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Transportation Research Part B-Methodological最新文献

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A multitrait-multimethod model linking consumers’ priorities and preferences in public transport usage 一个连接消费者在公共交通使用中的优先事项和偏好的多特征-多方法模型
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-09-05 DOI: 10.1016/j.trb.2025.103307
Thomas J. Magor, Leonard V. Coote
The interrelatedness of stated choice and best worst experiments motivates the core question of the current research; that is, does a shared behavioural process given rise to responses to both preference elicitation methods. The basic premise of the paper is that a common, underlying behavioural process gives rise to the responses of decision makers to both preference elicitation methods; and therefore, the patterns of preference heterogeneity between both methods can be mapped. We investigate this using a multitrait-multimethod specification of mixed logit which has the following two defining characteristics: firstly, the partitioning of the unobserved sources of preference heterogeneity into components that are general to the methods and second, components that are unique to each method. The core findings show that common underlying sources of preference heterogeneity do give rise to decision makers’ responses in both stated choice and best worst experiments (following a specifically theorised stucture). Our primary empirical contribution is – for within subjects deigns where data is generated from the same respondents across two or more preference elicitation methods – that the preference heterogeneity for alike attributes in each of the elicitation methods can be empirically shown to converge on the same source. This remains an assumption in much prior research of this type, but until now, method effects related to the use of different preference elicitation tasks have confounded the interpretation of jointly estimated models that combined attribute priorities and preferences. The implications of our work for transport researchers is that best worst and stated choice experiments can be used in complementary ways – given the empirical support for this core assumption.
陈述选择和最佳最差实验的相互关系激发了当前研究的核心问题;也就是说,一个共同的行为过程是否会引起对两种偏好诱导方法的反应。本文的基本前提是,一个共同的、潜在的行为过程导致决策者对两种偏好激发方法的反应;因此,可以映射两种方法之间的偏好异质性模式。我们使用混合logit的多性状-多方法规范来研究这一点,该规范具有以下两个定义特征:首先,将未观察到的偏好异质性来源划分为方法的一般成分,其次,每种方法的独特成分。核心发现表明,偏好异质性的共同潜在来源确实会引起决策者在陈述选择和最佳最差实验中的反应(遵循特定的理论结构)。我们的主要经验贡献是-在受试者设计中,数据是通过两种或多种偏好引出方法从相同的受访者中生成的-每种引出方法中相似属性的偏好异质性可以在经验上显示为收敛于同一来源。这仍然是这类先前研究的一个假设,但到目前为止,与使用不同偏好激发任务相关的方法效应已经混淆了结合属性优先级和偏好的联合估计模型的解释。我们的工作对交通研究人员的启示是,考虑到对这一核心假设的经验支持,最佳最差选择实验和陈述选择实验可以以互补的方式使用。
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引用次数: 0
The value of partial and full pre-trip information under stochastic demand and bottleneck capacity in the morning commute 随机需求和瓶颈容量下的部分和全部出行前信息的价值
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-09-03 DOI: 10.1016/j.trb.2025.103311
Zihan Wu , Zi-Yang Wang , Xiao Han , Rui Jiang , Ronghui Liu
This paper studies the welfare effects of providing pre-trip information to morning commuters in a single-bottleneck model, where both bottleneck capacity and travel demand are exogenously stochastic and assumed to follow an arbitrary joint distribution. We first derive the equilibrium travel costs under varying levels of information completeness, and then examine how information completeness influences travel costs and the key factors driving the welfare outcomes of information provision. We find that the welfare effects of providing pre-trip information are associated with the information completeness, the degree of correlation between bottleneck capacity and demand, and the frequency and amplitude of bottleneck capacity and demand changes. Although providing full information is never welfare-reducing, providing partial information can increase travel costs compared to no information (i.e., information paradox) when demand and bottleneck capacity are moderately correlated. Nevertheless, transitioning from partial to full information consistently leads to a reduction in travel costs. Our numerical examples further confirm the theoretical results and highlight the necessity of accounting for uncertainties in both supply and demand when developing traveler information systems.
