Welcome to one of the most comprehensive courses in Transportation Engineering which will help you enlighten about everything in Traffic Engineering. In this course, there are three sections - Traffic Operations, Traffic Forecasting and Multinomial Logit Modelling in R programming. In the first section, you will get to know about traffic stream parameters such as speed, density and flow and discuss their inter-relationship. You will also learn about conceptual topics such as time headway and space headway as well as time space diagrams which immensely help academicians and practitioners to analyze the traffic streams. Further, you will learn about the moving observer method which is used in the field to collect values of traffic stream parameters such as density, speed and flow. Further, you will also get to know about statistical modelling of vehicle arrivals using headways as well as using counts. The latter is done using Poisson distribution. This lays the foundation for simulating traffic on any computer program such as VISSIM. Moreover, you will learn the definition of capacity and level of service (LOS) of the road facility as well as the definition of ramp metering and different strategies to implement it with their benefits. Afterwards, you will learn about cumulative plots such as arrival curves and departure curves which are important in finding delays along with queueing theory that is applicable in the operations field as well. You will also learn about steps in designing a traffic signal. In the second section, you will learn about how to forecast traffic i.e. after 20 years how many vehicles will be there on roads. This is especially important when we are creating development plans for the next 20 years or 30 years. In this section, you will learn about Trip Generation, Distribution, Mode Choice as well as Traffic Assignment. You will be able to find the number of trips generated using the Institute of Transportation Engineers (ITE) Manual as well as be able to interpret utility equations in mode choice models after completing this section. Third section deals with discrete choice theory and its application on a real life case study from city of Bengaluru in India. Concept of Multinomial logit modelling is fully explained. Real data sets from a survey are analyzed and multinomial logit model is applied on the same in R programming. This course is for traffic engineers, transportation engineers, urban planners, transportation planners, undergraduate students in civil engineering as well as post graduate students in transportation engineering. There is no prerequisite to take this course. This course is being taught by Parth Loya where he is putting his years of experience of working as a Senior Traffic Engineer in one of world’s most congested city - Mumbai. He is also an alumnus of world’s no. 1 transportation engineering institute - ITS, Berkeley. If you need discount coupon for this course, drop an email at loya. parth@gmail.com
