Travel Behaviour and Society最新文献_第9页

Elevating transportation models: A comparative study of variable selection techniques for predictive performance 高架运输模型：预测性能变量选择技术的比较研究

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-07 DOI: 10.1016/j.tbs.2025.101170

Cagdas Kara , Asuman S. Turkmen

Selecting the right variables is essential in travel behavior modeling for transportation planning. Traditional methods, like choosing from highly correlated predictors or relying on past studies, can reduce the effectiveness of models. Using robust methods to identify relevant variables helps minimize errors, enhances model understanding, and simplifies future predictions by focusing on key factors, making applications more reliable and efficient.

In this study, the data from the household travel survey within the Eskisehir Transportation Master Plan (conducted in 2001 and 2015) were used for the theoretical modeling. The objective of the study is to develop models for non-home-based travel purposes (e.g., banking, shopping, socializing, visiting, entertainment, recreation, sports, etc.) by incorporating socio-economic demographic parameters and the land-use data to understand the relationships between socio-demographic variables and Other-Purpose Trips (OPT) behavior.

Various theoretical methodologies, including classical Multiple Linear Regression (MLR) in travel models, Ridge Regression, advanced variable selection and machine learning techniques such as Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (ENet), Adaptive Lasso (ALasso), and Adaptive Elastic Net (AEnet) are applied in the study. Ridge Regression and machine learning techniques are implemented to address multicollinearity problem that cannot be handled with the traditional MLR models.

Among the 2001 production models, ENet is approximately 29% more successful than MLR in terms of Cross Validated Root Mean Square Error (CVRMSE). Similarly, ENet demonstrates a 17% higher success rate in predicting the target year (2015) based on Root Mean Squared Error (RMSE). In the 2015 production models, the most successful predictions according to CVRMSE are obtained from AEnet, with a prediction power approximately 45% higher than MLR. Among the 2015 attraction models, AEnet and ALasso, approximately 37% more successful than MLR according to CVRMSE, are found to be the most successful models.

在交通规划的出行行为建模中，选择正确的变量是至关重要的。传统的方法，比如从高度相关的预测因子中选择或依赖过去的研究，会降低模型的有效性。使用健壮的方法来识别相关变量有助于减少错误，增强模型理解，并通过关注关键因素简化未来预测，使应用程序更加可靠和高效。在本研究中，使用Eskisehir交通总体规划（2001年和2015年进行）中的家庭旅行调查数据进行理论建模。本研究的目的是通过结合社会经济人口参数和土地利用数据，开发非居家旅行目的（如银行、购物、社交、访问、娱乐、休闲、体育等）的模型，以了解社会人口变量与其他目的旅行（OPT）行为之间的关系。各种理论方法，包括经典的旅行模型多元线性回归（MLR），岭回归，先进的变量选择和机器学习技术，如最小绝对收缩和选择算子（Lasso），弹性网（ENet），自适应Lasso （ALasso）和自适应弹性网（AEnet）应用于研究。采用岭回归和机器学习技术来解决传统MLR模型无法处理的多重共线性问题。在2001年的生产模型中，ENet在交叉验证均方根误差（CVRMSE）方面比MLR大约成功29%。同样，ENet在基于均方根误差（RMSE）预测目标年（2015年）的成功率高出17%。在2015年的生产模型中，根据CVRMSE进行的最成功的预测来自AEnet，其预测能力比MLR高约45%。在2015年的吸引力模型中，AEnet和ALasso是最成功的模型，根据CVRMSE的数据，它们的成功率比MLR高约37%。

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引用次数: 0

Measuring perceived transfer inconvenience for multimodal commute trips combining bicycles and public transport 测量自行车和公共交通相结合的多式联运通勤出行的感知换乘不便

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-07 DOI: 10.1016/j.tbs.2025.101167

