Communications in Transportation Research最新文献_第2页

On the stochastic fundamental diagram: A general micro-macroscopic traffic flow modeling framework 论随机基本图：一种通用的微观宏观交通流建模框架

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-02-13 DOI: 10.1016/j.commtr.2025.100163

Xiaohui Zhang, Jie Sun, Jian Sun

The stochastic fundamental diagram (SFD), which describes the stochasticity of the macroscopic relations of traffic flow, plays a crucial role in understanding the uncertainty of traffic flow evolution and developing robust traffic control strategies. Although many efforts have been made to reproduce the SFD via various methods, few studies have focused on the analytical modeling of the SFD, particularly linking the macroscopic relations with microscopic behaviors. This study fills this gap by proposing a general micro-macroscopic modeling approach, which uses probabilistic leader–follower behavior to derive the macroscopic relations of a platoon and is referred to as the leader–follower conditional distribution-based stochastic traffic modeling (LFCD-STM) framework. Specifically, we first define a conditional probability distribution of speed for the leader‒follower pair according to Brownian dynamics, which is proven to be a general representation of the longitudinal interaction and compatible with classical car-following models. As a result, we can describe the joint distribution of vehicle speeds of the platoon through Markov chain modeling and further derive the macroscopic relations (e.g., the mean flow‒density relation and its variance) under equilibrium conditions. On the basis of this general micro-macroscopic framework, we utilize the maximum entropy approach to theoretically derive the SFD model, in which we provide a specific conditional distribution for longitudinal interaction and thus solve the analytical functions of the mean and variance of FD. The performance of the maximum entropy-based SFD model is thoroughly validated with the NGSIM I-80, US-101 and HighD datasets. The high consistency between the theoretical results and empirical results demonstrates the soundness of the LFCD-STM framework and the maximum entropy-based SFD model. Finally, the proposed SFD model has practical implications for promoting smoother driving behaviors to suppress stochasticity and improve traffic flow.

随机基本图（SFD）描述了交通流宏观关系的随机性，对于理解交通流演化的不确定性和制定稳健的交通控制策略具有重要意义。尽管人们已经通过各种方法对SFD进行了再现，但很少有研究关注SFD的分析建模，特别是将宏观关系与微观行为联系起来。本研究提出了一种通用的微观宏观建模方法来填补这一空白，该方法利用概率leader-follower行为来推导队列的宏观关系，被称为基于leader-follower条件分布的随机交通建模（LFCD-STM）框架。具体地说，我们首先根据布朗动力学定义了领队-随从对速度的条件概率分布，并证明了这是纵向相互作用的一般表示，与经典的汽车跟随模型兼容。因此，我们可以通过马尔可夫链建模来描述车队车速的联合分布，并进一步推导出平衡条件下的宏观关系（如平均流量密度关系及其方差）。在这一宏观微观框架的基础上，利用最大熵方法从理论上推导出SFD模型，该模型为纵向相互作用提供了特定的条件分布，从而求解出FD的均值和方差的解析函数。利用NGSIM I-80、US-101和HighD数据集对基于最大熵的SFD模型的性能进行了验证。理论结果与实证结果的高度一致性证明了LFCD-STM框架和基于最大熵的SFD模型的合理性。最后，本文提出的SFD模型对于促进驾驶行为的平稳性以抑制随机性和改善交通流具有实际意义。

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引用次数: 0

From global open multi-source data to network-wide traffic flow: A large-scale case study across multiple cities 从全球开放多源数据到网络范围的交通流量：跨多个城市的大规模案例研究

IF 14.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-11-18 DOI: 10.1016/j.commtr.2025.100222

