Route Planning Method Research Articles

Integrating Human Behavior and Safety Measure into Evacuation Route Planning in a Volcanic Crisis

Many traditional methods in evacuation route planning are motivated by the operational objective, such as total travel/clearance time. Little attention has been given to the human factor and the safety aspect of the evacuees during the planning phase. Our study aims to propose a simple yet practical route planning method that simultaneously considers human behavior, safety factor, and the travel time in a volcanic crisis context. The planning model is developed based on the shortest-path problem with a joint-cost parameter representing the three aspects. We present a large-scale street network in Merapi volcano as a case study. The result implies that employing a joint-cost parameter is effective for creating an evacuation route that is reasonably safe and in line with human cognition in navigation. The finding offers practical insights for the stakeholders as part of the greater effort to develop a systematic disaster management plan.

A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing

With the background of hyperurbanization and a jobs–housing imbalance in Beijing and other megacities in China, this study aims at developing a systematic toolkit of demand estimation and route planning for long-distance commuter bus lines. Taking the express bus services (EBS) in the Changping Corridor in Beijing as an example, this paper presents the use of a route-planning algorithm. Mobile phone location (MPL) data serves as a valid instrument for the origin–destination (OD) estimation, which provides a new perspective to identify the locations of homes and jobs. The OD distribution matrices are specified via geocoded MPL data. To minimize the aggregated travel time and attract potential passengers, this study subsamples long-distance commuting trips among the OD matrices by determining the operation distance threshold. The threshold can be solved by comparing the aggregated travel time of EBS to the travel time of local buses and private cars. Using the distance threshold and other operational parameters, we process an iterative computation to determine the length of routes, number of lines, and stop spacing and location. Compared to local buses and private cars, the planned EBS lines could reduce aggregated travel time by at least 20%. The results demonstrate that the method presented in this study is applicable and flexible. This paper may lead to new research directions for route planning of long-distance commuter buses. The limitations of this study and the future research agenda are also discussed.

Sailing Route Planning Method Considering Various User Categories

Abstract The article describes the methodology related to determining the multi-criteria routes for sailing ships. Details of sea area discretisation and discretisation of the description of the sailing vessel properties and manoeuvring principles are shown. User requirements were specified (for five different categories of users) and on this basis the criteria for selecting the most suitable shipping route were formulated. The presented algorithm recommends a route for a given user category by means of defined restrictions and configuration parameters. The applied multi-criteria approach proves the universality and usability of the sailing ship route planning method.

DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION

Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

A distributed location obfuscation method for online route planning

A novel location obfuscation method for online route planning is proposed which is robust to privacy inferences by the service provider regarding route source and destination. This is achieved by performing the task of route computation in a distributed manner. Specifically, the client decomposes the required route into a sequence of shorter routes between intermediate locations. These routes are subsequently requested from independent online route planners with the results being integrated by the client to give the route originally required. Robustness to privacy inferences is a consequence of the fact that, without significant coordination and sharing of information, an individual online route planner cannot infer with high probability the true route source or destination. An evaluation of the proposed method is performed in the context of route planning within the street network of Boston. This evaluation demonstrates that the proposed method offers robustness to privacy inferences while exhibiting a reasonable reduction in quality of service.

Surface roughening of Nafion membranes using different route planning for IPMCs

ABSTRACT In this paper, three different roughening route planning methods (free coarsening, vertical coarsening, circling coarsening) were designed to pre-treat the basement membrane. The platinum IPMC was prepared by electroless plating to observe their surface morphology and test their performances such as output displacement and blocking force. The results show that the platinum electrode of the vertical coarsening route planning of IPMC is distributed in the shape of fish scales and is the most compact and regular on the surface electrode. The thickness of the platinum electrode layer is about 13 μm, the maximum output displacement is 54.89 mm, the blocking force of 15.46 mN, and the maximum strain energy density of the IPMC of 2.44 KJ/m3. Vertical coarsening route planning can significantly improve the electrodynamic properties of IPMC, and it is recommended that the surface roughening of IPMC be utilized in the research and application of IPMC. It lays a certain experimental foundation for the performance improvement and innovation research and development of IPMC in the subsequent stage.

Optimal Express Bus Routes Design with Limited-Stop Services for Long-Distance Commuters

This research aimed to propose a route optimization method for long-distance commuter bus service to improve the attraction of public transport as a sustainable travel mode. Taking the express bus services (EBS) in Changping Corridor in Beijing as an example, we put forward an EBS route-planning method for long-distance commuter based on a solving algorithm for vehicle routing problem with pickups and deliveries (VRPPD) to determine the length of routes, number of lines, and stop location. Mobile phone location (MPL) data served as a valid instrument for the origin–destination (OD) estimation, which provided a new perspective to identify the locations of homes and jobs. The OD distribution matrices were specified via geocoded MPL data. The optimization objective of the EBS is to minimize the total distance traveled by the lines, subject to maximum segment capacity constraints. The sensitivity analysis was done to several key factors (e.g., the segment capacity, vehicle capacity, and headway) influencing the number of lines, the length of routes. The results suggest that the scenario with the segment capacity of 4000 passengers/h has a minimum of number and length of lines, but we recommend that the transit agency adopt 3000 passengers/h as the route segment capacity because this scenario results in minimum fleet size and minimum total operation length.