Ioannis Kosmidis, Daniela Müller-Eie

Public transport and bicycles are two essential components of a sustainable urban transport system, with their integration increasing their potential to reduce car use in urban areas. However, their combined use has one essential weakness, which is the transfer between two travel modes. This additional step creates a feeling of inconvenience to potential travellers. Understanding the factors that cause inconvenience allows to implement policies and measures that ensure an efficient integration and consequently an effective transition to more sustainable travel options. However, the factors that cause inconvenience are currently understudied. This study fills this knowledge gap by identifying these factors and measuring their effect on transfer inconvenience. It also explores the influence of perceived transfer inconvenience on the choice to use a bike as an access or egress mode to a public transport stop. Using data (n = 1007) from an online survey questionnaire from the region of Nord-Jæren in Norway, a Structural Equation Model (SEM) has been developed. The results of the analysis show that the perception of transfer inconvenience plays a substantial role in the choice to combine bicycles and public transport since it can predict up to 10 % of the variance in the choice to use bicycles. The results also suggest that practical elements, like physical infrastructure, play a vital role in shaping transfer inconvenience.

公共交通和自行车是可持续城市交通系统的两个重要组成部分，它们的结合增加了减少城市地区汽车使用的潜力。然而，它们的组合使用有一个本质的弱点，那就是两种旅行方式之间的转换。这个额外的步骤给潜在的旅客带来了一种不方便的感觉。了解造成不便的因素有助于实施政策和措施，确保有效整合，从而有效地过渡到更可持续的旅行选择。然而，造成不便的因素目前还没有得到充分研究。本研究通过识别这些因素并测量它们对交通不便的影响来填补这一知识空白。它还探讨了感知到的交通不便对选择使用自行车作为公共交通站点的进出方式的影响。利用挪威Nord-Jæren地区在线调查问卷的数据（n = 1007），开发了结构方程模型（SEM）。分析结果表明，交通不便的感知在选择自行车和公共交通的组合中起着重要作用，因为它可以预测到选择使用自行车的10%的方差。结果还表明，实际因素，如物理基础设施，在形成转移不便方面起着至关重要的作用。

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引用次数: 0

The impact of perceived walking accessibility on willingness to walk: evaluating different assessment methods 步行可达性对步行意愿的影响：不同评估方法的比较

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-06 DOI: 10.1016/j.tbs.2025.101173

Cong Qi , Jonas De Vos , Yibang Zhang , Xiucheng Guo

Accurately assessing perceived walking accessibility is essential for analysing its impact on people’s willingness to walk and walking behaviour. However, limited research has analysed and compared different assessments of perceived walking accessibility. This paper uses survey data from Nanjing’s central area to build binary logit models and analyse the impact of perceived walking accessibility on willingness to walk. Four different assessment methods are employed: the perceived accessibility scale, perceived walkability, overall perceived walking accessibility, and perceived impedance. The results show that perceived walking time is the most effective method for assessing perceived walking accessibility to specific locations, with the highest degree of explanatory power for willingness to walk. Older people, those unfamiliar with the city centre and those who arrive there by car or bicycle are less likely to walk in the central area. Perceived walking time is the primary factor influencing both enthusiastic walkers and reluctant walkers, while actual walking time primarily influences conditional walkers. Land use type at survey point has no significant effect on willingness to walk. These findings are valuable for designing an appropriate walking environment in the city centre and for encouraging walking by improving people’s perceived walking accessibility.

准确评估感知步行可达性对于分析其对人们步行意愿和步行行为的影响至关重要。然而，有限的研究分析和比较了感知步行可达性的不同评估。本文利用南京市中心城区的调查数据，建立二元logit模型，分析感知步行可达性对步行意愿的影响。采用感知可达性量表、感知步行可达性、整体感知步行可达性和感知阻抗四种不同的评价方法。结果表明，感知步行时间是评估特定地点感知步行可达性的最有效方法，对步行意愿的解释能力最高。老年人、不熟悉市中心的人以及开车或骑自行车到达市中心的人不太可能在市中心步行。感知步行时间是影响热心步行者和不情愿步行者的主要因素，而实际步行时间主要影响有条件步行者。调查点土地利用类型对步行意愿的影响不显著。这些发现对于在城市中心设计合适的步行环境以及通过改善人们感知的步行可达性来鼓励步行具有重要价值。

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引用次数: 0

Formatting scientific data to enable designers to query socio-economic and environmental impacts of autonomous vehicles 格式化科学数据，使设计人员能够查询自动驾驶汽车的社会经济和环境影响

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-04 DOI: 10.1016/j.tbs.2025.101158

Robin Lecomte , Bernard Yannou , Roland Cahen , Guillaume Thibaud , Fabrice Étienne

Autonomous vehicles (AVs) are about to expand quickly within a few decades, with some fully autonomous AV shuttles already in use worldwide. However, significant uncertainties still surround their development. Beyond technical and economic concerns, the long-term impacts on people and society are often neglected by car manufacturers in favor of immediate technical and economic considerations. Anticipating these impacts comprehensively is crucial to ensure that decision-makers make informed choices that benefit society.

