Route Planning Research Articles

Unveiling user interests: A deep user interest exploration network for sequential location recommendation

Sequential location recommendation, also known as next location prediction, is a crucial task aiming to compute the likelihood of a user visiting a certain point of interest (POI). It has significant applications in route planning and location-based advertisements. Despite the utilization of geographical information, existing methods often adopt a rigidly sequential prediction mode, focusing solely on predicting the next destination (i.e., where to go), which limits their ability to effectively model user trajectories in terms of predicting from where the user came. Moreover, these methods struggle to capture the evolving and deep user interests along their trajectory. To deal with these problems, we propose a novel approach called Deep User Interest Exploration Network (DUIEN) for sequential location recommendation. DUIEN incorporates two essential components to enhance recommendation accuracy. First, we employ a bidirectional neural network with the Cloze objective, enabling it to predict random masked POIs within user trajectories. This approach aids the model in gaining a better understanding of the context and dynamics of user trajectories. Second, we introduce a user interest exploration layer that seamlessly integrates into the sequential learning process. This layer effectively utilizes tag information associated with POIs and learns the intricate interplay between user interests and their trajectories using a deep user interest exploration method. In our experiments, DUIEN outperforms state-of-the-art sequential location recommendation models, demonstrating the effectiveness of the designed update mechanisms for deep user interest exploration. This showcases the potential of our approach to significantly enhance the accuracy of sequential location recommendation.

Guideline for Developing Glamping Tourism in China: A Case Study of Gansu

Background and Aim: In China, glamping is experiencing rapid growth. With its unique advantages, Gansu Province has created opportunities for the development of glamping tourism. Despite challenges such as inadequate facilities and low service standards, glamping tourism in the province holds immense potential. This study aims to analyze tourism development in Gansu Province and propose recommendations for the advancement of glamping tourism. Materials and Methods: This study employed a quantitative research approach. A questionnaire survey was conducted among 400 glampers aged 18 and above who had visited Gansu Province and stayed at ten glamping sites over the past three years. The survey covered demographic information, travel habits, motivations, and tourist satisfaction. Descriptive statistical analysis, mean, and variance analysis were conducted using SPSS to quantify the relationships between different factors. Results: The research findings indicate that Gansu's unique natural beauty and rich cultural heritage contribute to high levels of tourist satisfaction with the abundance of nature and cultural landscapes. However, significant differences in satisfaction were observed in various aspects of glamping, including the glamping site environment, shower and toilet facilities, power and drainage infrastructure, emergency medical assistance, and security assurance, suggesting the need for further improvement. Additionally, external traffic and internal route planning were identified as significant factors influencing tourist satisfaction. Conclusion: The survey results indicate that glampers have high satisfaction with natural and cultural landscapes. However, there are significant differences in satisfaction regarding the campground environment, sanitation facilities, power and drainage infrastructure, transportation, emergency medical assistance, and safety measures. Analyzing the data, the following development guidelines are derived: enriching experiences, improving facilities, enhancing safety measures, reducing costs, promoting environmental conservation, integrating cultural heritage, fostering cooperation, and establishing feedback mechanisms. These measures aim to facilitate the sustainable development of glamping, attract more visitors, and promote economic prosperity.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

Solving Neutrosophic Multi-Dimensional Fixed Charge Transportation Problem

In the decision making problems, transportation problem (TP) is one of the most significant applications that aim to optimize the objective function of products shipped from different sources to different destinations. When an additional charge such as toll tax, parking fee, permit fee and so on are included in the transportation cost then the problem becomes a fixed charge TP. If the shipped products are damageable, they are affected by the type of vehicle and the distance of route. So it is quite evident that the vehicle type and the distance of route play a significant role in TP. In reality, along with sources and destinations, the decision maker (DM) desires to consider the parameters such as the mode of transport and routes of transportation that are used for shipment of the products from sources to destinations in order to minimize the loss of damageable products and to maximize the profit. As a result, mode of transport and routes of transportation are added to TP, then the TP becomes a four-dimensional TP. Sometimes, the data/parameters of the problems may not be always precise due to incomplete information, inadequate data or shortage of evidence. To deal with these obstacles, the parameters of the problem can be represented as the single-valued trapezoidal neutrosophic numbers (SVTrNNs). The neutrosophic data facilitate a reasonable and practicable way for DMs to tackle decisionmaking problems by managing indeterminacy and providing an effective framework for analysis and synthesis of complex decision scenarios. In view of this, in this paper we have considered the single objective four-dimensional fixed charge transportation problem (4DFCTP) with parameters, supply, demand, conveyance, transportation cost, fixed charge as single-valued trapezoidal neutrosophic numbers and the distance of routes as real numbers. First, the score function is utilized to transform the neutrosophic parameter into its deterministic parameter in order to avoid the negative values for the decision variable. Then the deterministic problem comprises deterministic parameters such as supply, demand, conveyance, transportation cost with fixed charges and distance of routes. Secondly, a novel approach namely, min zeromin cost approach is introduced for finding the optimal solution to the equivalent deterministic problem in polynomial time. The main objective of this paper is to optimize the breakable products and routing plan of vehicles in a way to minimize the total transportation cost with fixed cost of the business organizations using the proposed min zero-min cost approach. To demonstrate the problem’s validity and relevance, two numerical examples are solved using our proposed approach. To highlight the proposed approach, comparison of the solution with the LINGO software is performed. The obtained optimal solution from the proposed approach is the same as the LINGO software. At last, conclusions as well as future work related to the study are presented.