本文研究了在单瓶颈模型下,向早晨通勤者提供出行前信息的福利效应,其中瓶颈容量和出行需求都是外生随机的,并假设服从任意联合分布。本文首先推导了不同信息完备性水平下的均衡出行成本,然后考察了信息完备性如何影响出行成本以及驱动信息提供福利结果的关键因素。研究发现,提供出行前信息的福利效应与信息完备性、瓶颈容量与需求的相关程度、瓶颈容量与需求变化的频率和幅度有关。尽管提供全部信息永远不会减少福利,但当需求和瓶颈容量适度相关时,提供部分信息比不提供信息(即信息悖论)会增加旅行成本。然而,从部分信息过渡到完全信息始终导致旅行费用的减少。我们的数值例子进一步证实了理论结果,并强调了在开发出行者信息系统时考虑供需不确定性的必要性。
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引用次数: 0
A geometric cognition pedestrian dynamics model considering heterogeneous collision avoidance behaviors 考虑异构避碰行为的几何认知行人动力学模型
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-09-01 DOI: 10.1016/j.trb.2025.103309
Bohan Li , Yunchao Qu , Yao Xiao , Jianjun Wu , Ziyou Gao
Collision avoidance is one of the crucial behaviors in pedestrian dynamics, especially in high-density scenarios where it frequently occurs to maintain crowd safety and stability. However, the heterogeneity of this behavior has not been well investigated. Given the advantages of geometric compatibility, a microscopic pedestrian flow model combining the Voronoi diagram and velocity obstacle approaches has been proposed to fully quantify heterogeneous collision avoidance behavior. In this model, the velocity region is dynamically partitioned into three sub-regions to describe the avoidance behaviors with different levels of aggressiveness. The concept of the difference to collision velocity is defined to characterize the evolution of aggressiveness, and a heuristic velocity region evolution rule is proposed to determine the walking velocity. To demonstrate the effectiveness of the model, a series of pedestrian flow scenarios are validated in terms of fundamental diagrams, self-organization phenomena, and behavioral heterogeneity. The impact of heterogeneous collision avoidance behaviors on crowd phenomena is then analyzed according to simulations. The DF-GLS algorithm is used to determine the crowd re-stabilization time. The results show that more aggressive behaviors lead to obvious stop-and-go wave and smaller Yamori’s band index of lane formation phenomenon. Sudden aggressive behaviors will disrupt crowd stability and even cause the crowd into a disorganized state. This model offers valuable insights and supports the optimization of emergency evacuation strategies with practical applications.
避碰是行人动力学的关键行为之一,特别是在高密度场景下,为了维护人群的安全和稳定,避碰经常发生。然而,这种行为的异质性尚未得到很好的研究。考虑到几何兼容性的优势,提出了一种结合Voronoi图和速度障碍方法的微观行人流模型,以充分量化异构避碰行为。在该模型中,速度区域被动态划分为三个子区域来描述具有不同攻击程度的回避行为。定义了碰撞速度差的概念来表征攻击性的演化,并提出了一种启发式速度区域演化规则来确定行走速度。为了验证该模型的有效性,从基本图、自组织现象和行为异质性等方面验证了一系列行人流场景。通过仿真分析了异构避碰行为对人群现象的影响。采用DF-GLS算法确定人群再稳定时间。结果表明:侵略性行为越强,车道形成的走走停停波动越明显,车道形成的Yamori波段指数越小;突然的攻击行为会破坏群体的稳定,甚至使群体陷入无序状态。该模型提供了有价值的见解,并为实际应用中的应急疏散策略优化提供了支持。
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引用次数: 0
Exact solution method for multi-stakeholder freight transportation systems under uncertainty 不确定条件下多利益相关者货运系统的精确解方法
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-09-01 DOI: 10.1016/j.trb.2025.103288
Gita Taherkhani , Mojtaba Hosseini , Ali Hassanzadeh
At a time where efficient freight logistics are crucial to global commerce, integrated multi-stakeholder freight transportation systems play a pivotal role in ensuring timely delivery and operational adaptability under uncertainty. This study focuses on the tactical planning of such a system, which processes time-sensitive requests from both carriers and shippers. It orchestrates operations spatially and temporally, combining loads from various shippers into unified transport units. Our approach utilizes a two-stage stochastic programming model that effectively captures the uncertainties inherent in demand. The model formulates an efficient service network that not only meets the immediate logistical demands but also adapts to fluctuating conditions by leveraging available service capacities. To solve this complex model, we develop and implement an exact Benders decomposition-based algorithm. Our solution methodology incorporates several advanced techniques including partial decomposition, cut-lifting for both optimality and feasibility cuts, and various preprocessing steps including variable fixing and the use of valid inequalities. Additionally, we implement acceleration techniques that capitalize on the repetitive nature of our algorithm to enhance efficiency. We design and generate test instances inspired by real-world freight logistics, capturing key operational constraints and varying demand uncertainty levels. These instances enable a systematic evaluation of our model under diverse settings. We then perform extensive computational experiments. Our solution methodology demonstrates superior performance compared to a commercial solver. We also explore the impact of varying service availability among other parameters and the benefits of using stochastic modeling over deterministic approaches. These experiments underscore our model’s capacity to improve operational efficacy and responsiveness when dealing with uncertainty, thereby providing significant insights for both practitioners and researchers involved in freight logistics.
