Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Three-Dimensional Planning of Arrival and Departure Route Network Based on Improved Ant-Colony Algorithm

In order to improve safety,economy efficiency and design automation degree of air route in terminal airspace,Three-dimensional(3D)planning of routes network is investigated.A waypoint probability search method is proposed to optimize individual flight path.Through updating horizontal pheromones by negative feedback factors,an antcolony algorithm of path searching in 3Dterminal airspace is implemented.The principle of optimization sequence of arrival and departure routes is analyzed.Each route is optimized successively,and the overall optimization of the whole route network is finally achieved.A case study shows that it takes about 63 sto optimize 8arrival and departure routes,and the operation efficiency can be significantly improved with desirable safety and economy.

Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

Unmanned air vehicles(UAVs) have been regularly employed in modern wars to conduct different missions. Instead of addressing mission planning and route planning separately,this study investigates the issue of joint mission and route planning for a fleet of UAVs. The mission planning determines the configuration of weapons in UAVs and the weapons to attack targets, while the route planning determines the UAV’s visiting sequence for the targets. The problem is formulated as an integer linear programming model. Due to the inefficiency of CPLEX on large scale optimization problems, an effective learningbased heuristic, namely, population based adaptive large neighborhood search(P-ALNS), is proposed to solve the model. In P-ALNS, seven neighborhood structures are designed and adaptively utilized in terms of their historical performance. The effectiveness and superiority of the proposed model and algorithm are demonstrated on test instances of small, medium and large sizes. In particular, P-ALNS achieves comparable solutions or as good as those of CPLEX on small-size(20 targets)instances in much shorter time.

A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles

An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility;this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Big data-driven automatic generation of ship route planning in complex maritime environments

With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.

Integrated Route Planning and Resource Allocation for Connected Vehicles

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds.Existing methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge clouds.To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route planning.In this paper,route planning and resource allocation are jointly optimized to enhance intelligent driving.We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality.In large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge clouds.In small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource allocation.The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

Three-Dimensional Computed Tomography Assessment and Planning for Severe Lower Limb Deformities: A Case Report of Bilateral Fibular Hemimelia

To correct a lower limb deformity, orthopedic surgeons must have an exact understanding of the deformity. In general, preoperative planning is carried out using anterior-posterior (AP) and lateral radiographs. However, for severe cases with a combination of angular and rotational deformities of the lower limb, obtaining true AP and lateral radiographs is difficult and accurate calculation of the rotational deformity from radiographs is impossible. In this report, we propose to focus on preoperative assessment using three-dimensional (3D) reconstruction images of computed tomography (CT) scans for severe lower limb deformity in a patient with bilateral fibular hemimelia type II according to the Achterman- Kalamchi classification. She underwent bifocal deformity corrections of the bilateral tibiae using Taylor spatial frames in combination with the Ilizarov external fixator. Complete bony union was achieved, without angular deformity or limb length discrepancy.

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing Planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based Route Planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

Saturation attack based route planning and threat avoidance algorithm for cruise missiles

According to the characteristic of cruise missiles,navigation point setting is simplified,and the principle of route planning for saturation attack and a concept of reference route are put forward.With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets,a reference route algorithm is built on the shortest range and threat avoidance.Then a route-flight-time algorithm is built on navigation points.Based on the conditions of multi-direction saturation attack,a route planning algorithm of multi-direction saturation attack is built on reference route,route-flight-time,and impact azimuth.Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.

Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information

Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.

3D-Route Planning Method to Realize Missile Cruise Flight

In order to increase the aircraft's survival in the flight mission, it is necessary to carry out flight mission planning, which includes TF/TA2(terrain following/terrain avoidance/threat avoidance). An approach to 3D-route planning based on A heuristic search algorithm was selected to determine the routes of fiber optic guidance missile's cruise segment. The cost function was discussed, which was mainly related to the physical obstacle, threat exposure, and aircraft performance characteristics. The digital map techniques were presented, which included setting “no-go” area according to fiber's safety requirement. The optimal or the sub-optimal route was obtained, while the cost function constraints were satisfied and the stored terrain obtained from a real terrain was digitized. The algorithm is validated through simulation and can fulfill the route planning task which focuses on the cruise segment of fiber optic guidance missile.

