Actual Trip Research Articles

Field trips as gateways to out-of-school learning: perspectives and practices among preschool teachers*

ABSTRACT This qualitative study addresses preschool teachers’ beliefs and practices regarding field trips emphasizing their role as gateways to out-of-school learning. Engaging 20 preschool teachers, with observations of six during field trip activities, the research highlights the teachers’ recognition of the substantial educational benefits that field trips offer in early childhood education. Participants emphasized that field trips provide valuable opportunities for learning, fostering several chances to think and act in diverse out-of-school settings. Despite their positive beliefs, practical implementations may be hindered by limited time, resources, and security concerns. The current study was concluded with recommendations aimed at reducing these barriers and enriching the benefits of out-of-school learning through field trips.

Shiga toxin-producing Escherichia coli O157:H7 outbreak associated with school field trips at a farm animal exhibit-Tennessee, September-October 2023.

In October 2023, the Tennessee Department of Health identified an outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 infections among elementary school students who attended school field trips to the same farm animal exhibit. Our aim was to determine STEC source and prevent additional illnesses by initiating epidemiologic, laboratory and environmental investigations. We identified cases using laboratory-based surveillance and by surveying caregivers of children who attended the exhibit. Probable cases were defined as illness with abdominal cramps or diarrhoea after attendance; confirmed cases were laboratory-confirmed STEC infection in an attendee or household contact. A site visit was conducted, and event organizers were interviewed. Human stool, animal faeces and environmental samples were tested for STEC O157:H7 by real-time polymerase chain reaction (PCR), culture and whole-genome sequencing (WGS). Approximately 2300 elementary school students attended the animal exhibit during 2 days. Field trip activities included contact with different farm animal species, drinking pasteurized milk outside animal enclosures and eating lunch in a separate building onsite. We received survey responses from 399 caregivers for 443 (19%) animal exhibit attendees. We identified 9 confirmed and 55 probable cases with illness onset dates during 26 September to 12 October. Seven children aged 1-7 years were hospitalized. Four children aged 1-6 years experienced haemolytic uraemic syndrome; none died. Laboratory testing identified STEC O157:H7 by culture from eight human stool samples with 0-1 allele difference by WGS. Three environmental samples had Shiga toxin (stx 2) genes detected by PCR, but no STEC isolates were recovered by culture. This is the largest reported STEC O157:H7 outbreak associated with an animal exhibit in Tennessee. We identified opportunities for educating school staff, event organizers and families about zoonotic disease risks associated with animal contact and published prevention measures.

Route Positioning System for Campus Shuttle Bus Service Using a Single Camera

A route positioning system is a technology that identifies the current route when driving from one stop to the next, commonly found in public transportation systems such as shuttle buses that follow fixed routes. This is especially useful for smaller-scale services, such as shuttle buses, where using expensive technology and sensors for location tracking might not be feasible. Particularly in urban areas with tall buildings or mountainous regions with lots of trees, relying solely on GPS can lead to many errors. Therefore, this paper suggests a cost-effective solution that uses just one camera sensor to accurately determine the location of small-scale transportation services on fixed routes. For this, this paper uses a single-stage detection network that quickly identifies objects and tracks them using a simple algorithm. These detected features are compiled into a “codebook” using the bag-of-visual-words technique. During actual trips, this pre-created codebook is compared with landmarks that the camera sees. This comparison helps to determine the route currently being traveled. To test the effectiveness of this approach, this paper used the route of a shuttle bus on the Gachon University campus, which is similar to a downtown area with tall buildings or a wooded mountainous area. The results showed that the shuttle bus’s route was recognized with an accuracy of 0.60. Areas with distinct features were recognized with an accuracy of 0.99, while stops with simple, nondescript structures were recognized with an accuracy of 0.29. Additionally, applying the SORT algorithm to enhance performance slightly improved the accuracy from 0.60 to 0.61. This demonstrates that our proposed method can effectively perform location recognition using only cameras in small shuttle buses.

