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Inspired by new technologies to monitor parking occupancy and process market signals, we aim to expand the application of demand-responsive pricing in the parking industry. Based on a graphical Hotelling model wherein each garage has information for its incoming parking demand, we consider a general competitive spatial pricing in parking systems under an asymmetric information structure. We focus on the impact of urban network structure on the incentive of information sharing. Our analyses suggest that the garages are always better off in a circular-networked city, while they could be worse off in the suburbs of a star-networked city. Nevertheless, the overall revenue for garages is improved and the aggregate congestion is reduced under information sharing. Our results also suggest that information sharing helps garages further exploit the customers who in turn become worse-off. Therefore, policy-makers should carefully evaluate their transportation data policy since impacts on the service-providers and the customers are typically conflicting. Using the SFpark data, we empirically confirmed the value of information sharing. In particular, garages with higher price-demand elasticity and lower demand variance tend to enjoy larger benefits via information sharing. These insights support the joint design of parking rates structure and information systems. [ABSTRACT FROM AUTHOR]

Many statistical estimators for high-dimensional linear regression are |$M$| -estimators, formed through minimizing a data-dependent square loss function plus a regularizer. This work considers a new class of estimators implicitly defined through a discretized gradient dynamic system under overparameterization. We show that, under suitable restricted isometry conditions, overparameterization leads to implicit regularization: if we directly apply gradient descent to the residual sum of squares with sufficiently small initial values then, under some proper early stopping rule, the iterates converge to a nearly sparse rate-optimal solution that improves over explicitly regularized approaches. In particular, the resulting estimator does not suffer from extra bias due to explicit penalties, and can achieve the parametric root- |$n$| rate when the signal-to-noise ratio is sufficiently high. We also perform simulations to compare our methods with high-dimensional linear regression with explicit regularization. Our results illustrate the advantages of using implicit regularization via gradient descent after overparameterization in sparse vector estimation. [ABSTRACT FROM AUTHOR]

To understand non-compliance behaviors in investment goods in pandemic preparedness and resilience, we resort to a form of bounded rationality that people suffer from a lack of self-control due to "present bias", and differ in their sophistication levels (the degree to which they are aware of such compliance barriers). We focus on (i) the manufacturer's pricing strategy under an advance selling framework, and (ii) the subsidy policy to mitigate under-adoption, to generate operational insights. We show that subsidizing the manufacturer is more cost-effective than subsidizing consumers, because the latter subsidy will not fully trickle down to consumers when the manufacturering manipulates prices in response. In particular, when the subsidy program is budget-constrained, the manufacturer subsidy should be provided only in the spot period. In contrast, when the budget constraint is relaxed, subsidies in both periods should be provided. Surprisingly, such a subsidy program has non-monotone impacts on consumers' adoption quantities. Intuitively, this is because the spot-period subsidy induces consumers' non-compliance behaviors in the advance period. In response, the manufacturer/seller will shift to a pricing strategy that may further reduce the advance-selling quantity. This result provides a natural explanation to reconcile the mixed effects of adoption subsidies. [ABSTRACT FROM AUTHOR]

To identify the characteristics of inflammatory fibroid polyps (IFP) in the terminal ileum and to investigate the methods, safety, and efficacy of colonoscopic minimally invasive dissection and resection therapies for its treatment. Colonoscopy and colonoscopic ultrasonography were used to diagnose patients with protruding mucosal lesions in the terminal ileum, and the results suggested a high suspicion of IFPs. Colonoscopic minimally invasive dissection and resection were performed for these patients, and IFP was confirmed by postoperative pathological examination and immunohistochemical staining. Twelve cases of IFP from April 2016 to December 2020 in our hospital were examined pathologically and immunohistochemically. The IFPs in the terminal ileum were all successfully excised by colonoscopy. There were no postoperative perforation, bleeding, or recurrence cases during the follow-up. The features of the lesions, as well as the efficacy of colonoscopic minimally invasive dissection and resection, were reviewed. Terminal ileum IFPs have corresponding colonoscopic and endoscopic ultrasonographic features. For IFPs less than 2 cm in size and within 10 cm of the ileocecal valve, removal by colonoscopy was proven less invasive, safe, and effective. [ABSTRACT FROM AUTHOR]