This paper presents the Study-Method-Impact (SMI) model, which aims to help designer simulate the long-term socio-economic and environmental impacts of AVs. The SMI model centralizes and structures data from scientific literature, making it more accessible for designers. It also provides detailed results to complex queries.

The paper compares the results of the SMI model with external synthesis reviews and concludes that it is a useful tool for providing accurate and comprehensive information to designers. The SMI model has the potential to inform decision making and policy surrounding autonomous vehicles. Further development and perspectives are also discussed to incorporate the model within industrial tools.

自动驾驶汽车（AV）将在未来几十年内迅速发展，一些全自动无人驾驶汽车已经在全球范围内投入使用。然而，它们的发展仍然存在重大的不确定性。除了技术和经济方面的考虑外，汽车制造商往往忽略了对人和社会的长期影响，而倾向于立即考虑技术和经济方面的问题。全面预测这些影响对于确保决策者做出有利于社会的知情选择至关重要。本文提出了研究-方法-影响（SMI）模型，旨在帮助设计者模拟自动驾驶汽车的长期社会经济和环境影响。SMI模型集中并组织了科学文献中的数据，使设计人员更容易获得这些数据。它还为复杂查询提供详细的结果。本文将SMI模型的结果与外部综合评价结果进行了比较，并得出结论，SMI模型是为设计人员提供准确和全面信息的有用工具。SMI模型有可能为自动驾驶汽车的决策和政策提供信息。还讨论了进一步的发展和前景，以将该模型纳入工业工具中。

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引用次数: 0

Exploring inter-user differences in bike-sharing origin-destination flows across rainfall intensities 探索不同降雨强度下共享单车始发至目的地流量的用户间差异

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-04 DOI: 10.1016/j.tbs.2025.101166

Qiuping Li , Yan Wei , Yang Zhou , Suhong Zhou , Ling Yin

Rainfall is one of the most frequent and impactful weather conditions linked to bike-sharing usage, yet limited research has examined its influence on spatial flow structures and inter-user behavioral differences. This study investigates how bike-sharing origin–destination (OD) flows vary during rainfall, with a particular focus on member versus casual users. Using one year of bike-sharing trip data from Chicago’s Divvy system, we construct OD flow networks and evaluate changes in volume, spatial structure, and community partitioning across four rainfall levels: no rain, light, moderate, and heavy rain. We further assess the relationships between OD flows and the surrounding built environment factors under different rainfall conditions using negative binomial regression models. Results indicate that casual users exhibit significantly greater sensitivity to rainfall, with trip volumes observed to be lower on days with moderate and heavy rain (56%–59%), compared to 35%–37% for member users. Network structure analysis reveals that rainfall is associated with reduced long-distance trips and community fragmentation, especially among casual users. Additionally, the effects of built environment factors (e.g., land use mix, bike infrastructure, and sidewalk availability) vary across rainfall intensities and user types, with some factors reversing direction under adverse conditions. This study highlights the importance of considering user heterogeneity and weather-related behavioral shifts in shared mobility research. The findings may inform the design of more resilient, equitable, and user-responsive bike-sharing systems in the context of climate variability.