Zijian Hu , Zhenjie Zheng , Monica Menendez , Wei Ma

Network-wide traffic flow, which captures dynamic traffic volume on each link of a general network, is fundamental to smart mobility applications. However, the observed traffic flow from sensors is usually limited across the entire network due to the associated high installation and maintenance costs. To address this issue, existing research uses various supplementary data sources to compensate for insufficient sensor coverage and estimate the unobserved traffic flow. Although these studies have shown promising results, the inconsistent availability and quality of supplementary data across cities make their methods typically face a trade-off challenge between accuracy and generality. In this research, we first advocate using the global open multi-source (GOMS) data within an advanced deep learning framework to break the trade-off. The GOMS data mainly refers to publicly available multi-type datasets, including road topology, building footprints, and population density, which can be consistently collected across cities. More importantly, these GOMS data are closely related to the traffic flow dynamics, thereby creating opportunities for accurate network-wide flow estimation. Furthermore, we use map images to represent GOMS data, instead of traditional tabular formats, to capture richer and more comprehensive geographical and demographic information. To address multi-source data fusion, we develop an attention-based graph neural network that effectively extracts and synthesizes information from GOMS maps while simultaneously capturing spatiotemporal traffic dynamics from observed traffic data. A large-scale case study across 15 cities in Europe and North America was conducted. The results demonstrate stable and satisfactory estimation accuracy across these cities, which suggests that the trade-off challenge can be successfully addressed using our approach.

网络范围内的流量是智能移动应用的基础，它捕获了一般网络中每个链路上的动态流量。然而，由于相关的高安装和维护成本，从传感器观察到的流量通常在整个网络中受到限制。为了解决这个问题，现有的研究使用各种补充数据源来补偿传感器覆盖范围的不足，并估计未观察到的交通流量。尽管这些研究显示出有希望的结果，但各城市补充数据的可用性和质量不一致，使得他们的方法通常面临准确性和普遍性之间的权衡挑战。在本研究中，我们首先倡导在先进的深度学习框架内使用全球开放多源（GOMS）数据来打破权衡。GOMS数据主要是指可公开获取的多类型数据集，包括道路拓扑、建筑足迹、人口密度等，这些数据集可以跨城市统一收集。更重要的是，这些GOMS数据与交通流量动态密切相关，从而为准确估计全网流量创造了机会。此外，我们使用地图图像来表示GOMS数据，而不是传统的表格格式，以获取更丰富、更全面的地理和人口信息。为了解决多源数据融合问题，我们开发了一种基于注意力的图神经网络，该网络可以有效地从GOMS地图中提取和综合信息，同时从观测到的交通数据中捕获时空交通动态。在欧洲和北美的15个城市进行了大规模的案例研究。结果表明，这些城市的估计精度稳定且令人满意，这表明使用我们的方法可以成功地解决权衡挑战。

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引用次数: 0

Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach 基于稀疏和噪声GPS数据的增强轨迹重建：一种渐进式分块变压器方法

IF 14.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-08-02 DOI: 10.1016/j.commtr.2025.100200

Yonghui Liu , Qian Li , Inhi Kim

Trajectory reconstruction from sparse and noisy GPS data is critical for applications such as urban mobility analysis, transportation planning, and navigation systems. However, large sampling intervals and the typically long output sequences required to reconstruct coherent travel trajectories significantly increase computational complexity, particularly in the presence of noise. To address these challenges, we propose a progressive chunked transformer (ProChunkFormer), which is a deep learning method for trajectory reconstruction that employs self-attention mechanisms and chunked processing to balance efficiency with accuracy. ProChunkFormer first generates intermediate trajectories at a semi-high frequency from low-frequency sampled data, and then the remaining trajectory is divided into manageable blocks and reconstructed parallelly in the condition of the semi-high-frequency trajectory. By combining progressive reconstruction with chunk processing, ProChunkFormer not only mitigates the cumulative errors commonly observed in autoregressive models but also alleviates the rapid increase in complexity associated with reconstructing ultralong trajectories. Specifically, our approach achieves quadratic optimization in time and space for attention modules, with cubic time savings compared with autoregressive decoding. A case study using an open-source taxi trajectory dataset confirms the effectiveness of our approach. The performance of ProChunkFormer is comparable to that of autoregressive transformers while offering better running efficiency. It improves the accuracy, F1 score (F1), mean absolute error (MAE), and road network mean absolute error (MAE_RN) by 23.1%, 18.6%, 22.3%, and 25.1%, respectively, for trajectories with a long interval time of up to 240 s. Furthermore, we investigate incorporating heuristic information to guide trajectory reconstruction for each block. The experimental results indicate an improvement in both the overall performance and convergence speed of the model.

