🤖 AI Summary
This study addresses the limitations of prevailing urban logistics traffic models, which predominantly focus on macroscopic traffic flow and rely on black-box AI predictions, thereby hindering interpretable research with open parameters in supply chain contexts. Departing from the vehicle-flow-centric paradigm, this work adopts an individual-vehicle perspective and introduces a kinetic parameter framework grounded in Art.Kinema to characterize driving cycle features. By integrating high-frequency empirical vehicle speed data, the authors employ factor analysis and generalized linear models to quantify the impacts of exogenous factors—such as time of day, road type, and weather—on driving behavior. The resulting context-driven predictive model demonstrates strong goodness-of-fit and robustness, offering a theoretically sound foundation for transparent route planning and logistics decision-making.
📝 Abstract
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.