SUSTAINABLE URBAN MOBILITY PLAN AS ONE OF THE TOOLS OF MANAGEMENT OF URBAN TRANSPORT SYSTEMS

The concepts of "urban mobility" and "sustainable urban mobility plan" are defined. Indicators of sustainable transport planning problems in various categories (trip activity, air pollution emissions, noise pollution, transport risk, economic productivity, general accessibility, land use impact, equity capital, transport policy) are also given, possible sub-categories are proposed and the evaluation of indicators according to the principle: A = suggested for use in almost all situations; B = proposed for use if appropriate/possible; C = proposed for use when appropriate to address specific community needs. A number of factors that should be paid attention to in the process of creating a sustainable urban mobility plan are described. It also describes the main stages of creating a plan and the features that should be considered during the planning process in order to successfully implement sharing systems in a mobility strategy.

The importance of recurring public transport delays for accessibility and mode choice

This paper looks at the relationship between recurring public transport delays, accessibility to jobs, and travel behaviour in the region of Scania, Sweden. The difference between potential (scheduled) accessibility, observed (actual) accessibility, and behaviour is an important part of this research. This paper contributes to the growing body of literature that uses GTFS data (for both scheduled and actual services) to provide a deeper understanding of temporal variations in accessibility with public transport. Historic public transport data were used to develop a measure for typical delays in the region. The accessibility analysis shows that, on average, recurring public transport delays result in a 4–9% reduction in accessibility to jobs in the region. The loss in accessibility varied depending on the travel time budget that was considered and the location within the region. The accessibility analysis also shows that areas with higher concentrations of households with a lower economic standard experience a smaller loss in job accessibility caused by public transport delays. However, the concentration of these effects depends on the measure that is used. The measurement of typical delays was also analysed in relation to actual trips from the regional travel survey. The statistical analysis found that recurring public transport delays were associated with a lower likelihood of using public transport compared to other motorised modes.

Can Electric Vehicle Carsharing Bridge the Green Divide? A Study of BlueLA’s Environmental Impacts among Underserved Communities and the Broader Population

This study aims to evaluate the potential of electric vehicle (EV) carsharing services to address social and environmental disparities in urban transportation through an evaluation of BlueLA, a one-way station-based carsharing service in Los Angeles, California. BlueLA provides a clean and affordable mobility option in underserved communities that face significant air quality burdens and have historically been excluded from environmental benefits. By incorporating BlueLA trip activity data from January 2021 to December 2022 (n = 59,112 trips) and an online user survey implemented in early December 2022 (n = 215 responses), we estimate the impacts of BlueLA on personal vehicle ownership patterns, vehicle miles traveled (VMT), and associated greenhouse gas (GHG) emissions. The results show an overall net reduction in VMT and GHG emissions of 463,845 miles and 656 metric tons, respectively, among the BlueLA user population (3074 registered users). When disaggregating impacts by BlueLA member type, our findings show a net reduction of 234 and 371 metric tons in GHG emissions for Standard (general population) and Community (low-income qualified) members, respectively. Additionally, our socio-demographic analysis highlights clear disparities between these two member groups, with Community members typically having lower incomes (i.e., 74% earning below USD 50,000 annually); lower educational attainment (i.e., 46% with at most an associate’s degree); and larger households (i.e., 23% living in households of four or more) compared to Standard members (i.e., 19% earning below USD 50,000, 24% with at most an associate’s degree, and 9% in households of four or more). Moreover, when comparing the VMT and associated GHG emissions due to BlueLA, we find that the presence of BlueLA reduces VMT and GHG emissions by 34% and 48% respectively, and each BlueLA vehicle replaces 16 personally owned vehicles (shed and postponed purchases). Last, when comparing the emissions produced by the electric fleet of BlueLA to those of a comparable fleet of internal combustion engine vehicles, we find that the use of an EV fleet reduces GHG emissions by 43% in comparison. The BlueLA carsharing service has led to notable net reductions in VMT and thus GHG emissions, with a major share of these reductions observed among Community members.