从稀疏和噪声GPS数据中重建轨迹对于城市交通分析、交通规划和导航系统等应用至关重要。然而，重建相干旅行轨迹所需的大采样间隔和典型的长输出序列显着增加了计算复杂性，特别是在存在噪声的情况下。为了解决这些挑战，我们提出了一种渐进式分块变压器（ProChunkFormer），这是一种用于轨迹重建的深度学习方法，采用自注意机制和分块处理来平衡效率和准确性。ProChunkFormer首先从低频采样数据中生成半高频的中间轨迹，然后将剩余的轨迹划分为可管理的块，并在半高频轨迹条件下并行重构。通过将渐进式重建与块处理相结合，ProChunkFormer不仅减轻了自回归模型中常见的累积误差，而且还减轻了重建超长轨迹时复杂性的快速增加。具体来说，我们的方法在时间和空间上实现了注意力模块的二次优化，与自回归解码相比节省了三次时间。一个使用开源出租车轨迹数据集的案例研究证实了我们方法的有效性。ProChunkFormer的性能可与自回归变压器相媲美，同时提供更好的运行效率。对于长达240 s的长间隔时间的轨迹，该算法将准确率、F1分数（F1）、平均绝对误差（MAE）和路网平均绝对误差（MAE_RN）分别提高了23.1%、18.6%、22.3%和25.1%。此外，我们研究了结合启发式信息来指导每个块的轨迹重建。实验结果表明，该模型的综合性能和收敛速度均有提高。

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引用次数: 0

Situational awareness using set-based estimation and vehicular communication: An occluded pedestrian-crossing scenario 使用基于集合的估计和车辆通信的态势感知：一个闭塞的行人过街场景

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-06-11 DOI: 10.1016/j.commtr.2025.100190

Vandana Narri , Amr Alanwar , Jonas Mårtensson , Henrik Pettersson , Fredrik Nordin , Karl Henrik Johansson

The safety of unprotected road-users is crucial in any urban traffic. Occlusions and blind spots in the field-of-view of a vehicle can lead to unsafe situations. In this work, a specific pedestrian-crossing scenario is considered with an occlusion in the ego-vehicle's field-of-view. A novel framework is presented to enhance situational awareness based on vehicle-to-everything (V2X) communication to share perception data between vehicle and roadside units. It leverages set-based estimation utilizing a computationally efficient algorithm, for which the pedestrian is guaranteed to be located in a constrained zonotope. The proposed method has been validated through both simulation and real experiments. The real experiments are carried out on a test track using Scania autonomous vehicles.

在任何城市交通中，无保护的道路使用者的安全至关重要。车辆视野中的遮挡和盲点会导致不安全的情况。在这项工作中，考虑了一个特定的行人过街场景，在自我车辆的视野中遮挡。提出了一种基于车联网（V2X）通信增强态势感知的新框架，以在车辆和路边单元之间共享感知数据。它利用一种计算效率高的算法，利用基于集合的估计，保证行人位于受限的分区中。通过仿真和实际实验验证了该方法的有效性。真正的实验是在斯堪尼亚自动驾驶汽车的测试轨道上进行的。

引用次数: 0

Urban visual clusters and road transport fatalities: A global city-level image analysis 城市视觉集群与道路交通死亡：全球城市级图像分析

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-07-03 DOI: 10.1016/j.commtr.2025.100193

