FREIGHT GENERATION CHARACTERISTICS IN METROPOLITAN CITY OF HYDERABAD

Transcription

1 FREIGHT GENERATION CHARACTERISTICS IN METROPOLITAN CITY OF HYDERABAD M. Amal Datta Dr. Sanjay Gupta School of Planning & Architecture NEW DELHI

2 Structure of Presentation Role & Importance of Urban Freight Urban Freight Trends in India Freight Tonnage Generation in Indian Cities Freight Generating Activities Implication of Freight Traffic on Environment Conclusions Recommendations Areas of Further Research

3 Percentage Role & Importance of Urban Freight Logistics cost in India account for 10 to 13% share of GDP, the freight transport account for 35% share of total logistics cost. Goods vehicle & operating costs contribute 5% to 8% to total metropolitan city tax revenue Urban Goods Transport account for)- 18% of Vehicle kilometres 31 % of Energy Use 31% of CO2 Emissions In the developed world)), there are truck trips per 1,000 people/day tons of goods per person/year Goods Traffic Share in Urban Traffic in EUROPE CO2 Veh.Km Energy Use Freight Transit Urban Freight Total *Urban Freight in Developing Cities-GTZ report, **-Urban Freight Distribution-Relevance to Indian Context-Cambridge Systematics

4 Aim & Objectives NEED FOR THE STUDY Urban freight infrastructure planning is neglected during Preperation of City Development Plans (CDP) & Master Plans proposals lack rationale basis. Very little empirical studies have been carried out assess the goods movement characteristics. Conventionally the freight traffic is not explicitly accounted in the transport planning process in urban areas. There is an urgent need to assess urban freight patterns at disaggregate level. OBJECTIVES To appreciate the role & importance of freight in urban areas & Identify issues affecting their operation To review existing urban freight movement characteristics across Indian cities& establish freight generation rates. To estimate the existing demand for freight movement in Hyderabad both at aggregate & disaggregate level. To estimate the likely freight traffic in the horizon year & assess its impact under business as usual (BAU) scenario. To evolve alternate strategies to minimize the impact of urban freight in horizon year

5 Research Methodology STAGE I : Literature Review of Past Studies on Urban Freight STAGE II : Data identification and Collection STAGE III : Goods Traffic Demand Assessment STAGE IV : Goods Traffic Demand Forecast STAGE V : Freight Traffic Impact Assessment (Scenario Building)

6 Tonnage Urban Freight Trends in India Cities Population (in Lacs) Share( in%) Inbound Outbound Intracity Ahmedabad Nagpur Indore Vizag Agra Asansol Nashik Bhubaneshwar Ajmer Yamunanagar Adoni Darjeeling Samastipur Chickballapur Source: Estimation of Short Haul Urban & Suburban Freight Traffic. New Delhi: CRRI 1998 With increase in city size (population) there is an increase in freight movement Goods Tonnage Handled ( In Tonnes) Inbound Outbound Intracity Chickballapur Samastipur Darjeeling AdoniYamunanagarAjmerBhubaneshwar Nashik Asanol Agra Vizag Indore NagpurAhmedabad City (with Population) Inter City Inbound IIV = 33.1 (P) 0.63 IIT = 91.2 (P) 0.75 Inter City Outbound IOV = (P) IOT = (P) Intra-City Flows IV = 1.32 P 1.08 IT = 1.62 P 1.08

7 Generated Tonnage( in Tonnes) Freight Tonnage Generation in Indian Cities Sl.no City Estimated Popn (in lakhs) Generated Freight Traffic (O+D) ( in Tonnes) Generation Rate /Lac Popn (Tonnes) 1 Shimla Agartala Udaipur Rourkela Guwahati Vijayawada Varanasi Vododara Ludhiana Bhopal Kanpur Ahmedabad Calcutta Guruvayur Panipat Tiruppur Hubli-Dharwad Dhanbad Vishakapatnam Nagpur Pune Source: Comprehensive Study of 21 Cities in India. New Delhi: RITES Hilly terrain cities like Shimla, Agartala, Rourkela etc.. Have generation rates ranging from 1872 to 2793 tonnes/lakh population Population Vs. Tonnage Generation y = x R² = Population( In Lakhs)

