Trip Planning Research Articles

Customized Trip Planning Using Modified Clustering Algorithm with Personalized Points of Interests and Association Rule Generation

Data mining technologies are widely used to mine the interesting patterns and data from the huge collections of data. This work is intended to develop and implement an efficient, economic and customized software for trip plan for final customers. The main functions of this software are listed in the following. In this itinerary planning system, the users specify source, destination, days of travel and budget, then the system will generate a set of Points Of Interests (POIs) for a given destination, so the users can select their customized points of interests and can plan travels with k-days itinerary. All POIs are considered and ranked based on the users preference. Nearby POIs are put in the same day itinerary for the effective utilization of the plan. Inconsistent and incomplete data are removed and the list of POIs will be generated using the Clustering technique so that users can select their interested POIs. Shortest path will be displayed once they select their customized POIs. Frequent visits and suggestions for a particular destination are listed to the customers using Association rule mining technique. Clustering takes major advantage to compute the data and it has fast processing time. Each cluster holds the number of countries with the relevant information and the list of points of interests for a specified cost. With the help of shortest path, users can understand and view what is the travel distance between the POIs. Users can delete the POIs if they have relatively long distance to visit and travel. Using the association rule mining technique users can know the frequent visits of a particular destination, and it helps the system to predict the travelling patterns of the people. In this work, a customized itinerary planning service is provided, and the users can plan for multiday itineraries, designed effectively by managing the cost and time to the users.

협업적 추천 기반의 여행 계획 시스템

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as ‘Tourist Trip Design Problems’, and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters’ specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user’s preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3(PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users’ preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

Multi-objective re-synchronizing of bus timetable: Model, complexity and solution

This work is originally motived by the re-planning of a bus network timetable. The existing timetable with even headways for the network is generated using line by line timetabling approach without considering the interactions between lines. Decision-makers (i.e., schedulers) intend to synchronize vehicle timetable of lines at transfer nodes to facilitate passenger transfers while being concerned with the impacts of re-designed timetable on the regularity of existing timetable and the accustomed trip plans of passengers. Regarding this situation, we investigate a multi-objective re-synchronizing of bus timetable (MSBT) problem, which is characterized by headway-sensitive passenger demand, uneven headways, service regularity, flexible synchronization and involvement of existing bus timetable. A multi-objective optimization model for the MSBT is proposed to make a trade-off between the total number of passengers benefited by smooth transfers and the maximal deviation from the departure times of the existing timetable. By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex. Therefore, we design a non-dominated sorting genetic (NSGA-II) based algorithm to solve this problem. Numerical experiments show that the designed algorithm, compared with enumeration method, can generate high-quality Pareto solutions within reasonable times. We also find that the timetable allowing larger flexibility of headways can obtain more and better Pareto-optimal solutions, which can provide decision-makers more choice.

Guest Editorial: Environmental Multimedia Retrieval

Guest Editorial: Environmental Multimedia Retrieval

Promoting field trip confidence: teachers providing insights for pre-service education

Pre-service teachers need experiences in practical matters as a part of field trip preparations programmes. For 14 years, a private, non-profit university in Turkey has involved pre-service teachers in field trip planning, implementation and evaluation. A programme assessment was conducted through a case study to examine the long-term effects of pre-service field trip preparation. Through a survey created for the study, teachers shared their field trip activities and reported confidence levels. The survey was administered to 44 alumni of the biology education department with a response rate of 72.7% (N = 32). This study will help researchers learn which programme areas need to be improved and can serve as a model for other institutions interested in evaluating field trip preparation programmes.

Monochromatic and bichromatic reverse top-k group nearest neighbor queries

The Group Nearest Neighbor (GNN) search is an important approach for expert and intelligent systems, i.e., Geographic Information System (GIS) and Decision Support System (DSS). However, traditional GNN search starts from users’ perspective and selects the locations or objects that users like. Such applications fail to help the managers since they do not provide managerial insights. In this paper, we focus on solving the problem from the managers’ perspective. In particular, we propose a novel GNN query, namely, the reverse top-k group nearest neighbor (RkGNN) query which returns k groups of data objects so that each group has the query object q as their group nearest neighbor (GNN). This query is an important tool for decision support, e.g., location-based service, product data analysis, trip planning, and disaster management because it provides data analysts an intuitive way for finding significant groups of data objects with respect to q. Despite their importance, this kind of queries has not received adequate attention from the research community and it is a challenging task to efficiently answer the RkGNN queries. To this end, we first formalize the reverse top-k group nearest neighbor query in both monochromatic and bichromatic cases, and then propose effective pruning methods, i.e., sorting and threshold pruning, MBR property pruning, and window pruning, to reduce the search space during the RkGNN query processing. Furthermore, we improve the performance by employing the reuse heap technique. As an extension to the RkGNN query, we also study an interesting variant of the RkGNN query, namely a constrained reverse top-k group nearest neighbor (CRkGN) query. Extensive experiments using synthetic and real datasets demonstrate the efficiency and effectiveness of our approaches.

