A report on
Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – Mumbai Metropolitan Region
Munish K. Chandel Ishant Sharma Arti Soni Anil K. Dikshit Abhishek Sada
Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
April 2018 Contents
Contents 2 List of Figures 4 List of Tables 8 Abbreviations 9 1. 11 1.1 11 1.2 Population Growth in MMR 11 1.3.1 Slum Population 11 1.4 Resident Workers and Employment 11 1.5 Travel Characteristics of MMR 12 1.5.1 Suburban Railways 12 1.5.2 Travel by Bus 12 1.5.3 Road system 13 1.5.4 Registered Vehicles 13 2. 15 2.1 General 14 2.1.1 Traffic Analysis Zones 14 2.1.2 Road and Rail Network 14 2.1.3 Transport System Attributes and Inputs 15 2.1.4 Internal Goods Vehicles 16 2.1.5 External Travel Demand 16 2.1.6 Mode Share 16 2.2. Methodology 17 2.3 Travel Demand Modeling Approach 19 2.3.1 Trip Generation: 19 2.3.2 Trip Distribution: 20 2.3.3 Modal Split: 20 2.3.4 Trip Assignment: 22
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
2.3.5 Model Validation: 25 2.4 Vehicle Kilometres Travelled 26 2.5 Horizon Year Travel Demand Model 27 3. Travel Demand Forecast – BAU Scenario 28 3.1 Metro Rail 28 3.2 Suburban Rail 28 3.3 Mono Rail 28 3.4 Road Projects 29 3.4.1 Eastern Freeway Road Project 29 3.4.2 Mumbai Trans Harbor Link 29 3.4.3 Multimodal Corridor from Virar to Alibaug 29 3.4.4 Coastal Road 30 3.5 Population and Employment Forecasts 30 3.6 Travel Demand Forecast 32 3.6.1 Trip Generation 32 3.6.2 Mode Share 32 3.6.3 Trip Assignment 35 3.6.4 Vehicle Kilometres Travelled (VKT) 42 4. Assessment of Emission Levels 44 4.1 Methodology for estimation of emissions 44 4.1.1 Conventional Vehicles’ Emission Factors 44 4.1.2 Electric Vehicles’ Emission Factors 44 4.2 Estimation of emissions 46 5. Conclusions 65 References 67 Appendix A 68 Appendix B 72 Appendix C 85
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
List of Figures
Figure 1.1: Mumbai Metropolitan Region 10
Figure 1.2: Motor Vehicles Registered Trend of Greater Mumbai 13
Figure 2.1: Rail and Road Network of Mumbai Metropolitan Region (CTS, 2008) 15
Figure 2.2 Observed Mode Share of Base Year 16
Figure 2.3 Methodology for Travel Demand Modeling………………………………………...…………..17
Figure 2.4 Calibration of Base Year Model - Methodology 19
Figure 2.5: Assigned Base Year Mode Share 21
Figure 2.6: Comparison of estimated and observed Mode Share of Base Year 21
Figure 2.7: Suburban Rail Passenger flows 23
Figure 2.8: Bus Passenger flows 23
Figure 2.9: IPT Passenger flows 24
Figure 2.10: Private Vehicle Flows 24
Figure 2.11 Survey Locations (MMRDA, 2008) 25
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Figure 3.1 MMR Present Transit Network (Rail, Metro and Mono Rail) 30
Figure 3.2: Population Forecast for MMR 31
Figure 3.3: Employment Forecast for MMR 31
Figure 3.4: Mode Share for Horizon Year 2021 33
Figure 3.5: Mode Share for Horizon Year 2031 33
Figure 3.6: Mode Share for Horizon Year 2050 34
Figure 3.7: Mode Share Comparison of Base Year and Horizon Years 34
Figure 3.8: Assignment Result for Private Vehicle Flows for HY2021 35
Figure 3.9: Assignment Result for IPT Passenger Flows for HY2021 36
Figure 3.10: Assignment Result for Bus Passenger Flows for HY2021 36
Figure 3.11: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2021 37
Figure 3.12: Assignment Result for Private Vehicle Flows for HY2031 37
Figure 3.13: Assignment Result for IPT Passenger Flows for HY2031 38
Figure 3.14: Assignment Result for Bus Passenger Flows for HY2031 38
Figure 3.15: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2031 39
Figure 3.16: Assignment Result for Private Vehicle Flows for HY2051 39
Figure 3.17: Assignment Result for IPT Passenger Flows for HY2051 40
Figure 3.18: Assignment Result for Bus Passenger Flows for HY2051 40
Figure 3.19: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2051 41
Figure 3.20: Comparison of VKTs of Internal vehicles in Base Year and Horizon Years 43
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Figure 3.21: Comparison of VKTs of External vehicles in Base Year and Horizon Years 43
Figure 4.1: Total CO emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix 50
Figure 4.2: Total HC emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix 50
Figure 4.3: Total NOx emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix 51
Figure 4.4: Total CO2 emissions (million tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix 51
Figure 4.5: Total PM2.5 emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix 52
Figure 4.6: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1 52
Figure 4.7: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 53
Figure 4.8: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 53
Figure 4.9: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4 54
Figure 4.10: CO2 emissions (tonne/year) of External Vehicles in Base Year and Horizon Years 54
Figure 4.11: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1 55
Figure 4.12: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 55
Figure 4.13: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3 56
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Figure 4.14: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4 56
Figure 4.15: HC emissions (tonne/year) of External Vehicles in Base Year and Horizon Years 57
Figure 4.16: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1 57
Figure 4.17: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 58
Figure 4.18: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3 58
Figure 4.19: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4 59
Figure 4.20: PM2.5 emissions (tonne/year) of External Vehicles in Base Year and Horizon Years 59
Figure 4.21: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1 60
Figure 4.22: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 60
Figure 4.23: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3 61
Figure 4.24: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4 61
Figure 4.25: NOX emissions (tonne/year) of External Vehicles in Base Year and Horizon Years 62
Figure 4.26: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1 62
Figure 4.27: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2 63
Figure 4.28: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3 63
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Figure 4.29: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4 64
Figure 4.30: CO emissions (tonne/year) of External Vehicles in Base Year and Horizon Years 64
List of Tables
Table 2.1 Population and Estimated Productions / Attractions for the base year 2005 (MMRDA, 2008) 20
Table 2.2 Private, Commercial. Bus & IPT Vehicle Flows (PCU) 26
Table 2.3 Rail passenger flows 26
Table 2.12 Vehicle Kilometres Travelled by Each Mode in Base Year (2005) 26
Table 3.1 Planned Phases of Mumbai Metro Rail 28
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Table 3.2 Population forecast for Mumbai Metropolitan Region 31
Table 3.3 Employment forecast for Mumbai Metropolition Region 31
Table 3.5 Trip Productions / Attractions Forecast for Horizon Years 32
Table 3.6 VKTs of Base year and Horizon Years 42
Table 4.2 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S1 46
Table 4.3 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S2 47
Table 4.4 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S3 48
Table 4.5 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S4 49
Table C-1 Calculated Conventional Vehicles’ Emission factors 85
Table C-2 Calculated Electric Vehicles’ Emission factors (Scenario 1: Average Energy Consumption)86
Table C-3 Calculated Electric Vehicles’ Emission factors (Scenario 2: Average Energy Consumption)87
Table C-4 Calculated Electric Vehicles’ Emission factors (Scenario 3: Average Energy Consumption)88
Table C-5 Calculated Electric Vehicles’ Emission factors (Scenario 4: Average Energy Consumption)89
Abbreviations
ARAI – Automotive Research Association of India BAU – Business as Usual BEST – Brihanmumbai Electricity Supply and Transport BKC – Bandra Kurla Complex CAGR – Compound Annual Growth Rate CIDCO – City and Industrial Development Corporation CR – Central Railway CST – Chhatrapati Shivaji Terminus CTS – Comprehensive Transportation Study EBZ – Employment GDP – Gross Domestic Product HBW – Home Based Work HB – Home Based HBEAM – Home Based Education AM Peak HIS – Household Interview Survey HWFAM – Home based Work purpose employed in Office type jobs AM peak
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
HWIAM – Home based Work purpose employed in Industry type jobs AM peak HBOAM – Home Based Other AM Peak HWOAM – Home based Work purpose employed in other type jobs AM peak HCV – Heavy Commercial Vehicle IJ – Industry type Jobs IPT – Intermediate Public Transport ITES – Information Technology Enabled Services IVTT – In Vehicle Travel Time IVTC – In Vehicle Travel Cost LCV – Light Commercial Vehicle MbPT – Mumbai Port Trust MMR – Mumbai Metropolitan Region MMRDA – Mumbai Metropolitan Region Development Authority MSRTC – Maharashtra State Road Transport Corporation MTHL – Mumbai Trans Harbor Link NHB – Non-Home Based NHBAM – Non-Home-Based AM Peak NMMT – Navi Mumbai Municipal Transport OJ – Office Type Jobs OtJ – Other type Jobs OVDIST – Out Vehicle Distanced Travelled PCTR – Per Capita Trip Rate PCU – Passenger Car Unit Pop – Population RS – Resident Students RWF – Resident Workers Employed Office Type RWI – Resident Workers Employed Industrial-Type RWO – Resident Workers Employed Other Type SEEPZ – Santacruz Electronic Export Processing Zone SRC – Sub Region Cordon TAZ – Traffic Analysis Zone TMT – Thane Municipal Transport UTPS – Urban Transport Planning System VKT – Vehicle Kilometres Travelled WR – Western Railway
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
1. Introduction
1.1 Background Mumbai Metropolitan Region (MMR), a metropolitan area of Maharashtra - includes the Mumbai - the state capital with its satellite towns. With the development over a span of few decades, it has grown into nine municipal corporations and fifteen smaller municipal councils. Mumbai Metropolitan Region Development Authority (MMRDA) is an organization of Maharashtra State Government responsible for the development, town planning, housing and transportation in the region. The formation of MMRDA was mainly to mark the challenges in outlining and advancement of combined infrastructure for the metropolitan area. The areas outer from Greater Mumbai and Navi Mumbai have a disordered development. City and Industrial Development Corporation (CIDCO) - a Maharashtra Government-owned company, promoted one of the largest planned city in development - Navi Mumbai. The rapid urbanization has resulted in the problems of illegal and haphazard development. In some of the biggest warehousing areas in India, villages like Bhiwandi Taluka are examples of unexpected advancements in the MMR which are located along the NH3. (“MMRDA,” 2013)
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Figure 1.1: Mumbai Metropolitan Region (Source: https://commons.wikimedia.org/w/index.php?curid=47353448)
In 2005, with the population ~20.74 million in 4,455 km² area, MMR comes in the list of world's most crowded metropolitan areas. MMR comprises of Mumbai City District, Mumbai suburban regions, parts of Thane and Raigad districts and parts of Panvel, Karjat, Khalapur, Pen and Alibaugh Tehsils. The region comprises of 7 Municipal Corporations and 13 municipal councils and is divided into eight planning sub regions as per Regional Plan of MMR. (“MMRDA,” 2013) 1.2 Population Growth in MMR MMR’s population has progressed from 77.92 lacs in the year 1971 to 188.90 lacs in the year 2001 (Table 1.1). But, the CAGR for the population in MMR has diminished from 3.59% during the decade of the 1970s to 2.66% in the decade of the 1990s. It is further seen that MMR is a highly-urbanized area where an increase in the population of urban area is approximate 3% in the 1971-2001 period, whereas no change in the population of the rural area is recorded.(MMRDA, 2003) Although there is a continuous growth of the population of Greater Mumbai in total number, the CAGR has drastically decreased from 3.28% through 1971-81 to 1.84% during 1991-2001. On the other hand, Municipal Corporations of Navi Mumbai, Bhiwandi-Nizampur, Kalyan-Dombivali, Mira-Bhayandar and Thane have recorded substantial growth in their population, especially during the period 1981-2001. In MMR, the population percentage of Greater Mumbai's is reduced to 63.06% in 2001 from 76.63% in the year 1971. It concludes the higher growth rate of population in outer MMR as compared to Greater Mumbai (MMRDA, 2003). 1.3.1 Slum Population The increase of slums contributes to the scourge of urbanisation. The highly urbanised MMR is observing an increase in slums. It is observed that in 2001 in Great Mumbai, almost half of the population is found inhabiting in slums (48.88 %). Likewise, in Thane Municipal Corporation, about 33% of the population is found living in slums. In 2001 in MMR, more than 38% of the urban population inhabit in slums. This refers to the alarming requirement of improvement in the stock supply of affordable housing for many of the urban settlements of MMR (MMRDA, 2003). 1.4 Resident Workers and Employment Census gives knowledge about the resident workers divided as marginal and main workers. In Urban MMR, the resident workers in total were 66.16 lacs in the year 2001 which is 37.07% of total population of 178.46 lacs. For Greater Mumbai, the percentage of the total resident worker in its total population has changed over the years specifically to 34.69% in 1981, 34.59% in 1991 and 38.00% in 2001 from 36.81% in the year 1971 (MMRDA, 2003).
