Average Queuing Time Research Articles

Performance Evaluation of Reinforcement Learning-Based Adaptive Signal Control Deployment in Urban Network

The adaptive traffic control system (ATCS) has shown great potential to address traffic congestion, fuel consumption, and air pollution issues because of its strong adaption to traffic fluctuation. With the popularity of artificial intelligence, Reinforcement Learning (RL) has been applied to ATCS and achieved excellent performance. In RL-based ATCS, the controller collects traffic state information and learns a better signal control policy by minimizing travel delay. For ATCS of an area-wide network, most existing studies assumed that all intersections are installed with ATCS. However, fully ATCS networks are difficult to achieve in the real world due to the high upgrade cost. Therefore, a practical ATCS deployment strategy in a network is needed for agencies with limited budgets. This study proposes a concept of penetration rate, or the percentage of intersections with ATCS in an area, for intelligent intersections. The weighted PageRank algorithm determines a reasonable deployment plan based on road connectivity and link volume. The relationship between penetration rate and traffic performance is investigated in the SUMO simulation. It reveals that the average stopped and control delays gradually decreased with the penetration rate increase. When the penetration rate reaches 75%, the ATSC performance is about 90% of the optimum. However, inequity and inefficiency exist in non-intelligent intersections regarding average queueing time and length at the penetration rate below 40%. Consequently, the suitable range of penetration rate should be between 40% and 75% to balance the costs and benefits of ATCS deployment with budget constraints.

Smart control of traffic lights based on traffic density in the multi-intersection network by using Q learning

In urban areas, utilizing traffic lights to prioritize vehicles at the intersection is a solution to control traffic. Among the smart traffic light methods, the methods based on machine learning are particularly important due to their simplicity and performance. In this paper, Q-learning with deep neural network are combined and used in two different intersection models. The first one is an individual intersection, and the second one is two intersections that are connected and shared their actions. By using this method, the traffic light can make an intelligent decision at the intersection to reduce vehicle consumption time by managing the allocation of phases. The proposed smart traffic light is studied and simulated via SUMO. The results illustrated that compared to fixed-time traffic lights, the average queue time of each vehicle in different traffic scenarios has been reduced by 34% in the individual intersection. In the case of two intersections, awareness and communication between agents led to a 24% reduction in the queue time of all cars in the heavy traffic scenario.

Integrating Artificial Neural Networks, Simulation and Optimisation Techniques in Ambulance Deployment for Heterogeneous Regions under Stochastic Environment

The paper focuses on the development of a strategy to integrate forecasting using artificial neural networks (ANN), simulation and optimisation techniques for ambulance deployment to predefined locations with heterogeneous demand patterns under stochastic environments. The metropolitan city of Bulawayo was used as a case study with high variability in call inter-arrival rates, response times, service times, and proportions of severity of emergencies by geographical zones covered by sub-stations. These stochastic environments complicate the decision-making process at strategic, tactical and operational level, in pursuit to achieve high levels of equality, efficiency and effectiveness in resource allocation and utilisation. This paper proposes an integrated simulation optimisation methodology that integrates future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call-in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the number of ambulances influences the average response time below a certain threshold. Beyond this threshold, no significant changes occur in the performance measures. As the fleet size is increased, the ambulance utilisation levels decreased, hence there is always need to balance resource allocation and capacity utilisation to avoid idleness of essential equipment and human resources. Numerical experiments conducted to align the response time to international standards resulted in reduction in number of ambulances required for optimal deployment. For medical resources such as ambulances, deploying more resources do not always translate to better performance, hence there is need to simultaneously consider multiple performance measures. Decision makers in EMS must seriously consider ways of reducing the response time as it has significant bearing in reducing the required number of ambulances, a critical but scarce resource. Efforts must be directed towards digitisation of switch boards in the call center, training of the paramedics and provision of relevant modern equipment to the response teams as it will go a long way in reducing the pre-trip delay time, chute time and ultimately the response time. Based on the scientific evidence, management could lobby for de-congestion and resurfacing of old and dilapidated roads in order to increase access and speed when responding to emergency calls.

