Travel Behaviour and Society最新文献_第2页

Left behind: forgone medical care due to transportation barriers among adults with physical impairments and disabilities that prevent physical activity in small and rural communities 掉队：在小社区和农村社区中，由于交通障碍，身体有缺陷和残疾的成年人无法进行身体活动，因此无法获得医疗服务

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-30 DOI: 10.1016/j.tbs.2026.101242

Jiahe Bian , Wei Li , Xiao Li , Sun Quan , Andong Chen , Sinan Zhong , Muhammad Usman , Samuel Dominic Castiglione Towne Jr. , Muyang Li , Bahar Dadashova , Xinyue Ye , Marcia G. Ory

Adults with physical impairments or disabilities that prevent physical activity (PID-PA) face significant transportation barriers to essential healthcare, often forgoing care despite higher healthcare needs. While on-demand ride-sourcing services (e.g., Uber and Lyft) may improve mobility, concerns remain about the current level of inclusivity and equity, especially for individuals with more complex needs. Whether on-demand ride-sourcing will facilitate mobility or further isolate certain people with PID-PA is largely unknown. This study examined the transportation barriers to healthcare among people with temporary and chronic PID-PA and assessed the role of alternative access strategies, with particular attention to small and rural communities where residences are dispersed and transit options are limited. A cross-sectional online survey was conducted in nine such communities in Texas, yielding 416 valid responses for analysis. Fisher’s exact tests, logistic regression models, and mediation analysis were used to assess associations between adults with PID-PA and variables such as forgone necessary healthcare due to lack of transportation, use of on-demand ride-sourcing, and alternative transportation options.

Among participants, people with PID-PA were more likely to use rides provided by others and telemedicine. However, logistic regression models showed that having chronic PID-PA and using on-demand ride-sourcing for healthcare were positively associated with forgone necessary medical care due to transportation barriers. Moreover, on-demand ride-sourcing use did not mediate the relationship between chronic PID-PA and forgone necessary healthcare. This result indicates that ride-sourcing services do not effectively reduce transportation barriers for individuals with chronic PID-PA. Instead, dependence on such services may be associated with forgoing necessary medical care. The study highlights substantial challenges to using on-demand ride-sourcing in small and rural communities, including limited physical/digital accessibility, affordability concerns, and unreliable service. To improve transportation equity for people with PID-PA, interventions must address broader systemic issues affecting the accessibility of ride-sourcing.

患有身体损伤或残疾而无法进行身体活动（PID-PA）的成年人在获得基本医疗保健服务时面临严重的交通障碍，尽管有更高的医疗保健需求，但他们往往会放弃护理。虽然按需拼车服务（如优步和Lyft）可能会改善机动性，但目前的包容性和公平性水平仍令人担忧，尤其是对于需求更复杂的个人而言。按需拼车服务是否会促进移动性，还是会进一步隔离某些患有PID-PA的人，在很大程度上是未知的。本研究调查了临时和慢性PID-PA患者的交通障碍，并评估了替代访问策略的作用，特别关注了住宅分散且交通选择有限的小型和农村社区。一项横断面在线调查在德克萨斯州的9个这样的社区进行，产生了416个有效的回应进行分析。使用Fisher精确检验、逻辑回归模型和中介分析来评估患有PID-PA的成年人与诸如由于缺乏交通工具而放弃必要的医疗保健、使用按需乘车和替代交通选择等变量之间的关系。在参与者中，患有PID-PA的人更有可能使用他人提供的游乐设施和远程医疗。然而，逻辑回归模型显示，患有慢性PID-PA和使用按需乘车服务的医疗服务与由于交通障碍而放弃必要的医疗服务呈正相关。此外，按需乘车服务的使用并没有调解慢性PID-PA和放弃必要的医疗保健之间的关系。这一结果表明，乘车外包服务并不能有效降低慢性PID-PA患者的交通障碍。相反，对这些服务的依赖可能与放弃必要的医疗保健有关。该研究强调了在小型和农村社区使用按需乘车服务面临的重大挑战，包括有限的物理/数字可及性、可负担性问题和不可靠的服务。为了改善PID-PA患者的交通公平性，干预措施必须解决影响乘车资源可及性的更广泛的系统性问题。

