SYSTEM DYNAMICS MODELING for ...

2 downloads 5 Views 2MB Size Report
Jan 28, 2015 - The total permissible emissions is as per the standards is the summation of all ... Bharat Stage emission standard and Euro emission standard ...

SYSTEM DYNAMICS MODELING for TRANSPORTATION PLANNING in KANYAKUMARI DISTRICT, TAMIL NADU Tejas Rawal* &, V. Devadas,

*Corresponding Author: Tejas Rawal Ph.D. Research Scholar Department of Architecture & Planning, IIT-Roorkee Email: [email protected] M: +91 75002 07427 [email protected] M: +91 93758 53831 Dr. V. Devadas Professor Department of Architecture & Planning, IIT-Roorkee Email: [email protected] O: +91 13322 85298

ABSTRACT The transportation system in Kanyakumari District has met the grim situation over the years due to numerous parameters like increasing population, increasing economic activity, increasing vehicular population, negligence from the administrative officials, unethical practices of the locals, etc.; led it to a pathetic condition, especially on National Highway-47. However, to simplify the situation, the National Highway Authority of India (NHAI), Government of India (GoI) is taking up the effort for developing the new bypass road to the existing NH-47 by diverting form selected few junctions. Unfortunately, this partial solution for the existing NH-47 will give birth to numerous other physical, socio-economic, and ecological problems. The study aims at analyzing the impact of road transportation and developing plausible policy decision for sustainable development in Kanyakumari District. Survey research method has been adopted for this investigation. The methodology of the study consist primary household surveys and secondary data collection from various literature and competent government authority. Relevant statistical methods such as descriptive statistics correlation and regression have been used in the study. Systems theory and system dynamics modeling have been applied to the present investigation for comprehensive inclusion of diverse and dynamic behavior of the system with respect to transportation indicators. For present investigation the models that have been developed are road transportation network, vehicular growth, fuel consumption and carbon emission. Strategic simulation scenarios have been developed to analyse and compare the system and its behavior under varied circumstances. The simulated model results have been used for evolving policy interventions to achieve sustainable road transportation which can act as a catalyst for the socio-economic & environmental sustainability and integrated development in the study area. This study shows an assuring approach i.e, system dynamics modeling, for policy analysis. This sort of a pilot study is the proof that integration of all the aspects of regional development is possible for policy planning in India. Interesting findings and evolved strategic intervention from this study has potential to be developed as a strong policy document for sustainable regional development for Kanyakumari district.

KEY WORDS System Dynamics, Transportation Planning, Regional Planning, Integrated Development, Carbon Emission

Page 1 of 26

SYSTEM DYNAMICS MODELING for TRANSPORTATION PLANNING in KANYAKUMARI DISTRICT, TAMIL NADU Tejas Rawal & V. Devadas

1 Introduction India is undergoing rapid of urbanization. Evident population growth has ensued ever swelling demand for transportation infrastructure. The growth of vehicular population is constantly increasing over the years and its rate of growth is much higher than the growth rate of National Highways and other highway networks in length. As a consequence, the main arterial roads have been facing capacity saturation and a tremendous amount of pressure is exerted on them. The vehicular population grew at a compound annual growth rate (CAGR) of about 11 per cent compared to total length developed between 1951 and 2002 [1]. However, the increase in National Highway segment is observed as just 2.1 per cent, in recent years, it has grown around 5 per cent per year, while the growth of vehicular population is observed closer to 10 per cent per year during the period between 1991 and 2004. In India, about 50 per cent of roads are in abysmal state and are not apt for movement of vehicle to ply, The reasons observed for such a state of the roads are poor quality construction, poor or no maintenance, inadequate capacity, heavy vehicular pressure, weak pavement, distressed bridges, unabridged level crossings, congested cities (lack of by-pass roads), lack of wayside amenities and safety measures. Even the higher order roads, such as, National and State highways are not free from these challenges. Consequently, challenges like increase in travel time, congestion, pollution, road accidents, consumption of more fossil fuel, etc. are experienced. Increase of the capacity of roads, quality construction and regular maintenance of roads require large investments. However, it is seen that fund allocation for development of roads at regional level has decreased over the years. Although new roads are added into the national and regional road networks, both the road and traffic condition remain same without much improvement. Thus, it is an inevitable requirement to plan and design the transportation infrastructure with sustainability approach. In India the transportation planning process generally do not consider all the aspects which it affects indirectly as discussed above. There is a vacuum off an integrated approach especially in the policy development that has to be sustainable at the same time.

1.1 Study Area Background Kanyakumari district is facing grievous road transportation problems throughout, as a result of rapid modernization urbanization and intensely progressing vehicle growth, alike national road transportation scenario. There are many studies of urban areas or region that deals with measuring the effects of transportation crisis on various systems and evolve policy planning interventions for those systems. The system dynamics modeling approach is relatively new, very few studies are available where it has been used for transportation planning and specifically for transportation policy decision making process. In addition to that the system dynamic approach for evolving policy intervention based on impact of road transportation system on socio, economic and environmental considerations has never been attempted, at-least in Indian context. Thus, a case specific approach has been adopted in this study, for the road network assessment and its impact on the socio-economic and environmental factors using system dynamics modeling method to evolve policy interventions, for Kanyakumari district which is located at southernmost tip of peninsular sub-continent of India. Kanyakumari district is located in, Tamil Nadu State of India (Figure 1.1). It is the smallest district of the state having an area of 1672 sq.km. It has distinct geographical divisions which include Western Ghats in the North, a plain sea-coast in the South and the fertile area in the midland. Nagercoil is the biggest city and district headquarters in the district. Administratively it constitutes two Revenue Divisions, four Taluks, nine Community Development Blocks, 97 Village Panchayats, 56 Town Panchayats and four Municipalities. The Kanyakumari district has a total population of 1,863,174 (census 2011) [2] having a population density of 1114.34 per sq.km. Excluding the forest area, the density turns out to be 1638.65 person per Sq. Km. Although, the decadal population growth rate of the district is 11.6 per cent (census 2011), the rural areas have experienced a decrease in population growth. In contrast the proportion of urban population in this district has increased drastically from just 16.88 per cent in 1991 (Census 1991), 65.27 per cent in 2001 (Census 2001) to 82.33 per cent in 2011 (census 2011). [3, 4]

Page 2 of 26

Figure 1.1 Kanyakumari District (Source: www.kanyakumari.tn.nic.in)

Figure 1.2 Road Map of Kanyakumari District (Source: www.gis.nic.in)

Despite the district has higher literacy and education rates, this district is industrially backward. It also possesses a wide range of natural resources, well connected roads, transport and communication systems, availability of formal credit support through banks, etc. The district has well established tourism, which attracts numerous tourists from Page 3 of 26

all over the nation as well as foreign countries. It has unique architectural beauty, culture, customs and traditions, and are having blend of Kerala and Tamil Nadu State. This district has a well-developed road network (Figure 1.2) but the quality of the roads is very poor. In the year 2010-2011, the district had total 1630 lane km of roads, which include 137.5 lane km. of National Highway, 367.5 lane km. of State Highways, 297.5 lane km. of major district roads, and other roads of 760 lane km. Besides this, it has a single broad gauge railway line, and Nagercoil functions as the biggest junction of the district. Other facilities and services like electricity, postal services, telephone, banking, etc. are present in almost all the villages of the district. [5] A major road National Highway-47 (NH-47) passes through the district connecting Southern parts of Kerala and Tamil Nadu States including Salem city and Kanyakumari town. The portion of NH47 passing thorough Kanyakumari District of about 55 kms, lies from Kalyakkavilai to Kanyakumari. This highways has two way one lane system. This part of NH-47 has been facing numerous amounts of problems within itself due the flawed [6] practices of locals and neglect of State Government and the Central Government . Being one of the busiest roads, it is facing several problems ranging from physical problems like, accidents, traffic jams, increased travel time, wastage of fossil fuels; to social problems like, apathy among the travelers, and so on. However, to simplify the situation, the National Highway Authority of India (NHAI), Government of India (GoI) is taking up the effort for developing the new bypass road to the existing NH-47 by diverting form selected few junctions. Unfortunately, this partial solution for the existing NH-47 is expected to give birth to numerous other physical, socio-economic, and ecological problems, such as disruption of the entire existing road network system; destruction of the inhabitation; destruction of the fertile agricultural land; affect the trees, vegetation cover; destruction to the water bodies, interfere with the natural flow of streams, rivers, canal networks; medicinal herbs and other flora and fauna. A traffic study on different locations such as VC1, VC2 and VC3 as mentioned in Figure 1.3 showed that all the modes of vehicles such as two wheelers, three wheelers, buses, trucks, cars etc., were observed to be using the roads evenly to certain extent without any dominance of a particular mode (Figure 1.4). That means the traffic characteristics constitutes mixed mode on all the type of roads, which is a serious concern, in terms of quality and level of service, for the roads. Figure 1.3 Location of traffic studies on the road network in Kanyakumari district

