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AbstractAs urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators. [ABSTRACT FROM AUTHOR]

The urban logistics delivery planning problems are a crucial component of urban spatial decision analysis. Most studies typically focus on traditional urban logistics delivery planning problems and ignore real-time traffic information. With the advancement of urbanization, real-time traffic networks play a critical role. However, previous studies have utilized heuristic methods to solve urban logistics delivery planning with realtime traffic information problems, and few studies have applied deep reinforcement learning methods to tackle this problem. Deep reinforcement learning methods solving traditional logistics delivery planning problems overlook the impact of dynamic spatio-temporal features on route planning. In this study, we propose a new deep reinforcement method called DRLDSTG. The method introduces the dynamic spatio-temporal graph model into a deep reinforcement learning method to capture these dynamic features from urban logistics delivery planning tasks. The actor-critic with maximum entropy method is employed to train the model and determine the optimal policy function. The experimental results indicated that the proposed method can achieve a superior solution with faster computational efficiency compared to commercial software and heuristic methods. Compared to other deep reinforcement learning methods, our method can more effectively learn dynamic spatio-temporal features from environments, demonstrating promising applications in cities. [ABSTRACT FROM AUTHOR]

To provide limited delays for remote sensing and control, gaming, and virtual reality applications, the Wi-Fi 7 standard introduces the Restricted Target Wake Time (R-TWT) mechanism, which reserves time intervals for particular stations with such real-time traffic. As legacy stations do not support R-TWT, the access point forbids channel access during these intervals for legacy stations. Quiet Intervals have been announced for this purpose. Since the support for the Quieting Framework can be configured as mandatory in some networks, Quiet Intervals are assumed to be valid protection for R-TWT. The paper describes experimental results with mass-market devices that disprove this assumption. The paper reveals significant inconsistencies between the standard and widely used devices, e.g., the inability to schedule multiple Quiet Intervals. It will be a significant problem for Wi-Fi 7 devices using R-TWT in heterogeneous networks with legacy devices and will require much effort from academia and industry to solve. [ABSTRACT FROM AUTHOR]

A mathematical model of joint servicing of priority real-time traffic and elastic data traffic in multiservice access nodes has been constructed and studied. Definitions of quality indicators for joint servicing of incoming requests for information services are provided. A system of statistical equilibrium equations is formed and its use for calculating the exact values of the introduced characteristics is considered. A method for approximate calculation of characteristics is proposed, based on the construction of a system of simplified equilibrium equations. It has been established that the obtained estimates of customer service indicators are asymptotically accurate in the region of large and small losses. The use of the developed method is shown to solve the problem of estimating the volume of traffic offloaded in an overload situation to other access nodes or to other frequency ranges in order to achieve values of specified QoS indicators and to solve the problem of planning the volume of required transmission resource of a multiservice access node. [ABSTRACT FROM AUTHOR]

Content prefetching at road side units (RSUs) in vehicular networks can improve users’ experience by reducing the average data access latency. Prefetching algorithms proposed in the literature usually rely on a fixed modulation scheme, that corresponds to the lowest possible data rate for RSU-to-vehicles transmission. As a consequence, the network bandwidth is often inefficiently used. In this article, we propose to study the problem of efficient content broadcasting in vehicular networks and express it as a joint data-prefetching and broadcast transmission scheduling optimization problem, subject to cache size constraints and transmission rate adaptation constraints. In our study we consider a highway scenario covered by several RSUs, and take into account the deadline requirement of interactive applications. We express the problem as an integer linear programming problem, and solve it using a heuristic algorithm that determines: the content prefetching schedule, the broadcast transmission schedule and the rate adaptation schedule sequentially. Simulation results show that, i) it is beneficial to use adaptive rates to broadcast data instead of relying on single-rate with unicast transmissions; ii) using vehicular trajectories and content popularity in conjunction yields better results than simply relying on content popularity for content prefetching and caching; and iii) this holds even with inaccurately predicted vehicle locations and with delay-sensitive contents. [ABSTRACT FROM AUTHOR]

