Outbound Trips Research Articles

Tourism risk analysis and mitigation: Strategies for managing risks in a dynamic tourism environment

Tourism is interconnected with other industries and brings additional benefits to both the European economy and Ukraine in general. However, it also depends on external and internal factors and is the first to react to their changes. The article describes and uses a strategy for managing the risks posed by the fast-changing tourism market, where every second trip for travel purposes goes to EU countries. The EU countries comprise 50% of global tourism, while outbound tourism from European countries is almost 48% of all outbound trips and tourism activities worldwide. The COVID-19 pandemic had a severe impact on the tourism sector of many countries, leading to significant changes and negative consequences. However, the tourism industry is dynamic and constantly evolving, responding to any changes and demands. Therefore, the relevance of this study lies in exploring further ways to develop tourism in the European tourism macroregion, taking into account the impact of the COVID-19 pandemic. The unprecedented pace of transformation has affected the management system of tourism enterprises. Travel companies must respond quickly to constantly changing environmental conditions to protect their reputation and financial stability. Therefore, there is a real need to create a modern, comprehensive risk management system for tourism companies. Risks cannot be eliminated, yet any business owner who cares about the success of their business must investigate possible adverse factors affecting their profits and take action to reduce them. Risk management serves this purpose.

Impact Evaluation of COVID-19 on Transit Ridership: A Case Study of the Beijing Subway

A comprehensive understanding of the multifaceted ramifications of the coronavirus disease 2019 (COVID-19) on transit ridership is imperative for the optimization of judicious traffic management policies. The intricate influences of this pandemic exhibit a high degree of complexity, dynamically evolving across spatial and temporal dimensions. At present, a nuanced understanding remains elusive regarding whether disparate influencing factors govern inbound and outbound passenger flows. This study propels the discourse forward by introducing a methodological synthesis that integrates time series anomaly detection, impact inference, and spatiotemporal analysis. This amalgamation establishes an analytical framework instrumental in elucidating the spatiotemporal heterogeneity intrinsic to individual impact events, grounded in extensive time series data. The resulting framework facilitates a nuanced delineation, affording a more precise extraction of the COVID-19 impact on subway ridership. Empirical findings derived from the daily trip data of the Beijing subway in 2020 substantiate the existence of conspicuous spatiotemporal variability in the determinants influencing relative shifts in inbound and outbound ridership. Notably, stations situated in high-risk areas manifest a conspicuous absence of correlation with outbound trips, exhibiting a discernibly negative impact solely on inbound trips. Conversely, stations servicing residential and enterprise locales demonstrate resilience, evincing an absence of significant perturbation induced by the outbreak.

Aging behavior of an electric vehicle battery system considering real drive conditions

In this article, a comprehensive study of the aging behavior of an electric vehicle battery pack considering the vehicle's operation under real driving conditions, such as ambient temperature, humidity, and solar irradiation conditions, in all different months of the year for a 10-year time horizon with two different working scenarios is investigated for the first time. The vehicle duty cycle represents a personal vehicle traveling once at 7:00 AM from home to work and returning at 5:00 PM from work to home for scenario 1. For the second scenario, 8:30 AM is set for outbound trips, and 6:30 PM is set for return trips via the WLTP driving cycle. The target temperature for the cabin and battery pack are set to 22 °C and 28 °C, respectively. The results indicate the highest energy consumption is for the cold season. The EV's energy consumptions in scenario 2 for outbound and return trips were 20 % less and 0.6 % more than the EV's energy consumptions in scenario 1 for outbound and return trips, respectively. Next, the battery aging was evaluated for ten years, resulting in 7.16 % and 6.99 % total capacity loss for scenario 1 and scenario 2, respectively.

Resilience analysis of global agricultural trade

This study examined the transport network of global marine dry bulk carriers for agricultural trade during the period from 2018 to 2021. Firstly, the resilience of agricultural trade network is noteworthy throughout the COVID-19 pandemic. Agricultural trade initially plunged by 10.15% from 2019 to 2020 and bounced by a remarkable 11.45% in 2021, ultimately restoring trade volumes to the average level observed in the pre-pandemic year of 2019. However, the ports in Brazil and Argentina displayed less resilience in their agricultural trade with a continued decline in agricultural trade quantities in 2021. Additionally, the outbound trips increased in Ukraine, Canada, and Russia and decreased in Brazil and Argentina, leading to a more tightly knit agricultural network since 2020. Overall, this study provided evidence in comprehensively assessing the capacity and resilience of global food supply chains, especially in the context of constantly evolving circumstances and challenges.

