Transportation Research Part B-Methodological最新文献_第2页

Scheduling trucks in a time-space network of open-pit mines 露天矿山时空网络中的卡车调度

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.trb.2026.103400

Linying Yang , Jingwen Wu , Shuaian Wang , Lu Zhen

This study investigates a truck scheduling problem in open-pit mines, where trucks transport raw coal and rock from electric shovels to unloading stations. The raw coal is used to produce commercial coal for sale, which requires a consistent calorific value between the mined raw coal and the blended commercial coal. Truck congestion significantly impacts work efficiency, so proper scheduling is necessary to prevent congestion and improve efficiency. We model the problem as a mixed-integer linear programming model using a time-space network to minimize the total operation time of all trucks. We design a column generation-based algorithm to solve the model, integrating state-reduction-based dynamic programming and machine learning to enhance efficiency. Effective inequalities are also incorporated to accelerate the solution process and improve computational performance. Experimental results show that, for small-scale instances, the proposed algorithm reduces solving time by 24% compared to CPLEX while maintaining solution quality. For large-scale instances, CPLEX fails to find an optimal solution within 1800 seconds, but the proposed algorithm consistently produces better solutions in a shorter time. Sensitivity analyses based on real data from an open-pit mine show that the variable speed mode improves truck transportation efficiency and reduces congestion compared to the constant speed mode. Our results also suggest that mine operators should carefully choose truck speed modes, and truck size combinations, as well as the distribution of unloading stations and shovels. Utilizing speed modes with more choices, higher maximum speeds, and suitable gradients, along with incorporating larger trucks into fleets, can reduce the total operation time of all trucks.

本文研究了露天矿的卡车调度问题，在露天矿中，卡车将原煤和岩石从电动铲车运送到卸货站。原煤用于生产供销售的商品煤，这要求开采的原煤和混合的商品煤之间具有一致的热值。货车拥堵严重影响工作效率，合理调度是防止拥堵、提高效率的必要手段。我们将问题建模为一个混合整数线性规划模型，使用一个时空网络来最小化所有卡车的总运行时间。我们设计了一种基于列生成的算法来求解模型，将基于状态约简的动态规划和机器学习相结合来提高效率。有效不等式也被纳入，以加快解决过程和提高计算性能。实验结果表明，在保持求解质量的前提下，该算法的求解时间比CPLEX算法缩短了24%。对于大规模实例，CPLEX不能在1800秒内找到最优解，但该算法在更短的时间内始终能得到更好的解。基于某露天矿实际数据的敏感性分析表明，与恒速模式相比，变速模式提高了卡车运输效率，减少了拥堵。研究结果还表明，矿山经营者应谨慎选择卡车速度模式、卡车尺寸组合以及卸载站和铲的分布。利用更多选择的速度模式、更高的最大速度和合适的梯度，以及将大型卡车纳入车队，可以减少所有卡车的总操作时间。

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引用次数: 0

Dynamic deployment of pooled human-robot resources in urban parcel logistics 城市包裹物流中人机资源的动态调配

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.trb.2026.103409

Yujia Xu , Walid Klibi , Benoit Montreuil

Parcel logistics services play a crucial and expanding role in global economies. In urban parcel logistics hubs, resources support relay-based activities and ensure parcels are sorted, consolidated, and dispatched onto outbound trucks by specific deadlines. In light of labor shortages in logistics hubs, recent developments have introduced autonomous mobile robots (AMRs) to enhance operational efficiency, enabling carriers to meet time-definite customer service guarantees. Emerging market dynamics, driven by increasing customer expectations for swift delivery across diverse product categories, address the importance of dynamic resource deployment to meet uncertainties of parcel arrivals and resource availability in urban hub networks. This paper focuses on a novel operational problem: dynamic multi-location, multi-resource scheduling and deployment in an urban parcel logistics network. It formulates this novel problem as a stochastic optimization problem and presents its main features. Given the inherent stochastic-combinatorial setting of the problem, the solution methodology proposes a reformulation in a two-stage stochastic model. It develops a rolling-horizon framework to solve it sequentially with updated states and new observations. It also designs a solution approach based on Sample Average Approximation (SAA), Benders decomposition enhanced by acceleration methods, and relax-and-fix heuristics, which can solve large-scale instances of a real-sized megacity. Numerical results, inspired by the case of a large parcel express carrier in China, are presented to evaluate the computational performance of the proposed approach. Our results indicate potential savings of approximately 19% in costs and 22% in resources compared to a static resource deployment strategy. Also, necessary workforce-robot hybrid resource deployment yields the highest cost saving, surpassing the workforce-only and robot-only scenarios by 5% and 3%, respectively.

