Ferry Route Level of Service

1997 ◽  
Vol 1608 (1) ◽  
pp. 6-16 ◽  
Author(s):  
Joseph P. Savage
Keyword(s):  
Statistical Analysis ◽  
Waiting Time ◽  
Waiting Times ◽  
Absolute Number ◽  
Service Level ◽  
Washington State ◽  
Point Of View ◽  
Level Of Service ◽  
Transportation Service

Transportation service level measurements have been commonly used and accepted for highway systems, but similar service measures for ferry systems are less common, especially from the users’ point of view. An approach to measuring ferry route level of service is described that allows comparisons among ferry routes and between ferries and alternate modes such as highways (i.e., drive-around choices) and transit. The recommended approach focuses on excess user waiting times (excess delay) by mode (automobile, registered carpool or vanpool, bus, truck, and walk-on passenger), combined with calibrated relationships between volume-to-capacity (V/C) ratio and user delays for forecasting purposes. Data on waiting times for vehicles in the queues were collected on all ferry routes serviced by Washington State Ferries, and an extensive statistical analysis was performed to compute the relationships between V/C ratios and excess waiting times. Excess delay was defined as the waiting time for missed vessel sailings due to overloads, if any, after a ferry patron has arrived at the dock. User delays were expressed in two forms: absolute number of minutes of waiting time, and the number of boat sailings missed before boarding a ferry. The “boat wait” concept was introduced to differentiate between excess delays caused by congestion that prevents a driver from boarding the next ferry, and delays related to the amount of service provided on a route as reflected in the headways between vessels.

scholarly journals SP4.2.11 Access to emergency theatre during the COVID -19 pandemic

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Alexios Dosis ◽  
Jie Qi Lim ◽  
Dharsshini Reveendran ◽  
Kiara Paramjothy ◽  
Sonia Lockwood
Keyword(s):  
Statistical Analysis ◽  
Surgical Procedures ◽  
Waiting Time ◽  
Waiting Times ◽  
P Value ◽  
Surgical Emergencies ◽  
Time Periods ◽  
Spss Software ◽  
The Impact ◽  
Second Peak

Abstract Aims To investigate the impact of the COVID-19 pandemic on general surgical emergencies and access to theatre during the pandemic. Methods We retrospectively reviewed emergency theatre lists in three distinct time periods: October 2019 (pre-COVID-19 era), April 2020 (first peak) and October 2020 (regional second peak). We extracted and compared data from a prospectively maintained database to calculate patient waiting times. Statistical analysis was performed with SPSS software v21.0 to compare median waiting times between groups and significance was set to a p value of < 0.05. Results Conclusions Despite the initial major drop in general surgical procedures and waiting times, the decreased availability of theatre lists due to staff redeployment and sickness, the introduction of routine pre-operative COVID-19 testing have all resulted in a significant increase in waiting time for urgent (CEPOD 2A) cases during the second peak.

scholarly journals UTILITY 1 SERVER ON QUEUE SERVICE (STUDY: BANK ACCOUNT NUMBER CONVERSION)

2021 ◽  
Vol 2 (2) ◽  
pp. 187
Author(s):  
Johan Alfian Pradana
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Service Level ◽  
Bank Account ◽  
Queue System ◽  
Time Minimization ◽  
Queue Theory ◽  
And Performance ◽  
Number Of Customers

Fast-paced, precise demands and time minimization are dominant to support the service business. Service activities are always expected to be the best by customers. Especially ABC bank customers. Since the information about account conversion, many customers have come to the Bank. The server utility of the queue system plays an important role. One of them is about measuring the usefulness of the queue system, average expectations of waiting times, and expectations of the number of customers in the system. Services that focus on providing services always experience long lines. Therefore, the queue theory is used to assess utilities, waiting for time expectations, and expectations of customer numbers. Research methods using system performance. First, calculate the value of the speed, average service, service level, and performance of the queue system. The result is a queue system of 1 server on average - working with a utility value of 83.5% and the highest in the 4th week, with an average expectation - average waiting time of 0.428 or 25.6 minutes and an expectation of the number of customers in the system of 4.8 or 5 customers. The role of 1 server has not been practical to minimize waiting time expectations.

