Congestion Level Research Articles

Probabilistic modeling and simulation of drivers’ lane selection and its impact on highway traffic using fuzzy-based decision-making

Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company.

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

Measuring the level of urban traffic congestion for sustainable transportation in Addis Ababa, Ethiopia, the cases of selected intersections

Traffic congestion poses a persistent and escalating problem for major cities in both developed and developing countries, exerting a direct impact on the economic growth and development of these urban areas. Quantifying the extent of traffic congestion is the fundamental initial step in comprehending the severity of traffic congestion in order to devise effective methods for alleviation. The city of Addis Ababa is currently experiencing significant traffic congestion at its main intersections. The primary aim of this research is to assess the current level of traffic congestion at specific intersections. The assessment of traffic congestion was conducted using the travel time method. Data on travel time, traffic volume, and travel speed were gathered at three blocks and two intersections using a combination of quantitative and qualitative data collection methods. The travel rate, delay rate, and total travel delay (in vehicle-minutes) were computed. The total vehicle-minute delay for the selected three segments is estimated to be approximately 12,708 vehicle-minutes (or 212 vehicle-hours). The text reveals the significance of measuring the various components of traffic congestion in order to ensure a sustainable traffic system. It also highlights the importance of maintaining a satisfactory level of service for the future sustainability of City.

Planning Application for Mobility Assessment of Intersection Forms in Intersection Control Evaluation

The use of alternative intersections and interchanges (AIIs) has proliferated significantly over the past two decades. Their introduction is motivated by the need to improve mobility and safety at conventional signalized and unsignalized intersections. This paper presents a mobility assessment component of the intersection control evaluation (ICE). The focus of this component was to develop a planning application and computational tools for the Stage II mobility analysis that can fill the current gap in coverage of the diverse (and expanding) suite of AIIs. The design of the tool prioritized the simultaneous analysis of multiple AIIs within the same platform using a common input dataset. This feature of the tool guarantees that alternative forms can be compared side-by-side under the same set of inputs and parameter assumptions using overall system delay as the key performance measure. In addition, the research developed a framework and movement definition scheme that enables the extension of the current platform to accommodate new and yet to be conceived innovative intersection forms. A comparison against two widely used commercial analytical platforms yielded virtually identical system delays and level of service ratings across 17 case studies covering eight different AII forms and under varying intersection congestion levels. The maximum system delay difference between the two methods was about 5 s, with an average absolute difference of less than 2.5 s. Future work will focus on expanding the tool to unsignalized versions of AIIs along with new and innovative forms that appear on the horizon.

Development of Reward-Based Crowd Management System and Field Evaluation of Safety and Profitability

For railroad operators with a highly public nature, ensuring the safety of passengers and revitalizing the areas along the line are both tasks to be accomplished. To enhance passenger safety, it is necessary to keep congestion levels below a certain level on platforms and trains. Since traffic congestion generally occurs at certain times, such as in the morning, it is important to disperse traffic congestion during rush hours. In the past, railroad operators have encouraged passengers to voluntarily commute off-peak, but this has not worked as an incentive for passengers simply by improving comfort. Because of this failure, rail operators are considering a method of setting fare prices based on the time of day and congestion levels. However, past studies suggest that few users change their behavior because those who choose the time of day to use the railroads do not always coincide with the fare payers. Therefore, we devised a crowd management system in which coupons redeemable at stores along the rail line are given directly to the customers, encouraging voluntary off-peak commuting and stimulating the local economy. This system features dynamic updating of coupon ranks based on congestion forecast information from measured congestion levels to enhance the effectiveness of the service. The results of a three-month verification experiment that cooperated with Shizuoka Railway confirmed that 7.1% of passengers took congestion avoidance actions and that passengers who obtained coupons got off at a specific station an average of 29% more often.

