Route Planning System Research Articles

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

A design science-based case study of retail chain delivery operations and its implications

PurposeThe study aims to explore and develop a smart route planning system for the cross-docking delivery operations of a large supermarket chain using an action research (AR) approach and assessing through a design science research (DSR) lens.Design/methodology/approachThis study took a problem-solving AR (PAR) approach toward the delivery operational issue of the case firm. The research process has accorded with the solution incubation and the refinement phases defined by a DSR framework. An intervention-based research framework for DSR is developed to assess the validity of this study as a DSR research and derive mid-range theories.FindingsDramatic operational and financial improvements were achieved for the case firm. Significant and unintended environmental and social benefits were also found. A design proposition (DP) and several mid-range theories are proposed as an extension of AR research to DSR research.Research limitations/implicationsA problem-solving DSR research can be better assessed by the intervention-based DSR framework developed in this study. DSR studies should be encouraged for both practical and theoretical advancement purposes.Practical implicationsA challenging business problem-solving study can be tackled effectively through an industry/academic collaboration taking a PAR approach to deliver substantial values and organization transformational results.Social implicationsDrivers and store associates are safer with smart delivery operations in the case firm.Originality/valueThere are still limited PAR design science case studies in the supply chain/logistics research literature. The research experience and findings gained from this study provide more insights toward how this type of research can be conducted and assessed.

A Dynamic Route-Planning System Based on Industry 4.0 Technology

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

Application of Ant Colony Algorithm to Air Route Planning in Helicopter Submarine Searching

Air route planning system is the core of antisubmarine call-search mission which is carried out by an ASW helicopter, and the efficiency of mission completion is decided by the algorithm adopted. Considering actual army training and combat, this paper analyses the basic elements of responding-antisubmarine air route planning system systematically, constitutes assessment index of air route planning system, presents the constraint conditions which should be considered by the system and carries out simulation calculation by ant colony algorithm. Finally, this paper summarizes the applicable circumstances of responding-antisubmarine air route planning methods based on ant colony algorithm, which will be of great value to improve army combat and training efficiency.

Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

Real-Time Cache-Aided Route Planning Based on Mobile Edge Computing

Route planning is considered as one of the fundamental technologies in the navigation system, which finds an optimal route between a pair of source and target locations. Navigation services are required to provide real-time responses to route planning queries to promote user experiences on the road under different situations, such as sudden detour, unpredictable traffic congestion and loss of GPS signals. However, most commercial navigation products search the optimal path at the remote central server which suffer from several inherent limitations. First, the communication between the access network and the remote central server has a large uncertain Internet-induced time delay. Second, the computational cost of retrieving an optimal path is increasing exponentially with the distance from the source location to the destination in a large-scale road network. To address the above issues, we propose a real-time Cache-Aided Route Planning System based on Mobile Edge Computing (CARPS-MEC), aiming to greatly shorten the communication and computation time of route planning queries by caching those frequently requested paths. Different from traditional cache based route planning algorithms which require an exact path matching from point to point, CARPSMEC makes a rough path matching from region to region. Thus, it only needs to process unmatched road segments on a MEC server which is closer to the end users. This will significantly reduce the transmission latency due to the uncertainty of the Internet. Experiment results demonstrate that CARPS-MEC can increase the cache hit ratio and reduce the response time greatly.

Fuzzy polychromatic sets constraint model for route planning problem

Unmanned aerial vehicle route planning is a complex multiconstrained multiobjective optimization problem. Due to the complexity of various constraints and the mutual coupling between them, the expression of constraint conditions is not universal and normative. The development, maintenance, and upgrading of an existing route planning system are very difficult. In this article, by establishing the polychromatic sets of aircraft, aircraft equipment, and flight actions, creating the fuzzy relational matrix between equipment and actions and between actions and actions, this article realizes the standardized and generalized expression of the constraint condition of the route planning problem. Then the analysis and inspection of the constraint conditions are realized by the polychromatic sets operation rules.

