Waiting Time Estimation Research Articles

ВЛИЯНИЕ АДАПТИВНОГО УПРАВЛЕНИЯ СВЕТОФОРНЫМИ ОБЪЕКТАМИ НА ХАРАКТЕРИСТИКИ ДОРОЖНОГО ДВИЖЕНИЯ СМЕЖНЫХ ПЕРЕКРЕСТКОВ

The article is devoted to the study of the impact of adaptive algorithms for controlling traffic lights at one intersection on changes in traffic at adjacent intersections. The main purpose of the study is to identify the advantages and disadvantages of implementing such algorithms to assess the impact on adjacent intersections. Three algorithms have been proposed for the study: queue skipping, phase duration redistribution, and waiting time estimation. The study included traffic modeling, data collection on waiting times, queue lengths, and an assessment of changes in traffic flows. Data was collected using smart cameras, which allowed us to identify how changing traffic light work plans at one intersection affects the dynamics of traffic at neighboring intersections. Possible changes in traffic at adjacent intersections are proved, in particular, an increase in the number of waiting vehicles and the average waiting time at adjacent intersections, which indicates the negative impact of adaptive algorithms on adjacent intersections.

Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach

This study presents a hybrid solution for the charging station location-capacity problem. The proposed approach simultaneously determines the location and capacity of charging stations (i.e., number of charging piles), and assigns piles to electric vehicles based on their level of charge. The problem is formulated as a bi-objective mixed-integer nonlinear programming model to minimize the total cost of establishing charging stations together with the average customers’ waiting time. The proposed solution combines queueing theory with mathematical modelling to estimate the average waiting time. A deep learning algorithm is then developed to enhance the precision of waiting time estimation. Another contribution is involving a deep neural network model in improving NSGA-II algorithm. Numerical experiments are conducted in Halifax, Canada to assess the performance of the proposed framework. The results demonstrate the strong predictive performance of the deep learning algorithm and highlight the limitations of traditional queueing models in estimating waiting times in charging stations (i.e., 99.8% improvement in computation time, as well as accuracy improvement of time estimations from 13% to 1.6% deviation). Several valuable insights are obtained to improve the operational performance of charging stations such as achieving a significant (i.e., 61.5%) drop in the average waiting time across the network by a modest (i.e., 29.2%) increase in the initial investments. Also, it reveals that the variability of service rate significantly impacts the average waiting time (i.e., a 50% increase in the variability of service rate causes a substantial 950.56% surge in the average waiting time). The findings underscore the need to control service rate fluctuations to reduce wait times and boost driver satisfaction. The improved NSGA-II algorithm shows 12.77% improvement in the Pareto front solutions. Finally, the proposed prioritization strategy based on the charging level of vehicles could reduce the average waiting time and cost compared to the first-come-first-served strategy.

On a waiting-time result of Kontoyiannis: Mixing or decoupling?

We introduce conditions of lower decoupling to the study of waiting-time estimations of the cross entropy between two mutually independent stationary stochastic processes. Although similar decoupling conditions have been used in the literature on large deviations and statistical mechanics, they appear largely unexplored in information theory. Building on a result of Kontoyiannis, namely Theorem 4 in Kontoyiannis (1998), and replacing the ψ-mixing condition in this result with a lower decoupling condition, we considerably extend the validity of waiting-time estimation of cross entropy.

Probabilistic Forecasting of Patient Waiting Times in an Emergency Department

Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients’ overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes.Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.1210 .

Time swipes when you’re having fun: reducing perceived waiting time while making it more enjoyable

ABSTRACT Waiting for a system to respond always will be a factor in human–computer interaction. This is deployed by spinners, progress bars, skeleton-screens and other means. This project studies experiencing longer waits along the lines of ‘no activity’ (progress bar), ‘passive waiting’ (reading) and ‘active waiting’ (doing something). For the latter, a novel method is introduced: users actively swipe an image after which it disappears and the content underneath will unveil, as if it were a scratch card. A between-subjects experiment (n = 410) was conducted using a mobile website in 3 conditions to gauge the effects on estimated waiting time and enjoyment. The ‘no activity’ and ‘active waiting’ conditions were estimated faster than the ‘passive waiting’ condition. The ‘passive’ and ‘active’ waiting conditions were significantly more enjoyable than the ‘no activity’ condition. When combining waiting time estimation (shorter is preferable) and enjoyment (higher is preferable) the ‘active waiting’ condition yielded better results.

