Passenger waiting time prediction plays a critical role in enhancing both ridesharing user experience and platform efficiency. While most existing research focuses on post-request waiting time prediction with knowing the matched driver information, pre-request waiting time prediction (i.e., before submitting a ride request and without matching a driver) is also important, as it enables passengers to plan their trips more effectively and enhance the experience of both passengers and drivers. However, it has not been fully studied by existing works. In this paper, we take the first step toward understanding the predictability and explainability of pre-request passenger waiting time in ridesharing systems. Particularly, we conduct an in-depth data-driven study to investigate the impact of demand&supply dynamics on passenger waiting time. Based on this analysis and feature engineering, we propose FiXGBoost, a novel feature interaction-based XGBoost model designed to predict waiting time without knowing the assigned driver information. We further perform an importance analysis to quantify the contribution of each factor. Experiments on a large-scale real-world ridesharing dataset in
Everyone spends some time waiting every day. HCI research has developed tools for boosting productivity while waiting. However, little is known about how people naturally spend their waiting time. We conducted an experience sampling study with 21 working adults who used a mobile app to report their daily waiting time activities over two weeks. The aim of this study is to understand the activities people do while waiting and the effect of situational factors. We found that participants spent about 60% of their waiting time on leisure activities, 20% on productive activities, and 20% on maintenance activities. These choices are sensitive to situational factors, including accessible device, location, and certain routines of the day. Our study complements previous ones by demonstrating that people purpose waiting time for various goals beyond productivity and to maintain work-life balance. Our findings shed light on future empirical research and system design for time management.
Appointment scheduling problems under uncertainty encounter a fundamental trade-off between cost minimization and customer waiting times. Most existing studies address this trade-off using a weighted sum approach, which puts little emphasis on individual waiting times and, thus, customer satisfaction. In contrast, we study how to minimize total cost while providing waiting time guarantees to all customers. Given box uncertainty sets for service times and no-shows, we introduce the Robust Appointment Scheduling Problem with Waiting Time Guarantees. We show that the problem is NP-hard in general and introduce a mixed-integer linear program that can be solved in reasonable computation time. For special cases, we prove that polynomial-time variants of the well-known Smallest-Variance-First sequencing rule and the Bailey-Welch scheduling rule are optimal. Furthermore, a case study with data from the radiology department of a large university hospital demonstrates that the approach not only guarantees acceptable waiting times but, compared to existing robust approaches, may simultaneously reduce costs incurred by idle time and overtime. This work suggests that limiting instead of minimiz
To investigate the impact of social groups on waiting behaviour of passengers at railway platforms a method to identify social groups through the monitoring of distances between pedestrians and the stability of those distances over time is introduced. The method allows the recognition of groups using trajectories only and thus opens up the possibility of studying crowds in public places without constrains caused by privacy protection issues. Trajectories from a railway platform in Switzerland were used to analyse the waiting behaviour of passengers in dependence of waiting time as well as the size of social groups. The analysis of the trajectories shows that the portion of passengers travelling in groups reaches up to 10\% during the week and increases to 20 \% on the weekends. 60\% of the groups were pairs, larger groups were less frequent. With increasing group size, the mean speed of the members decreases. Individuals and pairs often choose waiting spots at the sides of the stairs and in vicinity of obstacles, while larger groups wait close to the platform entries. The results indicate that passengers choose waiting places according to the following criteria and ranking: shortes
Customers often have to wait during the process of acquiring and consuming many products and services. These waiting experiences are typically negative and have been known to affect customers' overall satisfaction with the product or service. To better manage these waiting experiences, many firms have instituted a variety of programs not only to reduce the actual duration of the wait but also to improve customers' perceptions of it. In this paper, we examine the impact of one such initiative, namely, the institution of a waiting time guarantee, on customers' waiting experiences. A waiting time guarantee is a commitment from a firm to serve its customers within a specified period of time. If the firm fails to meet this commitment for some customers then it compensates them for the delay. Today, a large number of firms in a variety of industries such as fast food, banking, industrial distribution, and healthcare offer such time guarantees to their customers. We develop a utility theory-based model of customers' satisfaction with waiting in line. The model is based upon