Trip Attraction Rates of Commercial Land Use: A Case Study

Background: Due to the high growth rate of urbanization in developing world leads to increase in vehicular traffic. Travel demand models are useful in managing the increased travel demand. Statistical Analysis: Trip generation step is essential in planning of transportation facilities for any city. This paper mainly focuses analyzing the trip attraction rates of commercial land use in different cities of Andhra Pradesh and Telangana states. As a part of the study, a total of seven commercial centres are studied by defining the four broad commercial centre typologies. Various factors considered for defining these typologies are physical features of study area (sq. ft), parking spaces, number of employees, number of stores, number of people attracting etc. Findings: The very first step in traditional travel demand modelling process is trip generation. It is very important for a planner to estimate the impact of changes due to establishment of new facilities like offices, shopping centres and any residential land use. In the study area, the shopping trips constitute the second largest share of trips after the work trips. Because of its major share, these trips not only influence the individual travel behaviour but also showing great impact on transport network. Unlike the previous studies, this study defined the typologies for commercial areas based on their physical characteristics and the same is used for the analysis. These typologies are defined based on the various characteristics of commercial area like floor area, number of stores and number of employees. Improvements: The accuracy of this analysis can be improved by considering more number of commercial areas. Since there are no earlier studies reported in estimating trip attraction rates in the study area, this study might provide an insight to the future researchers. Also the in depth literature study while defining typologies of commercial areas and more factors for analysing trip rates might further helpful in accurate prediction of trip rates.