Route Length Research Articles

Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles

As urban populations continue to grow and road traffic congestion worsens, traditional ground logistics has become less efficient. This has led to longer logistics times and increased costs. Therefore, unmanned aerial vehicle (UAV) logistics has become increasingly popular. However, free-planned routes cannot meet the safety and efficiency requirements of urban airspace mobility. To address this issue, a public air route network for low-altitude logistics UAVs needs to be established in urban areas. This paper proposes a public route network planning method based on the obstacle-based Voronoi diagram and A* algorithm, as follows: Firstly, construct a city airspace grid model in which the characteristics of the airspace are mapped onto the grid map. Introduce an obstacle clustering algorithm based on DBSCAN to generate representative obstacle points as the Voronoi seed nodes. Utilize the Voronoi diagram to establish the initial route network. Then, conduct an improved path planning by employing the A* algorithm for obstacle avoidance in route edges that pass through obstacles. To ensure the safe operation of drones, set constraints on the route safety interval. This process will generate a low-altitude public air route network for urban areas. After considering the flight costs of logistics UAVs at different altitudes, the height for the route network layout is determined. Finally, the route network evaluation indicators are established. The simulation results demonstrate that compared with the city road network planning method and the central radial network planning method, the total route length is shortened by 7.1% and 9%, respectively, the airspace coverage is increased by 9.8% and 35%, respectively, the average network degree is reduced by 52.6% and 212%, respectively, and the average flight time is reduced by 19.4s and 3.7s, respectively. In addition, by solving the network model using the Dijkstra algorithm, when the energy cost and risk cost weights are 0.6 and 0.4, respectively, and the safety interval is taken as 15 m, the total path cost value of the planned trajectory is minimized.

Properties of bundle valuations in carrier collaboration

Combinatorial auctions are an important part of many frameworks for carrier collaboration in logistics. In the last years, many innovative auction mechanisms have been developed in the economic literature that could potentially be applied in this context and that could significantly simplify the process of determining efficient allocations of requests. These mechanisms rely on certain assumptions about the valuations of bidders. In this paper, we perform computational experiments to analyze to which extent these requirements are fulfilled for valuations derived from a simple logistics problem, the Traveling Salesman Problem. We find that bidders’ valuations typically violate even very elementary requirements, but that these violations affect only a limited number of request bundles. We further analyze whether these violations exhibit a consistent pattern across multiple carriers and check consistency if route lengths are calculated exactly or approximated.

Dual Protection Routing Trees on Graphs

In IP networks, packet forwarding is destination-based and hop-by-hop, and routes are built as needed. Kwong et al. introduced a protection routing in which packet delivery to the destination node can proceed uninterrupted in the event of any single node or link failure. He then showed that “whether there is a protection routing to the destination” is NP-complete. Tapolcai found that two completely independent spanning trees, abbreviated as CISTs, can be used to configure the protection routing. In this paper, we proposed dual protection routing trees, denoted as dual-PRTs to replace CISTs, which are less restrictive than CISTs. Next, we proposed a transformation algorithm that uses dual-PRTs to configure the protection routing. Taking complete graphs Kn, complete bipartite graphs Km,n, hypercubes Qn, and locally twisted cubes LTQn as examples, we provided a recursive method to construct dual-PRTs on them. This article showed that there are no two CISTs on K3,3, Q3, and LTQ3, but there exist dual-PRTs that can be used to configure the protection routing. As shown in the performance evaluation of simulation results, for both Qn and LTQn, we get the average path length of protection routing configured by dual-PRTs is shorter than that by two CISTs.

Using an exact bi-objective decoder in a memetic algorithm for arc-routing (and other decoder-expressible) problems

We address the bi-objective Capacitated Arc Routing Problem (CARP) by considering two levels of solution interpretation: implicit and explicit solutions. An algorithm that translates implicit solutions into explicit solutions is called a decoder. In this work, the decoder takes as input a permutation of the required edges and generates a Pareto frontier of CARP solutions. While bi-objective CARP was our main focus and starting point, we could also use the proposed framework to solve a bi-objective version of the traveling salesman problem by plugging-in a different decoder. Recall that bi-objective CARP asks to service (the demands of) a set of required edges using a fleet of vehicles of limited capacity so as to minimize: (i) the total travelled distance and (ii) the length of the longest route. Any permutation s of the required edges constitutes an implicit CARP solution. The decoder constructs all non-dominated explicit solutions that service the edges in the order indicated by s, i.e., the decoder is an exact algorithm that returns the optimal Pareto frontier subject to the service order s. To achieve competitive CARP results it is also important to reinforce the decoder using a local search operator that acts on explicit routes (and that may change the service order s). For nine instances, the resulting algorithm was even able to find a new total-cost upper bound, improving upon the best solutions reported in the (considerably larger) mono-objective CARP literature. This shows that (some of) the proposed ideas can also be useful in single objective optimization: the second objective can be seen as a guide for the mono-objective search process.