the assumption that when a customer joins a queue he or she has some prior beliefs about the distribution of service times at the firm. The customer estimates the likely duration of the waiting time on the basis of these beliefs about the service times and the observed queue length. We further assume that as the customer observes the service times for other customers who are ahead in the queue, he or she successively updates these beliefs about the distribution of service times in a Bayesian manner. We then posit that the customer's satisfaction both during as well as the end of the wait is determined by the difference between the customer's updated and the prior estimates of the total waiting time. We apply the model to derive select hypotheses pertaining to the impact of a waiting time guarantee on customers' waiting experiences. These hypotheses are based upon the assumption that an offer of a time guarantee is a signal of reliability from the firm and reduces customers' perceived variance around the expected service times. We empirically test these hypotheses using data from a series of interactive, computer-based laboratory experiments. In these experiments, we used the computer to create animations of reallife waiting experiences. The computer display consisted of a queue of customers waiting for service at a counter. One of the customers represented the participant in the experiment. During the course of the experiment, each participant joined the queue, waited in line for service, and then exited the system. At several points during the wait, each participant reported his or her level of satisfaction with the waiting experience. Our results suggest that if customers observe the service times to be less than expected, their satisfaction increases monotonically during the wait. Further, under such circumstances, the explicit provision of a waiting time guarantee enhances satisfaction both during as well as at the end of the wait. However, if customers observe the service times to be more than expected, then their satisfaction typically declines at the beginning of the wait but increases toward the end of the wait. Further, under these circumstances, the initial positive impact of the provision of a waiting time guarantee declines over time. Moreover, at the end of the wait, customers in guaranteed environments are actually less satisfied than those in unguaranteed environments. Overall, we find that a time guarantee, if met, increases satisfaction at the end of a wait; however, if violated, then it decreases satisfaction at the end of the wait. We discuss the implications of these and other empirical findings for the management of customers' waiting experiences.
We study service scheduling problems in a slotted system in which agents arrive with service requests according to a Bernoulli process and have to leave within two slots after arrival, service costs are quadratic in service rates, and there are also waiting costs. We consider quadratic waiting costs. We frame the problems as average cost Markov decision processes. While the studied system is a linear system with quadratic costs, it has state dependent control. Moreover, it also possesses a non-standard cost function structure in the case of fixed waiting costs, rendering the optimization problem complex. We characterize optimal policy. We provide an explicit expression showing that the optimal policy is linear in the system state. We also consider systems in which the agents make scheduling decisions for their respective service requests keeping their own cost in view. We consider quadratic waiting costs and frame these scheduling problems as stochastic games. We provide Nash equilibria of this game. To address the issue of unknown system parameters, we propose an algorithm to estimate them. We also bound the cost difference of the actual cost incurred and the cost incurred using e
Waiting time distribution and the zero-frequency full counting statistics of unidirectional electron transport through a double quantum dot molecule attached to spin-polarized leads are analyzed using the quantum master equation. The waiting time distribution exhibits a non-trivial dependence on the value of the exchange coupling between the dots and the gradient of the applied magnetic field, which reveals the oscillations between the spin states of the molecule. The zero-frequency full counting statistics, on the other hand, is independent of the aforementioned quantities, thus giving no insight into the internal dynamics. The fact that the waiting time distribution and the zero-frequency full counting statistics give a non-equivalent information is associated with two factors. Firstly, it can be explained by the sensitivity to different timescales of the dynamics of the system. Secondly, it is associated with the presence of the correlation between subsequent waiting times, which makes the renewal theory, relating the full counting statistics and the waiting time distribution, not longer applicable. The study highlights the particular usefulness of the waiting time distribution
Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times of activity transitions can help analysts to identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose the waiting time observed in each activity transition in a process into multiple direct causes and to analyze the impact of each identified cause on the cycle time efficiency of the process. An empirical evaluation shows that the proposed approach is able to discover different direct causes of waiting times. The applicability of the proposed approach is demonstrated on a real-life process.
