metadata: authors: 0: author: Aritua,Bernard
1: author: Havenga,Jan
2: author: Simpson,Zane Paul
3: author: John,Esther Woon Lyn
count: India
docna: 0: docna: Unlocking India's logistics potential :
            the value of disaggregated macroscopic freight flow analysis
docty: Policy Research Working Paper
abstracts: cdata!: India is one of the fastest growing
            major economies. However, at 14 percent of gross domestic
            product, its logistics costs are high relative to the 8 to
            10 percent that is typical of most advanced economies. High
            logistics costs and poor logistics performance impact the
            competitiveness of the economy on multiple levels: (1) firms
            deliver less competitive goods and services; (2) consumers
            pay more than peers for goods; and (3) the cost of achieving
            improvements in gross domestic product is excessive. The
            development of a national transport and logistics network to
            facilitate competitiveness and sustainable development and
            uplift rural regions will play an increasingly important
            role in shaping spatial organization in emerging economies.
            An element that is absent, yet critically important for
            national logistics issues in emerging economies, is
            sufficiently detailed freight-flow analysis to facilitate
            targeted infrastructure investments and enable
            transformational change to improve national logistics
            performance. This paper presents the results of a
            disaggregated macroscopic freight demand analysis developed
            for India through a hybrid approach, calibrating the modeled
            input-output matrix and resulting freight flows with data
            where available. Data was obtained from multiple sources,
            such as agricultural statistics, national enterprise
            surveys, a financial performance database of Indian
            companies, population statistics, and transportation
            statistics from rail, inland waterways transport, highways,
            and ports. The model provides evidence for decision making
            on several levels. Aggregating freight flows enables
            planners to identify gaps in critical infrastructure and
            logistics chains. Disaggregated flows support decisions on
            the location of logistics clusters, maximizing the potential
            of multimodal transport systems, and designing the
            distribution and storage networks that underpin the economy.
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repnme: repnme: Unlocking India's logistics potential
            :  the value of disaggregated macroscopic freight flow analysis
display_title: Unlocking India's logistics
            potential : the value of disaggregated macroscopic freight
            flow analysis
listing_relative_url: /research/2018/02/29588320/unlocking-indias-logistics-potential-value-disaggregated-macroscopic-freight-flow-analysis
topicv3: Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Ports m1327399 167,Traffic Management m1583399 4231
publishtoextweb_dt: 2018-02-13T00:00:00Z
no_of_pages: 51
author_keys: 0: author_key: 1175216
1: author_key: 1052286
2: author_key: 1464434
3: author_key: 1303701
model_extractions: WPS8337 Policy Research Working Paper 8337

# Unlocking India’s Logistics Potential

The Value of Disaggregated Macroscopic Freight Flow Analysis _Bernard Aritua Jan Havenga Zane Simpson Esther Woon Lyn Chiew_ Transport and Digital Development Practice February 2018
Policy Research Working Paper 8337

# **Abstract**

India is one of the fastest growing major economies. However, at 14 percent of gross domestic product, its logistics costs are high relative to the 8 to 10 percent that is typical of most advanced economies. High logistics costs and poor logistics performance impact the competitiveness of the economy on multiple levels: (1) firms deliver less competitive goods and services; (2) consumers pay more than peers for goods; and (3) the cost of achieving improvements in gross domestic product is excessive. The development of a national transport and logistics network to facilitate competitiveness and sustainable development and uplift rural regions will play an increasingly important role in shaping spatial organization in emerging economies. An element that is absent, yet critically important for national logistics issues in emerging economies, is sufficiently detailed freightflow analysis to facilitate targeted infrastructure investments and enable transformational change to improve national logistics performance. This paper presents the results of a disaggregated macroscopic freight demand analysis developed for India through a hybrid approach, calibrating the modeled input-output matrix and resulting freight flows with data where available. Data was obtained from multiple sources, such as agricultural statistics, national enterprise surveys, a financial performance database of Indian companies, population statistics, and transportation statistics from rail, inland waterways transport, highways, and ports. The model provides evidence for decision making on several levels. Aggregating freight flows enables planners to identify gaps in critical infrastructure and logistics chains. Disaggregated flows support decisions on the location of logistics clusters, maximizing the potential of multimodal transport systems, and designing the distribution and storage networks that underpin the economy.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at baritua@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Unlocking India’s Logistics Potential** **_The Value of Disaggregated Macroscopic Freight Flow Analysis_**

# **_Authors_**

Bernard Aritua, Jan Havenga, Zane Simpson, Esther Woon Lyn Chiew
# **Introduction**

India has had steady economic growth since 2014, reporting 7.6% GDP growth for the 2015/16 fiscal year (Forbes, 2016). Despite a few shocks in 2016‐2017, the projections are for above 7% growth in the medium term. The growth is ascribed largely to an increase in private consumption due to a growing middle class, and significant public investment in the Indian economy (World Bank, 2016a and 2017). The Government of India forecasts economic growth of 7‐10% per annum over the next 20 years. Efficient freight transport is one of the key enablers to sustain these economic growth rates (Müller et al., 2012).