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Employment is the main pointer of the economic growth. There is an increase of MMR’s employment to 35.54 lacs in 1998 from 28.08 lacs in 1980. It is observed that the employment has recorded a rate of growth of 1.23% through 1980-90 period and 1.38% through 1990-98 period. Since 1980, a marginal increase has been found in Greater Mumbai’s employment. This can be viewed from the fact that employment in Greater Mumbai increased to 21.99 lacs in 1980, 24.26 lacs in 1990 and 26.26 lacs in 1998 from 15.28 lacs in 1971. The CAGR of employment was 3.71 % in 1971-80, which has remained constant at about 1% during both 1980-90 and 1990- 1998 (MMRDA, 2003).
1.5 Travel Characteristics of MMR In MMR, in the year 2005, the public transport; suburban rail and buses are the main modes which take up the 78% of passenger trips of the peak period. Remaining passenger trips are satisfied by private taxis and intermediate public transport (autos and taxis) with the share of 13% and 9%, respectively. Suburban rail is served by the Western and Central Zones of Indian Railways. Because of the lesser frequency and short distances between stations, they satisfy the requirements of metropolitan services by carrying passengers close to 6 million every day (MUTP, 2001). 1.5.1 Suburban Railways A total of about 2100 suburban rail operations per weekday are serving the city out of which Central Railway and Western Railway share 1186 and 913 services respectively between them with nearly a total route network of 400 km. Central Railway dominates the network by coverage of 280 km against 120 km of Western Railway. The MMR has a total of 100 suburban stations of which 23 are on Western Railway and 77 stations on Central Railway. Western Railway (WR) operations extend from Church Gate to Dhahanu Road, 124 km north from Church Gate. Central Railway (CR) suburban operations extend from Mumbai CST station as far as Kotapuri (136 km) and Khopoli (115 km) on the north-east and north-south respectively which are common corridors till Kalyan. The Harbor line suburban services extend from Mumbai CST/Masjid Station to as far as Panvel (46 km) (MMRDA, 2008). With 26 km as an average rail journey length in 2005, the suburban rail travel demand for an average weekday is about 15 million passenger-km in which the 12 persons/sqm being the average standing density and 16 persons/sqm being the average standing density in the space available between card doors. Overall, the average density is estimated to be nine persons/sqm (MMRDA, 2008). 1.5.2 Travel by Bus After Suburban Rail, the bus takes up about 26% of peak hour travel demand of City by operating over 5700 routes. Mainly, Buses in Mumbai act as a feeder service for the suburban rail.
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In MMR, bus services are dominated by Brihanmumbai Electricity Supply and Transport (BEST) with the services on 334 routes by a fleet of 3,380 buses in Greater Mumbai and its nearby destinations. Other than BEST, MMR is served by Maharashtra State Road Transport Corporation (MSRTC), Thane Municipal Transport (TMT) and The Navi Mumbai Municipal Transport (NMMT) to cover the whole MMR (MMRDA, 2008). If journey length varies from short to medium, Buses are favorable mode whereas for longer journey length the most economical suburban rail is preferred. 1.5.3 Road system The road network in Mumbai consists of predominantly radial road network along the peninsula and includes three main corridors - the Central Corridor, Eastern Express Highway and Western Express Highway. Since Expressways also act as an arterial road for some part of the city, their standards are
compromised in these portions (MUTP, 2001). 1.5.4 Registered Vehicles In Greater Mumbai, the number of registered vehicles grew from 5.21 lacs in 1985 to 8.21 lacs in 1991 and 18.70 lacs in 2011 (MCGM, 2016).
Figure 1.2: Motor Vehicles Registered Trend of Greater Mumbai (Source: MCGM, 2016)
The improvement in transport characteristics in MMR key factors like infrastructure, conditions of travel, application of institutional framework etc. play a vital role and require attention.
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
2. Base Year Travel Demand Model (2005)
2.1 General The transport demand modeling exercise for Mumbai Metropolitan Region is based upon the methodology of the Comprehensive Transportation Study (CTS) carried out for MMR in the year 2005 which is the base year for the study (MMRDA, 2008). The primary and secondary inputs which were obtained from CTS to build the model include: • Traffic Analysis Zones • Zonal Population • Zonal Employment • Road and Rail Network • Speed & Delays characteristics • Travel pattern • Road network attributes • PCU factor and Occupancy rates • Internal Goods Vehicle demand • External Vehicle Demand 2.1.1 Traffic Analysis Zones As per MMRDA (2008), 1030 Traffic analysis zones (TAZ’s) has been formed by dividing the study area (MMR) and seven external entry/exit points for the travel demand modeling purpose. It was estimated that the region has 20.8 million population and 7.76 million employments in the year 2005. Out of 1030 TAZ’s 557 are in Greater Mumbai and 453 in rest of MMR.
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2.1.2 Road and Rail Network Mumbai Metropolitan Region consists of an extensive road network of 6,266 km which are converted into 6,166 links for modeling exercise. The length of the major road network with the survey data available is about 2320 km covering details like physical characteristics such as lanes, observed right of way, divided or undivided carriageway. MMR road network includes Expressways, National Highways, State Highways, Major District Roads and Village Roads. The urban areas consist of Arterial, Sub-Arterial, Collector and local streets. The rail network of the study area is called Sub-Urban Rail Network and is nearly 400 km of which Central Railway and Western Railway share 280 km and 120 km respectively. A total of 100 suburban stations 23 on Western Railway and 77 on Central Railway. The road and rail network of the study area is presented in Figure 2.1 below.
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Figure 2.1: Rail and Road Network of Mumbai Metropolitan Region (CTS, 2008)
2.1.3 Transport System Attributes and Inputs
The basic elements of network considered are its nodes, links and transit lines. The modes that are considered for this exercise are public (Metro Rail, Mono Rail, Local Trains and Bus), private (2-wheeler, car and commercial vehicles). The nodes are categorized into intersections and centroids. Centroids are the geographical centres of the Traffic Analysis Zones (TAZ) which are the originating and terminating points of traffic. A link that connects centroid to a regular node is a centroid connector or a dummy link where as a link is a directional connection between two nodes. Links have the attributes like a number of lanes, divided/undivided carriageway, capacity attached to them. All these are obtained from MMRDA (2008). 2.1.4 Internal Goods Vehicles The MMRDA (2008) have estimated internal morning peak period goods travel in MMR is 12,530 vehicle trips in the year 2005. It also includes the CAGR for internal goods vehicles as 3%. 2.1.5 External Travel Demand External travel demand separately for goods vehicles, buses and personal modes of transport is taken from MMRDA (2008). The estimated total daily external travel in the base year 2005 for goods, buses and personalized vehicles is 38,517, 6,479 and 46,189 vehicle trips, respectively. The estimated CAGR reported for goods, buses and personalized vehicles is 2.35%, 5.48% and 10.47%. 2.1.6 Mode Share The travel demand is dominated by public transport in MMR by about 78% and rest of the trips are completed by the private mode (Cars, Two wheelers) and Para Transit modes (Autos, Taxis) MMRDA (2008).
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Figure 2.2 Observed Mode Share of Base Year (Source: MMRDA, 2008)
2.2. Methodology The methodology of the travel demand model is shown in the Figure 2.3 and also explained as follows: • The development of Base and horizon year travel demand model is based on the four-stage transport modelling.
o It is a conventional method used in Urban Transport Planning which includes the separate distribution of population and separate allocation of employment for land use and also called as Sequential Travel Demand.
o It includes following four stages: ▪ Trip Generation gives the number of trips generated at a zone for a given purpose. ▪ Trip Distribution distributes the generated trips between OD pairs.
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▪ Modal Split, gives the choice of mode for making the trip, and ▪ Trip Assignment describes the choice of travel route on the transport network. • Cube Voyager – an integrated modelling software package by the Citilabs has been used to formulate the model.
Figure 2.3 Methodology for Travel Demand Modeling
• Using the base year population and employment characteristics and household survey results, base year trip production and attraction models are developed. • Using the base year network, travel demand matrix and trip length frequency distribution graph, the spatial distribution of production and attractions are established. • The spatial flows of trips by various modes are assigned on the network of roads and public transport systems. • Evaluation of base year model is carried out by validating the assigned flows against observed flows on screen line locations as per MMRDA (2008) . • The model is considered to be validated when there existed a close relation (± 0-30%) between the observed and assigned traffic/ passenger flows on the network.
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
The formulation of each step with their results is explained in detail in the next section with their results.
2.3 Travel Demand Modeling Approach The following flow chart shows the modelling approach followed in formulating the base year model.
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Figure 2.4 Calibration of Base Year Model - Methodology 2.3.1 Trip Generation: Trip generation is the first stage of the demand models. It gives the prediction of the total number of trips produced and attracted to each zone of the study area. It consists of two types: 1. Trip Production: All the trip which are home-based or have a non-home-based origin are Trip Productions. Various independent variables like household income, vehicle ownership, household structure and family size influence trip productions. 2. Trip Attraction: Trip Attraction is the non-home end of the home-based trip and is the destination of a non-home-based trip. It is influenced by factors such as land use, employment, accessibility etc. Multiple Linear Regression Analysis is used for formulating this stage. All the planning variables with their production and attraction factors for trip production and attraction has been obtained from MMRDA (2008). Furthermore, the trips are modeled for six different purposes in the peak period which are Home based work purpose employed in Office type jobs AM peak (HWFAM), Home based work purpose employed in Industrial type jobs AM peak (HWIAM), Home based work purpose employed in Other type jobs AM peak
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period (HWOAM), Home-based Education Purpose AM peak period (HBEAM), Home based Other Purpose AM peak period (HBOAM) and Non-Home Based purpose AM peak period (NHBAM). The productions or attractions generated by the model for the base year 2005 are given in Table 2.1. Table 2.1 Population and Estimated Productions / Attractions for the base year 2005 (MMRDA, 2008) ted Trip Productions or Trip Attractions HWFA HWIAM HWOA HBEA HBOA NHBA M M M M M 20,000,00 2,883,14 1,072,72 738,652 928,580 291,586 15,236 0 5 1
The estimated Per Capita Trip Rate (PCTR) for the whole MMR region for private and public modes is 0.74. 2.3.2 Trip Distribution: Trip Distribution is the second stage of Travel Demand Modelling. The main aim of trip distribution stage is to synthesise the trips between traffic analysis zones providing the travel demand regarding the origin and destination matrices. A Gravity model of the following form is used to carry out the trip distribution stage.
