Traffic Breakdown Probability Estimation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles.

Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper, we propose a stochastic temporal queueing model to reliably depict the queue dynamics of mixed traffic flow at ramping bottlenecks. The new model is a specified Newell's car-following model that allows two kinds of vehicle velocities and first-in-first-out (FIFO) queueing behaviors. The jam queue join time is supposed to be a random variable for human driven vehicles but a constant for automated vehicles. Different from many known models, we check the occurrence of significant velocity drop along the road instead of examining the duration of the simulated jam queue so as to avoid drawing the wrong conclusions of traffic breakdown. Monte Carlo simulation results show that the generated breakdown probability curves for pure human driven vehicles agree well with empirical observations. Having noticed that various driving strategy of automated vehicles exist, we carry out further analysis to show that the chosen car-following strategy of automated vehicles characterizes the breakdown probabilities. Further tests indicate that when the penetration rate of automated vehicles is larger than 20%, the traffic breakdown probability curve of the mixed traffic will be noticeably shifted rightward, if an appropriate car-following strategy is applied. This indicates the potential benefit of automated vehicles in improving traffic efficiency.

A Channel Allocation Method Considering the Asymmetry of Available Channels for Centralized Underwater Cognitive Acoustic Networks.

Due to the unpredictable presence of Non-Cognitive Users (NCUs) in the time and frequency domains, the number of available channels (i.e., channels where no NCUs exist) and corresponding channel indices per Cognitive User (CU) may differ. In this paper, we propose a heuristic channel allocation method referred to as Enhanced Multi-Round Resource Allocation (EMRRA), which employs the asymmetry of available channels in existing MRRA to randomly allocate a CU to a channel in each round. EMRRA is designed to enhance the overall spectral efficiency and fairness of channel allocation. To do this, the available channel with the lowest redundancy is primarily selected upon allocating a channel to a CU. In addition, when there are multiple CUs with the same allocation priority, the CU with the smallest number of available channels is chosen. We execute extensive simulations in order to investigate the effect of the asymmetry of available channels on CUs and compare the performance of EMRRA to that of MRRA. As a result, in addition to the asymmetry of available channels, it is confirmed that most of the channels are simultaneously available to multiple CUs. Furthermore, EMRRA outperforms MRRA in terms of the channel allocation rate, fairness, and drop rate and has a slightly higher collision rate. In particular, EMRRA can remarkably reduce the drop rate compared to MRRA.

Impact of the Dropping Function on Clustering of Packet Losses.

The dropping function mechanism is known to improve the performance of TCP/IP networks by reducing queueing delays and desynchronizing flows. In this paper, we study yet another positive effect caused by this mechanism, i.e., the reduction in the clustering of packet losses, measured by the burst ratio. The main contribution consists of two new formulas for the burst ratio in systems with and without the dropping function, respectively. These formulas enable the easy calculation of the burst ratio for a general, non-Poisson traffic, and for an arbitrary form of the dropping function. Having the formulas, we provide several numerical examples that demonstrate their usability. In particular, we test the effect of the dropping function's shape on the burst ratio. Several shapes of the dropping function proposed in the literature are compared in this context. We also demonstrate, how the optimal shape can be found in a parameter-depended class of functions. Finally, we investigate the impact of different system parameters on the burst ratio, including the load of the system and the variance of the service time. The most important conclusion drawn from these examples is that it is not only the dropping function that reduces the burst ratio by far; simultaneously, the more variable the traffic, the more beneficial the application of the dropping function.

On the Time to Buffer Overflow in a Queueing Model with a General Independent Input Stream and Power-Saving Mechanism Based on Working Vacations.

A single server GI/M/1 queue with a limited buffer and an energy-saving mechanism based on a single working vacation policy is analyzed. The general independent input stream and exponential service times are considered. When the queue is empty after a service completion epoch, the server lowers the service speed for a random amount of time following an exponential distribution. Packets that arrive while the buffer is saturated are rejected. The analysis is focused on the duration of the time period with no packet losses. A system of equations for the transient time to the first buffer overflow cumulative distribution functions conditioned by the initial state and working mode of the service unit is stated using the idea of an embedded Markov chain and the continuous version of the law of total probability. The explicit representation for the Laplace transform of considered characteristics is found using a linear algebra-based approach. The results are illustrated using numerical examples, and the impact of the key parameters of the model is investigated.

On the Transient Queue with the Dropping Function.

We deal with a queueing system, in which arriving packets are being dropped with the probability depending on the queue size. Such a scheme is used in several active queue management schemes proposed for Internet routers. In this paper, we derive and analyze a selected transient characteristic of the model, i.e., the probability that in a given time interval the queue size is kept under a predefined level. As the main purpose of the discussed queueing scheme is to maintain the queue size low, this is a natural characteristic to study. In addition to that, the average time to reach a given level is derived. Theoretical results for both characteristics are accompanied by numerical examples. Among other things, they demonstrate that the transient behavior of the queue may vary significantly with the shape of the dropping function, even if the steady-state performance remains unaltered.

Comparison of emergency department crowding scores: a discrete-event simulation approach.

According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. Using data from a large academic hospital in North Carolina, we evaluate three crowding scores, namely, EDWIN, NEDOCS, and READI by assessing strengths and weaknesses of each score, particularly their predictive power. We perform these evaluations by first building a discrete-event simulation model of the ED, validating the results of the simulation model against observations at the ED under consideration, and utilizing the model results to investigate each of the three ED crowding scores under normal operating conditions and under two simulated outbreak scenarios in the ED. We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.