A Novel Method for Extracting and Analyzing the Geometry Properties of the Shortest Pedestrian Paths Focusing on Open Geospatial Data

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness, (c) road turns and intersections, and (d) road gradients. Deriving from the Hausdorff distance, a metric called the “dissimilarity ratio” was developed, allowing us to determine whether pairs of routes show any tendencies to be similar to each other. Using the “sinuosity index”, a segment-based technique quantified the route curviness based on the number and degree of the road turns along the route. Moreover, relying upon open elevation data, the road gradients were extracted to identify routes offering smoother motion and better accessibility. Lastly, the road turns and intersections were investigated as pedestrian convenience and safety indicators. A local government area of Greater Sydney in Australia was chosen as the case study. The analysis was implemented on OpenStreetMap (OSM) shortest pedestrian paths against Google Maps as a benchmark for real-world commercial applications. The similarity analysis indicated that over 90% of OSM routes were identical or roughly similar to Google Maps. In addition, while Spearman’s rank correlation showed a direct relationship between route curviness and route length, rS(758) = 0.92, p < 0.001, OSM, on average, witnessed more tortuous routes and, consequently, shorter straight roads between turns. However, OSM routes could be more suitable for pedestrians when the frequency of intersections and road slopes are at the center of attention. Finally, the devised metrics in this study, including the dissimilarity ratio and sinuosity index, showed their practicability in translating raw values into meaningful indicators.

A hybrid iterated local search algorithm for the multi-compartment vehicle routing problem

Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solution was improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 17 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods.

A genetic algorithm improvement by tour constraint violation penalty discount for maritime cargo delivery

The problem of minimizing the cost of maritime cargo delivery is considered. The cost is equivalent to the sum of the tour lengths of feeders used for the delivery. The problem is formulated as a multiple traveling salesman problem. In order to find its solution as the shortest route of the tours of feeders, a genetic algorithm is used where we present two inequalities constraining the tour length of every feeder to lie between the shortest and longest lengths. Apart from the constant tour constraint violation penalty in the genetic algorithm, we suggest a changeable penalty as an exponential function of the algorithm iteration, where we maintain the possibility of the penalty rate to be either increasing or decreasing, whose steepness is controlled by a positive parameter. Our tests show that the changeable penalty algorithm may return shorter routes, although the constant penalty algorithms cannot be neglected. As the longest possible tour of the feeder is shortened, the changeable penalty becomes more useful owing to a penalty discount required either at the beginning or at the end of the algorithm run to improve the selectivity of the best feeder tours. In optimizing maritime cargo delivery, we propose to run the genetic algorithm by the low and constant penalties along with the increasing and decreasing penalties. The solution is the minimal value of the four route lengths. In addition, we recommend that four algorithm versions be initialized by four different pseudorandom number generator states. The expected gain is a few percent, by which the route length is shortened, but it substantially reduces expenses for maritime cargo delivery.

Urban traffic flow management on large scale using an improved ACO for a road transportation system

PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

On strategies to help reduce contamination on public transit: a multilayer network approach

In times of a global pandemic, public transit can be crucial to spreading viruses, especially in big cities. Many works have shown that the human infection risk could be extremely high due to the length of exposure time, transmission routes, and structural characteristics during public transportation, and this can result in the rapid spread of the infection. Vaccines are often part of strategies to reduce contagion; however, they can be scarce in pandemic settings. Considering real-world and large-scale traffic data, this work proposes using time-varying multilayer networks to identify the main critical places to be prioritized in interventions, such as vaccination campaigns, to help reduce contagion on public transit. We exemplify our strategy in different vaccination scenarios. First, when considering only critical bus stops as priority vaccination points, determined by our approach, we indicate that focusing on vaccination in these locations reduces the spread of infection using fewer doses than a random vaccination. In another experiment, we demonstrate the flexibility of our approach in identifying other critical points of interest, healthcare units in this case. Vaccination in these vital health units could also be a viable strategy to curb contagion using a predetermined number of doses. The approach proposed in this study is not limited to vaccination strategies. It also applies to other problems that share similar properties, even in several different contexts, such as optimization in public transit or exploring different points of interest to gather insights from other issues of interest.