Novel Route Planning System for Machinery Selection. Case: Slurry Application

The problem of finding an optimal solution for the slurry application process is casted as a capacitated vehicle routing problem (CVRP) in which by considering the vehicle’s capacity, it is required to visit all the tracks only once to fully cover the field, as well as complying with a specified targeted application rate. A key objective in this study was to determine an optimized coverage plan in order to minimize the driving distance in the field, while at the same time allowing for varying the application rate. The coverage plan includes the optimal sequence of tracks with a specified application rate for each track. Two algorithms were developed for optimization and simulation of the slurry application cast as capacitated operations. In order to validate the proposed algorithms, a slurry application operation was recorded, and the results of the optimization algorithm were compared with the conventional non-optimized method. The comparison showed that applying the proposed new method reduces the non-working distance by 18.6% and the non-working time by 28.1%.

Route planning and track keeping control for ships based on the leader-vertex ant colony and nonlinear feedback algorithms

This paper aims at rapid route planning and tracking system design of intelligent ship. For rapid route planning, leader-vertex ant colony optimization (LVACO) algorithm is proposed based on the ant colony optimization (ACO), which ensures a leader of ant colony and optimizes the route by vertex method. For track keeping control, the indirect track keeping controller is designed, which utilizes the nonlinear feedback control and line of sight (LOS) algorithm. The nonlinear feedback control means that the feedback error of controller is decorated by the bipolar sigmoid function. Simulation results show that the route given by LVACO algorithm is less time to use and more suitable for navigation. Besides, compared with back-stepping control, the nonlinear feedback control saves more energy and mitigates abrasion of steering gear, simultaneously, obtains a better control effect.

A multi-objective coil route planning system for the steelmaking industry based on evolutionary algorithms

A multi-objective coil route planning system for the steelmaking industry based on evolutionary algorithms

Entropy-Based k Shortest-Path Routing for Motorcycles: A Simulated Case Study in Jakarta

Traffic congestion is a serious problem in rapidly developing urban areas like Jakarta, Indonesia’s capital city. To avoid the congestion, motorcycles assisted with navigation apps are popular solution. However, the existing navigation apps do not take into account traffic data. This paper proposes an open-source navigation app for motorcycle by taking into account the traffic data and wide road to avoid congestion. The propose navigation app uses entropy-balanced k shortest paths (EBkSP) algorithm to suggest different routes to different users to prevent further congestion. Tests show that the proposed route planning system in the app gives routes that are significantly shorter than motorcycle routes planned by Google Maps. The EBkSP algorithm also distributes vehicles more evenly among routes than the random kSP algorithm and does so in a practical amount of computing time.

Internet GIS-Based Multimodal Public Transport Trip Planning Information System for Travelers in Lithuania

The main purpose of this research is to present the developed VINTRA system, a comprehensive solution to a fully developed public transit system in Lithuania, and it is very important in encouraging travelers to use public transport. VINTRA is not simply a trip planner; it is capable of planning multimodal public transport trips, using different parameters in public transport trip planning. This system has the functionality to create and edit public transport route trajectories and to edit and calculate timetables according to the distance between stops. This research presents the public transport trip planning parameters procedure of the calculated walking route directions, integrated with the calculation results of public transit routes, as well as combining visualization in digital maps. This paper also discusses how route-planning systems could perform data exchange based on General Transit Feed Specification and how assessment of the public transport trip planning results, compared to the VINTRA system created with Google, was performed.

Dynamic on-line multimodal route planning with least expected travel time in the stochastic and time-dependent bus network

Route planning system in bus network is very important on providing passengers a better experience for public transportation services. This work presents a new on-line dynamic path planning algorithm in a stochastic and time-dependent bus network, with the objective of least expected travel time. Firstly, an initial optimal path is found when a passenger leaves from the starting location at time t for the destination location. Secondly, when the passenger reaches a bus stop, the real time traffic condition is checked to decide whether the optimal path should be modified and re-calculated. If so, the shortest path algorithm is re-applied to find the new path. The simulation scenario is executed to show the performance and efficiency of the proposed dynamic path planning system.

An Improved A* Algorithm Based on Hesitant Fuzzy Set Theory for Multi-Criteria Arctic Route Planning

This paper presents a new route planning system for the purpose of evaluating the strategic prospects for future Arctic routes. The route planning problem can be regarded as a multi criteria decision making problem with large uncertainties originating from multi-climate models and experts’ knowledge and can be solved by a modified A* algorithm where the hesitant fuzzy set theory is incorporated. Compared to the traditional A* algorithm, the navigability of the Arctic route is firstly analyzed as a measure to determine the obstacle nodes and three key factors to the vessel navigation including sailing time, economic cost and risk are overall considered in the HFS-A* algorithm. A numerical experiment is presented to test the performance of the proposed algorithm.