降雨是与共享单车使用相关的最常见和最具影响力的天气条件之一，但有关其对空间流结构和用户间行为差异影响的研究有限。本研究调查了共享单车的始发目的地流量（OD）在降雨期间是如何变化的，并特别关注会员用户和临时用户。利用芝加哥Divvy系统一年的共享单车出行数据，我们构建了OD流量网络，并评估了四种降雨水平（无雨、小雨、中雨和大雨）下的流量、空间结构和社区划分的变化。利用负二项回归模型进一步分析了不同降雨条件下OD流量与周边建筑环境因子的关系。结果表明，休闲用户对降雨表现出更大的敏感性，在中雨和大雨的日子里，他们的出行量较低（56%-59%），而会员用户的这一比例为35%-37%。网络结构分析表明，降雨与减少长途旅行和社区分裂有关，特别是在临时用户中。此外，建筑环境因素（如土地利用组合、自行车基础设施和人行道可用性）的影响因降雨强度和用户类型而异，有些因素在不利条件下会逆转方向。本研究强调了在共享出行研究中考虑用户异质性和天气相关行为转变的重要性。这些发现可能为在气候变化的背景下设计更有弹性、更公平、更符合用户需求的共享单车系统提供信息。

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引用次数: 0

Spherical fuzzy evidential reasoning for vehicle-to-grid enabled electric vehicle adoption: A data-driven analysis of public perception 车辆到电网的电动汽车采用球形模糊证据推理：公众感知的数据驱动分析

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-03 DOI: 10.1016/j.tbs.2025.101169

Muhammad Zahid , Ezzeddin Bakhtavar , Salim Khoso , Syed Naveel Hussain , Rehan Sadiq , Kasun Hewage

The successful adoption of Vehicle-to-Grid (V2G) technology depends on public awareness, infrastructure readiness, and policy support. This study presents a Spherical Fuzzy Set-Evidential Reasoning (SFS-ER) framework to assess public perceptions of V2G adoption, leveraging survey data from 729 respondents in Okanagan, British Columbia (BC), Canada. The study quantifies belief structures through three dimensions: True Belief (m_T), False Belief (m_F), and Uncertainty (m_U), and computes utility scores to evaluate adoption likelihood. K-Means clustering with the Elbow Method identifies three distinct respondent groups: Supporters (high belief and adoption potential), Skeptical (moderate false beliefs), and Resistant (high skepticism and reluctance toward adoption). The findings reveal that awareness (m_T = 0.78) and interest (m_T = 0.75) are the strongest predictors of adoption, whereas familiarity alone has a limited impact. Higher-income and university-educated respondents demonstrate greater awareness, while apartment and townhouse residents face structural barriers due to limited charging infrastructure. Moreover, sensitivity analysis confirms that awareness-driven interventions can increase utility scores by 5.4 %. Based on the results, the key policy recommendations include expanding educational initiatives, implementing financial incentives, and integrating V2G-ready infrastructure in multi-unit dwellings.

车辆到电网（V2G）技术的成功采用取决于公众意识、基础设施准备情况和政策支持。本研究提出了一个球形模糊集证据推理（SFS-ER）框架来评估公众对V2G采用的看法，利用了来自加拿大不列颠哥伦比亚省奥肯那根（BC） 729名受访者的调查数据。本研究通过真信念（mT）、假信念（mF）和不确定性（mU）三个维度对信念结构进行量化，并计算效用分数来评估采纳可能性。使用肘部方法的K-Means聚类确定了三个不同的受访者群体：支持者（高信念和采用潜力），怀疑者（中等错误信念）和抵抗者（高度怀疑和不愿意采用）。研究结果表明，意识（mT = 0.78）和兴趣（mT = 0.75）是采用的最强预测因子，而仅熟悉程度的影响有限。高收入和受过大学教育的受访者表现出更高的意识，而公寓和联排别墅的居民则由于充电基础设施有限而面临结构性障碍。此外，敏感性分析证实，意识驱动的干预措施可以将效用得分提高5.4%。根据研究结果，关键的政策建议包括扩大教育举措、实施财政激励措施，以及在多单元住宅中整合支持v2g的基础设施。

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引用次数: 0

Mobility of disadvantaged communities exhibits lower sensitivity to extreme weather 弱势群体的流动性对极端天气的敏感性较低

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-11-01 DOI: 10.1016/j.tbs.2025.101163

