Modeling for Urban Goods Movement - Indian Institute of · PDF fileModeling for Urban Goods...

Modeling for Urban Goods Movement – a case study of

Indian Cities

Dr. S. L. Dhingra Adjunct Professor

Ex. Institute Chair Professor & Emeritus FellowTransportation Systems Engineering

Civil Engineering DepartmentIIT Bombay, India

by

On April 9-10,2014

Workshop on

Urban Freight Transport : A Global PerspectiveBy

TSE/CE/IIT Bombay and Center of Excellence for SustainableUrban Freight Systems, RPI, Troy,NY

What makes it important?

• Traffic congestion

• Environmental impacts

• Traffic accidents

• Terminal facilities

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Goods movement pattern

Intra-city flows – Flowswhose origin anddestination are within thecity

Inter-city flows - Flowswhose one end (origin ordestination) is within thecity and other outside thecity

Regional flows - Flowswhose both ends (originand destination) areoutside the city

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Possible patterns of urban goods flows

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Intra urban freight movement

• Goods movement is directly related to populationand to understand that one must know thephysical, economic, and social make up of the city

• Urban goods may be classified depending on itsphysical state, handling needs, modes of vehiclesused, direction of movement etc., for analyzingthe demands

• Whole problem of goods movement would notbe solved all at once, but modeling frameworkcan be flexibly adopted to make progress in smallsteps

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Modeling frame work

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• Aggregate analysis of total establishments employingthe aggregated parameters to yield trip rates may beadequate and useful for planning process

• Urban goods movement forecasting techniques mustbe developed in terms of fairly simple measures ofeconomic activities

• Any modeling efforts should begin with the datacollection relating to urban goods movementsthrough primary surveys of consignment movementsand supplementing them by secondary sources

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Inter urban freight movement

• Situation in the case of inter urban freight movements has bright patches

• Consignment size and distance of haul are the most significant parameters in choosing the own transport, hired transport or railways for goods movement

• Firms owning transport generally utilized their own transport for medium and short hauls and preferred hired mode for long distance trips

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Selection of cities for study

• Cities of varying sizes with respect todemography and economic activities

• Federation of Indian Chamber of Commerceand Industry (FICCI) proposal that classifyingcities on the grounds of economy is anappropriate one as the urban economystructure has direct influence on the urbangoods flows

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India

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Case Study Cities

Data collection

• Truck operator surveys

• Traffic counts at selected points in the city

• Outer cordon surveys

• Focal point surveys

Owing to the complexity of the goodsmovement, no single method of datacollection could cover complete goodsmovement and its characteristics

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Traffic survey stations in selected cities

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Outer cordon surveys

Sample size between 6.8 to 100% dependingupon the city was taken

The following particulars of the sampled goodsvehicle were collected

1. Type of vehicle

2. Origin of trip

3. Destination of trip

4. Land use at destination

5. Type of commodity carried

6. Quantity of commodity carried

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Goods focal point surveys

• Major goods focal points are industries, whole sale trade, ware houses, freight terminals

• Goods focal point surveys are involved in identifying the extent of market in space and drawing a cordon line around these spaces

• In most of the cities whole sale markets were concentrated at one place

• Separate surveys were organized for each market in Delhi as different markets are located at different points

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• Data collected in goods focal point surveys was1. Type of activity2. Type of vehicle3. Origin4. Destination5. Destination of land use6. Type of commodity carried7. Quantity of commodity carried8. Average distance travelled in a day

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Goods transport flows

• Magnitude of goods transport flows in each ofthe cities was determined by analyzing datacollected through cordon surveys and focalpoint surveys

• Volume of incoming vehicles and quantum ofincoming goods increased with city size

• Outgoing goods traffic was also found to beincreasing with city size

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Urban goods flow characteristics

Commodity Classification is important to makethe analysis manageable. Eight broad categoriesare listed1. Perishable food products2. Non-Perishable food products3. Beverages4. Industrial Inputs5. Industrial Outputs (Consumer Products)6. Building materials7. Industrial Outputs (Intermediate Products)8. Other Categories

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Intercity Inbound flow characteristics Trucks and mini trucks are the major carriers of intercity

inbound flows with more than 91% of goods moving bythese vehicles

Building materials (28.9%), Industrial inputs (18.1%)and food products (16.7%) are the major constituentsof the intercity inbound flows

Whole sale markets (35.8%) and retail markets (21.3%)are the major attractors of the inbound goods flows

The intercity inbound flows are dominated by heavyconsignments with more than 55% consignmentsweighing more than 4 tonnes

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Mode split of intercity inbound goods flows

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Commodity wise composition of intercity inbound goods flows

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Intercity inbound goods flows destined to different land uses

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Intercity inbound goods flows as per consignment size

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Intercity Outbound flow characteristics The dominant carriers of Intercity Outbound flows are

trucks (70%) and mini trucks (18%) The major constituents of outbound flows are food

products, industrial outputs and industrial raw materials Whole sale trade (52.4%), industries (23.1%), transport

terminals (17.6%) are the major generators of outboundflows from the cities

Intercity outbound flows are also dominated by heavier consignments with more than 4 tonnes accounting for 44.4% of the total consignments

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Mode split of intercity outbound goods flows

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Commodity wise composition of intercity outbound goods flows

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Intercity outbound goods flows destined to different land uses

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Intercity outbound goods flows as per consignment size

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Regional flow characteristics

• The proportion of through traffic in a city is found tobe dependent more on its locations with respect totrunk routes

• The industrial raw materials (24.4%), buildingmaterials (16.9%) and food products (16.1%) arefound to be major constituents of the through flows