The Spatiotemporal Pattern Evolution Characteristics of Ship Traffic on the Arctic Northeast Passage Based on AIS Data

Warming weather has led to melting sea ice, and increasingly complex global geopolitics has drawn more countries’ attention to the Arctic. The Arctic Northeast Passage, as an emerging route connecting Eurasia, has seen a sharp increase in vessel activity. The period from 2015 to 2020, being a stable and undisturbed data period, is of significant theoretical importance for exploring the natural development of the Arctic Northeast Passage. The study found that the research period can be divided into three stages: from 2015 to 2017, the number of vessels grew slowly. In 2018 and 2019, the number of vessels and vessel activities saw significant growth, but an unexpected reverse growth occurred in 2020. Different types of vessels have unique activity characteristics and evolutionary patterns, influenced by the Arctic’s unique geographical environment, abundant natural resources, deepening Sino-Russian cooperation, and increasing global trade supply and demand. The results of this study aim to provide policymakers with analysis based on the initial development stage of the route, offering data support for future policy formulation, route planning, and research on the navigation safety of vessels on the Arctic Northeast Passage.

Spatial‐Temporal Distribution Characteristics of Linear Heritage Hiking Tourism Based on GPS Data Analysis: A Case Study of the Great Wall in Beijing, China

ABSTRACTHiking plays a significant role in experiencing linear heritage, and gaining insights into the spatiotemporal distribution of hikers' behavior is imperative for effectively utilizing heritage resources. This study employed GPS trajectory data to examine the spatiotemporal patterns of hiking behavior on the Great Wall in Beijing and investigate the utilization patterns of heritage resources in hiking tourism. The results revealed that: there were notable variations in trajectory quantity among different months with extended activity durations, early finishing times, and an absence of nighttime activities. Spatially, the trajectories exhibited dispersion and uneven distribution, leading to the underutilization of numerous heritage resources. The distribution of starting and ending points of trajectories demonstrated a substantial correlation with neighboring natural villages. Consequently, this study offers valuable insights for informing decision‐making processes related to the development, construction, and optimization of the scenic area along the Great Wall and the planning and design of tourist routes.

End-to-end drone route planning in flexible airspace design

Drone traffic, consisting of anything from small quadcopters for video and photography to large eVTOL transporting people, is expected to grow rapidly as soon as the challenges currently barring urban flights can be solved. One of the main challenges is how to automate authorization while both keeping full control over where and how drones fly over specific areas, and at the same time allowing the operators the freedom they require to successfully provide their services. While restrictions are necessary, being overly restrictive on plans has a negative impact on capacity, safety and efficiency. In this article we propose the combination of no-fly zones and flight grids into design elements for airspace design, to be used only where and when necessary. City planners can use these design elements to make both strategic decisions and real-time updates, and thereby set the rules for an automated system for planning and authorization. We describe the design elements, how to automatically find the optimal end-to-end route between or through these elements, a set of modifications or extension to improve flexibility even more, and demonstrate the efficacy of the approach through example airspace design patterns and by showing the resulting traffic in a drone traffic simulator.