Page 4 of 26

Figure 1.4 Distribution of traffic on the road network of the Kanyakumari district. 4500

Traffic Count Distribution

Numbers

3500

VC1 VC2 VC3 Average

2500 1500 500

Per cent

-500

30 25 20 15 10 5 0

Passenger Count Unit Distribution PCU VC1 PCU VC2 PCU VC3 PCU Avg

Figure 1.5 Average daily traffic scenario on the roads of Kanyakumari district

Apart from that, the average daily traffic counts and average daily passenger count unit shows the amount of hourly traffic throughout the day from morning 6 AM to 10 PM in the night. The average daily traffic count throughout the day is observed to be 9487 per hour. Average daily passenger count unit is 12477 PCU per hour, which is much higher than the capacity of the roads, i.e., 3500 PCU per hour in the system. This signifies the high level of traffic congestion on the roads of the district throughout the day (Figure 1.5).

1.2 Need of the study The ever increasing transportation crisis, severe condition of NH47 (Figure 1.6) and overall inefficiency of road network of Kanyakumari district poses some serious concern from socio-economic and environmental sustainability in the system. There is an immediate need of having a strong policy framework for the transportation Page 5 of 26

system that can help to resolve the current problems and enables the system to achieve sustainability in the longer run. Having the above understanding about the study area, the prevailing need and the knowledge gap of system dynamics approach for transportation policy planning, the present study tries to identify and formulate the plausible policy decision for sustainable transportation and ultimately sustainable development in the district. [6] Figure 1.6 Traffic Jam on National Highway – 47 (Source: http://www.skyscrapercity.com )

1.3 Aim and Objectives The study aims at analyzing the impact of road transportation and developing plausible policy decision for sustainable development in Kanyakumari District. The following objectives have been framed. They are: 1. To assess the existing conditions (physical, socio-economic, ecological, environmental, infrastructure and institutional functions of the system) in the study area. 2. To identify the control parameters, which decide the functions of the system. 3. To forecast the demand and supply of transportation infrastructure in the system for 2031 AD. 4. To assess the functions of the system in 2031 AD in different alternative conditions. 5. To evolve a set of policy planning guidelines, and strategies for sustainable transportation provisions in the system.

1.4 Scope and Limitation This study is an on-going PhD research. The scope of the study includes physical subsystem, social subsystem, economical subsystem, infrastructure subsystem, institutional subsystem, ecological subsystem and environmental subsystem. In this particular paper only infrastructure subsystem and environmental subsystem has been considered. The rest of the subsystems and their control parameters would be included as the study progresses.

1.5 Methodology A systematic and step wise methodology is developed and has been employed in the present investigation. Survey research methodology has been used for data collection. Primary data was collected through household survey by using systematic stratified random sampling process. Traffic and road related data were collected through traffic survey and physical survey of road sections. Also, relevant statistical data has been collected from secondary sources such as published and unpublished documents and literature. Data was analyzed by using relevant statistical methods such as descriptive statistics correlation, and regression. Followed by system dynamics models have been developed to comprehend various sustainable road transportation indicators and socio-economic and environmental indicators in region such as road network gap; vehicular growth; level of service (LOS) for hierarchical roads (like national highway, state highway, major district road, municipal roads and other/rural roads); carbon emission gap; fuel consumption gap; GDDP; man-hours employment; and transportation area wasted or unused. The simulated model results have been used for evolving policy interventions to achieve sustainable road transportation, which can act as a catalyst for the socio-economic and environmental sustainability in the study area.

1.6 APPLICATION OF SYSTEM THEORY In this present investigation, the Investigator has employed Systems theory based on systems concept and System Dynamics models are developed by considering the study area as a system. The Investigator observes that no systematic attempt has been made so far for evolving transportation policies, plans, programs, etc., for the development of the system. In fact, the Government is making sporadic and isolated attempts till today for Page 6 of 26

transportation planning. Therefore, the Investigator has attempted to establish the fact that transportation is an integral part of the system and that it functions as a catalyst for total development of the system. Further, attempts have been made to develop System Dynamics Models based on the survey data and the historical data, to understand the influence of the most important controlling parameters, which decide the function of the system for evolving optimal strategies for integrated development of the system. 1.6.1 Systems Concept A system is an organized or connected group or set of objects, principles, or ideas related by some common function or belief [7]. A system functions as a whole with the interaction of several subsystems. All the sub-systems of the system are interconnected, and interdependent to each other, and form a system. If one of the sub-systems of the system is defunct or functions with higher degree (taking lead role during its function) or functions partially, its effects can be visualized in the entire system over a period of time. In some cases, the system may not function at all, while in some cases the system may function, but with many disturbances or smooth functions of the system may be paralyzed [8]. System Characteristics The various major characteristics of a system as postulated by Jenkins [9] are:      

A system is a complex grouping of human beings and machine. A system comprises of many sub-systems, the amount of sub-systems detail depending on the problem being studied. The outputs from the given sub-system provide the inputs to the other sub-systems. A sub-system, therefore, cannot be studied in isolation. The system being studied will usually form part of a hierarchy of such-systems. The systems at the top are very important and exert considerable influence on the systems lower down. To function, a system must have an objective, but this is influenced by the wider system of which it forms a part. Usually, systems have multiple objectives, which are in conflict with one another, so that an overall objective is required which affects a compromise between these conflicting objectives. To function at maximum efficiency, a system must be designed in such a way that it is capable of achieving its overall objective in the best possible ways.

Thus, all living systems maintain steady state dynamic equilibrium keeping an orderly balance among its subsystems with respect to its super system and the environment. However, if an element of a system fails to handle a stress, other elements come forward and share this excess stress.

1.7 INTEGRATED SYSTEM MODEL In this investigation, the study area is considered as a system, and it has several subsystems. The various subsystems of the urban system are physical, social, economic, ecological, environmental, infrastructure, institution, etc. All these sub-systems are interlinked and interdependent to each other and function as a whole dynamically. The dynamic functions of the regional system along with its subsystems are presented in Figure 1.7 Figure 1.7 Functions of the regional system along with its sub-systems (Source: Devadas V. 2007)

Page 7 of 26

2 Model Development In this present investigation, Infrastructure and Environmental subsystems are considered for modeling. Various sub-models are prepared for Population growth, Vehicular population growth, Road network, Road network capacity, Vehicular carbon emission, and Vehicular fuel consumption. At the outset, individual models for each subsystem are developed separately, and then respective subsystems are amalgamated together to evolve an integrated model for transportation planning in Kanyakumari District. The detailed methods used for developing the models by subsystem wise are presented as below:

2.1 Population and Vehicle Growth Model Population is considered as one of the most important parameters, which influence the functions of the system. Therefore, a System Dynamic model is built to calculate the population, by considering the influential variables, such as, Birth Rate, Death Rate, In-migration Rate, Out migration Rate, Normal Birth Rate Fraction, Normal Death Rate Fraction, In-migration fraction, and Out migration fraction. In this model, population is considered as a function of birth rate, death rate, in migration rate and out migration rate; and the population is considered as the level variable, while the birth rate, death rate, in migration and out migration rate are taken as rate variables. Additionally, the vehicle population is also an important parameter to identify the impact on transportation system. The total number of vehicles is a function of population, vehicle growth rate, vehicle growth rate fraction, obsoletion rate, obsoletion time fraction and vehicles per thousand population. The model, which is employed for computing the population and total number of vehicles in the system, is presented in a functional flow diagram ( Figure 2.1) and the functional relationships among the variables are presented below. Population(t) =

