• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 44, Pages: 1-6

Original Article

MATLAB Adaptive Study of Traffic Related Accidents and Travel Demand Forecasting Case Study: Jalandhar

Abstract

The present studies arises with a need of elaborate computer adaptive simulation of traffic variables and provide models for accidents and travel demand using various variables and respective graphical representations, which would augment the conventional methods of forecasting providing rationality and validation of models. Objectives: 1. Improving the substantiality of traffic accidents forecasting by using Mathematical modeling and Computer programming such as MATLAB. 2. Understanding inter-dependence of traffic components. Method/Analysis: Taking in view of the constraints and resource availability, the study is subjected to the area of Jalandhar city. Changes in transportation system have its links attached to change in traffic characteristics and travel demand. Such effects have also been considered. MATLAB, a high level language of technical computing is used for mathematical modeling of various traffic related data and analyzing it by creating algorithms. The output of model gave relationship of total number of accidents and travel demand (values of coefficient of variables using 95% confidence limits) with respect to the traffic variable with r-square value (Coefficient of Determination) of the model comparing the actual values with simulated values to be 0.82, which shows the fitness of the model and assures the model to be good. Applications/Improvements: The study has resulted in providing a model for forecasting the total number of accidents and the travel demand of the study area. Various factors have been judged to perform the analysis inferring the significant impact of factors in predicting total number of traffic related accidents in Jalandhar.

Keywords: Accidents, MATLAB, Simulation, Traffic, Travel Demand

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