Spatiotemporal Variations in PM2.5 Concentration in Different Areas of Bus Cabin: A Case Study in Shenzhen, China

Inhalable particulate pollution has adverse effects on human health. Many studies have been conducted to investigate levels and risks of exposure to particulate pollution in public transit, but scant attention has been paid to variations in the pollution levels among different positions inside the cabin. Differences can be observed among different positions inside transit vehicles, like passenger density and activities, which may lead to differentiated exposure risks between different positions. Therefore, this study investigates the level of PM2.5 concentrations and the spatiotemporal variations among three different positions (front, middle, and rear) inside the cabin during bus travel. The three positions represent the three areas inside the bus cabin, i.e., front area (FA), middle area (MA), and rear area (RA). We developed a portable monitoring system for in-cabin measurements, which consists of a mobile device with the designated app, a PM2.5 collecting device, and a temperature-relative humidity monitor. Surveys were carried out on buses of a route in Shenzhen, China, which include inbound and outbound trips during both morning and evening peaks of two working days. Based on spatial and temporal information embedded in data collection devices, PM2.5 concentration data were spatially and temporally referenced. Comparative and descriptive statistical analyses were employed to examine the differences and variations in PM2.5 concentration among the areas inside the bus cabin. There are three major findings. First, for all of the surveyed trips, FA showed the highest and RA showed the lowest PM2.5 concentration. Second, concerning the average pollution level of route segments along the routes, the PM2.5 concentrations in the three cabin areas were higher during inbound trips than during outbound trips. Third, within route segments, PM2.5 concentrations in FA and MA increased rapidly after the bus stopped at stops, and then gradually decreased, while in RA they were relatively stable. In general, a higher level of passenger density increased in-cabin PM2.5 concentration. The findings of our study could provide insight into improving the bus in-cabin microenvironment, such as the seating arrangement for vulnerable passengers.

Modeling tourists’ preference between hotels and peer-to-peer (P2P) sharing accommodation: a pre- and post-COVID-19 comparison

PurposeWith the rapid development of sharing economy, travelers are facing choices between conventional hotels and the peer-to-peer sharing accommodation in urban tourism. The purpose of this study is to examine how travelers form their preferences in such choice situations and whether/how their preference formation mode would change with the COVID-19 pandemic.Design/methodology/approachA relative preference model was constructed and estimated for both domestic and outbound tourists, based on two waves of survey data collected before and after the COVID-19. The results of this study were compared to derive the evolution of preference formation patterns.FindingsA set of 15 key value attributes and personal traits was identified, together with their differential effects with the pandemic. Their divergent effects between domestic and outbound trips were also delineated. Based on these findings, the competitive edges and advantageous market profiles were depicted for both hotel and sharing accommodation sectors.Originality/valueThis study contributes to the knowledge of tourists’ preference between accommodation types and adds empirical evidences to the impact of the pandemic on tourist behavior patterns. Both hotel and sharing accommodation practitioners can benefit from the findings to enhance their competitiveness.

An Advanced Travel Demand Synthesis Process for Creating a MATSim Activity Model: The Case of Ústí nad Labem

In this study, an activity-based travel demand model of the Ústí nad Labem district (Czech Republic) is created. To do this, an advanced travel demand synthesis process is presented by utilizing the Eqasim framework, which is a pipeline-processing, initial raw data to simulation step. The framework is extensively modified and extended with several algorithms in order to utilize multiple data points for increasing realism in mobility for travel demand models. Two major extensions are provided. First, the pipeline framework is improved to estimate inbound and outbound trips of the study area, comprising a main city and 23 surrounding municipalities. The extended framework assigns synthetic gates for the study area as hubs for the inclusion of inbound and outbound trips. Second, the pipeline framework is advanced to provide a more compatible match of travel destination and activity location state. To do this, the extended framework assigns a capacity for each facility identified for the study area, the expected number of visitors to each facility, and the number of residents in each building. The resulting demand model is presented and the generated trips are evaluated based on locational, transport mode, and sociodemographic characteristics with origin–destination (OD) bundling. Additionally, distribution analyses of the present model are conducted to understand the matching results on a detailed level. The results demonstrate that the present model provides a reasonable output for transport researchers when testing different mobility scenarios and the provided extensions helps them to reduce implausible reflections of the distribution of travel and activity characteristics in household travel surveys while creating demand models, thus increasing realism. Lastly, open-source playground and code repository for further future improvement of synthetic travel synthesis methods are created, which enhances a deep understanding of the preparatory and methodological backgrounds required for complex activity-based simulations in order to inspire transport planners.