However, in 2015, logistics costs in India were estimated at 14% of GDP (Kumar and Shah, 2016), compared to North America’s 8% and Europe’s 9‐10% in 2015 (Armstrong and Associates Inc., 2017). Total freight traffic is expected to grow at 9.7% per annum to reach over 13 trillion tonne‐kilometers (tonne‐km) in 2031/32 from about 2 trillion tonne‐km in 2011/12. A balanced modal share is critical to supporting national logistics, which will underpin the economy and associated freight volumes. The National Transport Policy Development Committee (2014) estimated that the modal share of rail and road needs to shift from 35:65 in 2016/17 to 50:50 by 2031/32. Under an optimistic scenario where rail and road excel in their respective areas of domain competence, i.e., rail serving the medium and long‐distance (above 300 km) high‐volume traffic, and road transport serving low volume short‐ distance traffic in a complementary role, rail and road shares could reach 52% and 49%, respectively (Planning Commission, 2008 [Chapter‐XI]). Nonetheless, to achieve this scenario the public sector and policy makers would have to make targeted evidence‐based decisions for rail and a burgeoning inland waterway transport (IWT), to play an important role in servicing freight transport demand, reducing transport costs and mitigating the environmental impacts of freight transport (Kallas, 2011; Woodburn and Whiteing, 2012; Sanchez‐Triana et al., 2013).

However, recent analysis cautioned that, without intervention, India’s freight rail market share could decline to 25% in 2020 from 36% in 2009, compared to an almost 50% rail share in China and 40% in the United States, similar continent‐size nations. Under this scenario, waste caused by poor logistics infrastructure will increase from US$45 billion (equivalent to 4.3% of GDP in 2009), to US$140 billion or more than 5% of the GDP in 2020. However, if logistics challenges are addressed in an integrated and coordinated manner, rail share could move towards the 50% targets mentioned above (McKinsey, 2010).

Achieving this modal shift will require four major interventions:

- 1) Building the right network and ensuring freight flows on the right mode, comprising an integrated network of high‐density long‐distance corridors (rail and waterways), medium‐ distance rail and road connectors, and efficient last‐mile links;

- 2) Creating enablers to maximize the efficient use of the network, which includes developing logistics hubs and focusing on skills development;

- 3) Extracting more from existing assets, for example through increasing carrying capacity; and 4) Increasing the investment allocation to rail to more than 50% of total road/rail spending, with large sums spent on building high‐density traffic corridors, connectors and last‐mile links.

Some of the above interventions are already underway. To realize these transformational changes, India’s National Transport Development Policy estimated the average annual transport infrastructure investment to amount to US$114 billion by 2032 (Makwana, 2016). One of the enablers of optimal allocation of these resources is access to consistent, reliable, disaggregated freight‐flow information 2
to quantify the efficiency and capacity of the logistics network over the intermediate and long term, thereby enabling the management of transport as a strategic national resource (Tavasszy & De Jong, 2014).

The objective of this working paper is to report on the development of a disaggregated freight‐flow model for India. This model was developed as part of engagement between the World Bank and Government of India on national logistics. The model provides a sectoral and regional disaggregated quantification of total national freight flows and related costs to (1) improve the understanding of the national freight‐flow landscape and facilitate evidence‐based policy formulation and investment prioritization, (2) establish intermodal freight potential along the country’s most dense freight corridor, the Eastern Corridor, and (3) identify and prioritize logistics improvement and cost saving interventions for rail, road and ports on the Eastern Corridor.

Freight flows are a natural and important part of logistics, which is defined as an integrative and systemic support function, applying trade‐offs to determine optimal cost levels in the provision of the transport and storage of goods to address the time and place discrepancy between supply and demand for conforming to customer requirements (Lummus et al., 2001). Transport plays an important part in logistics and for all three of the economies that will be referenced in this report, the United States, South Africa and India; it constitutes more than 50% of logistics costs (64% in the case of the United States [AT Kearny, 2017] and 58% in the case of South Africa [Havenga, et. al. 2016]). The work in this report focuses on transport, but the context within a logistics system for further study should not be ignored.