Multi-period mine planning with multi-process routes

This paper attempts to optimize optimal capacities, block routing and mine sequencing problems in a mining system. The solution approach is based on a heuristics and the mixed integer programming (MIP). Unlike previous sequential solution approaches, the problems are herein solved at the same time. Furthermore, the proposed approach guarantees practical solutions because it considers ore material distribution within orebody. The paper has two main contributions: (a) the proposed approach generates production rates in a manner that the capacities are satisfied; (b) the proposed approach does not use pre-defined marginal cut-off grades. Thus, idle capacity problem is eliminated and different scheduling combinations are allowed. To see the performance of the approach proposed, a case study is carried out using a gold data. The schedule generated shows that the approach can determine optimal production rates, block destination and sequencing effectively.

Multi-objective route planning approach for timely searching tasks of a supervised robot

To performance efficient searching for an operator-supervised mobile robot,a multiple objectives route planning approach is proposed considering timeliness and path cost.An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm(MOGA)for multiple objectives traveling salesman problem(MOTSP).Then,the path between two route nodes is generated based on the heuristic path planning method A*.A simplified timeliness function for route nodes is proposed to represent the timeliness of each node.Based on the proposed timeliness function,experiments are conducted using the proposed two-stage planning method.The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.

An optimization model of UAV route planning for road segment surveillance

Unmanned aerial vehicle(UAV)was introduced to take road segment traffic surveillance.Considering the limited UAV maximum flight distance,UAV route planning problem was studied.First,a multi-objective optimization model of planning UAV route for road segment surveillance was proposed,which aimed to minimize UAV cruise distance and minimize the number of UAVs used.Then,an evolutionary algorithm based on Pareto optimality technique was proposed to solve multi-objective UAV route planning problem.At last,a UAV flight experiment was conducted to test UAV route planning effect,and a case with three scenarios was studied to analyze the impact of different road segment lengths on UAV route planning.The case results show that the optimized cruise distance and the number of UAVs used decrease by an average of 38.43% and 33.33%,respectively.Additionally,shortening or extending the length of road segments has different impacts on UAV route planning.

Cruise missile multiple routes planning based on hybrid particle swarm optimization

In order to solve cruise missile route planning problem for low-altitude penetration,a hybrid particle swarm optimization( HPSO) algorithm is proposed. Firstly,K-means clustering algorithm is applied to divide the particle swarm into multiple isolated sub-populations,then niche algorithm is adopted to make all particles independently search for optimal values in their own sub-populations. Finally simulated annealing( SA) algorithm is introduced to avoid the weakness of PSO algorithm,which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route,multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate,and the planned routes have flat ballisticpaths and short ranges which meet the lowaltitude penetration requirements.

Overall Planning of Tour Routes: A Case Study of Tour Route Planning in Panzhihua City

Taking overall planning of tour routes as the guiding concept of regional tourism development,one core,three subjects,three sections and three levels in the overall planning of tour routes were explored. Range,planning principle,optimized layout of travel channels,construction of tourist towns,construction of scenic areas,construction of highway landscapes along travel channels,construction of tourist distribution centers in the overall planning of tourist routes for Panzhihua City,Sichuan Province were elaborated. Signifi cance of the overall planning of tour routes was summarized.

Planning of Urban Cycling Routes Aiming at Improving Cycling Quality

Urban cycling environment is an important part of urban slow-moving system,and reasonable route planning is beneficial to the health of residents.In order to improve residents’ cycling quality and enrich travel experience,the evaluation system of cycling quality in the main urban area of Handan City was established from the four aspects of convenience,safety,comfort and experience.According to different travel needs,the cycling evaluation index of road sections was calculated and modified to obtain the cycling time of road sections.The shortest route algorithm was used to generate the best riding route for convenience,the best riding route for safety,the best riding route for comfort,and the best riding route for experience.Cycling evaluation indicators were combined with travel route planning to provide residents with a variety of humanized route options,and effectively improve the cycling quality and travel health of residents,which is of great significance to promoting green travel and improving residents’ health.

Study on the Hierarchical Route Planning Underthe Emergent Threats

Hierarchical route planning method under the emergent threats is put forward for the emergent threats in the route planningproblem. First, the basic ideas and framework of hierarchical planning are put forward on the basis of constraints of routeplanning. Then, the primary flight corridor planning of the bacterial foraging algorithm based on Gaussian distribution estimationalgorithm and secondary optimal route planning based on heuristic A ＊ algorithm are put forward in this paper. The primaryplanning effectively reduces planning domain, and the secondary planning is based on the primary planning. Real-time routeplanning can be carried out quickly and efficiently and the emergent threats can be effective to deal with. Finally, the simulationresults prove that this method is not only scientific and reasonable, it also can effectively narrow the domain range and plan theroute in real-time to avoid emergent threats.