Trip Destination Research Articles

Penerapan Metode Field Trip Observation untuk Meningkatkan Wawasan Siswa di MTs Muhammadiyah 10 Gresik

The aim of this research is to examine the implementation of the field trip observation method in the learning process of students, focusing on their ability to adapt and modify themselves in their surroundings, and to identify the strengths and weaknesses of this method. This qualitative study employs ethnographic or field research methodology, with teachers and students under observation as the primary data sources. Secondary data sources include school profile information, field trip destination observation data, and observations of field trip learning strategies. Data collection techniques involve documentation, interviews, and observations. Data analysis proceeds incrementally, leading to conclusions. Findings indicate that using field trip observation methods enhances students' insights, exposes them to new experiences, and alleviates boredom associated with classroom learning. However, this method requires thorough planning and significant financial resources to implement effectively.

Consecutive attractive local regions – bidirectional long short-term memory for trip destination prediction

We propose a sequence-to-sequence model to capture a partial trajectory that contains a few attractive local regions and forecast its destination. Therefore, the main goal of this work is to predict the destination, given the trip’s trajectory, effectively by learning from its attractive local sub-trajectory only. Conceptually, the proposed model incorporates Bidirectional Long Short-Term Memory (BiLSTM) for finding frequent trip trajectories and a Consecutive Attractive Local Regions (CALR) mechanism for only taking their few attractive local regions, called CALR-BiLSTM. Existing related works focused on observing whole regions of each trip trajectory at once to decide its attractive ones from a Global Attention Mechanism (GAM). However, GAM may hold similar attractive sub-trajectories for different destinations, which leads to worse predictions. To overcome the issue, the proposed model observes the entire trip trajectory part by part within a small window, such as focusing on a few regions that are attractive. In this work, we demonstrated the proposed model on large public datasets. As a result, we can enjoy the proposed model as the winner against the state-of-the-art models. Moreover, we present ablation studies on various recurrent neural networks and attention mechanisms to ensure the proposed model is in the right settings.

The role of everyday mobility in adaptation to air pollution hazard: A mixed-method approach combining big and traditional data

The empirical study aims to examine how residents perceive and respond to air pollution in their daily lives, whether they use mobility as an adaptation strategy to avoid or mitigate their exposure, and how socioeconomic and demographic factors modify such responses in mobility. To this end, this study conducts an analysis in the city of Chengdu using a mixed-method approach combining surveys and large-scale mobile phone data. It is found that most at-risk individuals take protective measures, and some choose to change mobility patterns to protect themselves from exposure to air pollution. Regression results suggest that engagement with air quality information and the perceived effectiveness of protective measures are the most important predictors of human mobility changes in response to air pollution. The use of mobility as an adaptation strategy occurs despite the availability of in-situ strategies in general, while low-cost and effective in-situ adaptation choices and high-cost mobility strategies are considered as substitutes. Using changes in origin–destination trips in Sichuan generated from 5,393,739 cellphone users in Chengdu, this study reveals that an increase in the difference of the air quality index at origin versus at destination is associated with more trips from the origin to the destination, and travelers are more sensitive to air quality at origin that drives them to escape from the polluted areas. The findings suggest the (re)production of inequality and marginalization of some population groups in hazard adaptation.

DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destination

AbstractIndividual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real‐world smartcard dataset in urban railway systems and comparing with state‐of‐the‐art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real‐world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.

Pollution-induced trips: Evidence from flight and train bookings in China

Utilizing a novel database including nearly 2.2 billion booking records in China, we examine whether people escape from pollution by traveling to “cleaner” places. Combining an instrumental variable approach with high-dimensional fixed effects, we find a 50-unit increase in the AQI gap between a city pair leads to a 1.30% (1.33%) increase in train and airline ticket bookings from the origin to the destination city departing within one day (2–7 days). In addition, the destination of such pollution-induced trips is more likely to be an intra-province city with more tourist attractions. We also measure willingness to pay for clean air.