The next section contains a critical review of literature which provides an overview of India’s freight logistics environment, highlighting the exponential growth in transport sector investments planned across all modes. These investment plans are however not underpinned by a current disaggregated macroscopic freight demand model to facilitate the optimal allocation of limited resources to support India’s growth trajectory. The macroeconomic relevance of disaggregated macroscopic freight demand modeling is therefore discussed. This is followed by an overview of the development of freight demand models to inform the methodology to develop India’s freight demand model (FDM), which is subsequently described. Next, the results of India’s FDM are presented, firstly providing an aggregate overview of the Indian freight transport environment, followed by a description of the Eastern Corridor, including the identification of logistics cost savings potential on this corridor. The paper concludes with a summary and the identification of next steps.

# **Overview of India’s freight logistics environment**

India is the 18th largest export economy in the world (with exports amounting to $292 billion in 2014) and the 13th largest import economy (with imports amounting to $421 billion in 2014). Between 2009 and 2014, exports increased at an annualized rate of 12%, from $165 billion to $292 billion. Over the same period, imports increased at an annualized rate of 11%, from $249 billion to $421 billion (The Observatory of Economic Complexity, 2015).

The optimistic growth outlook of 7‐10% per annum over the next 20 years by the Government of India is, in part, predicated on improvements in the business environment. According the 2018 ‘ _Doing Business_ ’ report, India improved from 134th in 2015 to 100th in 2018. The _Global Competitiveness Index_ published by the World Economic Forum (2015‐16) reflects a similar trend. In the 2015‐2016 report, India jumped 16 ranks to 55th place (although it still ranks seven places lower than in 2007). Per the report, this dramatic reversal is largely attributed to the pro‐business and pro‐growth initiatives by the Government of India. An ambitious reform agenda based on a series of high‐impact initiatives ranging from access to electricity and sanitation to smart cities and transport infrastructure 3
<!-- Start of picture text --> (6)<br>35% 33% 1% 30%<br>30%<br>24%<br>25% 22%<br>20% 17%<br>15%<br>10%<br>5%<br>ov<br>2 8 oS iJ oS<br>é r: 2 z<br><!-- End of picture text --> <!-- Start of picture text --> (Rs tn) (6)<br>25.0<br>149<br>15.3 153 160 161 158 16.2 15.7 45) 16.9<br><!-- End of picture text --> <!-- Start of picture text --> “gg 8 8g 228 23 ele<br>ek EER EEE EEE S<br>femme Manufacturing GDP (Rs tn) —=%of GDP (RHS)<br><!-- End of picture text -->
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<!-- Start of picture text --> Capacity is mostly allocated to passengers Freight income cross subsidises passengers (data<br>for India)<br>100% = , 4000<br>seihda| | 3.500 180%<br>20%|-. |F 3000 160%san +| — ee<br>60% - F 2300 30% 4<br>50% + + 2000 400% 4<br><!-- End of picture text -->

<!-- Start of picture text --> 40% +; + 1500 8 go% 4+<br>20% + F 1000 60% | SE<br>10% + - 500 40% +}<br>oe 4 . r = + O 20% +<br>China United States Russia India Canada 0% | = =<br>mums Tonne-km per person mmm Passenger km per person 1999-2000 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013<br>—t— Passenger + freight tonne-km ——Passengercost recovery 9 ——Freight cost recovery<br><!-- End of picture text -->
<!-- Start of picture text --> (INR bn)<br>9,000 8,560<br>8,000<br>7,000<br>6,000<br>5,000<br>4,000<br>3.000 2,557<br>2,000 1,539<br>1,000 | 225 285 933<br>FY91-95 FY96-00 FY01-05 FYO6-10 FY11-15 FY16-20<br><!-- End of picture text -->

