Traffic Scheduling Research Articles

Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network

Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. In order to solve this problem, this paper proposes a model based on Dynamic Diffusion Spatial-Temporal Graph Convolutional Network. It first combines the dynamic generation matrix and the static distance matrix to grasp real-time traffic conditions, and then introduces the diffusion random walk strategy to capture the correlation of spatial nodes. Finally, the convolutional LSTM module is used to mine the spatiotemporal dependence of traffic data to improve the accuracy of traffic prediction. Compared to several baseline models, the experimental results show that the model is 7% better than other models on several metrics and demonstrates the necessity of the module through ablation experiments.

Efficient and Secure Traffic Scheduling Based on Private Sketch

In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission (RA) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the RA structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and RA structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework’s efficiency and reliability.

Network-wide mixed-rail traffic scheduler: challenges and implementation aspects

Network-wide mixed-rail traffic scheduler: challenges and implementation aspects

The effect of dedicated lanes of urban passenger transport on the conditions of synchronization of inter-route passenger transfers

Abstract. Problem. The work is aimed at studying the problematic issue of reducing the waiting time for a transfer at a transport interchange hub. Due to the effect of external factors, the synchronization of the arrival schedule of buses at the transport interchange hub becomes practically impossible, which leads to an increase in the time that the passengers spend for a transfer. The solution to this problem is proposed through the introduction of dedicated traffic lanes on the approach to the transport interchange hub, within which it is possible to eliminate traffic deviations from the schedule. Goal. The purpose of the work is to establish the characteristic effect of the conditions of bus traffic on the designated lanes on the effectiveness of the traffic schedule synchronization, which is determined by the reduction of the passengers’ transfer time at the transport interchange hub. Methodology. The methodological basis for the analysis of the impact of bus traffic conditions on the designated lanes is a structural model that reflects the relationship between the high-speed mode and the time of deviation of arrival at the transport interchange hub. The evaluation of the effectiveness of the traffic schedule synchronization on the routes is carried out on the basis of the time parameters of bus traffic and the accounting of additional inter-journey idle time at stops. Based on the analysis of the traffic conditions along the route with a dedicated lane, an algorithm has been developed to determine the actual time of the bus's arrival at the stopping points of the route. Results. It was established that the deviation of the arrival time of buses is critically important for the effective organization of the synchronization of interroute transfers of passengers. Accounting for the key traffic factors in the conditions of the dedicated lane, such as the length of the dedicated lane section and the maximum speed of steady traffic, makes it possible to increase the accuracy of modeling the time parameters of the arrival of buses at the transport interchange hub. Originality. It is proposed to use the introduction of areas with dedicated traffic lanes as a basic element of effect on increasing the efficiency of synchronization of inter-route passenger transfers. The obtained regularities reproduce the effect of the deviation of bus arrivals on the change in the waiting time of an inter-route transfer. It was determined that the duration of inter-journey downtime can be reduced by increasing the regularity of traffic on the route. Practical value. According to the results of experimental studies, the regularities of changes in the average waiting time for passengers to transfer in the transport interchange hub "Str. Vokzalna" between routes No. 32 and No. 141 (Dnipro, Ukraine) were established. It was proven expedient to introduce dedicated bus lanes on the approach to the 6.7 km long and 1.8 km long transport interchange hub. The implementation of such measures makes it possible to reduce the deviation of bus arrivals from the synchronized schedule from 9 minutes to 2 minutes. This makes it possible to reduce the transfer time of passengers from 4.7 minutes to 1.8 minutes (by 61.7 %) and reduce the inter-journey idle time from 7 minutes to 2 minutes, which has a positive effect on increasing the productivity of buses and the quality of passenger service

Probabilistic prediction of ship destinations based on traffic pattern awareness in maritime networks

As maritime traffic becomes increasingly heavy, issues such as traffic congestion and inefficient transportation emerge as more prominent concerns. Ship destination prediction is essential for optimizing scheduling and improving maritime transportation efficiency. Currently, data quality and computational resources frequently limit the accuracy of most existing prediction methods. Therefore, this paper proposes a novel destination prediction method based on traffic pattern awareness. It uses AIS data to construct a maritime route network that integrates the navigational experience of ship groups. By modeling marine traffic patterns through abstract network topology and analyzing the target's real-time trajectory, the method employs a random forest-based prediction model to calculate the probabilities of candidate destinations and determine the final port. Experiments using 2020 Pearl River Estuary AIS data validated the proposed model. Through comparative analysis with four machine learning methods, the model demonstrated the highest accuracy and stable predictive performance. Additionally, the effectiveness of the model was further highlighted through multi-step dynamic predictions. At last, comparison with an advanced prediction model confirmed the proposed method's accuracy, demonstrating its ability to provide valuable decision-making support for ports in enhancing maritime traffic scheduling efficiency.

