高效的城市物流需要对交通动力学形成全面理解，尤其是影响能耗与行程时长估算的运动学参数。尽管实时交通信息日益可得，但当前嵌入路径规划中的高精度预测服务对用户而言往往呈现为不透明的‘黑箱’。此类服务通常依赖经人工智能处理的车流计数数据，在管理研究（特别是供应链管理）所需的开放设计参数支持方面存在明显不足。本研究重新审视城市物流语境下的交通状况建模，强调从用户视角出发的重要性，并聚焦于两个方向：其一，关注对象并非车流本身，而是车辆个体及其所受交通状况对其驾驶行为的影响；这意味着需拓展所考察的指标范围，超越单一车速，系统刻画驾驶行为的运动学与动力学特征；为此，我们采用专为表征驾驶循环而设计的Art.Kinema参数。其二，本研究考察驾驶情境（即交通流之外的外生因素）如何决定前述驾驶行为；具体而言，我们探究在时间、星期、道路类型、朝向、坡度及天气条件等有限外生因素下，车辆运动学行为的可预测程度。为回答该问题，我们基于包含高频车速实测数据的真实驾驶数据集开展统计分析，并构建因子分析（Factor Analysis）与广义线性模型（Generalized Linear Model），以建立运动学参数与独立的分类情境变量之间的关联。

Efficient urban logistics requires a comprehensive understanding of traffic dynamics, particularly as it pertains to kinetic parameters influencing energy consumption and trip duration estimations. While real-time traffic information is increasingly accessible, current high-precision forecasting services embedded in route planning often function as opaque 'black boxes' for users. These services, typically relying on AI-processed counting data, fall short in accommodating open design parameters essential for management studies, notably within Supply Chain Management. This work revisits the modelling of traffic conditions in the context of city logistics, emphasizing its significance from the user's point of view, with two focuses. Firstly, the focus is not on the vehicle flow but on the vehicles themselves and the impact of the traffic conditions on their driving behaviour. This means opening the range of studied indicators, beyond vehicle speed, to describe extensively the kinetic and dynamic aspects of the driving behaviour. To achieve this, we leverage the Art.Kinema parameters designed to characterizing driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determine the mentioned driving behaviour. Specifically, we explore how accurately the kinetic behaviour of a vehicle can be predicted based on a limited set of exogenous factors, such as time, day, road type, orientation, slope, and weather conditions? To answer this question, statistical analysis was conducted on real-world driving data, which include high-frequency measurements of vehicle speed. A Factor Analysis and a Generalized Linear Model have been established to link kinetic parameters with independent categorical contextual variables. The results include an assessment of the adjustment quality and of the robustness of the models, as well as an overview of the models' outputs.