Trip Planning Research Articles

A user perspective on the avalanche danger scale – insights from North America

Abstract. Danger ratings are used across many fields to convey the severity of a hazard. In snow avalanche risk management, danger ratings play a prominent role in public bulletins by concisely describing existing and expected conditions. While there is considerable research examining the accuracy and consistency of the production of avalanche danger ratings, far less research has focused on how backcountry recreationists interpret and apply the scale. We used 3195 responses to an online survey to provide insight into how recreationists perceive the North American Public Avalanche Danger Scale and how they use ratings to make trip planning decisions. Using a latent class mixed-effect model, our analysis shows that 65 % of our study participants perceive the avalanche danger scale to be linear, which is different from the scientific understanding of the scale, which indicates an exponential-like increase in severity between levels. Regardless of perception, most respondents report avoiding the backcountry at the two highest ratings. Using conditional inference trees, we show that participants who recreate fewer days per year and those who have lower levels of avalanche safety training tend to rely more heavily on the danger rating to make trip planning decisions. These results provide avalanche warning services with a better understanding of how recreationists interact with danger ratings and highlight how critical the ratings are for individuals who recreate less often and who have lower levels of training. We discuss opportunities for avalanche warning services to optimize the danger scale to meet the needs of these users who depend on the ratings the most.

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.

Explicating affordances in travel information search: investigating device use in relation to goals and personal characteristics

Despite increasing attention to technology development and its impact on travel information search, research about information search behaviours across multiple devices (i.e., smartphones, laptops, tablets, and desktop computers) has received limited attention. Guided by technology affordance theory, this paper examines the link between device use and information search goals, and the relationship between personal characteristics and device use. The study is based on two independent studies. Data were collected using a mixed-methods approach utilising an online survey and an online trip planning diary. The findings revealed that information searchers were essentially multi-device users and switched between devices based on their information search goals. Moreover, personal characteristics such as demographic characteristics, experience and personality influenced device use. The findings of the research shed light on the elusive concept of affordance to enhance the understanding of the complexity of device usage during travel information search.

Social-Aware Long-Distance Trip Planner for Electric Vehicles Using Genetic Algorithm

Abstract In the past decade, the number of battery electric vehicles (BEVs) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes genetic algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is first tested numerically based on two real-world routes. Then, Monte Carlo simulations are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world tests are conducted to further validate the accuracy of the mixed-integer programming model.

Efficient Top-k Matching for Publish/Subscribe Ride Hitching

With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called <i>ride hitching</i> , which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel type of top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> subscription queries called <i><u>T</u>op-<u><inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq3-3124232.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></u> <u>R</u>ide <u>S</u>ubscription (TkRS)</i> query, which continuously returns the best <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ride orders that match drivers’ trip plans to them. We propose two efficient algorithms to enable the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> result maintenance. We also design a novel hybrid grid index and a two-level buffer structure to efficiently track the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> results for all <i>TkRS</i> queries. Finally, extensive experiments on real-life datasets suggest that our proposed algorithms are capable of achieving desirable performance in practical settings.

AI-POWERED RECOMMENDATION SYSTEMS FOR TRIP PLANNING

Trip planning can be difficult due to the overwhelming amount of information available and the time-consuming process of finding the best deals on flights, accommodation, and activities, making it hard for individuals to make informed decisions and plan an itinerary efficiently. This survey paper's goal is to explore how artificial intelligence is used in web applications for trip planning. The paper will conduct a comprehensive review of existing approaches and technologies for trip planning and will evaluate the use of natural language processing and the GPT-3 language model in generating personalized itineraries. The survey will also examine the integration of flight and accommodation booking APIs in trip planning web applications and the impact on user satisfaction and efficiency. The results of this survey will provide valuable insights into the current state of the field and will help to inform the development of future trip planning web applications that use artificial intelligence to provide a more efficient and enjoyable trip planning experience for users.

Influences of Gamification on Repurchase Intention and Intrinsic Motivations in Egyptian Hotels and Travel Agencies: The Mediating Role of Customer Engagement

ABSTRACT The current study aims to identify the direct influence of gamification features performed by hotels& travel agencies on customer/traveler repurchase intention and intrinsic motivations. Indirect connections are also observed by highlighting the mediating role of customer engagement. A quantified survey was employed to gather data from a sample of pertinent hotel guests (n = 330) and tourists/travelers of travel agencies (n = 311), with a total of 641 valid responses. Structural equation modeling (PLS-SEM) was used for data examination. The study findings have revealed that adopting gamification has positively affected repurchase intention, customer engagement, and intrinsic motivations. In addition, customer engagement has partially mediated the relationship between gamification; repurchase intention, and intrinsic motivations. Moreover, intrinsic motivations acted as a consequence of repurchase intention. The findings led to various theoretical and managerial implications for scholars, policymakers, trip planners, hoteliers, and travel marketers.