Unleashing the Potential: Investigating the Empowering Effects of Field Trips on Autonomous English Learners’ Learning

Field trips have long been recognized as a significant component of educational endeavours. While students at the primary and secondary levels commonly engage in such activities, the integration of field trips into tertiary education remains infrequent. University studend who attend the English classes are seldom afforded the opportunity to directly observe authentic professional environments or explore potential workplaces related to their future careers, experiences that can be facilitated through field trips. Additionally, given the increasing emphasis on the internship program held by Indonesian Ministry of Education, Culture, Research, and Technology, a gap arises as students are limited in their exposure to real-world work environments. The objective of this research endeavour was to investigate university students' perceptions of field trip activities and assess how these experiences contribute to the development of students' autonomous learning skills. Qualitative data for this study were collected through two questionnaires, a preparation and a reflection Google sheet. The participants consisted of 27 students enrolled in the International Relations Study Program at UPN Veteran Jakarta. The findings of the study revealed that field trips offer numerous benefits to English for Professional Purposes students, including enhancing their understanding of academic concepts, providing a deeper real-world context for learning, and fostering social interaction. The questionnaire results demonstrated a predominantly positive perception among the students. Furthermore, a majority of the participants reported engaging in autonomous learning activities during the field trip, facilitated by the opportunity to interact with industry professionals and seek their guidance through questioning.

What determines the real-world CO2 emission reductions of ridesplitting trips?

Ridesplitting has great potential to alleviate the negative impacts of ridesourcing on the environment. However, not all ridesplitting trips are effective in reducing CO2 emissions. The actual trip-level environmental impact of ridesplitting and its determinants remain unclear. To bridge this gap, this study reveals what determines the real-world CO2 emission reductions of ridesplitting trips based on the observed data of ridesourcing services in Chengdu, China. First, the CO2 emission reduction of each ridesplitting trip compared with its substituted single rides is calculated using a vehicle emission model (COPERT) coupled with GPS trajectory data. Then, four implementations of gradient boosting machines, namely GBDT, XGboost, LighGBM, and CatBoost models, are employed to explore the relationships between the ridesplitting CO2 emission reduction rate and its explanatory variables. Finally, the nonlinear and interaction effects of these variables are analyzed based on partial dependence plots and SHapley Additive ExPlanations. Empirical results show that the ridesplitting CO2 emission reduction rate varies from trip to trip, with an average of 43.15 g/km. In the real world, approximately 15 % of ridesplitting trips even increase CO2 emissions, mainly due to the low overlap rate and high detour rate of the shared rides. To enhance the CO2 emission reduction rate of ridesplitting, the overlap rate, number of shared rides, average speed, and ride distance ratio should be increased, while the detour rate, actual trip distance, and ride distance gap should be decreased. This study can help the ridesourcing companies better match the shared rides and improve the environmental benefits of ridesplitting.

The Role of Field Trips in Teacher Education for Teaching Social Studies in a Developing Country

This study investigated the role of field trips in teacher education for teaching Social Studies in a developing country, focusing on Ghana and specific Colleges of Education. A descriptive survey approach was used, with 35 Social Studies tutors and 316 students participating. The study examined tutor engagement in field trips and the challenges faced during these trips. Ethical procedures were followed, and data was collected through questionnaire. The findings revealed that field trips were used occasionally or infrequently by most teachers, with virtual realities and models being used more frequently. Field trips were considered significant for both teachers and students in enhancing comprehension. Challenges in organizing field trips included the stressful planning process and obtaining approval from stakeholders. Strict restrictions and lack of funding posed additional challenges. The study recommends improving pre- and post-field trip activities, creatively engaging students in relevant environments, and addressing the complexities of planning and obtaining permissions for field trips.
 