Terrain Classification of Norwegian Slab Avalanche Accidents

It is difficult to rely on snow conditions, weather, and human factors when making judgments about avalanche risk because these variables are dynamic and complex; terrain, however, is more easily observed and interpreted. Therefore, this study aimed to investigate (1) the type of terrain in which historical fatal snow avalanche accidents in Norway have occurred, using the Avalanche Terrain Exposure Scale (ATES) and (2) how many accidents could have been prevented if the recreationists had followed the actions suggested by the Avaluator Trip Planner (ATP). The ATES classifies terrain as simple (Class 1), challenging (Class 2), and complex (Class 3), whereas the ATP uses these classifications in relation to the danger rating to suggest normal caution, extra caution, and areas in which people are not recommended to travel. We investigated 30 fatal slab avalanche accidents in Norway over a 10-year period (2005–2014) involving 42 deaths. According to the ATES, 77% of the accidents occurred in complex terrain and 23% occurred in challenging terrain. According to the ATP, 97% of the accidents could have been prevented if the recreationists had followed the recommended actions (not going under conditions requiring extra caution or when travel is not recommended). Our results indicate that the ATES and ATP are practical tools that help recreationists with trip planning in the type of terrain that suits their level of experience and knowledge. These tools can be valuable to novices by helping them to acquire experience in recognizing relevant terrain features that might compromise their safety and interfere with their plans. We conclude that most avalanche accidents occurred in complex terrain and that most of the accidents could have been prevented by using the ATP. Thus, for novices and advanced beginners, learning to use the ATES and ATP provides a sound basis for safe travel and gaining experience in avalanche terrain.

Travel Behavior Profiling Using a Trip Planner

The article addresses a solution based on an empirical example proposed for planning of transport system elements through application of a sufficiently extensive trip planner. The tool described, i.e. GTPlanner, developed under implementation of the international program of ERANET Transport III, was provided with an additional feature allowing for the queries submitted by travelling persons to be archived and individual needs of travellers to be profiled within selected areas. The added value connected with the foregoing is also that the problem of transport system planning was perceived from a different perspective, assuming that actual preferences of travelling persons (rather than those imposed by the existing system) should provide the grounds for establishing orientations in terms of organizational and technical changes to be introduced into the transport system of the chosen territory. Besides the information function, GTPlanner also performs an educational role, as it highlights the impact of individual travelling modes on the environment, consequently fostering desirable travel behaviors.

An Impact of Social Media and Online Travel Information Search in Vietnam

An Impact of Social Media and Online Travel Information Search in Vietnam Abstract Social media is increasingly prevalent among and beneficial for travelers. A large percentage of travelers use search engines when it comes to making travel-related decisions. In addition, social media websites which appear on the search results are becoming preferable information sources to travelers. In Vietnam, numerous customers (83%) use search engines before making ground travel purchase decisions and over one fifth of the population, which is equivalent to 19.6 million, is currently Facebook users. Therefore, the goal of this research is to investigate the role of social media presented by a search engine within Vietnam trip planning context. The research employs a set of selected searching keywords together with ten Vietnam urban destination names in order to unveil the information searching for travel planning. The findings show to some extent that social media websites can help display searching results, and how certain types of social media websites are distributed across the result pages in online tourism domains. Implications for online marketers targeting Vietnamese audience are also mentioned.

