scholarly journals Waiting Endurance Time Estimation of Electric Two-Wheelers at Signalized Intersections

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mei Huan ◽  
Xiao-bao Yang
Keyword(s):  
Motor Vehicle ◽  
Waiting Times ◽  
Red Light ◽  
Wait Time ◽  
Time Estimation ◽  
Personal Characteristics ◽  
Signalized Intersections ◽  
Normal Crossing ◽  
Phase Time ◽  
Time Motor

The paper proposed a model for estimating waiting endurance times of electric two-wheelers at signalized intersections using survival analysis method. Waiting duration times were collected by video cameras and they were assigned as censored and uncensored data to distinguish between normal crossing and red-light running behavior. A Cox proportional hazard model was introduced, and variables revealing personal characteristics and traffic conditions were defined as covariates to describe the effects of internal and external factors. Empirical results show that riders do not want to wait too long to cross intersections. As signal waiting time increases, electric two-wheelers get impatient and violate the traffic signal. There are 12.8% of electric two-wheelers with negligible wait time. 25.0% of electric two-wheelers are generally nonrisk takers who can obey the traffic rules after waiting for 100 seconds. Half of electric two-wheelers cannot endure 49.0 seconds or longer at red-light phase. Red phase time, motor vehicle volume, and conformity behavior have important effects on riders’ waiting times. Waiting endurance times would decrease with the longer red-phase time, the lower traffic volume, or the bigger number of other riders who run against the red light. The proposed model may be applicable in the design, management and control of signalized intersections in other developing cities.

scholarly journals A Reliability-Based Analysis of Bicyclist Red-Light Running Behavior at Urban Intersections

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mei Huan ◽  
Xiaobao Yang
Keyword(s):  
Waiting Times ◽  
Red Light ◽  
Video Recording ◽  
Personal Characteristics ◽  
Proportional Hazard ◽  
Red Light Running ◽  
Reliability Models ◽  
Traffic Conditions ◽  
Covariate Effects ◽  
Cox Hazard Model

This paper describes the red-light running behavior of bicyclists at urban intersections based on reliability analysis approach. Bicyclists’ crossing behavior was collected by video recording. Four proportional hazard models by the Cox, exponential, Weibull, and Gompertz distributions were proposed to analyze the covariate effects on safety crossing reliability. The influential variables include personal characteristics, movement information, and situation factors. The results indicate that the Cox hazard model gives the best description of bicyclists’ red-light running behavior. Bicyclists’ safety crossing reliabilities decrease as their waiting times increase. There are about 15.5% of bicyclists with negligible waiting times, who are at high risk of red-light running and very low safety crossing reliabilities. The proposed reliability models can capture the covariates’ effects on bicyclists’ crossing behavior at signalized intersections. Both personal characteristics and traffic conditions have significant effects on bicyclists’ safety crossing reliability. A bicyclist is more likely to have low safety crossing reliability and high violation risk when more riders are crossing against the red light, and they wait closer to the motorized lane. These findings provide valuable insights in understanding bicyclists’ violation behavior; and their implications in assessing bicyclists’ safety crossing reliability were discussed.

scholarly journals Crossing at a Red Light: Behavior of Cyclists at Urban Intersections

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaobao Yang ◽  
Mei Huan ◽  
Bingfeng Si ◽  
Liang Gao ◽  
Hongwei Guo
Keyword(s):  
Waiting Time ◽  
Traffic Safety ◽  
Red Light ◽  
Personal Characteristics ◽  
Effective Measure ◽  
Red Light Running ◽  
Normal Crossing ◽  
Unfavorable Condition ◽  
Internal And External Factors ◽  
The Relationship

To investigate the relationship between cyclist violation and waiting duration, the red-light running behavior of nonmotorized vehicles is examined at signalized intersections. Violation waiting duration is collected by video cameras and it is assigned as censored and uncensored data to distinguish between normal crossing and red-light running. A proportional hazard-based duration model is introduced, and variables revealing personal characteristics and traffic conditions are used to describe the effects of internal and external factors. Empirical results show that the red-light running behavior of cyclist is time dependent. Cyclist’s violating behavior represents positive duration dependence, that the longer the waiting time elapsed, the more likely cyclists would end the wait soon. About 32% of cyclists are at high risk of violation and low waiting time to cross the intersections. About 15% of all the cyclists are generally nonrisk takers who can obey the traffic rules after waiting for 95 seconds. The human factors and external environment play an important role in cyclists’ violation behavior. Minimizing the effects of unfavorable condition in traffic planning and designing may be an effective measure to enhance traffic safety.

scholarly journals Online Ratings of Primary Care Physicians: Comparison of Gender, Training, and Specialty

2021 ◽  
Vol 8 ◽  
pp. 237437352110077
Author(s):  
Daliah Wachs ◽  
Victoria Lorah ◽  
Allison Boynton ◽  
Amanda Hertzler ◽  
Brandon Nichols ◽  
...  
Keyword(s):  
Primary Care ◽  
Primary Care Physicians ◽  
Waiting Times ◽  
Wait Time ◽  
Primary Care Providers ◽  
Wait Times ◽  
Care Providers ◽  
Female Physicians ◽  
Online Ratings ◽  
And Gender

The purpose of this study was to explore patient perceptions of primary care providers and their offices relative to their physician’s philosophy (medical degree [MD] vs doctorate in osteopathic medicine [DO]), specialty (internal medicine vs family medicine), US region, and gender (male vs female). Using the Healthgrades website, the average satisfaction rating for the physician, office parameters, and wait time were collected and analyzed for 1267 physicians. We found female doctors tended to have lower ratings in the Midwest, and staff friendliness of female physicians were rated lower in the northwest. In the northeast, male and female MDs were rated more highly than DOs. Wait times varied regionally, with northeast and northwest regions having the shortest wait times. Overall satisfaction was generally high for most physicians. Regional differences in perception of a physician based on gender or degree may have roots in local culture, including proximity to a DO school, comfort with female physicians, and expectations for waiting times.

scholarly journals An Audit of Wait Times for Service Offered by the Gynaecology Oncology Unit at the University Hospital in Jamaica: Are We Meeting the Standards?