Population(t - dt) + (Birth_Rate + In_Migration_Rate - Death_Rate - Out_Migration_Rate) * dt Birth_Rate = Population*Birth_Rate_Fraction In_Migration_Rate = Population*In_Migration_Fraction Death_Rate = Population*Death_Rate_Fraction Out_Migration_Rate = Population*Out_Migration_Fraction

Total_Number_of_Vehicles(t) = Total_Number_of_Vehicles(t - dt) + (Vehicle_Growth_Rate - Obsoletion) * dt Vehicle_Growth_Rate = Total_Number_of_Vehicles*((Vehicle_Growth_Fr+ (Total_Number_of_Vehi /(Population*100)))) Obsoletion = Total_Number_of_Vehicles*Obsoletion_Time_Fr Figure 2.1 Population and Vehicle Growth Model

In Migration Fraction In Migration Rate

Birth Rate Fraction

Population Death Rate

Birth Rate

Death Rate Fraction Out Migration Rate Out Migration Fraction

Page 8 of 26

Fr 3Wh

Num 3Wh

Num Other

Fr Other

Demand Vehicle Density

Population

Vehicle growth per 1000

Vehicle Growth Fr

Vehicle Growth Rate

Gap Vehicle Density

Total Road Network

Total Number of Vehicles

Obsoletion Time Fr

Vehicles in thousand

Floating Vehicle Population

Actual Vehicle Density

Obsoletion

Total Number of Vehicles

Vehicles Number in thousand

Fr Car

Num Cars

Num Bus

Fr Bus

Fr 2Wh

Num 2Wh

Num Truck

Fr Truck

Fr 3Wh

Num 3Wh

Num Other

Fr Other

2.1.1 Road Network Gap Index Model

Demand Vehicle

Population

The road network gap is a significant component for determining theDensity shortcomings in the transportation infrastructure provision. This gap is determined by the difference between the road network demand and normal supply of road network. The road network gap index is the ratio of the difference between demand and supply of Vehicle growth roads to supply of roads. This gap index model consists of various sub-models for different hierarchical roads like per 1000 Vehicle National Highway (NH), State Highway (SH), Major District roads (MDRd), Municipal roadsGap(MuRd), and other Vehicle Total Road Network Fr or rural roads (OthRd). Growth The demand for all the hierarchical roads are calculated based of the requirement per 1000 Density population, that adds up to form the total road network demand. Due to varying widths and right of ways of the different hierarchy roads all the roads categories are considered in lane kilometers, where 1 lane measures for 3.75 meter. Vehicle roads Growthin Rateeach

Obsoletion

Actual Vehicle Density

Total Number The supply of category of roads is considered as level variables. The level variable, National of Vehicles Highway lane kilometer, is a function of national highway growth rate (NH_Growth_Rate) and national highway Obsoletion growth rate fraction (NH_Gr_Fr). Further, the investment component of transportation infrastructure, i.e. Time Fr Vehicles in thousand investment in National highways (Inv_NH) contributes to the national highway growth rate fraction (NH_Gr_Fr). Floating Vehicle The demand for the Population national highway (Demand_NH) is the function of required national highway per 1000 population (Required_NH_per_1000) and total population. Similarly algorithms for calculating the demand for other roads such as State Highway lane kilometer (SH), Major District roads lane kilometer (MDRd), Municipal roads lane kilometer (MuRd), and other roads lane kilometer (OthRd) were developed and employed in the model. The model, which is employed for computing the respective hierarchical roads lengths and their demands in the system and finally total road network, total road network demand and road network gap index, is presented in a functional flow diagram (

Figure 2.2) and the functional relationships among the variables are presented below. National_Highway_Lane_Km(t) = National_Highway_Lane_Km(t - dt) + (NH_Growth_Rate) * dt NH_Growth_Rate = National_Highway_Lane_Km*NH_Gr_Fr Page 9 of 26

NH_Gr_Fr = 0.01+(Inv_NH/1000) Demand_NH = (Required_NH_per_1000*Population)/1000 Major_District_Rd_Lane_Km(t) = Major_District_Rd_Lane_Km(t - dt) + (MDRd_Growth_rate) * dt MDRd_Growth_rate = Major_District_Rd_Lane_Km*MD_Gr_Fr MD_Gr_Fr = 0.01+(Inv_MDRd/1000) Demand_MDRd = (Required_MDRd_per_1000*Population)/1000 State_Highway_Lane_Km(t) = State_Highway_Lane_Km(t - dt) + (SH_Growth_Rate) * dt SH_Growth_Rate = State_Highway_Lane_Km*SH_Gr_Fr SH_Gr_Fr = 0.01+(Inv_SH/1000) Demand_SH = (Required_SH_per_1000*Population)/1000 Municipal_Rd_Lane_Km(t) = Municipal_Rd_Lane_Km(t - dt) + (MuR_Growth_Rate) * dt MuR_Growth_Rate = Municipal_Rd_Lane_Km*MuR_Gr_Fr MuR_Gr_Fr = 0.01+(Inv_MuRd/1000) Demand_MuRd = (Required_MuRd_per_1000*Population)/1000 Other_Roads_Lane_Km(t) = Other_Roads_Lane_Km(t - dt) + (OthR_Growth_Rate) * dt OthR_Growth_Rate = Other_Roads_Lane_Km*OthR_Gr_Fr OthR_Gr_Fr = 0.01+(Inv_OthRd/1000) Demand_OthRd = (Required_OthRd_per_1000*Population)/1000 Total_Road_Network =

Major_District_Rd_Lane_Km +Municipal_Rd_Lane_Km +National_Highway_Lane_Km +Other_Roads_Lane_Km +State_Highway_Lane_Km

Total_Road_Network_Demand = Demand_MDRd +Demand_MuRd +Demand_NH +Demand_OthRd +Demand_SH Gap_Total_Road_Network =

Total_Road_Network_Demand - Total_Road_Network / Total_Road_Network

Figure 2.2 Road Network Gap Index Model

Page 10 of 26

2.1.2 Road Network Capacity Gap Index Model Apart from road network length, the capacity of the same road network is also important to determine shortfal in the transportation infrastructure provision. The road network capacity gap (Gap_Vehicle_Km) is identified by calculating the difference between the vehicle kilometre travelled and vehicle kilometre capacity of the road network. Vehicle kilometre travelled is worked out by calculating the passenger count units (PCU) for all the available modes of transportation like two wheelers (TW), three wheelers (ThW), cars, buses, trucks, and other modes (non-motorised vehicles), and multiplying the total PCU count with the road network. Vehicle kilometre capacity is calculated by adding all respective PCU capacities of various hierarchical roads, like National Highway (NH), State Highway (SH), Major District roads (MDRd), Municipal roads (MuRd), and other or rural roads (OthRd), and multiplying the total PCU capacities with the road network. The model, which is employed for computing the vehicle kilometre travelled and vehicle kilometre capacity of the road network and finally the road network capacity gap index, is presented in a functional flow diagram (Figure 2.3) and the functional relationships among the variables are presented below. Vehicle_Km_Traveled = Total_PCU*Total_Road_Network Total_PCU = Bus_PCU+Car_PCU+Other_PCU+ThW_PCU+Truck_PCU+TW_PCU TW_PCU = TW_Pcu_Fr*Num_2Wh*100*365 ThW_PCU = Num_3Wh*100*ThW_Pcu_Fr*365 Car_PCU = Num_Cars*100*Car_Pcu_Fr*365 Bus_PCU = Num_Bus*100*Bus_Pcu_Fr*365 Truck_PCU = Num_Truck*100*Truck_Pcu_Fr*365 Other_PCU = Num_Other*100*Other_Pcu_Fr*365 Vehicle_Km_Capacity = Total_PCU_Capacity*Total_Road_Network Total_PCU_Capacity =

