Real-time Routing Research Articles

UAV real-time route planning logical architecture in complex threat environment

UAV real-time route planning logical architecture in complex threat environment

Distributed Learning for Vehicle Routing Decision in Software Defined Internet of Vehicles

With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment.

Customized Bus Route Optimization with the Real-Time Data

This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds.

Inventory Planning and Real-Time Routing for Network of Electric Vehicle Battery-Swapping Stations

Battery-swapping stations (BSSs) are one of the main types of electric vehicle (EV) refueling facilities. By battery swapping, EVs first replace their depleted batteries (DBs) with fully charged ones, and then, the demounted DBs can be recharged in charging facilities in a stand-alone mode, leading to a decouple between batteries and EVs during refueling. This article targets the planning and operation of a network of geographically distributed BSSs, termed BSS-Net. In particular, we focus on two important decisions being made within two different timescales, namely, a long-term decision on planning the initial inventory in each individual BSS and a short-term decision on real-time vehicle-to-station (V2S) routing of EVs. We formulate a two-stage optimization problem and propose a two-step solution scheme. Specifically, in the first step, we determine the long-term initial inventory by sample average approximation, and the resulting planning decision leads to a maximized total expected revenue for the BSS-Net. Based on the optimal initial inventory, we design a randomized online algorithm in the second step to perform real-time V2S routing, without assuming any future EV arrival information. We rigorously prove that the worst case performance of the randomized online algorithm is theoretically bounded by a closed-form competitive ratio.

Digital connectivity in an innovative joint distribution system with real-time demand update

A joint distribution system based on digital connectivity to share logistics resources constitutes an innovative method of intelligent logistics. In general, O2O deliverymen are subjected to delivery capacity imbalances in different hours—peak hours and off-peak hours in particular—throughout the day. To solve the insufficient capacity problem in peak hours and the idle capacity problem in off-peak hours of O2O deliverymen, this paper introduces an innovative digital connectivity enabled B2C and O2O joint distribution system. In this system, all pickup and delivery nodes of both B2C and O2O orders are interconnected through the Physical Internet. In response to the dynamic arrivals of O2O orders, this paper develops a center dispatching system to collect and schedule the real-time logistics information via digital connectivity. An improved savings algorithm is proposed to support the real-time routing optimization of deliverymen. By making a comparative analysis with the traditional separate distribution mode, this paper proves the effectiveness of the joint distribution system. Results show that it outputs better optimization with larger order sizes, the same B2C and O2O order proportions as well as longer lengths of O2O time window. With the benefit of the digital connectivity, the joint distribution system contributes to providing real-time logistics services with high efficiency and reducing carbon emissions to realize sustainability. The prospect of this system exhibits the potential for use in richly varied delivery services, most notably meal delivery in urban areas.

Fastest adaptive estimation algorithms for topological structure errors in smart grid networks

Compared with traditional wired networks, wireless sensor networks(WSN) have the characteristics of low cost and rapid deployment, and also guarantee the same level of fault tolerance as wired networks. The WSN can also monitor the operating status of the power grid in real time, collect physical information such as related parameters, and provide more comprehensive and complete power grid operation data as a reference basis for smart grid operation and related management personnel, and complete the diagnosis, monitoring and power statistics of smart grid equipment The rapid construction of the data communication network has become a key technology to effectively solve the problems of difficult optimization management and high cost and economic benefits in the smart grid. This paper discusses the application of WSNs in smart grids from two aspects. Firstly, construct a WSN topology that complies with the smart grid architecture, and establish a real-time routing mechanism that meets the requirements of smart distribution network communication reliability; secondly, propose a fastest adaptive algorithm for the fault of the WSN topology in the smart grid . The proposed adaptive routing mechanism has certain advantages in node energy consumption, which reduces energy consumption by nearly 4% compared to the directional diffusion method and the LEACH algorithm. Therefore, the algorithm is more suitable for the adaptation of WSN topology, and the method can Improve the life cycle of sensor nodes and networks.