Congestive mode-switching and economies of scale on a bus route

This paper introduces a type of circular causation called Congestive Mode-Switching (CMS) that may arise when an increase in congestion penalizes transit relative to driving. In turn, rising congestion persuades some transit riders to drive, which exacerbates congestion further, and so on. This circular causation can beget multiple equilibria with different levels of congestion and transit ridership. The paper explores this logic with a static model of a bus route. When the bus fleet size is fixed, CMS applies because congestion raises the bus cycle time and thus lowers bus frequency, resulting in higher wait times. When the fleet size depends on bus ridership, CMS is joined by economies of scale as a second source of circular causation. We derive the system’s equilibria using a static model in the vein of Walters (1961), which permits us to graphically characterize equilibria in useful ways. The comparative statics of a road improvement show how feedback alters first-order effects. A Downs-Thomson paradox is not possible, because a road improvement aids buses even more than cars. Continuous-time stability analysis shows that multiple equilibria may be stable.

Taxi-out time prediction at Mohammed V Casablanca Airport

Airports are vital for global connectivity. However, the increasing volume of air travel has presented significant challenges in airport managing. Accurate predictions of taxi-out times (TXOT) offer potential to enhance airport performance, minimize delays, optimize airline schedules, and enhance customer satisfaction. This paper focuses on developing a machine learning model to forecast taxi-out times at Mohammed V Airport. Historical taxiing data from various airports will be analyzed to predict taxi-out times based on diverse runway-stand combinations and congestion levels. we used neural network (NN), support vector machines (SVM), and regression tree (RT) in order to create a real-time model that forecasts TXOT and congestion levels for different runway-stand combinations. The result showed that the NN model outperformed other forecasting models when their performances are compared using the mean absolute percentage error, root mean square error as accuracy measures.

Dynamic berth allocation under uncertainties based on deep reinforcement learning towards resilient ports

With the evolving global trade landscape and the post-pandemic effects, the resilience of ports has become paramount. The unforeseen disturbances bring substantial challenges, especially in berth allocation, a vital task ensuring seamless resilient port operations. The unpredictability of vessel arrivals and the variability in loading/unloading times intensify these issues, pushing traditional static allocation methods beyond their limits. Fortunately, the advent of smart ports has led in an era of big data availability, enabling the application of advanced deep reinforcement learning (DRL) techniques. To capitalize on this shift, this research presents a DRL-based methodology specially designed to solve the berth allocation problem with the uncertainties in vessel arrival and container handling time to enhance port resilience. A Markov Decision Process model (MDP) of the berth allocation problem is established to minimize the mean waiting time with tailored state space, rule-based action space, and reward function to address the issue. An offline training method is designed to train the agent in selecting the optimal action based on the current state of the port berth system at each decision point even in uncertain environments, deep Q-network (DQN) is implemented for this problem. Comprehensive experiments across different problem scales are conducted to validate the effectiveness and generality of the proposed method in solving berth allocation challenges under uncertain conditions. Furthermore, the trained model also performs better than other methods in different vessel congestion levels through learning.

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks

Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators.

Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS

This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.

A Relationship of Traffic Congestion and Class Attendance Motivation among College Students in Davao City, Philippines

Traffic congestion has been observed to be one of the biggest and most prevalent problems on the society. With the policies’ implementation of face-to-face classes to the schools, numbers of college students frequently travel and mainly experience traffic congestion. Thus, transportation is one of many potential obstacles that students may experience when attempting to attend class particularly, students in Davao City as they are exposed to traffic and their classes need face-to-face interaction as a requirement to the institution. The study focuses on the significant relationship between traffic congestion and class attendance motivation. This study utilized a descriptive-correlational approach to research where it was used to describe the levels of traffic congestion and the levels of class attendance motivation, as well as to assess if there is a correlation between these variables. The 378 gathered results from one of the higher educational institutions in Davao City serves as respondents has indicated that the traffic congestion is highly observed, and class attendance motivation is manifested in high level. Furthermore, it was found in the study that motivation to attend class and traffic congestion are significantly and positively correlated. Hence, the study concluded that there is substantial proof that the motivation of students to attend class is significantly impacted by traffic congestion. The researcher recommend that future researchers widen the scope of the research and use this as a foundation and source of perspective leading as they explore possibilities for enhancing the study and to provide solution to alleviate this concern through implementation of various public and transportation regulations.

