Priority Scheme Research Articles

Transit Signal Priority Evaluation Model Based on Interval D-S Evidence Theory

Striving to develop public transport is an effective way of alleviating traffic congestion in large and medium-sized cities, and arterial transit signal priority is an important measure for improving the reliability of bus operations. However, only few studies have evaluated the quality of arterial transit signal priority scenarios. To address this gap this article proposes a transit signal priority scheme evaluation model. First, an evaluation index for arterial transit signal priority is established. Second, a decisive model of interval D-S evidence theory for arterial transit signal priority is built. Finally, to address the uncertainty of the index attribute and the one-sidedness of a single decision maker, the advantages and disadvantages of the model is assessed on the basis of D-S evidence theory, and the optimal scheme is selected. This research provides a new theory and method for the transit signal priority scheme that can effectively avoid the one-sidedness of a single decision maker in evaluating transit signal priority and the uncertainty of index attributes, and provides technical support for extending the application of transit signal priority.

Content Based Status Updates

Consider a stream of status updates generated by a source, where each update is of one of two types: high priority or ordinary (low priority). These updates are to be transmitted through a network to a monitor. However, the transmission policy of each packet depends on the type of stream it belongs to. For the low priority stream, we analyze and compare the performances of two transmission schemes: $(i)$ Ordinary updates are served in a First-Come-First-Served (FCFS) fashion, whereas, in $(ii)$ , the ordinary updates are transmitted according to an M/G/1/1 with preemption policy. In both schemes, high priority updates are transmitted according to an M/G/1/1 with preemption policy and receive preferential treatment. An arriving priority update discards and replaces any currently-in-service high priority update, and preempts (with eventual resume for scheme $(i)$ ) any ordinary update. We model the arrival processes of the two kinds of updates, in both schemes, as independent Poisson processes. For scheme $(i)$ , we find the arrival and service rates under which the system is stable and give closed-form expressions for average peak age and a lower bound on the average age of the ordinary stream. For scheme $(ii)$ , we derive closed-form expressions for the average age and average peak age of the high priority and low priority streams. We finally show that, if the service time is exponentially distributed, the M/M/1/1 with preemption policy leads to an average age of the low priority stream higher than the one achieved using the FCFS scheme. Therefore, the M/M//1/1 with preemption policy, when applied on the low priority stream of updates and in the presence of a higher priority scheme, is not anymore the optimal transmission policy from an age point of view.

Improving Societal Outcomes in the Organ Donation Value Chain

In the context of the mismatch between organ demand and supply, a statistic worth noting is that across the donor service areas in the United States, the average (median) percentage of eligible potential donors from whom no organs are recovered is 28.3% (29%). The socially suboptimal quantity and quality of recovered organs form the context of our study, which takes the perspective of the social planner that has an overall quality‐adjusted life‐year (QALY) improvement objective. We model the operational decisions of two key supply‐side entities in a cadaver organ donation value chain (ODVC)—the organ procurement organization (OPO), and the hospital (trauma center) where potential donors arrive. We consider the OPO’s effort level in seeking authorization for organ donation, the hospital’s effort level in identifying and referring potential donors to the OPO, and the hospital’s priority scheme for scheduling organ recovery and other procedures in its operating room (OR). We develop an analytical model to study the effects of contextual factors (including reimbursement rates for the hospital, shared OR capacity between organ recovery and other procedures, donor heterogeneity, and increments in QALYs for organ recipients and the hospital’s other patients), and the decisions of the OPO and the hospital, on their respective payoffs and on societal outcomes. Our analysis identifies possible misalignments in the objectives of the social planner, the OPO, and the hospital, and we recommend contracts that can help the ODVC achieve socially‐optimal QALY performance while ensuring that neither the OPO nor the hospital would be worse off.

Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control

Real-time control of large-scale urban networks has been attracting significant research attention. This paper, using the information provided by connected vehicles, proposes a novel control approach based on the concept of perimeter control to maximize the social welfare of all vehicles. The contributions of this paper are threefold. First, we consider vehicle heterogeneity (i.e. corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.) and integrate a priority scheme into perimeter control to improve both the traffic performance and the social welfare. This is achieved by installing priority lanes at some of the perimeter intersections. Unlike the existing research works that provide priority to certain traffic modes, we dynamically identify the groups of vehicles that we should prioritize, in order to maximize the social welfare. Second, we develop a model predictive control approach to simultaneously optimize the toll for using the priority lanes and the traffic signal timings at the perimeter intersections. This approach can explicitly handle the constraint of the storage capacity of each intersection link. Third, we propose a recursive estimation algorithm to update our knowledge on the distribution of the value of times (VOTs), using the lane choice information of connected vehicles. The proposed approach is tested in a simulated network which resembles the main features of the city center of Zurich, Switzerland. By using the proposed strategy, the traffic accumulation inside the network is still stabilized, and the monetary costs due to delay are significantly reduced at the entire network (up to 25.8%) compared to the strategy without priority. The distribution of the combined cost (including cost due to delay and tolls) is more uniform across VOT groups than that resulting from the strategy without priority. It is also shown that the proposed recursive estimation algorithm quickly converges and further improves the social welfare.

Designing a transit priority network under variable demand

ABSTRACT While transit exclusive lanes are statically introduced for an entire day (or for the peak period), the total number and distribution of trips vary from one hour to another during a day. In this study, a Transit Priority Network Design Problem (TPNDP) under variable demand has been formulated by a bi-level robust optimization approach. At the upper level, a non-linear mathematical program is adapted to find the optimum set of exclusive bus lanes at the network level such that the total travel time as well as its hourly variation is minimized and at the lower level, a four-step demand model is applied. The results show the importance of considering the hourly travel demand variation in designing a transit priority scheme and how such results can be compared with previous TPNDP models with a fixed demand. The proposed algorithm identifies the location of full time and part-time transit lanes in the network.

Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on shannon entropy

An assessment model is built to investigate the degree of order in the organisational structure of the electricity regulatory institution (ERI) in China. The model is based on Shannon entropy and is constructed from the perspective of timeliness and accuracy of the flow of information. The model is then used to evaluate the degree of order in the organisational structures of the ERI during three stages of reform that occurred during 2002–13. The results indicate that the reforms and improvements made in the organisational structure of China's ERI have resulted in a stepwise increase in their degree of order (corresponding to 0.3156, 0.3277, and 0.3324 in the three stages, respectively). On this basis, a scheme is put forward to optimise the degree of order in the structure of the energy regulatory institution in the current stage. The results show that downsizing the internal and subordinate departments appropriately and creating more governmental agencies to regulate energy are conducive to further improving the degree of order of the energy regulatory institution. Finally, we use principal component analysis to propose a priority scheme for adding more regulatory governmental agencies based on sorted energy production and consumption data.

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Wireless sensor networks have recently been used to monitor bioelectric signals generated by the human body. However, since the dimensions and capabilities of the network nodes are small and limited, a problem is generated inherently by the energy consumption and radioelectric interference. In view of this, we propose two priority schemes to improve the performance of the network. In the first one, we aim at increasing the system lifetime by reducing the number of transmissions of nodes with low energy levels. In the second scheme, we aim at conveying priority data to the sink node as fast and efficient as possible. In the former scheme, users can be monitored for long time periods considering a low data generation. On the other hand, the latter scheme allows expediting transmission of important data when energy efficiency is not relevant. In this article, we mathematically model, analyze, and study the performance of the system for both priority schemes considering cognitive radio capabilities to make efficient use of resources and limit the radioelectric interference when the network transmits both continuous monitoring and event detection information. Numerical results provided in this work allow a careful parameter selection for practical implementation of BANETs.

A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

Cognitive radio has emerged as an enabling technology in the realization of a spectrum-efficient and delay-sensitive industrial wireless communication where nodes are capable of responding in real-time. However, particularly for time-critical industrial applications, because of the link-varying channel capacity, the random arrival of a primary user (PU), and the significant delay caused by spectrum handoff (SH), it is challenging to realize a seamless real-time response which results in a quality of service (QoS) degradation. Therefore, the objectives of this paper is to increase spectrum utilization efficiency by allocating channel based on the priority of a user QoS requirements, to reduce SH delay, to minimize latency by preventing avoidable SHs, and to provide real-time response. To achieve an effective spectrum utilization, we proposed an integrated preemptive/non-preemptive priority scheme to allocate channels according to the priority of user QoS requirements. On the other hand, to avoid significant SH delays and substantial latency resulting from random PU arrival, a unified spectrum sensing technique was developed by integrating proactive sensing and the likelihood estimation technique to differentiate between a hidden and a co-existence PU, and to estimate the mean value of the busy and the idle periods of a channel respectively. Similarly, to prevent poor quality channel selection, a channel selection technique that jointly combines a reward system that uses metrics, e.g. interference range, and availability of a common channel to ranks a set of potential target channels, and a cost function that optimizes the probability of selecting the channel with the best characteristics as candidate channels for opportunistic transmission and for handoffs was developed. The simulation results show a significant performance gain of the delay-PritSHS in terms of number of SHs, Latency, as well as throughput for time-critical industrial applications in comparison to other schemes.

