Route Recommendation 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.

Fair and Efficient Ridesharing: A Dynamic Programming-based Relocation Approach

Recommending routes by their probability of having a rider has long been the goal of conventional route recommendation systems. While this maximizes the platform-specific criteria of efficiency, it results in sub-optimal outcomes with the disparity among the income of drivers who work for similar time frames. Pioneer studies on fairness in ridesharing platforms have focused on algorithms that match drivers and riders. However, these studies do not consider the time schedules of different riders sharing a ride in the ridesharing mode. To overcome this shortcoming, we present the first route recommendation system for ridesharing networks that explicitly considers fairness as an evaluation criterion. In particular, we design a routing mechanism that reduces the inequality among drivers and provides them with routes that have a similar probability of finding riders over a period of time. However, while optimizing fairness the efficiency of the platform should not be affected as both of these goals are important for the long-term sustainability of the system. In order to jointly optimize fairness and efficiency we consider repositioning drivers with low income to the areas that have a higher probability of finding riders in future. While applying driver repositioning, we design a future-aware policy and allocate the areas to the drivers considering the destination of requests in the corresponding area. Extensive simulations on real-world datasets of Washington DC and New York demonstrate superior performance by our proposed system in comparison to the existing baselines.

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

On Exploring the Carrying-Charging Demand Balance in Cruising Route Recommendation for Vacant Electric Taxis

On Exploring the Carrying-Charging Demand Balance in Cruising Route Recommendation for Vacant Electric Taxis

A Cultural Route Recommendation Based on Optimization Techniques in Urban Spaces

A Cultural Route Recommendation Based on Optimization Techniques in Urban Spaces

Effective Route Recommendation Leveraging Differentially Private Location Data

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy.

Feeder–Line Concept Development For Public Transportation Network In Urban Area Based On O-D Matrix And Performance Evaluation

The management of the public transport network system is very important because it affects the future of the city and supports the activities of the community in travelling and movement. This research aims to develop a feeder-line concept on the public transport network based on O-D Matrix and Operational Performance Evaluation. The research method used is the operational performance standard indicator approach based on the Directorate General of Land Transportation SK 687/AJ.206/DRJD/2002 and the O-D Matrix method in determining new route recommendations. This research uses secondary data as well as primary surveys to assess operational performance. The results show that the level of operational performance of public transport in the research area there are still several routes below the standard with a total of routes operating from D1 to D8 of which 75% of the routes are in good condition and the other 25% of the routes have substandard assessment results with a total value of ?18. Thus, it is recommended that rerouting to be carried out on routes whose operational performance is still below standard, namely routes D7, and D8 based on the characteristics of the feeder-line concept to BRT Corridor 2.

Informed sampling and recommendation of cycling routes: leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation

Attractive cycling routes can effectively promote active mobility, thus reducing the twin pressures of the population boom and the greenhouse effect. However, the existing approaches for cycling route recommendation primarily concentrate on identifying the most efficient routes while ignoring the urban spatial context, which is essential to meet the user’s particular preferences. This article proposes a novel method for informed sampling and recommending cycling routes leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation (WLDA). Precisely, spatial context mapping, incorporating a weighting mechanism into LDA, latent topics mining, and cycling route recommendation based on informed sampling are introduced. We collected 1,016 cycling trajectories around Cologne, Germany, for experimental analysis. The experimental results show that the three latent topics within the trajectories, leisure, city, and green tours, are clearly presented in the line density analysis. The insightful recommendation for unfamiliar cyclists could also be actively sampled upon the WLDA model. These findings suggest that our approach could shift the route recommendation paradigm from GIS analysis to a semantic mining perspective, yielding highly interpretable results and offering novel research avenues for applying machine learning in route planning.

A two-context-aware approach for navigation: A case study for vehicular route recommendation

In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.

Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.

Multi-source auxiliary information tourist attraction and route recommendation algorithm based on graph attention network

Abstract In the field of tourism recommendation systems, accurately recommending scenic spots and routes for users is one of the hot research directions. In order to better consider the complex interaction between user preferences and attraction features, as well as the potential connections between different information sources, this study constructed a graph attention network model using knowledge graphs for tourist attraction and route recommendations, and extracted features from visual images using visual geometry group-16. The results indicate that, in Xian, when the learning rate is 0.01, the area under the curve value is 0.916. The area under the curve of New York is 0.909, and the learning rate is 0.001. The area under the curve value of the Tokyo dataset is 0.895. When the learning rate is moderate, the model quickly stabilizes in the first 16 rounds and reaches its optimal state in 26–30 rounds. When the propagation depth is 2, the accuracy is 0.920, 0.905, and 0.895, respectively. After introducing visual features, the accuracy, recall, and F1 score improved by 10 to 15.7%. The multi-layer perceptron further increased the effect by 4–6%. These experimental data fully demonstrate the effectiveness and accuracy of the recommendation algorithm. This study provides a powerful tool for tourism recommendation systems, which helps to further improve user experience.