Evaluation of a Split Flow Model for the Emergency Department

Problem definition: Split flow models, in which a physician rather than a nurse performs triage, are increasingly being used in hospital emergency departments (EDs) to improve patient flow. Before deciding whether such interventions should be adopted, it is important to understand how split flows causally impact patient flow and outcomes. Methodology/results: We employ causal inference methodology to estimate average causal effects of a split flow model on time to be roomed, time to disposition after being roomed, admission decisions, and ED revisits at a large tertiary teaching hospital that uses a split flow model during certain hours each day. We propose a regression discontinuity design to identify average causal effects, which we formalize with causal diagrams. Using electronic health records data (n = 21,570), we estimate that split flow increases average time to be roomed by about 4.6 minutes (95% confidence interval (95% CI): 2.9, 6.2 minutes) but decreases average time to disposition by 14.4 minutes (95% CI: 4.1, 24.7 minutes), leading to an overall reduction in length of stay. Split flow is also found to decrease admission rates by 5.9% (95% CI: 2.3%, 9.4%) but not at the expense of a significant change in revisit rates. Lastly, we find that the split flow model is especially effective at reducing length of stay during low congestion levels, which mediation analysis partly attributes to early task initiation by the physician assigned to triage. Managerial implications: A split flow model can improve flow and may have downstream effects on admissions but not revisits. Funding: This work was supported by the National Institutes of Health [Grants KL2TR002374 and UL1TR002373]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0003

Interface-Based Loan Fund Performance Monitoring And Evaluation System At BKM Ngesrep Semarang

The monitoring and evaluation system is a routine process of collecting data and measuring progress on program objectives/monitoring changes, which focuses on processes and outputs. Monitoring involves accounting for what is done. Monitoring involves observing the quality of the services provided while evaluation is the use of social research methods to systematically investigate program effectiveness/assess the program's contribution to change (goal/objective) and assess the need for improvement, continuation or expansion of the program (recommendations). The first aim of this system is to be able to process data efficiently and accurately, the second aim is to assist the financial management unit (UPK) in inputting loan fund installments to make it easier to process data so as not to make calculation errors and record errors and to improve services to Community Self-Help Groups (KSM) better as borrowers, the third objective is to facilitate monitoring of loan fund evaluation which can be monitored and evaluated at any time, both the rate of return of loan funds and the level of loan fund congestion. In this system, the interface-based monitoring and evaluation model is one of the services provided by the operating system as a means of interaction between the user and the operating system. The interface is an operating system component that comes into direct contact with the user. This application was created using Microsoft Visual Basic 6.0 and a Client Server-based MySQL database.

Working from home, commuting time, and intracity house‐price gradients

AbstractThe popularity of working from home (WFH) in the US has surged over the past two decades, with the COVID‐19 pandemic further accelerating this trend. We hypothesize that WFH not only reduces the frequency of physical commutes but also lowers the time cost of commutes due to decreased urban congestion levels; both factors would flatten house‐price gradients. Analyzing big data from Google Maps on travel time in California, we first confirm that COVID‐19, as a WFH‐boosting shock, induced larger decreases in morning travel time in cities with a higher WFH potential. We then empirically validate the effect of WFH on house‐price gradients, channeled through its impact on commuting time; this effect explains 20% of the total WFH‐induced flattening of house‐price gradients during the pandemic in California.

Pulmonary congestion and systemic congestion in hemodialysis: dynamics and correlations.