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Intra-city flow characteristics The major generators of intracity goods flows are whole sale

markets and warehouses with a contribution of 62% of totalintracity flows

Retail trade is found to be the major attractor (39.4%) of theintracity flows

Trucks carry 45% and mini trucks carry 16% of the goodstransported within the city

Slow moving vehicles constitute 70% of the intracity goodsvehicle trips and carry about 40% of goods transported in cities

Non-Perishable food products (21.3%), industrial raw materials(20.6%), building materials (17.4%) and intermediate industrialoutputs (16.5%) are the major contributors of Intracity flows

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Mode split of intracity goods flows

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Intracity goods flows originating from different land uses

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Commodity wise composition of intracity goods flows

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• The average consignment size of intracity flows ranged from 0.5 tonnesto 2 tonnes and also the average consignment size of differentcommodities varied widely

• The average distance of haul varied with city size and it is found toincrease with the city size

• As expected the average trip length of trucks was higher and they alsocarried heavier consignments

• Trucks accounted for 40.8% of the tonne kilometers made in the citiesand is followed by LCVs with 18.8%

• Fast moving vehicles contributed to about 30% of the vehicle km whilethe slow moving vehicles contributed to more than 70% of the vehiclekm while the slow moving vehicles contributed more than 70% of thevehicle kilometers made in urban areas

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Intracity veh-km made by different vehicles

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Intracity tonne-km made by different vehicles

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Urban Goods Transport Demand Modeling

• Input – Output model

Goods demanded by each sector of economy from all other sectors of economy can be determined but non-availability of Input – Output tables in terms of commodities makes the use of model difficult

• Sequential model

Similar to urban passenger transport planning modeling with certain variations in the specifics of the models

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• Variables Selection

Urban goods flows = f(Population, Industrial Workers, Workers in Trade and Commerce)

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Proposed modeling approach

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Sequential Flow Models

• Intercity Inbound Vehicle Trips Model

Vehicle trips = -233+0.00102(Pop)+59.6(PIW)

where,

Pop = Population of city

PIW = Industrial workers as % of total workers

• Intercity Inbound Goods Flows Model

Flow in tonnes = -556+0.00736(Pop)+281.1(PIW)

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Intercity Outbound Vehicle TripsVehicle trips = -417+.00139 Pop

Intercity Outbound Goods FlowsFlow in tonnes = -3242+.00913 Pop

Intracity Outbound Vehicle TripsVehicle trips = -1814+.00510 PopVehicle Kilometers = -19251+.0394 Pop

Intracity Outbound Goods FlowsFlow in tonnes = -3167+.0077 PopTonne Kilometers = -46109+.0846 Pop

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Commodity Flow modelsIntercity Inbound flows

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Outbound flows

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Intracity flows

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Mode Split Models

• Regression analysis was conducted withpercentage of consignments of a givencategory, going by a designated mode, asdependent variable and the size ofconsignment and length of haul asindependent variable

• Linear and exponential forms of functions areestablished

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Linear ModelsPCS 1 = 96.33 - 2.83 LPCS 2 = 85.15 - 4.96 LPCS 3 = 36.42 - 3.94 L

Exponential ModelsPCS 1 = 3.78 L^0.859PCS 2 = 14.91 L^0.589PCS 3 = 24.53 L^0.486

Where,L = Haulage length in kmPCS 1 = % of consignment < .5 tonnes by slow moving VehiclesPCS 1 = % of consignment <1 & >.5 tonnes by slow moving VehiclesPCS 1 = % of consignment <2 & >1 tonnes by slow moving Vehicles

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• Validation of Demand Models

Data generated in Hyderabad is used to validate the models. Estimated and observed trips are matching closely

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Strategy for goods transport facility planning

• Quantum of goods categorized commodity wise must be known for planning terminal facilities more rationally and efficiently

• Standardize the total requirements of various types of commodities and flows for given population and percentage of industrial workers in city as it is cumbersome to use large number of models practically

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Facilities Required In Goods Terminals

• Docking and loading/unloading area

• Fuel and servicing stations

• Office spaces

• Parking spaces

• Covered storage space for handling goods

• Lodges and dormitories

• Shops selling motor spares

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Spacing Required

• Assuming that loading / unloading takes placefrom 8.00 am to 8.00 pm and loading /unloading time per intercity truck is 2 hoursand for intracity truck is one hour

Number of spaces = (Number of trips/Working hours )*Timerequired for loading

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Time series techniques for forecasting truck traffic

• Spectral analysis which include line spectrum and power spectrum are required to check whether time series models are suitable for the data sets

• Two types of models – ARMA and ARIMA

1.Auto Regressive Moving Average

2.Auto Regressive Integrated Moving Average

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• Auto Regressive Moving Average Model

2

1

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j

m

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jt twCjtwjtyu

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• ARIMA model is given by

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m

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jdttt twCjtwqjtyfyyu

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Where,

Y(t), t=1,2,…n is the series being modeled

M1 is the number of AR parameters

Fj is the jth AR parameter

M2 is the number of MA parameters

Qj is the jth MA parameter

C is a constant and

{w(t)=1,2,…n} is the residual series

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• Model selection is based on two criteria

1.Maximum likelihood rule

2.Minimum mean square error

• Model is validated for the data available in Mumbai Metropolitan region

• Utilizing time series models weekly truck traffic on national highways 3,4 and 8 and L.B.S road at Vashi, Mulund(E), Dahisar and Mulund(W) in Mumbai Metropolitan region were modeled

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• Complexities associated with the urban goodstransport analysis and modeling do not permitcomprehensive planning of the transportsystem

• Development of planning methods is inhibitedby the absence of urban goods movementdata and the inadequate knowledge of theurban goods transport needs

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Thank You62