In this paper, dynamic programming (DP) economical cruise control algorithms under discrete distance and discrete time strategies are designed to improve battery electric vehicle (BEV) energy consumption and riding comfort. In our experiment, energy consumption of constant-speed driving is reduced by up to 21.6% using the optimum velocity profiles from discrete distance DP. 'Variable step length and boundary conditions' are utilized to reduce discrete distance DP processing time by 90.8% with no effect on accuracy. Under discrete time DP, position is introduced as an additional variable, so discrete time DP allows more inputs including real-time preceding vehicle position and traffic information. The experiment results indicate that under traffic situation, discrete time DP further improves energy consumption and riding comfort by avoiding unnecessary stop at intersections compared with discrete distance DP. Further analysis indicates that under properly controlled velocity, the transfer between potential energy and kinetic energy is more efficient than that between potential energy and electric energy. These DP economical cruise control algorithms are effective for BEVs, hybrid electric vehicles and plug-in hybrid electric vehicles. 'Variable step length and boundary conditions' method in DP can also be applied in other domains where DP processing time is crucial. [ABSTRACT FROM AUTHOR]

Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway ("Attiki Odos") located in Greater Athens Area in Greece for the 2008–2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways. [ABSTRACT FROM AUTHOR]

In transportation-based cyberphysical systems (TCPS), also known as intelligent transportation systems (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining optimal routes for their travels. To determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information collected by vehicular networks to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and to reduce travel time and fuel consumption. DEDR considers real-time traffic information generation and transmission by vehicular networks. Based on the shared traffic information, DEDR introduces the trust probability to predict traffic conditions and to dynamically, en route, determine alternative optimal routes. DEDR also considers multiple metrics to comprehensively assess traffic conditions so that drivers can determine the optimal route with a preference to these metrics during travel. DEDR considers effects of external factors (bad weather, incidents, etc.) on traffic conditions as well. Through a combination of extensive theoretical analysis and simulation experiments, our data show that DEDR can greatly increase traffic efficiency in terms of time efficiency, balancing efficiency, and fuel efficiency, in comparison with existing schemes. [ABSTRACT FROM PUBLISHER]

The emerging vehicular ad hoc network (VANET) enables vehicles to access the Internet through roadside access points (APs). An important issue in system deployment is determining how many roadside APs shall be installed on a road. However, the existing works fail to provide rigorous and accurate analysis for VANETs. In this paper, we propose a general structure for Internet access in VANETs. It allows both real-time traffic and delay-tolerant traffic to be delivered to users in the most efficient ways. An analytical model is also proposed to analyze the system performance with random arrival of the vehicles. We finally develop an AP placement algorithm based on theoretical results derived from the model to deploy the minimal number of roadside APs with quality-of-service (QoS) guarantees. The simulation results have demonstrated the accuracy of the proposed analytical model and the efficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In spectrum aggregation (SA), two or more component carriers (CCs) of different bandwidths in different bands can be aggregated to support a wider transmission bandwidth. The scheduling delay is the most important design constraint for the broadband wireless trunking (BWT) system, especially in the cognitive radio (CR) condition. The current resource scheduling schemes for spectrum aggregation become questionable and are not suitable for meeting the challenge of the delay requirement. Consequently, the authors propose a novel component carrier configuration and switching scheme for real-time traffic (RT-CCCS) to satisfy the delay requirement in the CR-based SA system. In this work, the authors consider a sensor-network-assisted CR network. The authors first introduce a resource scheduling structure for SA in the CR condition. Then the proposed scheme is analyzed in detail. Finally, simulations are carried out to verify the analysis on the proposed scheme. Simulation results prove that our proposed scheme can satisfy the delay requirement in the CR-based SA system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a QoS-aware dynamic bandwidth allocation scheme to mitigate congestion problem in gateway-based multi-hop WiFi-based long distance networks and thereby enhance QoS guarantees for real-time traffic. It presents a dynamic slot scheduling scheme which efficiently distributes the unused TDMA time slots among the needy nodes. The distribution process is carried out in a hierarchical manner through the use of parent-child relationship of a tree topology. By doing so, end-to-end performance of real-time traffic is enhanced manifold. Furthermore, the proposed protocol provides assured packet forwarding for already admitted flows by allowing transmission of nodes according to the capacity of ancestral links. Simulation results show that the proposed protocol achieves a significant performance improvement in terms of throughput and delay of real-time traffic. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the schedulability of traffic with arbitrary end-to-end deadline constraints in Wireless Mesh Networks (WMNs). We formulate the problem as a mixed integer linear optimization problem, and show that, depending on the flow aggregation policy used in the network, the problem can be either convex or non-convex. We optimally solve the problem in both cases, and prove that the schedulability does depend on the aggregation policy. This allows us to derive rules of thumb to identify which policy improves the schedulability with a given traffic. Furthermore, we propose a heuristic solution strategy that allows good suboptimal solutions to the scheduling problem to be computed in relatively small times, comparable to those required for online admission control in relatively large WMNs. [ABSTRACT FROM AUTHOR]