Exploring Children's Travel to School in Upgraded Informal Settlements: A Qualitative Case Study of Ezbet El-Haggana

The majority of residential settlements in Greater Cairo are informal, yet little is known about travel behavior within its informal settlements. The present paper explores aspects of travel behavior in one of Egypt's largest and most dense informal settlements, Ezbet El-Haggana, through a qualitative study of children's travel to school. The key research question was: How do children interact with transport options to access schools in a dense informal settlement in Egypt? Ten Focus Group Discussions (FGDs) for this research were conducted, 4 with mothers, 5 with children, and 1 with transport service providers. Design of FGDs leveraged existing behavioral theories commonly used in exploring travel behavior and also included photo-elicitation. Qualitative data was also complimented with field observations and interviews with community leaders and government officials. Findings highlighted the high degree of children's independent mobility (CIM) and the diversity of modes of transport used and multitude of trip-to-school arrangements facilitated by community collaboration and entrepreneurial initiatives standing in contrast to current mainstream knowledge about trips to school in formal urban areas. Another distinct feature is prevalence of strong socio-cultural factors strongly inhibiting the consideration of cycling as a mode of transport, more so for older females, but also for all other segments. Implications for accompanying culturally-sensitive gender-specific soft measures that are needed together with the planned hard interventions are discussed in order to (a) maintain existing elements of sustainable travel behavior observed today (mode choices and efficient trip planning) sustaining their relatively low carbon footprint, and (b) mainstream underutilized modes such as cycling and Walking School Buses (WSB), and (c) inhibit adoption of unsustainable travel behavior in the future as found in the formal counterpart of the city that informal settlements are converging with.

Retrip: A Learning Environment for Augmenting and Presenting Knowledge from Field Trip

In this work, we have focused on experiential learning in field trip. In the field trip, we think experiential knowledge from field trip could be obtained, augmented, and strengthened via the following three phases: pre-trip (planning), trip (investigation), and post-trip (augmentation and presentation). However, learners often fail to investigate historical and cultural spots as planned due to some accidents. In addition, their interests in the spots could change during the trip, and then the new goals may arise. So, it is not easy for the learners to obtain their experiential knowledge as expected. In this paper, we demonstrate an experiential learning environment on iPad called Retrip. Retrip provides the learners with several scaffolds for seamlessly obtaining, augmenting, and presenting their experiential knowledge from trip planning to presentation.

ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas.

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.

An online tool for obesity intervention and public health.

BackgroundThough the United States of America (U.S.A.) obesity rate shows signs of leveling off, rates remain high. Poor nutrition contributes to the development of obesity, and physical inactivity is an important cause of numerous diseases and directly linked to obesity. Efforts to improve diet, increase physical activity and pursue other behavioral changes seem imperative. However, the effective management of intervention strategies for large number of participants are challenging because services in primary, secondary, and tertiary cares are often under-resourced, relatively uncoordinated with other parts of the health system. It is thus necessary to have accompanying intervention strategies that can be carried out at population level. In this paper, we describe an online intervention tool designed for the Obesity Prevention Tailored for Health II project to help achieve such goals.ResultsThe first part of the online tool locates healthy food stores and recreational programs within a specified distance of a participant’s home or a place of interest. The food environments include fruit & vegetable stores, farmers’ markets and grocery stores, and the companying popup window shows the street address and contact information of each store. The parks and recreational programs are displayed on names of park or recreational program, types of program available, and city each amenity belongs to. The tool also provides spatial coverage of vegetation greenness, air pollution and of historical traffic accidents involving active travel.The second part of the tool provides optimized travel options for reaching various amenities. By incorporating bicycling, walking and public transit into the trip planner, this online tool helps increase active transport and reduce dependence on automobiles. It promotes transportation that encourages safety awareness, physical activity, health, recreation, and resource conservation.ConclusionsWe developed the first Google-based online intervention tool that assists obese and overweight participants in finding food and recreational amenities around locations of interest and identifying optimized routes that fit their personal preferences. This tool can also serve general public and policy makers for education, disease prevention and health promotion.

Segmenting the 'visiting friends and relatives' travel market to a large urban destination: The Case of Nizhni Novgorod, Russia

Abstract This study examines the heterogeneity of the 'visiting friends and relatives' (VFR) travel market with respect to a large urban destination in Russia, including trip planning, travel concerns, on-site destination experience, and post-travel behavior variables. Specifically, the study investigates whether place of residence, accommodation type, and VFR type can serve as segmentation bases for the city's destination management organization. It was found that place of residence and accommodation type best discriminate among VFR tourists. Marketing implications for improving destination experience and communications with tourist segments are discussed.