Active group route guidance based on DODE: A novel modeling framework integrating dynamic driver behavior

This work aims to provide effective and reliable traffic guidance strategies for improving the system performance and travel reliability, wherein the drivers’ route diversion behavior is the key determinant in these strategies. This study presents a novel modeling framework that can incorporate the dynamic driver behavior into a real-time group route guidance model based on dynamic origin–destination demand estimation and prediction (DODE) for information-based active traffic management. Experiments are conducted to test the effectiveness of the proposed model on the basis of the traffic dataset of the Route Guidance Pilot Project. Experimental results show that the effect of route diversion on DODE under information provision, which can improve the accuracy of DODE, must be considered. Compared with the traditional guidance model, the proposed model considers the system objective and the actual route diversion behavior and can provide better performance and ensure system sustainability.

Real-Time Route Recommendations for E-Taxies Leveraging GPS Trajectories

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run.

Research on Optimization of Food Cold Chain Logistics Distribution Route Based on Internet of Things

with the Rapid Development of Social Economy and the Continuous Improvement of People’s Living Standards, People Pay More and More Attention to the Quality of Life, and Put Forward Higher Requirements for the Quality of Products, Especially the Quality of Cold Chain Products. as a Guarantee of the Quality of Cold Chain Products, Cold Chain Distribution Has Received the Attention of Enterprises. Based on the Technology of Internet of Things, This Paper Establishes a Path Optimization Model for Cold Chain Logistics by Adding Refrigeration Cost and Cargo Loss Cost to the Traditional Path Optimization Model. the Experimental Results Show That the Real-Time Route Optimization Method for Refrigerated Vehicles under the Internet of Things Environment is Effective. the Method Has Practical Significance in Achieving Fine Control of Cold Chain Logistics Distribution Costs and Improving Distribution Service Efficiency.

An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization

Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems in urban traffic. With the help of IoT technologies, online data from road segments are captured by monitoring devices, while real-time data from vehicles are collected through preinstalled sensors. Based on these data, a CPS model is constructed to depict real-time status and dynamic behavior of road segments and vehicles. An online learning data-driven model is developed to extract prior knowledge and enhance collaboration between road segments and vehicles by combining short-term traffic forecasting and real-time routing optimization. A case study based on Xi’an city is presented to demonstrate the feasibility and efficiency of the proposed method, showing a reduction in the travel time with reasonable computation time, without much compromising the travel distance and fuel consumption. This work potentially strengthens the transparency and intelligence of urban traffic systems.

Optimization of Classified Municipal Waste Collection Based on the Internet of Connected Vehicles

The development of 5G technology has brought the information revolution of the Internet of Things (IoT). With the emergence of intelligent products, such as the Internet of connected vehicles (IoCV) and wireless sensor nodes, the classification and disposal of municipal waste is now more intelligent and efficient. How to arrange waste collection vehicles reasonably and optimize their service routes in real time based on signals sent by intelligent waste bins to meet the daily needs of residents has become an urgent problem. An improved multiobjective model for the split-delivery vehicle routing problem (SDVRP) is proposed based on the multiple cleaning services for the same collection point in a certain period of time according to the real-time waste volume. The process of classified waste disposal in the Tianjin Wudadao area is analyzed as an example. The experimental results show that the Pareto-optimal solutions obtained by the algorithm can effectively arrange the vehicle service routes, meet the needs of classified waste collection in the area, and provide a scientific theoretical basis for the further development of waste classification.