��� = �����������
Where, 1 1 �� = � , �� = � � 1 ������� � 1 �������
∑ = ∑ = Tij = number of trips from zone I to zone j; Pi = number of trip productions in zone i; Aj = number of trip attractions in zone j; fij = “friction factor” or “deterrence function” relating the spatial separation between zones i and j; n = total number of zones The deterrence function or Friction factor is obtained from MMRDA (2008) . 2.3.3 Modal Split: The purpose of mode choice model is to develop a procedure to simulate the manner in which people decide on which mode to choose purely based on their economic conditions and ownership of vehicles to travel from origin to destination. The choice is primarily between private and public transport modes. Mode choice is influenced by the service characteristics of public transport modes, traffic congestion on roads, fares of different modes of transport and passenger’s affordability. A Multinomial Logit model with choice and captive criteria is used for obtaining the modal split. In multinomial logit model the proportion of choosing a particular mode over the other modes is given by the formula:
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
��� � = � � �� � 1 �
= ∑ Where Pi = Probability of mode i to be chosen;
Ui = Utility equation of mode i; n = total no. of mode choices. Modal split models were proposed for six different purposes as carried out for Trip Generation stage. The data for choice riders and utility models along with the statistical values without walk purpose are taken from MMRDA (2008). Figure 2.5 gives the mode share of the base model and figure 2.6 gives the comparison of observed mode share of the base with the estimated mode share of the base year.
Figure 2.5: Assigned Base Year Mode Share
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Figure 2.6: Comparison of estimated and observed Mode Share of Base Year 2.3.4 Trip Assignment: The purpose of the trip assignment is to develop a technique in which the trips from origin to destination of different modes gets distributed over the links of respective networks. Every link has travel time calculated from the speed observed on the network from speed and delay survey. Multimodal capacity restrained equilibrium technique is used for trip assignment process. There will be two choices for the public to choose from which are Transit lines and highway network. Transit lines are the exclusive public transport networks like Bus, Metro or Trains whereas the highway network comprises of the road based private modes like cars, two wheelers and taxi’s, auto rickshaws and other commercial vehicles. 2.3.4.1 Commercial Vehicle Trips The commercial vehicles trips in MMR are both internal and external. These commercial vehicles are further classified into Light Commercial Vehicles (LCV) and Heavy Commercial Vehicles (HCV). HCV are further subdivided into 2 Axle, 3 Axle and Multi Axle Vehicles (MAV). For base year, total number of internal commercial vehicle trips have been taken as 62465 per day with the share of LCV being the 39.17%. The Average Trip lengths of internal LCV and HCV have been taken as 27.86 km and 30.86 km, respectively. The external commercial vehicles are considered along with external vehicles (MMRDA, 2008). 2.3.4.2 External Vehicle Trips All the external vehicle trips including goods vehicle, buses and personalised vehicles are also assigned along with the private mode of assignment. For base year, total number of external vehicle trips have been taken as 88810 per day with the composition of goods vehicles, buses and personalized vehicles as 43.37%, 7.02% and 48.55%, respectively. The Average Trip lengths of external LCV (19.34% of external
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
commercial vehicles) and HCV have been taken as 59.48 km and 61.93 km, respectively (MMRDA, 2008). The Average Trip length of buses and personalized vehicles has been considered same as the internal modelled vehicles due to unavailability of sufficient data. 2.3.4.3 Peak Hour Factors and Directional Distribution The peak hour factor has been assumed as 10% based on observed peak hour traffic at various locations and considering future perspective. 2.3.4.4 Assigned Traffic Volume The traffic assignment has been performed as follows: ● Public Mode Trips: Train, Bus & IPT ● Private Vehicle Trips: Car, 2W, Commercial vehicles, External vehicles. The model has been developed to include the transit routes with all the relevant information like the routes grouped by the operator, the frequency of each route, the capacity of each public transit mode and each transit stop. After assigning of all trips by different modes on to the network, the assigned traffic streams have been compared with those observed on the ground along the screen lines. The peak hour traffic assignment in PCU, passengers and vehicle for the base year is presented in Figures 2.7 to 2.10 for Suburban Rail trips, Bus Trips, IPT trips and Private vehicle trips respectively.
Figure 2.7: Suburban Rail Passenger flows
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Figure 2.8: Bus Passenger flows
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Figure 2.9: IPT Passenger flows
Figure 2.10: Private Vehicle Flows
2.3.5 Model Validation: Travel demand assessed from household interview survey transformed into matrices are assigned to network with transport characteristics like travel speeds, bus routes, bus frequency and headway, etc. The volumes observed on the network are compared with the ground counts (Traffic Volume Counts) which are factored as Passenger Car Units (PCU’s) for highway network and passenger counts for Rail network. The screen line locations, Inner Cordon, Sub-Regional Cordons are targeted for validation purposes. The survey locations consist four inner Survey locations are presented in the map presented in the Figure 2.11. Along with the highway assignment, the rail network is also assigned with its respective demand for which four inner cordon locations are validated. Table 2.2 and 2.3 show the validation results for Vehicle flows (PCU) and Rail passenger flows respectively. As difference is in the acceptable limit of +-30%, model is validated.
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Figure 2.11 Survey Locations (MMRDA, 2008) Table 2.2 Private, Commercial. Bus & IPT Vehicle Flows (PCU) Locatio Observe Assigne Differenc n d d e IC1 64,532 45,384 30% IC2 57,032 57,684 -1% IC3 1,29,111 90,377 30% IC4 65,220 82,612 -27%
Table 2.3 Rail passenger flows Locatio Observed Peak Passengers Assigned Peak Differenc n Passengers e IC1 3,93,968 443,825 -13% IC2 766,927 806,114 -5%
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IC3 1,270,867 1,315,117 -3% IC4 991,761 768,223 23%
2.4 Vehicle Kilometres Travelled To measure the travel activity in the study area, Vehicle Kilometres Travelled (VKT) is considered. It is obtained as a product of a total number of trips and average trip length. Alternatively, it can also be obtained as a product of addition of all the volume on a particular link of road network and length of that link. The vehicle kilometres travelled in the base year model are observed to be 18.41 million km per day for all modes operating upon their respective links in the network. The mode wise share of VKT is shown in Table 2.12. Table 2.12 Vehicle Kilometres Travelled by Each Mode in Base Year (2005) Vehicle Kilometers Travelled Modes (millions/day) Internal Vehicles Car 2.89 2w 12.19 Auto 1.88 Taxi 0.41 Bus 2.00 Train 0.96 Metro 0.00 Mono Rail 0.00 Light Commercial Vehicles (LCV) 0.68 Heavy Commercial Vehicles 1.17 (HCV) Total 22.19 External Vehicles Car 0.28 2w 0.07 Auto 0.01 Taxi 0.02 Bus 0.10 Light Commercial Vehicles (LCV) 0.44 Heavy Commercial Vehicles 1.92 (HCV) Total 2.84 Total (External & Internal) 25.04 2.5 Horizon Year Travel Demand Model The horizon years for the study are 2021, 2031, and 2050. The base year model is used to forecast the travel demand model for Horizon years with population, employment and incorporation of proposed road infrastructure into the horizon year road network. From the trip end equation (Trip Generation Model)
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay developed for a base year, zone wise population and employment of horizon years is calculated and given as an input to predict the horizon year trip productions and attractions. External travel demand is estimated separately for goods vehicles, buses and personalised vehicles. These are estimated based on the average annual growth rate of goods vehicles, buses and personalized vehicles as 2.35%, 5.48% and 10.47% respectively (MMRDA, 2008).
3. Travel Demand Forecast – BAU Scenario
In the horizon years’ model, upcoming and proposed projects are added to the base road and transit network. So, in the base year 2005, only two main modes of public transport were available i.e. Suburban Rail and
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Bus. For horizon years, two more modes are being added into the public transportation i.e. Metro and Mono Rail. 3.1 Metro Rail Mumbai Metro is the new addition to the public transport mode of Mumbai City as a rapid transit system. It is planned to be completed in three phases of which only a part of it started its operation in the year 2014. The MMRDA has also proposed a Mumbai Trans Harbour Link (MTHL) Metro Link to facilitate less fuel consumption and decongestion and enable the development of Navi Mumbai. It will be constructed in two phases with the total length of 49.6 km. The phase-wise plan of Metro is presented in Table 3.1 below of which Versova-Andheri -Ghatkopar is the only line in operation today.(“Mumbai Metro Rail Project,” 2004) Table 3.1 Planned Phases of Mumbai Metro Rail
S. No Route Phase Length (km) 1 Versova-Andheri-Ghatkopar I 11.4 2 Charkop-Bandra-Mankhurd-Dahisar I 40 3 Colaba-Bandra-SEEPZ I 33.5 4 Wadala-Ghatkopar-Thane (Teen Haath Naka) - Kasarwadavli II 32 5 Teen Hath Naka- Kasarwadavali (Thane)-Bhiwandi-Kalyan III 34.6 6 SEEPZ-Kanjurmarg III 10.5 7 Andheri (E) – Dahisar III 18 8 Sewri-Prabhadevi III 3.5 9 Sidhivinayak – Dhutum MTHL - I 21.70 10 Dhutam - Dushmi MTHL - II 27.90 (Source:“Mumbai Metro Rail Project,” 2004) The plan of MMRDA is to complete phase I by 2021, part of phase III and phase II by 2031, and complete all the phases by 2050. (“Mumbai Metro Rail Project,” 2004) 3.2 Suburban Rail The Harbour Line of Suburban Rail is set to extend up to Goregaon under MUTP - II by April 2017. It will be further extended till Borivali under MUTP-III by 2031. (“Mumbai Urban Transport Project,” 2010) 3.3 Mono Rail For the further public transport expansion in the city, Monorail project was introduced and implemented by MMRDA with opening its Phase I partially for operation in February 2014. It will become the "fifth largest monorail system" after the full completion of Phase I in mid-2017. Phase I, Jacob Circle to Chembur (20.21 km) line 1, is fully elevated and services the person trips in Eastern Mumbai by supplementing the Suburban Rail. (“Mumbai Monorail Project,” 2007).
