ABSTRACT
Consumer behavior has changed over time, influenced by the emergence of e-commerce and e-delivery services. The COVID-19 pandemic has intensified the use of technologies influencing people's behavior for shopping activities and access to services. This study aims to understand the changes in commuting patterns for purchases, trip chaining, and the decision of internet purchases during the pandemic, by identifying groups with similar behaviors and relationships with their socioeconomic characteristics. For this purpose, data were collected in May 2020, considering the travel patterns for purchases and the use of the Internet during this period. Shopping patterns, chained travel, and the decision to purchase over the Internet are characterized by descriptive statistical analysis. Next, cluster analysis is used to identify groups of individuals with similar behavior changes and subsequent analysis of socioeconomic variables related to the groups. Preliminary results showed that travel reduction due to purchases is higher among women and users of public transport. There has been a significant reduction in travel for the consumption of durable goods and chained trips for general purchases. Consumption of convenience products and essential goods over the Internet increased among people who had never bought such products online and started doing so during the pandemic. Among the factors of choice for internet purchase, the absence of direct contact with people during the purchase gained significant relevance during the pandemic. These changes in behavior bring to light the need for a reflection on the effectiveness of urban logistics solutions and a discussion of the impacts of reducing trips to purchases.
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This study evaluates the effect of the COVID-19 pandemic on tourists' length of stay and daily expenditures at a destination. The paper compares detailed microdata for visitors to a Northern Spanish region in the summer periods of 2019 (pre-pandemic) and 2020 (after the pandemic outbreak). We estimate the pandemic-induced impacts on the length of stay and expenditures per person for several categories using regression adjustment, inverse probability weighting regression and propensity score matching. We find clear evidence of a drop in the length of stay of around 1.26 nights, representing a 23.8% decline. We also show that, although total expenditures per person and day have remained constant, there has been a change in the allocations for categories in the tourism budget.
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The outbreak of COVID-19 has made a profound impact on mobility, especially for public transport users. Extensive research has been conducted on the change of travel patterns in major cities where public transport systems have been well developed and heavily used. However, in small cities, the public transport network is relatively sparse, especially in suburban areas, which makes the corresponding travel patterns differ from those in major cities. Therefore, proper investigation of the public transport usage in such small cities is still needed, especially under the COVID-19 impact. This paper aims to reveal the change of public transport users' travel patterns based on a comparative study of public transport usage Pre-COVID and during the COVID-19 period. The Illawarra, a coastal region close to Sydney in Australia is used as a case study. Smart card data is used to reveal relevant changes in both intraregion (in the Illawarra) and inter-region (between the Illawarra and Sydney) travels in consideration of heterogeneous user groups. The results show a significant decrease (around 47%) in public transport ridership by both train and bus. However, compared to intra-region ridership, the inter-region trips by train drop much more (around 62%). Moreover, heterogeneous age group passengers show different changes after the COVID-19 outbreak. The research findings are expected to provide valuable suggestions for policy making and public transport service adjustment when a similar crisis occurs again. © 2022 IEEE.
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Sudden changes in urban mobility were caused due to the COVID-19 pandemic. The impacts are yet to be furtherly measured and analyzed. Our article uses GPS records provided by three different micromobility operators in Madrid to study how the pandemic affected their service usage and its relationships with land use. Thus, spatio-temporal travel patterns are compared between pre-COVID 19 (from January 2019 to February 2020) and COVID times (from March to December 2020). Additionally, multiple regression analyses are conducted to assess how the two scenarios differentiate in relation to micromobility trips, generated or attracted, to or from different land uses, and during morning or afternoon peak hours. Results show that the most pandemic-resilient shared mode is bike-sharing, and that COVID-19 has caused a downfall in micromobility trips of approximately 10%, which is relatively lower compared to the 80% ridership drop reported by the public transport system. Our models reveal that residential and commercial areas gained importance after the pandemic, while workplace locations (office and industrial), educational and transport facilities lost relevance with teleworking and online studying. These findings could help authorities to plan future policies and improve the infrastructure needed to promote micromobility services. © 2022 Taylor & Francis Group, LLC.
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The coronavirus pandemic had a significant effect on traffic volumes;the closures lead to a change in travel patterns and driver behaviors. This study evaluates the trip generation associated with commercial properties that remained open using a before, during, and post-pandemic lockdown approach. The study identified an anomaly in trip generation during the height of the pandemic pertaining to a specific commercial property type, where trips increased by 12% and 56% in the AM and PM periods. The results during the post-pandemic period were found to remain elevated at a rate of 8% to 36% higher in the AM and PM periods, and indicate a lasting change in driver behavior. This change should be considered when conducting future traffic studies or using data collected during the pandemic. An adjustment factor was also developed to adjust data collected during the pandemic and current post-pandemic lockdown times. © ASCE. All rights reserved.
