This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Autonomous vehicle technologies are anticipated to transform road transportation systems, promising enhanced traffic safety and efficiency across different modes, including public buses (PB) and ride-pooling services (RPS). However, in India, there is a growing security concern/fear of crime concerning conventional PB and RPS because of the recent rise in crimes committed on them. Moreover, the introduction of driverless modes of PB and RPS may further heighten commuters’ crime concerns on such services because of the absence of a driver. Thus, this study investigates the acceptance of autonomous public buses (APB) and autonomous ride-pooling services (ARPS), as well as how commuters’ characteristics influence the perceived risks of crime and victimization and their willingness to use the modes. To achieve this, a stated preference survey was designed and conducted across India. The survey resulted in 732 complete responses. The results show that socioeconomic attributes, vehicle automation, and security-related measures significantly influence commuters’ perceived fear of crime and willingness to use APB and ARPS in India. More specifically, young commuters demonstrate higher willingness to use APB and ARPS, while females exhibit lower willingness to use APB and ARPS. In addition, the presence of a security officer on these modes decreases commuters’ concerns about crime. Moreover, travel distance is positively associated with commuters’ perceived level of crime and victimization, while it has a negative relationship with their unwillingness to use APB and ARPS. APB and ARPS are yet to be introduced in India, and Indian commuters have not experienced the security concerns associated with them; thus, the results of this study can serve as the base for guideline formulation for security concerns in India. Based on the results of this study, a set of policy implications, such as female-only transit units , enhancing security measures on the automated modes , and design framework and infrastructure , were proposed. These policy implications can be instrumental in increasing the acceptability of APB and ARPS in India and other countries with similar characteristics.