包裹物流服务在全球经济中发挥着至关重要的作用。在城市包裹物流中心，资源支持以接力为基础的活动，并确保包裹在特定的期限内被分类、合并并发运到出站卡车上。考虑到物流中心的劳动力短缺，最近的发展引入了自主移动机器人（amr），以提高运营效率，使运营商能够满足特定时间的客户服务保证。由于客户对不同产品类别的快速交付的期望越来越高，新兴市场的动态解决了动态资源部署的重要性，以满足包裹到达和城市枢纽网络中资源可用性的不确定性。本文研究了城市包裹物流网络中动态多地点、多资源调度与部署的新问题。将该问题表述为随机优化问题，并给出了其主要特征。考虑到问题固有的随机组合设置，求解方法提出了一种两阶段随机模型的重新表述。它发展了一个滚动视界框架，用更新的状态和新的观测值来顺序地解决它。设计了一种基于样本平均近似（SAA）、基于加速方法增强的Benders分解和松弛修复启发式的求解方法，可以求解实际超大城市的大规模实例。以中国一家大型包裹快递公司为例，给出了数值结果来评估该方法的计算性能。我们的结果表明，与静态资源部署策略相比，潜在的成本节约大约19%，资源节约22%。此外，必要的劳动力-机器人混合资源部署产生了最高的成本节约，分别比仅使用劳动力和仅使用机器人的方案节省了5%和3%。

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引用次数: 0

Capturing stochastic variabilities in pedestrian flows: A dynamic continuum modeling approach 捕捉随机变化的行人流量：一个动态连续体建模方法

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.trb.2026.103405

Zepeng Liu , Haoyang Liang , S.C. Wong , Chi-Wang Shu , Mengping Zhang

Stochastic phenomena are commonly observed in pedestrian flow. However, the existing models for pedestrian dynamics rely on averaged inputs and yield deterministic outputs only, and thereby fail to capture the stochastic variabilities inherent in pedestrian dynamics. This study builds upon Hughes’ dynamic continuum model to develop mathematical models for stochastic pedestrian dynamics that explicitly consider two types of stochastic characteristics: demand stochasticity and behavioral stochasticity. The proposed system, represented as a set of time-dependent stochastic partial differential equations, is solved using a combination of the Monte Carlo (MC) method or Quasi Monte Carlo (QMC) method and efficient numerical schemes, such as a fifth-order weighted essentially non-oscillatory finite difference scheme and the fast sweeping method. Benchmarking scenarios are designed and simulated, and the numerical results demonstrate the convergence and computational performance of the MC and QMC methods. The advantage of stochastic modeling is evident given the significant differences between the averaged stochastic outputs and deterministic outputs, attributable to the strength of stochasticity and extent of stochastic dimensions. Moreover, based on stochastic data inputs, the proposed stochastic models and numerical solutions can clarify the probabilistic distributions of key indicators, such as density, which are valuable for the design and improvement of pedestrian facilities.

