Lower Travel Costs Research Articles

Tourists’ preferences for managing invasive species in national parks: a choice experiment study in Taiwan

ABSTRACT This research study utilises the choice experiment methodology to examine tourists’ preferences regarding the adverse effects of managing invasive species in national parks. The study estimates the willingness of tourists to pay for mitigating various negative externalities associated with invasive species management. By understanding these willingness to pay estimates, national park management authorities can develop invasive species management strategies that have minimal impact on tourists. The study focuses on tourists visiting Yushan National Park and Taroko National Park in Taiwan. The findings indicate that tourists recognise the significant impact of negative externalities resulting from invasive species management, yet they still exhibit a preference for management over non-management. Among the negative externalities, tourists prioritise avoiding ecological disturbances and health risks over reducing recreational areas. National parks that are more easily accessible tend to have lower travel costs, making them more attractive to tourists visiting other destinations, particularly those on group tours. However, these characteristics result in stronger reactions from tourists towards management measures or lesser impacts from not managing invasive species, leading to reduced support for management initiatives. Furthermore, the study’s questionnaire format and sampling method are deemed consistent and reliable, effectively addressing potential biases associated with on-site visitor sampling.

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.

POI Route Recommendation Model Based on Symmetrical Naive Bayes Classification Spatial Accessibility and Improved Cockroach Swarm Optimization Algorithm

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

The important factors nurses consider when choosing shift patterns: A cross-sectional study.

To gain a deeper understanding of what is important to nurses when thinking about shift patterns and the organisation of working time. A cross-sectional survey of nursing staff working across the UK and Ireland collected quantitative and qualitative responses. We recruited from two National Health Service Trusts and through an open call via trade union membership, online/print nursing profession magazines and social media. Worked versus preferred shift length/pattern, satisfaction and choice over shift patterns and nurses' views on aspects related to work and life (when working short, long, rotating shifts) were analysed with comparisons of proportions of agreement and crosstabulation. Qualitative responses on important factors related to shift preferences were analysed with inductive thematic analysis. Eight hundred and seventy-three survey responses were collected. When nurses worked long shifts and rotating shifts, lower proportions reported being satisfied with their shifts and working their preferred shift length and pattern. Limited advantages were realised when comparing different shift types; however, respondents more frequently associated 'low travel costs' and 'better ability to do paid overtime' with long shifts and 'healthy diet/exercise' with short shifts; aspects related to rotating shifts often had the lowest proportions of agreement. In the qualitative analysis, three themes were developed: 'When I want to work', 'Impacts to my life outside work' and 'Improving my work environment'. Reasons for nurses' shift preferences were frequently related to nurses' priorities outside of work, highlighting the importance of organising schedules that support a good work-life balance. General scheduling practices like adhering to existing shift work guidelines, using consistent and predictable shift patterns and facilitating flexibility over working time were identified by nurses as enablers for their preferences and priorities. These practices warrant meaningful consideration when establishing safe and efficient nurse rosters. This survey was developed and tested with a diverse group of stakeholders, including nursing staff, patients, union leads and ward managers. The Strengthening the Reporting of Observational Studies (STROBE) checklist for cross-sectional studies was used to guide reporting.

Reducing strategic uncertainty in transportation networks by personalized routing advice: A route-choice laboratory experiment

One fundamental reason causing traffic flow instability and the difficulties in predicting traffic conditions is strategic uncertainty. This paper experimentally investigated how routing advice influenced strategic uncertainty and analyzed compliance behavior and decision time that might affect strategic uncertainty. Subjects participated in a route-choice game in which they were required to choose among three routes from one origin to one destination. Two routing advice strategies, user-optimal recommendation (UOR) and system-optimal recommendation (SOR), taking into account subjects’ choice inertia, were investigated. The experimental results indicated that (i) UOR could reduce strategic uncertainty and stabilize traffic flow, whereas SOR was counterproductive, (ii) subjects were more likely to comply with UOR compared with SOR and preferred to follow the advice with the same choices as the previous round rather than the route switching advice, (iii) the subjects with faster decisions (intuition) had higher compliance rates and lower travel costs than those with slower decisions (deliberation). Our experimental findings suggest that fairness and psychological factors are crucial in route choice, providing valuable guidance in designing personalized advanced traveler information systems.