Optimal finite state predictive direct torque control without weighting factors for motor drive applications

An optimal finite state predictive direct torque control without flux and switching frequency weighting factors (WFs) is proposed to avoid the tuning of WFs and to provide high dynamic performances with reduced switching frequency and computational burden. The proposed method is a combination of the well-known direct torque control and multi-objective ranking (MOR) priority methods. The classical MOR, used to avoid the flux WF, increases the computation time compared to the standard predictive torque control (PTC) and does not consider switching frequency issues. These drawbacks impose the need for high sampling rates, which increase the implementation cost and the inverter power losses, an important issue for medium and high-power drives. In the proposed method, the direct torque control principle is used to reduce the number of voltage vectors from eight to three, which reduces the computational burden of MOR. Also, MOR is associated with a proposed priority scheme to reduce the switching frequency and avoid WFs. Simulation and experimental results confirm that similar steady-state response as in the traditional PTC based on MOR is obtained. However, fast transient response is achieved, the computational burden is reduced by about 25%, and the switching frequency is substantially reduced compared to classical PTC.

Safety Message Verification Using History-Based Relative-Time Zone Priority Scheme

Safety message verification plays an important role in securing vehicular ad hoc networks (VANETs). As safety messages are broadcasted several times per second in a highly dense network, message arrival rate can easily exceed the verification rate of safety messages at a vehicle. As a result, an algorithm is needed for selecting and prioritizing relevant messages from received messages to increase the awareness of vehicles in the vicinity. This paper presents the history-based relative-time zone (HRTZ) priority scheme for selecting and verifying relevant received safety messages. HRTZ is an enhanced version of our previously proposed relative-time zone (RTZ) priority scheme. HRTZ achieves higher awareness of nearby vehicles and works in different road configurations. To increase awareness of neighboring vehicles, the average velocity of neighboring vehicles in the range of communication is used to determine the range of the danger zone and other zones. The messages are ranked based on the zone of transmitting vehicles, road configuration (with/without a barrier) and transmitting vehicle location and direction, and relative time between transmitting and receiving vehicles. Only the most up-to-date message from each vehicle is kept in the receiver’s buffer. As a result, each neighboring vehicle has only the most recent safety message in the buffer at any time. The simulation results show that HRTZ achieves a higher rate of verified messages with low delay for nearby vehicles and achieves higher awareness for vehicles in the vicinity, when compared to RTZ and other existing schemes.

Workflow Scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses Markovian chain stationary probabilities as a measure of importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other, and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget splitting strategy, which splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including FFT, Gaussian elimination, typical scientific workflows, randomly generated workflows and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with the-state-of-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand in average for most cases, and the Markovian based prioritization further improves the workflow makespan.

Handoff in Various Mobile Network Technologies

Mobile cellular networks are an indispensable part of modern life, where the need for customer satisfaction in the use of many different services by consumers is constantly increasing. The requirements for higher transmission speed, lossless transmission, reliability, efficiency, low latency, mass connectivity, guarantee of high Quality of Service criteria are repeatedly increasing. All this requires the continuous development of the used technologies as well as the introduction of new generations of networks. Handover mechanism is extremely important in cellular network because of the cellular architecture employed to maximize spectrum utilization. To ensure the quality of service in wireless cellular networks, the report proposes the use of a Horizontal Handoff Priority Scheme. Simulation experiments have been carried out, the probability parameters of the scheme have been evaluated and the probabilities of losses occurrence have been classified as rare events. The proposed material are various algorithms and techniques for the implementation of Vertical and Horizontal Handoff in 3G, 4G and fifth-generation networks to provide the required QoS for mobile users with Ultra-High Definition.

Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud

Mobile devices may offload their applications to a virtual machine running on a cloud host. This application may fork new tasks which require virtual machines of their own on the same physical machine. Achieving satisfactory performance level in such a scenario requires flexible resource allocation mechanisms in the cloud data center. In this paper we present two such mechanisms which use prioritization: one in which forked tasks are given full priority over newly arrived tasks, and another in which a threshold is established to control the priority so that full priority is given to the forked tasks if their number exceeds a predefined threshold. We analyze the performance of both mechanisms using a Markovian multiserver queueing system with two priority levels to model the resource allocation process, and a multi-dimensional Markov system based on a Birth-Death queueing system with finite population, to model virtual machine provisioning. Our performance results indicate that the threshold-based priority scheme not only performs better, but can also be tuned to achieve the desired performance level.

Priority Scheme for Enhancing the Capacity of Peer-to-Peer Networks in Mobile Environments

Given the pervasive use of wireless systems, based on the IEEE 802.11 and LTE standards, Peer-to-Peer (P2P) networks take relevance due to their inherent flexibility and scalability. In a mobile environment, users experience different channel conditions. This has a major impact on the system’s performance as it is perceived by users in different coverage zones. Additionally, users have different degrees of mobility which can improve or degrade the quality of the received signal. In this work, a wireless mobile P2P network is studied. Building on this, two different scenarios are considered. Namely, a WLAN environment where nodes usually know the placement of the access point and can move closer to it in order to improve their channel quality and a cellular system where nodes usually do not know where their attending base station is located. Building on this, a priority model to improve the system’s performance is proposed, studied and analyzed. This priority scheme is based on serving first users that experience a better channel condition in order to improve the file downloading process. The aforementioned networks are analyzed using both a fluid model and a Continuous Time Markov Chain.

Social tie-driven content priority scheme for D2D communications

Priority scheduling is an efficient way to support the high-rate content transmission, and user subjectivity plays an important role in D2D communications. The previous priority schemes mainly focus on the routing protocol and ignore the impact of social relationship on data transmission. On this basis, a social tie-driven priority scheme, which combines social and physical D2D networks with priority decision issue, is proposed and analyzed in this paper. Specifically, considering the time-decaying property of social tie, the priority issue is modeled as an increment of social tie strength (ISTS) maximization problem based on the social trust and social reciprocity. Additionally, due to the interplay between social tie and physical link, the ISTS-maximization decision model is transformed into another one aiming at maximizing the system capacity, which is solved eventually by the simulated annealing (SA) algorithm with low complexity. Finally, the effectiveness and the superior performance of the proposed scheme are both verified by some simulations.

T-junction priority scheme and road user’s yielding behavior

Four studies investigated yielding behavior in yield-controlled T-junctions that differed for two priority schemes. In one case road users in the intersecting arm had to give way to road users in the straight arm (priority to straight arm). In the other case road users in the straight arm had to give way to road users approaching from the intersecting arm (priority to intersecting arm). In two studies, yielding behavior was assessed with approaching speed and gaze behavior to the critical areas of the intersection. Two additional studies monitored road users’ speed and eye movements approaching the intersection. The results of the two behavioral studies showed a significant speed reduction and an increase of driver’s visual inspection to the intersection area in the priority-to-straight-arm condition in comparison to the priority-to-intersecting-arm condition. The eye movement analysis showed that total fixation time towards the intersection critical area and horizontal eye movements were significantly higher in the priority-to-straight-arm condition. The results emphasize the importance of considering perceptual affordances and expectations for priority in intersection design to increase drivers’ compliance to yielding rules.