Personalizing trip recommendations: A framework proposal

Personalized trip planning is a very common problem in tourism domain. There are several studies in this area each one of all aims to provide recommendations based on user preferences. Recommendation engines mostly use two common methods: content based filtering and collaborative filtering. As a combination of these two methods, hybrid approaches are also popular for recommendation systems. This study provides a deep analysis about recent studies in trip recommendation domain. Applied techniques and mentioned methodologies in literature is discussed at all points. Insights about the proposed systems are provided clearly. Besides a literature survey, this study also proposes a novel travel recommender method based on a tourism datasource. A hybrid approach involving demographic, content-based and collaborative filtering techniques are proposed in order to eliminate drawbacks of each approach. Recommendations will be based on many factors including users’ demographic information, past travel locations and favorite seasons. Based on such inputs, recommender engine predicts possible travel locations along with various flight options. Possible challenges and future trends are concluded as a result of this study. Keywords: Recommender systems, trip recommendation, personalized recommendation, information filtering

Multi-modal transportation with multi-criteria walking (MMT-MCW): Personalized route recommender

Existing wayfinding and navigation services are designed primarily for vehicle trip planning. Of these services, currently only a few consider walking for pedestrians, and none is capable of providing multi-modal transportation with walking always as one mode. To fill the gap, multi-modal transportation with multi-criteria walking (MMT-MCW), as a personalized route recommender, such that walking is one mode and is optimized based on traveler's characteristics and criteria, is proposed. The premise of MMT-MCW is based on the observations that: (a) walking can be performed for other purposes besides merely reaching destinations, such as to maintain or improve health; (b) traveler's behavior and physical capabilities play an important role in determining optimal route choices; (c) some location parameters, such as sidewalk slope, may have much more influence on walking than on vehicular modes; and (d) environmental parameters, such as sun exposure, impact walking route choices at a specific location and time. Wayfinding and navigation services equipped with MMT-MCW will be able to provide travelers with routing options where walking is always a part of the route meeting traveler's preferences.

Robust Natural Language Processing for Urban Trip Planning

Navigating large, urban transportation networks is a complicated task. A user needs to negotiate the available modes of transportation, their schedules, and how they are interconnected. In this article we present a Natural Language interface for trip planning in complex, multimodal, urban transportation networks. Our objective is to provide robust understanding of complex requests while giving the user flexibility in their language.We designed TRANQUYL, a transportation query language for trip planning; we developed a user-centric ontology, which defines the concepts covered by the interface and allows for a broad vocabulary. NL2TRANQUYL, the software system built on these foundations, translates English requests into formal TRANQUYL queries. Through a detailed intrinsic evaluation, we show that NL2TRANQUYL is highly accurate and robust with respect to paraphrasing requests, as well as handling fragmented or telegraphic requests.

Travel by public transit to mammography facilities in 6 US urban areas.

We examined lack of private vehicle access and 30 minutes or longer public transportation travel time to mammography facilities for women 40 years of age or older in the urban areas of Boston, Philadelphia, San Antonio, San Diego, Denver, and Seattle to identify transit marginalized populations - women for whom these travel characteristics may jointly present a barrier to clinic access. This ecological study used sex and race/ethnicity data from the 2010 US Census and household vehicle availability data from the American Community Survey 2008-2012, all at Census tract level. Using the public transportation option on Google Trip Planner we obtained the travel time from the centroid of each census tract to all local mammography facilities to determine the nearest mammography facility in each urban area. Median travel times by public transportation to the nearest facility for women with no household access to a private vehicle were obtained by ranking travel time by population group across all U.S. census tracts in each urban area and across the entire study area. The overall median travel times for each urban area for women without household access to a private vehicle ranged from a low of 15 minutes in Boston and Philadelphia to 27 minutes in San Diego. The numbers and percentages of transit marginalized women were then calculated for all urban areas by population group. While black women were less likely to have private vehicle access, and both Hispanic and black women were more likely to be transit marginalized, this outcome varied by urban area. White women constituted the largest number of transit marginalized. Our results indicate that mammography facilities are favorably located for the large majority of women, although there are still substantial numbers for whom travel may likely present a barrier to mammography facility access.

Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers’ trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers’ choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers’ opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers’ preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers’ preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers’ behavioral tendencies concerning toll road utilization in support of traffic information dissemination.

The influence of motivations in tourists’ involvement

One of the main aspects in the consumer behaviour is the concept of involvement because it influences the decision rules used by tourists to reach the final decision. An empirical study of 600 international tourists reveals that motivation to relax, influences tourists’ involvement with the trip. A structural model shows that when tourists are motivated to relax they get directly more involved with their trip (pleasure and information seeking). The motivation to relax also influences indirectly the tourist involvement with the evaluation and quality perception of the trip through its influence on involvement with the trip planning. Discussion centres on the implications of this model to theory and management specifically to the development of tourism and services strategies.