CREATING A PLAN FOR IMPLEMENTING MEASURES FOR INCREASING RAILWAY INFRASTRUCTURE CAPACITY BY APPLYING MULTI-CRITERIA DECISION-MAKING METHODS

This study addresses the challenge of increasing railway infrastructure capacity. It investigates the key factors that lead to a reduction in this capacity. The study focuses on the Lithuanian railway line, assessing train traffic schedules to determine actual capacity. An evaluation of potential measures to enhance this capacity has been conducted, and a comprehensive expert survey questionnaire was developed based on these measures. A strategic plan to improve railway capacity for Lithuanian railways was formulated using Kendall’s rank correlation and ARTIW average rank for weight transformation methods. The study proposes a novel approach to creating these improvement plans and ends by presenting recommendations and conclusions.

Wavelet and VMD enhanced traffic forecasting and scheduling method for edge cloud networks

With the proliferation of Intelligent applications, more and more organizations migrate their applications from cloud to edge cloud network to reduce the latency of applications and alleviate the workload of cloud. When the network congestion occurs, network monitoring and detection system may disable, and edge cloud network will be vulnerable to be attacked. Traffic forecasting can identify the traffic patterns in advance, and dynamically allocate network resources to decrease latency of applications and avoid security risks caused by network congestion. Therefore, we propose a network scheduling framework WVNF (Wavelet VMD Based Network Flow Management) based on traffic prediction, which utilizes neural networks to forecast network traffic and deploys route strategies to optimize network scheduling. Specifically, in order to accurately forecast network traffic, we propose a neural network model, named TSWNet (Traffic Sequence Wavelet Network). TSWNet uses VMD (variational mode decomposition) to decompose time series and extract signal structure information on different time scales, and adopts wavelet transformation to extract the local and global features of the traffic sequence in the time and frequency domain. In addition, we model this traffic scheduling problem and propose a route strategy, which utilizes the result of TSWNet to find the best path. In extensive tests, TSWNet significantly outperformed existing models, reducing MSE and MAE by up to 48.8% and 27.8% respectively, demonstrating its effective traffic prediction and network scheduling capabilities.

A Receiver-Driven Named Data Networking (NDN) Congestion Control Method Based on Reinforcement Learning

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate.

Adaptive scheduling for Internet of Vehicles using deconfounded graph transfer learning

With the rapid development of Internet of Things technology, the Internet of Vehicles has emerged as a significant application. The Internet of Vehicles connects vehicles with traffic infrastructure and other vehicles, enabling real-time information sharing and vehicle dispatching. However, in the Internet of Vehicles environment, traffic scheduling systems face challenges such as sparse raw data and environmental confounding bias, which limit the effectiveness of traditional graph-based reinforcement learning methods. In this paper, we propose a Cross-City Federated Continual Learning Framework for Spatiotemporal Graph Transfer Learning called CCFTL, which removes confounding effects on reinforcement learning and enhances few-shot transfer learning in data-scarce cities. Our approach aims to significantly improve the adaptability and scalability of cross-city vehicle networking technologies in ever-changing environments. This study acts as an innovative research of the deconfounding strategy with spatiotemporal graph meta-knowledge learning, which enables optimization of cross-city meta-knowledge transfer through a federated continual learning approach. This approach effectively reduces learning bias caused by regional differences, which enhances the model’s generalization and adaptability to complex, heterogeneous urban scenarios. Experimental results show that our framework significantly outperforms traditional methods, especially in improving the efficiency and accuracy of traffic dispatch systems in Internet of Vehicles environments with scarce data and the presence of confounding factors.

Development of Traffic Scheduling Based on TSN in Smart Substation Devices

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems.

Deep Reinforcement Learning for Channel Traffic Scheduling in Dry Bulk Export Terminals

Deep Reinforcement Learning for Channel Traffic Scheduling in Dry Bulk Export Terminals

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Joint Optimization of Personalized Route Planning and Global Traffic Scheduling: A DT-Enabled Integrated Framework

Joint Optimization of Personalized Route Planning and Global Traffic Scheduling: A DT-Enabled Integrated Framework

Increasing the theoretical and practical capacity of the Eastern Operating Domain by means of advanced train separation systems

Aim. Currently, the mainline railway network does not fully meet the market’s needs in terms of capacity, which is why every day JSC RZD is forced to reject 2 to 4% of applications for the transportation of goods across routes passing through limited-capacity railway infrastructure facilities. Increasing the practical capacity of railway lines by building additional tracks at stations and on open lines is unacceptably long and requires massive capital investments. Methods. It is proposed to solve the above problem by increasing the utilization of railway infrastructure by reducing the succession times and station operation times in the traffic schedule by deploying moving block and virtual coupling. This approach allows reducing capital investments up to 32 times and the duration of project implementation up to 8 times with a comparable capacity increase. The paper details the functionality of train separation technology and its technical parameters. Conclusion. The train separation technology has been deployed and is in operation on the Far Eastern, Trans-Baikal, East Siberian, and Krasnoyarsk Railways, branches of JSC RZD, that are part of the Eastern Operating Domain of JSC RZD network. By the end of 2023, the total length of track covered by the train separation solution was 5,734 km. Conclusion. In 2023, the lines of the Eastern Operating Domain handled almost 36 thousand pairs of trains with reduced spacing. The train separation technology also allows improving the safety and the level of digitalisation of the transportation process.