随机现象在行人流中很常见。然而，现有的行人动力学模型依赖于平均输入，只产生确定性输出，因此无法捕捉行人动力学固有的随机可变性。本研究以Hughes的动态连续模型为基础，建立了随机行人动力学的数学模型，该模型明确考虑了两种随机特征：需求随机性和行为随机性。将蒙特卡罗（MC）方法或拟蒙特卡罗（QMC）方法与五阶加权本质非振荡有限差分格式和快速扫描方法等有效数值格式相结合，将所提出的系统表示为一组随时间变化的随机偏微分方程。设计和模拟了基准测试场景，数值结果证明了MC和QMC方法的收敛性和计算性能。随机建模的优势是显而易见的，因为平均随机输出和确定性输出之间存在显著差异，这可归因于随机性的强度和随机维度的范围。此外，基于随机数据输入，提出的随机模型和数值解能够明确密度等关键指标的概率分布，为行人设施的设计和改进提供参考。

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引用次数: 0

The fragile nature of road transportation networks 道路交通网络的脆弱性

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.trb.2025.103386

Linghang Sun , Yifan Zhang , Cristian Axenie , Margherita Grossi , Anastasios Kouvelas , Michail A. Makridis

Major cities worldwide experience problems with the performance of their road transportation networks, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. The operation of transportation systems is widely considered to display fragile property, i.e., the loss in performance increases exponentially with the linearly growing magnitude of disruptions. Meanwhile, the risk engineering community is embracing the novel concept of antifragility, enabling systems to learn from past events and exhibit improved performance under disruptions of previously unseen magnitudes. In this study, based on established traffic flow theory knowledge, namely the Macroscopic Fundamental Diagram (MFD), we first conduct a rigorous mathematical analysis to theoretically prove the fragile nature of road transportation networks. Subsequently, we propose a skewness-based indicator that can be readily applied to cross-compare the degree of fragility for different networks solely dependent on the MFD-related parameters. Finally, we implement a numerical simulation calibrated with real-world network data to bridge the gap between the theoretical proof and the practical operations, with results showing the reinforcing effect of higher-order statistics and stochasticity on the fragility of the networks. This work aims to demonstrate the fragile nature of road transportation networks and guide researchers towards adopting the methods of antifragile design for future networks and traffic control strategies.

世界主要城市的道路交通网络性能存在问题，交通需求的持续增长对城市道路网络的优化运行和交通控制策略的效率提出了重大挑战。人们普遍认为运输系统的运行表现出脆弱的特性，即随着中断程度的线性增长，性能损失呈指数级增长。与此同时，风险工程界正在接受反脆弱性的新概念，使系统能够从过去的事件中学习，并在前所未有的严重破坏下表现出更好的性能。本研究基于已有的交通流理论知识，即宏观基本图（MFD），首先进行了严格的数学分析，从理论上证明了道路交通网络的脆弱性。随后，我们提出了一个基于偏度的指标，可以很容易地应用于交叉比较不同网络的脆弱性程度，仅依赖于mfd相关参数。最后，我们利用真实网络数据进行了数值模拟，以弥合理论证明与实际操作之间的差距，结果显示了高阶统计量和随机性对网络脆弱性的强化作用。这项工作旨在展示道路交通网络的脆弱性，并指导研究人员在未来的网络和交通控制策略中采用反脆弱性设计方法。

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引用次数: 0

Benchmarking intra-driver heterogeneity car-following models using a behavioural and numerical evaluation framework 使用行为和数值评估框架对驾驶员内部异质性汽车跟随模型进行基准测试

