An Efficient Construction of Itinerary Planning for Multi-Users D.Rajasekar1 1
Sri Krishna College of Engineering and Technology, Computer Science and Engineering, rajasekardcse@gmail.com
C.Dhanamani2
2
Sri Krishna College of Engineering and Technology, Computer Science and Engineering, cdhanamani@skcet.ac.in
Abstract— Itinerary planning is the important process for the travelling agencies which need to be done carefully to satisfy the requirements of the users. The satisfaction level of users can be increased by creating the itinerary planning in the customized manner by gathering the details from the users itself. There will be more burden of processing the large search space will occur where the number of POI present will be large. The time complexity and cost of processing those requests will be increased due to the large search space. And also it will be more difficult when there is a need of creating an similar type of itinerary plan to the multiple users. To overcome this problem in this work multi-user itinerary plan generation was proposed which intends to generate the multi-user customized itinerary plan by using the fuzzy-c-means clustering algorithm. In this approach, membership values are defined for clustering process. Based on the membership values of users we are clustering the users. From the experimentation result, the proposed system is used to reduce the search complexity as well as time complexity of the system. Index Terms— Point of Interest, MapReduce, Fuzzy C-Means Clustering, Itinerary plan, multiday itinerary —————————— ——————————
1 INTRODUCTION
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OUTE planning is a most time consuming process in the real world which need to take under consideration more. The itinerary planning for the popular tourist place will consumes more time and also it needs to be analyzed the behavior of a number of places to bring forth a better itinerary planning. The itinerary planning generated should also include the details of interest mentioned by the users within the budget and time mentioned by the users to improve the user satisfaction level. Thither are many online services are available online which offers just the packaged itinerary planning to the users with their fixed costs and monetary value. Merely it is having more limitations like, it cannot meet the user’s needs and their budget and besides it will not give timely assistance to the users. The individual who wants to go tourist place in the short time manner cannot make use of the already available built-in itinerary packages. The other drawback present in this traditional itinerary planning, construction is the engagement of the brokers who will behave as an average between the users and the travelling agency to make the route planning. The security need to be ruled out in order to provide the privacy to the users. The automatic construction of the itinerary planning will eliminate the need of users to discover their personal ————————————————
Rajasekar is currently pursuing masters degree program in computer science and engineering in Sri Krishna College of Engineering and Technology, India, PH-9715506445. E-mail:rajasekardcse@gmail.com Dhanamani is currently working as Assistant Professor, computer science and engineering in Sri Krishna College of Engineering and Technology, India,E-mail: cdhanamani@skcet.ac.in
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involvement to the unknown users. Constructing the itinerary planning with the user satisfaction is the greatest problem in the real world which contributes to an interesting research for many of the researchers. The itinerary planning needs to be made even with the small number of points of interests as well. Although, the popular travel agencies provide more dependable services to the group of users, it lacks from the satisfying the individual users' requirements and likewise neglected to achieve better customization level. Yet they try to offer the best itinerary planning, it lacks from the high price of the software. And also travelling agencies will not arrange the hotel reservation and flight reservation services. They will simply provide the travelling plan and the users need to select their path and plan to pass the corresponding places by booking hotels and flights by themselves. The techniques offered in this work mainly concentrate on offering the best route planning for the users with the gratification of the constraints mentioned by the users. To accomplish this finish, the points of interest to be inflicted by the user and the time and budget details will be collected from them before preparing the program. The budget and the time constraint will also collect from the users in order to generate the user friendly plan. The generation of route planning is automated in this work by presenting the novel concepts. And also to improve the accuracy of the travelling plan, in this work, Fuzzy C means clustering algorithm is presented in this study. A novel approach presented in this work will leads to an optimized construction of an itinerary planned with the customer Satisfaction level.
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In the next section we will describe the itinerary planning, construction process in the detailed manner.