Exploring the Influence of Parking Penalties on Bike-Sharing System with Willingness Constraints: A Case Study of Beijing, China

Dockless bike-sharing has experienced explosive growth, establishing itself as an integral component of urban public transport systems. Challenges such as parking violations have spurred operators and users to pursue standardized management. While electronic parking spots are employed to promote standard parking, suboptimal parking layouts can lead to illegal parking. Inadequate post-violation penalties fail to achieve standard parking, while excessive punishment diminishes user engagement. This study combines parking spot density and penalties to incentivize standard parking, and Beijing, China, was selected as the research object. Using an SP questionnaire survey, a binary logistic model analyzes bike-sharing users’ standard parking behavior and willingness to adhere to different rules. Findings reveal that optimal walking distances range from 300 to 450 m for service levels and exceed 400 m for service efficiency. Influential factors include gender, age, occupation, usage behavior, and travel preferences. Users with high-frequency, low-convenience expectations, low travel costs, and flexible travel exhibit strong adherence. Additionally, user acceptance of the maximum distances without penalties follows an exponential distribution, with 80% accepting 400 m and 40% accepting 800 m. Enforcement has a visible effect within 300 m, but diminishes with longer distances. Excessive penalties result in significant user loss.

Scale effects in ridesplitting: A case study of the City of Chicago

Ridesplitting -- a type of ride-hailing in which riders share vehicles with other riders -- has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips.

The Effect of Driving Style on Battery Electric Vehicle Range

The demand for electric vehicles is high due to the fact of their low travel costs. Meanwhile, an increase in the car driving range is expected. Hence, this paper examines different concepts related to driving a battery electric vehicle. The driving scenarios were divided into two parts. The first part consisted of four stages: driving in a mixed cycle, charging the battery, driving a very short distance, and driving distances that were within the maximum theoretical range of the batteries. The second part involved driving a distance until the range extender system was activated. The outcomes of these experimental investigations are described and the key findings are presented in the discussion.

Comparative Study on Tourism Image of China and Japan for the Promotion of Visitation of Korean Travelers

Purpose – The image of destination has an influence on the satisfaction of tourists therefore, DMO (Destination Management Organization) try to find perceived image of own destination for the visitors. This study examined to understand the image of popular tourism destination of Korean outbound market. Design/Methodology/Approach – To conceptualize the image of destination of China and Japan using destination attributes which was suggested by the studies on push and pull factor theory of tourists motivation. According to push-pull theory, choosing a certain destination is the result of the tourists choice based on the interaction between push and pull factors. 24 destination image attributes were adopted for importance-performance analysis. The research was conducted by 220 questionnaires to convention attendees. Findings – According to the survey, it was found that Korean travelers who want to travel to China or Japan prefer destinations that provide intellectual information about rich history and culture, low travel costs and local prices, exciting tourist attractions, beautiful natural scenery, and history and culture. In the regression analysis, respondents were satisfied with natural beauty, historical and cultural places, and low travel expenses in China, and in case of Japan, they were satisfied with various regional festivals and cuisine. Research Implications – This research on image based on the tourism destination attributes give us understanding to understand the motivation of Korean travelers to China and Japan in the perspective of what kind of experience they pursuit in.

The geography of agricultural input markets in rural Tanzania

The expansion of agro-dealers into remote areas can be seen as conducive to more smallholders adopting new technologies and inputs, to include improved seed and fertilizer. However, lower travel costs may be offset by agro-dealer decisions on stocking and pricing, reflecting both travel time from wholesale markets as well as the level of competition in localized areas. This paper investigates the geographical distribution of agro-dealers and related patterns of local market competition on the availability and prices of maize seed and fertilizer. We use a unique census of agro-dealers in eight districts of Tanzania (n = 299) which maps distribution points for agricultural inputs in these areas. Results suggested that despite a high number of agro-dealers, almost 30% of farmers lived more than an hour travel time from at least one agro-dealer. Instead of wide geographical coverage, agro-dealers tended to be found in clusters, with strong variation in cluster sizes between different districts. Overall, more remote agro-dealers faced less competition, resulting in fewer stocked product choices and charging higher prices to customers, even after controlling for travel time from district headquarters. Remote farmers are disadvantaged in their uptake of new technologies and critical production inputs due to lack of competition among agro-dealers. Our results suggest that highly aggregated and/or simplified measures of market access fail to reflect important heterogeneity in the market access conditions faced by farmers; a better understanding of distribution networks and competition is needed.

