Finally, the convergence speed of the proposed algorithm was tested. The data shows that the average convergence speed of AACO-LST is more than twice that of the comparison algorithm. The relevant code can be found on our project homepage.

The varying effects of residential built environment on travel behavior of internal migrants and locals

The built environment impacts multiple dimensions of travel behavior of urban residents. However, limited research has explicitly unveiled how these impacts vary between diverse socioeconomic groups, especially internal migrants versus locals, notwithstanding hundreds of millions of internal migrants are living in cities in developing countries. Hence, employing a large-scale travel behavior survey dataset collected in Xiamen (China) in 2015 and applying the propensity score matching method, this study comparatively examines the differentiations in responsiveness of such two groups to the built environment. The results demonstrate that (1) the effects of the built environment on travel behavior differ between internal migrants and locals in some dimensions (e.g., active travel frequency and duration); (2) the built environment has larger effects on all the travel dimensions of locals than on those of internal migrants, implying the existence of “transportation assimilation” and a temporal lag effect; (3) the internal migrants are less impacted by residential self-selection effects. These findings can enlighten decision-makers about more nuanced, targeted, and socially inclusive intervention strategies, thus effectively accommodating the travel demands of both locals and internal migrants.

Inferring nonwork travel semantics and revealing the nonlinear relationships with the community built environment

The relationship between the built environment and nonwork travels reveals potential supply-demand status between urban functional facilities and diverse living travels, which provide critical indicative information for urban spatial structure optimization. Benefiting from the large-scale travel datasets, it would be possible to investigate deeper research by modeling the implied nonwork travel semantics (such as travel purposes). This study designs a framework to reveal the nonlinear relationships between the community built environment and the potential semantics of nonwork travel trajectories. First, we construct indicators to characterize the built environment of individuals’ living communities. Then, focusing on the definition of activity areas around drop-off points, latent Dirichlet allocation (LDA) is utilized to infer the implied semantics of nonwork travel. The random forest model is employed to quantify the relationships between the community built environment and nonwork travel with various semantics. The experimental results obtained with real-life datasets from Haikou, China uncover both compensatory nonwork travel derived from diverse facility accessibility and the contributions of housing attributes to travel semantic discrepancies. The findings provide scientific references for the optimization of urban spatial layouts of both functional facilities and residential communities to meet the living demands of diverse residents without long-distance intracity travel.

Assessing carbon reduction benefits of teleworking: A case study of Beijing

Teleworking can efficiently decrease the energy consumption and carbon emissions related to physical commuting. Previous studies on assessing the carbon reduction benefits of teleworking were customarily performed according to hypotheses or qualitative methods, and disregarded different potentials of different industries for teleworking implementation. In this study, a quantitative approach was proposed to assess the carbon reduction benefits of teleworking in different industries, which was illustrated via the case study of Beijing, China. The teleworking penetrations of different industries were first estimated. Then, the carbon reduction of teleworking was assessed through the decreased commuting distance using the large-scale travel survey data. Finally, the study samples were extended to a citywide scale and the uncertainty of carbon reduction benefits was evaluated with Monte Carlo simulation. The results showed that (1) teleworking can lead to an average of 1.32 (95 % confidence interval (CI): 0.70–2.05) million tons of carbon reduction, accounting for 7.05 % (95 % CI: 3.74 %–10.95 %) of the total carbon emissions by road transport in Beijing; and (2) information and communication, and professional, scientific and technical service industries had higher carbon reduction potential. Additionally, the rebound effect slightly weakened the carbon reduction benefit of teleworking, which was necessary to be considered and mitigated through relevant policies. The proposed method can be also applied to other regions worldwide, helping to exploit future work patterns and realize global carbon neutrality targets.

Shared dynamics learning for large-scale traveling salesman problem

In this work, we study generalization in reinforcement learning for traveling salesman problem (TSP). While efforts have been made for designing deep reinforcement learning-based solvers to achieve near optimal results in small tasks, it is still an open problem to apply such solvers to larger-scale tasks by retaining performance. In this research, we learn the shared dynamics in TSP environments based on multi-task learning, which can be generalized to new tasks. To accurately estimate such dynamics, we consider leveraging the node visitation information. Besides designing RL-based models to attentively aggregate the visitation information during decision making, we propose a scheduled data utilization strategy to stabilize learning with various problem sizes. The experimental result shows that our model achieves improved generalizability for unseen larger TSPs in both zero-shot and few-shot settings.