The emergence of bike-sharing systems has considerably improved last- and first-mile transportation systems. To ensure attractiveness to end users, operators aim to design effective service-oriented operational strategies to meet the desired service targets for users. Most existing studies focus on the service efficiency of bike-sharing systems, while service equity is overlooked. In this study, we propose a target-based stochastic distributionally robust optimization (TSDRO) model that addresses both the efficiency and equity of the service level in docked bike-sharing systems under demand uncertainty. We first employ a dissatisfaction risk measure to jointly quantify the probability and magnitude of user dissatisfaction in a zone. Then, we apply a lexicographic-order approach to define the objective function to achieve equity of service among different zones. This lexicographic approach optimizes the worst-off individual and the second-worst zone in an iterative manner. To address demand ambiguity, we use a data-driven method to explore the relationship between the demand distribution and several exogenous factors, including weather and weekends, and then construct a scenario-based distributionally robust optimization model. Based on duality theory and linear decision approximation, this model can be reformulated as a tractable equivalent deterministic model, which can be solved via a bisection-search approach to optimality. Numerical experiments based on real operational data show that compared with the benchmark models, the TSDRO model achieves (i) better out-of-sample performance in terms of service efficiency and (ii) higher service equity among users in different zones. Moreover, setting a lower target level may generate a better solution. • We propose a target-based service efficiency and equity optimization model. • We consider the spatial service equity by employing the lexicographic approach. • We address demand uncertainty by a stochastic distributionally robust approach. • Numerical experiments show that our model has good out-of-sample performance. [ABSTRACT FROM AUTHOR]

Over the past two decades, bike-sharing systems have grown significantly worldwide. Compared with that of other business sectors, the means by which revenue is obtained in the bike-sharing industry is unique. When the market in a region or city is saturated, an efficient way for a firm to increase revenue is to enter a new market. In this study, we design a revenue maximization-oriented decision tool to support the operational decisions of a new competitor firm entering into competition against a local firm. It is assumed that operational-level information from the local firm is unknown to the competitor firm. A new multi-stage max–min–max robust maximization model is proposed. It aims to optimize the dynamic bike inventory to maximize the worst-case revenue that the competitor firm may achieve. The worst-case revenue is treated as the baseline revenue for the competitor firm according to which rational decisions can be made. Specifically, we construct an uncertainty set to capture the uncertainty in the bike distribution of the local firm. To work with this nonconvex model, we design a myopic method, inspired by a special two-stage model that can be solved with a customized constraint-and-column approach, for obtaining the upper and lower bounds of the potential optimal revenue in our multistage model. The results of numerical experiments illustrate that the approximation approach has satisfactory computational efficiency and generates a tight bound of the optimal baseline revenue. Sensitivity analyses show that the new competitor firm should not increase its investment in bikes when the local firm increases its quantity of bikes because of high depreciation costs. Moreover, the two firms would allocate a similar proportion of bikes to each zone when either of them provides a large number of bikes. When the firms both provide a small number of bikes, the allocation of bikes by the new competitor firm is widely dispersed, while the local firm tends to allocate bikes across several zones with high demand. Furthermore, on the premise of a given target, increasing the bike acquisition can improve the robustness of revenue estimation for the competitor firm. • A revenue-oriented model is presented for a new firm entering into competition against a local firm. • An uncertainty set is defined to capture the uncertainty in the bike distribution of the local firm. • A customized myopic method with CC&G approach is designed to obtain suboptimal solutions. • The results show the new firm may decrease the number of bikes as the local firm provides more bikes. [ABSTRACT FROM AUTHOR]