2016 ◽  
Vol 2 (3_suppl) ◽  
pp. 74s-74s
Author(s):  
Ian Bambury ◽  
Christopher Fletcher ◽  
Carole Rattray ◽  
Matthew Taylor ◽  
Charmaine Mitchell ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Conflicts Of Interest ◽  
Waiting Times ◽  
Wait Time ◽  
International Standards ◽  
University Hospital ◽  
Wait Times ◽  
Oncology Unit ◽  
The University ◽  
Mean Time

Abstract 53 Background: The European Board and College of Obstetrics and Gynaecology recommends that time from referral of suspected or proven gynaecological cancers to consultation should be within two weeks and that initiation of treatment should occur within six weeks. It is has been shown that a delay in waiting times beyond these international standards results in significantly increased morbidity and mortality. Methods: An audit of wait times was performed for all patients who presented to the gynaecology oncology unit at the University Hospital in Jamaica for consultation between January 1, 2013 and December 31, 2013. Wait time for consultation was calculated as the date of first referral to the date of initial consultation. Wait time for treatment was calculated as time from consultation to either surgery or initiation of radiation therapy or chemotherapy. Primary site, stage, and the region from which the referrals came were abstracted from the medical record. Results: A total of 1,289 unique patients were seen at least once during the audit period; of these, 108 were new consultations and 1,219 were patients seen for follow-up. 72% were from the greater metropolitan area (Kingston & St. Andrew), while the others were from the surrounding parishes of Jamaica. Of the 108 new patients, malignancy was confirmed in 70 (65%). Case make-up included 23 cases of endometrial cancer (33%); 20 cases of cervical cancer (29%); 16 cases of ovarian cancer (23%); and 11 cases of other gynecologic cancers (vulvar, vaginal, choriocarcinoma) (15%). At presentation, there were 23 patients with stage 1 disease (33%), 16 patients with stage 2 disease (23%), 27 patients with stage 3 disease (38%); and 4 patients with stage 4 disease (6%). 39 out of 70 patients with malignancy (56%) underwent surgery; 11 (15.7%) were treated with radiation therapy; and 22 (31%) were treated with chemotherapy. Among patients with a cancer diagnosis, the mean time from referral to consultation was 2.1 weeks. Mean time from consultation to surgery was 7.6 weeks; mean time from consultation to start of radiotherapy was 16 weeks; and mean time from consultation to start of chemotherapy was 11.6 weeks. 66% of patients underwent surgery within the international standard of six weeks from referral. Only 36% initiated radiation therapy and 14% initiated chemotherapy within six weeks from referral. Conclusion: While the majority of patients met international standards for time to consultation to surgery, wait times for initiation of radiation and chemotherapy were sub-standard. This audit has provided information that will help us to assess the inadequacy of available services and could potentially inform national cancer policies in Jamaica. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST: No COIs from the authors.

scholarly journals Patient-centred method to evaluate the spatial accessibility of primary health care in a case study in Shanghai

2020 ◽  
Vol 26 (4) ◽  
pp. 344
Author(s):  
Xuechen Xiong ◽  
Li Luo
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
Waiting Times ◽  
Care Facility ◽  
Wait Time ◽  
Spatial Accessibility ◽  
Quantitative Methodology ◽  
Time Cost ◽  
Primary Health

Quantitative methodology investigating medical resource accessibility does not incorporate patients’ feelings about the adequacy and fairness of primary health care (PHC). In this study we quantified the spatial accessibility of PHC from the patient perspective. The main obstacles regarding access to PHC services are: (1) distance from the medical facility; and (2) waiting times after reaching the facility. The total time cost to access PHC is calculated as the sum of the time cost to access the PHC facility and the time cost waiting to receive health care; the total time cost was used in this study to reflect the potential spatial accessibility (i.e. probable entry into the healthcare system) of PHC. In Shanghai, it took residents approximately 13min to reach the nearest primary care facility, with an approximate 23min wait time after arriving at the facility. Thus, the spatial accessibility of PHC in Shanghai is approximately 36min. The method of assessing the spatial accessibility of PHC from the perspective of patients is more explanatory and easier to interpret. In the case of Shanghai, the regional accessibility of PHC is much better than its regional availability. Relevant managers should focus on increasing the resource supply capacity of existing facilities providing primary care in the suburbs of Shanghai.

scholarly journals Robust Multiclass Queuing Theory for Wait Time Estimation in Resource Allocation Systems

2019 ◽  
Vol 65 (1) ◽  
pp. 152-187 ◽  
Author(s):  
Chaithanya Bandi ◽  
Nikolaos Trichakis ◽  
Phebe Vayanos
Keyword(s):  
Resource Allocation ◽  
Queuing Theory ◽  
Wait Time ◽  
Time Estimation ◽  
Resource Allocation Systems
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