Route Recommendation Method Research Articles

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

Route recommendation method for frequent passengers in subway based on passenger preference ranking

To provide personalized and precise guidance services to subway passengers, a route recommendation method for frequent subway riders based on passenger preference ranking is proposed. Firstly, a data-driven analysis method of travel preference is proposed to identify the travel preference of frequent passengers in different origin-destinations (ODs) and time periods. Secondly, a route recommendation model of dictionary order preference based on just noticeable difference (JND) is proposed to solve the problem of different passenger perception in route attributes. Thirdly, the model of depth deterministic strategy gradient (DDPG) is proposed to consider dynamic parameters of JND. A case study of Guangzhou Metro is illustrated to verify the accuracy of the route recommendation model. The results show that the individual passenger recommendation in the proposed method is more accurate than the traditional logit model and the JND model without parameter optimization by 30% and 20%. The group recommendation in the proposed method is more accurate than the comparative models by 37% and 11%, respectively. Specifically, the accuracy of this method in group recommendation fluctuates less, and its accuracy variance is much smaller than the traditional logit model. Therefore, the method proposed in this study performs better in reflecting personalized need of passenger travel.

POI Route Recommendation Model Based on Symmetrical Naive Bayes Classification Spatial Accessibility and Improved Cockroach Swarm Optimization Algorithm

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

Personalised leisure tourism route recommendation method based on knowledge map

Personalised leisure tourism route recommendation method based on knowledge map

Study on travel route recommendation method based on improved ant colony optimisation algorithms

Study on travel route recommendation method based on improved ant colony optimisation algorithms

Study on travel route recommendation method based on improved ant colony optimisation algorithms

Study on travel route recommendation method based on improved ant colony optimisation algorithms

Personalized route recommendation for passengers in urban rail transit based on collaborative filtering algorithm

AbstractThe rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

Empowering A* Algorithm With Neuralized Variational Heuristics for Fastest Route Recommendation

Fastest route recommendation (FRR) is crucial for intelligent transportation systems. The existing methods treat it as a pathfinding problem on dynamic graphs, and extend A* algorithm with neuralized travel time estimators as cost functions. However, they fail to provide effective heuristic cost due to the neglect of its admissibility and the utilization of noise path information, resulting in sub-optimal results and inefficiency. Besides, path sequentiality is also ignored, affecting algorithm accuracy as well. In this paper, we propose a variational inference based fastest route recommendation method, which follows the framework of A* algorithm and provides effective costs for routing. Specifically, we first adopt a sequential estimator to accurately estimate the travel time of a specific path. More importantly, we design a variational inference based estimator, which models the distribution of travel time between two nodes and provides an effective heuristic cost with high probability of being admissible. We further take advantage of adversarial learning to enrich the fastest path information. To the best of our knowledge, we are the first to use variational estimator to consider the admissibility of heuristics in FRR. Extensive experiments are conducted on two real-world datasets. The results verify the performance advantage of our proposed method.

A personalized bikeability-based cycling route recommendation method with machine learning

Urban built environment quality along the travel route is positively correlated with active mobility. However, most previous personalized route recommendation research for cycling emphasizes non-visual attributes, ignoring the significance of visual attributes such as the built environment. Therefore, to take the built environment into consideration in the cycling route planning, we propose the personalized bikeability-based cycling route recommendation (PBCRR) method. The method consists of three steps: (1) deep neural networks are utilized to achieve statistics on built environment attributes and features of bikeability; (2) the weights road impedance calculation function is used to calculate route recommendation indicators according to minimum impedance sum of road segments; and (3) the recommended cycling route is found and shown to the user based on the calculated indicators. The results indicate that our PBCRR presents a high level of effectiveness and differences, and achieves better results than the current route recommendation algorithm in guiding cyclists towards routes with a better built environment.

Multi-objective route recommendation method based on Q-learning algorithm

With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q-value table search mechanism, the proposed algorithm can generate the recommended route almost in real time.