Research on task assignment to minimize travel cost for spatio-temporal crowdsourcing

Online task assignment is one of the core research issues of spatio-temporal crowdsourcing technology. The current researches on minimizing travel cost all focus on the scenario of two objectives (task requesters and workers). This paper proposes a two-stage framework (GH) based on Greedy algorithm and Hungarian algorithm for three-objective online task assignment to minimize travel cost. In order to further optimize the efficiency and average travel cost, this paper proposes GH-AT (Adaptive Threshold) algorithm based on GH algorithm, and redesigns the Hungarian algorithm into the sHungarian algorithm. sHungarian algorithm has lower time complexity than Hungarian algorithm. sHungarian algorithm is not only suitable for the problem studied in this paper, but also for all task assignment problems with constraints. Compared with Greedy algorithm, GH-AT algorithm has lower travel cost and higher total utility. In terms of the number of matches, GH-AT is slightly lower than Greedy algorithm. In terms of time cost, GH-AT algorithm is higher than Greedy algorithm, but much lower than GH algorithm.

What do we (Not) know about our future with automated vehicles?

While research on developing and testing automated vehicle (AVs) technologies is well underway, research on their implications on travel-related behavior is in its infancy. The aim of this paper is to summarize and analyze literature that focuses on travel-related behavior impacts of AVs, namely levels 4 and 5, as well as highlight important directions of research. We review five methods used to quantitatively investigate these implications and how each method contributes to this literature: 1) controlled testbeds, 2) driving simulators and virtual reality, 3) agent-based and travel-demand models, 4) surveys, and 5) field experiments. We also present five critical research questions regarding the implications of AVs on the demand side of transportation and summarize findings from the current literature on: 1) what is the willingness to adopt the technology? and what are the impacts of the technology on 2) in-vehicle behavior? 3) value of time? 4) travel-related behaviors (activity pattern, mode, destination, residential location)? and 5) vehicle miles traveled (VMT)? Results can be divided into four categories. The first category corresponds to results on research questions with numerous data points where the direction of the impact is consistent across the literature, albeit the magnitude varies considerably. For instance, surveys indicate 19% to 68% of people are unwilling to adopt AV technology, a sentiment that is fading over time. Moreover, people prefer owning AVs over sharing them and don’t believe their car ownership will decrease. Regarding VMT, most studies predict an increase that varies from a low of 1% to a high of 90% depending on the scenario and assumptions under study. The second category of findings corresponds to research questions with limited and consistent, albeit highly variable data points. For example, a few stated preference survey studies indicate that reduced stress and multitasking during travel will reduce the value of time between 5% and 90%. The third category of results is on research questions with a few but conflicting data points. For instance, surveys indicate that people (80% to 85%) do not believe their residential location will be impacted by the adoption of AVs. Some simulation studies, however, indicate that lower travel costs will drive people away from cities and into suburbs while other studies report the opposite. The final category of results corresponds to research question with a single or no data points. For instance, one study explores how users will use vehicles to run errands while no studies investigate user preferences for vehicle types (e.g. mobile-homes vs. right-sized) or how they plan to use their vehicles when they are not needed (e.g. rent out vs. leave them idle). Moving forward, the goal is to shift all results into the first category while simultaneously tightening the prediction interval of the magnitude of the impacts. This can be achieved by: 1) focusing more efforts on research questions that fall under the three remaining categories to fill the holes in the literature, and 2) establishing consistency and clarity of assumptions used by researchers to enable comparisons and transferability of results.

