The freedom of movement for people in urban cities in the twenty-first century is curtailed due to traffic congestion and parking problems. It has brought out the need for development of smart cities through incorporation of Internet of Things and other technologies in the planning of cities. Chennai, a city in India with a population size of 9.1 million and a vehicle pool of more than 3.7 million units, is in dire need of intelligent solutions to its traffic and transportation issues. Sustainable and practical city planning not only requires considering increased air pollution, longer travel duration, accidents, but also with frustration among travelers. Hence, smart city planning measures are incomplete if parking space issues are ignored. In this paper, we address the parking space problem and how they can be tackled in smart cities in the interest of both government and travelers. Based on the Stackelberg game model, our proposed scheme considers a game between the public authority and the travelers. The government with the help of its policies can influence the decisions taken by the travelers. In this study, we work upon proposing two utility function—one for traveler and one for government. The utility function of the government aims to maximize the public transit usage while maintaining flow of travelers to urban city centers in Chennai. City center refers to an urban center such as a shopping mall, heritage site, or government building. The utility function of an individual traveler aims to minimize travel duration, cost of travel, and other inconveniences. A trade-off demand of the players is sought using variable parking space. A small version of the game is envisioned for urban reality-based scenarios like strong center, weak center, weak transit system, strong transit system, respectively. Consequently, on the basis of the results, optimized parking space allotment is obtained and we infer that reducing parking space works more promisingly in a strong urban center scenario. The solution provided in our paper can be collaborated with existing smart parking systems for optimal results. Conclusions inferred can be applied by the government towards development of practical, efficient, and sustainable parking schemes. © 2020, Springer Nature Singapore Pte Ltd.