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The layout and description of MMR’s Present Transit network has been shown in figure 3.1. 3.4 Road Projects
3.4.1 Eastern Freeway Road Project Because of the travel demand, future traffic and the person trips originating from South Mumbai to Thane, Panvel, Nashik and Pune, 16.9 km long Eastern Freeway Project has been introduced to save the time and the cost of travel. It has contributed to the decongestion of the Island city. (“Eastern Freeway,” 2016) The Project is divided into three parts – Part-I Eastern Freeway – Chhatrapati Shivaji Maharaj Vastu Sangrahalay to Anik Junction (Elevated corridor) To facilitate the faster movement of the traffic to Mumbai Port Trust (MbPT), the 17.2 m wide and 9.29 km long first part of Easter freeway will join PD' Mello Road with Anik- Panjarpol Link Road via MbPT. It has already been opened to the public on 14 June 2013. (“Eastern Freeway,” 2016) Part-II: Anik-Panjarpol Link Road It includes 4.3 km long eight lane road joining Anik with Panjarpol Junction and in operation since 12 April 2013. (“Eastern Freeway,” 2016) Part-III: Panjarpol-Ghatkopar Link Road It includes the 17.2 m wide and 3 km long road joining Panjarpol Junction with Chembur - Mankhurd Road via Tukaram Patil Marg and in operation since 16 June 2014. (“Eastern Freeway,” 2016) 3.4.2 Mumbai Trans Harbor Link Mumbai Trans Harbor Link has been proposed to join the Island City with Navi Mumbai to decongest the Island city. It will include six lanes and 22 km long bridge out of which 16.5 km and 5.5 km will be on sea and land respectively and its construction will be started in mid-2017 and will be completed by the end of year 2021. (“Mumbai Trans Harbour Link,” 2016) It will be completed in three parts: 1. A 10.4 km long bridge joining Interchange at Sewri with Thane Creek 2. A 7.8 km long bridge joining interchange at Shivaji Nagar with Thane Creek. 3. A 3.6 km long interchange & viaduct to join NH 4B with SH 52 & 54 at Navi Mumbai (Chirle). 3.4.3 Multimodal Corridor from Virar to Alibaug With the incorporation of multiple modes such as cars, buses, BRTS, Metro Rail and non-motorized track, a 99 m wide and 126 km long Multi Modal Corridor between Alibaug and Virar is proposed by MMRDA. All the important national highways like NH3 (Agra – Delhi), NH4 (Pune-Bangalore), NH8 (Ahmedabad), Mumbai Pune Expressway will get easy access due by this corridor. Some major projects coming up in Mumbai like Mumbai Trans Harbor Link (MTHL), Navi Mumbai International Airport will also get
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay benefitted by this corridor. It will be completed by the year 2031 with a total cost estimate of 13000 Crores.(“Multimodal Corridor from Virar to Alibaug,” 2012) 3.4.4 Coastal Road An eight-lane freeway 29.2 km is proposed along Mumbai’s Western Coastline connecting Kandivli in the north to the Marine Lines in the South. With the estimated cost of about 15000 crores, construction of the project will start in 2017 and will be completed in two phases. There is also a provision of its extension up to Mumbai-Ahmedabad Highway in the proposal. First phase construction of 9.8 km from marine lines to Bandra-Worli Sea Link (Worli end) is set to be completed by 2019.
Figure 3.1 MMR Present Transit Network (Rail, Metro and Mono Rail) (Source: https://commons.wikimedia.org/w/index.php?curid=46754806) 3.5 Population and Employment Forecasts Population and employment forecast for MMR are based upon the MMRDA (2008). The MMRDA (2008) predicts compound annual growth rate (CAGR) for the population of 1.9% for 2011 to 2031. We assumed
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay that the CAGR would be 1.9% from 2031 to 2050. Similarly, CAGR for employment are assumed 2.19% for 2021 to 2031 as well as for 2031 to 2051. Table 3.2 Population forecast for Mumbai Metropolitan Region Yea Population Populatio CAG r (Millions) n R 197 7.9 7900000 ---- 1 198 9.9 9900000 2.3% 1 199 13.4 13400000 3.1% 1 200 18.5 18500000 3.3% 1 201 22.4 22400000 1.9% 1 202 27.0 27000000 1.9% 1 203 34.0 34000000 2.3% 1 204 41.0 40982143 1.9% 1 205 49.4 49398119 1.9% 1
Figure 3.2: Population Forecast for MMR
Table 3.3 Employment forecast for Mumbai Metropolition Region Yea Employment r Total employment in CAG million R 200 7.8 5 201 9.3 1.77% 1 201 10.93 3.28% 6
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202 12.32 2.42% 1 203 15.3 2.19% 1 205 24.0 2.19% 1
Figure 3.3: Employment Forecast for MMR 3.6 Travel Demand Forecast After forecasting the population and employment data for horizon years, models are prepared by taking these as inputs. Following are the results and their comparison with the base year. 3.6.1 Trip Generation The trip end equations developed for the base year have been used to forecast the productions and attractions by using the forecasted population and employment respectively. The productions and attractions estimated are shown in Table 3.5 below: Table 3.5 Trip Productions / Attractions Forecast for Horizon Years Yea Populatio Trip Productions or Trip Attractions r n HWFA HWIAM HWOA HBEAM HBOA NHBA M M M M 2005 20,000,000 2,883,14 1,072,72 738,652 928,580 291,586 15,236 5 1 2021 27,000,000 3,803,35 1,509,87 993,511 1,245,80 379,237 24,351 9 9 5 2031 34,000,000 4,774,45 1,895,40 1,247,193 1,560,62 476,052 30,348 6 5 4 2050 49,400,000 6,827,09 2,710,29 1,783,405 2,231,55 680,686 45,927 0 6 5
3.6.2 Mode Share Base year calibrated parameters are used to develop and estimate the future year's’ modal shares in 2021, 2031and 2050. The modal split estimates for future shows that there is a decrease in the share of suburban rail from base year share of 52.2% to around 32 % in horizon years.
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This decline in share is mainly due to the metro as its share starts around 31% in the year 2021 and reaches up to 38% in the year 2050. There is a shift of passengers from suburban rail to metro as connectivity by the metro network is extensive and the metro stations are spaced at one km interval. Monorail is estimated to be taking just 0.07 to in 0.13% share in horizon years. Due to the inclusion of Metro, there is a big decrease in the share of the bus also. The estimate shows that bus share is decreasing from 26% to 10 % in the year 2021 and further decreases up to 7% in the year 2050. So, in combination, the mode share of public transport from 78% in the base year is expected to decrease to 77% in the year 2050. Further, there is a simultaneous increase in the share of two-wheeler riders from 9.7% in the base year to 12% in the year 2031 and again decreasing to 9% in the year 2050 with having a maximum value of 14% in the year 2021. The share of cars is increasing from 3% in the base year to 8 % in the year 2050. Figures 3.4 to 3.6 shows the mode wise percentage share of trips in the cardinal years 2021, 2031 and 2050. Figure 3.7 shows the comparison mode share of Horizon years with base year.
Figure 3.4: Mode Share for Horizon Year 2021
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Figure 3.5: Mode Share for Horizon Year 2031
Figure 3.6: Mode Share for Horizon Year 2050
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Figure 3.7: Mode Share Comparison of Base Year and Horizon Years 3.6.3 Trip Assignment The external and internal vehicles have been forecasted for the Horizon years using their respective annual growth rates from MMRDA (2008). Due to insufficient data and since there is no effect of the new transit lines coming up in horizon year on goods vehicles there share and average trip length has been considered same as the base year. The horizon year trips for 2021, 2031 and 2050 were assigned on the proposed transport network and congested links were identified as shown in Figures 3.8 to 3.19.
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Figure 3.8: Assignment Result for Private Vehicle Flows for HY2021
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Figure 3.9: Assignment Result for IPT Passenger Flows for HY2021
Figure 3.10: Assignment Result for Bus Passenger Flows for HY2021
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Figure 3.11: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2021
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Figure 3.12: Assignment Result for Private Vehicle Flows for HY2031
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Figure 3.13: Assignment Result for IPT Passenger Flows for HY2031
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Figure 3.14: Assignment Result for Bus Passenger Flows for HY2031
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Figure 3.15: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2031
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Figure 3.16: Assignment Result for Private Vehicle Flows for HY2051
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Figure 3.17: Assignment Result for IPT Passenger Flows for HY2051
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Figure 3.18: Assignment Result for Bus Passenger Flows for HY2051
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Figure 3.19: Assignment Result for Suburban Rail, Metro and Mono Rail Passenger Flows for HY2051
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3.6.4 Vehicle Kilometres Travelled (VKT) VKTs were obtained as a result for all the horizon years and the estimate shows that due to increase in external vehicles, cars and commercial vehicles, there is a huge increase in VKTs. Also, due to the inclusion of new routes and services of Public transport, there is an increase in their VKTs. Table 3.6 and figures 3.20 & 3.21 shows the comparison of VKTs of the internal and the external vehicles in base year and horizon years.
Table 3.6 VKTs of Base year and Horizon Years
Modes Vehicle Kilometers Travelled (millions) 2005 2021 2031 2051
Internal Vehicles Car 2.89 14.04 22.28 38.46 Two-Wheeler 12.19 37.81 45.75 46.26 Auto 1.88 3.89 3.73 6.20 Taxi 0.41 0.85 0.82 1.36 Bus 2.00 2.27 2.47 3.57 Train 0.96 0.98 1.57 2.67 Metro 0.00 0.03 0.07 0.10 Mono Rail 0.00 0.00 0.00 0.02 Light Commercial Vehicles (LCV) 0.68 1.09 2.36 8.92 Heavy Commercial Vehicles (HCV) 1.17 1.88 4.06 15.35 Total 22.19 62.85 83.11 122.90
External Vehicles Car 0.28 1.33 3.64 24.17 Two-Wheeler 0.07 0.46 1.22 8.10 Auto 0.01 0.06 0.17 1.11 Taxi 0.02 0.28 0.68 4.51 Bus 0.10 0.33 0.53 1.46 Light Commercial Vehicles (LCV) 0.44 0.64 0.81 1.26 Heavy Commercial Vehicles (HCV) 1.92 2.79 3.52 5.47
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Total 2.84 5.90 10.57 46.09 Total (External & Internal) 25.04 68.75 93.68 168.99
Figure 3.20: Comparison of VKTs of Internal vehicles in Base Year and Horizon Years
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Figure 3.21: Comparison of VKTs of External vehicles in Base Year and Horizon Years 4. Assessment of Emission Levels
4.1 Methodology for estimation of emissions The present study estimates the emissions using vehicle kilometres travelled (VKT) method. The VKTs for each category of the vehicle are obtained for the years 2005, 2021, 2031 and 2050. The next step is to obtain emission factors for conventional and electric vehicles. The pollutants considered are Carbon Monoxide (CO), Hydrocarbons (HC), Nitrogen Oxides (NOx), Carbon
Dioxide (CO2) and Particulate Matter (PM). Since vehicle tail pipe has negligible coarser fraction of particulates, we have considered PM2.5 emissions only. 4.1.1 Conventional Vehicles’ Emission Factors The emissions factors for the conventional vehicles have been obtained from ARAI (2008) and CPCB (2015). The emission factors are based on vehicle type, vintage, fuel used and type of technology. On emission factors, the following assumptions are made. 1. The distribution of technology (2-stroke, 4-stroke), vintage etc. within a particular vehicular category in Mumbai has been assumed to be the same as that in Delhi. Since
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consumer choices and availability of technology can be assumed to be similar in both cities and was hence taken from Aggarwal and Jain( 2016). The distributions have been shown in in Appendix A. 2. Based on emission characteristics within a particular vehicular category, the vehicles have been further divided into the groups depending upon vintage. It is in correspondence with the emission factors provided by ARAI (2008). The emission factors have been attached in the Appendix B and C. 4.1.2 Electric Vehicles’ Emission Factors The electric vehicle emissions have been estimated as follows: Emission Factors (gm/km) = Emissions per unit of electricity generated (g/kWh) * Energy consumption (kWh/km) Emissions for the electricity (g/kWh) are calculated based upon different energy mix scenarios. Due to different projections of the energy mix in the horizon years 2031 and 2050, four different scenarios are assumed: 1. Scenario 1: New Policies Scenario (IEA, 2015) 2. Scenario 2: Electricity from non-renewable Sources (100%) 3. Scenario 3: Half electricity from renewable and another half from non-renewable sources (50%-50%) 4. Scenario 4: Electricity from Renewable Sources (100%) The Energy or Grid Mix of India for the base year (2005) is obtained from CEA (2017). In Scenario 1, the electricity grid mix for future, horizon years 2031 and 2050, is taken from IEA (2015) which was further modified in the other three scenarios assuming the different shares of Renewable and Non-Renewable sources in the electricity generation. The estimated electricity emission factors for all the four scenarios, with average electricity consumption of electric vehicles obtained from the different sources, are attached in Appendix C. For the Suburban, Metro and Mono Rail, we have used the Emission factors calculated in all the four scenarios of electricity grid mix with average energy consumption. Therefore, BAU is further estimated in the four scenarios i.e. BAU S1, BAU S2, BAU S3 and BAU S4. After the factors have been obtained, inventory for a particular pollutant has been generated using the following equation:
�� = (���)� × ��
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Here Ei represents emission in gm for the pollutant i, (veh) j represents km travelled by vehicle j and ej is the corresponding emission factor which depends upon the type of vehicle, age, electricity consumption and electricity grid emission factors as described above.