<!-- Start of picture text --> Drop in utilisations at major ports Growth in share of non-major ports in cargo trade<br>1,000 120 100%<br>900<br>800 100 80%<br>700 80<br>600 60%<br>500 60<br>400 40%<br>300 40<br>200 20 20%<br>100<br>- 0 0%<br>§ 5B5sesgeergeez:zez SSSSSSSSSSoeSssssssersere<br>cee rt ct EEE E qccaceciceere cae ae eae ae a<br>ae Capacity (‘OOOMT) ——= Utilisation % Major Ports mi Mnor Ports<br><!-- End of picture text -->
<!-- Start of picture text --> 1,40,000 559<br>1,20,000 209<br>1,00,000<br>80,000 159<br>60,000 109<br>40,000<br>5%<br>20,000<br>0 0%<br>$3333mon oO 3342sH“aAMYHNORDAAOK-AMYH<br>eeod oe ood Oi ee ome od<br>Container traffic (in MMT)<br>== Container Cargo % of Total cargo (RHS)<br><!-- End of picture text -->
<!-- Start of picture text --> 35,000<br>30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>B88eeeS8SE58eERS?<br>MRAAmAOanDaDWaAqDmMdadriecaoancedodooncdcsd<br>- Fr re Fr rH Tr TN NNN NON ON ON ON<br>=——=WNo. of cold storage =———= Capacity (‘000 MT)<br><!-- End of picture text --> <!-- Start of picture text --> Load capacity for various modesoftransport (per1 Horse power) _ Efficiency perlitreof fuel consumption<br>(in Inkkg) (Ton-Kmiitr)ADO<br>4,500 4,000 -<br>4,000 100<br>85<br>3,500<br>3,000 BO<br>2,500 60<br>2,000<br>1,500 40 24<br>1,000 500 20<br>500 150<br>0 0<br>Water Rail Road Inland w ater Rail Road<br>(Load capacity for various modes of transport Efficiencyper litre of fuel consumption<br><!-- End of picture text -->
2014. As shown in Table 1, India has five declared National Waterways, of which three are operational, with freight market share of 0.7%. The ambition of the government is to increase this several fold and unlock the full potential of IWT.

_Table 1: India’s declared National Waterways (IWAI 2017)_

|**National**<br>**waterway**|**Location**|**Stretch**<br>**(km)**|
|---|---|---|
|**NW 1**|Ganga‐Bhagirathi‐Hooghly river system from Allahabad to Haldia|1620|
|**NW 2**|Brahmaputra river from Sadiya to Dhubri|891|
|**NW 3**|West Coast Canal from Kottappuram to Kollam along with Champakara<br>and Udyogmandal canals|205|
|**NW 4**|Godavari & Krishna rivers & Canals between Kakinada and Puducherry|1095|
|**NW 5**|Brahmani river & Mahanadi delta system alongwith East Coast Canal|623|

In March 2016, Government of India declared its ambition to develop an additional 106 inland waterways to be National Waterways. To back this ambition, specific programs and investment plans have been made to develop IWT infrastructure while private stakeholders are expected to invest approximately US$10 billion in barges and other operational assets (Phillip Capital, 2016). As a priority, IWAI is developing NW‐1, from Haldia port in West Bengal to Allahabad in Uttar Pradesh with support from the World Bank (IWAI 2017). Initially, bulk commodities such as coal can be targeted for more efficient transportation (Singh, 2015) contributing to road decongestion and freeing up rail capacity. Other bulk commodities and food can follow soon, leading to further decongestion, cost savings and a decrease in environmental and accident‐related externality costs (Gupta, 2016).

# **<u>Integration of Modal Plans</u>**

The integration of the port, IWT and coastal shipping investment initiatives is managed through Sagarmala, the flagship program of India’s Ministry of Shipping. This program was introduced to boost port‐led development and is a testament to the Government of India’s ambition to further open India to trade. Sagarmala anchors the port‐led development ambition on four key pillars, namely port modernization, port connectivity improvement, port‐led industrial growth and development of coastal communities (Government of India, 2015). Sagarmala has identified three areas of urgent intervention required if the Indian port sector is to realize the ideal of port‐led development namely: policy and institutional interventions, port infrastructure expansion and upgrades and efficiency of land‐side evacuation through better hinterland connectivity by integrating rail, road and IWT (Mitra et al., 2016).

The success of the road, rail and port integration hinges on integration with logistics hubs. India already has more than 200 functional dry ports, which primarily provide customs clearance services, while also enabling the storage and consolidation of goods. There are more than 30 operators around JNPT alone with a total annual capacity of 3 million TEUs operating at only 50‐60% utilization on average (Kumar and Shah, 2016). CONCOR (the largest container train operator in India with an ExIm market share of nearly 75%) is developing five Multi‐Modal Logistics Parks (MMLPs) along the Western DFC. Indian Railways produced a private freight terminal policy and investment program in 2011 to increase efficient evacuation of trains and enhance its freight income through providing additional warehousing space and a reduction in the amount of handling/transportation for the customer (Kumar and Shah, 2016). The current hubs are however not being developed within the context of an overarching national freight transport strategy and taking cognizance of modal shift and consolidation opportunities.

