Smart innovative congestion pricing strategy for Traffic demand management in smart cities using Structural Equation Modelling

In today's urban scenario, the demand for travel has drastically increased due to certain factors such as urbanization, increased desire for car ownership and improper land use transport integration which on the other hand has an impact on the physical, social, economic and environmental factors of the city along with externalities such as pollution and loss of man hours etc. The real question is regarding the elasticity of increasing the infrastructure supply to meet the increasing demand for the transportation. So, there needs to be innovative and effective measures that would optimize the demand through managing the existing infrastructure rather than increasing the supply of roads. Congestion pricing, one of the travel demand management strategies, is the charging of vehicles to enter the Congested Zones (CZ) during the certain times of the day (mainly peak hour travel times) to reduce the private vehicular flow and make people move towards the sustainable modes of transport. Congestion pricing is addressed as a reliable tool for enhancing the effectiveness of large cities and their surroundings within the case of public transportation policies. Understanding the psychological processes and perceptions of vehicle users toward pricing schemes, as well as the public's acceptance of new pricing schemes, is critical for developing a successful policy. The focus of this research is on how road pricing influences people's willingness to change their current modes of transportation and their psychological response towards the congestion pricing strategy among the private car users. The relation between the socio-demographic characters and their response towards shifting their mode from private means of transport(car) towards sustainable transport systems are evaluated through chi-square test of significance. The user responses from the stated preference survey towards the modal shift are evaluated using Reliability analysis and Structural Equation modelling in response to the psychological latent variables to know the significant factors.