Zhuangyuan Fan, Becky P.Y. Loo

Road traffic crashes are among the leading causes of death and injury worldwide. While urban planning and design are known to influence road safety, it is not clear how features of the built environment contribute to traffic fatalities. In this study, we analyze road fatality data from 106 cities across six continents via a combination of computer vision and unsupervised clustering on 26.8 million Google Street View images. We use deep learning tools to extract 25 features from the images. Among these features, 19 are relatively static built environment features, and 6 are dynamic usage-related features (such as pedestrians, cars, buses, and bikes). On the basis of the built environment features, we group the urban streetscapes into six distinct visual clusters. We then examine how these clusters are related to city-level road fatality rates when various control variables (e.g., population size, carbon emissions, income, road length, road safety policy, and continent) and dynamic features are combined. Our findings show that cities with Open Arterials streetscape (extensive road surface, open-sky views, and railings) tend to have higher road fatality rates. After accounting for differences in the built environment, cities with better public transit (proxied by buses detected) tend to have fewer traffic deaths—specifically, a 1% increase in bus presence is linked to a 0.35% decrease in fatalities per 100,000 people. This study demonstrates the power of using widely available street view imagery to uncover global disparities in urban design and their connection to road safety.

道路交通碰撞是全世界造成死亡和伤害的主要原因之一。众所周知，城市规划和设计会影响道路安全，但目前尚不清楚建筑环境的特点如何导致交通事故死亡。在这项研究中，我们通过计算机视觉和无监督聚类的结合，对2680万谷歌街景图像分析了来自六大洲106个城市的道路死亡数据。我们使用深度学习工具从图像中提取了25个特征。在这些特征中，19个是相对静态的建筑环境特征，6个是与使用相关的动态特征（如行人、汽车、公共汽车和自行车）。根据建筑环境特征，将城市街景划分为6个不同的视觉集群。然后，我们研究了在各种控制变量（如人口规模、碳排放、收入、道路长度、道路安全政策和大陆）和动态特征相结合时，这些集群与城市一级道路死亡率的关系。我们的研究结果表明，具有开放动脉街景（宽阔的路面、开阔的天空景观和栏杆）的城市往往具有更高的道路死亡率。在考虑了建筑环境的差异之后，拥有更好的公共交通（以检测到的公交车为代表）的城市往往交通死亡人数更少——具体来说，每10万人中公交车数量增加1%，死亡人数就会减少0.35%。本研究展示了使用广泛可用的街景图像来揭示城市设计的全球差异及其与道路安全的联系的力量。

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引用次数: 0

Customized recursive model for drivers’ navigation compliance behaviors under abnormal events 自定义异常事件下驾驶员导航遵从行为递归模型

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-06-23 DOI: 10.1016/j.commtr.2025.100187

Kaijie Zou, Yaming Guo, Ke Zhang, Meng Li

In recent years, the resilience of road traffic during abnormal events has drawn considerable attention. Intelligent navigation systems, which proactively guide drivers along optimal routes in such situations, are viewed as a promising solution to facilitate recovery of road network performance. A key question arises: How do drivers choose routes when guided by navigation systems? This study addresses that question by modeling drivers’ decision-making behavior at each decision point using a nested framework. At the upper level, drivers decide whether to strictly follow the route recommended by the navigation system, while at the lower levels, they make route choices in the absence of guidance. A Customized Nested Dynamic Recursive Logit (C-NDRL) model was developed to capture these behaviors. Parameters for both decision levels were jointly estimated using a Broyden-Fletcher-Goldfarb-Shanno (BFGS) Method-based algorithm, and the model was verified on the Sioux-Falls network. The model was then applied to real navigation route and driving trajectory data from Canton, China, for parameter estimation and the analysis of the additional utility provided by navigation. The results indicate that the C-NDRL model significantly outperformed other models. Furthermore, the study quantifies the substantial impact of external environmental factors and navigation-related internal factors on drivers’ compliance on navigation systems, highlighting that during rainstorm days, the additional utility from navigation increases by 17%.

