Abstract
India is undergoing economic change. Cities now have better quality of life, and they are now crucial hubs for human existence. The increase in urbanization of Indian cities was facilitated by this influx of people. Prosperity and changing lifestyles brought about by a booming economy made reliance on private vehicles essential. The total demand for travel has accelerated along with population expansion and increased car ownership, but the supply side has lagged behind demand and there are numerous external variables associated to transportation, such as accidents, congestion, pollution, inequality, etc. Promoting and promoting sustainability is important in the contemporary urban transportation environment, sustainable transportation policies. These policies' principal goal is to change people's travel habits, or, in other words, to alter the travel environment. However, many of these rules' ramifications are unclear or complicated. As a result, it is critical that decision-makers are informed of the effects of such policies before adopting and putting them into practice. Models of travel demand can be used in this situation to forecast future travel demand under various policy scenarios. In order to analyze sustainable transportation strategies, this study analyses the possibilities of travel demand models already in use in India. The study discovered that the trip-based, four-step aggregate method used in India as the standard model system was nsufficient for studying sustainable transportation policy. An analysis of an alternative strategy known as activity-based travel demand modeling revealed that it could manage such policies better than traditional models and was useful in selecting the best combination of policies for particular circumstances. Since India has not yet created an operational activity-based travel demand modeling system, the study concludes by proposing a conceptual framework for an integrated activity-based demand model based on the needs identified within the review's framework. In accordance with people's current activity-travel behavior, it can be utilized to create modified and verified applications for existing travel demand models. The final result is done by using the EDAS method. Delhi is highest Value and Agra is lowest value.
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