Real-Time Route Search by Locations

With the proliferation of GPS-based data (e.g., routes and trajectories), it is of great importance to enable the functionality of real-time route search and recommendations. We define and study a novel Continuous Route-Search-by-Location (C-RSL) problem to enable real-time route search by locations for a large number of users over route data streams. Given a set of C-RSL queries where each query q contains a set of places q.O to visit and a threshold q.θ, we continuously feed each query q with routes that has similarity to q.O no less than q.θ. We also extend our proposal to support top-k C-RSL problem where each query continuously maintains k most similar routes. The C-RSL problem targets a variety of applications, including real-time route planning, ridesharing, and other location-based services that have real-time demand. To enable efficient route matching on a large number of C-RSL queries, we develop novel parallel route matching algorithms with good time complexity. Extensive experiments with real data offer insight into the performance of our algorithms, indicating that our proposal is capable of achieving high efficiency and scalability.

Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks.

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

An enhanced rerouting cost estimation algorithm towards internet of drone

When a drone flies along with the waypoint flight plan, rerouting has to pay a cost to change its route in real time through the Internet. The inflight rerouting cost estimation algorithm can predict the rerouting cost as a function of the flight direction and the speed of the drone. Previous studies proved that the rerouting cost estimation would apply to the cases where the entire waypoint route flight, including the modified waypoints, had been completed, as it was merely about changing the next waypoint at any point in the overall path. The drone must traverse all the waypoints to complete its flight. If the estimated flight time, including the rerouting cost, is almost equal to the real flight time when the waypoint is changed once, or more times, we can validate the accuracy of the inflight rerouting cost estimation algorithm. In this paper, we modified the inflight rerouting cost estimation algorithm proposed in our previous works. We compared the total estimated rerouting cost to the total flight time derived through the actual flight according to the number of rerouting. To prove the enhancement of the modified inflight rerouting cost estimation algorithm under the multiple inflight reroutings, the experimental flight trial was performed ten times on various routes that have five waypoints up to five times rerouting. The experimental results indicate that the modified rerouting cost estimation algorithm has a rerouting cost estimation of more than 92% accuracy under the multiple rerouting waypoints.

A Routing and Wavelength Assignment Algorithm Based on Two Types of LEO Constellations in Optical Satellite Networks

Research on LEO inter-satellite laser communication based on wavelength division multiplexing inter-satellite links and its routing and wavelength assignment (RWA) technology is an important way to solve the problem of low-latency, high-capacity, high-speed transmission, and low-cost on-orbit real-time routing of free-space optical communications. Since both the transmitting and receiving sides of the inter-satellite communication are high-speed moving satellites, the network topology in the whole inter-satellite communication process is time-varying. Therefore, based on this particularity, we propose the SpacePro constellation and combined the classical NeLS constellation for mathematical modeling, and the network topology and inter-satellite links are predicted and analyzed in their entire cycles. The RWA under the super satellite nodes with computation and storage functions algorithm (SCSA) is proposed and its effect is compared and analyzed in the wavelength resource requirement and connectivity. The results showed that the SCSA algorithm can save up to 50% in wavelength requirements, and the SpacePro constellation can achieve the same network connectivity effect with fewer on-board processors and wavelengths than NeLS. Satellite optical network node connectivity and wavelength demand are a trade-off problem. Studying the RWA problem in this scenario has a very critical reference for how the future satellite optical network performs wavelength usage configuration to help improve the performance of networks.

Routing in a Stochastic Network with Nonrecurrent Incidents: Behavioral Interpretation of Dynamic Traffic Assignment

AbstractIn an advanced traveler information system (ATIS), this study examines how to map a driver’s time constraints and risk-taking behavior to real-time routing in a probabilistic time-dependent...

Prediction and avoidance of real-time traffic congestion system for Indian metropolitan cities

The expansion of sensors and data is the catalyst for traffic simulators to help monitor and manage transportation systems in real-time to improve key performance metrics and overall efficiency. The number of accidents has rapidly increased in Indian perspective over the past few decades. Owing the accidents, a large number of losses occur in form of infrastructure, resources and people. In this paper, we have proposed an architectural framework for Indian urban transport system to gather, store, data mining and getting information to avoid the traffic accidents, congestions and predicting the alternative routes in real time. A combination of machine learning and data mining techniques are used to implement the proposed system. The implemented system will help in reduction of traffic accidents and suggesting alternative routes to avoid the traffic congestion for an Indian urban transport network operation in real-time. Real-time data is fed into a traffic simulation, which generates future states of the road network and alternative routes to avoid congestion. Because the results of the system need to be both faster than real-time and accurate, the acceleration of the simulation execution and the accuracy of the prediction models are critical.