On using lumped-parameter models in groundwater protection zones to predict aquifer response to reduced agrochemical input

• Waiting times for aquifers contaminated by agrochemicals are useful for management. • Lumped-parameter models can be used to estimate these waiting times. • Waiting times useful for management must not be confused with mean transit times. In groundwater protection zones, water managers and water providers will typically need to know the time scale of recovery for aquifers contaminated by agrochemicals. Despite the large body of work on the topic in the scientific literature, there still seems to be some need to clarify which waiting times are most relevant for water management planning, and how they relate to metrics such as the mean transit time of tracer. Firstly, we propose a simple nomenclature for the different waiting times and how they relate to the evolution of agrochemical concentration (increasing and decreasing concentration trends, trend reversal, etc.). Secondly, we describe how to select and fit a lumped-parameter model to contaminant time series and environmental isotopes in order to characterize aquifer response to agrochemical contamination. Thirdly, we explain how waiting times can be calculated from the fitted lumped-parameter model. Finally, we present a case study focussing on the contamination of several springs of the city of Luxembourg by a fungicide transformation product combining environmental tracers and pesticide measurements. We find that on the study site, the parameters estimated separately from the environmental tracers and from the pesticide data are nearly the same although they correspond to different areas within the groundwatershed. Although this could be incidental, it might also indicate that robust waiting time estimates can be obtained from agrochemical time series and environmental tracer measurements, at least in simple cases.

Feature Selection for Waiting Time Predictions in Semiconductor Wafer Fabs

Based on real operational data from the Robert Bosch GmbH, we investigate influencing features for waiting time estimation of operations in high product-mix / low-volume semiconductor manufacturing fabs. We define waiting time as the elapsed time between completing the previous operation and starting the next one. In addition to well-established features, we introduce novel features to capture the complexity of the manufacturing environment. To the best of our knowledge, we are the first to attempt waiting time estimation in a high product-mix / low-volume semiconductor fab. We present a framework for feature selection which is composed of three steps: First, random forest models are trained for each operation. Second, a permutation feature importance (PFI) for the full set of features for each operation is computed and the performance is statistically evaluated. The optimal subset of features is then chosen by a sequential backward search based on the PFI values. Third, the performance in terms of the coefficient of determination of each optimized model is evaluated by means of the initial performance. We apply the framework to real operational data from the production areas Lithography and Diffusion and conclude that the feature set can be reduced significantly, while the prediction performance remains equal. The novel features are found to be frequently used when estimating waiting times in the investigated use case.

Electric Vehicle Charging Right Trading: Concept, Mechanism, and Methodology

With the increasing penetration of electric vehicles (EVs), uncoordinated EV charging and the resulting chaos, disorder, and long waiting times at EV charging stations (EVCSs) will no longer be tolerable. An EV charging right (CR) is the right to reserve a predefined charging service. By purchasing CRs, EVs can reduce their charging waiting time, and the price of CRs can guide EVs toward optimized charging behaviors. In this article, we define CR, propose the CR trading mechanism (CRM), and analyze the effect of CRM on reducing waiting times and mitigating congestion in EV charging. In the proposed CRM, EVs can purchase CRs in advance, and the CRs are used to estimate the waiting time and update the price of charging. Queue theory is utilized in the waiting time estimation, in which the impact of disclosing queue states at EVCSs is considered for the first time. The simulation results verify the accuracy of the waiting time estimation and the effect of the proposed mechanism.

Improving patients’ experience concerning insufficient informational flow to patients during COVID-19 pandemic: Case study of a traditional Chinese medicine clinic

ABSTRACT Objective To improve patients’ experience concerning insufficient informational flow to patients waiting at the dispensary station – case study of a traditional Chinese medicine clinic. Method The service system was explored via qualitative techniques such as interviews and constructing the customer journey map. Then, experience and operational issues were defined through experience ratings at each touchpoint, patient interviews, and root cause analysis. The solution was identified via brainstorming and prototyping. A prescription tracking system was proposed with a finish time estimation algorithm. Results The overall satisfaction score at the dispensary station, comparing before-after system implementation in 2019, was increased 37% from the average score of 3.27 to 4.47 using 5-point Likert scales. The mean absolute percentage error (MAPE) of the waiting time estimation model that included the buffer time (upper bound at 95%) is 52%. Conclusion This methodology, which included both patient experience and operational improvement viewpoints, could successfully improve both the patient experience and the related operation system at its critical touchpoints. Moreover, the data from the prescription tracking system could be used to estimate the number of patients waiting in the system, allowing the clinic to better manage its waiting space and capacity during the COVID-19 pandemic.