Research on the Distribution of Emergency Supplies during the COVID-19 Epidemic

The COVID-19 virus spreads quickly and is highly contagious, and the timeliness of emergency supplies is extremely demanding, so adequate and efficient emergency supplies have become a necessary prerequisite for strict prevention and control during the outbreak. However, the available research results are still relatively few, and using the brain storm optimization algorithm to solve the emergency supplies distribution problem is still blank. In order to optimize the emergency supplies distribution system in the context of COVID-19, this paper studies the open vehicle routing problem by considering the load weight constraints, demand constraints, and time window constraints. With the shortest total distribution distance as the objective function, a mathematical model of mixed integer linear programming is established, and the crossover operation of the genetic algorithm, the destruction operator and the repair operator of the large neighborhood search algorithm are introduced in the brain storm optimization algorithm to enhance the diversity of the solution while making the solution toward the optimal direction. Through the verification of two sets of examples, we found that compared with the other 13 solution algorithms, the solution capability and solution efficiency of the brain storm optimization algorithm are better than the comparison algorithms, which can significantly reduce the length of the distribution route and lower the distribution cost, and has excellent convergence and stability. Meanwhile, it can effectively improve the distribution efficiency of emergency supplies, which is of great significance to help the emergency reception and regular prevention and control during the outbreak of the COVID-19 epidemic. In addition, it has the necessary guidance for the improvement of personnel efficiency in the supply chain and logistics industry.

CHOICE OF OPTIONS OF LOCOMOTIVE CHARACTERISTICS TAKING INTO ACCOUNT OPTIMUM EFFICIENCY

The saturation of the locomotive park of railways with various series of locomotives does not always correspond to their effective use given their inherent special characteristics. Modern conditions require all participants in design, construction, operation and repair to make correct and well-founded decisions. The variety of series of locomotives, which provide different requirements of economic activity, is not always the basis of their effective operation. This especially applies to locomotives that are purchased abroad. As the experience of their operation has shown, they are not always adapted to the real conditions of operation in our country. This is, first of all, their intensive exploitation and the lack of adaptability of the repair base to maintenance and repair of rolling stock. On the other hand, the capacity range, for example, of the freight locomotives that were purchased, does not correspond to the traditional ones, which take into account both the length of the warehouse and the length of the receiving-departing routes, etc. In our country, sufficient experience has been accumulated and there were appropriate conditions for a comprehensive coverage of the tasks of both design and construction of locomotives for the provision of industrial enterprises in freight transportation, as well as the population in intercity and suburban transportation. Unfortunately, there are many factors that currently make even the minimal needs for updating both the locomotive fleet and the modernization of the operational and repair activities of railway enterprises impossible. In these conditions, perhaps the most important task is the correct, justified choice of locomotive characteristics, taking into account its technical condition. The article deals with the procedure of forming requirements for the characteristics of locomotives based on the efficiency criterion. Based on the verification of the conditions of the corresponding substitutions for the dimensionless scales of the pure indicators of the locomotives, an effective set of some solutions is determined, on the basis of which the optimal solution is formed for the selection of locomotive characteristics for specific conditions of use. Based on specific data shown decision-making procedures regarding some characteristics of locomotives.

Economic comparison of electric fuels for heavy duty mobility produced at excellent global sites - a 2035 scenario

The transformation of economies worldwide towards climate neutrality will require not only renewable electricity but also climate-neutral energy carriers such as hydrogen and its derivatives. The production cost of electric fuels (e-fuels) are to a large extent driven by the energy-intensive electrolytic water splitting. The option of producing e-fuels in highly industrialized countries, as Germany, competes with production at international locations, with excellent conditions for renewable energies and thus very low levelized cost of green hydrogen and its derivatives. In this paper, we examine the economic efficiency of various imported e-fuels that are under consideration in different scenarios for the year 2035: Fischer-Tropsch diesel, methanol, and hydrogen transported as cryogenic liquid or in form of liquid organic hydrogen carriers. We develop a mathematical model that covers the entire process chain, from the production of the e-fuels at various excellent locations worldwide to the energetic utilization of the fuels in a heavy duty vehicle. We find that the choice of production site has a major impact on the mobility cost using the respective fuels. Under the selected boundary conditions and assumptions, methanol, cryogenic hydrogen, and liquid organic hydrogen carriers are the most favourable options for all seven locations examined. Which e-fuel is cheapest varies with the production site. Especially in case of diesel, the levelized cost of electricity determined by the full load hours of the applied renewable energy source have a huge impact. An LOHC-based system is shown to be less dependent on electricity source compared to other technologies due to its comparatively low electricity consumption. The length of the transportation route and the price of the filling station infrastructure, however, increase mobility cost for LOHC and LH2.

