Route Selection Model Research Articles

Optimal route selection model in freight transport with customer collection approach using genetic and fuzzy algorithms

Optimal route selection model in freight transport with customer collection approach using genetic and fuzzy algorithms

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

The optimization method for alleviating the congestion during peak hours of Guangzhou metro line 3 is based on thorough research and analysis

The proportion of subway usage in citizens' daily travel increases everyday, but overcrowding during peak hours remains a problem. Studying the characteristics of passenger flows in subways during peak hours is meaningful and a basic requirement to ensure the operation of stations. Although there are various reasons for the surge in passenger flows during peak hours, the fundamental solution is to control the passenger volume within the acceptable range of the station. Therefore, studying appropriate flow control methods to alleviate overcrowding during peak hours is practically valuable for urban development and citizens' lives. To solve this problem, the existing solutions usually involve flow restrictions or diversion, resulting in additional consumption with limited effectiveness. This paper focuses on the overcrowding phenomenon during the evening rush hour of the subway. A route selection model is proposed based on passengers' tendency to choose the route with the minimum negative utility (long travel time, discomfort, etc.) to achieve the feasibility of reducing passenger flow during peak hours, with the objective function set as the minimum value. To optimize the model, decision nodes for constrained routes are provided based on the calculation process of the search decision points and passenger volume threshold set by facility capacity, i.e., when the actual capacity exceeds the threshold. Subsequently, reasonable restrictions are set on the decision nodes of the minimum utility function route to prevent many passengers from entering the platform. Several methods, case analysis and data importation, can effectively ensure the safe and efficient operation of the station.

A real-time automatic fire emergency evacuation route selection model based on decision-making processes of pedestrians

After a fire occurs, it is imperative that people in danger evacuate as soon as possible. However, the current emergency plan based on the pre-established static exiting route is unable to considering the real-time fire scenario. In addition, the selection of evacuation routes significantly relies on the decision-maker's experiences. These issues seriously affect evacuation efficiency, decreasing the likelihood of survival. This paper developed an effective real-time evacuation guidance method that can automatically select the evacuation route in accordance with real-time fire scenarios. The model is established based on the on-policy learning algorithm SARSA (State–action–reward–state–action), an algorithm for learning a Markov decision process policy, which could mimic the decision-making of pedestrian behaviors in an emergency. In addition, two types of radar (exit radar and fire radar) are introduced into the SARSA algorithm to facilitate the wayfinding process, which formulated the so-called Radar-assisted SARSA (RSARSA). The results have shown that RSARSA can swiftly decide a safer evacuation route for pedestrians or crowd at arbitrary location. The convergence time of initial successful route planning is between 0.05 and 4.5 s under the tests in this paper. The evacuation route determined by this algorithm can well consider the fire, and timely avoid routes with potential dangerous. Moreover, RSARSA can flexibly respond to different fires under various heat release rates and development speeds. By applying this technology, fire evacuation can be guided by routes that are more attuned to the mindset of pedestrians. It can provide a good basis for route selection of crowd evacuation.

Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

Travel Route Selection Model After the Operation of the Semarang-Demak Toll Road

The highway is an infrastructure that is very supportive for the movement of community activities. So that the government develops a road network specifically in the Java area in the form of the Semarang-Demak toll road construction. The purpose of this study is to obtain a route choice model that can explain the probability of road users choosing toll roads, and to estimate the sensitivity of route choice if there is a change in its attributes. The stated preference questionnaire was used as a medium to obtain primary data. This study uses two variables: the difference in toll rates and the difference in travel time. The survey results were processed using the binomial logit model. Regression analysis using SPSS program to obtain the utility function. The model results obtained are Utol – Unontol = -5.813 - 0.000727(X1) + 0.251(X2). The results of the utility calculation are used to obtain the probability value of choosing a travel route. If the toll rate remains or does not change, the probability of respondents to use the toll road will be greater. On the other hand, if there is an increase in toll rates, respondents tend to choose to use the national road

Optimizing the drinking water gallon distribution using vehicle routing problems with pick-up and delivery approach (Case Study of Berkah RO Drinking Water Depot)

Distribution activities play an essential role in the industry nowadays. Every company will design new effective and efficient strategies to improve its distribution systems, such as route selection, scheduling the number of vehicles, and many others. The optimal route selection model is an adequately considered issue in product distribution. The problem model commonly arises in the distribution activity is the vehicle routing problem (VRP). This distribution problem model also occurred in one of the drinking water gallon companies, Berkah RO Drinking Water Depot, where they still subjectively selected the route. In addition, the factor of damaged vehicles causes some demands for water gallons not to be fulfilled. The problem in this company is categorized as a vehicle routing problem (VRP) model with pick-up and delivery. This research uses a saving matrix and nearest-neighbor methods to solve the problem of determining the optimal vehicle route. An application is then developed based on the algorithm for determining the optimal vehicle route with a saving matrix and nearest-neighbor methods. This application aims to acquire faster results than manual calculations if each customer's demand changes. The final result of this research is determining the optimal vehicle routing with the shortest distance considering the arrival and departure time allocation and total distribution cost.