Network traffic management based on differentiated and integrated services

The problem of traffic control and management for the purpose of forecasting and prevention of congestion of a telecommunication network is considered. The definition of the concept of traffic policy is given. The solution of traffic control problems is based on the application of a policy that allows the harmonious distribution of information flow. A promising way to prevent congestion is the management of traffic parameters ‒ policing and shaping, i.e the management of statistical characteristics of traffic. The necessity of using probabilistic and statistical methods of traffic control, based on correlation and regression analysis, is substantiated. The concept of end-to-end network diagnostics is considered. End-to-end management includes network management as a whole, congestion management and traffic parameters management. Further development of traffic policy is the use of differentiated and integrated services. The method involves the dynamic allocation of bandwidth of transmission channels between users based on their priorities. This approach avoids periodic local congestion of routes that have been identified as optimal at the current stage. The algorithm involves the selection of routes according to the criterion of equable loading of transmission lines. To implement a policy of traffic with differentiated services, it is necessary to solve the problem of flow distribution between locally optimal or quasi-optimal routes in real time. The process model of the total flow formation due to the imposition of separate flows from different sources is considered. It is shown that, under the conditions of maintaining the balance of network flows the intensities of flows to nodes with limited buffer memory exceed the corresponding intensity of the network with unlimited memory. This saves storage and reduces packet delays due to latency in switching buffers.

Prediction and avoidance of real-time traffic congestion system for Indian metropolitan cities

The expansion of sensors and data is the catalyst for traffic simulators to help monitor and manage transportation systems in real-time to improve key performance metrics and overall efficiency. The number of accidents has rapidly increased in Indian perspective over the past few decades. Owing the accidents, a large number of losses occur in form of infrastructure, resources and people. In this paper, we have proposed an architectural framework for Indian urban transport system to gather, store, data mining and getting information to avoid the traffic accidents, congestions and predicting the alternative routes in real time. A combination of machine learning and data mining techniques are used to implement the proposed system. The implemented system will help in reduction of traffic accidents and suggesting alternative routes to avoid the traffic congestion for an Indian urban transport network operation in real-time. Real-time data is fed into a traffic simulation, which generates future states of the road network and alternative routes to avoid congestion. Because the results of the system need to be both faster than real-time and accurate, the acceleration of the simulation execution and the accuracy of the prediction models are critical.

Real-time wavelength routing based on Bragg reflection with integrated forward Raman for long-reach networks

We experimentally demonstrate the all-optical efficient technique for dynamic full-duplex wavelength routing and reach extension through adoption of passive fibre Bragg grating (FBG), energy efficient vertical cavity surface emitting lasers (VCSELs) and high gain forward Raman pump. In this study, two VCSEL channels at 1 549.62 nm and 1 550.01 nm are directly modulated with a 8.5 Gbit/s data signal each, and transmitted over 25.5 km fibre to a passive single mode FBG-based wavelength routing node. The precise wavelength selectivity of the FBG is exploited to achieve all-optical real-time wavelength routing of the 1 549.62 nm VCSEL channel with a maximum insertion loss of 22.1 dB. The 1 550.01 nm channel is transmitted through the FBG with a maximum penalty of 1.03 dB. By utilizing the 7.2 dB flat gain of a forward Raman pump, the routed 1 549.62 nm VCSEL channel is transmitted over 76.7 km fibre with a maximum penalty of 3.84 dB at bit error rate (BER) of 5−5. Our technique is all-optical, power efficient as it employs passive FBG and circulators and has low cross talk allied to precise wavelength selectivity of the technique.