Charging Station Location and Sizing for Electric Vehicles Under Congestion

This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .

No food left behind: foraging route choices among free-ranging Japanese macaques (Macaca fuscata) in a multi-destination array at the Awajishima Monkey Center, Japan.

Animals must make route choices every day when moving through their habitat while foraging. Choosing an optimal route can be cognitively costly, and primates and other animals have been shown to use simple heuristics, "rules of thumb", to make foraging route choices. We investigated the potential use of heuristics among foraging free-ranging Japanese monkeys (Macaca fuscata) during solitary foraging trials. We also investigated the potential influence of individual variables (age and sex) and social variables (presence in the central group, presence of potential inter- and intraspecific competitors), on the use of heuristics, route length and trial time. We used a multi-destination foraging experiment with 6 platforms in a (4m×8m) Z-array, completed by 29 Japanese macaques in 155 runs at the Awajishima Monkey Center in Japan. Our results showed that the macaques chose routes consistent with heuristics (e.g. nearest neighbour heuristic 19.4%, convex hull heuristic 4.5%) and selected optimal routes (shortest path in 23.9% of the trials). We also identified a potential new heuristic that was used most frequently, that we termed the "sweep heuristic" (27.1% of trials), which we interpreted as a strategy to deal with competitive foraging trade-offs - choosing routes to prioritize not leaving isolated food pieces behind. Age was significantly related to trial time; juvenile macaques were faster than adults and young adults, using speed to gain access to resources. Solitary trials with conspecifics present took significantly longerroutes. Our results suggest that contextual factors led to variation in Japanese macaque decision-making, and we suggest that the preferential use of a sweep heuristic may have been a response to high intragroup competition.

Research and Evaluation on Dynamic Maintenance of an Elevation Datum Based on CORS Network Deformation

This paper presents a method for dynamically maintaining a regional elevation datum using CORS stations as core nodes. By utilizing CORS station data and surface mass loading data (including land water storage, sea level, and atmospheric pressure), the normal height changes of each station can be determined and dynamically maintained. The validity of this method is verified using multiple leveling survey results from five CORS stations in Beijing’s subsidence area between January 2012 and June 2021. Results show that it is necessary to derive and correct the height anomaly variation of CORS stations caused by surface mass loading using the remove-calculate-restore method and the Green’s function integration method, with the influence of surface mass changes reaching a subcentimeter level. CORS stations exhibiting great observation quality achieve a mean accuracy of 2.7 mm in determining normal height changes. Such accuracy surpasses the requirements of second-class leveling surveys covering route lengths exceeding 1.35 km, as well as conforming/closed loop routes with distances greater than 0.46 km. By strategically selecting CORS stations with long-term continuous observations and high-quality data as core nodes within the elevation control network, dynamic maintenance of the regional elevation datum can be achieved based on CORS station data.

Optimization of Simultaneous Pickup and Delivery Vehicle Routing with Three-Dimensional Balanced Loading Constraints

In order to promote the cost reduction and efficiency improvement of the logistics distribution process and to guarantee the safety of goods transportation, this paper studies the portfolio optimization of goods loading and the problem of simultaneous pickup and delivery vehicle routing. A balanced loading constraint was introduced to restrict loading through two aspects of axle weight bearing and lateral center-of-gravity offset. With the shortest total route length as the objective, this paper constructs a simultaneous pickup and delivery vehicle routing model with three-dimensional (3D) balanced loading constraints (3BL-VRPSPD). Additionally, a hybrid tabu search (TS) algorithm embedded loading test was proposed to solve this problem. Firstly, a heuristic insertion method was applied to determine the initial routing scheme, and the node swapping and relocation operators were designed to construct the tabu neighborhood scheme for routing optimization. On this basis, the 3D balanced loading was incorporated into the routing iteration process. A balanced loading algorithm, combining multiple-indicator ordering and maximum space division strategies (MOMD), was formulated to develop a 3D-balanced loading plan for goods with a pickup and delivery vehicle routing scheme. Finally, standard instances verified the effectiveness of the method. The results show that the proposed method can effectively optimize 3BL-VRPSPD and outperform other algorithms.

