scholarly journals En-route headway-based bus reliability with real-time data at network scale

2020 ◽  
Vol 2 (3) ◽  
pp. 236-245 ◽  
Author(s):  
Yiyi Li ◽  
Huadong Tan
Keyword(s):  
Real Time ◽  
Service Reliability ◽  
Time Data ◽  
Automatic Vehicle Location ◽  
Reliability Improvement ◽  
Real Time Data ◽  
Network Scale ◽  
The Difference ◽  
Bus Travel Time ◽  
Route Reliability

Abstract Bus reliability has long attracted attention and been extensively studied to enhance service quality. However, existing research generally evaluates bus reliability of specific routes or stops. To this end, this study explores en-route bus reliability with real-time data at network scale. Drawing on data of bus automatic vehicle location and smart card usage in Ningbo, China, this study calculates headway-based reliability with the difference between actual and scheduled headway at each stop. To demonstrate the trend of stop-level reliability along a bus route, reliability is graded and visualized on a map with ridership at each stop, which is then weighted with passenger-boarding volume. Route-level reliability is then quantified and mapped, where unreliable service basically concentrates in or extends through the centre area. With respect to network-level reliability, temporal changes are demonstrated with ridership on weekdays and at the weekend. It is observed that on weekdays, the reliability trend is similar to that of ridership, implying a causal relationship between bus travel-time variation and bus waiting-time at stops. Furthermore, a reliability comparison between weekdays in December and October shows the necessity of evaluating periodically and around important events to avoid negative riding experiences that discourage public transport usage. This research provides insights for bus agencies to systematically evaluate service reliability both spatially and temporarily, in order to identify and prioritize the routes and stops where the scope for reliability improvement and the expected benefit are greatest.

scholarly journals The Unreliability of Output-Gap Estimates in Real Time

2017 ◽  
Vol 56 (3) ◽  
pp. 193-219 ◽  
Author(s):  
Ahsan Ul Haq Satti ◽  
Wasim Shahid Malik
Keyword(s):  
Real Time ◽  
Linear Trend ◽  
The State ◽  
Output Gap ◽  
Policy Decisions ◽  
Time Data ◽  
Uncertainty Measurement ◽  
Real Time Data ◽  
Final Data ◽  
The Difference

Most research on monetary policy assumes availability of information regarding the current state of economy, at the time of the policy decision. A key challenge for policy-makers is to find indicators that give a clear and precise signal of the state of the economy in real time—that is, when policy decisions are actually taken. One of the indicators used to asses the economic condition is the output gap; and the estimates of output gap from real time data misrepresents the true state of economy. So the policy decisions taken on the basis of real time noisy data are proved wrong when true data become available. Within this context we find evidence of wrong estimates of output gap in real time data. This is done by comparing estimates of output gap based on real time data with that in the revised data. The quasi real time data are also constructed such that the difference between estimates of output gap from real time data and that from quasi real time data reflects data revision and the difference between estimates of output gap from final data and that from quasi real time data portray other revisions including end sample bias. Moreover, output gap is estimated with the help of five methods namely the linear trend method, quadratic trend method, Hordrick-Prescott (HP) filter, production function method, and structural vector autoregressive method. Results indicate that the estimates of output gap in real time data are different from what have been found in final data but other revisions, compared to data revisions, are found more significant. Moreover, the output gap measured using all the methods, except the linear trend method, appropriately portray the state of economy in the historical context. It is also found that recessions can be better predicted by real time data instead of revised data, and final data show more intensity of recession compared with what has been shown in real time data. JEL Classification: E320 Keywords: Data Uncertainty, Measurement Uncertainty, Output Gap, Business Cycle, Economic Activity

scholarly journals Influence of load balancing on quality of real time data transmission

2009 ◽  
Vol 6 (3) ◽  
pp. 515-524 ◽  
Author(s):  
Natasa Maksic ◽  
Petar Knezevic ◽  
Marija Antic ◽  
Aleksandra Smiljanic
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Data Transmission ◽  
Routing Algorithm ◽  
Network Throughput ◽  
Time Data ◽  
Real Time Data ◽  
The Difference

The routing algorithm with load balancing presented in [1] represents the modification of OSPF protocol, which enables the optimization to achieve higher network throughput. In the case of routing with load balancing, packets belonging to the same stream use different paths in the network. This paper analyzes the influence of the difference in packet propagation times on the quality of real-time data transmission. The proposed algorithm was implemented and the simulation network was formed to measure the jitter. .

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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