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It is widely recognized that the tourism and travel industry was among the most affected by the COVID-19 pandemic. Even in this context, the tourism sector has proven its strength to recover and adapt to the new tourist perceptions and behaviours, which have led to changes in travel patterns, resulting new travel trends. There is concern in the scientific literature for the topic of travel trends in terms of identifying them and finding solutions for tourism recovery. The research methodology of this paper is based on content analysis and the main purpose is to answer the question, what are the travel trends during the COVID-19 pandemic? The results of the research are derived from the perspective of the world's leading online travel agencies. In this study, several travel trends were identified during the COVID-19 pandemic, the main ones being domestic travel, tourists' preference for bookings with flexible cancellation terms, buying holidays shortly before the departure date, choosing destinations and locations where safety and health measures are valued, opting for shorter trips and moving towards more sustainable tourism. The authors consider that the contribution of this study in the field of tourism is represented by the actuality of the topic analysed.
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The COVID-19 pandemic marked a global disruption of unprecedented scale which was closely associated with human mobility. Since mobility acts as a facilitator for spreading the virus, individuals were forced to reconsider their respective behaviors. Despite numerous studies having detected behavioral changes during the first lockdown period (spring 2020), there is a lack of longitudinal perspectives that can provide insights into the intra-pandemic dynamics and potential long-term effects. This article investigates COVID-19-induced mobility-behavioral transformations by analyzing travel patterns of Berlin residents during a 20-month pandemic period and comparing them to the pre-pandemic situation. Based on quantitative analysis of almost 800,000 recorded trips, our longitudinal examination revealed individuals having reduced average monthly travel distances by â¼20%, trip frequencies by â¼11%, and having switched to individual modes. Public transportation has suffered a continual regression, with trip frequencies experiencing a relative long-term reduction of â¼50%, and a respective decrease of traveled distances by â¼43%. In contrast, the bicycle (rather than the car) was the central beneficiary, indicated by bicycle-related trip frequencies experiencing a relative long-term increase of â¼53%, and travel distances increasing by â¼117%. Comparing behavioral responses to three pandemic waves, our analysis revealed each wave to have created unique response patterns, which show a gradual softening of individuals' mobility related self-restrictions. Our findings contribute to retracing and quantifying individuals' changing mobility behaviors induced by the pandemic, and to detecting possible long-term effects that may constitute a "new normal" of an entirely altered urban mobility landscape.
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Background: After coronavirus disease 2019 (COVID-19) was declared a pandemic, movement restrictions were implemented across sub-Saharan Africa. There has been much speculation on what the long-term impacts on urban transport might be. Objectives: The aim of this paper is to identify the revealed and future travel impacts of the pandemic. Method: To pursue this aim, evidence was compiled from two sources: secondary big data;and a ( n = 15) two-wave Delphi panel survey of experts in the region. Results: It is predicted that longer-term impacts will take the form of: reduced travel by, and accessibility for, low-income households residing in peripheral locations because of decreased welfare;reduced transport service availability;operator reduction (particularly amongst unsubsidised formal operators);increased remote activity participation for a minority of better resourced households with white-collar workers;and disrupted trip distributions as the mix of city-centre land use changes in response to business attrition in economic recession rather than to disrupted bid rents. Conclusion: The major impact of the pandemic is likely to be on welfare, rather than on trip substitution. There is a need, therefore, to focus policy on the mitigation of these impacts and, more particularly, on ways of measuring changes in transport disadvantage and exclusion so that reliable data are available to inform mitigation strategies. The mitigation strategies considered should include investment in affordable ‘digital connectivity’ as a means of complementing accessibility from physical proximity and mobility. The pandemic also highlights the need to develop more robust transport planning practices to deal with uncertainty.
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Carbon emission is largely reduced during the COVID-19 due to the lockdown. However, the accurate impact in the personal transport sector after the epidemic is still not clear. To accurately measure the travel pattern variation effects on utility factor of plug-in hybrid electric vehicles (PHEV s) due to COVID-19, travel pattern, charging pattern, and utility factors (UF) are compared in a typical city based on actual travel data before and after the pandemic. The result shows that the number of trips and the daily vehicle kilometers travelled decreased significantly during the pandemic while the average daily travel mileage increased quickly after the pandemic and is only 9% lower than that before the pandemic. Some consumers even travel longer with personal vehicles to avoid possible health risks from public transportation. The electricity utility factor after the pandemic is 0.022 larger compared to that before the pandemic due to the variation of travel patterns, a 60-km-range PHEV has a pre-pandemic standard UF of 0.745 and a post-pandemic standard UF of 0.767. Besides, the actual UF is 15% smaller compared to the standard UF due to the actual charging frequency in reality. © 2021 IEEE.