Emergency Evacuation Based on Cellular Automata

With the increasing number or scale of large-scale assembly activities, emergency evacuation has become increasingly important. In order to understand the evacuation behavior of people, this paper uses European distance, simulation and other methods to optimize the research and design of evacuation in emergency. The main contents of this paper are as follows: First, based on the principle and update rules of cellular automata, determine the three key factors of gender, age and emotional intensity, and the evacuation model based on cellular automata, and then use MATLAB software to simulate the change of people flow under different anxiety conditions, and output relevant data and visual images. Finally, the differences of the experimental results caused by the key factors are discussed. Secondly, study the evacuation rate change curve, as well as the key factors and their effects. Based on the average speed and average flow model of the system, simulate and output the corresponding visual images. Thirdly, in order to study and determine the best simulated evacuation route and the dynamic process of personnel evacuation in the case of different door widths, based on the evacuation route selection model with the shortest time to reach the two doors, the best pedestrian evacuation route is selected according to the shortest time rule. Fourth, in order to study the impact of reduced visibility in the hall on the whole process. In this paper, the regional discretization method is used to establish the perception range model under visibility and analyze the factor changes in the whole process.

Deep Learning-based Prioritized Packet Classification and Optimal Route Selection in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks suffer network congestion due to increased traffic leading to data packet loss and increased energy consumption. Thus, it is essential to have a mechanism for priority-based packet classification to improve the performance and decrease the delay in real-time packets. This paper introduces priority-based packet classification using a word embedding mechanism to extract packet semantics and classification using the long short-term memory model that works based on the different characteristics of the packet header to decide the priority of each packet. Furthermore presents a hybrid meta-heuristic algorithm termed butterfly-based rider optimization algorithm, which considers the packet priority and other parameters in optimal route selection. This hybrid meta-heuristic algorithm is developed by merging rider optimization algorithm, and butterfly optimization algorithm. Finally, the experimental results validate the analytical result as well as the performance comparison between the proposed optimal route selection model and the conventional models.

Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithm

The facility location problem is extended by a new two-stage zero-one programming system (2S-ZOPS). It is a type of design optimization issue that exists in logistics implementations such as supply chain planning in healthcare or agriculture. Along with concerns regarding PD delivery time manner for connecting logistics centers and customers, recent studies have considered the zero-one location design model. This research discussed a route selection model for the 2S-ZOPS that did not exist in the published studies by taking into account the level of risk associated with physical appearance. The mathematical models were developed in response to a PD supply chain design that occurred in Thailand’s National Health Insurance Program. By combining the virus optimization algorithm (VOA) with a large neighborhood search (LNS), we created a hybrid metaheuristic method for solving the 2S-ZOPS. Experiments with real-world data demonstrated that the hybrid algorithm was efficient in terms of time consumption and solution quality, saving approximately 6% on total costs. The presented practice benefits not only the healthcare industry but also various other businesses.

Tourism Route Selection Model for Tourism Sustainable Development Based on Improved Genetic Algorithm

The improvement of people’s quality of life also promotes the development of tourism. The traditional travel mode is no longer suitable for the needs of modern people. How to quickly determine the optimal tourist route according to the needs of different families is the current sustainable development direction of tourism. Scientific planning of tourist routes to minimize the cost and time of tourists is very important for the improvement of tourism experience. This study employs an improved genetic algorithm (IGA) to find the best tourist route based on this requirement. With the increase of tourist attractions, routes, and demands, the traditional genetic algorithm (GA) has problems such as premature convergence and poor local search ability when planning the tourist route. The existence of these problems will affect the effect of route planning. IGA proposes three improvements to traditional GA. One is to introduce the ant colony algorithm (ACA) to initialize the parameters in the GA. This algorithm is introduced to alleviate the over-reliance of GA on initializing the population and the poor adaptability of individual populations. Second, because the crossover probability parameter in GA has such a large influence on the final solution, this study proposes an adaptive strategy for adjusting the crossover probability to improve population fitness. Third, considering the weak local search ability of GA and the problem of premature convergence, this study introduces the 2-opt optimization algorithm to improve the quality of the solution. The results of the experimental analysis confirm the effectiveness of the proposed method.