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引用次数: 0

Hands off the wheel, hands off the choice? A discrete choice experiment on trolley dilemma in autonomous vehicles 放开方向盘，放开选择权？自动驾驶车辆小车困境的离散选择实验

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-29 DOI: 10.1016/j.tbs.2026.101251

Woojae Kim , Youngsang Cho , Taeho Park , Kyuho Maeng

As autonomous vehicles (AVs) are increasingly integrated into everyday mobility systems, ethically complex crash scenarios have become a critical issue. Therefore, trolley dilemmas have attracted significant attention. However, little is known about how moral programming influences consumers’ acceptance of an AV. This study investigated the relative impact of ethical decision logic, accident liability, and safety performance on AV adoption preferences. A stated-choice experiment was conducted with 1,032 Korean respondents, and a mixed logit model with the Bayesian estimation method was used to estimate heterogeneous utility parameters. The experiment included five attributes: whether the AV protects the driver or pedestrian, the party responsible for the accident, annual accident probability, algorithm personalization, and purchase price. Demographic characteristics were also examined. The results indicated that the attribute “whether an AV protects drivers or pedestrians” had no significant effect on consumer utility. By contrast, a lower accident probability and assigning responsibility to manufacturers or software developers rather than to drivers substantially increased AV acceptance. Male, urban, and lower-income respondents were more likely to prefer AVs that protect drivers and shift the liability toward institutional actors. These findings suggest that consumers prioritize measurable safety and institutional accountability over abstract ethical logic. For AV developers and policymakers, these results highlight the value of adaptive algorithmic frameworks and clearly defined liability structures. This study contributes to the design of socially acceptable AV systems that align with public expectations in the age of algorithmic decision-making.

随着自动驾驶汽车（AVs）越来越多地融入日常出行系统，道德上复杂的碰撞场景已成为一个关键问题。因此，电车困境引起了极大的关注。然而，关于道德规划如何影响消费者对自动驾驶汽车的接受程度，我们知之甚少。本研究调查了道德决策逻辑、事故责任和安全性能对自动驾驶汽车采用偏好的相对影响。对1032名韩国受访者进行了状态选择实验，并使用混合logit模型和贝叶斯估计方法来估计异质性效用参数。实验包括5个属性：自动驾驶汽车是保护驾驶员还是行人、事故责任方、年度事故概率、算法个性化、购买价格。还审查了人口特征。结果表明，“自动驾驶汽车是保护驾驶员还是保护行人”属性对消费者效用没有显著影响。相比之下，较低的事故概率和将责任分配给制造商或软件开发商而不是驾驶员，大大提高了自动驾驶汽车的接受度。男性、城市和低收入受访者更倾向于选择能够保护司机并将责任转移给制度参与者的自动驾驶汽车。这些发现表明，消费者优先考虑可衡量的安全性和制度责任，而不是抽象的伦理逻辑。对于自动驾驶汽车开发者和政策制定者来说，这些结果突出了自适应算法框架和明确定义的责任结构的价值。这项研究有助于设计符合算法决策时代公众期望的社会可接受的自动驾驶系统。

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引用次数: 0

Neural integrated choice model with ParetoTail and Gaussian copula for travel behavior analysis 基于ParetoTail和高斯copula的出行行为分析神经集成选择模型

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-28 DOI: 10.1016/j.tbs.2026.101252

Yue Liu, Guohua Liang, Ziyu Chen, Zhixiang Gao

Travel behavior modeling is essential for transportation demand analysis and policy-making, yet traditional discrete choice models often struggle with real-world data complexities, such as heavy-tailed distributions and strong feature correlations. This study proposes a novel neural network framework integrated with advanced statistical techniques to effectively address these issues. Specifically, a ParetoTail transformation is employed to normalize heavy-tailed travel attributes, such as travel time and cost, reducing the undue influence of extreme values. To explicitly capture complex dependencies among features, a Gaussian copula approach is integrated, improving the robustness of the model against traditional independence assumptions. Furthermore, a gating mechanism is introduced to dynamically balance the contributions of continuous and discrete features, incorporating random noise to account for preference heterogeneity across individual travelers. Extensive empirical analyses, initially on the Swissmetro dataset and validated in three additional diverse public datasets, demonstrate that the proposed model consistently and significantly outperforms the baseline models (MNL, MXL, L-MNL, E-MNL, EL-MNL) in terms of prediction accuracy, F1 score, and AUC values. Crucially, the interpretability of the model reveals nuanced behavioral insights, such as the heterogeneity of decision-making styles across the population and non-linear responses to cost in long-distance travel. Additional ablation studies underscore the essential roles of the ParetoTail, Gaussian copula, and gating components. In general, this integrated framework provides a flexible, robust, and generalizable approach to modeling travel behavior, offering transport planners a more accurate tool for policy evaluation in complex real-world scenarios.