4.2 Estimation of emissions The yearly emissions estimated in tonne with their classification in direct and indirect emissions for the base year as well as horizon years 2021, 2031 & 2050 under all the four scenarios of electricity grid mix are shown in Table 4.2 to 4.5 below: Table 4.2 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S1
Carbon Carbon Nitrogen Particulate Year Emission Hydrocarbons Dioxide monoxide oxides matter Type (HC) (CO2) (CO) (NOx) (PM2.5)
All the emissions are in tonne/year Total 36,447 9,225 31,405 4,011,953 4,563 emissions Indirect 878 0 5,944 1,975,577 981 emissions 2005 Percentage indirect 2.41% 0.00% 18.93% 49.24% 21.50% emissions Direct 35,569 9,225 25,460 2,036,375 3,582 emissions
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Total 27,952 8,930 23,316 5,724,505 1,054 emissions Indirect 922 0 4,206 2,090,781 300 emissions 2021 Percentage indirect 3.30% 0.00% 18.04% 36.52% 28.44% emissions Direct 27,030 8,930 19,110 3,633,724 754 emissions
Total 39,545 6,821 13,730 8,455,108 746 emissions Indirect 1,344 0 5,165 2,919,693 367 emissions 2031 Percentage indirect 3.40% 0.00% 37.61% 34.53% 49.12% emissions Direct 38,201 6,821 8,566 5,535,415 380 emissions
Total 76,335 10,269 15,383 17,356,622 845 emissions Indirect 1,989 0 5,623 4,226,137 412 emissions 2050 Percentage indirect 2.61% 0.00% 36.55% 24.35% 48.76% emissions Direct 74,346 10,269 9,760 13,130,484 433 emissions
Table 4.3 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S2 Carbon Carbon Nitrogen Particulate Year Emission Hydrocarbons Dioxide monoxide oxides matter Type (HC) (CO2) (CO) (NOx) (PM2.5)
All the emissions are in tonne/year Total 36,447 9,225 31,405 4,011,953 4,563 emissions Indirect 878 0 5,944 1,975,577 981 emissions 2005 Percentage indirect 2.41% 0.00% 18.93% 49.24% 21.50% emissions Direct 35,569 9,225 25,460 2,036,375 3,582 emissions
Total 28,141 8,930 24,137 6,250,162 1,114 2021 emissions
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Indirect 1,111 0 5,026 2,616,438 360 emissions Percentage indirect 3.95% 0.00% 20.82% 41.86% 32.29% emissions Direct 27,030 8,930 19,110 3,633,724 754 emissions
Total 39,899 6,821 15,011 9,391,793 839 emissions Indirect 1,698 0 6,445 3,856,377 460 emissions 2031 Percentage indirect 4.25% 0.00% 42.94% 41.06% 54.77% emissions Direct 38,201 6,821 8,566 5,535,415 380 emissions
Total 76,913 10,270 16,888 18,861,182 959 emissions Indirect 2,567 0 7,127 5,730,698 526 emissions 2050 Percentage indirect 3.34% 0.00% 42.21% 30.38% 54.86% emissions Direct 74,346 10,269 9,760 13,130,484 433 emissions
Table 4.4 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S3 Carbon Carbon Nitrogen Particulate Year Emission Hydrocarbons Dioxide monoxide oxides matter Type (HC) (CO2) (CO) (NOx) (PM2.5)
All the emissions are in tonne/year Total 36,447 9,225 31,405 4,011,953 4,563 emissions Indirect 878 0 5,944 1,975,577 981 emissions 2005 Percentage indirect 2.41% 0.00% 18.93% 49.24% 21.50% emissions Direct 35,569 9,225 25,460 2,036,375 3,582 emissions
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Total 39,170 6,821 12,375 7,463,604 648 emissions Indirect 969 0 3,809 1,928,189 268 emissions 2021 Percentage indirect 2.47% 0.00% 30.78% 25.83% 41.38% emissions Direct 38,201 6,821 8,566 5,535,415 380 emissions
Total 39,899 6,821 15,011 9,391,793 839 emissions Indirect 1,698 0 6,445 3,856,377 460 emissions 2031 Percentage indirect 4.25% 0.00% 42.94% 41.06% 54.77% emissions Direct 38,201 6,821 8,566 5,535,415 380 emissions
Total 75,813 10,269 14,023 15,995,833 742 emissions Indirect 1,467 0 4,262 2,865,349 309 emissions 2050 Percentage indirect 1.93% 0.00% 30.40% 17.91% 41.62% emissions Direct 74,346 10,269 9,760 13,130,484 433 emissions
Table 4.5 Emissions levels in Base Year and Horizon Years for Business as Usual Scenario S4 Carbon Carbon Nitrogen Particulate Year Emission Hydrocarbons Dioxide monoxide oxides matter Type (HC) (CO2) (CO) (NOx) (PM2.5)
All the emissions are in tonne/year Total 36,447 9,225 31,405 4,011,953 4,563 emissions 2005 Indirect 878 0 5,944 1,975,577 981 emissions
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Percentage indirect 2.41% 0.00% 18.93% 49.24% 21.50% emissions Direct 35,569 9,225 25,460 2,036,375 3,582 emissions
Total 27,203 8,930 20,052 3,633,724 816 emissions Indirect 173 0 941 0 62 emissions 2021 Percentage indirect 0.64% 0.00% 4.69% 0.00% 7.54% emissions Direct 27,030 8,930 19,110 3,633,724 754 emissions
Total 38,441 6,821 9,738 5,535,415 456 emissions Indirect 240 0 1173 0 76 emissions 2031 Percentage indirect 0.62% 0.00% 12.04% 0.00% 16.72% emissions Direct 38,201 6,821 8,566 5,535,415 380 emissions
Total 74,712 10,269 11,158 13,130,484 524 emissions Indirect 366 0 1397 0 91 emissions 2050 Percentage indirect 0.49% 0.00% 12.52% 0.00% 17.41% emissions Direct 74,346 10,269 9,760 13,130,484 433 emissions
The total emissions i.e. CO, HC, NOx, CO2 and PM2.5 in all the Scenarios of BAU in the years 2005, 2021, 2031 and 2050 are shown in figures 4.1 to 4.5 below.
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Figure 4.1: Total CO emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix
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Figure 4.2: Total HC emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix
Figure 4.3: Total NOx emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix
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Figure 4.4: Total CO2 emissions (million tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix
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Figure 4.5: Total PM2.5 emissions (tonne/year) in Base Year and Horizon Years in all the scenarios of Grid mix
Comparisons of CO2, HC, PM, NOx and CO emissions due to internal and external vehicles in base year and Horizon years are given in the figures 4.6 to 4.30. For internal vehicles, these emissions are shown separately for all the four scenarios of the grid mix.
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Figure 4.6: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1
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Figure 4.7: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
Figure 4.8: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
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Figure 4.9: CO2 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4
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Figure 4.10: CO2 emissions (tonne/year) of External Vehicles in Base Year and Horizon Years
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Figure 4.11: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1
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Figure 4.12: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
Figure 4.13: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3
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Figure 4.14: HC emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4
Figure 4.15: HC emissions (tonne/year) of External Vehicles in Base Year and Horizon Years
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Figure 4.16: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1
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Figure 4.17: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
Figure 4.18: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3
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Figure 4.19: PM2.5 emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4
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Figure 4.20: PM2.5 emissions (tonne/year) of External Vehicles in Base Year and Horizon Years
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Figure 4.21: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1
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Figure 4.22: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
Figure 4.23: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3
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Figure 4.24: NOX emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4
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Figure 4.25: NOX emissions (tonne/year) of External Vehicles in Base Year and Horizon Years
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Figure 4.26: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S1
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Figure 4.27: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S2
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Figure 4.28: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S3
Figure 4.29: CO emissions (tonne/year) of Internal Vehicles in Base Year and Horizon Years in BAU S4
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Figure 4.30: CO emissions (tonne/year) of External Vehicles in Base Year and Horizon Years
5. Conclusions
Base year model for the year 2005 is developed by making use of the secondary data from Comprehensive Transportation Study (CTS). Future Year model is prepared by forecasting these parameters for Horizon years 2021, 2031 and 2050.
For the base year 52% of the trips are served by Suburban Train. Buses served for 26% of travel, IPT provided for 9% of the trips and the private mode completes 13%. The mode share is similar to the observed mode share in CTS. For horizon years, the mode share of the suburban train is estimated to decrease from 52% to about 32% in 2050. Similarly, mode share of the bus is expected to drop from 26% to as low as 7 % and from 9% to 4% for IPT. The proportion of the metro which was included only after the horizon year 2021 onwards increased up to 38% in 2050 from 31% in 2021. Monorail has managed to capture only minuscule share of 0.13% in 2050 from 0.07% in 2021.
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For the base year, the estimated Vehicle Kilometers Travelled (VKT) are 25.04 million/day and for horizon years 2021, 2031 and 2050 the figures are 68.75, 93.68 and 168.99 million/day. The VKTs of internal vehicles and external vehicles are estimated to increase by factor of 5.5 and 16.21 respectively in the year 2050 with respect to the base year 2005. An overall increase in the VKTs of all the vehicles in the MMR (Internal and external) are estimated to be 6.75 times of their base levels in the year 2050.
The Business Usual Scenario has been further subdivided into four scenarios under different projection of Electricity grid mix of India. The Scenario 1 (IEA, 2015) and Scenario 3 (50% Non-Renewable Energy- 50% Renewable Energy(RE)) are the most feasible and plausible one as these are more or less similar to the current trends of Indian power sector. However, the Scenario 2 is completely out of scope as it includes the electricity generation from Non-Renewable sources. Scenario 3 (100% RE) is more like ambitious to Indian power sector. Therefore, we are giving emphasis to the results of BAU scenario 1.
The CO2 emissions are estimated to be 4.01, 5.72, 8.46 and 17.36 million tonne/year for the base year,
2021, 2031 and 2050, respectively. The share of External Vehicles and Commercial Vehicles in CO2
emissions together in BAU scenario is 35% in 2031 which increases to 54% in 2050. Particulate matter emissions are expected to be 4563, 1054, 746 and 845 tonne/year for the base year, 2021, 2031 and 2050, respectively. Nitrogen Oxides emissions are expected to be 31405, 23316, 13730 and 15383 tonne/year for base year, 2021, 2031 and 2050, respectively. Similarly, hydrocarbon emissions are expected to be 9225, 8930, 6821 and 10269 tonne/year for different years. Carbon Monoxide emissions are expected to be 36447, 27592, 39545 and 76335 tonne/year for base year, 2021, 2031 and 2050, respectively.