Spatial bus priority schemes, implementation challenges and needs: an overview and directions for future studies

Urban transit systems consist of multiple modes of transport of which cars and buses make up the largest portion, particularly in developing countries. Thus, theories on the best means of allocating existing road spaces for cars and buses in city-centers and suburban corridors in order to improve operational transport efficiency has become a frontier research topic. Today, bus priority (BP) methods, as one of the most widely used Public Transport Priority (PTP) strategies, are commonly recognized as effective in lowering traffic congestion and reducing bus travel times. PT authorities, urban planners/designers, and researchers dealing with PT issues (i.e. buses), need to investigate existing BP methods in detail and assess relevant research before setting about designing or implementing new policies. Of course, evaluating such studies is a time-consuming process. In order to address this constraint, the present study proposes a general typology of existing BP methods and evaluates the challenges and requirements regarding their implementation by conducting a comprehensive literature review of existing studies related to PTP. Academic papers (theoretical, analytical, and simulation-based), technical reports, and design manual/guidelines in the context of both developed and developing countries have been searched as part of the literature review process. The study concludes by proposing two comparative summary tables of all spatial bus priority schemes in terms of “min. requirements to justify BP schemes” and “advantages and disadvantages of time-based and spatial BP treatments”. These may provide engineers, urban planners or transit policy-makers with an accessible guide with which to refer in the evaluation stage. However, future research is recommended in order to bridge existing gaps concerning conditions required for the successful implementation of spatial bus priority strategies.

Unified Algorithm for Demand-Side Appliance Commitment

Recent energy efficiency and conservation programs have created an unprecedented demand for home energy management systems (HEMS) in the residential sector aimed at reducing electricity consumption and saving on electricity bills. This paper gives a brief review of the basic algorithms found in the literature for HEMS that target optimum scheduling for home appliances participating in demand response (DR) programs. The working principles, as well as the pros and the cons, of these algorithms are explained and analyzed. Then, a unified algorithm to manage the hourly power consumption of home appliances on a daily basis is suggested using two scenarios. The first scenario aims to simultaneously achieve dual utility/customer benefits while avoiding the individual drawbacks of each presented algorithm. The second scenario aims to actively involve DR customers in making the optimum decision regarding their appliances in the face of their dynamic desires. The proposed algorithm is generic in the sense that it has the ability to achieve three different objectives for dual utility/customer benefits. Moreover, the paper takes into consideration a range of constraints, such as load priority, customer preferences, utility request, and electricity dynamic pricing scheme. The essential goal of this algorithm is not only to curtail or control the power consumption of appliances but to also shift it to a better price period based on different tariff rates. The results reflect the effectiveness of the proposed algorithm, which extends the previous findings in the literature by considering a wider range of limitations applied on HEMS.

Improving long-term fuel treatment effectiveness in the National Forest System through quantitative prioritization

Predicting the efficacy of fuel treatments aimed at reducing high severity fire in dry-mixed conifer forests in the western US is a challenging problem that has been addressed in a variety of ways using both field observations and wildfire simulation models. One way to describe the efficacy of fuel treatments is to quantify how often wildfires are expected to intersect areas prioritized for treatment. In real landscapes treatments are static, restricted to a small portion of the landscape and against a background of stochastic fire and dynamic vegetation, thus the likelihood of fire encountering a treatment during the period treatments remain effective is small. In this paper we simulate a wide range of different treatment prioritization schemes using the forest landscape simulation model Envision to examine 50 years of fire-treatment interactions and forest succession. We first reviewed 47 fuel management projects in Oregon, USA to build prioritization schemes that addressed different fuel management objectives. We then simulated different priority schemes in the 18 planning areas of the Deschutes National Forest in central Oregon and measured potential fire-treatment interactions over time. Simulated annual area burned was used to calculate the success odds for each priority scheme and planning area. Out of the ten metrics considered only three had higher success odds than a random prioritization of planning areas. Spatial allocation of projects based on burn probability and transmitted wildfire had the highest success odds among the tested metrics. However, success odds declined sharply as desired success levels increased suggesting that fuel management goals need to be tempered to consider the stochastic nature of wildfire. Meeting long-term multiple management goals over time can benefit from consideration of short- and long-term tradeoffs from different treatment prioritization schemes. Our work contributes towards a better framing of both management and public expectations regarding the performance of fuel treatments programs.

Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks

The Robot Operating System (ROS) is a popular and widely used software framework for building robotics systems. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes presents several issues regarding limited-bandwidth, delays, and jitter, when used in wireless multi-hop networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance by reducing delay and jitter in data exchanges, especially in multi-hop networks. Pound allows the use of multiple ROS masters (roscores), features data compression, and importantly, introduces a priority scheme that allows favoring more important flows over less important ones. We compare Pound to the state-of-the-art solutions through extensive experiments and show that it performs equally well, or better in all the test cases, including a control-over-network example.