 Received: 20 March 2023 / Accepted: 27 October 2023 / Published: 5 November 2023

The Role of Generative Pre-trained Transformers (GPT) in Recreational Tourism: An Interview with ChatGPT

This study explores the potential benefits of employing generative pre-trained transformers (GPTs) in recreational tourism, the difficulties that arise when implementing such technology, the impact it has on tourist behaviour, and the ways it can be utilised in recreational tourism management. The original aspect of the study is that it is the first to give detailed information about the use of GPT in recreational tourism. ChatGPT was used as an interviewer in the study. ChatGPT is a software application that utilizes the high-powered machine learning software called Generative Pre-trained Transformer (GPT-3), developed by the OpenAI organization. Six questions were posed on the ChatGPT query screen (https://chat.openai.com/chat). The interview queries were prepared with reference to the study by Fusté-Forné and Orea-Giner (2023). The question statements in this study on the use of GPT in gastronomy tourism were changed to recreational tourism. ChatGPT's replies were tabulated and presented descriptively. Inferences and suggestions were made in line with the answers given by ChatGPT. The study showed that using GPT technology in recreational-based tourism can offer better customer interaction, decision-making, and a personalized travel experience. ChatGPT underlined that new technologies continue to evolve, and recreational-based tourism will become more personalized, informative, and immersive, ultimately enhancing the overall travel experience, and contributing to the growth and sustainability of the tourism industry. ChatGPT also noted GPT models have the potential to shape and enhance the entire travel experience for tourists, from the initial planning stages to the actual trip itself. Although GPT makes significant contributions to recreational tourism management and recreational tourists, there are deficiencies in ethical, privacy, and authenticity concerns.

Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.

Science Teaching Materials Based on Field Trips with Local Wisdom to Improve Elementary School Students' Critical Thinking

Objective: Education is the most essential thing in the life of a human being. Components supporting its implementation are needed to realize the quality of education. Cultivating critical thinking character education is urgently required in the era of globalization. Field trip teaching materials with local wisdom can be an attractive alternative. This study aims to develop science teaching materials based on field trips with local wisdom to improve elementary school students in critical thinking skills. Method: This research method uses research and development, Borg and Gall. Data collection techniques used unstructured interviews, observation, assessment tests, and non-tes. At the same time, data analysis uses quantitative data and descriptive elaboration. Results: The study showed the validity of teaching materials in this research with a percentage gain of 82.00% if converted to a valid category. In comparison, the practicality of implementing lesson plans obtained a percentage of 94.00%, and student activity obtained a percentage of 95.00% with an outstanding category. The effectiveness of science teaching materials showed pretest results with an average of 66.30, while the post-test data was 87.22. Novelty: This research shows a significant increase in the critical thinking skills of elementary school students through learning science teaching materials based on field trips with local wisdom. The update of this research is to implement field trip activities into teaching materials so that learning in cultivating critical thinking is exciting for students.

Development of freight trip generation model using observed and unobserved information of supply chain characteristics for a sustainable urban transformation

Supply Chain Characteristics (SCC) drive the city logistics to control the economic activities that support urban life and transformation. The dynamics of freight trip activity and SCC in city logistics are less understood by city planners, disrupting the prediction of urban Freight Trip Generation (FTG) models. To develop robust urban FTG models, the present study introduces the concept of estimating the freight trip activity using observed and unobserved (subjective) information of SCC for sustainable urban transport movement. Traditionally, the objective variable excludes the unobserved information of SCC while developing the FTG model; this gap can be addressed by incorporating subjective variables to measure both observed and unobserved information of SCC. A unique framework was designed by a combination of objective and subjective variables through Exploratory Factor Analysis (EFA) and Structural Equation Modelling (SEM) to estimate the freight trip activity. An Establishment Based Freight Survey (EBFS) collected from an Indian smart city (1793 samples) comprising various SCC was used to test the developed framework. Deployed EFA can identify the subjective variables using latent constructs to explain the observed and unobserved information in the SCC. Then, sequential execution of SEM with hypothesis path structure for estimating the freight trip activity. The direct, indirect, and total effects of the FTG model revealed added advantage of causal inter-relationship of urban freight trip activity and SCC between intermediate and pure receiver establishment. The developed FTG models using observed and unobserved information of SCC predicted freight trip activity with an accuracy of 57% for intermediate and 63% for pure receiver establishment. The effect of SSC on freight trip activity aids in creating sustainable urban freight transportation that could be an agile, responsive, and flexible adaptive system for the global market demand.