MDRd_Capacity +MuRd_Capacity +NH_Capacity +OtherRd_Capacity +SH_Capacity NH_Capacity = (National_Highway_Lane_Km*NH_Cap_Fr) MDRd_Capacity = (Major_District_Rd_Lane_Km*MDRd_Cap_Fr) SH_Capacity = (State_Highway_Lane_Km*SH_Cap_Fr) MuRd_Capacity = (Municipal_Rd_Lane_Km*MuRd_Cap_Fr) OtherRd_Capacity = (Other_Roads_Lane_Km*OthRd_Cap_Fr) Total_Road_Network =

Major_District_Rd_Lane_Km +Municipal_Rd_Lane_Km +National_Highway_Lane_Km +Other_Roads_Lane_Km +State_Highway_Lane_Km

Gap_Vehicle_Km =

(Vehicle_Km_Traveled - Vehicle_Km_Capacity) /Vehicle_Km_Capacity) Figure 2.3 Road Network Capacity Gap Index Model

Page 11 of 26

2.1.3 Carbon Emission Gap Model Vehicular carbon emission is a very crucial parameter that affects the environment and human health directly. Vehicle emissions contribute to air pollution and are a major ingredient in the creation of smog in cities. Emission standards focus on reducing pollutants contained in the exhaust gases from vehicles. Bharat stage emission standards are instituted by the Government of India to regulate the output of air pollutants from motor vehicles. While the norms help in bringing down pollution levels, it invariably results in increased vehicle cost due to the improved technology & higher fuel prices. However, this increase in private cost is balanced by savings in health costs for the public, as there is lesser amount of disease caused by particulate matter and pollution in the air. For each mode, the carbon emission has been calculated as follows. Car carbon emission (CO2_Cars) is the function of annual car mileage (Ann_Car_Milage) and car carbon emission per kilometre fraction (Co2_Car_Fr); two-wheeler carbon emission (CO2_2Wh) is the function of annual two-wheeler mileage (Ann_2Wh_Milage) and two-wheeler carbon emission per kilometre fraction (Co2_2Wh_Fr); three-wheeler carbon emission (CO2_3Wh) is the function of annual three-wheeler mileage (Ann_3Wh_Milage) and three-wheeler carbon emission per kilometre fraction (Co2_3Wh_Fr); bus carbon emission (CO2_Bus) is the function of annual bus mileage (Ann_Bus_Milage) and bus carbon emission per kilometre fraction (Co2_ Bus_Fr); truck carbon emission (CO2_Truck) is the function of annual truck mileage (Ann_Truck_Milage) and truck carbon emission per kilometre fraction (Co2_Truck_Fr); and other vehicles carbon emission (CO2_Other) is the function of annual other vehicles mileage (Ann_Other_Milage) and other vehicles carbon emission per kilometre fraction (Co2_Other_Fr). The total carbon emission is the summation of carbon emission of all the transportation modes. The permissible carbon emissions for all the modes of transportation are derived based on the Bharat Stage emission standards and Euro Emission Standards. For each mode, permissible carbon emission has been calculated as follows: Permissible car carbon emission (Permi_Co2_Car) is the function of annual car mileage (Ann_Car_Milage) and permissible car carbon emission per kilometres fraction (Permi_Co2_Car_Fr); permissible two-wheeler carbon emission (Permi_Co2_2Wh) is the function of annual two-wheeler mileage (Ann_2Wh_Milage) and permissible two-wheeler carbon emission per kilometre fraction (Permi_Co2_2Wh_Fr); permissible three-wheeler carbon emission (Permi_Co2_3Wh) is the function of annual three -wheeler mileage (Ann_3Wh_Milage) and permissible three -wheeler carbon emission per kilometre fraction (Permi_Co2_3Wh_Fr); permissible bus carbon emission (Permi_Co2_Bus) is the function of annual bus mileage (Ann_Bus_Milage) and permissible bus carbon emission per kilometre fraction (Permi_Co2_Bus_Fr); permissible truck carbon emission (Permi_Co2_Truck) is the function of annual truck mileage (Ann_Truck_Milage) and permissible truck carbon emission per kilometre fraction (Permi_Co2_Truck_Fr); permissible other vehicles carbon emission (Permi_Co2_Other) is the function of annual other vehicles mileage (Ann_Other_Milage) and permissible other vehicles carbon emission per kilometre fraction (Permi_Co2_Other_Fr). The total permissible emissions is as per the standards is the summation of all the permissible carbon emission for various modes of transportation. The gap index of carbon emission (Gap Co2_Emission) is the function of total carbon emission and permissible carbon emission. The model, which is employed for computing the total emissions and permissible, is presented in a functional flow diagram (Figure 2.4) and the functional relationships among the variables are presented below. CO2_2Wh = Ann_2Wh_Milage*Co2_2Wh_Fr Ann_2Wh_Milage = Ann2Wh_Milage_Fr*Num_2Wh Num_2Wh = Total_Number_of_Vehicles*Fr_2Wh CO2_3Wh = Ann_3Wh_Milage*Co2_3Wh_Fr Ann_3Wh_Milage = Ann3Wh_Milage_Fr*Num_3Wh Num_3Wh = Total_Number_of_Vehicles*Fr_3Wh CO2_Bus = Ann_Bus_Milage*Co2_Bus_Fr Ann_Bus_Milage = AnnBus_Milage_Fr*Num_Bus Num_Bus = Total_Number_of_Vehicles*Fr_Bus CO2_Cars = Ann_Car_Milage*Co2_Car_Fr Ann_Car_Milage = AnnCar_Milage_Fr*Num_Cars Num_Cars = Total_Number_of_Vehicles*Fr_Car CO2_Other = Ann_Other_Milage*Co2_Other_Fr Ann_Other_Milage = AnnOth_Milage_Fr*Num_Other Num_Other = Total_Number_of_Vehicles*Fr_Other CO2_Truck = Ann_Truck_Milage*Co2_TTruck_Fr Ann_Truck_Milage = AnnTruck_Milage_Fr*Num_Truck Num_Truck = Total_Number_of_Vehicles*Fr_Truck

Page 12 of 26

Total_Carbon_Emission = CO2_2Wh+CO2_3Wh+CO2_Bus+CO2_Cars+CO2_Other+CO2_Truck Permi_Co2_2Wh = Ann_2Wh_Milage*Permi_Co2_2Wh_Fr Permi_Co2_3Wh = Ann3Wh_Milage_Fr*Permi_Co2_3Wh_Fr Permi_Co2_Bus = Ann_Bus_Milage*Permi_Co2_Bus_Fr Permi_Co2_Car = Ann_Car_Milage*Permi_Co2_Car_Fr Permi_Co2_Oth = Ann_Other_Milage*Permi_Co2_Oth_Fr Permi_Co2_Truck = Ann_Truck_Milage*Permi_Co2_Truck_Fr Permicible_Carbon_Emission =

Gap_Co2_Emission =

Permi_Co2_2Wh +Permi_Co2_3Wh +Permi_Co2_Bus +Permi_Co2_Car +Permi_Co2_Oth+Permi_Co2_Truck

(Total_Carbon_Emission- Permicible_Carbon_Emission) /Permicible_Carbon_Emission Figure 2.4 Carbon Emission Gap Model