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.trb.2026.103399

Mohammad Tamim Kashifi , Anshuman Sharma , Yasir Ali , He Haitao

Intra-driver heterogeneity significantly impacts traffic dynamics yet remains poorly understood and insufficiently assessed in existing car-following models. Whilst various modelling approaches have been proposed, the lack of a unified benchmarking framework has obscured their limitations, particularly regarding behavioural soundness and intra-driver heterogeneity recovery. This study benchmarks four well-established car-following models (Intelligent Driver Model, Optimum Velocity Model, Full Velocity Difference Model, and Newell) using four methods for incorporating intra-driver heterogeneity. We propose a benchmarking methodological framework to comprehensively evaluate these models from both numerical and behavioural perspectives. Six experiments are performed: (i) evaluating traditional models without heterogeneity, (ii) testing heterogeneity models on heterogeneity-free data, (iii) analysing simplified scenarios excluding the standstill regime, (iv) assessing models’ ability to recover heterogeneity in controlled data, (v) evaluating traditional models with real-world data, and (vi) testing heterogeneity models with real-world data. Numerical evaluation (using Percentage Parameter Estimation Error, Root Mean Square Error, and Percentage of Intra-driver Heterogeneity Error) and behavioural consistency (e.g., unrealistic accelerations, oscillations, and concavity of oscillations growth) are used for comparison. Results indicate that some traditional models struggle with behavioural soundness, whereas incorporating intra-driver heterogeneity improves certain aspects but introduces new challenges. Among model-method combinations, combining the Langevin method with the Intelligent Driver Model is promising for capturing realistic intra-driver heterogeneity behaviour and fewer behavioural issues. Yet the variable parameter method is generally robust in reproducing the concave growth curve of oscillations when integrated with any model. The proposed benchmarking framework offers a comprehensive approach for rigorously evaluating intra-driver heterogeneity models.

驾驶员内部异质性显著影响交通动态，但在现有的车辆跟随模型中仍未得到充分的理解和评估。虽然已经提出了各种建模方法，但缺乏统一的基准框架掩盖了它们的局限性，特别是在行为稳健性和内部驱动异质性恢复方面。本研究采用四种方法纳入驾驶员内部异质性，对四种成熟的汽车跟随模型（智能驾驶员模型、最优速度模型、全速差模型和Newell模型）进行了基准测试。我们提出了一个基准方法框架，从数值和行为角度全面评估这些模型。进行了六项实验：(i)评估没有异质性的传统模型，（ii）在无异质性数据上测试异质性模型，（iii）分析排除停滞状态的简化情景，（iv）评估模型在受控数据中恢复异质性的能力，(v)用真实数据评估传统模型，以及（vi）用真实数据测试异质性模型。数值评估（使用参数估计误差百分比、均方根误差和驾驶员内部异质性误差百分比）和行为一致性（例如，不切实际的加速、振荡和振荡增长的凹凸性）用于比较。结果表明，一些传统模型在行为合理性方面存在问题，而纳入驱动因素内部异质性可以改善某些方面，但也会带来新的挑战。在模型-方法组合中，朗格万方法与智能驾驶员模型相结合有望捕获现实的驾驶员内部异质性行为和更少的行为问题。而变参数法在与任意模型集成时，对于再现振动的凹增长曲线具有一般的鲁棒性。提出的基准框架为严格评估内部驱动异质性模型提供了一种全面的方法。

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引用次数: 0

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing 基于高性能并行计算的超大规模电动汽车充电基础设施规划

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.trb.2026.103403

Waquar Kaleem , Taner Cokyasar , Jeffrey Larson , Omer Verbas , Tanveer Hossain Bhuiyan , Anirudh Subramanyam

This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

研究了电动汽车最后一英里配送过程中排队拥堵情况下的随机充电桩位置与分配问题。目标是在预算和等待时间限制的情况下，决定在哪里开设充电站，以及安装哪种类型的充电器的数量。我们将问题表述为一个混合整数非线性程序，其中每个充电站对被建模为具有随机到达和服务时间的多服务器队列，以捕获车队操作中等待的概念。这个模型非常大，对于一个典型的大都市区来说，有数十亿个变量和约束；甚至在求解器内存中加载模型都很困难，更不用说求解了。为了应对这一挑战，我们开发了一个基于拉格朗日的对偶分解框架，该框架按工位分解问题，并利用高性能计算系统的并行化，其中子问题通过使用切割平面方法解决，并在主级别收集其解决方案。我们还开发了一个三步舍入启发式方法，将分数子问题解转化为可行的积分解。对芝加哥大都会地区数十万户家庭和数千个候选站点的数据进行的计算实验表明，在现有精确方法甚至无法在内存中加载模型的情况下，我们的方法产生了高质量的解决方案。我们还分析了各种政策情景，表明在多机构合作下，将现有的车站与新建的车站相结合，大大降低了成本和拥堵。这些发现为开发可持续的大规模电动汽车充电网络提供了一个可扩展和有效的框架。