Alireza Ermagun, Ella Zhang

This study investigates the extent to which extreme weather conditions (e.g., extreme heat, extreme precipitation, extreme wind, extreme humidity) affect bikeshare and rail transit usage of disadvantaged and non-disadvantaged communities. Three findings emerge from the evaluation of average daily bikeshare and average daily rail transit ridership in Chicago. First, bikeshare usage is more sensitive to extreme weather conditions than rail transit ridership. This indicates that bikeshare users are more likely to adjust their travel plans in response to adverse weather than rail transit users and reflects the greater environmental exposure associated with cycling relative to the more sheltered nature of rail transit. Second, heavy precipitation has the most significant impact on reducing ridership for both bikeshare and rail transit. Extreme rainfall leads to a 64% decrease in bikeshare usage and a 9% decrease in rail transit ridership. This demonstrates that heavy rainfall deters bikeshare users much more than rail transit riders. Third, trips originating from disadvantaged communities are less sensitive to extreme weather than those from non-disadvantaged communities. For bikeshare ridership, non-disadvantaged communities are, at the 90-100th weather percentile, approximately 1.29 times more sensitive to extreme heat, 1.19 times more sensitive to heavy rainfall, 1.07 times more sensitive to strong winds, and 1.29 times more sensitive to high humidity than disadvantaged communities. For rail transit ridership, at the 90-100th weather percentile, non-disadvantaged communities exhibit about 1.26 times greater responsiveness to temperature, 1.13 times greater responsiveness to precipitation, and 1.11 times greater responsiveness to humidity, while wind shows an insignificant effect. Although these ratios should be interpreted as indicative behavioral contrasts rather than precise statistical estimates, they suggest that individuals in disadvantaged communities may have fewer travel alternatives or a greater necessity to travel regardless of weather conditions.

本研究调查了极端天气条件（如极端高温、极端降水、极端大风、极端湿度）对弱势社区和非弱势社区共享单车和轨道交通使用的影响程度。通过对芝加哥平均每日共享单车和平均每日轨道交通客流量的评估，得出了三个结论。首先，与轨道交通相比，共享单车的使用对极端天气条件更为敏感。这表明，与轨道交通用户相比，共享单车用户更有可能调整他们的出行计划，以应对恶劣天气，并反映出与轨道交通更隐蔽的性质相比，与骑自行车相关的更大的环境暴露。其次，强降水对共享单车和轨道交通客流量的减少影响最为显著。极端降雨导致共享单车使用量减少64%，轨道交通客流量减少9%。这表明，与轨道交通乘客相比，暴雨对共享单车用户的影响更大。第三，来自弱势社区的旅行对极端天气的敏感性低于来自非弱势社区的旅行。在90-100个天气百分位数上，非弱势社区对极端高温的敏感度是弱势社区的1.29倍，对强降雨的敏感度是弱势社区的1.19倍，对强风的敏感度是弱势社区的1.07倍，对高湿度的敏感度是弱势社区的1.29倍。对于轨道交通客流量，在90-100个天气百分位数，非弱势社区对温度的响应性高1.26倍，对降水的响应性高1.13倍，对湿度的响应性高1.11倍，而风的影响不显著。虽然这些比率应该被解释为指示性的行为对比，而不是精确的统计估计，但它们表明，处于不利地位的社区的个人可能有更少的旅行选择，或者更有必要旅行，无论天气状况如何。

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引用次数: 0

Understanding the elements of the tourism accessibility chain 了解旅游无障碍链的要素

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-10-30 DOI: 10.1016/j.tbs.2025.101161

Francisco López-del-Pino , José M. Grisolía , Juan de Dios Ortúzar

This study investigates how different components of the accessibility chain influence tourist destination choices, using a Hybrid Discrete Choice (HDC) model that integrates stated preferences and latent attitudinal constructs. Drawing on a sample of visitors to Gran Canaria, the results show that staff preparedness, accessible environments, and reliable information significantly affect destination choice, with staff training emerging as the most influential factor. The research is grounded in the Social Model of Disability, which views disability as a social construct, and the Capability Approach, which focuses on individuals’ freedom to live the lives they value. These frameworks reframe accessibility not as an isolated technical issue but as a systemic condition enabling or constraining effective participation. The study also adopts a Universal and Inclusive Design perspective within this framework, recognising human diversity as a central design parameter in tourism systems. The estimated model reveals substantial heterogeneity in preferences: individuals with direct or indirect experience of disability place greater importance on staff support and trustworthy information. At the same time, younger and non-disabled tourists prioritise price and location. By combining behavioural and attitudinal data, the study demonstrates the added value of HDC models for understanding how accessibility is cognitively evaluated and behaviourally enacted. The findings have practical implications for inclusive destination design. Rather than addressing accessibility through isolated improvements, tourism systems should adopt a chain-based approach, ensuring coherence across all stages of the travel experience. As societies age and accessibility becomes a strategic imperative, this model provides a transferable tool for evaluating service gaps and guiding inclusive policy design.