近年来，道路交通在异常事件中的恢复能力受到了广泛的关注。在这种情况下，智能导航系统可以主动引导司机沿着最优路线行驶，这被视为促进道路网络性能恢复的一个有希望的解决方案。一个关键的问题出现了：在导航系统的引导下，司机如何选择路线？本研究通过使用嵌套框架在每个决策点对驾驶员的决策行为进行建模来解决这个问题。在上层，驾驶员决定是否严格按照导航系统推荐的路线行驶；在下层，驾驶员在没有引导的情况下进行路线选择。开发了一个定制的嵌套动态递归Logit （C-NDRL）模型来捕获这些行为。采用基于Broyden-Fletcher-Goldfarb-Shanno （BFGS）方法的算法对两个决策层的参数进行了联合估计，并在Sioux-Falls网络上对模型进行了验证。然后将该模型应用于中国广州的实际导航路线和行驶轨迹数据，进行参数估计和分析导航提供的附加效用。结果表明，C-NDRL模型显著优于其他模型。此外，该研究量化了外部环境因素和与导航相关的内部因素对驾驶员遵守导航系统的实质性影响，强调在暴雨天，导航的额外效用增加了17%。

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引用次数: 0

A causality-based explainable AI method for bus delay propagation analysis 基于因果关系的可解释人工智能总线延迟传播分析方法

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-04-10 DOI: 10.1016/j.commtr.2025.100178

Qi Zhang , Zhenliang Ma , Zhiyong Cui

Public transportation networks are highly interconnected, where disruptions like traffic congestion propagate bus delays and impact performance. Identifying delay causes is crucial, yet most studies rely on correlation-based methods rather than causal analysis. Attribution methods like the Shapley value quantify factor contributions but often overlook causal dependencies, leading to potential bias. This study uses a causal discovery model to uncover causal relationships between bus delays and various factors (e.g., operational factors, calendar, and weather). Based on this causal graph, an explainable Artificial Intelligence (AI) method quantifies each factor's contribution to delays, focusing on how these contributions vary at different stops along a route. By integrating scheduled route data and real-time vehicle locations, we analyze factor contributions over time and space, exploring various scenarios along the route. Cross-validation is conducted by comparing the importance ranking of factors with the Seemingly Unrelated Regression Equations (SURE). Results show significant variations in factors contributing to delays along the route. Delays at upstream stops propagate downstream, indicating a cascading effect. Operational factors dominate, accounting for 50%–83% of delays. Notably, delays from the preceding two to three stops have a larger impact than just the immediately preceding one stop, and origin delays strongly affect the first half of the route.

公共交通网络高度互联，交通拥堵等中断会导致公交车延误，影响性能。确定延迟原因是至关重要的，但大多数研究依赖于基于相关性的方法，而不是因果分析。像Shapley值这样的归因方法量化了因素的贡献，但往往忽略了因果关系，导致潜在的偏差。本研究使用因果发现模型来揭示公共汽车延误与各种因素（如运营因素、日历和天气）之间的因果关系。基于这张因果图，一种可解释的人工智能（AI）方法量化了每个因素对延误的影响，重点关注这些影响在路线上不同站点的变化。通过整合预定路线数据和实时车辆位置，我们分析了时间和空间上的因素影响，探索了路线上的各种场景。通过比较各因素的重要性排序与看似不相关回归方程（SURE）进行交叉验证。结果显示，导致沿线延误的因素存在显著差异。上游站点的延迟向下游传播，表明级联效应。运营因素占主导地位，占延误的50%-83%。值得注意的是，前两到三站的延误比前一站的延误影响更大，始发点的延误对路线的前半段影响很大。

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引用次数: 0

Integrating micro and macro traffic control for mixed autonomy traffic 混合自治交通宏微观一体化控制

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-06-10 DOI: 10.1016/j.commtr.2025.100188

