Transportation Research Part A-Policy and Practice最新文献

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Explaining patterns of cycling speed stability and disruption 解释循环速度、稳定性和中断的模式

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-03 DOI: 10.1016/j.tra.2025.104855

Hong Yan , Kees Maat , Bert van Wee

Cycling speed is an important attribute of bicycle traffic flow, being related to travel times, safety and road capacity. Although cycling speed changes constantly during a trip, it is typically measured at the trip-average or aggregated level, and microscopic speed fluctuations are rarely studied. This study aims to quantitatively understand the cycling speed stability within a trip and the determinants of speed stability and disruption. To this end, data from bicycle trips tracked with GPS devices are used. A change point detection method, the pruned exact linear time (PELT) algorithm, is adapted to split trip trajectories into segments differing in speed stability. Then, a rule-based algorithm is developed to classify segments into six speed (in)stability patterns: stable, increase, decrease, V-shape (speed decreases followed by increases), reverse V-shape (speed increases followed by decreases) and complicated unstable patterns. Finally, a two-level multinomial model is estimated to examine the determinants of different patterns. The findings suggest that stable patterns account for half the trip distances, and their speed is higher than the speed of unstable patterns. The V-shape pattern is the most frequent unstable type. Intersections, turns and built-up land use are the main causes of unstable speeds. Cycling on physically separate paths tends to involve more unstable speeds than on mixed-use infrastructures, such as bicycle streets and bicycle tracks. This study finds that daily cycling involves a considerable amount of unstable speed. While its effects have not been directly examined, speed instability likely increases travel times and physical effort and is perceived negatively by cyclists. This underscores the potential benefits of a smooth cycling network and highlights the need for future research on the role of speed stability.

骑行速度是自行车交通流的一个重要属性，它关系到骑行时间、安全性和道路通行能力。虽然骑行速度在行程中不断变化，但通常是在行程平均值或汇总水平上测量的，很少研究微观速度波动。本研究旨在定量了解行程内的循环速度稳定性以及速度稳定性和中断的决定因素。为此，使用了GPS设备跟踪的自行车旅行数据。采用一种变化点检测方法，即修正精确线性时间（PELT）算法，将行程轨迹分割为不同速度稳定性的路段。然后，开发了一种基于规则的算法，将路段划分为稳定、增加、减少、v型（速度先降低后增加）、反v型（速度先增加后降低）和复杂不稳定的6种速度稳定模式。最后，估计了一个两级多项式模型来检查不同模式的决定因素。研究结果表明，稳定模式占行程距离的一半，其速度高于不稳定模式的速度。v型模式是最常见的不稳定类型。十字路口、转弯处和建筑用地是造成车速不稳定的主要原因。与混合用途的基础设施（如自行车街和自行车道）相比，在物理上分开的道路上骑自行车往往涉及更不稳定的速度。这项研究发现，日常骑行涉及相当多的不稳定速度。虽然它的影响还没有被直接研究，但速度不稳定可能会增加旅行时间和体力消耗，骑自行车的人对此持负面看法。这强调了一个平稳的自行车网络的潜在好处，并强调了对速度稳定性的作用进行未来研究的必要性。

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引用次数: 0

Non-linear effects of built environment on cyclists’ perceived safety and comfort using online pairwise voting 基于在线两两投票的建筑环境对骑行者感知安全性和舒适度的非线性影响

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-02 DOI: 10.1016/j.tra.2025.104850

Lurong Xu , Shuli Luo , Alexa Delbosc , Steve O’Hern , Zhuo Chen

Understanding how built environment (BE) features influence cyclists’ perceptions is critical for designing human-centric and bike-friendly spaces. While previous research has largely focused on objective cycling conditions, this study addresses a key gap by investigating the non-linear relationships between BE features and cyclists’ subjective perceptions of safety and comfort, through a novel integration of Street View Images (SVIs), computer vision (CV), and explainable machine learning (ML). Using a pairwise voting survey, 150 participants evaluated 300 SVIs, with the results extrapolated to 5,176 locations using CV and ML models. The Shapley Additive Explanations (SHAP) framework quantified threshold effects and highlighted asymmetries between perceived safety and comfort, revealing three key insights: (1) Over 80% of subjective perceptions are explained by spatial chaos, greenery, cycle lane type, feature entropy, perceived road width, and crowdedness, though their relative importance and thresholds vary; (2) Several BE features exhibit threshold effects, including notable inverted “V-shaped” trends, with safety demonstrating higher sensitivity and lower tolerance to BE features compared to comfort; (3) Comfort-oriented interventions are particularly effective in heritage areas where structural constraints limit safety improvements. These findings emphasize the need for localized and context-sensitive strategies that differentiate between safety-oriented and comfort-prioritized interventions, enabling more effective cyclist-friendly urban design.

了解建筑环境（BE）特征如何影响骑自行车者的感知，对于设计以人为本和自行车友好型空间至关重要。虽然之前的研究主要集中在客观的骑行条件上，但本研究通过街景图像（SVIs）、计算机视觉（CV）和可解释机器学习（ML）的新颖集成，通过调查BE特征与骑行者对安全性和舒适性的主观感知之间的非线性关系，解决了一个关键空白。使用两两投票调查，150名参与者评估了300个svi，使用CV和ML模型将结果外推到5176个地点。Shapley加性解释（SHAP）框架量化了阈值效应，并强调了感知安全性和舒适性之间的不对称性，揭示了三个关键见解：(1)超过80%的主观感知由空间混沌、绿化、自行车道类型、特征熵、感知道路宽度和拥挤程度解释，尽管它们的相对重要性和阈值各不相同；(2)几个BE特征表现出阈值效应，包括显著的倒“v”型趋势，与舒适性相比，安全性对BE特征的敏感性更高，容忍度更低；(3)在结构约束限制安全改进的遗产地区，以舒适为导向的干预措施尤其有效。这些发现强调了本地化和环境敏感策略的必要性，以区分以安全为导向和以舒适为优先的干预措施，从而实现更有效的自行车友好型城市设计。

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IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

IF 6.8 1区工程技术 Q1 ECONOMICS

Transportation Research Part A-Policy and Practice

Pub Date : 2026-01-01

引用次数: 0

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