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Purpose>This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions.Design/methodology/approach>This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions.Findings>This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them.Originality/value>The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.
ABSTRACT
According to the current body of literature published, the outbreak of coronavirus is estimated to have an immense influence both on the supply and demand sides of the tourism industry. The lockdown and the risk of disease has in/directly forced consumers to respond to their regular life patterns in general and consumption patterns in tourism, travel and hospitality operations in particular. Consumers have mostly begun using online shopping platforms for varying reasons, and online platforms have become a great opportunity for businesses to transform their regular operations and collect the individualized data of consumers across the globe. On the other hand, the service providers have had a short break in their operations by thinking about different ways on how to respond to possible changes both in production and consumption patterns. For instance, passengers, particularly those who are elderly and less risk aversive, are less likely to board on flights and take cruise tours in the short term. Using own cars instead of benefiting from car rental services, on the other hand, have appeared to be an alternative way of travelling to short-haul destinations. As a result, this chapter aims to provide a generic discussion of how the coronavirus is likely to influence the services operated by the aviation industry. The chapter, to a greater extent, benefits from the results of existing conceptual/empirical studies, media news as well as the reports of various international organizations such as UNWTO and IATA. © 2021 by Emerald Publishing Limited.
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Access to adequate and appropriate transport options enables older people to continue as thriving community participants, to reach services and to maintain social connections. While transport needs are diverse, and tend to change over time, there is little information on current and future transport patterns, and the awareness, acceptance and adoption of new technologies. A national online survey was administered to current drivers in Australia. A sample of 705 drivers provided information on available travel modes and use of these modes, awareness of in-vehicle technologies and future use of vehicle technologies. The findings revealed high use of private vehicles, walking and taxis but little use of other travel modes (bicycles, motorcycles, rideshare, community services and public transport). Age, gender and residential location influenced the availability and use/potential use of some transport options. Overall awareness of in-vehicle technologies was generally low and particularly so amongst older and female participants. There was some appetite to use emerging technologies in the future. The findings inform the development of effective strategies and initiatives aligned with healthy ageing and wellbeing targets, increased sustainability, resilience and connectedness, creation of healthier travel choices and healthier environments to promote acceptance and use of a range of transport options and uptake of safer vehicles equipped with in-vehicle technologies to ultimately enhance safe and sustainable mobility of older road users.
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The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3% compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events. © 2021 IEEE
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The many benefits of cycling, such as eco-friendliness, low cost, health benefits, and efficiency in congested areas, had encouraged governmental strategies to promote it, triggering a global growth of bike-sharing systems (BSS). In this line, it is important to avoid service termination by assessing the evolution, identifying drawbacks and success factors, that could be determinant on the system’s future. Nonetheless, in many cases, subjective and objective information regarding BSS was not collected nor compared. In this study, we analyse the evolution of Madrid’s pioneer electric system, by combining the subjective data of three surveys, conducted since 2014 to 2019, with objective data from the service operator. The insights extracted shed light on the key factors determining the system’s success, and its influence on travel behaviour. Results suggest that the user profile of the young early adopters evolved to middle-aged workers. Strong maintenance campaigns and network expansions improved bikes availably and user satisfaction. Slope of the streets is one of the lowest importance factors, whereas pedelec assistance the highest. It is likely to believe that there is a relationship between both, suggesting that electric assistance encourages cycling in a hilly city like Madrid. Transferable experiences to other cities evolving from traditional to e-BSS could be valuable, like the results suggesting that the introduction of an electric BSS is a potential trigger for bicycle adoption in dense urban environments. In addition, that subscribers tend to reduce the use of private car while increase cycling. This longitudinal analysis offers valuable policy implications, like those related with bike maintenance, network extension, and measures focused on keeping the new subscribers of the COVID-19 post-lockdown.
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Combined bus rapid transit and buses in a dedicated bus lane (CBBD) is a measure that bus rapid transit (BRT) operators implement to reduce overlapping routes between BRT and fixed-route buses. The CBBD measure can combine the passengers of both systems on the same route, which helps increase passenger demand for the BRT, and reduce fuel consumption and emissions from utilizing the exclusive lanes for the combined route. However, the CBBD could affect some bus and BRT passengers in terms of either losing or gaining travel time-saving benefits depending on their travel pattern. This research proposed a methodology to determine the travel distance initiating disadvantage for BRT passengers (DDB) to justify the potential success of the CBBD operations. The number of passengers gaining a benefit from the CBBD was sensitive to the distance between the CBBD stops and the operational period of the CBBD. The CBBD reform would be beneficial to transit agencies to improve the travel time of passengers and be able to promote environmental sustainability for the public transportation system in urban cities.