Proper orthogonal decomposition of large-eddy simulation data over real urban morphology

Proper orthogonal decomposition (POD) is implemented to the atmospheric surface layer (ASL) over real urban morphology based on the data of large-eddy simulation (LES) with explicitly resolved buildings. Turbulence structures with different scales in the inertial (ISLs) and roughness (RSLs) sublayers are unveiled by decomposing the fluctuating velocity components into a range of spatial eigenmodes ψm(x) and time coefficients am(t). POD spatial modes, which are ranked according to their turbulence kinetic energy (TKE), are visualized to contrast their sizes. These dominant structures illustrate the footprints of most energetic eddies at the ground level which could modify the dynamics locally. Moreover, their temporal behaviors are examined by energy spectrum and Lissajous curve, elucidating the existence of large-scale traveling structures. It is also shown that around 80 and 100 modes (out of 1000) are able to retrace 90% of the original TKE and momentum flux, respectively. The aforementioned remarkable data reduction demonstrates the capability of POD for flow characteristic extraction and city-scale, reduced-order model (ROM) development. Besides, dominant modes transport more efficiently especially in ISLs. With proper urban planning, such as building height and orientation, these energy-carrying eddies would sweep into street canyons, improving pedestrian-level air quality as well as city ventilation. (200 words)

How different network disturbances affect route choice of public transport passengers. A descriptive study based on tracking

Public transport networks are affected daily by disturbances with different entities. While big service disturbances (or disruptions) are rare, delays and cancelled runs are more frequent and they affect daily the passengers. Despite this, most of the work on passengers’ behaviour in case of disturbances focuses on big disturbances and not on common and daily ones. In this work, we analyse how different network disturbances affect public transport passengers, regarding the chosen route and the travel cost. For this purpose, we exploit a large-scale travel survey based on GPS tracking, and AVL data of public transport operations in Zürich, Switzerland. We quantify the disturbances in the network, which are relevant for a specific passenger’s trip, with a metric of service degradation. In that way, we can analyse passengers’ route choices in case of different disturbances. In particular, our study evaluates the effects of disturbances on travel costs, comparing the passenger’s chosen route with three expected behaviours (“timetable”, “no information”, “full information”), based on the available alternatives with and without disturbances.Our analysis identifies that small disturbances and delays have a significant effect on travel cost, although marginal effects on route choice, since 72% of times users stick to the most likely route in planned conditions. In contrast, “good disturbances”, i.e. variations of the operations from the timetable, which generate less costly alternatives, have a significant effect on route choice, since only 55% of times users choose the most likely route. Moreover, we identified that passengers do not exploit new available alternatives, suggesting a need of better information in these cases.

Discrete choice modeling with anonymized data

This paper presents an approach to estimate mode-choice models from spatially anonymized revealed preference travel survey data. We propose an algorithm to find a feasible sequence of activity locations for each individual that minimizes the maximum error of each trip’s Euclidean distance within the activity chain. The synthetic activity locations are then used to create unchosen alternatives within the choice set for each individual. This is followed by the mode-choice model estimation. We test our approach on three large-scale travel surveys conducted in Switzerland, Île-de-France, and São Paulo. We find that our methodological approach can reconstruct activity locations that accurately match trip Euclidean distances but with location errors that still provide location protection. The discrete mode-choice models estimated on the synthetic locations perform similarly, in terms of goodness of fit and prediction, to the ones obtained from the observed activity locations.

Entrepreneurial responses to uncertainties during the COVID-19 recovery: A longitudinal study of B&Bs in Zhangjiajie, China.

The COVID-19 pandemic has brought sweeping changes to global tourism alongside large-scale travel restrictions, posing complex challenges to entrepreneurs and firms seeking to find their footing in a turbulent climate. This study presents a theoretical framework linking uncertainty, capital, and innovation to analyse how bed-and-breakfast small and medium-sized enterprises have innovatively responded to unprecedented obstacles during COVID-19 recovery. Three-stage longitudinal interviews were conducted with more than 30 entrepreneurs between April and November 2020 to unpack their ongoing responses to the pandemic. The recovery process was found to be non-linear due to the shifting nature of sources of uncertainty and changes in entrepreneurs' capital. These alterations shaped interviewees’ responses, especially in terms of product and marketing innovations, which ultimately generated new uncertainty.