The meanings of friluftsliv in Physical Education Teacher Education

ABSTRACT The aim with this article is to contribute knowledge about the meanings of friluftsliv in educational practice. This is done by investigating what friluftsliv becomes in the ongoing practices of PETE in Sweden and how these meanings are established in the studied activities. The empirical material consists of different kinds of material from friluftsliv activities in PETE programmes: study guides, field trip plans, students’ vlogs from overnight stays outdoors, video recordings from two longer field trips, audio recordings from evening seminars during the field trips and students’ written reflections after them. In order to identify meanings of friluftsliv and how these are established we used a transactional analysis based on Dewey. Five different ways of what friluftsliv becomes in PETE practice were identified in this study. The different meanings reflect the complex picture of contradictions and lack of common ground for the content and motives identified in the outdoor education field. Our study also confirms how the meaning of friluftsliv as skills is established by putting up tents, lighting fires, building wind shelters and so on, and how this contributes to a focus on the instrumental values in PETE. On the other hand, our study shows that other meanings of friluftsliv are established in the ongoing practice, where more intrinsic values are at stake even if they are often overshadowed in PETE practice. In conclusion the results also point to the potential of fritluftsliv in terms of a suggested move away from an activity-based personal and social development discourse in favour of experiences of educating for environmentally sustainable human-nature relations. The challenge is how to make these experiences educational in PETE and how to guide students in transforming experiences in more exclusive friluftsliv into pedagogical competence as future teachers using friluftsliv for different purposes in school PE.

Graph Neural Network for Traffic Forecasting: The Research Progress

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.

On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model

Travel time is an essential indicator for trip planning, transportation service planning, and operation. This study aims to propose a novel Burr mixture autoregressive (BMAR) model for the intermediate-to-long term period of bus section travel time prediction, which is useful for bus service and schedule planning. The BMAR model exhibits greater flexibility, allowing it to effectively capture the multi-peak and non-peak, non-linear with heteroscedasticity characteristics of travel time. The model is trained and well-validated with 6-month bus section travel time data collected via the automatic vehicle location system. Results show that the BMAR model gives promising results in travel time point and interval prediction, especially for the higher degree of variability and irregular pattern of travel time observed on urban roads and highways. The reliability ratio index derived from the BMAR model could be used to measure the bus service reliability and aid in the bus scheduling of maintenance activities.

Smartphone App Usage Patterns for Trip Planning Purposes and Stated Impacts in the City of Bhopal, India

With the considerable growth in the information and communication technology (ICT), several smartphone-based mobility platforms have already sprung up and they have the potential of transforming the mobility ecosystem completely. However, there is close to no knowledge available about how ICT-based smartphone apps meant for day-to-day trip planning tasks are being used across various user groups and how they influence travel outcomes, especially in Indian cities. Therefore, this study is an effort to close this gap by gathering data from the city of Bhopal and carrying out an exploratory statistical analysis on the usage of smartphone apps for different types of trip planning purposes, as well as their influence on travel outcomes. The study provides empirical evidence of relationships between smartphone app usage for trip planning (such as departure time, destination selection, mode selection, route selection, communicating and coordinating trips, and performing tasks online rather than visiting) and the resulting travel outcomes, such as kilometres travelled by vehicle (for purposes, such as work/education, shopping, and recreation), social gatherings, new destinations, and group trips. The chi-square test has been used to test and interpret several socioeconomic variables that could influence this relationship, such as gender, age, income, etc. The study’s findings provide important behavioural insights that may be useful in policy discussions.

Mobile Application Development to Solve Vehicle Routing Problems in Marketing or Tour Trip Planning

The vehicle routing problem (VRP) is a combinatorial optimization problem faced by transportation services related to pick up or delivery, such as industrial raw materials distribution, tour and travel, or travel routing problems in general. VRP is an NP-hard problem where the higher the dimensions of the problem will have a higher computational complexity. Without realizing it, VRP problem are often encountered every day. Therefore, it will be very useful if VRP solver is implemented in mobile application media. So, the aim of this work is developing a mobile application to get the shortest path and minimal cost in VRP problem. It is integrated by both Mapbox API and Google Maps API to get a real distance for modeling problem. The result show that the developed application can run well in all possibility condition.