Collaborative Intelligence Enabled Routing in Green IoV: A Grid and Vehicle Density Prediction-Based Protocol

Green Internet of Vehicles (IoV) is a newly-emerged research area which focuses on reducing networking overhead and improving communication efficiency in future vehicular cyber-physical systems (VCPS). A critical requirement to realize such tasks in Green IoV is the achievement of effective routing protocols for data dissemination. However, maintaining end-to-end communication and reducing the communication overhead at the same time is quite challenging due to high vehicle dynamics and complex traffic environments. In this paper, we propose a vehicle density prediction-based routing protocol called VDPGrid. Firstly, we introduce a vehicle density prediction model according to the spatio-temporal features of vehicle trajectories. Then, to reduce the communication overhead and improve communication efficiency, we divide the map into grids and present a routing path evaluation scheme that jointly considers the vehicle density, link quality, and routing length. Moreover, a grid-based routing method is proposed to select the optimal relay node according to real-time traffic information. Finally, extensive experiments using real-world vehicle trajectories are conducted to validate the effectiveness of our method.

OQ‐IICA: Optimal QoS‐aware intra‐inter cluster data aggregation technique for IoT‐assisted WSNs using hybrid optimization techniques

SummaryThe wireless sensor network (WSN) contains sensor nodes for understanding, communicating, and storing battery capacity data. Data collection can be defined as the process that base stations use to eliminate unwanted transmissions and provide mixed information. This improves energy efficiency and extends the life of low‐energy WSNs enabled by IoT (IoT‐WSNs). In this article, we propose an optimal QoS‐aware intra‐inter cluster data aggregation technique for WSNs using hybrid optimization techniques (OQ‐IICA). First, we introduce a modified bowerbird optimization (MBO) algorithm for balanced clustering which improves energy efficiency. Second, we develop a multi‐objective seagull optimization‐based decision‐making (MSO‐DM) algorithm to estimate the CH of clusters in the network. Next, we introduce a teacher‐inspired cappuccino search algorithm to ensure the quality of data transfer between nodes by learning through internal and cluster routing. Finally, the proposed OQ‐IICA algorithm compares the latest technologies in ICA, Leach, and Leach‐C power consumption, latency, throughput, latency, number of live nodes, routing, and grid length.

Research on the influence of pilot workload on local route path planning of helicopters in low-altitude penetration

Targeting the problem of high pilot workload caused by trajectory planning of low altitude penetration, this paper proposed a trajectory planning method considering the pilot workload. Firstly, by decomposing the three-dimensional trajectory into horizontal and vertical directions, the trajectory tree method based on the dynamic programming technique is used for horizontal trajectory planning, and the comprehensive slope-curvature limitation method is used for altitude trajectory planning. Then, a method was proposed to quantify the control inputs and pilot workload based on trajectory tracking simulation. The pilot workload cost, together with flight altitude cost and route length cost, was added to the local planning of a reference trajectory. To solve the problem of dimension explosion in trajectory tree search, a segmented optimal route tree algorithm is proposed. Finally, the simulation results show that the proposed method can effectively consider the pilot workload in trajectory planning, which is helpful to improve the flight ability of the planning trajectory and reduce the pilot workload at the same time.

Comparison of New Solutions in IP Fast Reroute

Currently, network requirements are placed on the efficiency and size of the networks. These conditions can be ensured by modern converged networks that integrate the functions of both data and telecommunication networks. Line or router failures have always been a part of transmission networks, which is no different from converged networks. As a result of outages, which can take from ms to tens of seconds, packets are lost. These outages cause degraded transmission quality, which is undesirable when transmitting real-time multimedia services (Voice over IP, video). To solve the mentioned problems, the IETF organization has developed IP Fast Reroute mechanisms to minimise the time to restore the connection after a line or node failure and, consequently, less packet loss. The article reviews and compares the latest IP Fast Reroute mechanisms deployed in the last three years. First, we have Optimistic Fast Rerouting, which calculates optimistic and fallback scenarios. The second is Post-processing Fast Reroute, which decomposes the network according to metrics such as load and route length. Third, Local Fast Reroute focused on low congestion and random access.