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引用次数: 0

Reliability premium: A generic conceptual framework for evaluating the cost of travel time variability 可靠性溢价：评估旅行时间可变性成本的一般概念框架

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.trb.2026.103408

Zhaoqi Zang , Richard Batley , David Z.W. Wang , Hong K. Lo

In this paper, we propose the reliability premium as a generic concept that eliminates the utility difference between the expected utility and the utility of a travel choice that is subject to randomness in travel time–without specifying the underpinning utility function nor the travel choice domain to which it relates. Mathematically, the reliability premium quantifies the buffer or additional time a traveller is willing to pay beyond the expected outcome of a travel choice to eliminate the extra disutility due to travel time variability (TTV), thereby conceptualising the cost of TTV directly and intuitively in time units. We then discuss the reliability premium under, first, the Bernoulli approach, which focuses on route choice only, and second, the scheduling delay approach, which encompasses both departure or arrival time choice and route choice. Under the Bernoulli approach, we show that it is convenient to derive the monetary cost of travel time variability based on the reliability premium. In addition, we discuss the preservation of first-order and second-order stochastic dominance (SD) of the reliability premium, which removes the computational concern of using the reliability premium in reliable path-finding problems or related assignment models. Under the schedule delay framework, we derive formulations of the reliability premium for different applications and show the detailed impact of TTV on the resulting valuations. We find that the reliability premium can be effective in capturing the asymmetry and distributional tail of travel times for quantifying the TTV cost, especially for risk-averse users, making it suitable for evaluating the impact of TTV on travellers’ route choice decisions. Numerical examples are employed to elucidate the concept of the reliability premium and illustrate its practical application.

在本文中，我们提出了可靠性溢价作为一个通用概念，它消除了期望效用和受旅行时间随机性影响的旅行选择效用之间的效用差异，而没有指定基础效用函数或与其相关的旅行选择域。从数学上讲，可靠性溢价量化了旅行者愿意在旅行选择的预期结果之外支付的缓冲或额外时间，以消除由于旅行时间可变性（TTV）而产生的额外负效用，从而以时间单位直接直观地概念化了TTV的成本。然后，我们讨论了在伯努利方法下的可靠性溢价，伯努利方法只关注路线选择，其次是调度延迟方法，它包括出发或到达时间选择和路线选择。在伯努利方法下，我们证明了基于可靠性溢价的旅行时间变异性的货币成本是方便的。此外，我们还讨论了可靠性溢价的一阶和二阶随机优势（SD）的保留，从而消除了在可靠寻径问题或相关分配模型中使用可靠性溢价的计算问题。在进度延迟框架下，我们推导了不同应用的可靠性溢价公式，并详细展示了TTV对结果估值的影响。我们发现，可靠性溢价可以有效地捕捉出行时间的不对称性和分布尾部，用于量化TTV成本，特别是对于风险厌恶的用户，使其适用于评估TTV对旅行者路线选择决策的影响。通过数值算例说明了可靠性溢价的概念和实际应用。

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引用次数: 0

Distributed adaptive signal control optimization and stability analysis based on nonlinear small-gain theorem 基于非线性小增益定理的分布式自适应信号控制优化及稳定性分析