Applications of video streaming and real-time gaming, which generate large amounts of real-time traffic in the network, are expected to gain considerable popularity in Long Term Evolution networks. Maintaining the QoS such as packet delay, packet loss ratio, median, and cell border throughput requirements in networks dominated by real time traffic, is critical. The existing dimensioning methodology does not consider QoS parameters of real-time traffic in network dimensioning. Moreover, exhaustive and time-consuming simulations are normally required to evaluate the performance and QoS of real-time services. To overcome this problem, we propose an improved radio network dimensioning framework that considers the QoS of real-time traffic in network dimensioning. In this framework, an analytical model is proposed to evaluate the capacity and performance of real-time traffic dominant Long Term Evolution networks. The proposed framework provides a fast and accurate means of finding the trade-off between system load, packet delay, packet loss ratio, required median, and cell border throughput. It also provides network operators with an analytical means for obtaining the minimum number of sites required by jointly considering coverage, capacity and QoS requirements. The accuracy of the proposed model is validated through simulations. [ABSTRACT FROM AUTHOR]

Highlights: [•] Transceivers’ traffic flow(s) are adjusted for prevailing network conditions. [•] Traffic is asynchronously off-loaded if control actions are not enough. [•] Delay and change of delay are used for packet size definition. [•] Needs for control actions and off-loading are automatically detected. [•] Number of concurrent users are increased and quality of the connections improved. [ABSTRACT FROM AUTHOR]

In IEEE 802.16 specifications, a CRC field can be attached in the rear of a WiMAX MPDU. Thus, each intermediate node (relay node or base station) in an 802.16-based multihop network can perform error detection on a received WiMAX MPDU. On the sender, the ARQ mechanism will be enabled to retransmit the MPDU if a transmission error occurs and an ACK message cannot be received. However, the ARQ mechanism is unsuitable for real-time traffic due to the delay consideration and the CRC field becomes an option to reduce the overhead of a WiMAX MPDU. Once the CRC field is removed from a WiMAX MPDU, the error occurrence cannot be detected by the intermediate nodes in time, and the erroneous data is still forwarded in the network but discarded by the destination. The resources of nodes in a network are wasted. Hence, the error detection mechanism should be retained for real-time traffic, but the overhead of CRC should be reduced. In this paper, we study an efficient discrete-error-checking scheme (DECS) to reduce the CRC overhead for real-time traffic. The DECS only needs several CRC checking bits, which are attached behind some WiMAX MPDUs. The DECS can efficiently provide the error detection for the intermediate nodes to discard these erroneous and useless data early. Thus, the utilization of node resources and wireless bandwidth can be improved significantly. [ABSTRACT FROM AUTHOR]

The proliferation of different wireless access technologies, together with the growing number of multi-radio wireless devices suggest that the opportunistic utilization of multiple connections at the users can be an effective solution to the phenomenal growth of traffic demand in wireless networks. In this paper, we consider the downlink of a wireless network with N access points (\ssr APs) and M clients, where each client is connected to several out-of-band \ssr APs, and requests delay-sensitive traffic (e.g., real-time video). We adopt the framework of Hou and study the maximum total timely throughput of the network, denoted by C{\ssr T^{3}}, which is the maximum average number of packets delivered successfully before their deadline. Solving this problem is challenging since even the number of different ways of assigning packets to the \ssr APs is N^M. We overcome the challenge by proposing a deterministic relaxation of the problem, which converts the problem to a network with deterministic delays in each link. We show that the additive gap between the capacity of the relaxed problem, denoted by Cdet and C{\ssr T^{3}} is bounded by 2\sqrtN(Cdet+N\over4), which is asymptotically negligible compared to Cdet, when the network is operating at high-throughput regime. In addition, our numerical results show that the actual gap between C{\ssr T^{3}} and Cdet is in most cases much less than the worst-case gap proven analytically. Moreover, using LP rounding methods we prove that the relaxed problem can be approximated within additive gap of N. We extend the analytical results to the case of time-varying channel states, real-time traffic, prioritized traffic, and optimal online policies. Finally, we generalize the model for deterministic relaxation to consider fading, rate adaptation, and multiple simultaneous transmissions. [ABSTRACT FROM PUBLISHER]