Personalized Route Recommendation with Hybrid Tabu Search Algorithm Based on Crowdsensing

In the postmodern era of tourism, tourists’ behavior has undergone a substantial change and the demand of customized experience dominates the tourism market. The traditional single-objective travel route recommendation method fails to meet the multiobjective needs of users. To handle this problem, a multiobjective hybrid tabu search algorithm for urban travel route recommendations is proposed in this paper. First, the rating and level of attractions, as well as the corresponding information of hotels and restaurants within a certain radius, are considered. Then, based on this information, a crowdsensing scoring method is established. Second, the hybrid particle swarm genetic optimization algorithm is exploited to generate a single-object route, and the fast nondominated Pareto sorting algorithm is exploited to find the optimized solution. Then, the hybrid tabu algorithm is used to optimize the personalized route chosen according to multiple objects set by users. This algorithm combines the global search ability of the genetic algorithm and the neighborhood search ability of the tabu algorithm to prevent convergence from falling into a local optimum. Finally, the experiments are conducted on the real-world data collected from the Dianping and Ctrip web sites. The comparison with baseline algorithms indicates that the algorithm proposed in this paper provides accurate and reasonable route recommendations for users.

Study on Intelligent travel route recommendation method based on popularity of interest points

Study on Intelligent travel route recommendation method based on popularity of interest points

Personalized leisure tourism route recommendation method based on Knowledge Map

Personalized leisure tourism route recommendation method based on Knowledge Map

Group popular travel route recommendation method based on dynamic clustering

Group popular travel route recommendation method based on dynamic clustering

A personalised travel route recommendation method based on an improved greedy algorithm

A personalised travel route recommendation method based on an improved greedy algorithm

A personalised travel route recommendation method based on an improved greedy algorithm

A personalised travel route recommendation method based on an improved greedy algorithm

Travelling Route Recommendation Method Based on Graph Neural Network for Improving Travel Experience

With the rapid development of Internet technology, people can learn all kinds of travel information anytime and anywhere. However, the serious information overload causes travelers to be unable to make accurate and reasonable travel routes that meet tourists’ tastes for a while, thus reducing the quality of travel. The recommendation system as the mainstream solution to the information explosion of two means has received the attention of the majority of scholars and industry. Based on the research theory of tourist route recommendation, this paper analyzes the characteristics of attractions, factors affecting travelers’ travel experience when touring attractions and factors affecting travelers’ travel experience along tourist routes. Furthermore, we propose a tourist route recommendation model that meets tourists’ preferences. Then, this paper uses the graph neural network (GNN) algorithm to build a framework for tourist route recommendations based on the GNN using the relationship of preference and commonality existing among groups, tourists and attractions. The GNN algorithm is optimized and improved using multiple graphs and an attention mechanism. Finally, the effectiveness of this paper’s algorithm is verified by conducting experiments on different data sets.

Personalized Route Recommendation Method for Field Survey Officers using Social Media Information and Administrative Border Maps

Changes in business processes in pandemic conditions are a must. The field survey were most affected, not only the interview process but also the route selection to survey location. To support the field survey officer, it is necessary to provide alternative route choices to the survey location as fast as possible. This research proposed a methodology that combines three information source, administrative border maps, google maps services, and information from social media that elaborated to provide best recommendation route to the assigned survey location. The combination of three different sources can enhance the current existing route that only relies on google map services. Our mechanism was tested on custom my maps application provided by Google and evaluated using system usability scale. This research aims to give the personalized route to field survey officers based on the assigned survey location and information from social media. The limitation of this research is that the social media channels used are still few, in the future, this research can be leveraged by integrating other platforms owned by the government and other public services to enrich the information.

Intelligent recommendation of personalised tourist routes based on improved discrete particle swarm

In order to overcome the problems of low accuracy and long time consuming in traditional personalised travel route recommendation methods, this paper proposes an intelligent recommendation of personalised tourist routes based on improved discrete particle swarm. This method analyses the key problems of tourism recommendation according to the personalised tourism characteristics, collects the information of tourists' interest, and establishes the model of tourists' interest. On this basis, the discrete particle swarm optimisation algorithm is improved, and the improved discrete particle swarm optimisation algorithm is used to select the personalised travel route, and the selection results are recommended to the passengers, so as to realise the personalised travel route intelligent recommendation. The experimental results show that the recommendation accuracy of this method is between 82.5% and 96.9%, and the recommendation time is always less than 0.5 s, which can realise the accurate and rapid recommendation of personalised tourist routes.

Group popular travel route recommendation method based on dynamic clustering

Group popular travel route recommendation method based on dynamic clustering