Calibration of the Gipps Car-following Model Using Trajectory Data

• Autor(a) principal: Vasconcelos, Luís
• Data de Publicação: 2014
• Outros Autores: Neto, Luís, Santos, Sílvia, Bastos Silva, Ana, Seco, Álvaro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.19/2645
• topic: Car Following; Gipps; Gipps; Acceleration; Reaction Time; Genetic Algorithm

One of the most important tasks in the microscopic simulation of traffic flow, assigned to the car following sub-model, is the modelling of the longitudinal movement of vehicles. The calibration of a car-following model is usually done at an aggregated level, using macroscopic traffic stream variables (speed, flow, density). There is an interest in calibration procedures based on disaggregated data. However, obtaining accurate trajectory data is a real challenge. This paper presents a low-cost procedure to calibrate the Gipps car-following model. The trajectory data is collected with a car equipped with a datalogger and a LIDAR rangefinder. The datalogger combines GPS and accelerometers data to provide accurate speed and acceleration measurements. The LIDAR measures the distances to the leading or following vehicle. Two alternative estimation methods were tested: the first follows individual procedures that explicitly account for the physical meaning of each parameter; the second formulates the calibration as an optimization problem: the objective function is defined so as to minimize the differences between the simulated and real inter-vehicle distances; the problem is solved using an automated procedure based on a genetic algorithm. The results show that the optimization approach leads to a very accurate representation of the specific modeled situation but offers poor transferability; on the other hand, the individual estimation provides a satisfactory fit in a wide range of traffic conditions and hence is the recommended method for forecasting purposes.