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.trb.2026.103404

Zhun Yin , Tong Liu , Guohui Zhang , Hong Wang , Zhong-Ping Jiang

Network-wide signal control optimization is of practical importance to shorten and stabilize travel time, improve productivity, enhance energy consumption efficiency, mitigate congestion, and reduce vehicle emissions. In this study, a deep learning–empowered distributed control strategy is developed to adaptively optimize network-wide traffic signal control coordination. To simplify the problem formulation and enhance its applicability, the entire traffic system is decomposed into multiple areas, and multilayer perceptron concepts are used to formulate traffic control system operations in each area. The distributed deep learning, velocity-based model predictive control (MPC) strategy is designed to optimize traffic signal coordination. Furthermore, a gain-scheduling control model is developed to linearize each learned nonlinear system around its most recent operating status, and then a distributed MPC controller is applied to the linearized systems. Simulation results demonstrate that the proposed control strategy can effectively reduce travel time by 15.1% compared with fixed-time control plans and by 8.0% compared with a decentralized control plan. This study is the first research effort to integrate the deep learning framework and multiagent MPC to optimize traffic control coordination. Moreover, a sufficient condition is theoretically formulated for the bounded-input, bounded-output stability of the closed-loop, large-scale traffic system based on the nonlinear small-gain theorem.

全网信号控制优化对于缩短和稳定行车时间、提高生产率、提高能耗效率、缓解拥堵、减少车辆排放具有重要的现实意义。本文提出了一种基于深度学习的分布式控制策略，用于自适应优化全网交通信号控制协调。为了简化问题的表述，增强问题的适用性，将整个交通系统分解为多个区域，并使用多层感知器概念来表述每个区域的交通控制系统操作。为优化交通信号协调，设计了分布式深度学习、基于速度的模型预测控制（MPC）策略。此外，建立了增益调度控制模型，将每个学习到的非线性系统围绕其最近的运行状态进行线性化，然后对线性化后的系统应用分布式MPC控制器。仿真结果表明，所提出的控制策略与固定时间控制方案相比，行程时间可有效减少15.1%，与分散控制方案相比，行程时间可有效减少8.0%。本研究首次将深度学习框架与多智能体MPC相结合来优化交通控制协调。此外，基于非线性小增益定理，从理论上给出了闭环大交通系统有界输入、有界输出稳定性的充分条件。

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引用次数: 0

Competitive ride-hailing markets with double-sided heterogeneity: Impacts of customers’ service valuation and drivers’ reservation earnings 具有双边异质性的网约车竞争市场：客户服务价值和司机预约收益的影响

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-11 DOI: 10.1016/j.trb.2026.103389

Yaqian Zhou , Xinwei Li , Hai Yang

We consider competition between a certain number of platforms (two or more) that facilitate matches between a market of customers and a pool of independent drivers. Each platform sets a price charged to customers and a wage paid to drivers for service through that platform to maximize its own profit. As such, equilibrium supply and demand for each platform are endogenously dependent on the price and wage interactions within the platform and the strategic competition across different platforms. This paper examines how two key features of ride-hailing markets—demand heterogeneity in service valuation and supply heterogeneity in reservation earnings—factor into the platforms’ price and wage decisions and resulting system performances. Our analysis suggests that an increase in the degree of heterogeneity in customers’ service valuation (drivers’ reservation earnings) may result in either higher or lower prices and wages, with lower (higher) prices and wages more likely when the demand (supply) level is high and/or the number of platforms is large. Based on these understandings, we further explore how the platforms’ price and wage decisions in response to changes in market characteristics including the potential demand, the potential supply, and the number of platforms, would be affected by demand heterogeneity in service valuation and supply heterogeneity in reservation earnings. In contrast to the two usual arguments in ride-hailing literature that (i) prices and wages increase in the potential demand and (ii) wages decrease in the potential supply, we find that equilibrium prices and wages may not be monotonic in the potential demand or supply, depending on the degrees of demand heterogeneity in service valuation and/or supply heterogeneity in reservation earnings. Besides, contrary to the common intuition that platform competition results in lower prices and higher wages, we find that entry of an additional platform may result in higher prices (lower wages), when the degree of demand (supply) heterogeneity in service valuation (reservation earnings) is quite high and the number of platforms is quite large.