11
Three decades ago, Raza and Aggarwal (1986) noted the paradox that while, following independence, the Government of India recognized balanced regional development as a key policy objective, in the early 1980s effective national economic planning was still being pursued without statistics on inter‐ regional interdependencies demonstrated through freight flows. This has remained a challenge, as one‐off modeling initiatives typically precede freight transport and logistics, without an overarching freight‐flow strategy providing national context and enabling annual tracking of key indicators. In 2007, a major study was conducted on logistics and it identified several shortcomings in logistics‐related policy dialogue: (1) inadequacy of updated data on modal freight flows, (2) the time lapse since similar previous studies (1978 and 1986), and (3) the major changes in the economy impacting demand‐supply linkages, freight flow volumes, flow patterns and modal options. Almost a decade has passed since that study and, unfortunately, these exact reasons pertain to the need for the activity discussed in this paper. For example, consider spatial development options for Uttar Pradesh. A recent study by Mehta et al (2014) suggests that the key criteria for locating logistics hubs include availability of land and fiscal space and government capacity to implement these investments, which are needed to spread the effects of trunk infrastructure. In the absence of detailed and disaggregated analysis, it would be difficult to quantify the trade‐offs or decide policy options that affect logistics. In the next section, the macroeconomic relevance of disaggregated macroscopic freight‐flow analysis is discussed.

# **The Macroeconomic Relevance of Disaggregated Macroscopic Freight Flows**

## **<u>Background</u>**

Transport forms part of the so‐called network industries, which provide services and infrastructure of general economic interest. These industries have a significant impact on national and regional competitiveness as they typically contribute a significant portion of GDP and employment, while impacting the success of other industries (Commission of the European Communities, 2002). Globally, unabated population growth, urbanization, and resulting increased consumption is expected to intensify pressure on transportation services (Ivanova, 2014), exacerbated by the global reality that transportation infrastructure is approaching capacity levels (Müller et al., 2012). Significant changes on both the supply and demand‐side of logistics could however impact these trajectories. On the supply side, disruptive technologies such as driverless trucks, drone delivery (Van Meldert and De Boeck, 2016; Connolly and Coughlin, 2017) and the physical internet ‐ an analogy with the electronic internet to unitize shipments into globally standardized ‘packets’ and optimize routing through portals‐ could transform the supply landscape significantly (Crainic and Montreuil, 2015). On the demand side, alternative business models including a departure from just‐in‐time business practices, a return to more localized consumption, recycling at source and additive manufacturing, have the potential to reduce the demand for logistics and interrupt the growth trajectory of freight transport (European Parliament, 2010; Attaran, 2017).

The network industries are however also characterized by several market distortions such as scale economies and externalities and are therefore typically subject to some form of regulation (Commission of the European Communities, 2002). Ultimately, this should include total cost internalization to enable both suppliers and consumers to make full cost trade‐offs in their purchasing and logistics decision making to support the scale of change that is made possible through technological developments (Sustainable Aotearoa New Zealand, 2009). The latter is important as the stock of transport infrastructure has been identified as the main explanatory factor for the level of transport costs (Ivanova, 2014), leading to an argument that higher‐quality road infrastructure reduces transit times which negates the negative effects of generalized transportation costs on trade flows (Arbués and Baños, 2016). Currently this argument is possible because the price signal fails as the full 12
ecological and social impacts of freight activity are not accounted for in transport costs (Lewis and Conaty, 2012).

Macroscopic freight demand modeling will be a key enabler for the management of transport and logistics as a macroeconomic production factor within this changing landscape as logistics‐ and connectivity‐related interventions are estimated to have the highest potential to reduce trade costs and to boost global value chain integration (World Bank, 2016d). This thinking is beginning to take root in national logistics policy. For example, in preparing the U.S. _National Freight Strategic Plan,_ understanding of major freight flows was considered a key input for informed planning (US Department of Transportation, 2015).

According to Ivanova (2014), existing empirical literature on freight demand modeling focuses on aggregate trade flows and ignores the differences between various commodities and region pairs in terms of the impact of transport costs on trade patterns and volumes. This aggregated approach does not allow for making policy‐relevant conclusions related to particular industries and commodities and diminishes the usefulness to macro‐econometric analysis. Three decades ago Raza and Aggarwal (1986) understood that aggregate freight‐flow analysis does not reflect the diversities of and the disparities in either the production or consumption processes, nor can it reflect the regional structure of the economy. Tavasszy and De Jong (2014) reiterated that, ideally, freight flow modeling should commence from economic linkages, as freight transport is an outcome of these interactions. De Vries et al. (2012) highlighted that analyses of structural change in developing countries are constrained by the lack of detailed sector data, obscuring a proper assessment of the role of structural transformation in driving aggregate productivity growth through resource allocation to value‐added industries. A more in‐depth understanding of the freight transport market presupposes access to reliable, disaggregated freight‐flow information (Lyk‐Jensen 2011). Disaggregation is required on _inter alia_ commodity flows and geography, and an increased linking of freight modeling with the broader economy, geographically as well as functionally (Tavasszy, 2006).