Approximating a ride-sourcing system with block matching

On-demand matching between waiting passengers and idle drivers is one of the most important components in a ride-sourcing system. A variety of matching mechanisms have been developed to meet different needs of ride-sourcing platforms, e.g. mitigating supply–demand imbalance, maximizing platform revenue. In this paper, we focus on a block matching system, a special type of matching mechanism, where the region of interest is partitioned into blocks, and on-demand matching is separately and simultaneously conducted in each block. Block matching can bring many benefits, such as limiting order assignment with long pick-up distance, simplifying the process of deployment, etc. However, it still remains a challenging yet interesting issue to determine the block size for the matching system, which is a key decision variable governing passengers’ waiting time. To solve the problem, we model the ride-sourcing system with block matching via a M/M/c queue, in which the service rate is endogenous and partially determined by passengers’ average pick-up time. Based on the model, we find that the average queueing time of passengers decreases with block size increasing, while the average pick-up time may increase instead. In addition, the average total waiting time (sum of average queueing and pick-up time) become nearly invariant to the change of block size when the block size is large, which we call plateau phenomenon. In the plateau, ride-sourcing platforms can choose the block size based on other standards while the average total waiting time is always maintained at the nearly lowest value. The findings are verified via an agent-based simulation study, demonstrating that the proposed model can be an effective tool to approximate block matching system.

Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions

ABSTRACT This study combines an integrated transport, land-use, and energy (iTLE) modeling system with traffic microsimulation model and emission simulator for a holistic analysis of COVID-19 pandemic related changes in traffic flows and emissions. An activity-based travel demand model within iTLE informs pandemic traffic operation scenarios for traffic microsimulation modeling. Link-based simulation outputs inform a finer-grained emission estimation process within a MOtor Vehicle Emission Simulator. Results suggest that the overall network performance improves during lockdown as average delays and queue time decrease by 42.04% and 5.9% respectively compared to pre-COVID condition. Emission results reveal that GHG emissions significantly decrease (64%) in lockdown while it starts increasing gradually in post-pandemic period. Link-based emission analysis indicates that major arterial streets achieve a significant reduction in air pollutant emission. The findings of this study will help transportation planners, engineers, and policymakers to devise effective policies for the improvement of transport operations and emissions.

RETRACTED ARTICLE: Supply chain and logistics optimization management for international trading enterprises using IoT-based economic logistics model

Under the development trend of economic globalization, the financial difficulties of the supply chain may cause a serious crisis to the enterprises in the whole supply chain. It is necessary to alleviate the transformation pressure faced by international trading enterprises (ITE), to enhance the core competitiveness of ITE. This paper takes the SCLM (Supply Chain and Logistic Management) of ITE as the research object. Based on the analysis of the existing SCLM experience of ITE and the Internet of Things (IoT) technology, this paper puts forward the economic analysis model of the IoT-based SCLM. Specific data of some ITE prove the effectiveness of the proposed model. Further optimization is carried out through process analysis. The results show that the proposed SCLM economic analysis model based on IoT can effectively maximize the interests of enterprises and shorten the supply chain process by adding information groups. The introduced IoT-based method reduces the Average Queue Time (AQT) of vehicles by nearly 7 times and the Average Waiting Time (AWT) by nearly 4 times. After the IoT technology is applied to the SCLM of ITE, this paper significantly improves the cooperation efficiency of various departments, optimizes the relationship between customers and suppliers, and improves the core competitiveness of the enterprise. Therefore, this paper has important reference value for promoting the development of the small and micro machinery industry in China.

Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

In a wireless sensor network (WSN), data gathering is more effectually done with the clustering process. Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network. Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station. Moreover, existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes. Here, an improved clustering approach is anticipated to attain energy efficiency by implementing MapReduction for regulating mapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping. In order to optimize the network performance, this work considers intelligent behaviors’ to adapt with network changes and to introduce computational intelligence ability. In the proposed research, improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption. Node upgradation is performed by integrating Map reduction to attain modification in Hop size of nodes. This variation reduces communication complexities. Therefore, network lifetime is increased, and redundancy is reduced. While comparing with existing approaches here, sleep and wake-up nodes are considered for data transmission. The proposed algorithm clearly demonstrates 50%, 16% & 12% improvement in nodes lifetime, residual energy and throughput respectively compared to other models. Also it shows progressive improvement in reducing average waiting time, average queuing time and average energy utilization as 30%, 20% and 46% respectively. Simulation has been done in NS-2 environment for distributed heterogeneous networks.

Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.

Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium

In this study, the optimal design of location and capacity of charging facilities for electric vehicles (EVs) is investigated. A bi-level mathematical model is proposed to derive optimal design considering the equilibrium of route choice and waiting time for charging. The objective is to minimize the joint cost of facility constructions and EV drivers’ travel and waiting time over the network. The upper-level model allocates the facilities and their capacity, while the lower-level model characterizes equilibrium behavior of drivers’ route and charging facility choices. In particular, we model drivers at each charging facility as the M(t)/M/n queue and approximate the average queuing time and probability of waiting time as functions of facility capacity and demand arrival rate. The bi-level model is then converted into a single-level model, and the solution algorithm is proposed for iteratively solving the relaxed problems. Comprehensive experiments are conducted on three networks to evaluate algorithm performances, assess solution robustness and understand the scalability of the solution approach on large networks.

Simulation of queuing time in crowd evacuation by discrete time loss queuing method

We have proposed a new evacuation model based on discrete time loss queuing method in order to effectively depict the queuing of pedestrians in an indoor space and its effect over evacuation performance. In this model, the calculation formula of pedestrian movement probability is given first based on field value and average queuing time; the average queuing time is depicted with the discrete time loss queuing method and the adopted evacuation strategy is set forth through defining cellular evolution process. Moreover, with the use of the established simulation platform for experiment, we have made a deep study of relations of parameters such as evacuation time, pedestrian density, exit number and average queuing time to obtain the pedestrian flow characteristic more in line with the reality. The result has shown that there is a great change in the evacuated population in the change of crowded state at the exit, and in the background of high population density, it is beneficial for reducing queuing time to prefer faraway exit to overcrowded exit for evacuation.

MMedia: An Efficient Transmission Policy for Multimedia Applications using Mobile Cloud Computing

Background & Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.

Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows.

An optimal evacuation strategy for parking lots can shorten evacuation times and reduce casualties and economic loss. However, the impact of dynamic background traffic flows in a road network on the evacuation plan is rarely taken into account in existing approaches. This research develops an optimal evacuation model with total evacuation time minimization by dividing the evacuation process in a parking lot into two periods. In the first period, a queuing theory is used to estimate the queuing time, and in the second period, a traffic flow equilibrium model and an intersection delay model are employed to simulate vehicles’ route choice. To deal with these models, a modified ant colony algorithm is developed. The results of a numerical example prove that the proposed method has an advantage in improving evacuation efficiency. The results also show that background traffic flows affect not only vehicles’ average queuing time in parking lots but also optimal evacuation route choice. Additionally, a sensitivity analysis indicates that the minimum threshold of headway time that allows vehicles out of a parking lot to merge into the background traffic flows on the roads connecting the exits has a great impact on average queuing time, average travel time, and total evacuation time.