我们考虑一定数量的平台（两个或更多）之间的竞争，这些平台促进了客户市场和独立司机之间的匹配。每个平台都设定了向客户收取的价格，并为通过该平台提供服务的司机支付工资，以实现自身利润最大化。因此，每个平台的均衡供给和需求内生地取决于平台内的价格和工资相互作用以及不同平台之间的战略竞争。本文考察了网约车市场的两个关键特征——服务估值的需求异质性和预订收益的供给异质性——如何影响平台的价格和工资决策以及由此产生的系统绩效。我们的分析表明，客户服务估值（司机预定收益）的异质性程度的增加可能导致价格和工资的上涨或下跌，当需求（供应）水平高和/或平台数量大时，更有可能出现价格和工资的下跌（上涨）。在此基础上，我们进一步探讨了平台的价格和工资决策对潜在需求、潜在供给和平台数量等市场特征变化的响应，如何受到服务估值需求异质性和预留收益供给异质性的影响。与网约车文献中常见的两种观点(i)价格和工资在潜在需求中增加，（ii）工资在潜在供给中减少相反，我们发现均衡价格和工资在潜在需求或供给中可能不是单调的，这取决于服务估值中的需求异质性程度和/或预订收益中的供给异质性程度。此外，与平台竞争导致价格降低和工资提高的普遍直觉相反，我们发现，当服务估值（预留收益）的需求（供给）异质性程度相当高且平台数量相当大时，额外平台的进入可能导致价格升高（工资降低）。

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引用次数: 0

Optimizing for green skies: How do herd effects and customer heterogeneity impact airline optimal pricing and reputation investment? 优化绿色天空：羊群效应和客户异质性如何影响航空公司最优定价和声誉投资？

IF 6.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part B-Methodological

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.trb.2026.103401

Jinshu Cai , Yanyan Ding , Sisi Jian

As public scrutiny of aviation emissions intensifies, airlines face the challenge of balancing corporate social responsibility (CSR) with profitability. This study examines how CSR reputation, particularly concerning adopting sustainable aviation fuel (SAF), influences airlines’ investment and pricing strategies. We develop a two-stage sequential decision model to analyze the interplay between CSR reputation, pricing strategies, herd effects, and customer heterogeneity. This model considers both congestion effects and the impact of CSR reputation on herd behavior among customers. We examine two pricing strategies: uniform pricing with standard green service and differentiated pricing with tailored services. Using backward induction, we determine the optimal reputation investment and pricing strategy under various market conditions. Further, we introduce an integrated efficiency indicator that captures the combined influence of sensitivity to herd effects, investment costs, customer heterogeneity, and congestion to guide airlines in selecting the optimal pricing strategy. This indicator gives airlines a practical tool for navigating the complex trade-offs between CSR investment, pricing, and market dynamics. Results indicate that while herd behavior incentivizes reputation investment, a critical threshold exists for the relative efficacy of uniform versus differentiated pricing. The inherent cost of implementing differentiated pricing necessitates a more substantial efficiency gain to surpass uniform pricing.

随着公众对航空排放审查的加强，航空公司面临着平衡企业社会责任（CSR）与盈利能力的挑战。本研究探讨了企业社会责任声誉，特别是关于采用可持续航空燃料（SAF），如何影响航空公司的投资和定价策略。我们开发了一个两阶段顺序决策模型来分析企业社会责任声誉、定价策略、羊群效应和客户异质性之间的相互作用。该模型既考虑了拥塞效应，也考虑了企业社会责任声誉对顾客群体行为的影响。我们研究了两种定价策略：标准绿色服务的统一定价和定制服务的差异化定价。利用逆向归纳法，确定了不同市场条件下的最优信誉投资和定价策略。此外，我们引入了一个综合效率指标，该指标捕捉了对羊群效应的敏感性、投资成本、客户异质性和拥堵的综合影响，以指导航空公司选择最优定价策略。该指标为航空公司在企业社会责任投资、定价和市场动态之间进行复杂权衡提供了实用工具。结果表明，虽然羊群行为激励声誉投资，但统一定价与差异化定价的相对效果存在一个临界阈值。实施差异化定价的内在成本要求有更大的效率收益来超越统一定价。

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引用次数: 0