在高效的货运物流对全球商业至关重要的时代,综合的多利益相关者货运系统在确保及时交付和不确定性下的运营适应性方面发挥着关键作用。本研究的重点是这样一个系统的战术规划,该系统处理来自承运人和托运人的时间敏感请求。它在空间和时间上协调操作,将来自不同托运人的货物组合成统一的运输单元。我们的方法利用了一个两阶段的随机规划模型,有效地捕获了需求中固有的不确定性。该模型制定了一个高效的服务网络,不仅满足即时的后勤需求,而且通过利用现有的服务能力来适应波动的条件。为了解决这个复杂的模型,我们开发并实现了一个精确的基于Benders分解的算法。我们的解决方法结合了几种先进的技术,包括部分分解,最优性和可行性切割的切割提升,以及各种预处理步骤,包括变量固定和有效不等式的使用。此外,我们实现了加速技术,利用我们算法的重复特性来提高效率。我们设计和生成受现实世界货运物流启发的测试实例,捕捉关键的操作约束和不同的需求不确定性水平。这些实例能够在不同的设置下对我们的模型进行系统的评估。然后我们进行了大量的计算实验。与商业求解器相比,我们的求解方法表现出优越的性能。我们还探讨了不同服务可用性对其他参数的影响,以及使用随机建模优于确定性方法的好处。这些实验强调了我们的模型在处理不确定性时提高操作效率和响应能力的能力,从而为涉及货运物流的从业者和研究人员提供了重要的见解。
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引用次数: 0
Evidence and quantification of cooperation of driving agents in mixed traffic flow 混合交通流中驾驶主体合作的实证与量化
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-31 DOI: 10.1016/j.trb.2025.103285
Di Chen , Jia Li , Michael Zhang
Cooperation is a ubiquitous phenomenon in many natural, social, and engineered systems with multiple agents. Understanding the formation of cooperation in mixed traffic is of theoretical interest in its own right, and could also benefit the design and operations of future automated and mixed-autonomy transportation systems. However, how cooperativeness of driving agents can be defined and identified from empirical data seems ambiguous and this hinders further empirical characterizations of the phenomenon and revealing its behavior mechanisms. Towards mitigating this gap, in this paper, we propose a unified conceptual framework to identify collective cooperativeness of driving agents. This framework expands the concept of collective rationality from our recent model (Li et al., 2022), making it empirically identifiable and behaviorally interpretable in realistic (microscopic and dynamic) settings. This framework integrates mixed traffic observations at both microscopic and macroscopic scales to estimate critical behavioral parameters that describe the collective cooperativeness of driving agents. Applying this framework to NGSIM I-80 trajectory data, we empirically confirm the existence of collective cooperation and quantify the condition and likelihood of its emergence. This study provides the first empirical understanding of collective cooperativeness in human-driven mixed traffic and points to new possibilities to manage mixed autonomy traffic systems.
