Urban Logistics Dynamics: A User-Centric Approach to Traffic Modeling and Kinetic Parameter Analysis

Authors: Emilienne Lardy, Eric Ballot, Mariam Lafkihi

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 modeling 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 behavior. This means opening the range of studied indicators beyond vehicle speed, to describe extensively the kinetic and dynamic aspects of the driving behavior. To achieve this, we leverage the Art. Kinema parameters are designed to characterize driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determines the mentioned driving behavior. Specifically, we explore how accurately the kinetic behavior 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 includes 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 the robustness of the models, as well as an overview of the model’s outputs.

Keywords: Factor analysis, generalized linear model, real world driving data, traffic congestion, urban logistics, vehicle kinematics.

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