The S-Curve of Technological Adoption: Mobile Communication Devices on Commuter Trains in the Chicago Region, 2010–2015

Urban transit riders’ use of mobile communication devices has grown markedly in recent years. Studies evaluating the usage of these devices have generally focused on only one or two points in time, limiting their ability to describe long-range trends. To foster insights into this issue, this study evaluated data from 15,531 passenger observations collected on 156 commuter trains on the metropolitan commuter rail system of Chicago, Illinois, from 2010 through 2015. The data show that the rate of technological usage is following an S-shaped pattern among passengers. The share of passengers using mobile communication devices at observed points grew sharpest during the first three years, rising from 25.6% in 2010 to 47.8% in 2013, a compounding annual rate of 23.1%. Between 2013 and 2015, the share rose to 56.2%, an annualized rate of just 8.4%. Over the five-year period, the share of passengers conducting visually-oriented activities on their devices increased at a faster rate than usage as a whole, whereas the share of passengers engaged in audio-only tasks has dropped. Multiple regression analysis shows that the rate of device usage on trains is highest on outbound trips (traveling away from downtown) and positively related to the income associated with the route traveled, with differences of more than five percentage points between lines of varying levels of affluence.

STUDY ON TRAVEL HABITS AND LEISURE ACTIVITIES IN THE LIGHT OF COVID-19 TRIGGERED CHANGES IN ROMANIA AND HUNGARY

Tourism is a widely known concept as it is part of our modern lifestyle that we opt for useful and quality free time that provides us with the opportunity to relax and boost our energy. Our research focused on travel habits and the leisure activities people undertake during their trips, relying on answers provided by respondents aged 18-55. Most of the respondents said that they quite like to go on recreational journeys /tourism trips, with the most popular activities being nature walks, hiking, visiting places of historic interest, and viewing architecture and buildings. A quintessential role during these trips is played by relaxation and recharge, as well as restoring capacity for work. The most popular destinations in descending order were domestic attractions, neighboring countries, and other countries in Europe. Covid-19 had a major impact on destination choice; the number of outbound trips decreased, and beside the lowering frequency of inbound trips their length also shortened. As traveling is a popular holiday activity, it is particularly important to pay due attention to tourists’ travel motivations, assess their needs and habits so that they can be provided with the best possible experiences during their trips, and can return well rested and with renewed vigor to face their everyday challenges.

Empirical analysis of cycling distances in three of Europe’s most bicycle-friendly regions within an accessibility framework

In this paper, we study observed cycling distances within an accessibility framework, using data from the Netherlands, the Copenhagen Metropolitan Area and the Freiburg Region. As a scope, we look at outbound trips in home-based tours which include a single destination. We relate these observed cycling distances to a rich set of explanatory variables using both quantile and ordinary least square regression models. The results provide evidence that cycling distances are similarly distributed in all three regions. Most cycling distances are rather short, with a median of only two and a mean of three kilometers. These values vary depending on the type of activity at the destination, gender and age of the traveler and the type of bicycle that has been used. Moreover, a few remarkable differences have been found between the three regions, such as substantially different effects of age and e-bike use on observed cycling distances. Noteworthy is the missing effect of urban density. The findings of this research provide urban planners with differentiated information about how far people cycle to daily-life destinations. As shown for the example of the “15 minutes city,” the outcomes can also be used to refine existing concepts of bicycle accessibility. Finally, this research offers valuable insights into three of Europe’s most developed bicycle cultures.

Оценка состояния, проблем и трендов развития рынка туристских услуг в регионах России на фоне пандемии коронавируса

The article examines the impact of the novel coronavirus infection (COVID-19) pandemic on the tourism and hospitality industry. Based on an assessment of the scale of losses incurred by enterprises in this sector of the economy in an unfavorable epidemiological situation, it was concluded that tourism was among the industries most affected by the COVID-19 pandemic. The identified problems faced by small and medium-sized businesses from among tour operators and travel agencies confirmed the correctness of the measures of state support provided by the state to this sector of the economy. A comparative analysis of the development of the tourism sector in 2019 and 2020 in terms of such parameters as the number of inbound and outbound trips of citizens, the volume of demand for tourism services, made it possible to assess the threats and opportunities for the functioning of the tourism sector during the pandemic for the Russian economy and suggest that the recovery of the tourism industry it may take at least three years. There have been illustrated the diagrams comparing the demand for outbound and inbound tourism in 2019 and 2020, the demand for tourist services among different strata of the population, and hotel occupancy in Russia. In addition to identifying general factors that hinder the balanced growth of the Russian market of tourist services, the problem of information asymmetry was stated, which hinders the realization of the tourist potential in the regions: lack/ insufficient data, or distorted data on the tourism potential in certain territories. The highlighted trends in the development of the Russian tourism industry in the current conditions and promising trends in the tourism and hospitality industry include greening, digitalization, individualization of both demand and supply, an orientation towards domestic tourism.

