Tanveer Qaiser et al., International Journal of Emerging Trends in Engineering Research, 9(5), May 2021, 565 – 569 565 ABSTRACT Travel time is one of the simplest yet the most important parameter for transportation facility users as well as transportation engineers. Travel time data is valuable for wide range of transportation analysis including congestion management, transportation planning and passenger’s decision making. Traffic simulation models are now becoming necessary tools to understand the behavior of traffic and reduce vehicular travel times, but it is very important to calibrate these models first. This study attempts to determines the values of those parameters, using microsimulation, that significantly affect the travel time. These parameters are then used for calibrating the traffic simulation model that results in realistic travel time. Study was conducted on an urban road and field data was collected during weekdays for peak hours. The traffic network was modelled using VISSIM ® . The calibration parameters were desired speed distribution, number of lanes, average standstill distance and minimum headway. After calibrating the model, the travel times collected from field data and those by simulations for different modes of transportation were in close agreement. Keywords: Desire Speed Distribution, Microsimulation, Transportation Planning, VISSIM ® . 1 INTRODUCTION Transportation sector is one of the most vital and significant sectors of a city that plays a key role in the social, economic and cultural development of a city and ensures its successful functioning. Though, due to rapid increase in the population of metropolitan cities, this sector is facing major problems including traffic congestion, low fuel efficiency, increased vehicular travel times and exhaust emissions. To represent on-field conditions on urban roads as close as possible and study the behavior of vehicles for effective traffic management and control analysis, microscopic traffic simulation tools have been widely used [1]. [2] has briefly discussed the different tools used for traffic modelling and analysis. These traffic simulation tools are now becoming integral part of Intelligent Transportation Systems (ITS) for traffic regulatory control in urban areas. The complication of traffic stream behavior and the difficulties in performing experiments with real world traffic make these computer-generated simulations an important analysis tool in transport and traffic engineering. However, traffic simulation models need to be calibrated and validated first. Various microscopic traffic simulation tools consist of several controllable and uncontrollable input parameters to explain the existing traffic flow especially the driver behavior. The simulation tools have default input values for these parameters; however, they offer the user to put the values of these parameters according to the local conditions to accurately represent the on-field traffic conditions. This process is called the model calibration. Model validation is the process to find out whether the simulation model is an accurate representation of the system under study or not by the comparison of values generated from simulation models of certain measure of effectiveness to the on-field values with the condition that the on-field values must be of the same measure of effectiveness. It is necessary to select certain measure of effectiveness like vehicular travel time or queue length and then determine parameters that affects these measures of effectiveness (travel time/ queue length) for the proper calibration and validation of these models. After the calibration and validation of traffic simulation models, they can be used for urban congestion management, and analyze the impact of urban development plans through graphical depiction of traffic flows. [3] described the calibration process of microscopic traffic simulation model in detail having three main phases: (Phase 1) tasks and activities which are done before the start of any calibration model like identification of goals and field data which is to be collected,(Phase 2) initial calibration of the simulation model and (Phase 3) comparison of the results from the simulation model with filed data. The study provides a very thorough procedure for the calibration of the model however, there is no direct method of model validation of the model. [4] developed a methodology for the validation of simulation model using 5 key elements: (1) context, (2) data, (3) uncertainty, (4) feedback and (5) prediction. The simulation tool used was CORSIM ® to validate the signal times in Chicago by the numerical comparison of the collected field data with the CORSIM ® [5] model through visualization . Calibrating Microsimulation Parameters for Vehicular Travel Time Tanveer Qaiser 1 , Muhammad Umair Khan 2 , Salman Saeed 3 1 Department of Civil Engineering, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan, tanveerqaiser@cecos.edu.pk 2 Department of Civil Engineering, Abasyn University, Peshawar, Pakistan, muhammad.umair@abasyn.edu.pk 3 National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar, Pakistan, salmansaeed@uetpeshawar.edu.pk ISSN 2347 - 3983 Volume 9. No. 5, May 2021 International Journal of Emerging Trends in Engineering Research Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter05952021.pdf https://doi.org/10.30534/ijeter/2021/05952021