出行行为建模对于交通需求分析和政策制定至关重要，但传统的离散选择模型往往难以应对现实世界数据的复杂性，如重尾分布和强特征相关性。本研究提出了一个新的神经网络框架与先进的统计技术相结合，以有效地解决这些问题。具体来说，使用ParetoTail变换来规范重尾旅行属性，如旅行时间和费用，减少极端值的不当影响。为了明确捕获特征之间的复杂依赖关系，集成了高斯copula方法，提高了模型对传统独立性假设的鲁棒性。此外，引入了一种门控机制来动态平衡连续和离散特征的贡献，并结合随机噪声来解释个体旅行者的偏好异质性。广泛的实证分析，最初是在Swissmetro数据集上进行的，并在另外三个不同的公共数据集上进行了验证，表明所提出的模型在预测精度、F1分数和AUC值方面一致且显著优于基线模型（MNL、MXL、L-MNL、E-MNL、EL-MNL）。至关重要的是，该模型的可解释性揭示了细微的行为洞察，例如人口决策风格的异质性和长途旅行成本的非线性反应。其他消融研究强调了帕累托尾、高斯联结和门控分量的重要作用。总体而言，这一综合框架提供了一种灵活、稳健和可推广的方法来模拟出行行为，为交通规划者在复杂的现实场景中进行政策评估提供了更准确的工具。

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引用次数: 0

Exploring peer-to-peer paid carpooling in Bogotá: A path to sustainable shared mobility 探索波哥大的点对点付费拼车：一条可持续的共享交通之路

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-26 DOI: 10.1016/j.tbs.2026.101243

César A. Merchán-Núñez , Mauricio Orozco-Fontalvo , Luisa F. Morales-Moreno , Aquiles Darghan , Sonia C. Mangones M. , Lenin A. Bulla-Cruz

Carpooling is a sustainable transportation alternative that allows users with similar destinations to share private vehicles, contributing to reductions in energy consumption, pollutant emissions, and traffic congestion. While not a comprehensive solution, carpooling can lower private car use and increase vehicle occupancy rates. However, most carpooling initiatives have been limited in scope, often operating on a small scale or within corporate frameworks, restricting their potential for widespread adoption. In Latin America, where ride-hailing services are popular despite regulatory issues, carpooling remains uncommon. In Bogotá, an exception is the informal service called “Wheels,” which operates through WhatsApp groups to coordinate rides, focusing on university communities. This service quickly became a preferred mode of transport for students, faculty, and staff. This study aims to identify the factors driving the success of this informal initiative. A survey of 470 university community members was conducted, incorporating a discrete choice experiment to evaluate the attributes influencing their stated likelihood of use. Analytical methods included multiple correspondence analysis, logit models, and machine learning techniques. Our findings reveal that the adoption of carpooling is significantly influenced by price sensitivity, safety perceptions (particularly among women), reluctance to share rides with strangers, and demographic factors such as age and socioeconomic status. These insights offer valuable guidance for enhancing the appeal and scalability of carpooling as a door-to-door, reliable transportation alternative, particularly in similar sociocultural contexts as our case study.

拼车是一种可持续的交通方式，它允许目的地相近的用户共享私家车，有助于减少能源消耗、污染物排放和交通拥堵。拼车虽然不是一个全面的解决方案，但它可以降低私家车的使用，提高车辆入住率。然而，大多数拼车计划的范围有限，通常是小规模或在公司框架内运作，限制了它们广泛采用的潜力。在拉丁美洲，尽管存在监管问题，但叫车服务很受欢迎，拼车仍然不常见。在波哥大，一个例外是名为“Wheels”的非正式服务，它通过WhatsApp群组来协调乘车，主要针对大学社区。这项服务很快成为学生、教师和工作人员的首选交通方式。本研究旨在确定推动这一非正式倡议成功的因素。对470名大学社区成员进行了调查，结合离散选择实验来评估影响他们陈述的使用可能性的属性。分析方法包括多重对应分析、logit模型和机器学习技术。我们的研究结果表明，拼车的采用受到价格敏感性、安全观念（尤其是女性）、不愿与陌生人拼车以及年龄和社会经济地位等人口因素的显著影响。这些见解为提高拼车作为门到门的可靠交通选择的吸引力和可扩展性提供了有价值的指导，特别是在与我们的案例研究相似的社会文化背景下。