Bike-sharing systems are important components of urban transportation systems that facilitate short-distance travel. In recent years, free-floating bike-sharing systems have become popular. However, a major problem in such systems is the increasing number of idle bikes due to their spatial dispersion, which leads to a loss of demand and difficulty in bike rebalancing. To overcome these drawbacks, operators reserve parking areas, and laborers are incorporated into the bike rebalancing operation. We propose a novel dynamic bike rebalancing strategy based on trucks and laborers for free-floating bike-sharing systems. This strategy combines bike collection by laborers and bike transportation by trucks for rebalancing operations. In addition, we consider broken bike detection by laborers and user dynamics in two extension models. Because of the demand uncertainty in bike-sharing systems, we employ an enhanced distributionally robust optimization (EDRO) approach to protect against uncertainty. A target-level constraint is added to the general distributionally robust optimization (DRO) model to further improve the out-of-sample performance. An adaptive robust strategy is also employed to avoid overly conservative solutions. Furthermore, a linear decision rule is adopted to reformulate the EDRO model into a mixed-integer programming model, which guarantees computational tractability. The results of numerical experiments show that the EDRO model outperforms other benchmark models on a series of measures. Moreover, we verify the value of laborers in dynamic rebalancing operations and explore the effect of different bike supply levels and several other parameters on the value of laborers. We also test how the target-level parameter affects the model performance. • We propose a novel dynamic bike rebalancing strategy integrating laborers. • Several special features in free-floating bike-sharing systems are incorporated. • An enhanced adaptive distributionally robust optimization approach is employed. • A linear decision rule is employed to maintain the computational tractability. • The practical managerial insights are demonstrated by the real-world instance. [ABSTRACT FROM AUTHOR]

The rise of autonomous driving may sharply reshape ride-sourcing markets by influencing drivers' income and participation, customers' choices, ride-sourcing platforms' operations, and governments' regulations. We introduce a model that characterizes a ride-sourcing market with a mixed fleet of non-atomic autonomous vehicles (AVs) and atomic human-driven vehicles (HVs). Human drivers decide whether and where to offer ride-sourcing services independently, while AVs are fully controlled and dispatched by the platform to serve on-demand ride requests that are distributed heterogeneously. We solve for the optimal platform operating strategy and examine the implications of different government regulations on the platform's optimal decisions and the resulting market equilibrium. Although the emergence of AV might undermine human drivers' welfare, it can simultaneously increase platform profit, customer surplus, and social welfare. Moreover, by looking into the regulatory outcomes of a few prevailing regulations (i.e., price cap, minimum wage, minimum fleet size, and maximum fleet size), we find that the policymakers should determine the regulations according to the conditions of labor supply and the development stage of AV technology. • Develop an analytical model to characterize a mixed fleet of autonomous vehicles (AVs) and human-driven vehicles (HVs). • Identify the platform's operating strategies under distinct market structures. • Compare the monopoly optimum with the social optimum. • Examine multiple regulations that can potentially improve social welfare. [ABSTRACT FROM AUTHOR]

• Cooperative and competitive analysis for a duopoly of TNC and public transit. • TNC can be hurt by a good-intentioned subsidy in areas with high public transit accessibility. • Win-win outcome achieved via subsidy design in areas with low public transit accessibility. The substitution and complement of ride-sourcing services and public transit services greatly affect the mobility and accessibility of a multi-modal transportation system. Since the transportation network company (TNC) (operator of ride-sourcing) concerns the profit and the government (operator of public transit) promotes social welfare, the two policy-makers will both cooperate and compete with each other to enhance their benefits. In this paper, we develop a bi-level game-theoretic approach to model the cooperative and competitive relationship between the TNC and the government. In the upper level, the two policy-makers design a cooperative plan such that the government provides subsidies to public transit riders who use ride-sourcing to solve first- or last-mile problems. In the lower level, they attempt to optimize operational strategies to compete with each other for achieving their objectives. The outcome of the bi-level game depends on passengers' choices among alternative travel modes, which is characterized by an aggregate multi-modal market. Based on the proposed game-theoretic approach, we analytically and numerically examine the impact of the competition with passenger-side subsidies on the multi-modal system, and the existence of optimal strategies that lead to a win–win situation for both the policy-makers. We find that a socially-optimal subsidy level may exist. In areas with high public transit accessibility, a good-intentioned subsidy that boosts first- or last-mile ride-sourcing services can reduce the TNC's profit. Our research highlights the value of a carefully designed subsidy structure that benefits both the TNC (profit) and the society (social welfare), especially in areas with low public transit accessibilities. [ABSTRACT FROM AUTHOR]