A travel demand modeling framework based on OpenStreetMap

Demand modeling is an important part of the setup of a traffic model for a city. All travel demand models rely on land use data as the demand for traveling fundamentally stems from activities occurring at different locations; however, many cities lack these data, or experience in estimating travel demand in their region. In response, this study develops a methodology for generating highly detailed land use data in the form of points of interest (POIs) specifically aimed at travel demand estimation purposes. The framework includes a procedure to extract, clean, enhance, and categorize freely available land use data from OpenStreetMap (OSM) into different POI categories, such as residences, schools, and shops. These residential and activity POIs, which are typical origins and/or destinations of trips, serve as the starting point for estimating travel demand. This paper demonstrates the framework’s utility through three case studies across different cities in Belgium. It validates the effectiveness of OSM-derived POIs for travel demand estimation by replicating Antwerp’s existing demand model, examines the POIs classification’s suitability for various travel demand purposes in Leuven, and assesses the transferability of correlations between OSM data and travel demand from Antwerp to Ghent. Beyond the applications illustrated in this paper, the framework provides opportunities for future research on the consistent disaggregation of existing zonal demand estimates and design-based research in which future demand is estimated given the development of POIs. The framework is openly available as a Python tool called Poidpy.

AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant; however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising; however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to the EV driver’s preference, AFARM checks the set of candidate paths at the source point and selects the appropriate path for the vehicle to drive based on its current status. Consequently, AFARM provides an anxiety-free navigation model that allows for a reliable and environmentally friendly driving experience, promoting this alternative mode of transportation.

Differences in route choice behavior when riding shared e-scooters vs. bicycles – A field study

Problem: The surge in popularity of electric kick scooters (e-scooters) poses new challenges for traffic planning, demanding a comprehensive understanding of route choice behavior to see how e-scooters are used, how they affect traffic flow, and where improvements can be made to the road infrastructure. Therefore, this study aimed to analyze route choices and preferences of e-scooter riders and cyclists in a quasi-experimental setup with both user groups having the same trip destinations. Method: Two groups of participants (n = 52) completed a ride with either a shared e-scooter or bicycle to reach four predefined destinations in Dresden, Germany. The riders were supposed to choose their routes and subsequently reported the difficulty of decision-making and the importance of several route choice factors related to the ride. Results: E-scooter riders rated road surface and safety as significantly more important for route choice than cyclists and tended to perceive the decision-making as more difficult. Riding data revealed broad comparability between the groups, with e-scooter riders tendentially having longer routes for complex decisions (unknown destinations, scenic routes, more turns required). Discussion: The study suggests that the route preferences of e-scooter riders may be influenced by a combination of road surface and safety considerations, highlighting the need for high-quality cycling infrastructure. Limitations exist regarding the naturally occurring differences in riding experience in e-scooter riding versus cycling. Practical implications indicate that planning for e-scooter riders can benefit from insights drawn from activities designed for cyclists. The provision of real-time road quality information is proposed, considering its potential impact on overall road safety. Summary: This study contributes to a better understanding of how e-scooter riders navigate through cities and delivers a valuable foundation for transport planners and engineers considering the rise in cycling and micro-mobility use.

Moderating Effect of Social Media in Shaping Ecotourism Loyalty: A Two-Stage-Cross-Sectional Study

This study aims to evaluate the moderating role of social media influence before and after the trip in Vietnam and Czech Republic. Primary data, gathered during time lag data surveys conducted among international tourists travelling to the Czech Republic and Vietnam, was analyzed with the Structural Equation Modelling (SEM) in R. The findings revealed that social media influence with higher level positively strengthened the connection between tourist motivation and the post trip destination image. Furthermore, social media had a significant moderating effect on the relationship between tourist satisfaction and ecotourism loyalty. However, there are strong differences between the destinations of developed and emerging economies. For Czech Republic, as developed economy, social media influence plays a more important role as a value-expressive mechanism at the post-trip stage whereas for the developing economy of Vietnam, social media plays a more important role as an information source before the trip. This study extends the Expectation Confirmation Theory (ECT) by introducing the moderating effect of social media influence, which first time measures this moderating effect before and after the trip accounting for the dynamic nature of the destination image as an antecedent of ecotourism loyalty. Limitations, theoretical implications, and recommendations for practice and further research are addressed. JEL Classification: L83, Z30, Z39

Assessment of accessibility and equality of access in Long Island libraries from the perspective of community resilience

