Calibration of Vissim Model for Multilane Highways Using Speed Flow Curves

Abstract

Traffic flow is a complex phenomenon that needs better understanding of basic concepts and methods for its analysis. As a solution to practical problems, computer simulation has been proved to be a powerful tool in replicating complex traffic systems which allows experimentation to the basic traffic flow system. Various methods are available in literature, some of the methods use statistical tools to verify the difference between simulated and actual outputs and some are based on optimization which tries to minimize the error. For the present work, suitable methodology is adopted for the calibration process. Field data is collected, statistical tests are performed. For the simulation, VISSIM microscopic simulation tool is used and data analysis is performed by considering individual parameters and performance measures like speed, volume and random seed number. Statistical tests have been performed to check the sensitivity of the different simulation parameters. Calibration is done using trial and error method and optimization is performed using solver function. The maximum simulated flow rate was found with default values as 4599 veh/hr, and with calibrated values is 5147 veh/hr which is close to the target capacity 4958 veh/hr as obtained using field composition. Calibrated values of CC0 and CC1 and CC2 parameters were found as most optimized values to achieve target capacity. Finally, validation of calibrated parameter was performed by using different section of a multilane highway.

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

“… (GA) & (TS) Comparison between the observed and simulation. CC0, CC1, CC2, CC3, CC4, CC5, CC7 [ 33 ] W 99 Statistical Tests Solver Function Utilising distinct sections of a multi-lane highway. CC0 CC1 CC2 [ 34 ] W 99 ANOVA (GA) & Simultaneous Perturbation Stochastic Approximation (SPSA) The tool's functionality is assessed through a dataset acquired from the expressway.…”

Microscopic Traffic Simulation Calibration Level for Reliable Estimation of Vehicle Emissions

“…have been used to quantify estimation or prediction error and serve as performance measurements in various calibration-related research. Such research has focused heavily on numerical algorithms that minimize error by applying appropriate target values or fitness functions on their own [21][22][23][24][25]. It is, however, widely known that better performance can be expected when the measurement target vehicle driving in simulation [23].…”

“…e objective of the calibration is to fine-tune the parameters of the traffic model so that the discrepancy between the observed and simulated traffic flow is minimized. Most studies of microscopic traffic simulation calibration have focused on methods for adjusting complex combinations of behavioural parameters using optimization algorithms such as genetic algorithms (GA) rather than investigating calibration measures of effect (MOEs) [21][22][23][24][25]. Traffic engineers generally use aggregated traffic data as the MOEs such as volume, speed, travel time, and origin/destination volume for calibration of their models in order to replicate the actual traffic flows [24].…”

“…Most studies of microscopic traffic simulation calibration have focused on methods for adjusting complex combinations of behavioural parameters using optimization algorithms such as genetic algorithms (GA) rather than investigating calibration measures of effect (MOEs) [21][22][23][24][25]. Traffic engineers generally use aggregated traffic data as the MOEs such as volume, speed, travel time, and origin/destination volume for calibration of their models in order to replicate the actual traffic flows [24]. ough these basic MOEs seem to be sufficient to obtain realistic macroscopic traffic flow characteristics (i.e., capacity, average speed, and queuing and travel times), previous studies using them could not guarantee the sufficiently realistic microscopic details of vehicle characteristics (i.e., speed and acceleration) necessary to accurately predict emissions.…”

Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia

“…Researchers in recent years have attempted to calibrate (i.e., adjust the model parameters) different microscopic simulation tools for their respective traffic characteristics and driving conditions [9][10][11][12][13][14]. Most of the driving behaviors in traffic simulation models are determined through sub-models representing car following, gap acceptance, and lane changing behavior [15].…”