Multisource data integration for targeted bus exterior advertising

Bus exterior advertising plays a significant role in outdoor advertising, since it provides frequent exposure to a large number of residents. Traditional route selection methods are generally based on a rough estimation, for example, the number of total passengers of a bus route or the geographical features along the bus route. Targeted bus exterior advertising remains a challenge as little is known about the characteristics of the people along the bus route. In this study, we are aiming at determining a set of bus routes for a given ad category to maximize advertising effectiveness, by mining multiple data sources, including mobile phone data, bus GPS data, smart card data (SCD), and land use data. Specifically, we first estimated the distribution of potential target audiences using mobile phone data and land use data. Two optimization models are proposed considering different advertising requirements. For well-established brands that audiences are familiar with, a wide coverage-oriented bus route selection model is proposed to maximize the coverage of potential target audiences. For new brands that require a high level of exposure before they become recognizable, a deep coverage-oriented bus route selection model is proposed to maximize the total exposure times of the ads. Both models were demonstrated with a case study in Shenzhen, China to explicitly present the outcomes of the models and the differences between them. The calculation results show that the wide coverage-oriented model achieves an average of 84.8% improvement compared with baseline 1 which selects the bus routes with the most passengers, while an average of 9.2% improvement compared with baseline 2 which selects the bus route with the maximum coverage of the target area in reaching more potential target audiences. The exposure intensity of the deep coverage-oriented model is almost 3.7 times of the wide coverage-oriented model. The proposed models provide new options for advertisers to select a suitable advertising strategy according to their needs.

Research on GIS-Based Route Selection Model of Tour Trails in Sanqing Mountain National Park

Research on GIS-Based Route Selection Model of Tour Trails in Sanqing Mountain National Park

Behavior analysis of travel route selection with consideration of risk aversion

In order to study the effect of travelers' attitudes on route selection behavior when risk aversion is considered in intelligent transportation system, this study provides a methodology about multi-attribute route selection for travelers with different characteristics. A multi-objective decision-making model of route selection with consideration of risk aversion is established by integrating the cumulative prospect theory to describe the traveler's risk aversion attitude and the solution algorithm based on the assignment model. A case in practice verifies the rationality of the model. The result shows that traveler's risk aversion has a significant impact on the route selection result. The result of path selection varies with the traveler's different composite impedance and comprehensive influence weight. The target sensitivity with consideration of risk aversion is stronger than that without consideration of risk aversion, and the consideration of risk aversion is closer to the actual situation of route selection. The proposed travel route selection decision-making model which considers risk avoidance can be used to study the problem of multi-type travelers and multi-attribute route selection in intelligent transportation system, also can be used as the basis for the study of traffic problems such as traffic flow allocation and network design.

Multiobjectives for Optimal Geographic Routing in IoT Health Care System

In numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted by GPS devices, geographic‐oriented multicast routing schemes were chosen, because it induces lesser overheads. Nevertheless, the extant geographic‐oriented routing models are found to have particular disadvantages. After the advent of the IoT systems for remote healthcare, medical services can be rapidly provided to patients in rural areas. The IoT network encapsulates flexible sensors in the environment to collect environmental information. This gathered sensor information is sent to the nursing stations for timely medical assistance. The IoT network is wireless, which leads to security breaches. Therefore, there is a necessity to have a secured data transmission in the context of healthcare. Hence, this study intends to propose a novel optimal route selection model in IoT healthcare by deploying optimized ANFIS. Here, the optimal routes for medical data are selected using a new self‐adaptive jellyfish search optimizer (SA‐JSO) that is the enhanced edition of the extant JSO model. Accordingly, the optimal route selection for medical data is performed under the consideration of “energy, distance, delay, overhead, trust, quality of service (QoS), and security (high risk, low risk, and medium risk).” In the end, the performances of adopted work are compared and proved over other extant schemes.

The Optimal Route Selection Model of Fresh Agricultural Products Transportation Based on Bee Colony Algorithm

In order to optimize the distribution route of fresh agricultural products and reduce the distribution cost, the optimal route selection model of fresh agricultural products transportation based on bee colony algorithm is constructed. After establishing the transportation road network model of fresh agricultural products and analyzing the transportation road information, the transportation road network zoning is realized by the dynamic zoning method of transportation road network based on spectral clustering algorithm, taking the unblocked area and congested area as the zoning purpose. Based on the traffic zoning information, the transportation route optimization model of fresh agricultural products with time window is constructed. Solved by bee colony algorithm to obtain the distribution route of fresh agricultural products with the lowest distribution cost and the highest customer satisfaction. The experimental results show that the model can choose the fresh agricultural products distribution route with the lowest distribution cost and the highest customer satisfaction under the two working conditions of smooth traffic and congestion.