Num Cars

Ann Car Milage AnnCar Milage Fr

CO2 Cars

Num 2Wh

Num 3Wh

Ann 2Wh Milage Ann 3Wh Milage Ann2Wh Milage Fr Ann3Wh Milage Fr

Co2 Car Fr

CO2 2Wh

Co2 2Wh Fr

CO2 3Wh

Num Bus

Ann Bus Milage AnnBus Milage Fr

Co2 3Wh Fr

Num Other

Ann Truck Milage

CO2 Truck

Ann Other Milage AnnOth Milage Fr

AnnTruck Milage Fr Co2 Bus Fr

CO2 Bus

Num Truck

Co2 TTruck Fr

CO2 Other

Co2 Other Fr

Total Carbon Emission

Gap Co2 Emission Permicible Carbon Emission

Permi Co2 Car Ann Car Milage Permi Co2 Car Fr

Permi Co2 2Wh Ann 2Wh Milage Permi Co2 2Wh Fr

Permi Co2 3Wh

Ann3Wh Milage Fr Permi Co2 3Wh Fr

Permi Co2 Bus Ann Bus Milage Permi Co2 Bus Fr

Permi Co2 Truck Ann Truck Milage Permi Co2 Truck Fr

Permi Co2 Oth Ann Other Milage Permi Co2 Oth Fr

2.1.4 Fuel Consumption Gap Model In 2013, India was the fourth-largest consumer and net importer of crude oil and petroleum products in the world after the United States, China, and Japan. Most of India's demand is for motor gasoline, fuels used mainly in the transportation and industrial sectors. Vehicles are now becoming the main source of air pollution in urban India. Bharat Stage emission standard and Euro emission standard advocates reduction of fuel consumption and improvement in fuel efficiency, by introducing new technology and better engineered engines. These standards are referred to identify the optimal fuel consumption by various modes of transportation. The current fuel consumption for different vehicle mode is calculated as follows: Car fuel consumption (Fuel_Cars) is the function of number of cars (Num_Cars) and car fuel efficiency fraction (Fl_Car_Fr); two-wheeler fuel consumption (Fuel_2Wh) is the function of number of two-wheeler (Num_2Wh) and two-wheeler fuel efficiency fraction (Fl_2Wh_Fr); threewheeler fuel consumption (Fuel_3Wh) is the function of number of three-wheeler (Num_3Wh) and three-wheeler fuel efficiency fraction (Fl_3Wh_Fr); bus fuel consumption (Fuel_Bus) is the function of number of bus (Num_Bus) and bus fuel efficiency fraction (Fl_Bus_Fr); truck fuel consumption (Fuel_Truck) is the function of number of truck (Num_Truck) and truck fuel efficiency fraction (Fl_Truck_Fr); and other vehicles fuel consumption (Fuel_Other) is the function of number of other vehicles (Num_Other) and other vehicles fuel efficiency fraction (Fl_Other_Fr). The total vehicle fuel consumption (Total_Actual_Fuel_Consumption) is the summation of fuel consumed by all the modes of transportation. The optimal fuel consumption, based on the standards, for different vehicle mode is calculated as followed: car optimal fuel consumption (Opt_Fuel_Car) is the function of number of cars (Num_Cars) and car optimal fuel efficiency fraction (Opt_Fl_Car_Fr); two-wheeler optimal fuel consumption (Opt_Fuel_2Wh) is the function of number of two-wheeler (Num_2Wh) and twowheeler optimal fuel efficiency fraction (Opt_Fl_2Wh_Fr); three-wheeler optimal fuel consumption (Opt_Fuel_3Wh) is the function of number of three-wheeler (Num_3Wh) and three-wheeler optimal fuel efficiency fraction (Opt_Fl_3Wh_Fr); bus optimal fuel consumption (Opt_Fuel_Bus) is the function of number of bus (Num_Bus) and bus optimal fuel efficiency fraction (Opt_Fl_Bus_Fr); truck optimal fuel consumption (Opt_Fuel_Truck) is the function of number of truck (Num_Truck) and truck optimal fuel efficiency fraction Page 13 of 26

(Opt_Fl_Truck_Fr); and other vehicles optimal fuel consumption (Opt_Fuel_Other) is the function of number of other vehicles (Num_Other) and other vehicles optimal fuel efficiency fraction (Opt_Fl_Other_Fr). The total optimal vehicle fuel consumption (Optimal_Fuel_Consumption) is the summation of optimal fuel consumed by all the modes of transportation. The fuel consumption gap is the function of total vehicle fuel consumption (Total_Actual_Fuel_Consumption) and total optimal vehicle fuel consumption (Optimal_Fuel_Consumption). The model, which is employed for computing the actual and optimal fuel consumption and its gap index, is presented in a functional flow diagram (Figure 2.5) and the functional relationships among the variables are presented below. Fuel_2Wh = Fl_2Wh_Fr*Num_2Wh Fuel_3Wh = Fl_3Wh_Fr*Num_3Wh Fuel_Bus = Fl_Bus_Fr*Num_Bus Fuel_Cars = Num_Cars*Fl_Car_Fr Fuel_Other = Fl_Other_Fr*Num_Other Fuel_Truck = Fl_Truck_Fr*Num_Truck Total_Actual_Fuel_Consumption = Fuel_2Wh+Fuel_3Wh+Fuel_Bus +Fuel_Cars +Fuel_Other +Fuel_Truck Opt_Fuel_2wh = Num_2Wh*Opt_Fl_2Wh_Fr Opt_Fuel_3Wh = Num_3Wh*Opt_Fl_3Wh_Fr Opt_Fuel_Bus = Num_Bus*Opt_Fl_Bus_Fr Opt_Fuel_Car = Num_Cars*Opt_Fl_Car_Fr Opt_Fuel_Other = Num_Other*Opt_Fl_Oth_Fr Opt_Fuel_Truck = Num_Truck*Opt_Fl_Truck_Fr Optimal_Fuel_Consumption =

Opt_Fuel_2wh +Opt_Fuel_3Wh+Opt_Fuel_Bus

+Opt_Fuel_Car +Opt_Fuel_Other+Opt_Fuel_Truck Figure 2.5 Fuel Consumption Gap Model

Total Actual Fuel Consumption

Gap Fuel Cons

Optimal Fuel Consumption

Total Actual Fuel Consumption

Fuel Truck Fuel 2Wh

Fuel Cars

Fuel Bus

Fuel 3Wh

Fl 2Wh Fr

Fl Truck Fr

Fuel Other Fl Other Fr

Fl Bus Fr

Fl 3Wh Fr

Fl Car Fr Num 2Wh

Num Cars

Opt Fl Car Fr Opt Fuel Car

Opt Fl 2Wh Fr

Num 3Wh

Opt Fl Bus Fr

Opt Fl 3Wh Fr Opt Fuel 2wh

Opt Fuel 3Wh

Opt Fl Truck Fr

Opt Fuel Bus

Opt Fuel Truck

Optimal Fuel Consumption Total Vehicle Fuel Cons. in thousand Lit

Page 14 of 26

Num Other

Num Truck

Num Bus

Opt Fl Oth Fr Opt Fuel Other

2.1.5 Integrated Model

Figure 2.6 Integrated Transportation planning model for Kanyakumari District

Page 15 of 26

2.2 Model Validation The models are employed to compute the outputs from the set of inputs for the year 2011, which is considered as the base year for the model in this investigation. The model results are closely examined and are compared to the data available in the real system and the comparison between the model results and the real system data are presented in Table 2.1, Table 2.2, Figure 2.7 and Figure 2.8 Table 2.1 Model Validation for Kanyakumari District Population Year 1971 1981 1991 2001 2011

Real System Value 1222549 1423399 1600349 1676034 1902267

Model Result 1182163 1512665 1574868 1620743 1870374

Difference -40386 89266 -25481 -55291 31893

Percent Variation -3.42 5.90 -1.62 -3.41 1.68

Figure 2.7 Model Validation for Kanyakumari District Population 2100000 1900000 1700000

Real System Value

1500000

Model Result

1300000 1100000 1971

1981

1991

2001

2011

Table 2.2 Model Validation for Vehicles in Kanyakumari District

Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Real System Value 92427 101602 108808 116809 130384 145932 161273 181424 205591 305360

Model Result 103766 101212 103404 110344 122031 138466 159647 185576 216252 379044

Difference 11339 -390 -5404 -6465 -8353 -7466 -1626 4152 10661 13684

Percent Variation 10.93 -0.39 -5.23 -5.86 -6.84 -5.39 -1.02 2.24 4.93 5.44

Figure 2.8 Model Validation for Vehicles in Kanyakumari District 500000 400000 300000

Real System Value

200000

Model Result

100000 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Page 16 of 26

3 Base Year (2011) and Projected Year (2041) Model Results The System Dynamic models developed in this investigation are employed to understand the functions of the system. In these models, year 2011 is considered as the base year to understand the functions of the system and its various subsystems together.