OPTIMIZATION OF OVERFLOW POLICIES IN CALL CENTERS

2015 ◽  
Vol 29 (3) ◽  
pp. 461-471 ◽  
Author(s):  
G.M. Koole ◽  
B.F. Nielsen ◽  
T.B. Nielsen
Keyword(s):  
Performance Measures ◽  
Waiting Time ◽  
Optimal Policy ◽  
Call Center ◽  
Present Method ◽  
Call Centers ◽  
Waiting Times ◽  
Service Level ◽  
Markovian Approximation ◽  
Markov Decision

We examine how overflow policies in a multi-skill call center should be designed to accommodate performance measures that depend on waiting time percentiles such as service level. This is done using a discrete Markovian approximation of the waiting time of the first customers waiting in line. A Markov decision chain is used to determine the optimal policy. This policy outperforms considerably the ones used most often in practice, which use a fixed threshold. The present method can be used also for other call-center models and other situations where performance is based on actual waiting times and customers are treated in a FCFS order.

scholarly journals On the Stochastic Nature of the Rapid Climate Shifts during the Last Ice Age

2009 ◽  
Vol 22 (2) ◽  
pp. 446-457 ◽  
Author(s):  
Peter D. Ditlevsen ◽  
Ove D. Ditlevsen
Keyword(s):  
Residence Time ◽  
Waiting Time ◽  
Waiting Times ◽  
Point Of View ◽  
Ice Age ◽  
Data Series ◽  
Climate Shifts ◽  
Mean Waiting Time ◽  
Last Ice Age ◽  
The Mean

Abstract The rapid climate shifts observed in the glacial climate are analyzed. The transitions into the warm interstadial states, the onsets, are easily identifiable in the record. The distribution of waiting times between consecutive onsets is well fitted, assuming that the remaining residence time in each state is independent of the past. This implies that it has a simple no-memory exponential waiting time distribution, but with the mean waiting time depending on the climate state. The mean waiting time from one onset to the next is around 2400 yr. The most likely (maximum likelihood) distribution derived solely from the onset sequence is rather insensitive to the mean waiting time in the warm interstadials in the range of 400–1200 yr. When extending the analysis to include the transitions from the warm interstadials to the cold stadials observed with a larger uncertainty, the distributions in the two states are well fitted to exponential distributions, with mean waiting times of around 800 yr in the warm state and around 1600 yr in the cold state. This observation is an important piece in the climate puzzle, because the fact that the climate is a no-memory process indicates that the transitions are noise induced and the mean residence time in one state indicates how stable that climate state is to perturbations. The possibility of a hidden periodic driver is also investigated. The existence of such a driver cannot be ruled out by the relatively sparse data series (containing only 21 onsets). However, because the record is fitted just as well by the much simpler random model, this should be preferred from the point of view of simplicity.

scholarly journals Interhospital referral of colorectal cancer patients: a Dutch population-based study

2021 ◽  
Author(s):  
A. K. Warps ◽  
◽  
M. P. M. de Neree tot Babberich ◽  
E. Dekker ◽  
M. W. J. M. Wouters ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Waiting Time ◽  
Secondary Care ◽  
Advanced Disease ◽  
Waiting Times ◽  
Tertiary Care ◽  
Disease Stage ◽  
Colorectal Cancer Patients ◽  
Tertiary Care Hospitals

Abstract Purpose Interhospital referral is a consequence of centralization of complex oncological care but might negatively impact waiting time, a quality indicator in the Netherlands. This study aims to evaluate characteristics and waiting times of patients with primary colorectal cancer who are referred between hospitals. Methods Data were extracted from the Dutch ColoRectal Audit (2015-2019). Waiting time between first tumor-positive biopsy until first treatment was compared between subgroups stratified for referral status, disease stage, and type of hospital. Results In total, 46,561 patients were included. Patients treated for colon or rectal cancer in secondary care hospitals were referred in 12.2% and 14.7%, respectively. In tertiary care hospitals, corresponding referral rates were 43.8% and 66.4%. Referred patients in tertiary care hospitals were younger, but had a more advanced disease stage, and underwent more often multivisceral resection and simultaneous metastasectomy than non-referred patients in secondary care hospitals (p<0.001). Referred patients were more often treated within national quality standards for waiting time compared to non-referred patients (p<0.001). For referred patients, longer waiting times prior to MDT were observed compared to non-referred patients within each hospital type, although most time was spent post-MDT. Conclusion A large proportion of colorectal cancer patients that are treated in tertiary care hospitals are referred from another hospital but mostly treated within standards for waiting time. These patients are younger but often have a more advanced disease. This suggests that these patients are willing to travel more but also reflects successful centralization of complex oncological patients in the Netherlands.

