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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]

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable. [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 real-time 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.

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm. [ABSTRACT FROM AUTHOR]

In recent years, frequent fire disasters have led to enormous damage in China. Effective firefighting rescues can minimize the losses caused by fires. During the rescue processes, the travel time of fire trucks can be severely affected by traffic conditions, changing the effective coverage of fire stations. However, it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions. This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel. In addition, it proposes a new index of total effective coverage area (TECA) based on the time-weighted average of the effective coverage area (ECA) to evaluate the urban fire services. It also selects China as the case study to validate the feasibility of the models, a fire station (FS-JX) in Changsha. FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points, respectively. A total of 196 simulation scenarios throughout a consecutiveweek are analyzed. Eventually, 1,933,815 sets of valid sample data are obtained. The results showed that the TECA of FS-JX is 3.27 km2, which is far below the standard requirement of 7.00 km2 due to the traffic conditions. The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage. The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations. [ABSTRACT FROM AUTHOR]

Failure to obtain timely fire services will lead to thermal runaway phenomenon, which will seriously threaten the lives of people and property safety. In this study, a dynamic method was proposed to estimate fire service accessibility based on real-time traffic. To validate the method, a total of 110,055 POls extracted from online maps and existing 12 fire stations in Yiyang were used as fire service demand points and supply points. Travel times of fire trucks from supply points to demand points were calculated based on real-time traffic, and a total of 550,275 valid samples were obtained. The results showed that the ≥10 min fire service coverage rate in Yiyang is only 71.60 %. Based on the distribution area of demand points ≥10 min, the locations of three optimization solutions of new fire stations are proposed. By calculating the total average time (TAT) of the three optimization solutions of proposed new fire stations, it is concluded that TAT decreases by 743.99 s, 99.72 s, 92.59 s, respectively. From perspectives of safety and economy, the construction of the 10 fire stations of optimized solution I will effectively improve the regional fire service level.

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]

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

An improved road impedance function in conformity with Chinese city road traffic is designed for calculating the actual transportation time based on real-time traffic information and the complex road environment in the city. The objective function minimizes the total distribution cost, including fixed cost, transportation cost, damage cost, energy cost, penalty cost, and carbon emission cost. A mathematical model is constructed for cold-chain logistics distribution path optimization considering the influence of road impedance. The model is solved using three particle swarm optimization algorithms with improved weights. The experimental results show that the self-adaptive weighted particle swarm optimization algorithm is more efficient in solving this model. Experimental results obtained from the cold-chain logistics path optimization model considering road impedance compared to those of the model without road impedance indicate that the former are closer to the actual situation. Based on the changes in carbon emission and total cost under different carbon taxes, we analyze the critical carbon tax value and the optimal carbon tax value range to improve the economic and environmental benefits in the distribution process. This study should prove to be of great practical significance and application value for logistics enterprises to conduct rational planning of path problems.

As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of the standard, necessary traffic forwarding schemes are currently planned to be deployed in additional interfaces located at the edge of a 5G System. This analysis highlights the extent to which internal 5G mechanisms can be used to execute the traffic forwarding of TSN streams according to the requirements of the TSN control plane. It concludes with the recognition that a static prioritization of logical channels is not appropriate for the treatment of TSN streams over the 5G air interface. A novel prioritization mechanism of logical data channels is derived, which enables the execution of TSN-compliant traffic shaping over 5G RAN. Subsequently, a proof of concept is implemented and simulated for evaluation.

Urban areas have developed organically over time, driven by the economic success of cities. However, this development has usually been accompanied by the side effects of urbanization, such as increased traffic and its associated problems: traffic congestion, increased accident rates and pollution. As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data, including human-generated data. This data is necessary to understand the complexity of the city, set priorities, solve complicated planning problems and perform a variety of spatial analysis, which shows not only the feasibility but also the consistency of the proposed infrastructure with the requirements of a sustainable city. In this paper, we demonstrate the benefits of integrating real-time traffic data with GIS technology and remote sensing data for analyzing the impact of infrastructure works and COVID-19 on traffic in Oradea, Romania. The case study was focused on the historical center of Oradea and was based on remote sensing data collected before, during, and after traffic restrictions. The study also shows the need for using GIS and crowdsourcing-based applications in traffic analysis and planning. [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.