Investigating the credibility of online travel reviews adoption in trip planning intention among Indian travellers

Travelers evaluate online travel reviews usage through their argument quality and credibility of the source. As a result, travel marketers such as hoteliers, destination marketers and travel agency websites managers should set up mechanisms to assess tourists' concern to avoid unfavorable experience from social media reviews. Accordingly, this study aims to develop a scale and validate the modified Information Adoption Model (IAM) in the tourism context, while studying the impact of online review usefulness on its adoption. Secondly, the mediation role of travelers' attitude toward online reviews between review usefulness and its adoption is determined. Further, the credibility of online travel review adoption on the behavioral intentions of travelers has been assessed. This paper adapts the modified IAM given by Abedi et al. (2019) to develop scale and provide a comprehensive model in the tourism context. The study used 284 travelers who use online travel reviews from social media for their trip planning to evaluate the proposed model through SmartPLS software. The results show that online travel review usefulness significantly affects online travel review adoption. Secondly, the relationship between online review usefulness and its adoption is fully mediated by the mediating variable (attitude towards online travel reviews). Hence, this empirical paper intends to add to the research on online travel review adoption by travelers for their trip planning, considering the forwarding of information as one of the behavioral intentions along with purchase intentions, which depicts the novelty of this paper.

User-focused Trip Planner with an Expanded Suite of Features: Riga Case Study

The research aims to develop a sustainable and personalised trip planner concept for Riga city, which will add information covering safety, service quality, and environmental aspects and provide recommendations for building an optimal travel plan based on the ranking results. The set of route attributes, which cover transportation service quality indicators, was identified and includes time, price, accessibility, reliability, comfort, safety, and environmental aspects. One of the user-focused interactions within the trip planning exercise is to introduce the possibility for travellers to choose weights for selected route attributes. Then the different possible route realisation rates using the multi-attribute decision-making methodology. The Technique for Order Preferences by Similarity to an Ideal Solution method was used for this case study.

Electric Vehicle Charge Planning by Deep Reinforcement Learning

In this paper, we study the problem of planning the charging of an electric vehicle (EV) for long distance trips. In particular, a dedicated tool is needed in order to suggest the driver the best traveling and charging plan based on her/his preferences and on the available charge infrastructures along the route. An ad-hoc tool is developed using Matlab and Simulink software in order to model the EV energy charging/discharging, and to optimize the trip plan based on three alternative goals: minimizing the travel time; minimizing the charging cost; optimizing travel time and cost. The proposed model is based on the use of Deep Reinforcement Learning (DRL) algorithm in which an agent is rewarded or penalized while learning the best charging options along the route according to the three above goals. In particular, in a dynamic energy market context, the model is designed to be robust at energy price variations and at cruise speed variations as well. In addition, a dashboard is designed and developed to provide an user-friendly interface to set the EV and trip parameters and to monitor the trip planning results. A case study demonstrates the application of the proposed tool.

A Multi-Objective Situational Awareness Approach for Distribution Networks Using Drones

Unmanned aerial vehicles (drones) have gained a high level of practical confidence due to their ease of use in different applications as well as their advantages such as flexibility, ease of operation and low cost. In this paper, we propose the use of drones equipped with onboard sensors for monitoring the status of electrical distribution networks. Specifically, we formulate a multi-objective optimization approach for distribution networks’ situational awareness, which aims to minimize two conflicting objectives. These are the total annual cost and loss of observability. The proposed approach decides the optimal number of drones, their batteries, the nodes to be visited, and the trip plan. In order to obtain a set of Pareto solutions, we utilize the non-dominated sorting genetic algorithm (NDSGA) in conjunction with branch-and-bound algorithm to minimize both objectives. To validate the performance of the proposed approach, we applied it to a practical distribution system under different scenarios of monitoring frequency and working hours. The obtained results prove the effectiveness of the proposed approach.