We study the waiting time distributions of solar flares observed in hard X-rays with ISEE-3/ICE, HXRBS/SMM, WATCH/GRANAT, BATSE/CGRO, and RHESSI. Although discordant results and interpretations have been published earlier, based on relatively small ranges ($< 2$ decades) of waiting times, we find that all observed distributions, spanning over 6 decades of waiting times ($Δt \approx 10^{-3}- 10^3$ hrs), can be reconciled with a single distribution function, $N(Δt) \propto λ_0 (1 + λ_0 Δt)^{-2}$, which has a powerlaw slope of $p \approx 2.0$ at large waiting times ($Δt \approx 1-1000$ hrs) and flattens out at short waiting times $Δt \lapprox Δt_0 = 1/λ_0$. We find a consistent breakpoint at $Δt_0 = 1/λ_0 = 0.80\pm0.14$ hours from the WATCH, HXRBS, BATSE, and RHESSI data. The distribution of waiting times is invariant for sampling with different flux thresholds, while the mean waiting time scales reciprocically with the number of detected events, $Δt_0 \propto 1/n_{det}$. This waiting time distribution can be modeled with a nonstationary Poisson process with a flare rate $λ=1/Δt$ that varies as $f(λ) \propto λ^{-1} \exp{-(λ/λ_0)}$. This flare rate distribution represents a highly i
On the elementary level, electronic current consists of individual electron tunnelling events that are separated by random time intervals. The waiting time distribution is a probability to observe the electron transfer in the detector electrode at time $t+τ$ given that an electron was detected in the same electrode at earlier time $t$. We study waiting time distribution for quantum transport in a vibrating molecular junction. By treating the electron-vibration interaction exactly and molecule-electrode coupling perturbatively, we obtain master equation and compute the distribution of waiting times for electron transport. The details of waiting time distributions are used to elucidate microscopic mechanism of electron transport and the role of electron-vibration interactions. We find that as nonequilibrium develops in molecular junction, the skewness and dispersion of the waiting time distribution experience stepwise drops with the increase of the electric current. These steps are associated with the excitations of vibrational states by tunnelling electrons. In the strong electron-vibration coupling regime, the dispersion decrease dominates over all other changes in the waiting time
Metro systems in megacities such as Beijing, Shenzhen and Guangzhou are under great passenger demand pressure. During peak hours, it is common to see oversaturated conditions (i.e., passenger demand exceeds network capacity), which bring significant operational risks and safety issues. A popular control intervention is to restrict the entering rate during peak hours by setting up out-of-station queueing with crowd control barriers. The \textit{out-of-station waiting} can make up a substantial proportion of total travel time but is not well-studied in the literature. Accurate quantification of out-of-station waiting time is important to evaluating the social benefit and cost of service scheduling/optimization plans; however, out-of-station waiting time is difficult to estimate because it is not a part of smart card transactions. In this study, we propose an innovative method to estimate the out-of-station waiting time by leveraging the information from a small group of transfer passengers -- those who transfer from nearby bus routes to the metro station. Based on the estimated transfer time for this small group, we first infer the out-of-station waiting time for all passengers by de
We investigate the distribution of waiting times between electrons emitted from a periodically driven single-electron turnstile. To this end, we develop a scheme for analytic calculations of the waiting time distributions for arbitrary periodic driving protocols. We illustrate the general framework by considering a driven tunnel junction before moving on to the more involved single-electron turnstile. The waiting time distributions are evaluated at low temperatures for square-wave and harmonic driving protocols. In the adiabatic regime, the dynamics of the turnstile is synchronized with the external drive. As the non-adiabatic regime is approached, the waiting time distribution becomes dominated by cycle-missing events in which the turnstile fails to emit within one or several periods. We also discuss the influence of finite electronic temperatures. The waiting time distributions provide a useful characterization of the driven single-electron turnstile with complementary information compared to what can be learned from conventional current measurements.
Multiserver jobs, which are jobs that occupy multiple servers simultaneously during service, are prevalent in today's computing clusters. But little is known about the delay performance of systems with multiserver jobs. We consider queueing models for multiserver jobs in scaling regimes where the system load becomes heavy and meanwhile the total number of servers in the system and the number of servers that a job needs become large. Prior work has derived upper bounds on the queueing probability in this scaling regime. However, without proper lower bounds, the existing results cannot be used to differentiate between policies. In this paper, we study the delay performance by establishing sharp bounds on the mean waiting time of multiserver jobs, where the waiting time of a job is the time spent in queueing rather than in service. We first characterize the exact order of the mean waiting time under the First-Come-First-Serve (FCFS) policy. Then we prove a lower bound on the mean waiting time of all policies, which has an order gap with the mean waiting time under FCFS. Finally, we show that the lower bound is achievable under a priority policy that we call Smallest-Need-First (SNF).
