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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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

To solve the problem between the high QoS requirement and bandwidth impartiality when the real-time traffic transmitted in IP network,load-adaptive slotted priority queue（LASPQ） was introduced. According to the load of the real-time traffic,it was transferred with an appropriate triggering period τ. LASPQ keep real-time traffic’s delay,jitter in need,while improving the transferring efficiency of non-real-time traffic. It is proven from theory and simulation that LASPQ is better than traditional queue schemes such as PQ,WFQ,when transmitting real-time traffic.

To solve the problem between the high QoS requirement and bandwidth impartiality when the real-time traffic transmitted in IP network,load-adaptive slotted priority queue（LASPQ） was introduced. According to the load of the real-time traffic,it was transferred with an appropriate triggering period τ. LASPQ keep real-time traffic’s delay,jitter in need,while improving the transferring efficiency of non-real-time traffic. It is proven from theory and simulation that LASPQ is better than traditional queue schemes such as PQ,WFQ,when transmitting real-time traffic.

In the last few years wireless local area network (WLAN) technology and products have grown unusually. This growth has encouraged many researchers to employ WLAN technology in different application Areas. Recently, using IEEE 802.11 WLAN in industrial automation applications is attractive and more interesting. However, the currently used medium access mechanism makes WLANs nondeterministic and hence not suitable for industrial real-time traffic. Legacy IEEE 802.11 WLANs were initially designed for best effort traffic only and did not provide any QoS support for this kind of traffic. The new amendment IEEE 802.11e standard was introduced and ratified in 2005. It defines the concept of a Hybrid Co-ordination Function (HCF) at the MAC layer for medium access control. HCF is a combination of HCF Controlled Channel Access (HCCA) with parameterized quality of service (QoS) and Enhanced Distribution Channel Access (EDCA) with prioritized QoS. Enhanced Distribution Channel Access (EDCA) is an enhanced version of Distributed Coordination Function )DCF( mechanism used in the original IEEE 802.11 WLAN. This paper analyze the performance of the EDCA in an industrial automation network with real-time requirements by means of a simulation with the network simulator OPNET 11.5 and compares the results with the DCF in terms of latency and throughput in various scenarios.