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Understanding the patterns of human mobility between cities has various applications from transport engineering to spatial modeling of the spreading of contagious diseases. We adopt a city-centric, data-driven perspective to quantify such patterns and introduce the mobility signature as a tool for understanding how a city (or a region) is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland. First, we use mobility signatures to show that the well-known radiation model is more accurate for mobility flows associated with larger Finnish cities, while the traditional gravity model appears a better fit for less populated areas. Second, we illustrate how the SARS-CoV-2 pandemic disrupted the mobility patterns in Finland in the spring of 2020. These two cases demonstrate the ability of the mobility signatures to quickly capture features of mobility flows that are harder to extract using more traditional methods.
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With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the "intelligent lockdown", daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted. For this group, policy is required to ensure when (which days) and where (geographical) telework takes place, such that public transport operators can better plan and operate their services. For those with low willingness, it is essential that the government provides tools to companies (especially in education and vital sector) such that they can be better prepared for teleworking (mostly during but also after the pandemic). Employers on the other hand need to better support their employees, such that they stay in contact with colleagues and their concentration and productivity can increase.
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Until the COVID pandemic occurred city tourism has been one of the fastest growing industries in the last years and understanding travel patterns of tourists inside cities becomes for many cities important as it constitutes a substantial amount of daily trips. Kyoto, Japan, is a prime example for this as there were 53 million tourists visiting the city in 2019. To understand tourists' travel patterns different data sources need to be exploited as their behavior is less captured with traditional surveys. In this research we use data obtained from the “Arukumachi Kyoto” application, a mobile app that allows travelers to plan their public transport trips in Kyoto and is aimed at tourists. Around 6000 users have given their consent for their search data as well as their GPS traces to be stored. We focus our analysis on users for which we obtain several fairly regular location information. We first describe the biases and overall distribution of trips in the city obtained from the data as well as dominant tour patterns. We then employ a Markovian analysis to describe the movement between 37 touristic areas in the city. A basic model is improved by obtaining time specific transition probabilities. If no more records are observed during a day a tour is considered as entering an absorbing state. We furthermore discuss advances such as overcoming the fact that we miss visits due to irregular and not frequent enough GPS records as well as the potential for alternative analysis methods. © 2021 Proceedings of the 25th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2021: Sustainable Mobility. All Rights Reserved.
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The application of human factors and ergonomics in transportation is an example of human-automation interaction. Since the year 2020, the covid-19 pandemic has become an emerging factor that interacts with transportation from epidemiological and ergonomic perspectives. This study aims at capturing the emerging trends of covid-19 related human factors in transportation through conclusions from a systematic literature review of relevant publications. Analyses of content and bibliometrics were accomplished by using tools such as VOS Viewer, Citespace, Harzing, and MaxQDA to establish the findings of emerging trends in this field. Key findings from these analyses are: (1) Since the start of the covid 19, countries over the world have administered a variety of travel-related controls in an attempt to contain or slow down the spread of the virus both domestically and internationally. (2) The enforced travel restrictions not only impacted the spread of the pandemic but also transformed people’s activity and travel patterns into a new form. (3) The altered activity and travel patterns further brought changes in transportation policy design, air quality control, and industry disruptions. (4) The pandemic has motivated people to adopt new HCI technologies, and some previously HCI technologies are being challenged because of the pandemic mitigation policies. © 2021, Springer Nature Switzerland AG.
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The pandemic of COVID-19 has caused many changes and one of the changes is in people’s travel patterns. Travel patterns have potentially related to environmental factors. The patterns in an urban environment can affect the liveability increase and the planning in urban areas. The change of travel pattern data is necessary for transportation planners and policymakers in providing safe transportation during the pandemic. Therefore, forecasting travel patterns is particularly essential to plan a better environment to reduce the spread of COVID-19. This study tries to forecast travel patterns during the COVID-19 period in Province of Bangka Belitung using the Community Mobility Report provided by Google. In this study, a time series model is necessary to predict future mobility. The model used in this research is exponential smoothing. The results find that the prediction of travel patterns for each trip destination experiences a fluctuating trend. The prediction for going to retail and recreation, grocery stores and pharmacies, parks, and transit stations tends to increase. Otherwise, the prediction of travel patterns for going to workplaces and residential decreases. Furthermore, the results of this study are feasible to plan a better environment to reduce the spread of COVID-19.