8 Study Area Profile 'GHMC( The Greater Hyderabad Municipal Corporation(GHMC) covers an area of about 922 Sq.Km. The workforce participation rate in the GHMC area is 38 % The Population of the study area as per 2011 census is 7.1 Million GHMC Hyderabad is the major centre for the manufacture of Metal Products, Electrical Products and Small Machine Parts Land Use Residential (26%) Commercial 18.37(2%) P & SP 67.76(7%) Manufacturing 52.12(6%) Transport & Comm (4%) Source: CDP Hyderabad 2001

9 Database For the present study, it is important to know the commodity characteristics, movement pattern, etc.. The following data was identified to be required for the study: Type of establishment Commodity handled Location of establishment Commodity movement details Modal characteristics Markets The Case markets Selected for the Study are: Auto Parts Market Building Materials Market Electricals & Electronics Market Food Grains Market Readymade Garments Market Sl. Survey Name Total No 1 Establishment Survey Location Sample Establishments a. CBD b. OBD c. FRINGE Total Truck Operator Survey Market Sample a. Auto parts 24 b. Building Materials 33 c. Electricals & 19 Electronics d. Food Grains 27 Total 103 Commodity The above selected commodities account for 38% share of freight traffic movement. PRIMARY DATA SECONDARY DATA No.of Establishments Auto Parts 3771 Building Materials 5993 Electricals Food Grains Readymade Garments 8483 Others Total(approx.) 1,10,000 The selected commodity establishments also account for nearly 59% of the total establishments.

10 Analytical Framework The Data Collected has been analyzed with respect to Scale of Operations i.e. Wholesale/ Retail/ Wholesale-Retail and Location of Establishments i.e. Central Business District (CBD), Outlying Business District (OBD), Fringe Areas For the purpose of analysis the area delineation is as under- CBD : Distance of about 5 Km from the Centre OBD : Distance between 5 to 10 Km from the Centre Fringe : Distance exceeding 10 Km from the Centre BUILDING MATERIALS AUTO PARTS READYMADE GARMENTS FOOD ELECTRICALS & ELCTRONICS GRAINS

11 Freight Generation Characteristics '2014( Commodity Location Across Markets by Locations Auto Parts Building Materials Electricals Food Grains Readymade Garments Total Tonnage (In Tonnes) CBD OBD FRINGE Total Tonnage Source: Primary Survey 2014 Tonnage Generated Across Locations OBD 44% Fringe 13% CBD 43% 100% 80% 60% 40% 20% 0% Comparative Tonnage across Locations Auto Parts Building Materials Electricals Food Grains Readymade Garments CBD OBD PERIPHERY The Outlying Business District contributes 44% of the total freight Generation CBD and OBD alone account for 87% of the total tonnage generation The Foods Grains market among the commodities is the leading contributor(67%) in CBD.

12 Freight Generation Characteristics '2014( Across Markets by Scale of Operations Commodity Scale of Market Auto Parts Building Materials Electricals Food Grains Readymade Garments Total Tonnage (In Tonnes) Wholesale Retail Wholesale/Retail Total Tonnage Source: Primary Survey 2014 Tonnage Generated Across Typology W/R 43% Retail 23% Wholsale 34% 100% 80% 60% 40% 20% 0% 45% 41% 37% 28% 28% Comparitive Tonnage Across Typology 22% 38% 43% 22% 38% 20% Electrical Building Materials Readymade Garments 46% 38% 32% 24% Food Grains Autoparts Wholesale Retail W/R The Wholesale-Retail markets handles 43 % of the total urban freight generated. The Wholesale-Retail & Wholesale markets alone account for 77% of the total tonnage generated. As the Readymade garments are not a bulk traded commodity, its share is very high in Retail segment (43%)

