Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> Geotagged photos of users on social media sites provide abundant location-based data, which can be exploited for various location-based services, such as travel recommendation. In this paper, we propose a novel approach to a new application, i.e., personalized landmark recommendation based on users’ geotagged photos. We formulate the landmark recommendation task as a collaborative filtering problem, for which we propose a category-regularized matrix factorization approach that integrates both user-landmark preference and category-based landmark similarity. We collected geotagged photos from Flickr and landmark categories from Wikipedia for our experiments. Our experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple travel costs and personalization—they usually deliver the same routes that minimize a single travel cost (e.g., the shortest routes or the fastest routes) to all drivers. We study the problem of how to recommend personalized routes to individual drivers using big trajectory data. First, we provide techniques capable of modeling and updating different drivers' driving preferences from the drivers' trajectories while considering multiple travel costs. To recommend personalized routes, we provide techniques that enable efficient selection of a subset of trajectories from all trajectories according to a driver's preference and the source, destination, and departure time specified by the driver. Next, we provide techniques that enable the construction of a small graph with appropriate edge weights reflecting how the driver would like to use the edges based on the selected trajectories. Finally, we recommend the shortest route in the small graph as the personalized route to the driver. Empirical studies with a large, real trajectory data set from 52,211 taxis in Beijing offer insight into the design properties of the proposed techniques and suggest that they are efficient and effective. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users' rating behaviors. In order to predict user-service ratings, we focus on users' rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user's rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users' daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users' rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors—user personal interest (related to user and the item's topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users' rating behavior habits), and interpersonal rating behavior diffusion (related to users' behavior diffusions)—into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community-contributed photos. We map both user's and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> Route choice in multimodal networks shows a considerable variation between different individuals and the current situational context. Personalization and situation awareness of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences, as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes, such as biking, which crucially depend on personal characteristics and exogenous factors, such as the weather. As an alternative, this paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step, a mass preference prior (MPP) is used to encode the prior knowledge on preferences from the class identified in step one. Third, subsequently, the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better quality recommendations w.r.t. alternative learning algorithms from the literature. In particular, the definition of the MPP for initialization of step two is shown to provide better predictions than a number of alternatives from the literature. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 2) GROUPING OF AUTONOMOUS VEHICLES WITH PRIVACY PRESERVATION [1] <s> Ride sharing can reduce the number of vehicles in the streets by increasing the occupancy of vehicles, which can facilitate traffic and reduce crashes and the number of needed parking slots. Autonomous vehicles can make ride sharing convenient, popular, and also necessary because of the elimination of the driver effort and the expected high cost of the vehicles. However, the organization of ride sharing requires the users to disclose sensitive detailed information not only on the pick-up/drop-off locations but also on the trip time and route. In this paper, we propose a scheme to organize ride sharing and address the unique privacy issues. Our scheme uses a similarity measurement technique over encrypted data to preserve the privacy of trip data. The ride sharing region is divided into cells and each cell is represented by one bit in a binary vector. Each user should represent trip data as binary vectors and submit the encryptions of the vectors to a server. The server can measure the similarity of the users’ trip data and find users who can share rides without knowing the data. Our analysis has demonstrated that the proposed scheme can organize ride sharing without disclosing private information. We have implemented our scheme using Visual C on a real map and the measurements have confirmed that our scheme is effective when ride sharing becomes popular and the server needs to organize a large number of rides in short time. <s> BIB006
Autonomous vehicles (AVs) have emerged as a new phenomenon in modern era with the flexibility of driving without the need of a driver. These vehicles are normally equipped with advanced sensing and navigation facilities allowing them to route with accuracy. The work proposed in BIB006 tries to utilize these interesting features of AVs. Basically, for each AV, there is a set of primary and secondary users. A primary user (PU) is the owner of an AV and a secondary user (SU) is the one wanting to share a PU's vehicle. A PU decides whether to share its AV with SU(s) on the basis of nearest neighbor selection algorithm proposed in  . A PU also has the right to decide the number of SUs who can share its AV. Moreover, three different routing scenarios are presented such as: • A PU decides the pick and drop areas for SUs according to the start and the end locations agreed by both primary and secondary users. The vehicle is then routed accordingly. • A PU decides the pickup area but the dropping location is different from the end location, however it should not be far from the surrounding of route. • A PU decides the pick area but the dropping location is different from the end location and it can be far from the route. The AV can then drop the PU first, take SU to its destination, and finally route back to the PU (which is unlikely to happen with human driven cars). All the above routing strategies are tested on real maps of Tennessee State (USA) and the results show that the proposed scheme incurs less route searching time which is highly desirable for modern day users dealing with BD applications and AVs. 3) PERSONALIZED ROUTE RECOMMENDATION BASED ON BIG SOCIAL MEDIA As mentioned before (in Section III), Social media (such as Facebook, Twitter, and Instagram) is one of the biggest examples of BD BIB003 . We use social media to post photographs and videos, tag friends, make suggestions, and so forth. Thus, social media has become a necessary part of our daily routine. Among other important things shared on social media these days, most people prefer sharing their travel experiences in the form of photographs and posting status updates. This important source of travel information from social media along with various 'Travelogues' available on several websites can be used together to propose more personalized and efficient travel recommendations to users which is done in a very interesting work presented in BIB004 . The authors propose a learning model named as ''topical package model (TPC)'' which recommends personalized routes to users based on community-contributed photos and travelogues. In addition, the routes are learned and suggestions are made according to the average amount of money a user spends on a trip plus the visiting season and time. Different mathematical models are proposed to show how important information (such as users preferred season for traveling) can be extracted/mined from various social media photos, tags, and travelogues. Mining information is then used to recommend routes to users based on a ranking system. Extensive set of experiments are performed on real-time travelogues to show the impact of user interests, traveling season, time, and cost on mean average precision (MAP-which is calculated using the proposed mathematical model). This MAP reflects the change in choices of users when they are selecting their traveling packages. The results conclude that time and cost have more impact on MAP than traveling season which is kind of an obvious trend. We summarize the main points of BIB004 and other important works (similar to BIB004 ) which are based on recommending personalized routes to users using different criteria such as geographical locations, distances, time, cost, etc., in Table 5 . For example, in Table 5 , the work proposed in BIB001 is summarized that uses real-time datasets for the experiments collected from Flickr API. Similar to BIB004 , Shi et al. BIB001 use MAP as an important criterion for suggesting routes to users. Moreover, Dai et al. BIB002 and Campigotto et al. BIB005 discussed in our previous section can also be categorized under ''personalized route recommendation based on social datasets'' and thus are highlighted in Table 5 .