Tingting Fan , Jieming Chen , Edward Chung

During the transition to fully autonomous traffic systems, managing mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is imperative. Existing macroscopic and microscopic strategies have shown effectiveness in alleviating highway congestion. However, the integration of these strategies for mixed autonomy traffic remains under-explored. This study proposes a hybrid flow and trajectory control (HFTC) strategy that combines a macroscopic control, ramp metering (RM), with a microscopic control, cooperative merging (CM) for CAV trajectory optimization in mixed traffic scenarios. Specifically, the RM control considers CAV-penetration-dependent dynamics to regulate ramp flow, and the CM utilizes a centralized optimization model to enhance CAV merging trajectories. Independently implementing RM or CM proved effective only under heavy or moderate traffic flow, whereas our proposed integrated strategy, HFTC, demonstrated greater adaptability and suitability under various traffic conditions. Additionally, the impacts of CAV penetration rates and traffic flows on performance of different control strategies are thoroughly explored. Simulation results indicate that under low and moderate traffic conditions, microscopic control can be comparable to macroscopic control given sufficient CAV integration, while under heavy traffic flows, macroscopic control cannot be replaced by microscopic control.

在向全自动交通系统过渡的过程中，管理由联网自动驾驶汽车（cav）和人类驾驶汽车（HDVs）组成的混合交通势在必行。现有的宏观和微观策略在缓解公路拥堵方面都显示出了效果。然而，将这些策略整合到混合自主交通中仍有待探索。针对混合交通场景下CAV的轨迹优化问题，提出了一种将宏观控制匝道计量（RM）与微观控制协同归并（CM）相结合的流轨混合控制策略。具体来说，RM控制考虑了CAV穿透相关的动力学来调节坡道流，CM利用集中优化模型来增强CAV合并轨迹。独立实施RM或CM被证明仅在交通流量较大或中等的情况下有效，而我们提出的综合策略HFTC在各种交通条件下表现出更大的适应性和适用性。此外，深入探讨了自动驾驶汽车渗透率和交通流量对不同控制策略性能的影响。仿真结果表明，在低、中等交通条件下，如果CAV积分足够，微观控制可以与宏观控制相媲美，而在大交通流量下，宏观控制无法被微观控制所取代。

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引用次数: 0

The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories 自动驾驶汽车的基本图：交通状态估计和车辆轨迹证据

IF 14.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.commtr.2025.100212

Michail A. Makridis , Shaimaa K. El-Baklish , Anastasios Kouvelas , Jorge A. Laval

The fundamental diagram (FD) is a key tool in traffic flow theory, describing the relationship between traffic flow and density at the link level. Traditionally, FD estimation relies on data from static sensors, although vehicle trajectory data provides an alternative approach. Driver heterogeneity strongly influences the shape and scatter of the FD and is crucial for traffic management. Autonomous vehicles (AVs), exhibiting distinct driving behavior from human drivers, are expected to alter the FD. However, limited observations of AVs in stationary conditions have constrained research in this area. This study addresses this gap by introducing the platoon fundamental diagram (PFD), a simple method to infer empirical FDs from platoon trajectory data. PFD derives pseudo-states from vehicle trajectories and aggregates them to capture consistent relationships between flow, density, and speed—without requiring stationary conditions or backward wave speed estimation. The results highlight the impact of AVs on traffic flow capacity, driver heterogeneity, and oscillation propagation. Comparative analysis with human-driven experiments provides additional insights. Furthermore, the PFD's potential as a practical tool for traffic state estimation in mixed traffic conditions is demonstrated through real-world applications using NGSIM and I–24 Motion datasets.

基本图（FD）是交通流理论中的一个重要工具，它描述了交通流与交通密度之间的关系。传统上，FD估计依赖于静态传感器的数据，尽管车辆轨迹数据提供了另一种方法。驾驶员异质性强烈影响FD的形状和分布，对交通管理至关重要。自动驾驶汽车（AVs）表现出与人类驾驶员截然不同的驾驶行为，有望改变FD。然而，在固定条件下对自动驾驶汽车的有限观察限制了这一领域的研究。本研究通过引入从排轨迹数据推断经验fd的简单方法——排基本图（PFD）来解决这一问题。PFD从车辆轨迹中提取伪状态，并将它们聚合起来，以捕获流量、密度和速度之间的一致关系，而不需要固定条件或反向波速估计。研究结果强调了自动驾驶汽车对交通流容量、驾驶员异质性和振荡传播的影响。与人为实验的比较分析提供了更多的见解。此外，通过使用NGSIM和I-24运动数据集的实际应用，证明了PFD作为混合交通条件下交通状态估计的实用工具的潜力。