Development of spatial econometric models for estimating the bicycle sharing trip activity

In developing countries, the implementation of bicycle sharing programs is in a nascent stage; this paper provides a preliminary understanding of bicycle sharing trip activity introduced in Kerala, India. Fundamentally, a framework is utilized to incorporate spatial interaction effects and estimate the bicycle sharing trip activity using spatial econometric techniques. MYBYK is the associated bicycle rental company, with 67 stations in the various major locations at Cochin, Kalamasery, Choornikkarra, and Aluva cities in India. Information such as socio-demographic, bicycle sharing trip activity, weather, and built environment information have been collected in the study regions. Initially, spatiotemporal variation is analyzed to understand the bicycle-sharing trip activity. Then, spatial indicators are determined using Moran's I technique, and the study area's locational information is transmitted to the spatial weight matrix to illustrate the spatial neighbourhood structure. Finally, Trip Production and Trip Attraction models were developed for estimating the bicycle sharing trip activity using five spatial econometric techniques as Spatial Autoregressive (SAR) model, Spatially Lagged X model (SLX), Spatial Error Model (SEM), Spatial Durbin Error model (SDEM), and Spatial Durbin Model (SDM). The models are evaluated, and an appropriate model is found using the Bayesian approach. The findings indicate that SDM effectively estimates the bicycle sharing trip activity, and its spillover effects are also addressed for each spatial indicator variable.

Passenger‐perception dynamic ridesharing service based on parallel technology

AbstractWith the emergence of new information and communication technology, mobile‐based ridesharing services have received more attention and have improved. They offer timely and convenient service and exert an enormous influence on pollution and congestion by allowing passengers to share overlapping routes. However, ridesharing services have also been discussed and called into question by people with respect to their attractiveness, their comfort, and passenger safety. This paper proposes a ridesharing algorithm with a perceived mechanism by introducing multiple dimensions of perceived value to overall assess passengers’ perceptions. The approach involved can continuously track the psychological changes in passengers during travel, capture the dynamic and comprehensive perceptions of passengers, and make real‐time adjustments on this basis. Furthermore, the proposed partition‐based parallel strategy can ensure high‐quality services and further enhance computational efficiency. Finally, numerical experiments are conducted based on the urban transportation network and the actual trip data from Manhattan to evaluate the performance of the proposed algorithm. The results show that it is important to effectively capture the passengers’ perception of ridesharing services and apply them to optimization mechanisms, which will contribute to improving the perception of ride experiences and attracting potential passengers.

Mobility and Congestion in Urban India

We develop a methodology to estimate robust city-level vehicular speed indices, exactly decomposable into uncongested speed and congestion. We apply it to 180 Indian cities using 57 million simulated trips measured by a web mapping service. We verify the reliability of our simulated trips using a number of alternative data sources, including data on actual trips. We find wide variation in speed across cities that is driven more by differences in uncongested speed than congestion. Denser and more populated cities are slower, only in part because of congestion. Urban economic development is correlated with faster speed despite worse congestion. (JEL O15, O18, R23, R41)

Do people spend travel time the way they think they would? a comparative study of generic and trip-specific travel time allocation using hybrid multiple discrete continuous (MDC) framework