The relevance of spatial and sectoral disaggregation in macroscopic freight demand modeling is threefold (informed by Ludlow, 2011):

- Macroeconomic planning and policy applications to _inter alia_ assess the health and changing structure of the economy at the national and regional level over time, to conduct sector and regional analysis and to direct spatial planning;

- Macro logistics planning and policy applications to assess transportation demand, engineer an optimal modal balance, identify economic impacts of transportation network investments and support macroeconomic growth through directing capital investments; and

- Mesoscopic applications to target investments and logistics solution development for specific industries or geographies.

These are discussed in turn below using specific examples from the United States and South Africa, the two countries with the longest running time series of freight volume and cost data.

# **<u>Macroeconomic Planning and Policy applications</u>**

The efficient movement of freight within and through a region is vital to its growth and economic development. An understanding of commodity‐level freight flows allows for the assessment of the demand for specific transportation facilities and services (Mesenbourg, 2011) and supports a region’s infrastructure investment decisions (Harris and Anderson, 2011). In the United States, results from the Commodity Flow Survey (CFS) are for example applied to explore the feasibility of dedicated truck lanes on the Interstate 70 corridor connecting Indiana, Illinois, Missouri and Ohio (refer Figure 12). The 13
<!-- Start of picture text --> ey<br>LE RCPas Up aS<br>“SenWee ae<br>anges a8 4OMT:<br>ae Ie<br>FAF Truck Volume(Day<br>_<br><!-- End of picture text -->
Through this process, infrastructure spending and policy initiatives can be better informed, industrial clusters developed in the correct spatial context, and other bottlenecks and issues can be addressed within the context of the total national freight‐flow landscape.

# **<u>Macro Logistics Planning and Policy applications</u>**

Commodity‐level data enable an understanding of current modal shares, the sustainability of future freight flows given the current trajectories, and where to target modal shift given commodity characteristics.

Zhang et al. (2003) reported on the development of a methodology, based on the CFS data, to systematically estimate state‐wide truck travel demand using the state of Mississippi as a case study. The first step is a detailed commodity flow data analysis including modes and origin‐destinations, which facilitates conversion of road flows into truck trips by using average density of freight and average load per truck to estimate the composition of different truck types carrying different commodity groups. One of the objectives is to identify and prioritize intermodal infrastructure opportunities given that highway networks are experiencing severe congestion ‐ with concomitant externality costs ‐ while other inland transportation modes are underutilized. Feasibility studies for the development of intermodal technologies are enhanced through the availability of commodity‐level data, since specific industries and freight owners can be targeted per route.

Similarly, the State of Alabama used the CFS to develop a state‐wide model that showed the possible impact of new relocated industries and the movement of their goods on the state’s existing transportation infrastructure. Understanding the total character of freight movements along a corridor – its prevalent commodities and potential safety and operational constraints – facilitates the identification of potential options for shifting commodities to alternate modes of transport to alleviate congestion (Anderson and Harris, 2011).

In South Africa, the segmentation enabled by the country’s disaggregated macroscopic FDM informed the development of a domestic intermodal strategy (Havenga et al., 2012) and allowed for confidential input to the national rail policy to change the thrust of rail policy away from one that is focused only on institutional reform and increasing clarity towards one that encourages development and investment (Smith, 2012). The externality cost extension to the transport cost component of South Africa’s logistics cost model is providing ongoing input to national and international discussions on logistics’ contribution to sustainability, as well as providing inputs to carbon taxation (Havenga, 2010 and 2015). Outputs from the South African freight demand model also played a pivotal role in directing large‐scale infrastructure investments, notably the rail and port capital investment programs, with the intent of creating capacity ahead of demand (Havenga, 2012; Transnet, 2015).

The inclusion of imports and exports in spatial disaggregation unlocks analysis of the advantages of supply chain coordination and the elimination of trade barriers for cross‐border and international trade. Reducing delays at South Africa’s two major inland border posts can reduce the costs related to these delays by 55% due to reduced buffer stock required at the destination site, reduced costs of carrying inventory, and reduced vehicle utilization losses (Havenga et al., 2013). Havenga et al. (2016) showed that, for international trade, documentation costs and induced transport costs due to truck and ship standing times are approximately equal to direct port charges. These costs are at least in part avoidable and can be addressed through improved port efficiencies and collaboration between the ports, industry, and the South African Revenue Services.