Resource allocation in two-tier small-cell networks with energy consumption constraints

Small-cell networks (SCNs) technology is being considered as a promising solution to improve the coverage and capacity for small-cell wireless equipment (SWE). However, the deployment of SCNs is challenging due to wireless channel interference, stochastic tasks arrival, and more prominently, the long-term energy consumption constraint of SWEs. In this paper, we provide a novel distributed dynamic resource management approach for energy-aware applications in two-tier SCNs. The joint admission control (AC) at the transport layer and resource allocation (RA) at the physical layer in SWEs is proposed to solve these challenges. Specifically, the AC and RA problem under two-tier SCNs is formulated as a stochastic optimization model which aims at maximizing the long-term average throughput of SWEs in SCN subject to time-average energy consumption limitation of each SWE and network stability constraint. By adopting Lyapunov optimization theory and Lagrangian dual decomposition technique, we propose a distributed online energy-constraint throughput optimal algorithm (ETOA) to obtain optimal AC and RA decisions. Furthermore, we derive the analytical bounds for the time-average system throughput and the time-average queue backlog achieved by our proposed approach under the constraints of long-term average energy consumption and network stability. The evaluation confirms theoretical analysis on the performance of ETOA and also shows that our approach outperforms other resource allocation methods in satisfying the time-average energy consumption requirement of SWEs.

UAV-Enabled Multiple Traffic Backhaul Based on Multiple RANs: A Batch-Arrival-Queuing-Inspired Approach

In this paper, we investigate the air-to-ground multi-traffic backhaul enabled by unmanned aerial vehicle (UAV) equipped with multiple radio access networks (RANs). Faced with the challenging traffic designation problem over diverse RANs, an efficient packet delivery scheme based on multi-priority batch arrival queuing theory is proposed to match various traffics with multiple RANs. Specifically, our contributions are three-fold: 1) The average queuing time of UAV-enabled multi-RAN access is theoretically derived according to multi-priority batch arrival queuing theory. 2) The priority factor and z-type utility function are well tailored by leveraging the derived queuing time. 3) A multiple traffic backhaul strategy is further proposed by exploiting the priority factor and z-type utility function to optimize the packet delivery, i.e., maximizing the delay utility via joint network selection and power allocation for diverse RANs. The simulation results show that the average delay of delay-sensitive packets is reduced by 15%, and the overall packet delivery quality is improved considerably.

Analyzing airport security checkpoint performance using cognitive agent models

Modern airports operate under high demands and pressures, and strive to satisfy many diverse, interrelated, sometimes conflicting performance goals. Airport performance areas, such as security, safety, and efficiency are usually studied separately from each other. However, operational decisions made by airport managers often impact several areas simultaneously. Current knowledge on how different performance areas are related to each other is limited. This paper contributes to filling this gap by identifying and quantifying relations and trade-offs between the detection performance of illegal items and the average queuing time at airport security checkpoints. These relations and trade-offs were analyzed by simulations with a cognitive agent model of airport security checkpoint operations. By simulation analysis a security checkpoint performance curve with three different regions was identified. Furthermore, the importance of focus on accuracy for a security operator is shown. The results of the simulation studies were related to empirical research at an existing regional airport.

The Alive-in-Range Medium Access Control Protocol to Optimize Queue Performance in Underwater Wireless Sensor Networks

Time synchronization between sensor nodes to reduce the end-to-end delay for critical and real time data monitoring can be achieved by cautiously monitoring the mobility of the mobile sink node in underwater wireless sensor networks. The Alive-in-Range Medium Access Control (ARMAC) protocol monitors the delay of sensitive, critical and real-time data. The idea evolves as it involves reduction in duty cycle, precise time scheduling of active/sleep cycles of the sensors, monitoring the mobility of the sink node with the selection of appropriate queues and schedulers. The model for the path loss due to attenuation of electromagnetic wave propagation in the sea water is explained. The three-path reflection model evaluating reflection loss from the air-water and watersand interfaces as a function of distance between sensors and water depth is introduced. The algorithms for effective path determination and optimum throughput path determination are elaborated. The results verify that implementation of the Alive-in-Range MAC protocol has reduced the total number of packets dropped, the average queue length, the longest time in queue, the peak queue length and the average time in queue significantly, making it relevant for critical and real-time data monitoring.

