Transport Sector Energy Planning by Network Analysis: A Case Study of Kathmandu Valley

Abstract

Kathmandu Valley is undergoing rapid urbanization, high population growth, urban sprawl and increased motorization which has led to the problems of congestion, pedestrian/vehicular conflict, environmental degradation and poor public transport operation and services. Because of low cost and high mobility on congested and narrow roads, the two-wheeler population is dominant in valley whose share out of registered public vehicles and private LDV gradually increased from 80.07% in 2007/08 to 83.16 % in 2017/18. Meanwhile, the share of PT decrease from 3.92% in 2007/08 to 2.91% in 2017/18. Private cars and motorcycles, which make up 71% of the total number of operational vehicles, currently meet just 41% of the total travel demand but consume 53% of the total energy. High-occupancy public transport vehicles like buses and minibuses comprise only 1.4% of the total number of vehicles but meet 37% of the travel demand and consume just 13% of the total energy (Dhakal,2006). However, the existing PT modes are not serving well, not sufficient, inefficient, overcrowded. Currently, PT facilities are being operated through numerous individual private operators that are often poorly assigned to routes. Public vehicles operate in more than 200 routes in Kathmandu valley. (MOPIT/JICA, 2012; Sajha Yatayat,2013). The distribution of bus in those routes are randomly decided by private operators. Hence, this research work is all about finding out the optimum number of required buses in the top ten routes of Kathmandu Valley out of 163 routes of study. Out of 10 routes, in one route from Sankhu to Ratnapark, requirement of buses are more than the available vehicles plying at that route, where as in other routes, the requirement of buses is less than the available vehicles plying on the routes. Microsoft Excel-Solver tool is used for preparing the transportation optimization model. The total number of vehicles at present scenario was 618 for all routes of study & the number reduced by 41% to 365 at optimized scenario. Similarly, the total transportation cost for all routes at present scenario is Rs. 30,25,558 for 618 number of vehicle & the total transportation cost for all routes at optimized scenario is Rs.18,60,058 for 365 number of vehicles. The total saving is Rs.11,65,500 which is equal to 39% of total transportation cost at present scenario. Similarly, from the view point of energy consumption, the total energy Consumptions by vehicles at different routes is 890,594 MJ at present scenario which is reduced by 38% to 549,420 MJ at optimized scenario. The amount of energy consumption that can be saved is 341,174 MJ. This means that the requirement of fuel is also less in the optimized scenario which results in less environmental pollution. From the view of environmental emissions, the air pollutant gases (CO2, CH4, N2O, NOX, CO, NMVOC, SO2) & particulate matters PM2.5 decreases by 57%, 44%, 66%, 62%, 22%, 20%, 20%, & 42% in Route 1, Route 2, Route 3, Route 4, Route 5, Route 6, Route 7 & Route 8 respectively in optimized scenario. In case of Route 9, those gases & particulate matters under consideration except methane gas increases very high. Also, in case of route 10, the air pollutant gases such as: N2O, NMVOC, NOX, CO increases high in optimized scenario.