On the Improvement of Statistical Accounting for Tourist Trips Amidst Crisis

The article substantiates the relevance of the issues in question, which have been exacerbated by the coronavirus pandemic and provides a brief overview of the scientific and practical sources, as well as a description of current research related to both traditional methods of statistical accounting of tourist travel and methods based on the use of big data sources.The review shows current methodology as well as different suggestions, encountered in various research studies, on how to assess inbound, outbound and domestic travel. The evolution of Rosstat’s approaches to accounting for the number of inbound and outbound trips has been analyzed, and certain adjustments to existing methods relating to the accounting of organized and unorganized trips have been proposed.The results of calculations illustrating the predominance of unorganized tourism when making inbound and outbound trips are presented. In general, it is concluded that the most difficult is the comprehensive monitoring of internal travel. Three possible approaches to the accounting of trips are shown. The option of using the approach to accounting for domestic tourist travel based on monitoring of passenger traffic, which is consistent with international recommendations and the existing classification group «Tourism» in our country, is proposed. In addition, the option of accounting for domestic regional tourist trips based on official forms of reporting is shown.It is the author’s opinion that the implementation of the reasoned proposals should serve to improve the reliability and objectivity of statistics on tourist travel. This, in turn, will create the conditions for improving measures to support the tourism sector of the economy, particularly necessary in the economic crisis triggered by the coronavirus pandemic.

Research on the behavior and activities of Vietnamese female travelers before and during outbound trips

In 2020, more than 33 million people in Vietnam, over 50% of whom are women, join the middle class, living in large cities. Tourism, especially outbound tourism, is one of the 3 main spending needs of middle-class women. The female traveler market in Vietnam is gradually increasing in scale and considered to be a unique segment. In this research, based on the theory of planned behavior and diagram analyzing the process of visiting destinations, the author classified 4 groups of behaviors including destination selection, destination activities, destination spending, and reference information from 270 Vietnamese women on outbound trips by using descriptive statistics methods. The result showed that they share the common desires of culinary discoveries, the prevalence of self-sufficiency, technology proficiency, and the need to expand the geographical scope of destinations outside of Asia. From that, the research assisted international tour operator groups in Vietnam to formulate tour programs, PR and marketing strategy development proficiently, to offer the female travelers segment an excellent traveling experience.

The benefits of autonomous vehicles for community-based trip sharing

This work reconsiders the concept of community-based trip sharing proposed by Hasan et al. (2018) that leverages the structure of commuting patterns and urban communities to optimize trip sharing. It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners. In the considered problem, each rider specifies a desired arrival time for her inbound trip (commuting to work) and a departure time for her outbound trip (commuting back home). In addition, her commute time cannot deviate too much from the duration of a direct trip. Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%.This paper studies the potential benefits of autonomous vehicles in further reducing the number of vehicles needed to serve all these commuting trips. It proposes a column-generation procedure that generates and assembles mini routes to serve inbound and outbound trips, using a lexicographic objective that first minimizes the required vehicle count and then the total travel distance. The optimization algorithm is evaluated on a large-scale, real-world dataset of commute trips from the city of Ann Arbor, Michigan. The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%. These results demonstrate the significant potential of autonomous vehicles for the shared commuting of a community to a common work destination.

Suburban Demand Responsive Transit Service With Rental Vehicles

Demand responsive transit (DRT) services in suburban regions serve residents as the complements of the public transportation. Since suburban DRT service usually faces demand fluctuation in some special situations, such as terrible weather, DRT operators have recourse to temporal rental vehicles to satisfy all requests. There are two types of rental contracts. In the first case, the costs of rental vehicles are similar to the costs of owned vehicles, and the multi-trip vehicle routing problem is studied. In the second case, the costs of outbound trips are not covered by DRT companies, and the open vehicle routes are introduced. In this paper, a mixed integer linear programming formulation is proposed to model the route optimization problem of the suburban DRT service, in which the number of rental vehicles and two classes of routes should be decided simultaneously. To solve the proposed model, a branch-and-price algorithm is designed, which has two pricing subproblems to generate two classes of routes, respectively. From the results of experiments, it can be found that the service sequence is introduced, when open routes are involved. Renting vehicles can help DRT companies save operational costs even in daily operations.

