A Kinematic Analysis of Vehicle Acceleration from Standstill at Signalized Intersections: Implications for Road Safety, Traffic Engineering, and Autonomous Driving

Alfonso Micucci (), Luca Mantecchini, Giacomo Bettazzi and Federico Scattolin
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Alfonso Micucci: Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Luca Mantecchini: Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy
Giacomo Bettazzi: KINEMATICA SRL, Via Ferrarese 24/4, 40128 Bologna, Italy
Federico Scattolin: KINEMATICA SRL, Via Ferrarese 24/4, 40128 Bologna, Italy

Sustainability, 2025, vol. 17, issue 20, 1-26

Abstract: Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis.

Keywords: vehicle acceleration; electric vehicles; traffic safety; ADAS; forensic engineering; driver behavior; autonomous vehicles; traffic simulation models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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