3.1 Population and Vehicle Growth The growth projections of population and vehicle volume based on the base year projections are presented in the following Table 3.1 and Figure 3.1. The population is expected to grow almost twice the current population (2011) and number of vehicles is expected to grow more than three times by the year 2014. The population is estimated to grow from 1.87 million to 3.48 million and the vehicles are estimated to grow from 0.37 million to 1.27 million. The population growth will exert immense amount of demand and growth in the vehicles number plying on the roads for the district. Table 3.1 Population and Vehicle Growth Results Base Year (2011) 2021 2031 2041

Population 1,870,374.00 2,302,427.04 2,834,283.57 3,488,997.99

Vehicles ('000) 379.44 562.52 841.66 1,273.70

Figure 3.1 Population and Vehicle Growth Results 1: Population 1: 2:

2: Total Number of Vehicles

3500000 1350 1

2

1 1: 2:

2

2500000 850 1

2 1

1: 2:

2

1500000 350 2011.00

2017.00

2023.00

Page 1

2029.00 Years

2035.00

2041.00

1:56 PM Wed, Jan 28, 2015

Untitled

3.2 Road Network

The results for total road network, road network demand and road network gap index based on the base year projections are presented in the following Table 3.2 and Figure 3.2. The current road network is expected to grow twice the base year and rode network demand is expected to grow almost twice the base year demand by the year 2041. In the base year itself, the gap between existing road network and road network demand is quite high (1.52). However, from the gap index, it is evident that the gap in road network supply and demand is going to remain greater throughout the time spam, and is expected to be 1.36 by the year 2014. Road network is a potential parameter for plausible policy intervention to reduce this gap. The vehicle and population growth by the year 2041 will increase the demand of road network and compared to the road network supply as per the base case, will result in higher and accumulative gap in the district. Table 3.2 Total Road Network, Demand and Road Network Gap Results

Base Year (2011) 2021 2031 2041

Total Road Network (Lane Kilometer) 1,630.00 1,861.46 2,260.10 3,258.75

Road Network Demand (Lane Kilometer) 4,114.82 5,065.34 6,235.42 7,675.80

Page 17 of 26

Road Network Gap Index 1.52 1.72 1.76 1.36

Figure 3.2 Total Road Network, Demand and Road Network Gap Results 1: Total Road Network 1: 2: 3:

2: Total Road Network Demand

3: Gap Total Road Network

3500. 8000. 1.85 3 2

3 1: 2: 3:

2500. 6000. 1.6

1 2 3 1

1: 2: 3:

1500. 4000. 1.35

1

2

2011.00

2018.50

2026.00

Page 1

2033.50

Years

2041.00

5:25 AM Sun, Feb 22, 2015

Untitlednetwork, road network demand and road network Similar results are observed in all the hierarchical roads for road gap index based on the base year projections, and are presented in the following Table 3.3,

Table 3.4 & Table 3.5 and Figure 3.3, Figure 3.4 & Figure 3.5. Form the gap indexes of all the hierarchical roads, the national highway has the least base year and projected year gap index; and municipal roads have the highest base year and projected year gap index. Second highest base year and projected year gap index is observed in major district roads High amount of intra district and inter city traffic movement can be the reason for the higher municipal roads and major district roads demand, which has led the projections to such higher gaps throughout the time span. The improper network and inefficient internal connectivity of secondary and tertiary level of roads, throughout the district have led the demand to rise. The estimated gap index in the year 2041 for national highway will be 0.26, for state highway it will be 1.38, for major district road it will be 1.79, for municipal road it will be 2.54, and for other roads it will be 1.25. These demand versus supply gaps will be high in all the hierarchies of the roads in year 2041.The vehicle and population growth by the year 2041 will increase the demand of all hierarchical road and compared to the road network supply as per the base case, will result in higher and accumulative gap of all the hierarchical roads in the district.

Table 3.3 National Highway Gap and State Highway Gap

Base Year (2011) 2021 2031 2041

National Hwy(Lane Km)

National Hwy Demand (Lane Km)

National Hwy Gap Index

State Hwy (Lane Km)

State Hwy Demand (Lane Km)

State Hwy Gap Index

137.5

187.04

0.36

367.5

935.19

1.54

157.13 191.24 277.77

230.24 283.43 348.9

0.47 0.48 0.26

419.57 508.93 731.62

1,151.21 1,417.14 1,744.50

1.74 1.78 1.38

Figure 3.3 National Highway Gap and State Highway Gap

Page 18 of 26

Table 3.4 Major District Road Gap and Municipal Road Gap

Base Year (2011) 2021 2031 2041

Major District Roads (Lane Km) 297.5 340.87 418.61 624.61

Major Dist Rd. Demand (Lane Km) 935.19 1,151.21 1,417.14 1,744.50

Major Dist Rd. Gap Index 2.14 2.38 2.39 1.79

Municipal Roads (Lane Km) 67.5 77.72 97.04 151.81

Municipal Road Demand (Lane Km) 280.56 345.36 425.14 523.35

Municipal Road Gap Index 3.16 3.44 3.38 2.45

Figure 3.4 Major District Road Gap and Municipal Road Gap

Table 3.5 Other /Rural Roads Gap

Base Year (2011) 2021 2031 2041

Other/Rural Road (Lane Km) 760 866.17 1,044.28 1,472.93

Other/Rural Road Demand (Lane Km) 1,776.86 2,187.31 2,692.57 3,314.55

Other/Rural Road Gap Index 1.34 1.53 1.58 1.25

Figure 3.5 Other /Rural Roads Gap

3.3 Carbon Emission and Fuel Consumption The results for existing carbon emission, permissible carbon emission carbon emission gap, existing fuel consumption, optimal fuel consumption and fuel consumption gap based on the base year projections are presented in the following Table 3.6 and Figure 3.6 and Page 19 of 26

Figure 3.7. It is observed from the results that, the overall carbon emission gap and fuel consumption gap are about to increase over the period of time, and this gap will be 1.38 and 0.81 respectively in the year 2041. This behavior is likely to occur because of the increased number of vehicles and increased trips of the growing population.

Table 3.6 Carbon Emission and Fuel Consumption Gap Results Existing Carbon Emission (Kg/Km) 15,799.16 23,422.19 35,045.28 53,034.77

2011 2021 2031 2041

Permissible Co2 Em. (Kg/Km) 4,837.63 7,171.04 10,728.87 16,235.47

Existing Fuel Consumption ('000 Liter) 911.94 1,351.94 2,022.83 3,061.20

Co2 Em. Gap 0.79 1.02 1.38 1.93

Optimal Fuel Consn. ('000 Liter) 615.7 912.78 1,365.74 2,066.80

Fuel Consn. Gap 0.74 0.77 0.81 0.88

Figure 3.6 Carbon Emission, Permissible Carbon Emission and Emission Gap Results 1: Total Carbon Emission 1: 2: 3:

2: Permicible Carbon Emission

3: Gap Co2 Emission

55000. 20000. 2.5

2 1 1: 2: 3:

35000. 10000. 1.5

3

2 3 2

1 3

1: 2: 3:

15000. 0 0.5

1 2011.00

2018.50

2026.00

Page 1

2033.50

Years

2041.00

5:38 AM Sun, Feb 22, 2015

Untitled

Figure 3.7 Fuel Consumption, Optimal Fuel Consumption and Fuel Consumption Gap 1: Total Actual Fuel Consumption 1: 2: 3:

2: Optimal Fuel Consumption

3: Gap Fuel Cons

3500 2500 1 3

1 1: 2: 3:

2000 1500 1

2

3

1

2

3 1 1: 2: 3:

2

500 500 1 2011.00

Page 1

2018.50

2026.00 Years Untitled

Page 20 of 26

2033.50

2041.00

5:41 AM Sun, Feb 22, 2015

4 Simulation Scenarios 4.1 Simulation Scenario Development The base year results were tested under varied simulated scenarios, to derive systematic and reasonable plausible policy interventions and to achieve possible sustainability behaviour by the year 2041. Through development of the causal feedback loop model, literature review and simulation scenario analysis of system dynamics model; the most positively and negatively affecting variables for sustainability in road transportation system in Kanyakumari district, were observed to be transportation infrastructure investment and vehicle volume. Following are the simulation scenarios conditions presented in the Table 4.1 based on which the case based simulations for all the control parameter, as discussed in the previous sections, were conducted. Table 4.1 Simulation conditions for developing policy scenarios Simulation Conditions of variables Up gradation of lower order roads to higher order roads (without changing the total Km lane of road network) Change in vehicle volume

Variation in Conditions /Policy Interventions Improving the State Highway to National highway by 0 to 120% of the current level Improving of Major District Road to State Highway by 0 to 50% of the current level Improving of Other/Rural Roads to Major District Road by 0 to 50% of the current level Improving of Other/Rural Roads to Municipal Roads by 0 to 100% of the current level Reducing Other/Rural Roads by 0 to 100% of the current level Reduction in total vehicle volume by 1% to 4% of the existing volume Change in modal split without changing the total volume 20-30% for 2 Wheelers 2-7% for 3 Wheelers 10-20% for Cars 10-20% for Buses 15-25% for Trucks 5-10%% Others

Based on the aforesaid control parameters, a good number of policy runs are made by testing scenarios in the model individually and in combination. Amongst these, the simulations, which have considerable impact on various parameters of transportation planning, have been shortlisted. The scenarios which are tested in the projected year model with their values are presented. However, of the various simulated scenarios the following 9 scenarios were considered (Table 4.2) for policy analysis. Table 4.2. Scenarios Development Scenarios S1 From 137.5 to From 367.5 to From 297.5 to From 67.5 to From 760 to Total 1630 Km

S2 S3

S4

S5

Particulars Up-gradation (or readjustment) of lower hierarchy roads to the higher hierarchy roads, without changing the total kilometer lanes 250 Km Lane National Highway (Upgrading State Highway to National highway) 450 Km Lane State Highway (Upgrading Major District Road to State Highway) 325 Km Lane Major District Road (Upgrading Other/Rural Roads to Major District Road) 100 Km Lane Municipal Roads (Upgrading Other/Rural Roads to Municipal Roads) 505 Km Lane Other/Rural Roads (Reduction due to Up-gradation) Lane Total Road Network

Change in Vehicle volume of 3.2% (by 20 % decrease of existing 4%) Change in Vehicle volume of 2.8% (by 30 % decrease of existing 4%) 20 % Change in Vehicle volume proportions of different modes (without changing total vehicle volume) 27.2% 2 Wheelers 6% 3 Wheelers 17.6% Cars 18% Buses 22.8% Trucks 8.4% Others 30 % Change in Vehicle volume proportions of different modes Page 21 of 26

(without changing total vehicle volume) 23.8% 2 Wheelers 6.5% 3 Wheelers 15.4% Cars 20% Buses 24.7% Trucks 9.6% Others

Besides, a few combined scenarios are also taken in to considerations for evaluation for policy analysis. The reason for considering such scenarios are to get a holistic perspective which can assist to achieve the results faster compared to a single standalone policy. The combined scenarios are as follows: S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Page 22 of 26

4.2 Simulation Scenario Analysis It was observed while scenario testing that in few control parameters change in small amount did not give much effect in the results. Therefore, higher degree of change was attempted in them. This shows that system has acute problems pertaining to infrastructure service provisions and environment. The most important tested scenarios and their results are presented in the following section. Table 4.3 Simulated Scenarios Analysis Results Scenarios Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Total Road Network 1 Per Cent Value Variation 3,258.75 0.00 3,253.49 -0.16 3,232.62 -0.80 3,221.17 -1.15 3,280.60 0.67 3,291.57 1.01 3,245.85 -0.40 3,254.83 -0.12 3,233.26 -0.78 3,241.54 -0.53

Vehicle Km Traveled 4

Scenarios

Value Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

221,189,870,395.28 220,832,698,945.51 174,089,015,799.07 154,415,241,966.23 249,821,112,031.46 264,275,381,276.54 196,113,069,423.48 207,340,749,782.48 173,891,446,463.00 183,809,073,654.00

Scenarios Value Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Gap Road Network 2 Per Cent Value Variation 1.36 0.00 1.36 0.00 1.37 0.74 1.38 1.47 1.34 -1.47 1.33 -2.21 1.36 0.00 1.36 0.00 1.37 0.74 1.37 0.74

0.26 -0.29 0.27 0.27 0.25 0.24 -0.29 -0.29 -0.29 -0.29

Gap NH 7 Per Cent Variation 0.00 -211.54 3.85 3.85 -3.85 -7.69 -211.54 -211.54 -211.54 -211.54

Per Cent Variation 0.00 -0.16 -21.29 -30.19 12.94 19.48 -11.34 -6.26 -21.38 -16.90

Vehicle Km Capacity 3 Value 9,012,135,174.84 11,077,798,276.08 8,861,439,368.09 8,795,837,635.10 9,138,882,189.59 9,202,807,357.25 11,028,764,021.23 11,086,902,051.28 10,947,692,810.11 11,001,092,833.05

Gap Vehicle Km 5 Per Cent Value Variation 2.35 0.00 1.89 -19.57 1.86 -20.85 1.66 -29.36 2.63 11.91 2.77 17.87 1.68 -28.51 1.77 -24.68 1.49 -36.60 1.57 -33.19

State Highway Lane Km 8 Per Cent Value Variation 731.62 0.00 887.27 21.27 726.21 -0.74 723.85 -1.06 736.16 0.62 738.44 0.93 885.72 21.06 887.62 21.32 883.08 20.70 884.84 20.94

Page 23 of 26

Per Cent Variation 0.00 22.92 -1.67 -2.40 1.41 2.12 22.38 23.02 21.48 22.07

National Highway Lane Km 6 Per Cent Value Variation 277.77 0.00 491.15 76.82 275.12 -0.95 273.96 -1.37 279.98 0.80 281.08 1.19 490.38 76.54 491.35 76.89 489.06 76.07 489.95 76.39

Value 1.38 0.97 1.4 1.41 1.37 1.36 0.97 0.97 0.98 0.97

Gap SH 9 Per Cent Variation 0.00 -29.71 1.45 2.17 -0.72 -1.45 -29.71 -29.71 -28.99 -29.71

Scenarios Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Major District Rd Lane Km 10 Per Cent Value Variation 624.61 0.00 675.77 8.19 615.18 -1.51 611.05 -2.17 632.33 1.24 636.21 1.86 672.86 7.72 676.07 8.24 668.28 6.99 671.23 7.46

Scenarios Value Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Scenarios Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

Scenarios Projected Year S1 S2 S3 S4 S5 S1+S2+S4 S1+S2+S5 S1+S3+S4 S1+S3+S5

2.45 1.45 2.53 2.57 2.38 2.35 1.47 1.45 1.49 1.47

Gap MuRd 13 Per Cent Variation 0.00 -40.82 3.27 4.90 -2.86 -4.08 -40.00 -40.82 -39.18 -40.00

Total Carbon Emission 16 Per Cent Value Variation 53,034.77 0.00 53,034.77 0.00 42,078.78 -20.66 37,456.09 -29.37 62,392.99 17.65 67,072.10 26.47 49,503.77 -6.66 53,216.26 0.34 44,065.37 -16.91 47,370.02 -10.68 Total Actual Fuel Consumption 19 Per Cent Value Variation 3,061.20 0.00 3,061.20 0.00 2,428.81 -20.66 2,161.99 -29.37 3,617.58 18.18 3,895.77 27.26 2,870.26 -6.24 3,090.98 0.97 2,554.94 -16.54 2,751.41 -10.12