Cyclist’s waiting time estimation at intersections, a case study with GPS traces from Bologna

Waiting time plays an important role in the cyclists’ route choice, most likely because cyclists, after a stop, need to pedal harder to regain their previous speed. Literature review highlights that cyclists generally overestimate waiting time approximately three to five times higher than their actual waiting time. The aim of this paper is to quantify cyclists’ waiting time in function of specific intersection characteristics and person attributes. This aim is achieved in two steps: (1) a recent algorithm that estimates cyclists’ waiting time from GPS traces is validated, using data from a manual survey, (2) a second manual survey has been conducted to test the representativeness of a big data set of 270,000 GPS traces recorded in the city of Bologna, Italy; the same survey also showed how many cyclists pass with the red signal for different maneuvers; and finally (3) the mentioned algorithm is applied to the big data set in order to estimate the waiting time for different intersection types and cyclist attributes. Such estimations have not been addressed in literature due to the difficulty of associating the cyclists’ waiting times with infrastructure elements based using GPS traces. Results show that waiting time represents a not-negligible share of the bike trip (11% of total trip duration). On average, particularly large waiting times have been found (1) at complex intersections by (2) for cyclists younger than 25 years old, (3) for infrequent cyclists and (4) for women. During rush hour, cyclists have recorded waiting times only 6% above the daily average, demonstrating that traffic congestion has a limited effect on waiting times. Furthermore, approximately 14% of all cyclists have crossed the red traffic light, especially when the opposite traffic volume is not high and there is good visibility. The study contributes to provide a novel and validated tool to evaluate waiting times of cyclists from GPS traces, which can support the calibration of cyclists route choice models.

Conditional Waiting Time Analysis in Tandem Polling Queues

We analyze a tandem network of polling queues with two product types and two stations. We assume that external arrivals to the network follow a Poisson process, and service times at each station are exponentially distributed. For this system, we determine the mean conditional waiting time for an arriving customer using a sample path analysis approach. The approach classifies system state upon arrival into scenarios and exploits an inherent structure in the sequence of events that occur till the customer departs to obtain conditional waiting time estimates. We conduct numerical studies to show both the accuracy of our conditional waiting time estimates and their practical importance.

Effect of waiting time estimates on patients satisfaction in the emergency department in a tertiary care center.

Objectives:To examine the influence of emergency department (ED) waiting time estimate provision on the satisfaction of patients.Methods:This was a randomized controlled trial at King Abdulaziz Medical City, Riyadh, Saudi Arabia between September 2017 and May 2018. It included 18 to 70 years old Arabic-speaking acute care patients. After being divided into 2 groups, the intervention group alone was provided waiting time estimates. Both groups answered 2 questionnaires evaluating their satisfaction and illness perception before and after seeing a doctor.Results:One-hundred patients were included. No significant difference found in waiting time satisfaction scores between groups (intervention (5.92/10±3.13), control (5.45/10±3.38), p=0.476). Demographics and illness perception had an insignificant impact on satisfaction. Waiting time estimate was preferred by most participants (70%).Conclusion:Providing waiting time estimation did not affect satisfaction but was preferred to have in the ED by most.

Probabilistic Forecasting of Patient Waiting Times in an Emergency Department

We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. Aspects relating to communicating forecast uncertainty to patients, and implementing this methodology in practice, are also discussed.