Systemic congestion and pulmonary congestion (PC) are common in hemodialysis (HD) patients. However, the relationship between these two entities is not quite clear. We study this relationship and attempt to uncover the factors that may affect it considering different inter-dialytic intervals. A prospective pilot observational and interventional study including 18 HD patients was conducted. The following were obtained: i) B-line score (BLS) by lung ultrasound (LUS) (reflecting significant pulmonary congestion if BLS > 5), ii) echocardiography, iii) bioelectrical impedance analysis (BIA) (reflecting global volume status), and iv) inferior vena cava (IVC) dynamics (reflecting systemic congestion) before and after the first two consecutive HD sessions of the week, with different inter-dialytic intervals (68 hours and 44 hours). Serum N-terminal pro-brain natriuretic peptide type B (NT-proBNP) levels were obtained before each session. Then, patients were randomized into two groups: the active group, where dry weight was reduced according to BLS + standard of care, and the control group, where dry weight was modified according to standard of care. All the measures were repeated on day 30. We found no correlation between pulmonary congestion represented by BLS and IVC dimensions and dynamics reflecting systemic congestion, independent of different inter-dialytic intervals. Pulmonary congestion was quite prevalent, as mean pre- and post-dialysis BLSs were quite elevated (16 ± 5.53 and 15.3 ± 6.63, respectively) in the first session compared with the second session (16.3 ± 5.26 and 13.6 ± 5.83, respectively). Systolic (left ventricular ejection fraction) and diastolic cardiac function (e/è ratio) parameters from one side and pulmonary congestion (BLS) from the other were not always correlated. BLS was correlated to e/è ratio before HD (session 1) (R 2 = 0.476, p = 0.002) and after HD (session 2) (R 2 = 0.193, p = 0.034). Pulmonary congestion reflected by BLS was correlated to the global volume state reflected by BIA only in the second HD session (HD2) (R 2 = 0.374, p = 0.007). NT-proBNP levels and BLS were correlated before both sessions (R 2 = 0.421, p = 0.004, and R 2 = 0.505, p = 0.001, respectively). Systemic congestion was quite prevalent, as mean pre- and post-dialysis IVC dimensions and dynamics were quite elevated in both sessions, with a higher level of systemic congestion in the first HD session (diameter and collapsibility of 2.1 cm and 23%, and 2.01 cm and 19%, respectively) compared with the second session (1.98 cm and 17.5%, and 1.9 cm and 22%, respectively) without reaching statistical significance. IVC dimensions and global volume status measured by BIA were correlated in the second dialysis session (R 2 = 0.260, p = 0.031). No correlation was found between IVC dimensions and diastolic cardiac function (e/è ratio) parameters or with NT-proBNP levels. On day 30, BLS was significantly reduced in the active group, whereas no difference was found in the control group. However, no real impact was observed on IVC dimensions and dynamics or in total volume status by BIA. Pulmonary congestion is common in HD patients even after reaching their dry weight at the end of two consecutive sessions, and it is not correlated to systemic congestion, suggesting a complex multifactorial pathophysiology origin. Global volume status reflected by BIA and cardiac function are not always related to either systemic congestion represented by IVC dimensions or pulmonary congestion represented by BLS. Fluid redistribution anomalies may allow pulmonary congestion accumulation independently from systemic congestion and global volume status (non-cardiogenic pulmonary congestion). We recommend a personalised approach when managing HD patients by integrating systemic and pulmonary congestion parameters. Dry weight modification guided by repeat LUS may safely reduce pulmonary congestion. However, no impact was observed on systemic congestion or global volume status.

