Calibration of SUMO Car-Following and Lane-Change Models for Heterogeneous Traffic on Egyptian Two-Lane Two-Way Roads

Abstract

Traffic simulation models require calibration to capture local driving behavior, particularly in developing countries where conditions differ from Western defaults. This study calibrates three car-following models—Krauss, Wiedemann 99 (W99), and Intelligent Driver Model (IDM)—with lane-change parameters for heterogeneous traffic on six two-lane two-way roads in Egypt’s Nile Delta. A sensitivity analysis reduced eight lane-change parameters to three critical ones: lcOpposite, lcSpeedGain, and lcStrategic. A Genetic Algorithm then optimized car-following and lane-change parameters jointly across multiple seeds and generations. The best configurations achieved mean absolute percentage errors of 8.79% (IDM), 8.94% (W99), and 10.56% (Krauss), indicating that IDM and W99 performed similarly under the original four-metric validation, while Krauss was less accurate overall and exceeded the overtaking-rate threshold. suggesting calibration methodology matters more than model choice. Minimum standstill gap (minGap) treatment varied by model: freeing it improved IDM and Krauss performance, while fixing it at 1.50 m benefited W99. Constraining minGap in IDM increased error by 2.06 percentage points and altered parameter sensitivity, with lcStrategic becoming dominant and tau showing reduced sensitivity. Notably, SUMO’s uncalibrated Krauss defaults achieved 10.11% error under understaturated conditions, though this should not extend to congested scenarios. Simulated standard deviations underestimated observed variability, primarily due to aggregation mismatches rather than model inadequacy.