Gap MDRd 11 Per Cent Value Variation 1.79 0.00 1.58 -11.73 1.84 2.79 1.85 3.35 1.76 -1.68 1.74 -2.79 1.59 -11.17 1.58 -11.73 1.61 -10.06 1.6 -10.61

Municipal Rd Lane Km 12 Per Cent Value Variation 151.81 0.00 213.28 40.49 148.09 -2.45 146.47 -3.52 154.83 1.99 156.34 2.98 212.11 39.72 213.38 40.56 210.29 38.52 211.46 39.29

Other Roads Lane Km 14 Per Cent Value Variation 1,472.93 0.00 986.01 -33.06 1,468.01 -0.33 1,465.85 -0.48 1,477.31 0.30 1,479.50 0.45 984.77 -33.14 986.4 -33.03 982.56 -33.29 984.06 -33.19 Permissible Carbon Emission 17 Per Cent Value Variation 16,235.47 0.00 16,235.47 0.00 12,881.83 -20.66 11,466.82 -29.37 19,638.94 20.96 21,340.68 31.44 15,582.21 -4.02 16,932.40 4.29 13,870.54 -14.57 15,072.41 -7.16 Optimal Fuel Consumption 20 Per Cent Value Variation 2,066.80 0.00 2,066.80 0.00 1,639.84 -20.66 1,459.69 -29.37 2,433.10 17.72 2,616.25 26.58 1,930.47 -6.60 2,075.78 0.43 1,718.39 -16.86 1,847.74 -10.60

Page 24 of 26

Value 1.25 2.36 1.26 1.26 1.24 1.24 2.37 2.36 2.37 2.37

Gap OthRd 15 Per Cent Variation 0.00 88.80 0.80 0.80 -0.80 -0.80 89.60 88.80 89.60 89.60

Gap Co2 Emission 18 Per Cent Value Variation 1.93 0.00 1.93 0.00 1.59 -17.62 1.45 -24.87 2.28 18.13 2.45 26.94 1.87 -3.11 2.01 4.15 1.7 -11.92 1.83 -5.18 Gap Fuel Cons 21 Per Cent Value Variation 0.88 0.00 0.88 0.00 0.84 -4.55 0.82 -6.82 0.92 4.55 0.93 5.68 0.87 -1.14 0.88 0.00 0.84 -4.55 0.86 -2.27

5 Findings and Conclusion Kanyakumari district is the smallest district in Tamil Nadu State; and is blessed with good monsoons and various irrigation facilities; as a consequence, the entire land is almost cultivated for cereal crops, plantation crops and others. The NH-47 road stretch lies in Kanyakumari District from Kalyakkavilai to Kanyakumari, has been facing numerous amounts of problems, since this road is not yet widened. The existing NH-47 road functions as a backbone, and number of roads are intersecting at various junctions, which connect the edges of the district in all directions. The highway stretch is one of the busiest roads in India, and has been facing numerous amount of problems like accidents, traffic jams, time consuming travel, wastage of fossil fuels, apathy among the travelers, air pollution, etc. Larger sizes of encroachments are observed at few junctions, on existing NH-47 road by the local people, which has totally disturbed through traffic. Number of accidents occurs every month on existing NH-47, which results in heavy loss of qualified human resource, further resulting in to heavy loss to national development. The constant and over usage of NH-47, has generated tremendous amount of problems related to travel quality and deteriorated surface conditions of the roads. Besides NH 47, the road network of Kanyakumari district is still underdeveloped. Just because of the strategic location of NH 47, which lies exactly in the middle of the district, it is facing numerous problems. There is lack of connectivity between 2nd and 3rd hierarchical roads within the district road network. Because of this lack of connectivity there is a tremendous traffic pressure NH 47. From the literature survey, no study was found which includes the integrated development and transportation planning together for policy development for any region. In this case system dynamics has been found suitable for integrating all the subsystems and transportation in particular for sustainable development of the district. From the various surveys and expert opinions it was understood that strengthening the road network is one of the key element. Another parameter is number of vehicles. Mount of vehicles that have increased in the district almost 3 fold in last 10 years. This situation has caused tremendous congestion levels on the roads during peak hours. Because of this high congestion on the roads, pollution levels have increased drastically over the time in the region. Also because of the vehicular increase, the congestion on the roads and traffic jams, huge amount of fossil fuel is being wasted everyday which is a very huge loss to the nation. In this present investigation, Physical, Social, Infrastructure and Environmental subsystems are considered for modeling. Various sub-models are prepared for Population growth, Vehicular population growth, Road network, Road network capacity, Vehicular carbon emission, and Vehicular fuel consumption. Through model validation, it was established that real system and the model are very similar in nature and behaviour. The results system dynamics model for population and vehicles had very less variation compared to real system values. In the base year (2011) itself road network index value is 1.52, which will be 1.36 in the year 2041. It is evident that the gap will be reducing eventually over the next 30 years, still the gap is not low enough for sustaining the traffic demand. Similar observations were made for National highway state highway Municipal roads and major district roads, other rural roads. The carbon emission gap was observed 0.79 for the year 2011, which will increase to 1.93 in the year 2041. Similarly the fuel consumption gap will increase from 0.74 in the year 2011 to 0.88 in the year 2014. To improve the above said condition various scenarios were developed based on the identified control parameter like up-gradation of lower hierarchical roads to higher hierarchical roads and change in vehicular growth. Vehicle volume proportions of different modes and their changes were are also anticipated for building the scenarios. Out of various simulated scenarios, 9 scenarios were considered, including combination scenarios, for comparison. All the nine policy interventions assumed in the study have shown considerable reductions in the gaps. That means up-gradation of the roads in the road network aided by the reduction of vehicle volume can make real difference in the system. Reduction in two wheelers and cars supported by increase in number of buses and promoting nonmotorized vehicles and pedestrians, can make additional positive effect on the road network of Kanyakumari district. All the scenarios have shown promising results of reduction in the road network gap, road network capacity gap, carbon emission gap and fuel consumption gap. Infrastructure investment, the biggest influencing parameter to the sustainable development of Kanyakumari district, has not yet been considered in the study. Here it should be noted that the economic subsystem institutional subsystem social subsystem physical subsystem infrastructure subsystem ecological system and environmental subsystem are yet to be considered in the model building process in detail. The further scope of research may extend further to understand the impact of road transportation system on other entities like industries, health-care, education, housing, agriculture, agricultural infrastructure, tourism etc., to have a holistic perspective for integrated regional development planning. As mentioned previously, it is an ongoing research. Rest of the subsystems would be included as the study progress. This study shows an assuring approach i.e, system dynamics modeling, for policy analysis. This sort of a pilot study is the proof that integration of all the aspects of regional development is possible for policy planning in India. Page 25 of 26

Interesting findings and evolved strategic intervention from this study has potential to be developed as a strong policy document for sustainable regional development for Kanyakumari district.

References 1. The Working Group Report On Road Transport For The Eleventh Five Year Plan 2011 2. Kanyakumari District Statistical Handbook 2010-11, Nagarcoil, Tamil Nadu 3. Census of India 2001, Government of India. 4. Census of India 2011, Government of India. 5.

)

6. Devadas V., Nand Kumar & Tejas Rawal, 2013, Analysis of National Highway-47 in Kanyakumari District, Tamil Nadu., Journal of ITPI, Vol-10 No. 3, pp 33-65. 7. Dicky,J.W., Watts, T.M., 1978, Analytic Techniques in Urban and Regional Planning, McGrawHill, New York. 8. Devadas, V., Nand Kumar 2007. Integrated Development Plan: A Scientific Approach, Journal of Indian Building Congress, New Delhi, Vol. 14, No. 1, pp 35-42 9. Jenkins, G.M., 1969. The System Approach. Journal of System Engineering, 1 (1).

Acknowledgement This study is part of ongoing PhD research which is registered with Indian Institute of Technology Roorkee, India. This research is funded by the Ministry of Human Resource Development, Government of India. We, the authors, thank the funding agency and home institute for providing the monetary and infrastructure support for pursuing the research and this particular study.

Page 26 of 26

Suggest Documents