As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of the standard, necessary traffic forwarding schemes are currently planned to be deployed in additional interfaces located at the edge of a 5G System. This analysis highlights the extent to which internal 5G mechanisms can be used to execute the traffic forwarding of TSN streams according to the requirements of the TSN control plane. It concludes with the recognition that a static prioritization of logical channels is not appropriate for the treatment of TSN streams over the 5G air interface. A novel prioritization mechanism of logical data channels is derived, which enables the execution of TSN-compliant traffic shaping over 5G RAN. Subsequently, a proof of concept is implemented and simulated for evaluation. [ABSTRACT FROM AUTHOR]

The aim of this study is to collate valuable information about the hazard perception process of (young) drivers and to evaluate their hazard perception skills. For this, an eye-tracking system was used to record the visual behaviour of drivers. In addition, a system based on a data logger and sensors was developed to capture and log the relevant data from the vehicle. By tapping into the vehiclés CAN bus (Controller Area Network), the needed parameters (e.g. position of accelerator, brake, and clutch pedal, as well as the steering wheel angle) were acquired through the reverse engineering of the vehicle′s CAN stream. All the captured data was subsequently synchronized and overlaid in the eye-tracking video. The complete system, i.e. data-logger and sensors, was consistently integrated into a driving school car. Due to the fact that all the data was captured in real-time whilst driving in traffic, every analysed situation can be considered unique and therefore cannot be compared to other situations. The results of the study revealed that only in 18% of the analysed situations, the hazard-perception skills were adequate or the same as they were taught at the driving school just a few months previous. Confronted by the videos of their lessons, the participating drivers adjusted their self-assessment scores for their driving skills downwards by around 25% on average.

Data traffic is the most important data transmission between users inside every network. This data traffic can be videos, files, voice, pictures, and many more. It is divided into two types, real-time and non-real-time traffic. Most real-time traffic data have a low tolerance for delays during transmission, as they need to be quickly received between communication devices. In this paper, a comprehensive analysis was made to evaluate the two types of data transmitted through FANET drones, with different mobility models and two types of IEEE 802.11 2.4 GHz and 5 GHz using the optimized link state routing protocol. Metrics such as delay and throughput were measured. This paper gives an important overview of how real-time and non-real-time traffic will be handled during data transmission in FANET networks. [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]

Urban areas have developed organically over time, driven by the economic success of cities. However, this development has usually been accompanied by the side effects of urbanization, such as increased traffic and its associated problems: traffic congestion, increased accident rates and pollution. As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data, including human-generated data. This data is necessary to understand the complexity of the city, set priorities, solve complicated planning problems and perform a variety of spatial analysis, which shows not only the feasibility but also the consistency of the proposed infrastructure with the requirements of a sustainable city. In this paper, we demonstrate the benefits of integrating real-time traffic data with GIS technology and remote sensing data for analyzing the impact of infrastructure works and COVID-19 on traffic in Oradea, Romania. The case study was focused on the historical center of Oradea and was based on remote sensing data collected before, during, and after traffic restrictions. The study also shows the need for using GIS and crowdsourcing-based applications in traffic analysis and planning.

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In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants. [ABSTRACT FROM AUTHOR]

Today's network traffic can be broadly classified into two categories: real-time traffic and elastic one. Real-time traffic is delay sensitive and is to be conveyed at a fixed bit rate. This type of traffic comprises voice, videoconferencing of different quality, etc. Elastic traffic is generally used for data transmission; therefore its transfer rate can vary in proportion to the residual cell capacity. It might happen, for instance, while downloading files, transmitting machine-to-machine data for the Internet of things, etc. The process of conveying elastic traffic allows increasing the efficiency of network resources considerably. According to teletraffic theory, requests for file transmission at the expense of spontaneous resource seizing force requests for traffic transmission of real-time services out of processing. This is essential for mobile (wireless) communications networks of any standard where subscribers use devices with intelligent modems that increase the elastic traffic portion significantly. Simultaneous transmission of heterogeneous traffic over a network obviously needs some control tools that will ensure the desired quality of service (QoS) for arriving requests. Thereby when the transmission rate increases or decreases, the mean value of residual time for data file transfer decreases or increases proportionally. One of the ways to control the cellular network throughput is to set limits to the speed of elastic data traffic transmission. The given study considers the problem of planning the network capacity and acceptable amount of traffic transmitted through the network cell while transferring it with the desired QoS.

Fire stations play a key role in controlling typical thermal runaway phenomena (fires). Based on real-time traffic, we develop a data crawling and processing algorithm for travel times, and propose an evaluation model for the spatial configuration of fire stations. In the model, the fire service levels are divided into four levels based on the total fire service score (TFSS). The present work gathered 16,448 fire incidents in Changsha, China from 2013 to 2017 as fire service demand points, and the minimum travel times for each fire incident point in the four evaluation scenarios with different real-time traffics were extracted from online maps. The results show that the TFSSs of Changsha varied from 67.92 to 74.05 due to the insufficient firefighting resources and the dynamic traffic conditions, and the fire service level of Changsha can be defined as fair. By employing spatial visualization, we observed that the fire service demand points with moderate and poor fire service levels were mainly located in rural areas, and those with good and fair fire service levels were located in urban areas.