Potential Effects of the COVID-19 Pandemic through Changes in Outbound Tourism on Water Demand: The Case of Liège (Belgium)

The COVID-19 pandemic has led to many countries closing their borders, and numerous people spending their holidays at home instead of traveling abroad. This sudden reduction in travel activities, and other ‘new normals’, might have influenced people’s water usage. Hence, using Liège as a case study, this study aims to address the potential effect of outbound tourism on water consumption and how the current situation might affect the total water demand. Statistical models were developed and validated using the total daily volume of 23 municipalities in the Liège conurbation, the monthly total number of outbound trips, and other meteorological data. Results suggest significantly lower water demand in the months with high numbers of outbound travel activities. Though the projected risk of increased water needs due to fewer people traveling is moderate, the threat becomes much higher during long periods of dry and hot weather.

Are We There Yet? An Anticipation Account of the Return Trip Effect

Research has documented the psychological phenomenon in which the trip back from a destination is experienced as shorter than the trip to the destination. Deemed the “return trip effect” (RTE), prior work explained this phenomenon in terms of differential familiarity between home and outbound destination or an underestimation of initial travel time. The present article posits an anticipation account for the RTE: Outbound trips to a destination tend to foster higher levels of anticipation than return trips. Due to greater anticipation, people’s perception of time elongates. Importantly, the anticipation account makes novel predictions with regard to the occurrence of the RTE that cannot be accounted for by prior explanations. Multiple studies, with diverse methodologies, test and offer evidence in support of an anticipation account of the RTE.

Estimate travel time of ships in narrow channel based on AIS data

This paper proposes an AIS-data-based model to accurately estimate vessels' travel time and its distribution between any two points in a narrow channel. It is crucial to know the travel time (plus its distribution) between some critical points for traffic control. The model has two components: (1) to identify a vessel's destination dock, and its arrival and departure times (the key is how to separate a vessel's trips); and (2) to estimate a vessel's travel time from its destination dock (or a specific point) to another specific point, or vice versa. Travel time between two points is called traversal time. The proposed algorithms were then applied to the Houston Ship Channel (HSC) with the AIS data of 11 months in 2017, focusing on the outbound trips in the section above the Beltway-8 Bridge. Through the travel times of cargos and tankers from their destination docks to a point are likely to be normally distributed, their traversal times between two points are more likely to be lognormally distributed. The traversal time distribution is significantly impacted by vessel width, draft and type, but independent from time-of-day and month. The adjacent link traversal times are also highly positively correlated.

Study of travel behavior of vessels in narrow waterways using AIS data – A case study in Sabine-Neches Waterways

This paper proposed an AIS data based method of studying travel behavior of vessels when passing through a hotspot in a narrow waterway, by building their trip information within this hotspot. With trip information, all trips in a hotspot can be divided into three categories: inbound, outbound and moored. The last one refers to the trips that have ever stopped in a hotspot. Such stopping behavior is expected to be different from those directly passing through a hotspot without any stopping. If not excluding these trips, the revealed behavior pattern may be bias, especially when considering speed distributions. This proposed method was applied to the three hotspots in the Sabine-Neches Waterways (SNWW), identified through the analysis of vessel conflicts along the whole SNWW using AIS data collected in the whole year of 2012: more than 10% of total vessel conflicts occurred within these three hotspots. We also investigated the distributions of flow speeds and densities, as well as their relationships at these hotspots, respectively. The findings reveal the travel patterns of inbound and outbound trips passing these three hotspots in the SNWW, considering the heterogeneous behavior of different types of vessels. These revealed patterns also help examine the risk of vessel collisions at these three spots, respectively.

The characteristics of bike-sharing usage: Case study in zhongshan, china

Public bike systems have grown in popularity and are expanding rapidly across cities worldwide. Such systems provide access to pickup and drop-off public bikes at numerous bike stations for free or for an affordable fee and aim at increasing bike use and extending the accessibility of traditional public transport systems. A variety of studies have examined the characteristics of bike-sharing systems, mostly in American and European cities and with a focus on user demographics. The objective of this study is to investigate the general characteristics of system usage, in terms of system efficiency, trip characteristics and bike activity patterns, for Zhongshan’s public bike system during a five-month period. The findings show that the system is not very efficient based on usage metrics which are low compared to successful systems in other countries. Demand is relatively high in city centre zones due to high population and activity density. However, there is no clear direction of inbound or outbound trips in rush hours. This may be attributed not only to mixed land use patterns throughout the city, but also to the fact that most trips are local trips over short distance. This could indicate that public bike trips are mainly substitute for walking trips rather than for car or PT trips. On the outskirts, demand and system efficiency are low, indicating that location allocation of stations needs adjustment. In the conclusions, we discuss how these findings can be used for improving the system.