在许多具有多个主体的自然、社会和工程系统中,合作是一种无处不在的现象。理解混合交通中合作的形成本身就具有理论意义,也有利于未来自动化和混合自治交通系统的设计和运营。然而,如何从经验数据中定义和识别驱动主体的合作性似乎是模糊的,这阻碍了对这一现象的进一步经验表征和揭示其行为机制。为了缓解这一差距,在本文中,我们提出了一个统一的概念框架来识别驾驶代理的集体合作。该框架扩展了我们最近模型中的集体理性概念(Li et al., 2022),使其在现实(微观和动态)环境中具有经验可识别性和行为可解释性。该框架整合了微观和宏观尺度上的混合交通观察,以估计描述驾驶主体集体合作的关键行为参数。将这一框架应用于NGSIM I-80轨迹数据,实证证实了集体合作的存在,并量化了集体合作出现的条件和可能性。本研究首次提供了对人类驱动混合交通中的集体合作的实证理解,并指出了管理混合自治交通系统的新可能性。
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引用次数: 0
Solving connected automated vehicle merging problems: A generalized benders decomposition-based approach for mixed-integer nonlinear programming 求解网联自动车辆归并问题:基于广义弯曲分解的混合整数非线性规划方法
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-30 DOI: 10.1016/j.trb.2025.103293
Jieming Chen , Yiwei Wu , Yue Zhou , Edward Chung , Shuaian Wang
Intensive interactions among vehicles often lead to congestion and accidents, particularly at freeway merging sections. As connected automated vehicles (CAVs) become a reality, their collaborative driving offers a promising solution. However, the real-time scheduling and trajectory planning for multiple CAV streams remain challenging and are not adequately addressed in the existing literature. To this end, this study formulates an integrated mixed-integer nonlinear programming (MINLP) model to jointly optimize lane change decisions, vehicle sequences, and vehicle trajectories, with the objective of maximizing traffic efficiency and driving comfort at multi-lane freeway merging sections. Existing commercial software struggles to handle such a complicated model. To rapidly obtain solutions, this study designs a Generalized Benders Decomposition (GBD)-based solution algorithm to tackle the problem of multi-vehicle combinatorial optimization and nonlinear trajectory optimization. Meanwhile, the finite convergence property of the GBD approach is proved. Numerical experimental results demonstrate that the proposed model outperforms three benchmark CAV control methods and a two-step method under various traffic demands and mainline-ramp demand ratios, highlighting significant traffic benefits from jointly planning lane changes and driving sequences, as well as utilizing microscopic vehicle information. Furthermore, this study evaluates traffic delay and the number of lane changes under varying road lengths, i.e., the lengths of lane-changing and merging areas, identifying recommended lengths for the maximum traffic efficiency, and analyzing the performance trend under varying traffic demands.
车辆之间的密集互动经常导致拥堵和事故,特别是在高速公路合流路段。随着互联自动驾驶汽车(cav)成为现实,它们的协同驾驶提供了一个很有前景的解决方案。然而,多CAV流的实时调度和轨迹规划仍然具有挑战性,并且在现有文献中没有得到充分的解决。为此,本文建立了一种综合混合整数非线性规划(MINLP)模型,以多车道高速公路合流路段的交通效率和驾驶舒适性最大化为目标,对变道决策、车辆序列和车辆轨迹进行联合优化。现有的商业软件难以处理如此复杂的模型。为了快速求解,本文设计了一种基于广义弯曲分解(GBD)的求解算法来解决多车组合优化和非线性轨迹优化问题。同时,证明了GBD方法的有限收敛性。数值实验结果表明,该模型在不同交通需求和主干道匝道需求比下优于三种基准CAV控制方法和两步控制方法,突出了联合规划变道和行驶顺序以及利用微观车辆信息的显著交通效益。在此基础上,对不同道路长度(即变道区和并合区长度)下的交通延迟和变道次数进行评价,确定最大交通效率的推荐长度,并分析不同交通需求下的性能趋势。
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引用次数: 0
Choice-driven bilevel optimization for multiclass traffic congestion management via eco-routing incentives 基于生态路径激励的多级交通拥堵管理的选择驱动双层优化
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-29 DOI: 10.1016/j.trb.2025.103289
Mingye Luan , Taha Hossein Rashidi , S. Travis Waller , David Rey
This study contributes to sustainable transportation modeling by proposing a user-centric approach to incentivize eco-routing travel behavior. We propose a novel reward credit scheme to provide path-based commuter incentives with the goal of reducing CO2 emissions and the total system travel time. The scheme takes into account multiple classes of commuters in the network that differ by their value of time and their vehicle energy type. Users subscribing to the scheme may earn monetary reward credits which act as incentives to promote sustainable mobility. Two types of reward credits are considered: subscription- and path-based