DeepSneak: User GPS Trajectory Reconstruction from Federated Route Recommendation Models

Decentralized machine learning, such as Federated Learning (FL), is widely adopted in many application domains. Especially in domains like recommendation systems, sharing gradients instead of private data has recently caught the research community’s attention. Personalized travel route recommendation utilizes users’ location data to recommend optimal travel routes. Location data is extremely privacy sensitive, presenting increased risks of exposing behavioural patterns and demographic attributes. FL for route recommendation can mitigate the sharing of location data. However, this paper shows that an adversary can recover the user trajectories used to train the federated recommendation models with high proximity accuracy. To this effect, we propose a novel attack called DeepSneak, which uses shared gradients obtained from global model training in FL to reconstruct private user trajectories. We formulate the attack as a regression problem and train a generative model by minimizing the distance between gradients. We validate the success of DeepSneak on two real-world trajectory datasets. The results show that we can recover the location trajectories of users with reasonable spatial and semantic accuracy.

Comparative analysis of the optimal route recommendation models based on city public transportation network data

In recent years, studies on smart cities have attracted much attention in the literature. In the smart city concept, urban public transportation network studies are one of the most important issues. Afterthe 2000s, smart cards have been widely used in city public transportation systems. Using the data collected through the smart card, many models have been created in line with the smart city concept. In the literature, there are various optimal route recommendation models using urban public transportation network and city smart card data. In this study, some current models built on the basis of urban public transportation network and smart card data are discussed. The specifications of the models, their important differences, the topologies they use, optimization criteria, and computational complexities are analyzed. Dijkstra’s algorithm, which is widely used for the solution of optimal route recommendation models, and its various modifications are analysed. Additionally, various models developed by applying fuzzy logic are examined. Comparative analysis of public transportation network models is given.

A Sustainable mHealth Intervention to Promote Physical Activity for Healthy Aging: A Pilot Study of the “Every Walk You Take” Citizen Science Initiative

The objective of the Every Walk You Take initiative was to co-design and test, in a pilot study, a sustainable mHealth intervention prototype (mobile app) to promote physical activity. This prototype would help to identify the barriers to and facilitators of active living in individuals older than 55 years. A prototype of the intervention was co-designed by a community of stakeholders in Barcelona who were familiar with the social and economic burden of aging in high-income societies. The app’s functionalities included recommendations for healthy routes in the city (parks, pedestrian lanes, and streets) according to environmental variables (air quality and climate) and personal preferences (route difficulty, distance, and geolocation), and ecological momentary assessments (pictures and voice notes) were collected to identify the barriers to and facilitators of performing these routes. To test the app, a pilot study was conducted over two 7-day cycles with citizen scientists recruited at the life-long learning centers of two deprived neighborhoods in Barcelona. A total of 21 citizen scientists (mean age = 67 (standard deviation = 7)), 86% of them female, collected 112 comments and 48 pictures describing their perceived barriers to and facilitators of active living. Every Walk You Take is a new, validated, and sustainable mHealth intervention that is directly involved in health promotion, as it empowers the citizens of Barcelona to play an active role in their own healthcare. This intervention has the potential to be implemented in different cities around the world to collect information on the community determinants of health and health assets.

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

The study presents a hybrid model for recommending travel routes that takes into account multiple constraints. This model is based on a collaborative filtering recommendation algorithm and addresses the issue of disorganized travel route recommendations. To improve upon the k-mean clustering algorithm, the proposed model introduces the Dynamic Gaussian Kernel Density K-means algorithm. After the data was processed, the initial clustering center was determined and k-means clustering was performed. Subsequently, the travel route recommendation model was created by integrating various constraints. The study’s proposed algorithm was compared with alternative algorithms, and the experimental results demonstrated superior performance across a range of datasets, with the minimum sum of squared errors and a running time of approximately 1.4 seconds - a noteworthy improvement. Comparative experiments were conducted on various forgetting coefficients in the model, and the forgetting coefficient with the lowest sum-of-squares of errors was selected to replace the existing one. Upon comparing the proposed research model with other models, it was found that the former had greater accuracy and recall, amounting to 98.1% and 96.8% respectively. This suggests that the proposed research model serves as a more efficient solution for travel route recommendation.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

Dynamic migratory beekeeping route recommendation based on spatio-temporal distribution of nectar sources

Dynamic migratory beekeeping route recommendation based on spatio-temporal distribution of nectar sources

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.

Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.