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引用次数: 0

Investigating extra environmental exposure in bike-sharing trips: spatial patterns and built environment factors 调查共享单车出行中的额外环境暴露：空间模式和建成环境因素

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-23 DOI: 10.1016/j.tbs.2026.101241

Zijian Guo , Mei-Po Kwan , Jian Liu , Xintao Liu

Shared bikes provide flexible mobility but expose riders to harmful outdoor environments, such as humid and oppressive heat, negatively impacting the travel experience. Reducing extra travel time, particularly for open-air transport, is an effective way to minimize unnecessary environmental exposure (UEE). However, the role of extra travel time in exposure studies has received limited attention, and the relationship between the built environment and UEE remains underexplored. This study addresses these gaps by constructing complex networks of UEE based on shared bike travel flows. We first calculate each Shenzhen bike-sharing trip’s extra travel time by comparing the optimal and actual travel time. UEE is then defined as a combination of this extra travel time and the corresponding “feels-like” temperature. For each origin–destination pair, numerous UEE values of trips form a distribution, from which the maximum probability point (EP) and fluctuation (EF) are extracted as two key indicators. These two indicators, along with traffic volume, serve as the weights of network edges. After the network aggregation, spatial hotspot comparisons are conducted, followed by the application of a GCN-LIME model to explain the contribution of the built environment to UEE. The results indicate that areas associated with work, education, and high diversity inhibit the UEE, while areas with food, shops, services, and hospitals promote it. Notably, laborer communities experience higher UEE and are sensitive to changes in the built environment, underscoring issues of spatial justice. These findings provide valuable insights for policymakers to identify high-exposure areas and optimize facilities to mitigate exposure.

共享单车提供了灵活的机动性，但却让骑行者暴露在有害的室外环境中，比如潮湿和闷热，对出行体验产生了负面影响。减少额外的旅行时间，特别是露天运输，是减少不必要的环境暴露（UEE）的有效方法。然而，额外旅行时间在暴露研究中的作用受到了有限的关注，建筑环境与UEE之间的关系仍未得到充分探讨。本研究通过构建基于共享自行车出行流的复杂UEE网络来解决这些差距。我们首先通过比较最优出行时间和实际出行时间，计算出每次深圳共享单车出行的额外出行时间。然后，UEE被定义为额外的旅行时间和相应的“感觉”温度的组合。对于每个始发目的地对，无数次行程的UEE值形成一个分布，从中提取最大概率点（EP）和波动（EF）作为两个关键指标。这两个指标与流量一起作为网络边缘的权重。在网络聚合后，进行空间热点比较，然后应用GCN-LIME模型解释建筑环境对UEE的贡献。结果表明，与工作、教育和高度多样性相关的区域抑制了UEE，而与食品、商店、服务和医院相关的区域则促进了UEE。值得注意的是，劳动者社区的UEE较高，对建筑环境的变化敏感，强调了空间正义问题。这些发现为决策者确定高暴露区域和优化设施以减轻暴露提供了有价值的见解。

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引用次数: 0

Beyond daily travel: understanding shared autonomous vehicle usage behavior across emergency preparedness segments in the United States 超越日常旅行：了解美国应急准备部门的共享自动驾驶汽车使用行为

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-23 DOI: 10.1016/j.tbs.2026.101235

