Transportation Planning using Activity-Based Travel Demand Model

Ramesh Kumar*
Department of Civil Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
Periodicity:April - June'2023


According to Bangalore's Transportation Department, the number of transport vehicles in the city will be close to 85.6 lakh two wheelers, 50 lakh automobiles, and 20 lakh transport vehicles by the end of 2023. Two-wheelers account for 58.41% of all vehicles, while Light Motor Vehicles (LMVs) account for 22%, Heavy Motor Vehicle (HMVs) for 11%, and other vehicles account for 8.59%. Therefore, there is a need for efficient transportation planning. From earlier research, it was found that activity-based modeling is more efficient for evaluation. In this study, a method was proposed to develop an activity-based travel demand model for the selected zones of Bangalore city, and work trips were generated from the Global Tech Park. The trips were monitored, and it was found that people came from all around Bengaluru. Four zones in Bengaluru were chosen for this study because they provided the most travel to the Global Tech Park. The four zones were Kengeri, Mylasandra, RR Nagar, and Vijaynagar. The data were collected through individual person surveys, considering the influential parameters in developing person tours. The collected data were analyzed using SPSS software, and models were developed based on parameters such as age, sex, monthly income, distance traveled, active travel, daily travel cost, and vehicle ownership. The results obtained in the form of the models were compared with those of traditional models. In addition, the factors influencing the trips of each individual were studied, and the effects of these factors were analyzed. The results obtained were satisfactory in terms of the R2 value and other testing parameters. From the primary data analysis, it was found that the preference for modal choices of the public increased from public transport (i.e., buses) to private transport (i.e., owned cars) as per the increase in age, income, professions, travel distance, and daily travel cost.


Transportation, Activity-Based Travel Demand, Planning, Testing Parameters, Data Analysis.

How to Cite this Article?

Kumar, R. (2023). Transportation Planning using Activity-Based Travel Demand Model. i-manager's Journal on Civil Engineering, 13(2), 30-37.


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