Gospodarka współdzielenia a zachowania polskich młodych singli: przypadek BlaBlaCar

This article is a research exercise. The purpose of the study is to provide an insight into what young singles know about the sharing economy and what their key motives are for using the BlaBlaCar platform, the most popular sharing economy venture for passenger transport. In the theoretical part of the text, based on a critical analysis of the literature, the concept and essence of the sharing economy are explained, the BlaBlaCar transport service is characterised as a flagship example of the sharing economy, and an attempt is made to explain the terms “single” and “young single”. The empirical section focuses on the research conceptualisation and a description of the research sample and its characteristics. Subsequently, following the author’s own research, the knowledge of young singles about the sharing economy is analysed, and the most important motives encouraging them to use the BlaBlaCar platform are discussed. The analysis is based on a survey questionnaire administered from May 1 to July 30, 2018 in a sample of 826 young singles who made independent decisions in the market. The survey shows that young singles demonstrated fairly good knowledge of sharing economy ventures and the most popular car sharing websites. However, they are confused about the rich terminology associated with collaborative platforms and often misunderstand the sharing economy, identifying it with the access economy, gift economy or on-demand economy. BlaBlaCar is undoubtedly one of the most popular examples of the practical application of sharing economy fundamentals. The survey reveals that almost three-fifths of respondents use the BlaBlaCar platform. Its active participants are most often men in the 18–23 age group, with a bachelor’s or engineering degree, earning a monthly income not exceeding PLN 3,000, and living in cities with over 500,000 inhabitants. Lower travel costs, better travel conditions, the possibility of reaching a destination quickly and directly and environmental considerations proved to be the most important motives for the respondents to use BlaBlaCar. In turn, social relations that are established among the users of websites such as BlaBlaCar.pl are a kind of hybrid of relations developed in direct interaction during shared rides and those built up in cyberspace.

What Drives the Erasure of Protected Areas? Evidence from across the Brazilian Amazon

Protected areas (PAs) are a widely used strategy for conserving forests and ecosystem services. When PAs succeed in deterring economic activities that degrade forests, the impacts include more forest yet less economic gain. These economic opportunity costs of conservation lead actors with economic interests to resist new PAs, driving their sites away from profitable market centers and towards areas featuring lower opportunity costs. Further, after PAs are created, economic actors may want PA downgrading, downsizing, and degazettement (collectively PADDD). We examine reductions in PAs' spatial extent – downsizings (partial erasures) and degazettements (complete erasures) − that presumably reduce protection. Using data for the entire Brazilian Amazon from PADDDtracker.org, our empirical analyses explore whether size reductions from 2006 to 2015 resulted from bargaining between development and conservation. We find that the risks of PA size reductions are raised by: lower travel costs (as implied by distances to roads and cities), which affect economic gains and enforcement; greater PA size, which affects enforcement; and more prior internal deforestation, which lowers the impacts of size reductions. These dynamics of protection offer insights on the potentially conflicting factors that lead to PA size reductions, with implications for policymaking to enhance PA effectiveness and permanence.

Krahn's sales territory for PPG precipitated silica extended to cover Benelux

A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers.

How shopping motives, store attributes and demographic factors influence store format choice in Vietnam

PurposeDespite huge investments within the modern trade arena, Vietnam remains a traditional trade retailing country. The purpose of this paper is to establish the combined effects of motivation, store attributes and demographic factors on the predictive outcome of store format choice in Vietnam.Design/methodology/approachA logistic regression model is used to determine the effect of these factors on the predictive outcome of traditional markets or supermarkets in purchasing non-food products or processed food products.FindingsThe dichotomy between what supermarkets and traditional markets have to offer is simple but effective. Utilitarian-motivated shoppers are more likely to shop at traditional markets. They emphasize the need to buy products quickly, find a good price, with less travel time and hence lower travel costs. Hedonistic shoppers are motivated by feelings of happiness; they shop to relieve stress and to keep up with trends. Significant difference in store choice also exists between older and higher income shoppers.Research limitations/implicationsFuture research should examine the sociocultural dimensions of shopping at traditional stores by exploring how such shopping relates to, and is embedded in, the formation and changes of individual identity, especially in communities outside of Ho Chi Minh City where shoppers are almost entirely dependent on traditional stores.Practical implicationsTraditional stores have the benefit of convenient location and savings in both time and travel costs. These benefits are being eroded as supermarkets and transnational retail corporations establish new stores close to the traditional stores.Social implicationsShopping at traditional markets is part of the social culture and is embedded in individuals’ identity formation, despite increasing urbanization and shoppers’ higher incomes.Originality/valueThis study comprehensively explores the interactions between store choice and motivation, store attributes and demographic factors, taking into account contemporary and contextually relevant factors.