Flexible-queue-management-based bandwidth allocation in higher-speed PONs

Higher-speed passive optical networks (i.e., ITU-T 50G PONs) are envisioned to support various time-sensitive services with diverse quality of service (QoS) requirements (e.g., latency and jitter). To achieve this goal, higher-speed PON introduces multiple queues in each transmission container (T-CONT) so that each queue can carry a type of time-sensitive traffic. However, in the existing dynamic bandwidth allocation (DBA) schemes, the DBA engine in the optical line terminal (OLT) allocates bandwidth to four types of T-CONTs based on their buffer occupancies and priorities without considering queue status (e.g., the amount of data frames in queues) and services’ QoS requirements. To support multiple services belonging to the same T-CONT, the traffic scheduler in each optical network unit (ONU) further assigns the bandwidth of T-CONTs to their associated queues by using priority-based scheduling strategies. In this way, the bandwidth is allocated to queues by the DBA engine and traffic scheduler independently, which cannot guarantee network performance and meet the stringent QoS requirements of time-sensitive services. To solve this problem, we propose a DBA mechanism based on flexible queue management (FQM) to enable direct allocation of bandwidth to the queues under their QoS requirements. The proposed FQM mechanism enables the DBA engine to obtain the arrival time and QoS requirements of data frames in different queues as well as queue status based on the existing report/grant mechanism. By using these parameters, the required bandwidth for each queue in the next polling cycle can be calculated. Then, the DBA engine allocates bandwidth to these queues according to their bandwidth requests and priorities periodically. Simulation results show that the proposed scheme outperforms two benchmarks in the aspects of meeting time-sensitive services’ diverse QoS requirements, even when the traffic load is high.

A Division-of-Labour Approach to Traffic Light Scheduling

Traffic light scheduling is a critical aspect of traffic management with many recently developed solutions that incorporate computational intelligence approaches. This paper presents a traffic light scheduling algorithm based on a task allocation model that simulates the division of labour among insects in a colony, specifically ant colonies. The developed algorithm switches the green light based on a probability calculated every second from the traffic volume around the traffic light. The application of this algorithm to several benchmark simulated traffic scenarios shows optimal performance compared to five other traffic scheduling algorithms.

A Mixed-Criticality Traffic Scheduler with Mitigating Congestion for CAN-to-TSN Gateway

The network architecture that Time-Sensitive Networking (TSN) is used as the backbone network and the Controller Area Network (CAN) serves as the intra-domain network is considered as the CAN-TSN interconnection network architecture, which has gained considerable attention within industrial embedded networks, such as spacecraft, intelligent automobiles, and factory automation. The architecture employs the CAN-TSN gateway as a central hub for transmitting and managing a significant volume of communications between the CAN domains and TSN. However, the CAN-TSN gateway faces a high congestion challenge due to the rapid growth in data volume, making it difficult to effectively support different time planning mechanisms provided by TSN. In this article, we propose a two-stage mixed-criticality traffic scheduler. The scheduler in the first stage adopts a Message Optimization Algorithm (MOA) to aggregate multiple CAN messages into a single TSN message (including the aggregation of critical and non-critical CAN messages), which reduces the number of CAN messages requiring transmission. In the second stage, the scheduler proposes a Message Scheduling Optimization Algorithm (MSOA) to schedule critical TSN messages. This algorithm reassembles all the critical CAN messages (within the un-schedulable TSN messages) to generate new TSN messages for rescheduling. Experimental results show that our proposed scheduler effectively improves the acceptance ratio of critical and non-critical CAN messages and outperforms the state-of-the-art message scheduling method in terms of acceptance ratio while improving the bandwidth utilization and the number of schedule table entries. We further construct a hardware platform to evaluate the performance of MSOA. The consistency between practical results and theoretical results shows the effectiveness of MSOA.

Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial-Temporal Dependency.

Traffic prediction is a keystone for building smart cities in the new era and has found wide applications in traffic scheduling and management, environment policy making, public safety, and so on. Instead of creating a traffic predictor for each city, this article focuses on designing a unified network model that could be directly applied for traffic prediction in any city, by learning the essential spatial-temporal dependencies, i.e., the mutual relationship between traffic and the corresponding fine-grained road network. To achieve this goal, this article proposes a joint knowledge- and data-driven mechanism that novelly divides dependencies into three kinds of correlations, i.e., road segment, intra-intersection, and inter-intersection correlation, which capture the microcosmic, middle, and macroscopic dependencies between traffic and the road network, respectively. Specifically, we first construct traffic datasets that could cover all road segments from real-world trajectory datasets, which makes it possible to model the whole road network as a graph, with the help of fine-grained road topology. Then, we propose meta road segment learner, connection-aware spatial-temporal graph convolutional network (GCN), and multiscale residual networks for capturing the microcosmic, middle, and macroscopic dependencies, respectively. Our experiments on three real-world datasets demonstrate that our proposed method could: 1) achieve better prediction accuracy compared with several approaches and 2) capture the mutual relationship between traffic and the fine-grained road network since our model trained only using data from the source city achieves good performance when it is directly applied for traffic prediction in the target city, without any fine-tuning. The codes will be made publicly available.

Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network

QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.