Ningzhe Xu , Jun Liu , Steven Jones

Shared Autonomous Vehicles (SAVs) are viewed as a promising next generation mobility solution; however, most existing research has focused on their use in routine daily travel. Individuals’ preferences for using SAVs during emergencies remain largely underexplored. Gaining insight into these preferences is essential for designing effective emergency mobility strategies that can leverage advanced mobility technologies when available. This study investigates SAV usage preferences in emergency contexts across the United States using data from a nationally distributed online survey (N = 1,015). Specifically, the study examines (1) differences in SAV usage preferences between daily and emergency contexts, (2) variations in these preferences across segments defined by emergency preparedness levels, and (3) the factors that influence SAV adoption behavior within each preparedness segment. Methodologically, chi-squared tests were used to assess shifts in SAV usage preferences between contexts, while latent class analysis (LCA) was employed to classify respondents into two preparedness segments: under-prepared and well-prepared. Within each segment, Light Gradient Boosting Machine (LightGBM) models were developed and interpreted using feature importance rankings and partial dependence plots. Findings show increased willingness to use SAVs during emergencies, especially among individuals who were hesitant or unwilling in daily settings. SAV usage preferences also varied by preparedness segment. Several factors, including residential duration, vehicle access, race, and ethnicity, showed consistent effects across groups, while others, such as land use, household with children, home ownership, and household size, displayed divergent patterns. These results highlight the moderating role of preparedness in SAV adoption and caution against directly applying daily-use assumptions to emergency contexts. Policy efforts should consider preparedness-based segmentation to support effective SAV deployment during emergencies.

共享自动驾驶汽车（sav）被视为有前途的下一代出行解决方案；然而，大多数现有的研究都集中在日常旅行中的使用。个人在紧急情况下使用sav的偏好在很大程度上仍未得到充分研究。深入了解这些偏好对于设计有效的应急机动战略至关重要，这些战略可以在可用时利用先进的机动技术。本研究使用来自全国分布的在线调查数据（N = 1,015）调查了美国紧急情况下SAV的使用偏好。具体而言，该研究考察了(1)日常和紧急情况下SAV使用偏好的差异，(2)这些偏好在应急准备水平定义的不同部门之间的变化，以及(3)影响每个准备部门内SAV采用行为的因素。方法学上，卡方检验用于评估不同背景下SAV使用偏好的变化，而潜在类别分析（LCA）用于将受访者分为两个准备部分：准备不足和准备充分。在每个片段中，使用特征重要性排序和部分依赖图开发和光梯度增强机（LightGBM）模型并进行解释。研究结果显示，在紧急情况下使用sav的意愿增加，特别是在日常环境中犹豫不决或不愿使用sav的个人中。SAV的使用偏好也因准备阶段而异。有几个因素，包括居住时间、车辆进出、种族和民族，在不同群体中表现出一致的影响，而其他因素，如土地使用、有孩子的家庭、房屋所有权和家庭规模，则表现出不同的模式。这些结果强调了准备在SAV采用中的调节作用，并告诫不要直接将日常使用假设应用于紧急情况。政策努力应考虑基于准备情况的分割，以支持紧急情况下有效的SAV部署。

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引用次数: 0

Learning nonlinearity and measuring uncertainty--a multi-task neural network and additive gaussian process based travel choice model 学习非线性和测量不确定性——基于多任务神经网络和加性高斯过程的旅行选择模型

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-23 DOI: 10.1016/j.tbs.2026.101247

Sha Zhang , Yao Dong , Peter J. Jin , Shichao Sun , Fei Yang

Discrete choice model (DCM) is a classical framework for modelling an individual’s travel choice. However, its oversimplified architecture of utility function may limit its performance when faced with a complex decision process. In this paper, we develop a new framework called multi-task neural network and additive gaussian process based discrete choice model (MNNAGP-DCM). Specially, the multi-task neural network (MNN) is used to learn the representation of individual characteristics, while the additive Gaussian process regression (AGP) process is utilized to enhance flexibility of utility function. In multi-task neural network, the sub-learners learn the taste parameters between individuals’ characteristic in each alternative, while the global bias term is used to learn the cross effect between alternatives. The additive GPR framework is employed to substitute the linear term in utility function with a nonparametric probability framework. Additive GPR enables the modelling of nonlinearity, threshold effects and uncertainty, thereby providing a more comprehensive perspective on the decision-making process. Moreover, when combined with DCM, the GPRs become intractable. To address this, we employ variational inference to construct a tractable lower bound, thereby transforming the original model into a tractable one. Then MNNAGP-DCM can be optimized by gradient based algorithms such as Adam. The proposed model is tested on the open-source dataset and benchmarked with standard MNL, Mix-logit, XGBoost, TasteNet-MNL, MNN-DCM and MNNSGP-DCM. Results show that MNNAGP-DCM can not only capture individuals’ heterogeneity but also can learn the nonlinearity in utility function, showing great superiority in terms of predictability. Our model can also provide interpretable result with taste parameters and the fitted GPR models, while quantifying uncertainty through GPR’s probability framework.