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]

In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions. [ABSTRACT FROM AUTHOR]

This study aims to divide traffic into meaningful clusters (regimes) and to investigate their impact on accident likelihood and accident severity. Furthermore, the likelihood of powered-two-wheelers (PTWs) involvement in an accident is examined. To achieve the aims of the study, traffic and accident data during the period 2006–2011 from two major arterials in Athens were collected and processed. Firstly, a finite mixture cluster analysis was implemented to classify traffic into clusters. Afterwards, discriminant analysis was carried out in order to correctly assign new cases to the existing regimes by using a training and a testing set. Lastly, Bayesian logistic regression models were developed to investigate the impact of traffic regimes on accident likelihood and severity. The findings of this study suggest that urban traffic can be divided into different regimes by using average traffic occupancy and its standard deviation, measured by nearby upstream and downstream loop detectors. The results revealed potential hazardous traffic conditions, which are discussed in the paper. In general, high occupancy values increase accident likelihood, but tend to lead slight accidents, while PTWs are more likely to be involved in an accident, when traffic occupancy is high. Transitions from high to low occupancy also increase accident likelihood.

In order to achieve the load balancing between network nodes, improve the utilization of system resources and network throughput, and reduce network delay. Therefore, based on the Optimized Link State Routing (OLSR) protocol, this paper proposes an improved protocol of OLSR based on traffic load balancing and QoS. OLSR-TLB protocol takes the traffic load balancing and QoS into account. For real-time traffic, focus on the traffic load of the access point. If the access point traffic load is too high, it cannot be connected with the routing. However, for non-real-time traffic, QoS but not traffic load is considered, making routing decisions based on hop counts. In addition, the traffic service is divided into real-time traffic and non-real-time traffic. The former considers the real-time, making routing decisions based on traffic load, while the latter considers QoS, making routing decisions based on hop counts. Simulation results show that the proposed OLSR-TLB protocol can effectively improve the throughput and reduce the delay performance. Compared with the traditional OLSR protocol, the throughput of OLSR-TLB protocol is improved to 3.5Mbps, and the delay is reduced to 50 ms. [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 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.

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.

Wireless sensor networks (WSNs) have gained much attention in today's research domain for supporting a wide variety of applications including the multimedia applications. Multimedia applications that are regarded as the quality-of-service (QoS)-aware, delay sensitive, and bandwidth hungry applications require enough energy and communication resources. WSNs being the energy-scarce network have now been designed in such a way that they can support these delay-sensitive and time-critical applications. In this paper, we propose an energy-efficient routing protocol for heterogeneous WSNs to support the delay sensitive, bandwidth hungry, time-critical, and QoS-aware applications. The proposed QoS-aware and heterogeneously clustered routing (QHCR) protocol not only conserves the energy in the network, but also provides the dedicated paths for the real-time and delay sensitive applications. The inclusion of different energy-levels for the heterogeneous WSNs also provides the stability in the networks while minimizing the delay for the delay-sensitive applications. Extensive simulations have been performed to validate the effectiveness of our proposed scheme. Our proposed routing scheme outperforms other state-of-the-art schemes in terms of the delay performances.

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.

Vehicle emissions have become the dominant source of air pollutants in many cities. Relatively few studies have examined the impact of different ethanol concentrations into fuel on exhaust emissions from flex-fuel vehicles on real-time traffic conditions. In this study, emissions analyses of carbon monoxide, carbon dioxide, nitrogen oxides and hydrocarbons were carried out in a flex-fuel vehicle in an urban route in the Fortaleza city/Brazil. The vehicular exhaust emissions of the pollutants above mentioned were measured through a portable analyzer. Route was performed with different ethanol/gasoline blends (27%, 85%, and 100% ethanol). VSP methodology was used in this work. On-board system data used could register instantaneous emissions as a function of vehicle dynamics. It was observed that the acceleration operation mode was the major responsible for highest emissions of CO, CO 2 and NO x for all blends tested. C x H y emissions of <0.005 g km −1 were registered. The results show the relevance of performing sampling in real-time traffic conditions, since the emission standards set by approved real-time traffic conditions do not often represent local reality. [ABSTRACT FROM AUTHOR]

In this paper we propose estimation procedure in which traffic flows resulting from rerouting model are matched with traffic flows observed during unexpected events. We show practical value of observing a entire cut-set of the transportation network and propose theoretical closed-form formulation of estimation problem for the rerouting model. We apply proposed framework on field-data from Warsaw bridges to observe rerouting phenomena. Most importantly we observed that: a) around 20% of affected traffic flow reroutes, b) rerouting flows are increasing in time, c) drivers show strategic capabilities, d) and maximize their utility while rerouting. All of the which were hypothesized in Information Comply Model (Kucharski et. al., 2014) and are now supported with field observations.