Comparative study: International chain and local chain hotel COVID-19 readiness strategies in Indonesia

The global hospitality and tourism industry has been severely impacted by the spread of COVID-19 and large-scale travel restrictions. Due to the nature of the crisis of COVID-19, Hotel businesses in Indonesia need to adapting many new strategies in response to the evolving circumstances. This study presents a comparative coronavirus readiness strategy at International Chain Hotel and Local Hotel in Indonesia and their impacts on hotel guests’ trust and satisfaction. The study used quantitative approaches by using an online survey to obtain data from a total of 400 participants. The data were collected from respondents that have been stayed in both the International Chain hotel or Local Chain Hotel in Indonesia during pandemics. The data collected is analyzed using Structural Equation Modeling (SEM) with Smart PLS statistical software. The findings from this dataset show that both local and international hotel readiness had a significant effect on customer trust and satisfaction with COVID-19 readiness in the hotel industry.

Service Quality in Tourism Public Health: Trust, Satisfaction, and Loyalty.

The spread of COVID-19 and large-scale travel restrictions has caused serious damage to the global tourism industry. Tourists pay additional attention to public health services and their health during travel, but studies on tourism public health service quality (TPHSQ) are limited. Therefore, this study aims to define TPHSQ and revise and validate its scale. The result of exploratory factor analysis (EFA) indicates that TPHSQ includes two dimensions “overall environmental image” and “public health facilities and management.” And based on 456 valid samples, the relationship among TPHSQ, tourists’ trust, satisfaction, and loyalty was validated using the multiple linear regression models. Results revealed the importance of the TPHSQ in improving tourists’ satisfaction and recovering their trust and loyalty. These results provided several implications for research, practice, and society that can benefit diverse stakeholders, which could accelerate the recovery of the tourism industry.

Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data

ABSTRACT This paper proposes a framework to extract dynamic trip flows and travel demand patterns from large-scale 2 G and 3 G cellular signaling data. Novel data pre-processing techniques based on cell phone activity metrics are presented. The trip extraction method relies on the detection of stationary activities to form trip sequences related to resident users. A probabilistic solution is introduced to estimate the trip starting time, allowing to aggregate trips by time of the day and reconstruct hourly travel flows. To better characterize these flows, a spatial clustering process combined with land-use data is proposed based on the temporal demand profile of each zone. Empirical comparisons have been performed showing that the resulting dynamic travel demand patterns are consistent with those obtained from travel survey data with high correlation coefficients of about 0.9. The results prove the potential of signaling data to generate low-cost valuable information for large-scale travel demand modeling.

Revisiting the Effects of High-Speed Railway Transfers in the Early COVID-19 Cross-Province Transmission in Mainland China.

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was reported at the end of 2019 in Wuhan, China, and was rapidly disseminated to all provinces in around one month. The study aims to assess the changes in intercity railway passenger transport on the early spatial transmission of COVID-19 in mainland China. Examining the role of railway transport properties in disease transmission could help quantify the spatial spillover effects of large-scale travel restriction interventions. This study used daily high-speed railway schedule data to compare the differences in city-level network properties (destination arrival and transfer service) before and after the Wuhan city lockdown in the early stages of the spatial transmission of COVID-19 in mainland China. Bayesian multivariate regression was used to examine the association between structural changes in the railway origin-destination network and the incidence of COVID-19 cases. Our results show that the provinces with rising transfer activities after the Wuhan city lockdown had more confirmed COVID-19 cases, but changes in destination arrival did not have significant effects. The regions with increasing transfer activities were located in provinces neighboring Hubei in the widthwise and longitudinal directions. These results indicate that transfer activities enhance interpersonal transmission probability and could be a crucial risk factor for increasing epidemic severity after the Wuhan city lockdown. The destinations of railway passengers might not be affected by the Wuhan city lockdown, but their itinerary routes could be changed due to the replacement of an important transfer hub (Wuhan city) in the Chinese railway transportation network. As a result, transfer services in the high-speed rail network could explain why the provinces surrounded by Hubei had a higher number of confirmed COVID-19 cases than other provinces.