credits. A discrete choice model is embedded within a traffic assignment model to capture the endogenous impact of commuters’ scheme adoption onto network congestion effects. We introduce a bilevel optimization formulation to determine optimal non-additive, path-based reward credits and subscription-based reward credits within a predefined budget under traffic equilibrium conditions. In this formulation, the follower problem is a parameterized multi-class user equilibrium traffic assignment problem with non-additive path costs and incorporates a logit choice model for scheme adoption. The leader represent the network regulator whose goal is to maximize social welfare by minimizing the total system travel time and total CO2 emissions. We develop a single-level Karush–Kuhn–Tucker reformulation and propose a customized branch-and-bound algorithm to solve this bilevel optimization problem. Numerical experiments demonstrate the potential of eco-routing incentives to promote sustainable urban mobility and highlight the benefits of combining subscription- and path-based reward credits for traffic congestion management.
本研究通过提出以用户为中心的方法来激励生态路线出行行为,为可持续交通建模做出了贡献。我们提出了一种新的奖励积分方案,以减少二氧化碳排放和系统总旅行时间为目标,提供基于路径的通勤激励。该方案考虑了网络中不同类型的通勤者,他们的时间价值和车辆能量类型不同。订阅该计划的用户可获得货币奖励积分,作为促进可持续交通的激励措施。我们考虑了两种类型的奖励积分:订阅积分和基于路径的积分。在交通分配模型中嵌入离散选择模型,以捕捉通勤者方案采用对网络拥塞效应的内生影响。我们引入了一个双层优化公式,以确定最优的非加性,基于路径的奖励积分和基于订阅的奖励积分在预定义的预算在交通平衡条件下。在该公式中,follower问题是一个具有非加性路径成本的参数化多类用户均衡流量分配问题,并引入了一个logit选择模型用于方案的采用。领导者代表网络监管者,其目标是通过最小化系统总运行时间和总二氧化碳排放量来最大化社会福利。我们发展了一个单层次的Karush-Kuhn-Tucker重构,并提出了一个定制的分支定界算法来解决这个双层优化问题。数值实验证明了生态路线激励在促进可持续城市交通方面的潜力,并强调了将订阅和基于路径的奖励积分结合起来用于交通拥堵管理的好处。
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引用次数: 0
C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework C-MP:使用最大压力框架的分散自适应协调交通信号控制
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-29 DOI: 10.1016/j.trb.2025.103308
Tanveer Ahmed , Hao Liu , Vikash V. Gayah
Coordinated traffic signals seek to provide uninterrupted vehicular flow through a series of closely spaced intersections, typically using pre-defined fixed signal timings and offsets. However, coordination is typically not possible when using adaptive traffic signals that dynamically change signal timings based on observed traffic conditions, particularly in decentralized systems where these decisions are made independently at each intersection. To alleviate this issue, this paper introduces a novel Max Pressure-based traffic signal framework that specifically seeks to provide coordination even under decentralized decision-making. The proposed Coordinated Max Pressure (C-MP) algorithm uses the space mean speeds of vehicles – obtained from detectors or in a connected vehicle (CV) environment – to explicitly detect freely flowing platoons of vehicles and prioritizes their movement along a corridor. Specifically, upstream platoons are detected, and their weight in the MP framework is increased to provide priority, while downstream platoons are detected, and their weight is reduced to ensure smooth traffic flow across corridors. The study analytically proves that C-MP maintains the desirable maximum stability property, while micro-simulation analyses conducted on an arterial network demonstrate its ability to achieve a larger stable region compared to benchmark MP control policies. Simulation results also reveal that the proposed control algorithm can effectively coordinate traffic signals in both directions along an arterial without explicitly assigned offsets or constraints. The results also reveal C-MP's superiority to benchmark coordination strategies in reducing travel time, and fuel consumption both at the corridor level and the network level by balancing the negative impact imparted to vehicles in the minor direction. The proposed algorithm also functions effectively when a subset of vehicles provide input to the controller, as would be the case in a partial CV environment.