scholarly journals E-Triage Systems for COVID-19 Outbreak: Review and Recommendations

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2845
Author(s):  
Fahd Alhaidari ◽  
Abdullah Almuhaideb ◽  
Shikah Alsunaidi ◽  
Nehad Ibrahim ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Population Growth ◽  
Disease Severity ◽  
Waiting Time ◽  
Transmission Rate ◽  
Waiting Times ◽  
Triage System ◽  
Disease Symptoms ◽  
Comprehensive Review ◽  
Severity Levels ◽  
Growth And Aging

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients’ conditions and identifying their severity levels.

493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Z Hayat ◽  
E Kinene ◽  
S Molloy
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Time Frame ◽  
Outpatient Clinics ◽  
Contributing Factors ◽  
Time Data ◽  
Work Related ◽  
Mean Waiting Time ◽  
Appointment Time ◽  
Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

Some general results for many server queues

1973 ◽  
Vol 5 (01) ◽  
pp. 153-169 ◽  
Author(s):  
J. H. A. De Smit
Keyword(s):  
Integral Equations ◽  
Waiting Time ◽  
Queue Length ◽  
Joint Distribution ◽  
Waiting Times ◽  
Virtual Waiting Time ◽  
Server Queue ◽  
Many Server Queues ◽  
The Many ◽  
Busy Cycle

Pollaczek's theory for the many server queue is generalized and extended. Pollaczek (1961) found the distribution of the actual waiting times in the model G/G/s as a solution of a set of integral equations. We give a somewhat more general set of integral equations from which the joint distribution of the actual waiting time and some other random variables may be found. With this joint distribution we can obtain distributions of a number of characteristic quantities, such as the virtual waiting time, the queue length, the number of busy servers, the busy period and the busy cycle. For a wide class of many server queues the formal expressions may lead to explicit results.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

scholarly journals Preferences and Perceptions of Patients Attending Emergency Departments with Low Acuity Problems in Hong Kong

2009 ◽  
Vol 16 (3) ◽  
pp. 148-154 ◽  
Author(s):  
CA Graham ◽  
WO Kwok ◽  
YL Tsang ◽  
TH Rainer
Keyword(s):  
Health Care ◽  
Hong Kong ◽  
Health Care Providers ◽  
Waiting Time ◽  
Health Care Professionals ◽  
Waiting Times ◽  
Medical Advice ◽  
Care Providers ◽  
Convenience Sample ◽  
Waiting Area

Objective To explore why patients in Hong Kong seek medical advice from the emergency department (ED) and to identify the methods by which patients would prefer to be updated on the likely waiting time for medical consultation in the ED. Methods The study recruited 249 semi-urgent and non-urgent patients in the ED of Prince of Wales Hospital from 26th September 2005 to 30th September 2005 inclusive. A convenience sample of subjects aged ≥15 years old in triage categories 4 or 5 were verbally consented and interviewed by research nurses using a standardized questionnaire. Results From 1715 potential patients, 249 were recruited ad hoc (mean age 44 years [SD18]; 123 females). About 63% indicated that an acceptable ED waiting time was less than or equal to two hours, and 88% felt that having individual number cards and using a number allocation screen in the ED waiting area would be useful. Perceived reasons for attending the ED rather than other health care providers such as primary health care or the general outpatient clinic (GOPC) included: a desire for more detailed investigations (56%); a perception that more professional medical advice was given in the ED (35%); patients were under the continuing care of the hospital (19%); and patients were referred to the ED by other health care professionals (11%). Notably, 26% of participants had considered attending the GOPC prior to attending the ED. Patients educated to tertiary level expected a shorter waiting time than those educated to lesser degrees (p=0.026, Kruskal-Wallis test). Suggestions were made on how to provide a more pleasant ED environment for the wait for consultations, which included the provision of a television screen with sound in the waiting area (43%), more comfortable chairs (37%) and health care promotion programs (32%). Conclusion Patients chose ED services because they believed they would receive more detailed investigations and more professional medical advice than available alternatives. Clear notification of the likely waiting times and enhancement of comfort before consultation are considered desirable by patients. Enhanced public education about the role of the ED and making alternatives to ED care more accessible may be useful in reducing inappropriate ED attendances in Hong Kong.

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