Telephone call centers offer a convenient communication channel between businesses and their customers. Efficient management of call centers needs accurate modeling of customer waiting behavior, which contains important information about customer patience (how long a customer is willing to wait) and service quality (how long a customer needs to wait to get served). Hazard functions offer dynamic characterization of customer waiting behavior, and provide critical inputs for agent scheduling. Motivated by this application, we develop a two-way functional hazards (tF-Hazards) model to study customer waiting behavior as a function of two timescales, waiting duration and the time of day that a customer calls in. The model stems from a two-way piecewise constant hazard function, and imposes low-rank structure and smoothness on the hazard rates to enhance interpretability. We exploit an alternating direction method of multipliers (ADMM) algorithm to optimize a penalized likelihood function of the model. We carefully analyze the data from a US bank call center, and provide informative insights about customer patience and service quality patterns along waiting time and across different time
We consider sample to sample fluctuations of the waiting time between the detection of two consecutive electrons in quasi-one-dimensional disordered conductors at zero temperature. We compute the full distribution of the mean waiting time along the crossover from ballistic to localised transport in the framework of the Dorokhov-Mello-Pereyra-Kumar theory for an arbitrary number of conduction channels. In particular we show that its variance, with respect to disorder, displays universal fluctuations similar to the universal conductance fluctuations in the metallic regime. We then discuss the statistical properties of the jitter associated to quantum fluctuations of the waiting time.
In a video on demand system, the main video repository may be far away from the user and generally has limited streaming capacities. Since a high quality video's size is huge, it requires high bandwidth for streaming over the internet. In order to achieve a higher video hit ratio, reduced client waiting time, distributed server's architecture can be used, in which multiple local servers are placed close to clients and, based on their regional demands video contents are cached dynamically from the main server. As the cost of proxy server is decreasing and demand for reduced waiting time is increasing day by day, newer architectures are explored, innovative schemes are arrived at. In this paper we present novel 3 layer architecture, includes main multimedia server, a Tracker and Proxy servers. This architecture targets to optimize the client waiting time. We also propose an efficient prefix caching and load sharing algorithm at the proxy server to allocate the cache according to regional popularity of the video. The simulation results demonstrate that it achieves significantly lower client's waiting time, when compared to the other existing algorithms.
Waiting time is an important transport quantity that is complementary to average current and its fluctuation. So far all the studies of waiting time distribution (WTD) are limited to steady state transport (either dc or ac). In this work, we present a theory to calculate WTD for coherent electronic systems in transient regime. We express the generating function of full counting statistics using Keldysh non-equilibrium Green's functions formalism. Our analysis goes beyond the wideband approximation and is suitable for first principles calculation on realistic systems. Analytic solution has been obtained for short and long time behaviors of waiting times. At short times, the WTD shows a linear dependence on the waiting time while in the long time limit, WTD follows Poisson distribution. We have applied this theory to a quantum dot connected by two leads and calculated cumulants of transferred charge as well as WTD in the transient regime. We have demonstrated how to relate WTD to experimental measured data.
We present a Floquet scattering theory of electron waiting time distributions in periodically driven quantum conductors. We employ a second-quantized formulation that allows us to relate the waiting time distribution to the Floquet scattering matrix of the system. As an application we evaluate the electron waiting times for a quantum point contact, modulating either the applied voltage (external driving) or the transmission probability (internal driving) periodically in time. Lorentzian-shaped voltage pulses are of particular interest as they lead to the emission of clean single-particle excitations as recently demonstrated experimentally. The distributions of waiting times provide us with a detailed characterization of the dynamical properties of the quantum-coherent conductor in addition to what can be obtained from the shot noise or the full counting statistics.
Blockchain systems are being used in a wide range of application domains. They can support trusted transactions in time critical applications. In this paper, we study how miners should pick up transactions from a transaction pool so as to minimize the average waiting time per transaction. We derive an expression for the average transaction waiting time of the proposed mining scheme and determine the optimum decision rule. Numerical results show that the average waiting time per transaction can be reduced by about 10% compared to the traditional no-wait scheme in which miners immediately start the next mining round using all transactions waiting in the pool.
Asymmetric information in healthcare implies that patients could have difficulty trading off non-health and health related information. I document effects on patient demand when predicted wait time is disclosed to patients in an emergency department (ED) system. I use a regression discontinuity where EDs with similar predicted wait times display different online wait times to patients. I use impulse response functions estimated by local projections to demonstrate effects of the higher wait time. I find that an additional thirty minutes of wait time results in 15% fewer waiting patients at urgent cares and 2% fewer waiting patients at EDs within 3 hours of display. I find that the type of patient that stops using emergency care is triaged as having lower acuity and would have used an urgent care. However, I find that at very high wait times there are declines in all acuity patients including sick patients.