An efficiency improvement in warehouse operation using simulation analysis

In general, industry requires an efficient system for warehouse operation. There are many important factors that must be considered when designing an efficient warehouse system. The most important is an effective warehouse operation system that can help transfer raw material, reduce costs and support transportation. By all these factors, researchers are interested in studying about work systems and warehouse distribution. We start by collecting the important data for storage, such as the information on products, information on size and location, information on data collection and information on production, and all this information to build simulation model in Flexsim® simulation software. The result for simulation analysis found that the conveyor belt was a bottleneck in the warehouse operation. Therefore, many scenarios to improve that problem were generated and testing through simulation analysis process. The result showed that an average queuing time was reduced from 89.8% to 48.7% and the ability in transporting the product increased from 10.2% to 50.9%. Thus, it can be stated that this is the best method for increasing efficiency in the warehouse operation.

An efficiency improvement in warehouse operation using simulation analysis

In general, industry requires an efficient system for warehouse operation. There are many important factors that must be considered when designing an efficient warehouse system. The most important is an effective warehouse operation system that can help transfer raw material, reduce costs and support transportation. By all these factors, researchers are interested in studying about work systems and warehouse distribution. We start by collecting the important data for storage, such as the information on products, information on size and location, information on data collection and information on production, and all this information to build simulation model in Flexsim® simulation software. The result for simulation analysis found that the conveyor belt was a bottleneck in the warehouse operation. Therefore, many scenarios to improve that problem were generated and testing through simulation analysis process. The result showed that an average queuing time was reduced from 89.8% to 48.7% and the ability in transporting the product increased from 10.2% to 50.9%. Thus, it can be stated that this is the best method for increasing efficiency in the warehouse operation.

A model of check-in system management to reduce the security checkpoint variability

This article presents a model of dynamic management of the check-in desk system at an airport. The elaborated model enables the obtainment of time limits ensuring a uniformly distributed stream of passengers reporting to the security checkpoint on the basis of a schedule of check-in desk operations. The model also allows for the creation of a check-in desk operation schedule for an assumption of the longest acceptable time in queue and the maximum acceptable length of a queue. The model of dynamic management of the check-in desk system at an airport was implemented in Flexsim software. A significant advantage of the developed algorithms is that they consider the stream of passengers report for security control, which is generated by the check-in operating system. The developed model was verified and implemented for management of the check-in system at the Wrocław Airport. For input data obtained in the course of research conducted in 2014 (characteristics of the system and the stream of passengers reports - flight schedule), system efficiency measures were determined: the average queuing time for the check-in, number of work-hours of check-in operators and characteristics of the number of passengers reports for the security control. The developed model can be used in existing check-in systems, and it can also be used to evaluate the operation of a system being designed.

Avaliação do conhecimento dos candidatos a transplante de fígado

Objetivo: verificar a consistência interna de um questionário de avaliação de conhecimento sobre o processo de transplante de fígado e avaliar o conhecimento dos candidatos em fila de espera para transplante de fígado, bem como, sua percepção sobre o próprio conhecimento. Método: estudo quantitativo, descritivo/exploratório, com pacientes em lista de espera para transplante de fígado. Resultados: 62 pacientes constituíram a amostra, tempo médio de fila de 5,7 anos. A média de acertos no questionário foi de 7,4 com desvio padrão de 2,5. As questões relativas à fase pós-transplante tiveram os piores índices de acerto. 43% apontaram ter recebido informações sobre doença e 77,4% não as consideraram suficientes. Com relação à compreensão da doença e transplante, 37,1% e 45,2% apontaram respectivamente compreensão insuficiente. A consistência interna do questionário, verificada por meio do alpha de Cronbach foi 0,813. Conclusão: o questionário desenvolvido apresentou consistência interna suficiente para avaliar o conhecimento dos candidatos a transplante de fígado e apontou déficit de informação em relação ao período pós-operatório do transplante.