协调的交通信号寻求通过一系列紧密间隔的十字路口提供不间断的车辆流量,通常使用预定义的固定信号时间和偏移量。然而,当使用基于观察到的交通状况动态改变信号时间的自适应交通信号时,协调通常是不可能的,特别是在分散的系统中,这些决策是在每个十字路口独立做出的。为了缓解这一问题,本文引入了一种新的基于最大压力的交通信号框架,该框架专门寻求在分散决策下提供协调。提出的协调最大压力(C-MP)算法使用车辆的空间平均速度(从探测器或联网车辆(CV)环境中获得)来明确检测自由流动的车辆排,并优先考虑它们沿着走廊的移动。具体来说,上游排被检测到,并增加其在MP框架中的权重以提供优先权,而下游排被检测到,并减少其权重以确保走廊间的交通畅通。该研究分析证明了C-MP保持了理想的最大稳定性,而在动脉网络上进行的微观模拟分析表明,与基准MP控制策略相比,C-MP能够实现更大的稳定区域。仿真结果还表明,该控制算法可以有效地协调沿主干道方向的交通信号,而无需明确指定偏移量或约束。结果还表明,C-MP通过平衡对次要方向车辆的负面影响,在减少通道级和网络级的旅行时间和燃料消耗方面优于基准协调策略。当车辆子集为控制器提供输入时,所提出的算法也能有效地工作,就像部分CV环境中的情况一样。
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引用次数: 0
Robust planning for electric vehicle charging stations under congestion 拥堵条件下电动汽车充电站的稳健规划
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-27 DOI: 10.1016/j.trb.2025.103291
Yongzhen Li , Jia Shu , Chengyao Wang , Ting Wu , Yinghui Wu
The last decades have witnessed the rise of electric vehicle (EV) sales, accompanied by a growing demand for readily accessible public EV charging facilities. Unlike refueling a fossil fuel vehicle, charging an EV requires significantly more time, which may lead to congestion if the public charging infrastructure is not well-designed. In this paper, we study the strategic planning of public EV charging stations, aiming to place chargers with a limited investment budget to maximize the coverage of uncertain charging demand. To ensure service quality under possible congestion, we introduce two types of chance constraints to mitigate long waiting times and reduce demand loss in situations with limited waiting space. Given the challenges in accurately estimating charging demand and charging time, we apply a robust approach to model this problem with uncertain charging demand arrival and service rates. The robust model is then reformulated into an equivalent mixed integer linear program of moderate size, which is tractable by commercial solvers. A case study based on data from Nanjing demonstrates the effectiveness of the proposed robust approach and provides insights into real-world applications. Extensions with a general charging process and decentralized driver selection of charging stations are also discussed and verified through extensive numerical experiments, which indicates the stable performance of the proposed approach under general settings.