Dynamic Right-of-Way Allocation on Bus Priority Lanes Considering Traffic System Resilience

Bus priority is an effective way to improve traffic efficiency and sustainability. To achieve this, the Bus Priority Lane (BPL) is adopted to provide exclusive right-of-way for buses. However, the BPL is underutilized if the frequency of buses is low. To address this issue, many studies focus on improving the BPL’s utilization efficiency by intermittently allowing general vehicles to access it. However, these studies still have some shortcomings: (i) bus priority cannot be guaranteed if general vehicles run on the BPL; and (ii) the traffic system lacks resilience, especially when the traffic demand is unbalanced. This paper proposes a dynamic right-of-way allocation for the BPL, considering traffic system resilience. On the one hand, it ensures absolute bus priority by controlling Connected Automated Vehicles (CAVs), so as they do not interfere with buses. On the other hand, it can improve traffic system resilience by allocating right-of-way for CAVs with heavy turning-movement demand. To test the effectiveness, the proposed control strategy is compared with the non-control baseline. The experiments are conducted under seven unbalanced-traffic-demand levels, four congestion levels, and five CAV Penetration Rates. The results show that the proposed strategy can ensure absolute bus priority and improve traffic efficiency and traffic system resilience.

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction.

Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively.

Visitors’ perception of crowding. A case study from the Tatra Mountains

Protected areas (PAs) serve as significant tourist destinations, drawing a substantial number of visitors annually. Over the past century, managers and scientists overseeing PAs have sought to identify indicators that specify the maximum number of tourists that could visit a particular area without making a large impact on the environment. As a result, the carrying capacity theory was created, which now evolved into the concept called Limits of Acceptable Changes (LAC). This concept is based on the expected conditions that should be met in a protected area in both ecological and social aspects. This study aims to assess the tolerable level of congestion in a cable car destination situated in the Tatra Mountains and, in addition, to obtain information on the factors that affected the differences in visitors’ perception of crowding. Data used in the analysis were collected as part of two research projects: focused on the Kasprowy Wierch area (Poland) and the Skalnaté Pleso area (Slovakia). To gain information on the perception of crowding by visitors, on-site surveys were conducted during the summer season of 2014 and 2015 at both sites. The research results show that the acceptance of crowding decreases with the increasing number of visitors in research areas and it is affected by the type of research area. The analysis also reveals that the acceptance of crowding is also dependent on socio-demographic and social factors such as the level of educational attainment of the respondents, the type of trip or the size of the group.

Road Management with Mathematical Accuracy

Paraskevov, A. V. Improving road traffic management based on the use of mathematical and instrumental methods of economics [Sovershenstvovanie upravleniya dorozhnym dvizheniem na osnove ispolzovaniya matematicheskikh i intrumentalnykh metodov ekonomiki]. Moscow, Vologda, Infra-Inzheneriya publ., 2023, 84 p. ISBN 978-5-9729-1283-4.The article is a review of the monograph «Improving traffic management based on the use of mathematical and instrumental methods of economics», written by A. V. Paraskevov, a senior lecturer at the Department of Computer Technologies and Systems of the Kuban State Agrarian University named after I. T. Trubilin and published by Infra-Inzheneriya [Infra-Engineering] publishing house. The work summarises and systematises extensive material on innovative methods for improving management of road traffic congestion levels. An also considered application for motorists is designed to build optimal routes taking into account the current situation on urban roads.

Mail back or in-store dropoff? Optimal design of product-exchange policies in omnichannel retailing systems

Online retailing has been booming over the past several years as people grow increasingly comfortable with it and accustomed to its ease and speed. A major drawback of online retailing is the lack of consumer–product interaction before a purchase is finalized, which often leads to consumer dissatisfaction due to mismatched expectation of the received product. In response, retailers usually promise that online orders may be returned or exchanged free of extra charges, which can be processed either online (e.g., by mail) or onsite i.e., in-store dropoff. In this work, we study the implications of both online and onsite exchange policies in the setting of a queueing model that offers omnichannel services. Taking into account customers’ behavioral responses to these policies, we aim to inform the retailer of the one that generates a higher system revenue. Our results reveal that the online exchange policy is a double-edged sword: On the one hand, it helps eliminate the inconvenience cost for exchange customers to revisit the store; on the other hand, it can trigger more feedback orders and render a higher system congestion level, which in turn, deters future customers from placing orders. Specifically, we discover that online exchange becomes an inferior policy relative to in-store exchange when the market size is large.