Network coding is a promising technology that can be used to effectively enhance the throughput of wireless networks. In realistic wireless networks, due to the random nature of wireless medium access and possible rate mismatch among different codeable flows, there may be insufficient packets to be coded together at intermediate nodes, which compromises the potential throughput gain by exploiting network coding. In this paper, we propose a joint scheduling and network coding (SNC) framework to maximize coding opportunity for real-time traffic flows with diverse delay constraints. SNC consists of two mechanisms: Delaying Packet Transmission Policy (DTP) and Network Coding Group (NCG) based Scheduling Policy (GSP). DTP dynamically decides whether to delay packet transmission of an NCG or not and adjusts the encoding degree. We formulate this problem as a finite-horizon Markov decision process to obtain the optimal encoding degree adaptation policy. Based on the results of DTP and delay requirements of real-time traffic flows, GSP calculates a weight for each NCG and schedules the coded packets of an NCG for transmission according to the weight. Simulation results demonstrate the effectiveness and advantages of SNC, compared with NC schemes using opportunistic policy and greedy policy. [ABSTRACT FROM AUTHOR]

Network-on-Chip (NoC) is a preferred communication medium for massively parallel platforms. Fixed-priority based scheduling using virtual-channels is one of the promising solutions to support real-time traffic in on-chip networks. Most of the existing works regarding priority-based NoCs use a flit-level preemptive scheduling. Under such a mechanism, preemptions can only happen between the transmissions of successive flits but not during the transmission of a single flit. In this paper, we present a modified framework where the non-preemptive region of each NoC packet increases from a single flit. Using the proposed approach, the response times of certain traffic flows can be reduced, which can thus improve the schedulability of the whole network. As a result, the utilization of NoCs can be improved by admitting more real-time traffic. Schedulability tests regarding the proposed framework are presented along with the proof of the correctness. Additionally, we also propose a path modification approach on top of the non-preemptive region based method to further improve schedulability. A number of experiments have been performed to evaluate the proposed solutions, where we can observe significant improvement on schedulability compared to the original flit-level preemptive NoCs. [ABSTRACT FROM AUTHOR]

Cognitive radio technology can improve the spectrum utilization in wireless networks by providing the capability for the secondary users to opportunistically use the frequency spectrum which is originally dedicated to the licensed users. In this paper the quality of service parameters for the real-time (RT) secondary users are analyzed in an overlay cognitive radio cellular network. RT packets are delay sensitive and can tolerate a limited amount of waiting time. Otherwise they will be useless and hence will be dropped. On the other hand, the availability of the frequency channels for SUs in cognitive radio networks (CRNs) depends on the activity of licensed users. Therefore, the support of RT services in CRNs requires elaborate considerations in various functionalities of the network. Here a practical cellular structure for the network is considered. Furthermore, a packet scheduling scheme is proposed and an analytical framework is developed for the performance analysis of RT secondary users. The effect of errors of spectrum sensing is also taken into account in the performed analysis. The analytical results are verified by simulations. The obtained results are very helpful for the support of RT traffic in cellular CRNs. [ABSTRACT FROM AUTHOR]

The past decade witnessed the dramatic evolution from Quality of Service (QoS) to Quality of Experience (QoE) in the design of wireless networks, especially on the aspect of link scheduling. In many applications, end users are concerned more about transmission quality of an individual task rather than the quality of a link, where a task may refer to a piece of music, video, etc. and may include many packets. This paper proposes a new network model aiming at improving user experience by pushing the scheduling problem to the task layer. A novel QoE requirement is designed to generalize the QoS requirements of a task, which is the ratio requirement. Following this design, a corresponding scheduling policy is proposed to capture it for each task and then reach an application-aware transmission allocation. We theoretically analyze the performance of the scheduling policy, and discuss the design of an optimal solution and the impact of the QoE requirements. Finally, the simulation results indicate that our scheduling policy can significantly improve QoE. [ABSTRACT FROM AUTHOR]