In the horizon year 2050, CO2 emissions are estimated to be ~4.33 times of the base year levels. Similarly, CO emissions are twice of the base year levels. However, remaining emissions are decreasing or are same because of the improvement in emission standards of the conventional vehicles (Euro VI). For instance,
PM2.5 emissions are estimated to decrease by 80% of their base year levels in the year 2050. Similarly, NOx emissions are getting halved of their base year levels in the year 2050. HC emissions are increasing by 10 percent of their base year levels in the year 2050. Therefore, appropriate measures are required to reduce the CO and CO2 emissions.
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References Aggarwal, P., & Jain, S. (2016). Energy demand and CO2emissions from urban on-road transport in Delhi: current and future projections under various policy measures. Journal of Cleaner Production, 128, 48–61. https://doi.org/10.1016/j.jclepro.2014.12.012 ARAI. (2008). Emission Factor development for Indian Vehicles. Retrieved from http://www.cpcb.nic.in/Emission_Factors_Vehicles.pdf CEA. (2017). Growth of Electricity Sector in India From 1947-2017. Retrieved from http://www.cea.nic.in/reports/others/planning/pdm/growth_2017.pdf CPCB. (2015). Status of Pollution Generated from Road Transport in Six Mega Cities. Central Pollution Control Board, Government of India. Eastern Freeway. (2016). Retrieved October 21, 2016, from https://mmrda.maharashtra.gov.in/eastern- freeway#
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IEA. (2015). India Energy Outlook. World Energy Outlook Special Report. https://doi.org/https://www.iea.org/publications/freepublications/publication/africa-energy- outlook.html MCGM. (2016). Comprehensive Mobility Plan (CMP) for Greater Mumbai. Retrieved from http://portal.mcgm.gov.in/irj/go/km/docs/documents/MCGM Department List/Roads and Traffic/Docs/CMP for Greater Mumbai/Executive Summary/Executive Summary.pdf MMRDA. (2003). Population and Eimployment Profile of Mumbai Metropolitan Region. Retrieved from https://mmrda.maharashtra.gov.in/documents/10180/6867434/Population+and+Employment+Pr ofile.pdf/4e3d18f0-4c6c-4708-9130-09915551c5e9 MMRDA. (2008). Comprehensive Transportation Study for Mumbai Metropolitan Region. Retrieved from https://mmrda.maharashtra.gov.in/comprehensive-transport-study MMRDA. (2013). Retrieved September 28, 2016, from https://mmrda.maharashtra.gov.in/about-mmr Multimodal Corridor from Virar to Alibaug. (2012). Retrieved October 21, 2016, from https://mmrda.maharashtra.gov.in/multimodal-corridor-from-virar-to-alibaug Mumbai Metro Rail Project. (2004). Retrieved October 20, 2016, from https://mmrda.maharashtra.gov.in/mumbai-metro-rail-project Mumbai Monorail Project. (2007). Retrieved October 20, 2016, from https://mmrda.maharashtra.gov.in/mumbai-monorail-project# Mumbai Trans Harbour Link. (2016). Retrieved October 15, 2016, from https://mmrda.maharashtra.gov.in/mthl# Mumbai Urban Transport Project. (2010). Retrieved October 15, 2016, from https://mmrda.maharashtra.gov.in/mumbai-urban-transport-project-2# MUTP. (2001). CONSOLIDATED ENVIRONMENTAL ASSESSMENT. Retrieved from http://www.mumbaidp24seven.in/reference/Mumbai_Urban_Transport_Project_Enviornmental_ Assessment_Executive_Summary_5.doc.
Appendix A
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Table A-1 Share distribution of two wheelers with respect to technology, fuel use and vintage in Delhi
Mode of travel Technology Fuel use Vintage % share
Two-wheeler
Scooter 2 stroke Petrol >15 years old 3.7
2 stroke Petrol 10-15 years old 12.2
2 stroke Petrol 5-10 years old 8.0
2 stroke Petrol <5 years old 0.5
4 stroke Petrol >15 years old 0.1
4 stroke Petrol 10-15 years old 1.5
4 stroke Petrol 5-10 years old 7.5
4 stroke Petrol <5 years old 15.0
Motorcycle 2 stroke Petrol >15 years old 0.1
2 stroke Petrol 10-15 years old 0.3
2 stroke Petrol 5-10 years old 0.2
2 stroke Petrol <5 years old 0
4 stroke Petrol >15 years old 0.2
4 stroke Petrol 10-15 years old 1.9
4 stroke Petrol 5-10 years old 17.2
4 stroke Petrol <5 years old 31.0
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Table A-2 Share distribution of three wheelers with respect to technology, fuel use and vintage in Delhi
Mode of travel Technology Fuel use Vintage % share
Rickshaw (3W) 2 stroke CNG >15 years old 1
2 stroke CNG 10-15 years old 7.5
4 stroke CNG >15 years old 0
4 stroke CNG 10-15 years old 12.17
4 stroke CNG 5-10 years old 43.5
4 stroke CNG <5 years old 35.83
Table A-3 Share distribution of buses with respect to technology, fuel use and vintage in Delhi [22]
Mode of travel Technology Fuel use Vintage % share
Bus 4 stroke Diesel >15 years old 0.620945
4 stroke Diesel 10-15 years old 16.383395
4 stroke Diesel 5-10 years old 51.01302
4 stroke Diesel <5 years old 27.51264
4 stroke CNG >15 years old 0.029055
4 stroke CNG 10-15 years old 0.766605
4 stroke CNG 5-10 years old 2.38698
4 stroke CNG <5 years old 1.28736
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Table A-4 Share distribution of Cars with respect to technology, fuel use and vintage in Delhi
Mode of travel Technology Fuel use Vintage % share
Cars 4 stroke Petrol >15 years old 2.12
4 stroke Petrol 10-15 years old 10.28
4 stroke Petrol 5-10 years old 29.86
4 stroke Petrol <5 years old 29.3
4 stroke Diesel >15 years old 0.04
4 stroke Diesel 10-15 years old 0.34
4 stroke Diesel 5-10 years old 5.07
4 stroke Diesel <5 years old 8.17
4 stroke CNG >15 years old 0
4 stroke CNG 10-15 years old 0
4 stroke CNG 5-10 years old 7.86
4 stroke CNG <5 years old 6.96
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Appendix B
Table B-1 Emission factors
gm/km mg/km Vehicle 1-3 Vintage Benzen Formaldeh Acetaldehy Category CO HC NOx CO2 PM Butadien e yde de e LCV 1991- 0.99 Diesel 3.07 2.28 3.03 327.29 0.543 0.009 0.198 0.012 96 8 (<3000 cc) LCV 1996- 0.65 Diesel 3 1.28 2.48 333.31 0.202 0.215 0.118 0.006 2000 5 (<3000 cc) LCV 0.47 Diesel BSI 3.66 1.35 2.12 401.25 0.196 0.415 0.003 0.008 5 (<3000 cc) LCV 0.47 Diesel BSII 3.66 1.35 2.12 401.25 0.196 0.415 0.003 0.008 5 (<3000 cc) LCV 0.47 Diesel BSIII 3.66 1.35 2.12 401.25 0.196 0.415 0.003 0.008 5 (<3000 cc) LCV 0.94 1.48 0.08 Diesel BSIV 2.65 401.25 0.137 0.291 0.002 0.006 6 4 1 (<3000 cc) HCV Diesel 1991- 13.8 1.96 19.3 2.63 837.5 0.02 0.018 0.093 0.02 Truck 2000 4 5 (>6000cc) HCV Diesel BSI 6 0.37 9.3 762.39 1.24 0.005 0.007 0.061 0 Truck (>6000cc) HCV Diesel BSII 6 0.37 9.3 762.39 1.24 0.005 0.007 0.061 0 Truck (>6000cc) HCV Diesel BSIII 6 0.37 8.63 762.39 0.42 0.005 0.007 0.061 0 Truck (>6000cc) HCV Diesel 4.34 0.25 6.04 0.07 BSIV 762.39 0.003 0.005 0.043 0 Truck 5 9 1 1 (>6000cc)
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HCV 1991- 13.0 11.2 2.01 Diesel Bus 2.4 817.52 0.153 0.031 0.101 0.015 96 6 4 3 (>6000 cc) HCV 1996- 15.2 1.21 Diesel Bus 4.48 1.46 920.77 0.101 0.009 0.102 0.003 2000 5 3 (>6000 cc) HCV 0.79 Diesel Bus BSI 3.97 0.39 11.5 668 0.013 0.002 0.01 0.014 5 (>6000 cc) HCV 0.79 Diesel Bus BSII 3.97 0.39 11.5 668 0.013 0.002 0.01 0.014 5 (>6000 cc) HCV Diesel Bus BSIII 3.92 0.16 6.53 602.01 0.3 0.01 0.01 0.052 0.008 (>6000 cc) HCV 2.83 0.11 4.57 0.05 Diesel Bus BSIV 602.01 0.007 0.007 0.037 0.006 8 2 1 1 (>6000 cc) HCV Post 0.04 CNG Bus 3.72 3.75 6.21 806.5 0 0 0 0 2000 4 (>6000 cc) HCV Post 4.34 0.03 CNG Bus 3.72 3.75 806.5 0 0 0 0 2010 7 5 (>6000 cc) Passenger Cars 1991- 0.00 4.75 0.84 0.95 95.65 0.213 0.132 0.018 0.011 (Petrol) 96 8 (<1000 cc) Passenger Cars 1996- 0.00 4.53 0.66 0.75 106.96 0 0.007 0.001 0 (Petrol) 2000 8 (<1000 cc) Passenger Cars 0.00 BSI 1.3 0.24 0.2 126.37 0 0.003 0.003 0.001 (Petrol) 4 (<1000 cc) Passenger Cars 0.00 BSII 1.3 0.24 0.2 126.37 0 0.003 0.003 0.001 (Petrol) 4 (<1000 cc) Passenger Cars 0.00 BSIII 0.84 0.12 0.09 126.37 0 0.003 0.003 0.001 (Petrol) 2 (<1000 cc) Passenger Cars 0.36 0.04 0.00 BSIV 0.06 126.37 0 0.002 0.002 0.001 (Petrol) 1 8 2 (<1000 cc) Passenger 1991- 0.00 3.01 0.19 0.12 126.5 0.001 0.003 0.003 0.001 Cars 96 6