The determination of rescue route based on fuzzy analytic hierarchy process (AHP)

Geological disasters as earthquake, often accompanied with the landslide collapse. Although the earthquake is unpredictable, while the relief can be the most optimal. The earthquake area, often due to earthquake, lead to instability of rock and soil mass, the rescue route is blocked. Time equals life, how to choose a best way to rescue, is extremely important. The manuscript is a kind of measure to determine the rescue route based on fuzzy analytic hierarchy process (AHP), mainly aimed at the geological disaster area, involving the instability of rock and soil mass under seismic action lead to traffic jams, establishing the best rescue route. Considering the stability of rock and soil under earthquake inducement, this manuscript establishes the model of optimal route selection; determines the influence of earthquake on slope stability through analytic hierarchy process, and establishes the corresponding model; combines matlab programming, establishes the influencing factors of earthquake on slope stability, and combines probability model to realize the choice of optimal rescue route.

Route Selection of Multimodal Transport Based on China Railway Transportation

The advantage of multimodal transport is that it can deliver the goods to their destination in a reasonable combination of transport modes while ensuring security and punctuality. Multimodal transportation can effectively reduce logistics costs, improve logistics efficiency, and reduce environmental pollution. In the process of multimodal transportation, due to the interference of natural factors (weather, terrain, etc.) and some special human factors, it may have different degrees of impact on the transportation time and transportation safety of different transportation modes. Therefore, when choosing a transportation method, it is necessary to consider the transportation time and transportation safety under the interference. However, the current research on multimodal transport has not considered the impact of external interference on transportation time and transportation safety. Compared with other modes of transportation, external interference has a relatively small impact on railway transportation. Railways can safely deliver goods to their destinations on time. Under the background of China’s huge railway network and advanced heavy-duty technology, this paper establishes a multimodal transport route selection model for considering railway as the core, introduces time penalty cost and damage compensation cost, and takes the lowest comprehensive transportation cost as the model objective under the premise of considering transportation reliability and transportation safety. Finally, taking a multimodal transport network in China as an example, an improved ant colony algorithm is used to solve the model and the results verify the rationality of the model.

The Model of Vehicle and Route Selection for Energy Saving

The World Bank, United Nations, the Organization for Economic Cooperation and Development, and others are in line with the governments of countries that are strongly interested in the sustainable development of countries, regions, and enterprises. One of the aspects that affects the indicators and prospects of sustainable development is the efficiency of energy source use. Nationwide reductions in the greenhouse gas emissions of motor vehicles could have a direct effect on ambient temperature and reducing the effects of global warming, which can affect future environmental, societal, and economic development. Significant reductions in fuel consumption can be achieved by increasing the efficiency of use, and the performance, of current cargo vehicles. This aspect is directly related to cargo delivery systems and supply chain efficiency and effectiveness. The article solves the problem of increasing the effectiveness of cargo delivery and proposes a model that would minimize transportation costs that are directly related to fuel consumption, shortening transportation time. The model addresses the problem of a lack of models evaluating the efficiency of cargo to Lithuania that is using several different modes of transportation. For the solution to this problem, the article examines the complexity of the rational use of land and water vehicles depending on the type of cargo transported, the technical capabilities of the vehicles (loading, speed, environmental pollution, fuel consumption, etc.), and the type (cars, railways, ships). The novelty of the findings is based on the availability to select the most appropriate vehicles, on a case-by-case basis, from the available options, depending on their environmental performance and energy efficiency. This model, later in this article, is used for calculations of Lithuanian companies for selecting the most rational vehicle by identifying the most appropriate route, as well as assessing the dynamics of the economic and physical indicators. The model allows for creating dependencies between the main indicators characterizing the transport process—the cost, the time of transport, and the safety, taking into account the dynamics of economic and physical indicators, that lead to a very important issue—reducing the amount of energy required to provide products and services.

Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia.

In order to better accelerate the transition from traditional trade to cross-border e-commerce, a cross-border e-commerce transportation route optimization model was designed in the context of the prevention and control of new crown pneumonia. Against the background of the new coronavirus pneumonia, through the analysis and research of the current situation of domestic and foreign e-commerce logistics, optimize the cross-border e-commerce logistics distribution model, establish an environmental model, and use efficient search algorithms to search for walking paths that meet environmental requirements. Based on the Dijkstra algorithm model of demand, and based on the linear relationship between demand and delivery distance, an optimal route selection model is established to select the optimal route with the shortest total travel distance. The simulation results show that the cross-border e-commerce transportation time of this model is within 13 h, which is shorter than that of the traditional model. The search efficiency of the optimal route for cross-border e-commerce transportation is higher, and the time for cross-border e-commerce transportation is shorter.