2 RELATED WORKS This part talks about the researches that have done previously about the preparation of itinerary planning in the detailed manner. Senjuti Basu Roy et al [1] discussed the automatic creation of itinerary planning in an interactive manner. This study purports to shorten the user's burden by providing the previous user feedback visible to the users from which the most visited places is enough to look by the current users instead of reviewing the all the points of pursuit. This operation will be repeated by displaying the previous results to the users until the user satisfaction level is achieved. To enable this process, the user feedback model is enforced in this study which intends to pile up the feedback of every user who enters into the mesh. And also in this work, itinerary scoring semantic is introduced which is utilized to rank the itineraries utilized by every user in order to generate and offer an optimal route planning to the users. Barun Chandra et al [2] discussed at several problems occurred during the set packing algorithm. The set packing algorithm intends to group the objects into one for satisfying the user demand. Even so there will be many events occur when the aims from the different groups are taken together. The primary aim of this study is to find the sub collection of more objects with maximum weights from the finite group of available targets. To accomplish this end, this research work introduces a k-set packing problem which intends to pack k objects together. This study commences with an initial object taken into the set based on the greedy solution and the farther aims are seen based on the local search methodology. This work can be applied effectively in all the works which are entirely founded on the itinerary planning construction. Munmun De Choudhury et al [3] discussed an automatic expression of an itinerary planned based on social crumbs. In this work, itinerary planning is constructed by leveraging the personal interests of user by analysing the geo temporal bread crumps. This is performed by extracting the details of time stamps and the image contents of the available source information and grouping them to get out the most wanted point of interest of many users. To accomplish this, one must gather the photos of the popular cities and the time path need to be built. Grounded on this time paths, the optimized itinerary planning can be fabricated. Hyoseok Yoon et al [4] proposed an novel mechanism for itinerary plan construction based on the user generated GPS trajectories. This workplace is principally utilized to help the unfamiliar people in some seats where they wish to go for a holiday trip. This work mainly aims to recover out the start point and the terminal point of a position with the minimized cost and the travelling. This is answered by collecting the multiple users' location details based on the GPS tracking data. When the users are submitting their queries, the query will send to the cloud server where the multiple user location data will be present. The degree of interest submitted by the users will be coupled with the GPS tracked information, and the optimized itinerary planning will be fabricated.
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Maarten Clements et al [5] proposed details of discussed an approach for monitoring the user behavior by calling the user travelling. In this study, a novel approach is presented to predict the user interest by examining the user tagged location based images. In this work, Flickr tagged images are downloaded for the function of predicting user travelling behaviors. After finding the users' point of interest, those extracted places will be placed further to enable the users to choose the special places that he requires to visit within a special time point. Chao Chen et al [8] discussed a new means to make an itinerary planning by using the placement based information and the GPS traces. GPS traces are used to place the crowd sourced information’s that are the location which is mostly opted by the people to visit often. To achieve this, in this work, the heuristic algorithms are effectively utilized which focus to gather the user preferred locations and interest scores. Still all these works described above doesn’t try to achieve the user satisfaction level. And besides the method discussed in the related studies doesn’t try to fulfill the user requests in the fine grained manner. The fine grained itinerary planning construction of the group of users are implemented in our study which attempts to make the itinerary planning with the user satisfaction level. The work proposed in our study is hashed out in the next parts.
3 AUTOMATIC ITINERARY PLAN CONSTRUCTION Focus of this research is to reduce the burden of travelling agents and as well as to attain the satisfaction of users with the help of customized itinerary plan. The itinerary plan construction is done with the consideration of user interest points and as well as the time and cost objectives. The multi user environment is created in this work which is used to reduce the computational complexity. This multi user environment is used to create the itinerary plan with the knowledge of multiple users who are having the most similar requirements. This grouping of users with similar requirements is achieved by using the fuzzy c means clustering approach. The itinerary plan construction consists of the next steps:. 1. Piling up the points of interest (POI) from the different users 2. Group the users with similar POI in single cluster using fuzzy c means clustering approach 3. Make the single daily itinerary using the MapReduce programming 4. Single day itinerary has been constructed and an itinerary index is built for efficient itinerary retrieval. 5. Book the flight and hotels for the group of users By sticking with these steps, the better itinerary planning can be fabricated with the constraints of reduced cost and time as per the user demands. 3.1 Collecting the Point of Interest (POI) from the different users The optimized itinerary planning with the satisfaction of users can be built by pulling together the points of interests that are user wants to shoot the breeze. After gathering user interests,
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those requests will send to the server for the further proceedings. Along with this information, the budget data will also gather from the users. Grounded along the price level, the flight and hotel booking is too performed. In the server, the details of network traffic and road information will be usable. These details will be gathered from the various informants and that will be stored in the database for the further proceedings. In our work, data set which consists of Road traffic information’s are gathered from the Google API. This information set consists of a various location information and as well as the distance between the various positions.