Carbon Footprint of Academic Air Travel: A Case Study in Switzerland

Relatively low travel costs and abundant opportunities for research funding in Switzerland and other developed countries allow researchers large amounts of international travel and collaborations, leading to a substantial carbon footprint. Increasing willingness to tackle this issue, in combination with the desire of many academic institutions to become carbon-neutral, calls for an in-depth understanding of academic air travel. In this study, we quantified and analyzed the carbon footprint of air travel by researchers from the École Polytechnique Fédérale de Lausanne (EPFL) from 2014 to 2016, which is responsible for about one third of EPFL’s total CO2 emissions. We find that the air travel impact of individual researchers is highly unequally distributed, with 10% of the EPFL researchers causing almost 60% of the total emissions from EPFL air travel. The travel footprint increases drastically with researcher seniority, increasing 10-fold from PhD students to professors. We found that simple measures such as restricting to economy class, replacing short trips by train and avoiding layovers already have the potential to reduce emissions by 36%. These findings can help academic institutions to implement travel policies which can mitigate the climate impact of their air travel.

Scheduling Argentina’s professional basketball leagues: A variation on the Travelling Tournament Problem

Operations research methods are applied to design the season schedules of Argentina’s professional basketball leagues using a format adopted in 2014 by the top two divisions. Following the setup used by the National Basketball Association (NBA) in North America, games are played any day of the week and away games are scheduled in road trips of one to four consecutive games. The main scheduling objective is to reduce the teams’ total travel distance compared to previous season formats through the use of predetermined trips submitted by the teams. The mathematical form of the problem is a variation on the well-known Travelling Tournament Problem. The modelling is divided into two successive stages, the first one defining the sequences in which each team plays the other teams and the second one assigning the days on which each game is played. Both stages use integer programming models that incorporate a series of constraints reflecting criteria requested by the Argentine Basketball Club Association. Implementation of the models has generated average travel distance reductions of more than 30% per away game, with consequential benefits in lower travel costs and less player fatigue.

Joint Operating Revenue and Passenger Travel Cost Optimization in Urban Rail Transit

Urban rail transit (URT) scheduling requires designing efficient timetables that can meet passengers’ expectations about the lower travel cost while attaining revenue management objectives of the train operators. This paper presents a biobjective timetable optimization model that seeks maximizing the operating revenue of the railway company while lowering passengers’ average travel cost. We apply a fuzzy multiobjective optimization and a nondominated sorting genetic algorithm II to solve the optimization problem and characterize the trade-off between the conflicting objective functions under different types of distances. To illustrate the model and solution methodology, the proposed model and solution algorithms are validated against train operation record from a URT line of Chengdu metro in China. The results show that significant improvements can be achieved in terms of the travel cost and revenue return criteria when implementing the solutions obtained by the proposed model.

How Do Travel Costs Shape Collaboration?

We develop a simple theoretical framework for thinking about how geographic frictions, and in particular travel costs, shape scientists’ collaboration decisions and the types of projects that are developed locally versus over distance. We then take advantage of a quasi-experiment—the introduction of new routes by a low-cost airline—to test the predictions of the theory. Results show that travel costs constitute an important friction to collaboration: after a low-cost airline enters, the number of collaborations increases between 0.3 and 1.1 times, a result that is robust to multiple falsification tests and causal in nature. The reduction in geographic frictions is particularly beneficial for high-quality scientists that are otherwise embedded in worse local environments. Consistent with the theory, lower travel costs also endogenously change the types of projects scientists engage in at different levels of distance. After the shock, we observe an increase in higher-quality and novel projects, as well as projects that take advantage of complementary knowledge and skills between subfields, and that rely on specialized equipment. We test the generalizability of our findings from chemistry to a broader data set of scientific publications and to a different field where specialized equipment is less likely to be relevant, mathematics. Last, we discuss implications for the formation of collaborative research and development teams over distance. This paper was accepted by Toby Stuart, entrepreneurship and innovation.