Placing users at the core of mobility products development: the case of VoxPop in Lisbon

Carris, the bus and tram operator of Lisbon, identified the need to deliver improved information for people with reduced mobility, which would enable them to better plan and navigate their trips involving the use of buses or trams. In the frame of the Voxpop project, a wide reaching project to promote a better use and sharing of mobility data, Carris and other project partners carried out a study on the information needs of people with reduced mobility which led to the design of features to be included in trip planning and navigation applications that deliver on those needs.This document presents the objectives, methodology, developments and results of this project which will lead to the testing and delivery of new information features to make mobility more accessible and convenient to people with reduced mobility.

Commute Booster: A Mobile Application for First/Last Mile and Middle Mile Navigation Support for People With Blindness and Low Vision.

People with blindness and low vision face substantial challenges when navigating both indoor and outdoor environments. While various solutions are available to facilitate travel to and from public transit hubs, there is a notable absence of solutions for navigating within transit hubs, often referred to as the "middle mile". Although research pilots have explored the middle mile journey, no solutions exist at scale, leaving a critical gap for commuters with disabilities. In this paper, we proposed a novel mobile application, Commute Booster, that offers full trip planning and real-time guidance inside the station. Our system consists of two key components: the general transit feed specification (GTFS) and optical character recognition (OCR). The GTFS dataset generates a comprehensive list of wayfinding signage within subway stations that users will encounter during their intended journey. The OCR functionality enables users to identify relevant navigation signs in their immediate surroundings. By seamlessly integrating these two components, Commute Booster provides real-time feedback to users regarding the presence or absence of relevant navigation signs within the field of view of their phone camera during their journey. As part of our technical validation process, we conducted tests at three subway stations in New York City. The sign detection achieved an impressive overall accuracy rate of 0.97. Additionally, the system exhibited a maximum detection range of 11 meters and supported an oblique angle of approximately 110 degrees for field of view detection. The Commute Booster mobile application relies on computer vision technology and does not require additional sensors or infrastructure. It holds tremendous promise in assisting individuals with blindness and low vision during their daily commutes. Clinical and Translational Impact Statement: Commute Booster translates the combination of OCR and GTFS into an assistive tool, which holds great promise for assisting people with blindness and low vision in their daily commute.

Towards Green Freight Transportation Using Train Design Optimization

It is imperative to re-design the freight transport modal mix to ensure a shift from road to rail to limit energy consumption and global GHG emissions. However, one of the main barriers to the shift is the ability of the railways to handle consignments from customers with less than ‘unit’ train loads. In such cases, railways have to combine consignments from different customers to form ‘unit’ trains. Combining consignments is a train design optimization process involving designing a trip plan with the minimum number of trains formed and satisfying a set of conditions. However, manually optimizing train design for high-density freight traffic is challenging and practically impossible. Hence, it is essential to develop an automated train design optimization methodology that railways can quickly implement. Among several conditions of train formation, the two key constraints are the ‘number of work events’ and ‘number of block swaps’. However, the existing literature only considers either one of these two constraints in a single decision-making model. We have proposed a train design optimization method based on a genetic algorithm with a priority generator to simultaneously consider both the above-mentioned constraints. The train design optimization method developed has also been demonstrated using real-life data.

Exploring the antecedents influencing smartphone app use in post-pandemic destination recovery : A case study of Danang FantastiCity adoption

Smart technological apps have been adopted at tourism destinations to facilitate tourists’ information needs and destination experiences. Using such applications becomes even much more essential for solo travelers who are likely to rely on the apps to organize their trip plans. However, little has been known about whether such smart technologies have been tailored in a way that meets tourists’ particular requirements in the situation of post-pandemic recovery. This study aimed to examine tourists’ acceptance and smartphone app use by adopting the extended theory of acceptance and use of technology (UTAUT2) in a post-COVID-19 context. The two critical constructs including perceived convenience and context use are added to the theoretical framework to predict smartphone users’ satisfaction, travel experience satisfaction, and reuse intention. More specifically, a relevant smartphone app was selected to investigate the research phenomenon. The PLS-SEM method was utilized in this study to test the proposed research framework by employing 462 valid questionnaires from solo tourists visiting Da Nang City in a post-pandemic context. The findings indicate that using smartphone apps significantly influences the tourists’ satisfaction and intention to seek repetitive technology experiences while visiting a destination. Noticeably, the perceived convenience of technological apps indirectly influenced tourists’ use intention but the effort expectancy and performance expectancy of smartphone apps directly affect their behavioral intention. The study provides several practical implications for smart tourism destinations to tailor smartphone apps in a way that could improve tourists’ destination experience and foster post-pandemic destination recovery more sustainably.