离散选择模型（DCM）是一个经典的个人旅行选择建模框架。然而，在面对复杂的决策过程时，其过于简化的效用函数架构可能会限制其性能。本文提出了一种基于多任务神经网络和加性高斯过程的离散选择模型（MNNAGP-DCM）。其中，利用多任务神经网络（MNN）学习个体特征的表示，利用加性高斯过程回归（AGP）过程增强效用函数的灵活性。在多任务神经网络中，子学习者学习每个选择中个体特征之间的口味参数，而全局偏差项用于学习选择之间的交叉效应。采用加性探地雷达框架将效用函数中的线性项替换为非参数概率框架。加性探地雷达可以对非线性、阈值效应和不确定性进行建模，从而为决策过程提供更全面的视角。此外，当与DCM结合使用时，gpr变得棘手。为了解决这个问题，我们采用变分推理来构造一个可处理的下界，从而将原始模型转换为可处理的模型。然后利用Adam等基于梯度的算法对MNNAGP-DCM进行优化。该模型在开源数据集上进行了测试，并与标准MNL、Mix-logit、XGBoost、TasteNet-MNL、MNN-DCM和MNNSGP-DCM进行了基准测试。结果表明，MNNAGP-DCM不仅可以捕捉个体的异质性，还可以学习效用函数的非线性，在可预测性方面表现出很大的优势。通过探地雷达的概率框架对不确定性进行量化，该模型还可以利用味觉参数和拟合的探地雷达模型提供可解释的结果。

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引用次数: 0

Navigating the gig economy: transportation labor challenges facing California’s app-based ridehailing and courier drivers 驾驭零工经济：加州基于应用程序的叫车和快递司机面临的运输劳动力挑战

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-23 DOI: 10.1016/j.tbs.2025.101218

Susan Shaheen , Brooke Wolfe , Adam Cohen

Given the dynamic landscape surrounding the classification of workers in California, it is important to consider how the existing legal and regulatory environment may impact app-based gig drivers, including transportation network companies (TNCs, also known as ridehailing) and courier network services (CNS). Using a multi-method approach, we conducted a literature review (n = 41 sources), expert interviews (n = 8), and case study analysis (n = 7) between October 2022 to May 2024 to better understand how California’s gig drivers are impacted by state legislation and regulation (i.e., Assembly Bill 5, Proposition 22, and Senate Bill 1014). The expert interviews found that gig drivers are concerned with fair pay, benefits, labor classification, and transparency from the app-based platforms on issues related to punitive actions (i.e., deactivations). Drivers also raised concerns about California’s Clean Miles Standard (also known as SB 1014), which requires 90 % of vehicle miles traveled be electric by 2030, due to the financial costs associated with acquiring and operating electric vehicles (EVs) and limited public charging availability. The seven case studies examine gig labor policies from other states and countries (e.g., policies that may help enhance driver working conditions, platform regulation, and facilitate the EV transition). Together, these methods explore the tension between sustaining an app-based gig driving platform and providing fair compensation and working conditions for gig drivers. The study finds that state and/or local policies establishing minimum pay for drivers and policies enhancing transparency and appeal processes for driver deactivations could help improve working conditions for gig drivers. Various state agencies, such as the California Public Utilities Commission, could support gig drivers through incentives for the purchase of EVs and installation of EV charging near their homes and driving locations.