Deep-Learning-Based Joint Optimization of Renewable Energy Storage and Routing in Vehicular Energy Network

Recent development in renewable energy-enabled electric vehicles (EVs) has posed challenges to the stability and efficiency of the vehicular energy network (VEN), which is a concrete implementation of Internet of Things (IoT) in energy and vehicular networks. In this article, we study a VEN with time-varying point-to-point traffic flow and adjustable energy storage capacity at stations. The goal is to jointly optimize the routing and dynamic storage allocation of renewable energy so as to maximize the efficiency of plant-to-station energy transferring. We first adopt a time-expanded topology graph to describe the scenario and model it as a maximum flow problem. Next, we incorporate routing in our methodology and derive a joint energy storage capacity and route planning method based on linear programming. Then, the problem is extended to a more general case, where the traffic pattern of the VEN is unknown. We apply the long short-term memory model, a deep learning method, to predict the traffic pattern and utilize the concept of reinforcement learning to iteratively improve the prediction accuracy. To evaluate the performance, we implement our method first on regular buses, then extend to EVs based on the real trace data from the PeMS system in California. The simulation results show that the joint optimization can achieve near-optimal performance and performs well even in case of high rate of missing traffic information, with the help of deep reinforcement learning.

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.

A Tourism Route-Planning Approach Based on Comprehensive Attractiveness

In recent years, “free travel” has been increasingly popular. How to plan personalized travel routes based on the perspective of tourists, rather than that of tourism intermediaries, is in great need. However, some factors reflecting tourists’ preferences are ignored in the related work. What’s more, the evaluation about scenic spots is incomplete. Besides, real data sets are seldom used in existing works. We propose a novel route-planning method that considerate multiple factors (that is, the distance between sites, initial travel position, initial departure time, time duration of tour, total cost, scores and popularities of sites) comprehensively, and routes were rated by what we call a comprehensive attractiveness index. We conducted comprehensive case studies based on the real-world data of sites from the Baidu and Xiecheng websites and found that our proposed method is feasible. It is also found that the genetic algorithm outperformed two baseline ones in terms of run time.

A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV). To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm. It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed. In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning. Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability. The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments.

Route planning methods for a modular warehouse system

In this study, procedures are presented that can be used to determine the routes of the packages transported within a modular storage system. The problem is a variant of robot motion planning problem. The structures of the procedures are developed in three steps for the simultaneous movement of multiple unit-sized packages in a modular warehouse. The proposed heuristic methods consist of route planning, tagging, and main control components. In order to demonstrate the solution performance of the methods, various experiments were conducted with different data sets and the solution times and qualities of the proposed methods were compared with previous studies. It was found that the proposed methods provide better solutions when taking the number of steps and solution time into consideration.

A Vehicle Route Planning Method of Two-Phase Large-Scale Crowd Evacuation in Typhoon Relief Activities

Large-scale crowd evacuation is an important measure guaranteeing the safety of disaster-stricken victims in typhoon relief activities. Decision-making related to antityphoon crowd evacuation must take full consideration of the destructive effect of typhoons and their secondary disasters, time urgency, and resource limitation. To give full play to limited vehicle resources, the influence of a typhoon and its secondary disasters on antityphoon evacuation are mainly manifested during the execution of evacuation tasks in this article. The shortest time spent in completing all evacuation tasks was taken as the objective. Then, a vehicle route selection model for two-phase large-scale antityphoon crowd evacuation was built under an uncertain environment, and a matrix encoding–based genetic algorithm was designed to solve the model. Under the background of Super Typhoon Meranti in 2016, the model and algorithm were applied to crowd evacuation in a typhoon in Xiamen for a simulated analysis. Results indicate that in typhoon relief activities, emergency decision makers can use the proposed method to acquire a scientific and reasonable route selection scheme for antityphoon crowd evacuation according to related typhoon disaster data.

Optimization Method for Reducing the Air Pollutant Emission and Aviation Noise of Arrival in Terminal Area

In order to reduce the environmental impact of aircraft operation in the terminal area, this paper researched the route optimization method. Firstly, this paper constructed the air pollutant emission and noise assessment model, and the flight performance model. Secondly, aiming at reducing air emissions and noise level, the multi-objective terminal area route optimization model is established based on the principles of flight safety and flight procedure construction. Then this paper puts forward the path optimization method of emission and noise reduction of terminal area route network, through the research on the priority setting method of terminal area approach and departure route planning. The route segmentation method and NSGA-II algorithm are employed to solve the problem. Finally, a numerical case study is carried out for the Shanghai terminal area, and yields the following results: (1) Compared with the original route network, the optimized route network in the terminal area can significantly reduce emission and noise by reducing pollutant emission by 51.4% and noise influence by 21.5%; (2) The method can also reduce fuel consumption by 60.5% and the total route length by 21.1%.