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引用次数: 0

Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency 用于交通调查和访谈的模块化人工智能代理：提高参与度、透明度和成本效率

IF 12.5 Q1 TRANSPORTATION

Communications in Transportation Research

Pub Date : 2025-12-01 Epub Date: 2025-03-24 DOI: 10.1016/j.commtr.2025.100172

Jiangbo Yu , Jinhua Zhao , Luis Miranda-Moreno , Matthew Korp

Surveys and interviews—structured, semi-structured, or unstructured—are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. For example, distributed questionnaires lack the ability to provide real-time guidance and request immediate clarifications. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by generative artificial intelligence (AI) technologies. However, considering decisions in transportation investments and policies often carry significant socioeconomic and environmental consequences, surveys and interviews face unique challenges in integrating AI agents. This issue underscors the need for a rigorous, explainable, and resource-efficient approach that enhances participant engagement and ensures privacy. This paper bridges this gap by introducing a modular approach accompanied by a parameterized process for designing and deploying AI agents for surveys and interviews, thereby supporting decision-makings in high-stakes contexts. We detail the system architecture, integrating engineered prompts, specialized knowledge bases, and customizable, goal-oriented conversational logic. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies: (1) travel preference surveys, highlighting conditional logic and multimodal (voice, text, and image generation) capabilities; (2) public opinion elicitation on a newly constructed, novel infrastructure project, showcasing question customization and multilingual (English and French) capabilities; and (3) expert consultations about the impact of technologies on future transportation systems, highlighting real-time, clarification request capabilities for open-ended questions, resilience in handling erratic inputs, and efficient transcript postprocessing. The results suggest that the AI agent increases completion rates and response quality. Furthermore, the modular approach demonstrates controllability, flexibility, and robustness while addressing key ethical, privacy, security, and token consumption concerns. We believe this work lays the foundation for next-generation surveys and interviews in transportation research.

调查和访谈——结构化的、半结构化的或非结构化的——被广泛用于收集对新兴或假设场景的见解。传统的以人为主导的方法经常面临与成本、可伸缩性和一致性相关的挑战。例如，分发的问卷缺乏提供实时指导和要求立即澄清的能力。最近，各个领域已经开始探索由生成式人工智能（AI）技术驱动的会话代理（聊天机器人）的使用。然而，考虑到交通投资和政策决策往往会带来重大的社会经济和环境后果，调查和访谈在整合人工智能代理方面面临着独特的挑战。这个问题强调需要一种严格的、可解释的和资源高效的方法，以增强参与者的参与并确保隐私。本文通过引入模块化方法和参数化过程来设计和部署用于调查和访谈的人工智能代理，从而支持高风险环境中的决策制定，从而弥合了这一差距。我们详细介绍了系统架构，集成了工程提示、专门的知识库和可定制的、面向目标的会话逻辑。我们通过三个实证研究证明了模块化方法的适应性、普遍性和有效性：(1)旅行偏好调查，强调条件逻辑和多模式（语音、文本和图像生成）能力；(2)对新建的新型基础设施项目进行民意调查，展示问题定制和多语种（英语和法语）能力；(3)就技术对未来交通系统的影响进行专家咨询，强调对开放式问题的实时性、澄清请求能力、处理不稳定输入的弹性以及高效的记录后处理。结果表明，人工智能代理提高了完成率和响应质量。此外，模块化方法展示了可控性、灵活性和鲁棒性，同时解决了关键的道德、隐私、安全和代币消费问题。我们相信这项工作为下一代交通研究中的调查和访谈奠定了基础。

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引用次数: 0