13 Aggregate vs. Disaggregate Level Freight Generation Tonnage Generation Commodity (Tonnage/Ha) Aggregate Disaggregate Tonnage/Ha CBD OBD FRINGE Auto Parts Building Materials Electricals Food Grains Readymade Garments Employment Generation Commodity Aggregate Level Dis-Aggregate Level Employment/Ha CBD OBD FRINGE Auto Parts Building Materials Electricals Food Grains Readymade Garments The aggregate generation rates clearly over estimates the freight generation rates

14 Transport Operator Characteristics Characteristics NMT LCV 2 Axle Operational Time ( in Hrs.) Time of Vehicle in Operation(per Trip) Journey Time (in Min) Loading/Un-Loading (in Min) Idle Time (in Min) No.of Trips Performed Daily Commodity wise trip lengths Sl.No Commodity Average trip length (50 th percentile) (Km) The Food grains Market exhibit the highest trip length of 12Km & 16 Km respectively. Mode wise trip lengths Average Trip Length (75 th percentile) (Km) 1 Auto Parts Building Materials Electrical & Electronics Food Grains Source: Primary Survey 2014 NMT mainly consists of Cycle Rickshaws and carts, which exhibit trip lengths of 3Km, 4Km respectively The Light Commercial Vehicle has trip lengths of 11 Km, 14 Km respectively. Sl.No Mode Average trip length (50 th percentile) (Km) Average Trip Length (75 th percentile) (Km) 1 NMT LCV Axle truck 9 20 Source: Primary Survey 2014

15 Freight Demand Assessment The overall tonnage obtained in the year (2011) is compared with the tonnage estimated using the above mentioned two approaches. Movement CTS Hyd 2011 (In Tonnes) RITES Study (In Tonnes) CRRI Study (In Tonnes) Internal-External External-Internal Total Tonnage ( In tonnes) Generated Tonnage( in Tonnes) Population Vs Tonnage Generated y = x R² = Population Source: RITES 1994 Source: CRRI 1998 Inter City Inbound IIV = 33.1 (P) 0.63 IIT = 91.2 (P) 0.75 Inter City Outbound IOV = (P) IOT = (P) Intra-City Flows IV = 1.32 P 1.08 IT = 1.62 P 1.08 REGIONAL DEMAND Movement Inbound (T) Out Bound(T) Total ( in Tonnes) INTRA-CITY DEMAND Movement Intra-City Tonnage (in Tonnes) Share in Regional Demand (%) The Method of estimation by CRRI gives a reasonable accurate estimated freight tonnage(+9%) The regional freight traffic estimated increases by 57% from 1,31,829 Tonnes to 2,06,889 Tonnes over a period of 20 years

16 Implications of Freight Traffic on Environment Alternate Scenarios were developed to assess the implications of freight on the environment- Scenario I: Business As Usual Scenario II : Green Mode Scenario Scenario-I: Business As Usual Scenario For this particular scenario Same vehicular mix, distribution leads and distribution modes as in base year have been assumed The performed tonne/km doubled from the base year to horizon year resulting in 72% increase in emissions.

17 Implications of Freight Traffic on Environment Scenario-II: Green Mode Scenario It is assumed that a new mode which has similar characteristics to that of Light Commercial Vehicle (LCV) but with zero tail pipe emissions is likely to replace the LCV in a phased manner. This ratio of change in vehicle composition is assumed to be 100:0, 70:30, and 30:70 for LCV: Green Mode for the years 2014, 2024, and 2034 respectively. It is observed that the performed tonne/km doubled from the base year to horizon year resulting in 33% reduction in emissions. With comparison of BAU Scenario & Green Mode Scenario, it is observed that though the tonne km increases,the emissions are reduced by 60%

18 Conclusion Urban freight is neglected both in terms of database and empirical research Absence of micro-level demand assessment is resulting in irrational and inadequate planning for freight facilities. Disaggregate analysis can provide better & reliable estimates in tonnage generation and can assist in rational planning of freight facilities This study is a contribution to estimate micro level demand assessment based on cross-classification method. A variation in generation rates is observed at aggregate and disaggregate level of assessment. Recommendations Freight Planning should be based upon disaggregate demand estimate Transferability checks over various classes of cities should be carried out Areas of Further Research Micro level demand assessment of Urban Freight More Empirical Studies Should be carried out for different order of city sizes.