Estimation of passenger waiting time using automatically collected transit data

Among the many ways to improve a transit system is a reduction in travel time as experienced by the passenger. Hence, passenger waiting times remain a topic of interest among transit planners. In this study, the effects of transit vehicle delays on passenger waiting time is investigated, as well as the effects of transfer status, boarding location, time of day, and rider travel frequency. The data used in this study were collected using automatic fare collection (AFC) and automatic vehicle location (AVL) technology. A trip chaining algorithm is used to infer the trajectory of each passenger, and as a result produce measures of passenger waiting time and vehicle delay. An analysis of an arterial Bus Rapid Transit (aBRT) line in Saint Paul, Minnesota reveals a waiting time model consistent with previous literature, a positive relationship between vehicle delay and passenger waiting time, and an insignificant relationship between transfer status and passenger waiting time. Finally, a simple model relating waiting time and vehicle delay is provided for the purpose of transit planning and waiting time estimation.

Agent-based Truck Appointment System for Containers Pick-up Time Negotiation

Congestion in the seaports area is a common issue in many parts of the world. Fluctuating truck arrival has been identified as one of the significant determinants of congestion. In response, a truck appointment system (TAS) is introduced to manage truck arrival, particularly at peak times. In the existing TAS mechanism, the scheduling decision is centralized and disregards the concerns of trucking companies. Moreover, TAS may complicate the business operation of trucking companies that already have a constrained truck schedule. This study proposes a decentralized negotiation mechanism in TAS that allows trucking companies to adjust arrival times by utilizing the waiting time estimation provided by the terminal operator. We develop an agent-based model of a TAS in the container terminal pick-up procedure. The simulation results indicate that compared to the existing TAS mechanism, the negotiation TAS mechanism generates a shorter average truck turnaround time regardless of truck arrival rates. In terms of average net time cost, the negotiation TAS mechanism provides better value under high truck arrival rate conditions. The incentive for trucking companies to participate in the negotiations is even higher at peak times.

Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction

Data is increasingly being used to make everyday life easier and better. Applications such as waiting time estimation, traffic prediction, and parking search are good examples of how data from different sources can be used to facilitate our daily life. In this study, we consider an under-utilized data source: university ID cards. Such cards are used on many campuses to purchase food, allow access to different areas, and even take attendance in classes. In this article, we use data from our university to analyze usage of the university fitness center and build a predictor for future visit volume. The work makes several contributions: it demonstrates the richness of the data source, shows how the data can be leveraged to improve student services, discovers interesting trends and behavior, and serves as a case study illustrating the entire data science process.

Temporal Signatures of Passive Wi-Fi Data for Estimating Bus Passenger Waiting Time at a Single Bus Stop

This paper proposes an alternative method for bus passenger waiting time estimation using passive Wi-Fi data. With the underlying mechanism for Wi-Fi communication, the presence of Wi-Fi enabled devices in a particular area can be passively discoverable. The mobile devices carried by bus passengers can be exploited to estimate passenger waiting time at a bus stop without the passengers’ direct participation. Passenger waiting time estimation using such opportunistic data is challenging due to the particular characteristics of the Wi-Fi data collected from bus stop environments. This paper proposes a methodology to handle massive noise in Wi-Fi data and identify the potential Wi-Fi records which are derived from passengers’ devices. The filtered data can then be used to estimate passenger waiting time. The Wi-Fi data collected from a bus stop in Hong Kong are used as a case study for evaluating the proposed system. The practicality is investigated in terms of estimation accuracy and insightful analysis.

Machine learning for healthcare behavioural OR: Addressing waiting time perceptions in emergency care

Recent research has discovered links between patient satisfaction and waiting time perceptions. We examine factors associated with waiting time estimation behaviour and how it can be linked to patient flow modelling. Using data from more than 250 patients, we evaluate machine learning (ML) methods to understand waiting time estimation behaviour in two emergency department areas. Our attribute ranking and selection methods reveal that actual waiting time, clinical attributes, and the service environment are among the top ranked and selected attributes. The classification precision for the true outcome of overestimating waiting times reaches almost 70% and 78% in the waiting area and the treatment room, respectively. We linked the ML results with a discrete-event simulation model. Our scenario analysis reveals that changing staffing patterns can lead to a substantial drop-off in overestimation of waiting times. These insights can be employed to control waiting time perceptions and, potentially, increase patient satisfaction.

Cooperative Waiting Time Estimation Method Based on Crowd Behavior Characteristics Using Acceleration and RSSI

Cooperative Waiting Time Estimation Method Based on Crowd Behavior Characteristics Using Acceleration and RSSI

Announcing Waiting Time in Emergency Departments

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?