本研究使用混合离散选择（HDC）模型，结合陈述偏好和潜在态度构念，探讨可达性链的不同组成部分对旅游目的地选择的影响。通过对大加那利岛游客的抽样调查，结果表明，工作人员的准备、无障碍的环境和可靠的信息显著影响目的地的选择，其中工作人员培训成为最具影响力的因素。该研究基于残疾的社会模型和能力方法，前者将残疾视为一种社会建构，后者关注个人过自己所珍视的生活的自由。这些框架不再将无障碍视为孤立的技术问题，而是将其视为能够或限制有效参与的系统条件。该研究还在这一框架内采用了通用和包容性设计的视角，将人类多样性视为旅游系统的核心设计参数。估计的模型揭示了偏好的实质性异质性：有直接或间接残疾经历的个人更重视工作人员的支持和可靠的信息。与此同时，年轻和非残疾游客优先考虑价格和位置。通过结合行为和态度数据，该研究证明了HDC模型在理解可访问性如何进行认知评估和行为制定方面的附加价值。研究结果对包容性目的地设计具有实际意义。旅游系统不应通过孤立的改进来解决可达性问题，而应采用基于链条的方法，确保旅游体验各个阶段的一致性。随着社会老龄化和无障碍成为战略要求，该模型为评估服务差距和指导包容性政策设计提供了可转移的工具。

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引用次数: 0

Characterizing walkability in Hong Kong’s 15-minute transit-oriented development(TOD): insights from street view imagery and local accessibility 香港15分钟公共交通导向发展（TOD）的可步行性特征：来自街景图像和当地可达性的见解

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-10-30 DOI: 10.1016/j.tbs.2025.101157

Zidong Yu, Ketong Shen, Xintao Liu

As urban environmental challenges intensify, sustainable transport modes like walking are increasingly prioritized. The 15-minute city model is introduced to support this shift by promoting access to daily essentials within a short walk. While existing research often focuses on accessibility to urban functions, the perceptual experience of the walking environment—especially in transit-oriented developments (TODs) within a 15-minute framework—remains underexplored. This study addresses this gap by developing a data-driven framework to quantitatively assess walkability in Hong Kong’s TOD areas. Our approach integrates two key dimensions: perceptual feeling and local accessibility. First, we use Vision Transformers to analyze street view images (SVIs) and quantify perceptual feelings such as safety, liveliness, and beauty. We employ a generalized additive model (GAM) to analyze how these visual elements correlate with human ratings of walkability. Second, we integrate these perceptual metrics with traditional accessibility measures. These perceptual metrics are then combined with accessibility measures—such as the total number and diversity of local destinations—to create a composite walkability index, mapping and analyzing spatial variations in walkability. This multi-faceted approach provides a more comprehensive understanding of walkability in dense urban contexts. Analysis reveals that street perceptions of safety and liveliness peak in central business districts. These lively areas are characterized by a visual composition rich in buildings and sidewalks, underscoring a positive link between dense urban form and pedestrian activity. These findings suggest that in highly accessible transit-oriented development (TOD) areas, improving pedestrian safety by expanding sidewalk width should be prioritized. By integrating visual perception with accessibility in the 15-minute city framework, practical planning insights to enhance urban walkability are discussed.

