Optimization of BRTs Operations in Pakistan: Rawalpindi-Islamabad a Case Study

Abstract

PMBS is a BRT system in the Rawalpindi-Islamabad region comprising AADR of 110,000 passengers which is only 4% of total trips of the twin cities with a 41% model share of public transport. Being the Lower-Middle-Income country, with 5.8% of the Current Account Deficit of GDP (US$ 18.1 Billion), PMBS is important for sustainability and stability for the deplorable transport structure of the twin cities. Moreover, besides an average annual revenue of PKR 148.16 /KM, its expenses are PKR 539.56 /KM i.e. the fare-box recovery ratio is 0.27. About 60% of the total expenses of PMBS are due to the cost of per kilometers of 68 buses for Operations i.e.70,000 KM/bus/Year as guaranteed KM. Therefore, around 40 Million passengers who traveled during 2018, the demand at every station is studied using VBA of Microsoft Excel and SQL Software. The loss of ridership is observed i.e. 135,005 Passengers/day as per feasibility study to actual 110,000 Passengers/day due to 9 minutes or above Waiting Time of passengers during peak hours at some stations. Other issues are; above 40% Saturation Level of stations during peak hours, increased boarding & alighting time of passengers due to overloading of buses, passengers’ discomfort during travel, unavailability of park & ride service, women complaints, and pickpockets issues. The decisions are made based on 1-year data and three routes are suggested to be fully functional so that the demand can be increased to plan. The scheduling model is provided. Using this model, the bus schedule is re-formed and for a single day (Monday) Express & Limited Stop routes are suggested. By using passenger demand at stations at a Load Factor of 0.85, 310,243 minutes/day of passengers-minutes traveled or 2,281 minutes/day of bus trips can be saved. Moreover, around 900 KM/day of bus kilometers can be saved using this schedule which is PKR 100 million savings per annum. Keywords: BRT System, AADR = Average Annual Daily Ridership, Model Share, Public-Transport, fare-box recovery ratio, ridership, Waiting Time, Saturation Level, Boarding & Alighting Time, Scheduling Model, Park & Ride Service, bus schedule, Load Factor, passengers-minutes, Optimization of cost and comfort level.