Rail systems are highly complex and their control requires mathematical-computational tools. The main drawback of the models used to represent railway traffic, and to resolve any conflicts that occur, is the large computational time needed to obtain satisfactory results. Therefore, the purpose of this article is to study and design a discrete event-based model, characterized by the positioning of trains in block sections, that can represent the rail system, including the dynamic aspect, and a fixed block signaling system able to proactively detect and resolve potential conflicts that may occur within this system. This model can be developed and integrated as a specific module of a railway passenger-dedicated intelligent transportation systems (ITS) oriented to real-time traffic management in order to improve the service quality of a railway system's operations. The aim is to reduce the computational cost as much as possible and implement the proposed model in a railway network. A numerical investigation based on the Renfe Cercanís Madrid rail network (Spain) shows the high computational performance of the proposed approach in a real-life application context. [ABSTRACT FROM PUBLISHER]

In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,1 it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment. [ABSTRACT FROM AUTHOR]

Abstract: This study aims at ‘predicting’ the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes. [Copyright &y& Elsevier]

In multi-service wireless networks, asymmetric bandwidth allocation has been proposed to satisfy the requirements of asymmetric traffic load introduced by some data applications. However, it is difficult to promptly adjust bandwidth allocation on uplink and downlink according to the dynamics of traffic load. Inappropriate call admission control (CAC) policies in this environment may admit superfluous real-time (RT) calls or non real-time (NRT) calls and thus lead to low bandwidth utilization. In this paper, we propose and evaluate two new CAC schemes to address the problems caused by the mismatch of bandwidth allocation and traffic changing in multi-service wireless networks with bandwidth asymmetry between uplink and downlink. By determining the admissible regions for the RT calls and the NRT calls, the proposed schemes prevent the calls of a specific class from overusing the bandwidth resources. Mathematical analysis and simulation experiments are employed to study and compare the performance of the proposed schemes and the existing schemes. The numerical results show that the proposed schemes can achieve better performances in terms of call blocking probability and bandwidth utilization compared to some existing schemes, even those performing well in bandwidth asymmetry wireless networks. [ABSTRACT FROM AUTHOR]

This paper presents the architecture and performance of a new shared media ATM LAN protocol known as pipelined-access local ATM network (PLAN) which has high performance and operates in native ATM mode for efficient and simpler interworking with backbone ATM networks. PLAN is a reservation type bidirectional broadcast bus system protocol in which overhead due to bus propagation delay is limited to a total of twice the end-to-end propagation delay per transmission frame irrespective of the number of stations on the bus. It is designed to (i) support multimedia traffic, (ii) dynamically allocate bandwidth to various types of traffic and provide fair media access to stations, (iii) provide throughput close to 100%, (iv) operate at high bit rates and (v) use packets of ATM cell size and format for interoperability with wide-area ATM networks. In this paper we present the architecture, analysis and performance of the proposed PLAN protocol under various multimedia traffic conditions. The results show that its throughput approaches 100%, is independent of traffic composition and increases slowly with bit rate. It decreases only slightly as the bus length and/or number of stations increase. The proposed PLAN protocol is seen to be well suited for high-speed multimedia ATM LAN and MAN applications in the ATM networking environment. © 1998 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and Device-to-Device (D2D) service zones are composed into the 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the Quality of Service (QoS) and throughput of D2D and Cellular-to-Device (C2D) communication in a cellular network, Self-detecting Traffic Interference Control Scheme (STICS). The proposed STICS mechanism distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes. [ABSTRACT FROM AUTHOR]

Both economic and environmental costs are driving much research in the area of the energy efficiency of networking equipment. This research has produced a great amount of proposals. However, the majority of them remain unimplemented due to the lack of flexibility of current hardware devices and a certain lack of enthusiasm from commercial vendors. At the same time, Software-Defined Networking (SDN) has allowed customers to control switching decisions with a flexibility and precision previously unheard of. This paper explores the potential convergence between the two aforementioned trends and presents a promising power saving algorithm that can be implemented using standard SDN capabilities of current switches, reducing operation costs on both data centers and wired access networks. In particular, we focus on minimizing the energy consumption in bundles of energy-efficient Ethernet links leveraging SDN. For this, we build on an existing theoretical algorithm and adapt it for implementing with an SDN solution. We study several approaches and compare the resulting algorithms not only according to their energy efficiency, but also taking into account additional QoS metrics. The results show that the resulting algorithm is able to closely match the theoretical results, even when taking into account the requirements of delay-sensitive traffic. [ABSTRACT FROM AUTHOR]