Access to libraries is integral for day-to-day use and utilization as resilience hubs during times of disaster. However, challenges to library accessibility are exacerbated, particularly in low-density areas with high car dependency and limited transportation options. This study employs spatial analysis to investigate library accessibility on Long Island, NY, an area with a diverse range of population densities, with a particular emphasis on disadvantaged communities and various transportation modes (e.g., walking, public transportation, and driving). The Accessibility indicators used in this study are framed from the perspective of trip destination that resemble market/service/catchment areas or shares. This study assesses accessibility within three distinct geographical service areas: library districts, local areas (within a 3-mile buffer), and the entire Long Island region. Population coverage metric calculation, denoting the percentage of demographic segments within accessible areas concerning different geographical service areas, unveils disparities in library access. The regional analysis reveals that approximately 20% of the population can access a library within a 15-minute walking time, increasing to nearly 90% within a 60-minute walking timeframe. In contrast, 15-minute transit accessibility begins at 30% and only reaches around 70% within a 60-minute window. The Gini coefficient, employed to gauge inequality in accessibility, illustrates that “Limited English Proficiency” and “Non-White” groups consistently exhibit the highest coefficients across the population density spectrum. The analysis also shows that households relying on public transportation face unequal access to libraries. To address these disparities in library access, the study proposes solutions, such as mobile libraries, on-demand transportation shuttles, and non-point library services.

How and Where Did Older People Travel Before and After COVID? Insights from Washington, DC’s Smart Card Data

Using smart card data of subway trips, this paper analyzed travel behaviors of older adults in Washington, DC in three phases of COVID-19 (Pre: 2018-2019, Early: 2020, Late: 2021-2022). The findings show that the impact of COVID-19 on average daily travel patterns was more pronounced on weekday travels, compared to weekend trips. In addition, compared to the general population, older adults’ subway usage showed a slower recovery to normal patterns in both usage levels and trip destinations. The results reveal important insights for transportation planners and transit authorities about older adults’ travel patterns during normal times and unusual events.

Revealing Urban Color Patterns via Drone Aerial Photography—A Case Study in Urban Hangzhou, China

Urban color, primarily emanating from building façades and roofs, plays a pivotal role in shaping a city’s image and influencing people’s overall impression. Understanding the nuances of color patterns contributes significantly to unraveling the uniqueness and identity of a city. This study introduces a statistical method for the systematic analysis of urban color and macroscopic urban structure. Specifically, we employ drones to collect and extract building roof and façade colors in the main urban area of Hangzhou, mapping these colors to the HSV color space. Subsequently, we establish a random walk model and an origin–destination trip model within the urban transportation network to simulate the movement of people. Our experiments reveal robust correlations between façade and roof values and passing frequency (with the Pearson correlations reaching 0.70). Through a rigorous statistical analysis, we gain insights into the distribution of urban color and the impact of architectural structures on color variations, identifying potential patterns or trends. By integrating color data with architectural structure data, our systematic research method deepens the understanding of the visual features that define cities. Beyond theoretical exploration, this approach offers practical insights for building planning and design. This study not only sheds light on the relationship between architectural structures and urban color but also provides valuable guidance for future urban development initiatives.

The Flow Termini Coverage Model for Locating Bike-Sharing Stations

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters.

SPATIAL INTERACTION ANALYSIS OF URBAN FUNCTIONAL DISTRICTS BASED ON TAXI TRAJECTORIES

Abstract. The functional differences among various components of a city drive the circulation of logistics, human flows, and energy flows within the city. Qualitatively and quantitatively analyzing the spatial interactions among urban functional districts contributes to understanding the spatial structure of it and formulating more efficient urban planning strategies. Based on taxi trips, this paper compares spatial interactions between the Xudong Commercial District and the Zhongnan Road Commercial District in Wuhan, respectively, with other functional districts. Firstly, the spatial interaction based on taxi trips between these two commercial districts, acting as either the origins or destinations for taxi trips, with various functional districts was visualized. Subsequently, we analyzed the intensity and distance of the spatial interactions based on taxi trips during each typical time period (8:00-9:00, 18:00-19:00, and 20:00-21:00 on weekdays; and 9:00-10:00, 19:00-20:00, and 22:00-23:00 on weekends). These results can serve as reference for further research on the influence range, development planning, and transportation optimization of these commercial districts.