考虑到围绕加州工人分类的动态景观，重要的是要考虑现有的法律和监管环境如何影响基于应用程序的零工司机，包括运输网络公司（TNCs，也称为乘车服务）和快递网络服务（CNS）。利用多方法方法，我们在2022年10月至2024年5月期间进行了文献综述（n = 41个来源）、专家访谈（n = 8）和案例研究分析（n = 7），以更好地了解加州的零工司机是如何受到州立法和法规（即议会法案5、提案22和参议院法案1014）的影响的。专家访谈发现，零工司机关心的是公平的薪酬、福利、劳动分类，以及基于应用程序的平台在惩罚行动（即停用）相关问题上的透明度。由于购买和运营电动汽车的财务成本以及有限的公共充电设施，司机们还对加州的“清洁里程标准”（也称为SB 1014）提出了担忧。该标准要求，到2030年，电动汽车的行驶里程必须达到90%。这七个案例研究考察了其他州和国家的零工劳工政策（例如，可能有助于改善司机工作条件、平台监管和促进电动汽车转型的政策）。总之，这些方法探讨了维持基于应用程序的零工驾驶平台与为零工司机提供公平的薪酬和工作条件之间的紧张关系。研究发现，制定司机最低工资的州和/或地方政策，以及提高司机停职透明度和申诉程序的政策，可能有助于改善零工司机的工作条件。加州公用事业委员会（California Public Utilities Commission）等多个州政府机构可以通过奖励购买电动汽车，并在他们的家和驾驶地点附近安装电动汽车充电桩，来支持零工司机。

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引用次数: 0

Street environments and recovery experience: a cycling perspective analysis using machine learning and natural language processing 街道环境和恢复经验：使用机器学习和自然语言处理的自行车视角分析

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-22 DOI: 10.1016/j.tbs.2026.101238

Yiwen Zhang , Scarlett T. Jin , Haizhi Luo , Yang Yu , Hui Kong

Recovery experience refers to the process through which individuals achieve stress recovery via emotional responses and engagement in restorative activities. Although urban streets are known to support stress recovery, their specific effects on recovery experience, particularly in cycling-friendly environments amid growing bike-sharing adoption, remain underexplored. This study integrates multi-source data to examine how street environmental features and cycling density influence recovery experience. Using a Bidirectional Encoder Representations from Transformers (BERT) model and machine learning techniques, we systematically assess the impact of various street environmental features on recovery experience. Results indicate that traffic accessibility (e.g., metro and bus station density, intersection density), urban vitality (e.g., social vitality and commercial facility density), and aesthetic qualities (vegetation) significantly enhance recovery experience. Different dimensions of recovery experience exhibit distinct sensitivities: detachment and mastery are more responsive to traffic accessibility, while relaxation is more strongly influenced by street vitality. Specifically, shared-bike facility density exerts a positive effect on recovery experience, and moderate slope likewise contributes positively to mastery. Furthermore, high cycling density amplifies the beneficial influence of the street environments on recovery experience, particularly in terms of traffic accessibility and vitality. These findings highlight the critical role of cycling-friendly street environmental features and active cycling participation in promoting recovery experience. Based on the findings, this research provides evidence-based insights for designing urban streets that enhance sustainable mobility while fostering stress recovery.

恢复体验是指个体通过情绪反应和参与恢复性活动实现压力恢复的过程。虽然人们都知道城市街道有助于压力恢复，但它们对恢复体验的具体影响，尤其是在共享单车日益普及的自行车友好环境中，仍未得到充分探索。本研究整合多来源资料，检视街道环境特征与单车密度对复原体验的影响。使用双向编码器表示（BERT）模型和机器学习技术，我们系统地评估了各种街道环境特征对恢复经验的影响。结果表明，交通可达性（如地铁和公交车站密度、十字路口密度）、城市活力（如社会活力和商业设施密度）和审美品质（植被）显著增强了恢复体验。不同维度的恢复体验表现出不同的敏感性：超然和精通对交通可达性更敏感，而放松受街道活力的影响更强烈。其中，共享单车设施密度对恢复体验有正向影响，适度坡度对掌握也有正向影响。此外，高自行车密度放大了街道环境对恢复体验的有益影响，特别是在交通可达性和活力方面。这些发现强调了自行车友好型街道环境特征和积极的自行车参与对促进恢复体验的关键作用。基于这些发现，本研究为设计城市街道提供了基于证据的见解，这些街道在促进压力恢复的同时增强了可持续的流动性。

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引用次数: 0

Multimodal public transport, travel behaviour, and social equity 多式联运公共交通、出行行为和社会公平

IF 5.2 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2026-01-16 DOI: 10.1016/j.tbs.2026.101237

Sui Tao, Long Cheng, Jonathan Corcoran, Susan Shaheen

引用次数: 0