Development of mode choice models of a trip maker for Hyderabad metropolitan city

Abstract

The rapid development of urbanization, population growth and the rapid development of economy resulted in the rapid increase in the total number of motor vehicles in the modern cities of India. Consequently, the importance of forecasting of the travel demand model has been increased in the recent years. Forecasting of the travel demand model involves various stages of trip generation and distribution, mode choice and traffic assignment. Among these stages, the mode choice analysis is a prominent stage as it considers the travelers mode to reach their destination. Further, study of mode choice criteria has become a vital area of research as individual and household sociodemographics exert a strong influence on travel mode choice decisions. There is a huge literature on travel model choice modeling to predict the range of trade-offs of transportation of commuters considering travel time and travel cost. In such literature intercity mode choice behavior has gained significant attention by several authors. In this study an attempt has made in order to calculate the model share of the different modes between the circle to the circle, and it is found that the modal share of 2-wheeler is 70 %, bus is about 23 % and car is about 7% of the total trips.

Modeling of time series data for forecasting the number of foreign tourists in east Kalimantan using fuzzy inference system based on ARX model

“…Seasonality and volatility are important features of tourism data, therefore the context is a favorable feature for comparing the performance of linear model forecasting with non-linear alternative approach [3], [4]. A research has been conducted to forecast demand travel models involving stages of trip generation and distribution with different types of traffic density models [5]. Other studies have compared between modern and classical methods, in which classical methods are represented by Box Jenkins, ARIMA, SARIMA, Holt-Winters and time series regression methods [6]- [8].…”

Total asset prediction of the large Indonesian bank using adaptive artificial neural network back-propagation

“…To improve forecasting results, many researchers have applied machine learning methods in some forecasting activities. Machine learning methods have been used independently, combined with inter-methods, even combined with various statistical methods such as in [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In this study, the prediction of the total asset is based on the time series data modeling using AR and MISO-ARX models.…”