Traffic Intensity of Patients and Physicians in the Emergency Department: A Queueing Approach for Physician Utilization.

BACKGROUND: The unpredictable nature of patient visits poses considerable challenges to the staffing of emergency department (ED) medical personnel. There is a lack of common physician usage parameters at present. OBJECTIVE: The aim of this study was to quantify the ED traffic intensity of patients and physicians using a queueing model approach. METHODS: A retrospective administrative electronic data analysis was conducted in a tertiary medical center. All patients who registered at the ED in 2013 were included. Precisely recorded patient waiting time, service time, and disposition time were obtained. An M/M/s (Markovian patient arrival, Markovian patient service, s servers) queueing model was used, while taking account of the actual physician number and number of patients managed simultaneously. Physician utilization and performance indicators were measured. RESULTS: A total of 148,581 patients were analyzed after exclusion. The overall mean waiting time, service time, and disposition time were 0.23 (standard deviation [SD] = 0.24), 2.31 (SD = 3.89), and 2.54 (SD = 3.90) hours, respectively. Hourly physician utilization (ρ), stratified by different patient entities, was ρ = 0.75 ± 0.17 for adult non-trauma, ρ = 0.75 ± 0.28 for pediatric, and ρ = 0.53 ± 0.18 for trauma (p = 0.0004). There was a surge of utility for pediatric non-trauma patients in the late evening (ρ = 1.4 at 11 pm). The distribution of number of patients in the system was derived and compared by different patient entities and time points. CONCLUSIONS: A queueing model was built to model traffic intensity of physicians and patients, the physician utility trend disclosed the fluctuation of manpower utility. The estimated parameters serve as important factors for developing tailored staffing policies for minimizing ED waiting and improving ED crowding.

Flexible bed allocations for hospital wards.

Flexibility in the usage of clinical beds is considered to be a key element to efficiently organize critical capacity. However, full flexibility can have some major drawbacks as large systems are more difficult to manage, lack effective care delivery due to absence of focus and require multi-skilled medical teams. In this paper, we identify practical guidelines on how beds should be allocated to provide both flexibility and utilize specialization. Specifically, small scale systems can often benefit from full flexibility. Threshold type of control is then effective to prioritize patient types and to cope with patients having diverse lengths of stay. For large scale systems, we assert that a little flexibility is generally sufficient to take advantage of most of the economies of scale. Bed reservation (earmarking) or, equivalently, organizing a shared ward of overflow, then performs well. The theoretical models and guidelines are illustrated with numerical examples. Moreover, we address a key question stemming from practice: how to distribute a fixed number of hospital beds over the different units?

A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model.

Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of applications. Monitoring tasks can be performed in various environments. This may be beneficial in many scenarios, but it certainly exhibits new challenges in terms of security due to increased data transmission over the wireless channel with potentially unknown threats. Among possible security issues are timing attacks, which are not prevented by traditional cryptographic security. Moreover, the limited energy and memory resources prohibit the use of complex security mechanisms in such systems. Therefore, balancing between security and the associated energy consumption becomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the requirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of this problem, a hybrid continuous-time Markov chain (CTMC) and queueing model are put forward, and the tradeoff analysis of the security and performance attributes is carried out. By extending and transforming this model, the mean time to security attributes failure is evaluated. Through tradeoff analysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate of the performance and security tradeoff can be obtained.

A communication failure and repair mechanism with adjustable transmission rates for PU packets in CRNs.

As one key technology of future radio communication networks, cognitive radio networks (CRNs) can effectively handle the shortage of network resources. Two classes of users exist in traditional CRNs, namely, primary users (PUs) with higher priority and secondary users (SUs) with cognitive ability. In CRNs, during the communication process, packets need to travel through various servers, such as switches and routers, and these devices may fail at any time. We consider this type of problem to be a communication failure. The occurrence of communication failures trigger system repairs, which make the system return to regular work. In this paper, we present a communication failure and repair mechanism with adjustable transmission rates for PU packets in CRNs. We assume that PU packets can maintain low-speed transmission during the system failure state and resume high-speed transmission after the failure is repaired. We establish a three-dimensional Markov chain (3DMC) and build a queueing model based on discrete time. Through numerical experiments, we analyze some indicators' impact on the system capability. In addition, compared with the traditional communication failure and repair mechanism, our proposed mechanism can reduce the blocking rate while considerably increasing the throughput of data packets.

A Three-Level Health Inspection Queue Based on Risk Screening Management Mechanism for Post-COVID Global Economic Recovery.

Due to the serious impact of border control measures during the ongoing novel coronavirus (COVID-19) epidemic, new difficulties and challenges have been brought to the border lines of most countries and regions. Focusing on the post-COVID global economic recovery, we develop a computing method for studying the health inspection process based on a three-color risk screening management mechanism at border crossing checkpoints. In this article, to manage the cross-border risk efficiently during the epidemic prevention and control, we formulate a queueing model with hierarchical health inspection channels. The structural characteristics and properties of this three-level queueing model are also analyzed by studying the health inspection process with risk classification. Furthermore, we conduct a series of sensitivity analysis on several performance measures for the studied queueing system. In the numerical results, we figure out the monotonicity, convexity and complicated patterns of the derived formulas.