Mobility knowledge graph: review and its application in public transport

AbstractUnderstanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various domains but has limited applications in urban mobility. This paper aims to address this gap by reviewing existing KG studies, introducing the concept of a mobility knowledge graph (MKG), and proposing a general learning framework to construct MKG from smart card data. The MKG represents hidden travel activities between public transport stations, with stations as nodes and their relations as edges. Two decomposition approaches, rule-based and neural network-based models, are developed to extract MKG relations from smart card data, capturing latent spatiotemporal travel dependencies. The case study is conducted using smart card data from a heavily used urban railway system to validate the effectiveness of MKG in predicting individual trip destinations. The results demonstrate the significance of establishing an MKG database, as it assists in a typical problem of predicting individual trip destinations for public transport systems with only tap-in records. Additionally, the MKG framework offers potential for efficient data management and applications such as individual mobility prediction and personalized travel recommendations.

1383. Malaria Prophylaxis Use In a Travel Clinic, How Are We Doing?

Abstract Background Malaria chemoprophylaxis is indicated for travelers going to geographic areas designated as endemic for malaria. Often persons traveling to their native countries where malaria is present consider themselves immune and decline chemoprophylaxis. This study aimed to evaluate malaria chemoprophylaxis utilization in a travel clinic and whether it is correlated with the patient’s native place. Methods A retrospective study of patients attending a travel clinic between 2021 to 2023. Data were extracted on demographics, trip destinations, trip duration, and, malaria chemoprophylaxis prescribed. Pharmacies were contacted to verify prescription filling. Patients admitted with malaria were analyzed at the same time period. Comparison between groups was made using a chi-square test. p < 0.05 is considered significant. Results One hundred patients were included. Median age 55.5 years (21-80). 62 % (62) were females while 38% were males. There were ninety-four 94 travelers to Africa, 6 to South America, and 1 to Asia. Of the travelers, 44 were US natives while 56 were non-US natives. The median trip duration was 14 and 15 days among US and non-US natives respectively. All 100 patients received prescriptions for malaria prophylaxis: Atovaquone/Proguanil was prescribed to 94, while 6 were given mefloquine. Of the 100 prescriptions, 58 were filled. Of the 44 travelers who were US natives,33/44 (75 %) filled their prescriptions. Among the 56 non-US natives, 25/56 (44 %) filled prescriptions, p -0.002. During the study period, 10 patients (6 females and 4 males) were admitted with a diagnosis of malaria in returning travelers, median age of 54.5 years. The median trip duration was 19 days. All were non-US natives returning from Africa (Nigeria, Ghana, Guinea, and Mali). Only 1/10 patients took mefloquine chemoprophylaxis. All the patients were treated successfully without sequelae. Conclusion In our travel clinic malaria chemoprophylaxis was prescribed to all patients as indicated, US native patients filled their prescriptions significantly more than nonnative patients. Malaria in returning travelers was diagnosed exclusively in non-US natives who did not take malaria chemoprophylaxis. Disclosures All Authors: No reported disclosures

Trip purpose prediction using travel survey data with POI information via gradient boosting decision trees

AbstractAt present, data obtained from the Global Positioning System (GPS) is significantly valuable in mobility research. However, GPS‐based data lacks include trip purpose information. Consequently, many researchers have endeavoured to predict or impute these missing attributes. Existing studies have focused on constructing more features to improve prediction accuracy, but paid less attention to the model's applicability and transferability. In this study, five trip purposes are extracted, including education, recreation, personal, shopping, and transportation, from Chengdu Household Travel Survey (HTS) data. The individual and trip characteristics that are common and can be easily derived from GPS data are carefully selected and extracted. Point of Interest (POI) data of the trip destination are also collected to enhance input characteristics. To obtain more accurate results, an ensemble learning model, Gradient Boosting Decision Trees (GBDT), is employed to predict trip purposes. grid search and cross‐validation techniques are used to optimize the hyper‐parameters. Empirical results show that the proposed model achieves 0.788 accuracy, which is 22.17%, 14.53%, 10.36%, and 6.77% higher than Multinominal Logit (MNL), Artificial Neural Network (ANN), Random Forest (RF), and Deep Belief Network (DBN), respectively. It is also found that although increasing trip features improve the model's accuracy, it simultaneously impairs model's transferability and generalizability.

Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message

In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, inadequate accuracy, and a lack of individualization, thwarting the effective distribution of meteorological insights and inhibiting the fulfillment of personalized forecast demands. Addressing these concerns, our study proposes a personalized weather forecasting approach that harnesses machine learning techniques and leverages the 5G messaging platform. By amalgamating projected user travel data, we augment personalized weather reports and extend user coverage to achieve tailored, timely, and high-quality weather services. Concretely, our research commences with an extensive analysis of large-scale user travel behavior data to extract pertinent travel attributes. Subsequently, we construct a user’s future location prediction model—dubbed the Loc-PredModel—by employing the Extreme Gradient Boosting (XGBoost) algorithm to forecast users’ trip destinations and arrival times. Anchored in the anticipated outcomes of user travel behavior, personalized weather data reports are formulated. Experimental results underscore the Loc-PredModel’s remarkable predictive prowess, demonstrating a root mean squared error (RMSE) value of 0.208 and a coefficient of determination (R2) value of 0.935, affirming its efficacy in prognosticating users’ trip destinations and arrival times. Furthermore, our 5G message-driven platform, rooted in intelligent personalized meteorological services, underwent testing within Chengdu city and garnered positive user feedback. Our research effectively surmounts the limitations of conventional weather forecasting platforms by furnishing users with more precise and customized weather information predicated on behavioral analysis and the 5G information ecosystem. This study not only advances the theoretical groundwork of intelligent meteorology but also offers invaluable insights and guidance for future advancement. By providing users with a more personalized and timely intelligent meteorological service experience, our approach exhibits transferability, with the research methodology and model potentially extendable nationwide or even on a larger scale beyond the study’s Chengdu-based scope.

Barriers to Transportation in Rural Communities: Perspective of Older Adult Users.

Rural older adults who require transportation assistance face more challenges than their urban counterparts. By focusing on a historically underserved population, this study examined specific barriers from the perspective of older adult users and explored potential policy and technology solutions. A cross-sectional study was conducted throughout rural Wisconsin to identify specific barriers to transportation, uncover personal factors associated with identified barriers, and measure causal relationships between identified barriers and travel satisfaction. A total of 580 older adult respondents from 92% of rural counties across the state provided clear answers regarding specific transportation barriers. Of these, 67.6% identified at least one barrier, but only 12.8% had stopped using transportation due to any identified barriers. Top barriers to accessing transportation included service hours, service areas, trip destinations, getting in/out of service vehicles, service reservations, and operational scheduling. Although specific barriers were associated with different sociodemographics, trip purposes, and frequency of transportation usage, logistic regression findings suggested that concerns about service hours, service areas, and service reservation were the only 3 major determinants driving rural older adults' attitudes toward transportation usage. Rural older adults relying on transportation assistance have demonstrated diverse needs and constraints. Rural transportation could provide better support by extending service availability in terms of hours, areas, and destinations, improving door-to-door accessibility by providing "arm through arm" services, enhancing service responsiveness and reliability via age-friendly technology solutions, and implementing a sliding scale subsidy program that takes income level and trip frequency into account.

Assessing Korean writing ability through a scenario-based assessment approach

Advocating for measuring a broadened construct of writing ability, scenario-based assessment (SBA) has recently been used to assess writing ability. While SBA for writing has been explored in ESL contexts (e.g., Banerjee, 2019; Purpura, 2021), it has been rarely used or examined in the context of languages other than English, especially those that are typologically distant from English, such as Korean. This study explores the feasibility of designing a scenario-based Korean writing assessment (K-SBA). A pilot study was conducted with 51 participants from a Korean as a foreign language program at a US university. Through a goal-oriented scenario of a study abroad program in South Korea, examinees were presented with a collaborative problem-solving task where they were expected to learn about two potential class trip destinations and write a summary of the pros and cons of each destination based on what they learned. The results indicated that the K-SBA was psychometrically sound, providing adequate evidence of reliability. It elicited construct-relevant performances reflecting features unique to the Korean language, such as sociolinguistic competence through the use of honorifics or rhetorical control through a range of cohesive devices. Additionally, variations in examinee performances were observed across the different course levels.