Abstract
Calibrating the microsimulation model is essential to enhance its ability to capture reality. The paper proposes a Bayesian neural network (BNN)-based method to calibrate parameters of microscopic traffic simulators, which reduces repeated running of simulations in the calibration and thus significantly improves the calibration efficiency. We use BNN with probability distributions on the weights to quickly predict the simulation results according to the inputs of the parameters to be calibrated. Based on the BNN model with the best performance, heuristic algorithms (HAs) are performed to seek the optimal values of the parameters to be calibrated with the minimum difference between the predicted results of BNN and the field-measured values. Three HAs are considered, including genetic algorithm (GA), evolutionary strategy (ES), and bat algorithm (BA). A TransModeler case of highway links in Shanghai, China, indicates the validity of the proposed calibration method in terms of error and efficiency. The results demonstrate that the BNN model is able to accurately predict the simulation and adequately capture the uncertainty of the simulation. We also find that the BNN-BA model produces the best calibration efficiency, while the BNN-ES model offers the best performance in calibration accuracy.
Highlights
In the process of efficiently assessing the influence of traffic management schemes and even emerging technologies on traffic performance, simulations are powerful tools to simulate the proposed scenes without the need for field experiments [1]
A new parameter calibration method for microscopic traffic simulators combining Bayesian neural network (BNN) and heuristic algorithms (HAs) is proposed. e objective of the BNN is to quickly predict simulation results with uncertainty based on inputs of traffic simulator parameter values without real simulation
Based on the trained BNN model, the purpose of running HA is to search for the optimal values of the parameters to be calibrated in the traffic simulator to minimize the difference between fieldmeasured values and the predicted simulation results from BNN without simulation. e combination of BNN and HA avoids running the simulator repeatedly during the calibration, which can significantly decrease the computation time of calibration. e research innovation lies in the huge improvement of calibration efficiency and in which the method is universal for microscopic traffic simulators
Summary
In the process of efficiently assessing the influence of traffic management schemes and even emerging technologies on traffic performance, simulations are powerful tools to simulate the proposed scenes without the need for field experiments [1]. Considering that the traffic conditions vary from place to place, the default values of parameters from simulator developers are inconsistent with the specific characteristics of the places of simulation. Us, it is important to calibrate the parameter values, which enables the simulator to be used for traffic analysis under the preferred background. E calibration is still challenging in microscopic traffic simulation containing high uncertainty of the simulation system and a large number of parameters [3]. Erefore, finding specific parameter values during the calibration process will be time-consuming and computationally expensive, and it is necessary to improve calibration efficiency Considering that simulation is based on a certain scale of the road network and complex calculations, an inestimable and long period of time is spent on one Journal of Advanced Transportation simulation. e number of simulations is affected by calibration convergence. erefore, finding specific parameter values during the calibration process will be time-consuming and computationally expensive, and it is necessary to improve calibration efficiency
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call