UAV data link anti-interference via SLHS-SVM-AdaBoost algorithm: Classification prediction and route planning

The ability to predict the anti-interference communications performance of unmanned aerial vehicle (UAV) data links is critical for intelligent route planning of UAVs in real combat scenarios. Previous research in this area has encountered several limitations: Classifiers exhibit low training efficiency, their precision is notably reduced when dealing with imbalanced samples, and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary. This paper proposes the sequential Latin hypercube sampling (SLHS)-support vector machine (SVM)-AdaBoost algorithm, which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization. Additionally, it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm. Through comparison, the modeling efficiency, prediction accuracy on the test set, and macro-averaged values of Precision, Recall, and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%, 5.7%, 36.0%, 25.0%, and 34.2%, respectively, compared with Grid-SVM. Additionally, these values are improved by 22.2%, 2.1%, 11.3%, 2.8%, and 7.4%, respectively, compared with particle swarm optimization (PSO)-SVM-AdaBoost. Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm, the classification prediction model of anti-interference performance of UAV data links, which took factors like three-dimensional space position of UAV and antenna bearing into consideration, is established and used to assess the safety of the classical flying path and optimize the flying route. It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path, and that intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered while reducing the route length by at least 2.3%, thus benefiting both safety and operation efficiency.

Model of Tsunami Mitigation and Evacuation Routes in Pancer Banyuwangi

Banyuwangi has the largest tsunami disaster risk in East Java, about 45,305 affected people, Rp. 160,886,000,- for the risk of physical losses, Rp. 106,414,000,- for the risk of economic losses and 84 ha of environmental losses (BNPB, 2023). In addition, according to historical data, Banyuwangi was hit by tsunami on June 9, 1994 with magnitude of 7.8 which caused a maximum run up of 14.0 m and killed 238 lives (BNPB, 2023). Also based on Undang- undang No. 24 Tahun 2007 concerning Disaster Management, Indonesia should have preventive and mitigation measures for tsunami disasters both before and after as an effort to reduce the disaster risk (Putra, 2009) . Tsunami modeling in southern Java using COMCOT v1.7 as tsunami worst case scenario in Banyuwangi is the first step for mitigation. The initial results of COMCOT were then used for preparing tsunami disaster evacuation route planning based on the Tsunami Natural Disaster Evacuation Route Planning Guidelines, Dirjen Bina Marga, 2023. The final step is validation/routing survey of the 8 (eight) routes that have been determined. 2 (two) of the 8 (eight) routes fulfill the guidelines for planning tsunami mitigation routes. However, all routes can be considered due to the distribution of settlements, the number of affected residents and the number of routes needed

Citywalk in China: analysis of regional and route characteristics

ABSTRACT The West introduced Citywalk to China; since then, the concept has undergone a significant transformation from mere walking or hiking activities, impacting people’s choices in transportation and urban spatial experiences. However, the defining characteristics of Citywalk remain unclear. To address this gap, this study considers representative cities in China as examples and employs a comprehensive approach including website information retrieval, SPSS software, and word frequency analysis to collect and analyze data on 611 Citywalk routes in 48 cities. The study objective is to examine the regional characteristics of Citywalk routes and elucidate the factors that shape Citywalk characteristics. Descriptive statistics are used to describe the regional characteristics of Citywalk routes, and three indicators – urban attributes, route features, and node types – are identified. Subsequently, correlation analysis is applied within and between groups. The results indicate a significant correlation among urban attributes, route features, and certain node types with high reliability. This study provides bottom-up data support for the design of linear spatial experiences in Chinese cities and the improvement of tourist route planning, thus offering valuable references for future endeavors in this field.

A new model for fuel consumption and route time computations – a case study in the Quebec forest industry

ABSTRACT In forest transportation in Quebec, transportation rates are typically based on an estimated trip or travel time duration during which a truck travels loaded from an origin to a destination and then returns empty to the origin. These transportation rates implicitly consider fuel consumption for fuel surcharge costs in negotiation, but for most shippers and carriers, fuel consumption remains a rough estimate, leading to underestimation or overestimation of actual consumption. In this paper, we propose a new fuel consumption model and a more detailed trip time computation to support accurate estimations. The model takes into account road network characteristics that affect fuel consumption, such as road elevation profiles, including slopes that significantly affect fuel consumption compared to flat roads, and curves where trucks change speed, as well as intersections where trucks need to stop or slow down. The road network of the province of Quebec (Canada) is represented in a route network that integrates all the characteristics considered by the fuel consumption model. The model is validated in a case study using a timber truck equipped with GPS and information about overall fuel consumption between a set of refueling points. Utilizing the fuel consumption model and a route planner enables accurate estimation of fuel consumption and, consequently, associated greenhouse gas (GHG) emissions and travel times. A case study involving three companies is then conducted to analyze how more detailed information can inform new transportation rates.

