CAPACITY ANALYSIS OF BRTS USING MICROSIMULATION

Abstract

Urban Public Transportation, popularly known as "transit," has been properly referred to as the "lifeblood of cities." Its role now been recognized as essential for physically active, economically sound, and energy efficient cities. Transit systems and available technologies are much more complex and diversified today than they were a couple of decade ago. Estimating bus lane capacity of Bus Rapid Transit System is imperative for decision-makers and planners in developing countries. The main reason is to determine the state beyond which, buses operating on transitway cannot be increased further without making design changes in the 4 transitway. Moreover, the capacity has an influence over speed, travel time and reliability of BRT systems. These parameters affect the time required by the bus to make a round trip on a transit line, which further affects the number of buses, needed to serve the transit line at a given headway. In context of existing BRTS corridors in India, knowledge about bus lane capacity is scarce. Therefore, this study demonstrates the applicability of empirical and simulation models to estimate bus lane capacity of Bhopal BRTS, which started its operation in September 2013.India. P. In addition, this study also presents a procedure that schedules the departure headway of buses to achieve a desired operational reliability. The study shall be useful for planning and designing BRTS projects for other cities. For empirical model, a widely accepted approach in the literature namely Transit Capacity and Quality of Service Manual (TCQSM) methodology was considered. The input data required to calculate bus lane capacity have two parts, namely bus stop demand data which includes dwell time (waiting time at bus stop), dwell time variability, failure rate (probability of queue formation at bus stop), and bus stop location data which include position relative to roadway, ar position relative to an intersection, number of loading areas, traffic signal timing. All these input data collected during field surveys. A spreadsheet tool was framed based on the TCQSM procedure. After this the bus stop capacity of all 20 bus stops were calculated and lane capacity was estimated by considering the bus stop having least capacity (critical bus stop). Further, different failure rate values were assumed and corresponding bus lane capacity values were estimated. This study reports that there is linear relationship between failure rate and bus lane capacity. a The average dwell time values observed at 20 bus stops of the transitway range from 8 to 75 s. I- The estimated bus lane capacity values range from 41 to 294 buses per hour depending upon their relative bus dwell times and failure rate. Chanchal Chouraha stop is the critical bus stop, having capacity of 41 buses per hour and highest average dwell time. It was observed that a failure rate of 10% was observed at critical bus stop. The study also reports the reasons observed for such a low value of capacity and measures that can be implemented to increase the bus lane capacity of Bhopal BRTS. The presented study shall provide recommendations to implement the TCQSM guidelines in Indian conditions and the suitable changes undertaken according to the Indian scenario. In simulation, a base network of Bhopal BRTS transitway was developed, calibrated, and validated in microsimulation software VISSIM. Following this, two approaches, namely Failure Rate (FR) and Speed Reduction (SR) have been implemented in simulation model to estimate bus lane capacity. The comparison of simulation outcomes with TCQSM results show that FR approach is most reliable one to estimate bus lane capacity. Finally, both FR and SR approaches were implemented on simulation model to schedule the headway of BRT buses. The study concluded that if the existing schedule headway of buses is decreased by 60% then an increased capacity of 45 bus/h could be achieved at Bhopal BRT transitway. In addition, at this scheduled headway, the probability of queue formation at bus stops on transitway is less than 13 %. Keywords: Bus Rapid Transit (BRT), Transit Capacity, Micro-simulation.