ABSTRACT Unlike driving alone, public transportation allows one to engage in extraneous activities while traveling – often referred to as travel-based multitasking. Research involving travel-based multitasking often relies on either generic (i.e. not related to an actual trip) or revealed (i.e. related to an executed trip) data. The data collected for this study provided a unique opportunity to compare the generic and trip-specific preferences while traveling on public transportation. To this end, the study develops two integrated choice and latent variable models with multiple discrete-continuous (MDC) kernels to simultaneously model the activity selection and the time allocation for generic and trip-specific data, respectively. According to the results the trip-specific data could identify more nuances in the travel-based multitasking behavior than the generic data. The heterogeneity identified by the developed models will be helpful for the transit operators in providing appropriate facilities (e.g. internet access, reading lights) along various transit corridors.

City-scale synthetic individual-level vehicle trip data

Trip data that records each vehicle’s trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.

Anxiety prevalence and its association with physical activity in patients with non-communicable diseases during COVID-19 lockdown: a cross-sectional study in Shanghai, China

BackgroundQuarantine due to the COVID-19 pandemic may have created great psychological stress among vulnerable populations. We aimed to investigate the prevalence of anxiety and explore the association between physical activities (PA) and anxiety risk in people with non-communicable diseases during the period of COVID-19 lockdown.MethodsWe conducted a cross-sectional telephone survey from February 25 to April 20, 2020, the period of COVID-19 lockdown in Shanghai. Up to 8000 patients with type 2 diabetes and/or hypertension were selected using multi-stage cluster random sampling. PA level was measured based on the International Physical Activity Questionnaire using Metabolic Equivalent for Task scores, while symptoms of anxiety were assessed by the 7-item Generalized Anxiety Disorder scale. Multiple logistic regression analyses were performed to evaluate the associations of type and level of PA with the risk of anxiety.ResultsOf a total 4877 eligible patients, 2602 (53.4%) reported with anxiety, and 2463 (50.5%), 123 (2.5%) and 16 (0.3%) reported with mild, moderate, and severe anxiety. The prevalence of anxiety was higher in the females, the elders, non-smokers, non-drinkers, and patients with diabetes, and the associations of anxiety with sex, age, smoking, drinking and diagnosis of diabetes were significant. A significant negative association was observed for housework activities (OR 0.53, 95%CI: [0.45, 0.63], p < 0.001) and trip activities (OR 0.55, 95%CI: [0.48, 0.63], p < 0.001) with anxiety, but no significant was found for exercise activities (OR 1.06, 95%CI: [0.94, 1.20], p = 0.321). Compared with patients with a low PA level, those with a moderate (OR 0.53, 95%CI: [0.44, 0.64], p < 0.001) or a high PA level (OR 0.51, 95%CI: [0.43, 0.51], p < 0.001) had a lower prevalence of anxiety.ConclusionThis study demonstrates a higher prevalence of anxiety in patients with hypertension, diabetes, or both during the COVID-19 lockdown. The negative associations of housework and trip activities with anxiety highlight the potential benefit of PA among patients with non-communicable diseases.

EFEKTIVITAS KEGIATAN FIELD TRIP SEBAGAI METODE PEMBELAJARAN DI RUNIAH SCHOOL MAKASSAR

Education is a process of influencing students in adapting to the environment and being able to make changes that exist in students. Field trips are a method used by students to complete certain learning experiences and are part of the school's curriculum program. This service aims to determine the effectiveness of the field trip method as a learning method at Runiah School Makassar. This service uses the pre-post test control design method and mentoring outdoor learning activities. The subjects of the service were students from grades 3 to grade 6. The measuring instrument used was in the form of questions consisting of 8 questions covering general knowledge and special knowledge questions. This measuring tool aims to measure student knowledge according to the location of field trip activities. Based on the dedication that was carried out, it gave the result that field trips were effectively carried out as a learning method by increasing general and special knowledge.