Intelligent route planning system based on interval computing

We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

An intelligent model for assuring food quality in managing a multi-temperature food distribution centre

In the globalized cold chain network, the effective distribution of perishable food is of utmost importance when transporting multiple types of food with different handling requirements, such as temperature and humidity, for minimizing the food spoilage rate during transportation. Currently, mismanagement of premium fruit and vegetables leads to a huge amount of capital loss such that logistics service providers (LSPs) apply refrigerated trucks to deliver them for the sake of minimizing the food spoilage rate during transportation. Since different types of food have their own different handling requirements, traditional refrigerated distribution management at a fixed environmental condition is insufficient. Without considering such requirements, traditional route planning by merely minimizing the travelling distance is ineffective in maintaining food quality, resulting in an increased likelihood of food deterioration and food chilling injury. In addition, there is a lack of real-time product monitoring to control violations of the required handling requirements in order to prevent delivery of spoilt food to customers. In this paper, an internet of things (IoT)-based route planning system (IRPS) is proposed (i) to design a multi-temperature packaging model, (ii) to develop real-time product monitoring during transportation, and (iii) to optimize routing solutions. Under the IoT framework, the ambient environmental information can be collected automatically by building a wireless sensor network so as to develop total product monitoring during the distribution process. Experiments using the Taguchi method are conducted to examine the most effective packaging model for various products in terms of maximizing duration of optimal environment conditions in tertiary packaging. By integrating the above results and travelling constraints, the optimal delivery routes can be formulated by using genetic algorithms (GAs). With the aid of IRPS, the food spoilage rate during transportation and the time needed in route planning and in the delivery of deteriorated food can be reduced, while customer satisfaction is enhanced.

Regional Multicriteria and Multimodal Route Planning System for Public Transportation: A Case Study

Nowadays, the use of computer-based route planners is popular among private and public transportation passengers. A large range of websites and GPS navigation devices provide such services for their users. Here, we present an algorithm used by a route planning system which operates on a complete public transport network of two regions from two countries, namely Hungary and Serbia. The algorithm can handle the pedestrian traffic between stops not too far from each other. It can take into account individual user preferences like walking distances and modes of transport. The graph representing the transport network was very large, but with the help of some speed-up techniques, we managed to create an effective search algorithm that is able to handle user requirements.

유전 알고리즘 기반의 지능형 유연 수송 스케줄링 시스템 설계

유전 알고리즘 기반의 지능형 유연 수송 스케줄링 시스템 설계

A Robust GA/PSO-Hybrid Algorithm in Intelligent Shipping Route Planning Systems for Maritime Traffic Networks

The development of intelligent shipping route planning systems is important for maritime traffic networks, and has attracted considerable attention in the field of marine traffic engineering. In practical applications, the traditional experience-based planning scheme has been widely used due to its simplicity and easy implementations. However, the traditional manual procedure is experience-dependent and time-consuming, which may easily lead to unstable shipping route planning in different waters. The purpose of this study automatically and robustly determines that the optimal shipping route is based on artificial intelligence approaches. It is general that Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are almost the most popular methods in route planning. These two heuristic-based optimization techniques benefit from their specific advantages when solving different optimization problems. In this paper, we proposed a hybrid heuristic scheme by integrating GA and PSO to improve the accuracy and robustness of shipping route planning in restricted waters. The experimental results about both synthetic and real-world problems have demonstrated that our proposed hybrid approach outperforms the existing schemes in terms of both accuracy and robustness, and the approach is helpful for optimizing maritime traffic network for the links of terminals.

Application Teaching Case of Dijkstra Algorithm

Dijkstra algorithm has been widely applied in many fields, such as optimization, image processing and grid processing. In the tradition teaching process, we find that students could not understand the use of Dijkstra algorithm, think this algorithm is complex and have no interest. Based on analysis, we propose use airport route planning system as an example to learn this method. The result is an APP. Application teaching method could make students have a sense of accomplishment, improve students’ learning interest and enhance students’ practical ability.