In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

Compared with traditional recommendation algorithms based on collaborative filtering and content, the sequential recommendation can better capture changes in user interests and recommend items that may be interacted with by the next time according to the user's historical interaction behaviors. Generally, there are several traditional methods for sequential recommendation: Markov Chain (MC) and Deep Neutral Network (DNN), both of which ignore the relationship between various behaviors and the dynamic changes of user interest in items over time. Furthermore, the early research methods usually deal with the user's historical interaction behavior in chronological order, which may cause the loss of partial preference information. According to the perspective that user preferences will change over time, this paper proposes a long and short-term sequential recommendation model with the enhanced self-attention network, RP-SANRec. The short-term intent module of RP-SANRec uses the Gated Recurrent Unit (GRU) to learn the comprehensive historical interaction sequence of the user to calculate the position weight information in the time order, which can be used to enhance the input of the self-attention mechanism. The long-term module captures the user's preferences through a bidirectional long and short-term memory network (Bi-LSTM). Finally, the user's dynamic interests and general preferences are fused, and the following recommendation result is predicted. This article applies the RP-SANRec model to three different public datasets under two evaluation indicators of HR@10 and NDCG@10. The extensive experiments proved that our proposed RP-SANRec model performs better than existing models.

The results of road congestion detection can be used for the rational planning of travel routes and as guidance for traffic management. The trajectory data of moving objects can record their positions at each moment and reflect their moving features. Utilizing trajectory mining technology to effectively identify road congestion locations is of great importance and has practical value in the fields of traffic and urban planning. This paper addresses the issue by proposing a novel approach to detect road congestion locations based on trajectory stay-place clustering. First, this approach estimates the speed status of each time-stamped location in each trajectory. Then, it extracts the stay places of the trajectory, each of which is denoted as a seven-tuple containing information such as starting and ending time, central coordinate, average direction difference, and so on. Third, the time-stamped locations included in stay places are partitioned into different stay-place equivalence classes according to the timestamps. Finally, stay places in each equivalence class are clustered to mine the congestion locations of multiple trajectories at a certain period of time. Visual representation and experimental results on real-life cab trajectory datasets show that the proposed approach is suitable for the detection of congestion locations at different timestamps.

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

Accurately mapping the raw global position system (GPS) trajectories to the road network is the basis for studying the application of trajectory data. This study proposes a novel off-line map matching algorithm based on road network topology, to address the problems of low execution efficiency and poor matching accuracy of selective look-ahead map matching (SLAMM) algorithm. First, the noise points of the trajectory data are removed by data preprocessing. Second, the algorithm searches for critical samples in the trajectory data and segments the data accordingly. Then, the adjacent road segments around the transition node corresponding to the critical sample are selected as candidate arcs. Finally, the segmented trajectory data are matched to the road network by constructing an error ellipse. The algorithm fully considers the topology of the road network and the characteristics of high-frequency trajectory data. The experimental results, using Beijing trajectory data to perform matching on an actual road network environment, show that the proposed algorithm is more efficient and robust than other map matching algorithms for high-frequency trajectories.

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.