随着城市环境挑战的加剧，步行等可持续交通方式越来越受到重视。15分钟城市模式的引入是为了支持这一转变，通过促进在短步行内获得日常必需品。虽然现有的研究往往侧重于城市功能的可达性，但步行环境的感知体验——特别是在15分钟框架内的交通导向开发（TODs）——仍未得到充分探索。这项研究通过建立一个数据驱动的框架来定量评估香港TOD地区的步行性，从而解决了这一差距。我们的方法整合了两个关键维度：感知和局部可达性。首先，我们使用Vision transformer来分析街景图像（SVIs），并量化感知感受，如安全性、活动性和美感。我们采用广义加性模型（GAM）来分析这些视觉元素如何与人类对步行性的评价相关联。其次，我们将这些感知指标与传统的可访问性指标相结合。然后将这些感知指标与可达性指标（如当地目的地的总数和多样性）结合起来，创建一个复合的可步行性指数，绘制和分析可步行性的空间变化。这种多方面的方法提供了对密集城市环境中步行性的更全面的理解。分析表明，在中心商务区，街道对安全和活力的感知达到高峰。这些充满活力的区域以丰富的建筑和人行道的视觉构成为特征，强调了密集的城市形态和行人活动之间的积极联系。这些结果表明，在高度可达的公交导向开发（TOD）地区，应优先考虑通过扩大人行道宽度来改善行人安全。通过在15分钟城市框架中整合视觉感知和可达性，讨论了提高城市步行性的实际规划见解。

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引用次数: 0

Exploring the heterogeneity of changes in work-related mobility patterns throughout the COVID-19 crisis in Brazil: A latent Markov chain analysis of a 3-year panel data 探索巴西2019冠状病毒病危机期间工作相关流动模式变化的异质性：对3年面板数据的潜在马尔可夫链分析

IF 5.7 2区工程技术 Q1 TRANSPORTATION

Travel Behaviour and Society

Pub Date : 2025-10-25 DOI: 10.1016/j.tbs.2025.101160

Jorge Ubirajara Pedreira Junior , Cira Souza Pitombo

The COVID-19 pandemic catalyzed potential long-term shifts in the way people move. This research examined how individuals transitioned between work-related mobility patterns throughout the pandemic by conducting a 3‑wave longitudinal survey of Brazilian workers from 2020 to 2022. A combination of latent Markov models and exploratory data analysis was used to understand these transitions. Early in the pandemic, there was a significant shift towards telework, driven largely by the suitability of certain occupations for remote work, car-oriented mobility patterns with lower physical accessibility and increased concerns about contracting the coronavirus. However, after vaccination coverage surpassed 80%, coinciding with eased mobility restrictions and fewer COVID-19 cases, telework frequency decreased and commuting became more widespread. Nevertheless, the data suggest that car-dependent mobility patterns rebounded more strongly than others. Indeed, roughly 20% of those predominantly walking and using public transit before the pandemic were still working remotely at a later stage, whereas this figure was approximately 8% for car users. More concerning, carless-oriented individuals and teleworkers were more likely to switch to car-dependent travel at a later stage than the reverse. The research further reveals that the pandemic may have exacerbated existing disparities, particularly affecting low-income women with lower educational levels who were most vulnerable to COVID-19 exposure before widespread vaccination. These findings underscore the potential negative consequences of the pandemic on urban mobility and highlight the urgency of implementing effective policies to discourage car use and preserve urban density, particularly along mass transit routes and in city centers.

2019冠状病毒病大流行催化了人们出行方式的潜在长期变化。本研究通过对2020年至2022年巴西工人进行三波纵向调查，研究了在疫情期间个人如何在与工作相关的流动模式之间过渡。使用潜在马尔可夫模型和探索性数据分析相结合来理解这些转变。在大流行早期，人们向远程办公发生了重大转变，这主要是由于某些职业适合远程工作、以汽车为导向的移动模式较低的物理可达性以及对感染冠状病毒的担忧加剧。然而，在疫苗接种率超过80%之后，恰逢流动性限制放宽和COVID-19病例减少，远程工作频率下降，通勤变得更加普遍。然而，数据显示，依赖汽车的出行模式比其他模式反弹得更强烈。事实上，在疫情前主要步行和使用公共交通工具的人中，大约有20%的人在后期仍在远程工作，而汽车使用者中这一数字约为8%。更令人担忧的是，无车倾向的个人和远程工作者更有可能在以后的阶段转向依赖汽车的旅行，而不是相反。研究进一步表明，大流行可能加剧了现有的差距，特别是影响到受教育程度较低的低收入妇女，她们在广泛接种疫苗之前最容易感染COVID-19。这些调查结果强调了大流行对城市交通的潜在负面影响，并强调了实施有效政策以阻止汽车使用和保持城市密度的紧迫性，特别是在公共交通路线沿线和城市中心。

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