However, users’ specific travel preferences are influenced by many factors, such as distance, traffic volume, travelling time, weather, fuel consumption, safety, and many other implicit and hidden factors. Personalized travel route recommendation refers to the planning of an optimal travel route between two geographical locations based on the road networks and users’ travel preferences. Personalized travel route recommendation is an important research topic and has not been fully explored. One reason that users do not follow routes recommended by existing route planning services is that they do not take into account individual user’s travel preferences. As shown in the figure, two users (user #9 and user #10) travelling from the same origin to the same destination at a similar time (between 7 and 8 am) took different routes (shown in solid gray line and black line): neither of the routes was the shortest distance path (in dash line). Citation2008a, Citation2009) and routes provided using the shortest distance. Moreover, different users may prefer to travel on different routes.įigure 1 shows the travel routes of users extracted from a Geolife data set (Zheng et al. Current route recommendation services generally consider a certain metric such as the shortest distance or traveling time, and provide the shortest or quickest path between locations to users however, the recommended path would not often be chosen in real travel (Letchner, Krumm, and Horvitz Citation2006 Delling and Wagner Citation2009). Travelling is a critical component in daily life, and route recommendation is the most popular service for travelling. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. With new technology, personalized travel route recommendations are possible and have become a new research area. Please note, however, that if you delete cookies or refuse to accept them, you might not be able to use all of the features we offer, you may not be able to store your preferences, and some of our pages might not display properly.Travelling is a critical component of daily life. If you'd like to delete cookies or instruct your web browser to delete or refuse cookies, please visit the help pages of your web browser. In addition to our own cookies, we may also use various third-parties cookies to report usage statistics of the Service, deliver advertisements on and through the Service, and so on. We may use essential cookies to authenticate users and prevent fraudulent use of user accounts. We use both session and persistent cookies on the Service and we use different types of cookies to run the Service. We use cookies for the following purposes: to enable certain functions of the Service, to provide analytics to improve the Service, to store your preferences, to enable advertisements delivery, including behavioral advertising. When you use and access the Service, we may place a number of cookies files in your web browser. A cookie file is stored in your web browser and allows the Service or a third-party to recognize you and make your next visit easier and the Service more useful to you.Ĭookies can be "persistent" or "session" cookies. What are cookiesĬookies are small pieces of text sent to your web browser by a website you visit. Our Cookies Policy explains what cookies are, how we use cookies, how third-parties we may partner with may use cookies on the Service, and your choices regarding cookies. By using the Service, you consent to the use of cookies. Journi GmbH ("us", "we", or "our") uses cookies on its website (the "Service"). Journi Cookies Policy Last updated: 23-October 2018
0 Comments
Leave a Reply. |