The inclusion of rural areas in spatial disaggregation allows for detailed analysis which points to opportunities for a revival of rail branch lines in South Africa, which will reduce transport costs and 15
externality charges in rural areas and increase equitable access to the core transport network. The research also expounds the single‐network characteristic of South Africa’s railroad, based on density requirements, relative to the size of the network (Simpson and Havenga, 2010).

In sum, the benefits of understanding macro‐logistics facilitates decisions and discussions beyond transport and allow policy makers to focus on impacts of national logistics in a way that would not otherwise be possible. As can be seen from the case studies in the United States and South Africa the evidence can provide a basis for decisions with transformational impact. The challenge for emerging economies is that the research and application of macro‐logistics are still in their infancy.

# **<u>Mesoscopic Applications targeting Industries or Geographies</u>**

Commodity‐level freight‐flow analysis enables a more robust linking of freight activity with underlying economic activity, such as the increase in high‐value, low‐weight products which informs modal, service and transport technology requirements (Mesenbourg, 2011; Lawson et al., 2011).

Production of low‐value bulk commodities, such as grain or coal, generates a transport demand disproportionate to their value (Transportation Research Board, 1997). In New Zealand, the _National Freight Demand Study_ showed that almost 60% of the total freight transport demand consists of the movement of relatively low value commodities such as liquid milk, aggregate, timber and wood chips. For these goods, transport costs can represent a high proportion of the delivered costs. Minimizing the transport costs is therefore an essential part of keeping the overall logistics costs low and, for items which are converted into exports ensuring that these are competitive on world markets (Richard Paling Consulting, 2008).

For most commodity groups, the relationship between the value of output (in monetary terms) and transport volumes changes over time. These changes may be due to changes in the mix of commodities being produced within a given commodity group or changes in the average real value per ton of major products within the group (Transportation Research Board, 1997). In the United States, the Commodity Flow Survey (CFS) has allowed policy makers and researchers to understand how the growth in demand for higher value and time‐sensitive commodities has affected the transportation system by driving growth in trucking (Ludlow, 2011). For example, from 1999 to 2009 the percentage of electronic commerce sales in the retail sector increased sevenfold from 0.6% of total retail sales in 1999 to 4.1% in 2009. In manufacturing, e‐commerce shipments increased from 18% of total manufacturing shipments in 2000, to 39% in 2008 (Mesenbourg, 2011; Lawson et al., 2011). The CFS is also being applied to understand the impact of increasing globalization on furniture industry supply chains with U.S. furniture imports having grown 108% and exports increasing 41% from 1999 to 2007. The commodity forecasts and predicted flows will enhance understanding of the underlying global and domestic furniture supply chain requirements (Liu and Tolliver, 2011).

In South Africa, the commodity‐level segmentation within the national freight demand model enabled the identification of palletizable fast moving consumer goods for the domestic intermodal business case. More than half of South Africa’s potential intermodal freight moves on the country’s two most‐ dense freight corridors. Building three intermodal terminals to connect the three major industrial hubs – Gauteng, Durban and Cape Town – could enable modal shift to rail, increasing rail densities and thereby reducing logistics costs ‐ including externalities ‐ for the identified intermodal freight flows on these two corridors by two‐thirds (Havenga et al., 2012).

An understanding of the transportation of hazardous materials allows objective evaluation of the safety risks involved. Understanding the geographical flows of these commodities facilitates policy‐ 16
making, alleviates public safety concerns by providing data for conducting risk analyses and security assessments, and identifies emergency response and preparedness requirements (Duych, 2011).

The relevance of spatial and sectoral flows in macroscopic freight demand analysis is clear. In the next section, an overview of the development of freight demand models is provided to inform the methodology to develop India’s FDM.

# **Background to Macroscopic Freight Demand Modeling**

Transportation demand modeling was formalized within urban passenger transportation planning during the 1960s with the development of the sequential four‐step model. The four steps refer to trip generation, trip distribution, mode choice and route assignment (De Jong et al., 2004).