Optimal route planning for image-guided EBUS bronchoscopy

The staging of the central-chest lymph nodes is a major lung-cancer management procedure. To perform a staging procedure, the physician first uses a patient's 3D X-ray computed-tomography (CT) chest scan to interactively plan airway routes leading to selected target lymph nodes. Next, using an integrated EBUS bronchoscope (EBUS = endobronchial ultrasound), the physician uses videobronchoscopy to navigate through the airways toward a target node's general vicinity and then invokes EBUS to localize the node for biopsy. Unfortunately, during the procedure, the physician has difficulty in translating the preplanned airway routes into safe, effective biopsy sites. We propose an automatic route-planning method for EBUS bronchoscopy that gives optimal localization of safe, effective nodal biopsy sites. To run the method, a 3D chest model is first computed from a patient's chest CT scan. Next, an optimization method derives feasible airway routes that enables maximal tissue sampling of target lymph nodes while safely avoiding major blood vessels. In a lung-cancer patient study entailing 31 nodes (long axis range: [9.0 mm, 44.5 mm]), 25/31 nodes yielded safe airway routes having an optimal tissue sample size = 8.4 mm (range: [1.0 mm, 18.6 mm]) and sample adequacy = 0.42 (range: [0.05, 0.93]). Quantitative results indicate that the method potentially enables successful biopsies in essentially 100% of selected lymph nodes versus the 70–94% success rate of other approaches. The method also potentially facilitates adequate tissue biopsies for nearly 100% of selected nodes, as opposed to the 55–77% tissue adequacy rates of standard methods. The remaining nodes did not yield a safe route within the preset safety-margin constraints, with 3 nodes never yielding a route even under the most lenient safety-margin conditions. Thus, the method not only helps determine effective airway routes and expected sample quality for nodal biopsy, but it also helps point out situations where biopsy may not be advisable. We also demonstrate the methodology in an image-guided EBUS bronchoscopy system, used successfully in live lung-cancer patient studies. During a live procedure, the method provides dynamic real-time sample size visualization in an enhanced virtual bronchoscopy viewer. In this way, the physician vividly sees the most promising biopsy sites along the airway walls as the bronchoscope moves through the airways.

Research on Route Planning Method of Dual Aircraft Rescue Based on Block Chain Technology

The dynamic change of rescue environment after disaster can greatly improve the efficiency of rescue by adjusting flight routes in real time according to the latest rescue environment and rescue tasks detected by aircraft. In this paper, the block chain technology is used to complete the real-time transmission of dynamic tasks and environmental information, and then the AI algorithm is used to realize the dynamic adjustment of flight route: realizing the real-time adjustment of flight route under the circumstances of dynamic change of rescue mission and deterioration of planning environment; the experimental results show that with the real-time transmission of threat information, threat detection and processing are realized, and threat information is localized. In this way, the transmission time of threat information and mission information and command center is shortened, and the efficiency and reliability of rescue are improved.

Research on Laser Thread Form Bending of Stainless Steel Tube

Compared to traditional bending methods, laser bending can yield products with better shapes and higher quality using the simpler equipment. In this paper, the mechanism of Laser-assisted tube bending is elaborated, and then the influence of processing parameters (including laser power, scanning speed, spot diameter, and scanning times) on the bending angle is studied with both the finite element method and experiments. The bending angle increases with an increase of laser power and spot diameter and with a decrease in the scanning speed. Three-dimensional (3D) thread form bending can be achieved by planning the optimal route, scanning parameters, and the number of laser scans. In contrast to the desired thread diameter of 20 mm and thread pitch of 180 mm, the resulting thread of the stainless steel tube is close to the target shape with errors of 3% for thread diameter and 2.5% for thread pitch, which are both in an acceptable range and thus verify the validity of the parameters selection and route planning method.

Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments.

In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.

一类新算法研究智能飞行器航迹规划问题

一类新算法研究智能飞行器航迹规划问题

Optimizing emergency rescue and evacuation planning with intelligent obstacle avoidance in a chemical industrial park

A major chemical accident has the characteristics of being destructive, and potentially provoking a great loss of lives and property damage in any Chemical Industrial Park (CIP). Emergency rescue and evacuation are essential parts of emergency decision-making for enhancing the capacity and effectiveness of emergency handling and reducing the potential loss of accidents. Most of current literature concentrates on one-way route planning of emergency rescue and evacuation, and applies different models, optimization objectives and algorithms. However, when applying the one-way route planning model in a CIP, a road conflict is possible due to the inherent weak traffic capacity. Therefore, a new method of two-way route planning of emergency rescue and emergency evacuation which considers intelligent obstacle avoidance, is proposed in the paper. The method we developed integrates three modeling components: (i) a dynamic grid environment model to simulate the interaction between the road network and the time-varying location of emergency rescue and evacuation. (ii) a two-way route planning model to simultaneously optimize routes of emergency rescue and routes of emergency evacuation. (iii) an intelligent obstacle avoidance model to prevent potential road conflicts. The results illustrate that the proposed model is able to generate a set of two-way optimum routes and overcomes possible road conflicts successfully.