Creating a softening cultural-landscape to enhance tourist experiencescapes: The case of Lu Village

Tourist destination management involves many resources, including local culture and landscape but rarely are both considered together. To address this deficiency this study focused on the visual softening of a Chinese traditional village to enhance visitor experiences by planting vegetation consistent with cultural and heritage principles. It does so by using empirical data from the case study site of Lu Village in China. The study is directed toward explaining how a fusion of nature possessing cultural values might visually improve a village destination to make it more attractive for tourists. It represents a little explored aspect of the policy of “cultural consumption” currently espoused by the Chinese authorities, yet one that is low cost for Chinese villages. However, application of the policy requires planning of routes, locations, and messaging to provide information for tourists and maintain privacy for residents. The required consultative process may enhance senses of village cohesion.

Multi-Scale Spatio-Temporal Attention Networks for Network-Scale Traffic Learning and Forecasting.

Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets.

Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires

When a fire occurs underground, pre-existing emergency escape routes may become ineffective. Such scenarios necessitate the real-time planning of escape routes that consider evolving conditions and prioritize safety. This paper proposes a multi-objective real-time search method for emergency escape routes using dynamic programming to address these challenges. Firstly, this paper discusses how the evacuation of an underground mine fire caused by external factors depends on the fire monitoring system, personnel positioning system, and emergency evacuation system, as well as the modeling and solution methods of evacuation route planning. Then, based on fire smoke, CO, and temperature sensor data from the mine ventilation network structure and monitoring system, the possible smoke spread in the event of an underground fire was calculated. The objectives are to optimize both the shortest equivalent length of the path and the shortest time to walk through the smoke flow while ensuring that temporary escape time does not exceed the rated protection time of the self-rescuer. A mathematical model for emergency escape route planning is established under these conditions. A labeling algorithm based on dynamic programming is employed to find the Pareto optimal solution set of emergency evacuation routes that meet emergency requirements. Finally, two path evaluation indicators, namely “escape target priority” and “personnel temporary escape time”, are introduced to re-rank the solutions in the Pareto optimal set, thereby obtaining disaster evacuation routes with different priorities. Example verification shows that the algorithm can quickly solve the disaster evacuation routes that meet the actual disaster evacuation needs in complex networks. Example verification shows that the algorithm can quickly solve the emergency escape routes in complex networks that meet actual emergency escape needs.

Towards greener city logistics: an application of agile routing algorithms to optimize the distribution of micro-hubs in Barcelona

The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.

Route Planning for Intra-Hospital Patient Transportation Using Metaheuristics and Mixed Integer Linear Programming.

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.

Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives

The purpose of this study is to develop an optimal bus route search algorithm that considers both the user’s and supplier’s perspectives. The process of providing bus route service involves route network design, route allocation, and operation and management in sequence. Among these, establishing the optimal rationality for route network design in practical applications is challenging, and route modifications often occur during the operation process. To minimize these practical difficulties, this study proposes the Bus Route Sketch (BRS) methodology. This methodology, designed for network-level optimization, distinguishes itself from existing bus route setting methodologies by minimizing travel costs while taking user needs into account. This study yielded positive results, with the evaluation score improving from 8.83 to 9.50 from the supplier’s perspective and from 7.13 to 9.89 from the user’s perspective. This BRS methodology, developed to suit both route planning and operation processes, is expected to be utilized in the practical evaluation, adjustment, and design of bus routes.

Optimization of multi-vehicle charging and discharging efficiency under time constraints based on reinforcement learning

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

The whole process route planning algorithm based on AIS spatio-temporal big data analysis

ABSTRACT This paper proposes a route planning algorithm that can comprehensively integrate ship safety, economic benefits and algorithm universality. Firstly, the Polynomial Approximation with the Exponential Kernel method was used to smooth the repaired trajectory and the similarity of the smoothed trajectory was evaluated using Hausdorff distance. The results showed that the similarity of ship trajectories was effectively improved by 36.53%. Using the Douglas Pucker algorithm to compress the smoothed trajectory, the compression rate exceeded 92%. Then, the HDBSCAN self-adjusting clustering analysis method was used to cluster the ship trajectory and the silhouette coefficient index was used to quantitatively evaluate the clustering effect, the calculation result was 0.9032. Finally, using the average centre method to extract the centres of each cluster, 299 key turning points were obtained and connected to construct an undirected route network. Based on this, the Dijkstra algorithm was used to achieve optimal route planning.

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.