(b) Trip Id
4 PROPOSED WORK 4.1 Group the Users With Similar POI in Single Cluster using Fuzzy C Means Clustering (FCM) Approach Initially, the user, with the similar requirement is grouped to attain the best vacation. Fuzzy C- means clustering is presented in our work to group the users with the similar requirement. To accomplish this, the membership value will be imputed to every user presents in the network, so that users can be bunched up together based on those values. This clustering mechanism will produce ease of booking hotel and flight ticket process in the composite manner. The FCM clustering algorithm is based along the distance between the data present in the surroundings. In our study, the data is a POI represented by the users. This algorithm intends to organize the cluster by specifying the cluster center initially. The information will be appended into the corresponding cluster, if the distance between the data and the cluster center is minimal. 4.1 Hotel Selection In fact, hotels can be considered as a special type of POIs. It must appear as the last POI in the itinerary. Here need to calculate the traveling time from other POIs to the hotel POIs. Hotel POIs do not incur access cost and their weights are set as users’ rankings for the hotels based on the user’s preference.
(c) Route Id
(d) Longitude
(e) Latitude Fig.2. Sample Datasets
5 SIMULATION RESULTS From the corresponding datasets, with the help of MapReduce jobs single day itinerary can be constructed based on their weight and shortest distance.
(a) Stop Id
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Fig.3. Map Reduce Jobs
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REFERENCES
Fig.4. Single day Itinerary
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CONCLUSION
In this work, an automatic generation of route planning is executed for the user who wants to get to the vacation trip. In our work, the service is made out for creating the multiple day itineraries based on the multiple user preferences. In main rule is to manage the multiple users in an effective way. The fuzzy c means clustering mechanism is used to cluster the users with the standardized requirements. Membership values are very effective method for clustering the multi-users.
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FUTURE WORK
For the future work, multiday itinerary will be provided with the help of itinerary index and fuzzy c-means clustering is used for grouping the user according to their requirement similarity. In enhancement stage, more services like hotel selection, flight booking will be done
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[1] Senjuti Basu Royz, Gautam Dasz, Sihem Amer-Yahiay, Cong Yu, ―Interactive Itinerary Planning‖ Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, PP: 15-26, 2011 [2] Barun Chandra, Magnus M. Halldorsson, ―Greedy Local improvement and weighted set packing approximation‖, Journal of Algorithms, Volume 39, Issue 2, May 2001, Pages 223–240 [3] Munmun De Choudhury, Moran Feldman, Sihem Amer-Yahia, Nadav Golbandi, Ronny Lempel, Cong Yu, ―Automatic Construction of Travel Itineraries using Social Breadcrumbs‖, Proceedings of the 21st ACM conference on Hypertext and hypermedia, PP: 35-44, 2010 [4] Hyoseok Yoon, Yu Zheng, Xing Xie, and Woontack Woo, ―Smart Itinerary Recommendation based on User-Generated GPS Trajectories‖, Ubiquitous Intelligence and Computing, Volume 6406, 2010, pp 19-34 [5] Maarten Clements, Pavel Serdyukov, Arjen P. de Vries and Marcel J.T. Reinders, ―Using Flickr Geotags to Predict User Travel Behaviour‖, Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, PP: 851-852, 2010 [6] Asha Viswanath, Edgar Eugenio Samano Baca, and Amro M. Farid, ―An Axiomatic Design Approach to Passenger Itinerary Enumeration in Reconfigurable Transportation Systems‖, IEEE Transactions On Intelligent Transportation Systems, Vol. 15, No. 3, June 2014 [7] Le Minh Kieu, Ashish Bhaskar, and Edward Chung, ―Passenger Segmentation Using Smart Card Data‖, IEEE Transactions On Intelligent Transportation Systems [8] Chao Chen, Daqing Zhang, Bin Guo, Xiaojuan Ma, Gang Pan, and Zhaohui Wu, ―TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints‖, IEEE Transactions On Intelligent Transportation Systems [9] Sara Mehar, Sherali Zeadally, Guillaume Rémy, and Sidi Mohammed Senouci, ―Sustainable Transportation Management System for a Fleet of Electric Vehicles‖, IEEE Transactions On Intelligent Transportation Systems
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