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(Petrol) (1000- 1400 cc) Passenger Cars 1996- 0.00 (Petrol) 3.01 0.19 0.12 126.5 0.001 0.003 0.003 0.001 2000 6 (1000- 1400 cc) Passenger Cars 0.00 (Petrol) BSI 3.01 0.19 0.12 126.5 0.001 0.003 0.003 0.001 6 (1000- 1400 cc) Passenger Cars 0.00 (Petrol) BSII 3.01 0.19 0.12 126.5 0.001 0.003 0.003 0.001 6 (1000- 1400 cc) Passenger Cars 1.94 0.09 0.05 0.00 (Petrol) BSIII 126.5 0.001 0.003 0.003 0.001 5 5 4 3 (1000- 1400 cc) Passenger Cars 1.29 0.09 0.06 0.00 (Petrol) BSIV 126.5 0 0.002 0.002 0.001 4 5 4 2 (1000- 1400 cc) Passenger Cars 1991- 0.00 2.74 0.19 0.21 142.86 0.001 0 0.009 0.001 (Petrol) 96 6 (>1400 cc) Passenger Cars 1996- 0.00 2.74 0.19 0.21 142.86 0.001 0 0.009 0.001 (Petrol) 2000 6 (>1400 cc) Passenger Cars 0.00 BSI 2.74 0.19 0.21 142.86 0.001 0 0.009 0.001 (Petrol) 6 (>1400 cc) Passenger Cars 0.00 BSII 2.74 0.19 0.21 142.86 0.001 0 0.009 0.001 (Petrol) 6 (>1400 cc) Passenger Cars 0.00 BSIII 0.84 0.12 0.09 172.95 0 0 0 0 (Petrol) 2 (>1400 cc) Passenger 0.36 0.04 0.00 BSIV 0.06 172.95 0 0 0 0 Cars 1 8 2
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(Petrol) (>1400 cc) Passenger Cars 1991- 0.14 0.87 0.22 0.45 129.09 1.596 0.313 0.026 0 (Petrol) 96 5 (>1400 cc) Passenger Cars 1996- 0.14 0.87 0.22 0.45 129.09 1.596 0.313 0.026 0 (Diesel) 2000 5 (<1600 cc) Passenger Cars BSI 0.72 0.14 0.84 156.76 0.19 0.039 0.053 0.021 0.002 (Diesel) (<1600 cc) Passenger Cars BSII 0.3 0.26 0.49 156.76 0.06 0.039 0.053 0.021 0.002 (Diesel) (<1600 cc) Passenger Cars 0.01 BSIII 0.06 0.08 0.28 148.76 0.002 0.001 0.089 0.003 (Diesel) 5 (<1600 cc) Passenger Cars 0.04 0.04 0.00 BSIV 0.14 148.76 0.001 0 0.044 0.002 (Diesel) 7 8 8 (<1600 cc) Passenger Cars 1991- (Diesel) 0.66 0.25 0.61 166.14 0.18 0.003 0.012 0.04 0.001 96 (1600- 2400 cc) Passenger Cars (Diesel) BSI 0.66 0.25 0.61 166.14 0.18 0.003 0.012 0.04 0.001 (1600- 2400 cc) Passenger Cars 0.54 0.15 1.13 201.75 0.05 (Diesel) BSII 0.003 0.012 0.04 0.001 6 9 9 2 7 (1600- 2400 cc) Passenger Cars 0.01 (Diesel) BSIII 0.66 0.25 0.61 166.14 0.003 0.012 0.04 0.001 4 (1600- 2400 cc) Passenger 0.52 0.30 0.00 Cars BSIV 0.15 166.14 0.002 0.007 0.024 0 1 5 8 (Diesel)
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(1600- 2400 cc) Passenger Cars 1991- 0.00 0.85 0.79 0.53 149.36 0 0 0.011 0.002 (CNG) 96 1 (<1000 cc) Passenger Cars 1996- 0.00 0.85 0.79 0.53 149.36 0 0 0.011 0.002 (CNG) 2000 1 (<1000 cc) Passenger Cars 0.00 BSI 0.06 0.46 0.74 143.54 0 0 0.011 0.002 (CNG) 6 (<1000 cc) Passenger Cars 0.00 BSII 0.06 0.46 0.74 143.54 0 0 0.011 0.002 (CNG) 6 (<1000 cc) Passenger Cars 0.00 BSIII 0.06 0.46 0.74 143.54 0 0 0.011 0.002 (CNG) 6 (<1000 cc) Passenger Cars 0.00 BSIV 0.06 0.46 0.74 143.54 0 0 0.011 0.002 (CNG) 6 (<1000 cc) Passenger Cars 1991- 0.00 (LPG) 6.78 0.85 0.5 130.85 0.004 0.006 0.015 0.01 96 1 (1000- 1400 cc) Passenger Cars 1996- 0.00 (LPG) 6.78 0.85 0.5 130.85 0.004 0.006 0.015 0.01 2000 1 (1000- 1400 cc) Passenger Cars 0.00 (LPG) BSI 0.6 0.36 0.01 131.19 0.001 0 0.001 0.001 2 (1000- 1400 cc) Passenger Cars 0.00 (LPG) BSII 0.6 0.36 0.01 131.19 0.001 0 0.001 0.001 2 (1000- 1400 cc) Passenger 0.00 Cars BSIII 0.6 0.36 0.01 131.19 0.001 0 0.001 0.001 2 (LPG)
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(1000- 1400 cc) Passenger Cars 0.00 (LPG) BSIV 0.6 0.36 0.01 131.19 0.001 0 0.001 0.001 2 (1000- 1400 cc) MUV 1991- Diesel 2.49 1.39 1.7 163.56 0.57 0.013 0.006 0.014 0 96 (<3000 cc) MUV 1996- Diesel 1.38 1.39 0.65 189.48 0.56 0.006 0.059 0 0.003 2000 (<3000 cc) MUV Diesel BSI 1.94 0.89 2.46 242.01 0.48 0.008 0.006 0 0.003 (<3000 cc) MUV Diesel BSII 1.94 0.89 2.46 242.01 0.48 0.008 0.006 0 0.003 (<3000 cc) MUV 0.09 Diesel BSIII 0.25 0.19 0.67 255.98 0.268 0.04 0.014 0.008 6 (<3000 cc) MUV 0.19 0.11 0.33 0.05 Diesel BSIV 255.98 0.161 0.024 0.009 0.005 8 4 5 3 (<3000 cc) Moped (2 1991- 11.4 stroke) 7.7 0.02 15.37 0.06 0.004 0.002 0 0 96 1 (<80 cc) Moped (2 1996- stroke) 2.97 2.77 0.03 21.13 0.06 0.002 0.004 0.006 0.023 2000 (<80 cc) Moped (2 Post stroke) 0.45 3.1 0.04 29.69 0.06 0.001 0.003 0.002 0.001 2000 (<80 cc) Moped (2 Post 0.01 stroke) 0.46 0.6 0.02 36.81 0.001 0 0.037 0.007 2005 8 (<80 cc) Moped (2 Post 0.30 0.40 0.01 0.01 stroke) 36.81 0.001 0 0.025 0.005 2010 8 2 3 8 (<80 cc) Scooter (2 1996- 0.04 Stroke) 5.2 2.51 0.04 24.24 0.001 0.002 0.004 0.005 2000 9 (<80 cc) Scooter (2 Post 0.04 Stroke) 2.37 2.05 0.03 27.08 0.002 0.003 0.011 0 2000 9 (<80 cc) Scooter (2 Post 1.19 0.01 0.04 Stroke) 1.03 36.81 0.001 0.001 0.006 0 2010 1 5 9 (<80 cc)
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Scooter (2 1991- 0.07 Stroke) 6 3.68 0.02 24.75 0.006 0.004 0 0.017 96 3 (>80 cc) Scooter (2 1996- 0.07 Stroke) 5.1 2.46 0.01 25.05 0.001 0.007 0.003 0 2000 3 (>80 cc) Scooter (2 Post 0.02 Stroke) 2.76 2.16 0.03 33.31 1.037 0.055 0.008 0.002 2000 5 (>80 cc) Scooter (2 Post 0.05 Stroke) 0.16 0.86 0.02 38.54 0.011 0.012 0.04 0.057 2005 7 (>80 cc) Scooter (2 Post 0.10 0.57 0.01 0.04 Stroke) 38.54 0.007 0.008 0.027 0.038 2010 7 6 3 6 (>80 cc) Motorcycl e (2 1991- 5.64 2.89 0.04 23.48 0.06 0.009 0.008 0.002 0 Stroke) 96 (<80 cc) Motorcycl e (2 1996- 0.07 2.96 2.44 0.05 24.17 0 0.008 0 0.001 Stroke) 2000 3 (<80 cc) Motorcycl e (2 Post 0.07 2.96 2.44 0.05 24.17 0 0.008 0 0.001 Stroke) 2000 3 (<80 cc) Motorcycl e (2 Post 1.48 1.22 0.02 0.01 24.17 0 0.004 0 0 Stroke) 2010 7 6 5 3 (<80 cc) Moped (4 Post Stroke) 0.81 0.5 0.29 20.09 0.01 0.003 0.006 0 0.004 2000 (<100 cc) Moped (4 Post 0.40 0.25 0.14 Stroke) 20.09 0.01 0.002 0.003 0 0.002 2010 7 1 6 (<100 cc) Scooter( 4 1991- 0.01 Stroke) 0.93 0.65 0.35 33.83 0.005 0.017 0.006 0.001 96 5 (>100 cc) Scooter( 4 1996- 0.01 Stroke) 0.93 0.65 0.35 33.83 0.005 0.017 0.006 0.001 2000 5 (>100 cc) Scooter( 4 Post 0.01 Stroke) 0.93 0.65 0.35 33.83 0.005 0.017 0.006 0.001 2000 5 (>100 cc) Scooter( 4 Post 0.01 Stroke) 0.4 0.15 0.25 42.06 0.002 0.013 0.105 0.058 2005 5 (>100 cc)
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Scooter( 4 Post 0.26 0.10 0.16 0.01 Stroke) 42.06 0.001 0.009 0.07 0.039 2010 8 1 8 2 (>100 cc) Motorcycl e (4 1991- 3.12 0.78 0.23 22.42 0.01 0.004 0.002 0.001 0.002 Stroke) 96 (<100 cc) Motorcycl e (4 1996- 0.01 1.58 0.74 0.3 23.25 0.001 0.003 0.01 0.002 Stroke) 2000 5 (<100 cc) Motorcycl e (4 Post 0.03 1.65 0.61 0.27 24.97 0.002 0.01 0.003 0 Stroke) 2000 5 (<100 cc) Motorcycl e (4 Post 0.82 0.30 0.13 0.01 24.97 0.001 0.005 0.002 0 Stroke) 2010 9 7 6 3 (<100 cc) Motorcycl e (4 Post 0.03 Stroke) 1.48 0.5 0.54 24.82 0.017 0.002 0.001 0 2000 5 (100-200 cc) Motorcycl e (4 Post 0.74 0.25 0.27 0.02 Stroke) 24.82 0.009 0.001 0.001 0 2010 4 1 1 8 (100-200 cc) Motorcycl e (4 Post 0.01 0.72 0.52 0.15 45.6 0.002 0.002 0.006 0.005 Stroke) 2005 3 (>200 cc) Motorcycl e (4 Post 0.48 0.34 0.10 45.6 0.01 0.001 0.001 0.004 0.004 Stroke) 2010 2 8 1 (>200 cc) Three Wheelers 1996- 3.15 6.04 0.3 54.5 0.11 0.006 0.005 0.043 0.011 (2 Stroke) 2000 (<200 cc) Three Wheelers Post 0.04 1.37 2.53 0.2 62.41 0.003 0.004 0.016 0.018 (2 Stroke) 2000 5 (<200 cc) Three Wheelers Post 0.04 1.15 1.63 0.16 71.5 0.005 0.008 0.105 0.198 (2 Stroke) 2005 3 (<200 cc)
