Significant private motorization to meet increased mobility requirements has resulted in severe congestion and parking problems in urban areas. In such a scenario, Park-and-Ride (P&R) has been a potential and established travel demand management strategy that efficiently improves urban mobility by significantly reducing traffic congestion and parking burden in city centers. Thus, it is essential that urban planners and policy makers establish P&R facilities at appropriate locations. The current manuscript presents a state-of-the-art review of past and present practices in selecting appropriate locations for establishing P&R facilities. The present study provides useful insights into the critical factors influencing the identification of potential P&R locations, establishment of P&R facilities at optimal locations, and development of commuters’ P&R location choice behavior models. The study findings could help researchers, planners, and policy makers collate basic ideas of P&R facility planning and develop context-specific guidelines to suitably locate P&R facilities as potential urban travel demand management solutions in the context of developing countries.

Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility of using the GPS trajectory dataset for travel time prediction in Indian traffic conditions having heterogeneous disordered traffic and improvement in prediction accuracy by shifting from the traditional historical average method to modern machine learning algorithms such as linear regressions, decision tree, random forest, and gradient boosting regression. The present study uses massive location data consisting of historical trajectories that were collected by installing GPS devices on the probe vehicles. A 3.6 km long stretch of the Delhi–Noida Direct (DND) flyway is selected as a case study to predict the travel time and compare the performance as well as the efficiency of various travel time prediction algorithms.

This paper discusses a model for, user oriented selection of bus rapid transit (BRT) corridor for Jaipur city in GIS environment. The objective of the model is to select the BRT corridor based on spatial distribution of transit trips in the city for horizon year. The model uses the demographic, transit trip and land use characteristics of the city to identify the high ridership oriented BRT corridor. The methodology comprises of two models, first model deals with BRT transit demand forecasting and second model is responsible for selection of the BRT corridor based on some pre-defined conditions. The model generates graphical GIS based maps as output for the better understanding of the transit demand pattern and policy making, for the urban planners. The methodology can be utilized for any similar size cities in Indian context for mass transit planning effectively