过去几十年见证了电动汽车(EV)销量的增长,同时对公共电动汽车充电设施的需求也在不断增长。与化石燃料汽车加油不同,电动汽车充电需要更多的时间,如果公共充电基础设施设计不当,可能会导致拥堵。本文研究了公共电动汽车充电站的战略规划,目的是在投资预算有限的情况下放置充电站,以最大限度地覆盖不确定的充电需求。为了确保在可能出现的拥塞情况下的服务质量,我们引入了两种类型的机会约束,以减少在有限的等待空间下的长等待时间和减少需求损失。考虑到在准确估计充电需求和充电时间方面存在的挑战,我们采用了一种鲁棒的方法来对充电需求到达和服务费率不确定的问题进行建模。然后将鲁棒模型重新表述为一个中等大小的等效混合整数线性规划,该规划可由商业求解器处理。基于南京数据的案例研究证明了所提出的稳健方法的有效性,并为实际应用提供了见解。讨论了一般充电过程下的扩展和充电站驾驶员选择的分散,并通过大量的数值实验验证了该方法在一般设置下的稳定性能。
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引用次数: 0
Real-time vehicle location estimation in signalized networks using partial connected vehicle trajectory data 基于部分互联车辆轨迹数据的信号网络实时车辆位置估计
IF 6.3 1区 工程技术 Q1 ECONOMICS Pub Date : 2025-08-20 DOI: 10.1016/j.trb.2025.103292
Shaocheng Jia , S.C. Wong , Wai Wong
Real-time vehicle location estimation is essential for diverse transportation applications, such as travel time estimation, arrival pattern estimation, and adaptive signal control. Existing connected vehicle-based studies rely on either black-box neural networks requiring large training datasets or computationally intensive time-continuous movement simulations grounded in car-following models. However, they often overlook the distinct vehicle location patterns in source lanes, which define network boundaries and experience random arrivals, and intermediate lanes, situated between intersections and receiving traffic discharged from upstream. These patterns are critical for accurate vehicle location estimation. To address these limitations, this study proposes a generic and fully analytical CV-based vehicle location (CVVL) model for estimating vehicle locations within a signalized lane in a network using readily available partial CV trajectory data. The proposed model is applicable to any signal timing, traffic demand, and CV penetration rate and consists of two sub-models: CVVL-S and CVVL-I. The CVVL-S sub-model estimates vehicle locations in source lanes, where vehicle distribution tends to be relatively homogeneous owing to random arrivals. In contrast, the CVVL-I sub-model focuses on estimating vehicle locations in intermediate lanes, where sequential discharges from different upstream lanes can lead to the formation of multiple platoons, adding complexity to vehicle location estimation. The proposed model decomposes the complex task into three sequential sub-problems: identifying candidate platoons (CPs), estimating the number of vehicles in each CP, and determining the spatial distribution of vehicles within each CP. Extensive numerical experiments were conducted under various traffic conditions, CV penetration rates, and times of interest using the VISSIM platform and the real-world Next Generation Simulation dataset. The results demonstrate that the proposed CVVL model achieved improvements of 0–45 %, 0–37 %, and 4–34 % in precision, recall, and F1 score, respectively, compared with the competing method. These results highlight the model’s potential to enhance the accuracy and reliability of various downstream applications.
实时车辆位置估计对于各种交通应用至关重要,如行程时间估计、到达模式估计和自适应信号控制。现有的基于互联汽车的研究要么依赖于需要大量训练数据集的黑盒神经网络,要么依赖于基于汽车跟随模型的计算密集型时间连续运动模拟。然而,他们往往忽略了源车道上不同的车辆定位模式,源车道定义了网络边界并随机到达,而中间车道位于十字路口之间并接收上游排放的车辆。这些模式对于准确估计车辆位置至关重要。为了解决这些限制,本研究提出了一种通用的、完全分析的基于CV的车辆定位(CVVL)模型,该模型使用易于获得的部分CV轨迹数据来估计网络中信号车道内的车辆位置。该模型适用于任何信号配时、流量需求和CV渗透率,包括CVVL-S和CVVL-I两个子模型。CVVL-S子模型估计源车道上的车辆位置,由于随机到达,源车道上的车辆分布趋于相对均匀。相比之下,cvv1 - i子模型侧重于估计中间车道的车辆位置,其中来自不同上游车道的连续排放可能导致形成多个排,增加了车辆位置估计的复杂性。该模型将复杂的任务分解为三个连续的子问题:识别候选队列(CPs),估计每个CP中的车辆数量,以及确定每个CP内车辆的空间分布。使用VISSIM平台和真实世界的下一代模拟数据集,在各种交通条件、CV渗透率和兴趣时间下进行了大量的数值实验。结果表明,与同类方法相比,所提出的CVVL模型在查全率、查全率和F1分数上分别提高了0 - 45%、0 - 37%和4 - 34%。这些结果突出了该模型在提高各种下游应用的准确性和可靠性方面的潜力。
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Transportation Research Part B-Methodological
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