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Three Wheelers Post 0.77 1.09 0.10 0.03 71.5 0.004 0.005 0.071 0.133 (2 Stroke) 2010 1 2 7 4 (<200 cc) Three Wheelers 1991- 0.01 4.59 1.63 0.6 56.5 0.004 0.003 0.006 0.001 (4 Stroke) 1996 2 (<200 cc) Three Wheelers 1996- 0.01 4.59 1.63 0.6 56.5 0.004 0.003 0.006 0.001 (4 Stroke) 2000 2 (<200 cc) Three Wheelers Post 0.01 4.59 1.63 0.6 56.5 0.004 0.003 0.006 0.001 (4 Stroke) 2000 2 (<200 cc) Three Wheelers Post 0.01 2.29 0.77 0.53 73.8 0.001 0 0.013 0.013 (4 Stroke) 2005 5 (<200 cc) Three Wheelers Post 1.53 0.51 0.35 0.01 73.8 0 0 0.009 0.008 (4 Stroke) 2010 4 6 5 2 (<200 cc) Three Wheeler 1996- 0.78 9.16 0.63 0.93 140.87 0.018 0.001 0.016 0.005 Diesel 2000 2 (<500 cc) Three Wheeler Post 0.34 2.09 0.16 0.69 173.85 0.018 0.001 0.016 0.005 Diesel 2000 7 (<500 cc) Three Wheeler Post 0.09 0.41 0.14 0.51 131.61 0.012 0.011 0.007 0.006 Diesel 2005 1 (<500 cc) Three Wheeler Post 0.20 0.08 0.42 0.04 131.61 0.007 0.007 0.004 0.003 Diesel 2010 5 3 3 6 (<500 cc) Three Wheeler Post 0.01 CNG 1 0.26 0.5 77.7 0.036 0.004 0.007 0.001 2000 5 OEM 4S (<200 cc) Three Wheeler Post 0.11 CNG 0.69 2.06 0.19 57.71 0.005 0.006 0.005 0.007 2000 8 Retro 2S (<200 cc)
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Three Wheeler LPG 1996- 0.17 7.2 5.08 0.05 44.87 0.006 0.017 0.008 0.004 Retrofit 2000 1 2S (<200 cc) Three Wheeler LPG Post 1.7 1.03 0.04 68.15 0.13 0.006 0.017 0.008 0.004 Retrofit 2000 2S (<200 cc) Tractor 9.88 1.09 9.73 799.95 1.09 0.01 0.01 0.07 0.01
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Appendix C
Table C-1 Calculated Conventional Vehicles’ Emission factors
Yea gm/km Vehicle r CO HC NOx CO2 PM 2005 2.444 0.267 0.344 133.58 0.0276 2021 0.604 0.139 0.178 144.13 0.004 Conventional Car/Taxi 2031 0.528 0.133 0.130 144.29 0.004 2050 0.528 0.133 0.020 147.12 0.0038
0.02291 2005 1.153 0.790 0.112 31.07 7 0.02288 2021 0.628 0.416 0.122 36.33 Conventional Two-Wheeler 2 2031 0.933 0.201 0.057 42.71 0.011 2050 0.994 0.099 0.06 43.61 0.004
2005 4.468 1.889 0.558 77.17 0.203 Conventional Three- 2021 1.550 0.650 0.385 85.51 0.025 Wheeler 2031 0.487 0.316 0.147 81.74 0.017 2050 0.302 0.206 0.084 67.53 0.011
2005 7.997 1.601 11.96 789.18 1.38 2021 3.026 0.271 4.641 611.48 0.0851 Conventional Bus 2031 2.877 0.202 1.319 611.15 0.0225 0.77 2050 2.877 0.191 611.15 0.0178 4
3.25 1.73 2.59 353.96 2005 0.741 6 6 4 5 2.67 1.00 1.49 2021 401.25 0.133 8 4 6 Conventional LCV 0.91 0.94 0.30 2031 401.25 0.015 5 6 2 0.62 0.94 0.10 2050 401.25 0.005 3 6 3
14.7 1.85 12.2 811.43 2005 1.715 2 2 8 5 Conventional HCV 4.58 0.26 6.11 2021 762.39 0.123 3 4 4
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
4.34 0.10 1.54 2031 762.39 0.03 5 1 9 4.34 0.07 0.79 2050 762.39 0.024 5 4 5
Table C-2 Calculated Electric Vehicles’ Emission factors (Scenario 1: Average Energy Consumption)
Yea gm/km Vehicle r CO HC NOx CO2 PM 0.05 1.10E- 0.06 2005 0.390 129.55 8 05 4 0.05 1.02E- 0.01 2021 0.243 120.63 3 05 7 Electric Car/Taxi 0.04 8.97E- 0.01 2031 0.180 101.95 7 06 3 0.04 7.93E- 0.00 2050 0.117 88.18 2 06 9
0.01 2.14E- 0.01 2005 0.076 25.17 1 06 2 0.01 1.98E- 0.00 2021 0.047 23.44 0 06 3 Electric Two-Wheeler 0.00 1.74E- 0.00 2031 0.035 19.81 9 06 2 0.00 1.54E- 0.00 2050 0.023 17.13 8 06 2
0.02 3.95E- 0.02 2005 0.140 46.52 1 06 3 0.01 3.65E- 0.00 2021 0.087 43.32 Electric Three- 9 06 6 0.01 3.22E- 0.00 Wheeler 2031 0.065 36.61 7 06 5 0.01 2.85E- 0.00 2050 0.042 31.66 5 06 3
0.50 9.71E- 0.56 2005 3.442 1144.03 8 05 8 Electric Bus 0.47 8.98E- 0.15 2021 2.143 1065.24 0 05 3
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
0.41 7.92E- 0.11 2031 1.592 900.23 4 05 3 0.36 7.00E- 0.07 2050 1.036 778.63 6 05 6
2.49 4.77E- 16.91 2.79 2005 5621.01 7 04 3 0 2.30 4.41E- 10.52 0.75 2021 5233.86 8 04 8 0 Suburban Rail/Train 2.03 3.89E- 0.55 2031 7.824 4423.12 5 04 5 1.80 3.44E- 0.37 2050 5.090 3825.67 1 04 3
7.95 1.52E- 53.83 17892.6 8.88 2005 0 03 7 6 3 7.34 1.40E- 33.51 16660.2 2.38 2021 8 03 2 8 8 6.47 1.24E- 24.90 14079.5 1.76 Metro / Mono Rail 2031 9 03 5 7 7 5.73 1.10E- 16.20 12177.7 1.18 2050 2 03 3 8 7
Table C-3 Calculated Electric Vehicles’ Emission factors (Scenario 2: Average Energy Consumption)
Yea gm/km Vehicle r CO HC NOx CO2 PM 0.05 1.10E- 0.06 2005 0.390 129.555 8 05 4 0.05 1.13E- 0.01 Electric Car/Taxi 2031 0.225 134.65 9 05 6 0.05 1.02E- 0.01 2050 0.149 119.57 4 05 1
0.01 2.14E- 0.01 2005 0.076 25.17 1 06 2 Electric Two-Wheeler 0.01 2.20E- 0.00 2031 0.044 26.16 2 06 3
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
0.01 1.99E- 0.00 2050 0.029 23.23 0 06 2
0.02 3.95E- 0.02 2005 0.140 46.52 1 06 3 Electric Three- 0.02 4.07E- 0.00 2031 0.081 48.35 Wheeler 1 06 6 0.01 3.68E- 0.00 2050 0.053 42.94 9 06 4
0.50 9.71E- 0.56 2005 3.442 1144.03 8 05 8 0.52 1.00E- 0.14 2031 1.987 1189.04 Electric Bus 3 04 2 0.47 9.04E- 0.09 2050 1.313 1055.83 3 05 7
2.49 4.77E- 16.91 2.79 2005 5621.01 7 04 3 0 2.57 4.91E- 0.69 2031 9.764 5842.13 Suburban Rail/Train 2 04 6 2.32 4.44E- 0.47 2050 6.452 5187.66 4 04 6
7.95 1.52E- 53.83 17892.6 8.88 2005 0 03 7 6 3 8.18 1.56E- 31.08 18596.5 2.21 2031 Metro / Mono Rail 6 03 0 2 7 7.39 1.41E- 20.53 16513.2 1.51 2050 7 03 8 3 6
Table C-4 Calculated Electric Vehicles’ Emission factors (Scenario 3: Average Energy Consumption)
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
Yea gm/km Vehicle r CO HC NOx CO2 PM 0.05 1.10E- 0.06 2005 0.390 129.555 8 05 4 0.03 6.46E- 0.00 Electric Car/Taxi 2031 0.133 67.33 4 06 9 0.03 5.85E- 0.00 2050 0.089 59.78 1 06 6
0.01 2.14E- 0.01 2005 0.076 25.17 1 06 2 0.00 1.26E- 0.00 2031 0.026 13.08 Electric Two-Wheeler 7 06 2 0.00 1.14E- 0.00 2050 0.017 11.61 6 06 1
0.02 3.95E- 0.02 2005 0.140 46.52 1 06 3 Electric Three- 0.01 2.32E- 0.00 2031 0.048 24.18 Wheeler 2 06 3 0.01 2.10E- 0.00 2050 0.032 21.47 1 06 2
0.50 9.71E- 0.56 2005 3.442 1144.03 8 05 8 0.29 5.71E- 0.08 Electric Bus 2031 1.174 594.52 9 05 3 0.27 5.16E- 0.05 2050 0.785 527.92 0 05 7
2.49 4.77E- 16.91 2.79 2005 5621.01 7 04 3 0 1.46 2.80E- 0.40 Suburban Rail/Train 2031 5.770 2921.06 8 04 6 1.32 2.54E- 0.27 2050 3.859 2593.83 8 04 9
7.95 1.52E- 53.83 17892.6 8.88 2005 0 03 7 6 3 4.67 8.93E- 18.36 1.29 2031 9298.26 Metro / Mono Rail 2 04 8 2 4.22 8.08E- 12.28 0.88 2050 8256.62 6 04 2 9
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Table C-5 Calculated Electric Vehicles’ Emission factors (Scenario 4: Average Energy Consumption)
Yea gm/km Vehicle r CO HC NOx CO2 PM 0.05 1.10E- 0.06 2005 0.390 129.555 8 05 4 0.00 1.60E- 0.00 Electric Car/Taxi 2031 0.041 0.00 8 06 3 0.00 1.46E- 0.00 2050 0.029 0.00 8 06 2
0.01 2.14E- 0.01 2005 0.076 25.17 1 06 2 0.00 3.11E- 0.00 2031 0.008 0.00 Electric Two-Wheeler 2 07 1 0.00 2.84E- 0.00 2050 0.006 0.00 1 07 0
0.02 3.95E- 0.02 2005 0.140 46.52 1 06 3 Electric Three- 0.00 5.75E- 0.00 2031 0.015 0.00 Wheeler 3 07 1 0.00 5.24E- 0.00 2050 0.010 0.00 3 07 1
0.50 9.71E- 0.56 2005 3.442 1144.03 8 05 8 0.07 1.41E- 0.02 Electric Bus 2031 0.362 0.00 4 05 4 0.06 1.29E- 0.01 2050 0.257 0.00 7 05 7
2.49 4.77E- 16.91 2.79 2005 5621.01 7 04 3 0 0.36 6.95E- 0.11 Suburban Rail/Train 2031 1.777 0.00 4 05 5 0.33 6.33E- 0.08 2050 1.265 0.00 1 05 3
7.95 1.52E- 53.83 17892.6 8.88 2005 0 03 7 6 3
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Base Year Travel Demand Model and BAU Scenario Travel Demand Forecast – MMR | IIT Bombay
1.15 2.21E- 0.36 Metro / Mono Rail 2031 5.656 0.00 7 04 8 1.05 2.02E- 0.26 2050 4.027 0.00 5 04 3
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