Walter et al. (1998) expanded this approach and include freight modeling in their approach based on the premise that passenger flow models rely on socioeconomic factors rather than profit maximization which is often the case with freight which requires a mathematical optimizing algorithm. They highlight the problems associated with the absence of intercity data and propose and develop an approach to develop an investment analysis model that integrates regional input‐output models, spatial interaction models, cost‐benefit analysis and geographic information systems. Ortuzer and Willumsen (2011) admit that a large part of their seminal work is around passengers with a strong urban emphasis even though freight movement, and especially road haulage, is an important source of congestion, noise and other traffic problems. They maintain that policy options are limited, but that freight demand modeling can play an important role in developing countries where exports and market access for underdeveloped areas are important.

Specific characteristics of the freight transport industry therefore render freight transport demand modeling more challenging from a methodological point of view than passenger transport demand modeling. These include (De Jong et al., 2004; Müller et al., 2012; Ivanova, 2014):

- The heterogeneity of freight (from parcel deliveries with many stops to single bulk shipments of hundreds of thousands of tonnes) and the heterogeneity of geographies;

- The diversity of stakeholders (freight owners, customers, freight forwarders, logistics service providers, government and communities) and components (technology, infrastructure, fixed‐ and moving assets) within freight supply chains requiring coordination and integration. These lead to complex relationships and, frequently, conflicting requirements.

This is compounded by the mega scale and extended lifespan of freight transportation infrastructure such as railways and ports (Rodrigue, 2016). In addition, specific modeling requirements impact the freight modeling process. According to Müller et al., 2012, the requirements include:

- Modeling and tracking the link between freight transport and final consumption (often proxied by GDP growth);

- Accurate conversion of trade values to trade volumes (the majority of economic data is published in monetary terms);

- The choice of vehicle type as impacted by cost sensitivities and freight requirements; and

- Ability to link modal interaction, mainly rail, road and waterways, as well as the interaction with passenger capacity requirements.

The complexity induced by these characteristics and requirements is the main reason why the development of freight transport modeling has been lagging the development of passenger transport modeling. The basic premise is however straightforward: The spatial patterns of production and consumption generate freight transportation flows to meet the needs of end consumers (Ivanova, 17
2014). This translates into the following four‐step approach for freight demand modeling (De Jong et al., 2004):

- Production and attraction – Estimating the spatial distribution of economic activities, i.e. spatially‐ disaggregated supply (origin) and demand (destination) of commodities and shifts in this spatial distribution over time as the economy responds to policies, macro‐ and microeconomic trends. The output dimension is origin and destination tables in tonnes. In intermediate stages of the production and attraction models, the dimension could be monetary units (trade flows).

- Distribution – Modeling volumetric freight flows between origins and destinations (cells of the OD matrix).

- Modal split – The allocation of the volumetric freight flows to modes (e.g. road, rail and IWT).

- Assignment – The modal flows are converted to vehicle‐units and assigned to networks (ideally, incorporating both truck flows and passenger cars on road networks).<sup>1</sup>

There are essentially three methodologies available for developing freight supply and demand data: a demand‐side approach, a supply‐side approach and a survey approach (Havenga and Pienaar, 2012b).

A demand‐side approach addresses the geographical disparity between supply and demand through flow‐modeling techniques and modality assumptions. It is based on the macroeconomic input‐output (I‐O) model of the economy which describes the interdependencies between industries in terms of intermediate inputs, driven by the developments in final household demand. The country‐level multi‐ sectoral I‐O framework was developed by Leontief (1986) in the 1930s, based on the theory of Keynes who postulated that production is determined by consumption, so‐called market equilibrium, expanded to multiregional or spatial I‐O models by Chenery, Isard and Moses in the 1950s allowing for the calculation of inter‐regional trade (Ivanova, 2014). It requires voluminous disaggregated economic data but renders a deep understanding of freight‐flow patterns and resulting demand for logistics infrastructure. The demand‐side approach therefore has the potential to provide the most comprehensive information but is only possible if adequate data and funding are available (Transportation Research Board, 1997).

A supply‐side approach collects data regarding actual transportation activity on each mode of transportation and constructs a network view of the supply of freight transportation infrastructure. Typically, road freight volumes, due to its fragmented nature, are modeled based on truck counts at weighbridges and counting stations, while actual rail and port data are mostly available through agreements with incumbents. The supply‐side approach is the most cost‐effective option but does not provide detailed O‐D data or commodity visibility.

A survey approach entails the distribution of a questionnaire to a sample of freight logistics stakeholders and, through statistical analysis of the responses, the characteristics of the total freight market are estimated. The strength of the survey approach is that it can elucidate industry dynamics to explain freight flow trends and anomalies. Questionnaire‐based surveys however tend to underperform due to sampling biases and non‐ or partial responses (Kockelman et al., 2009), and present reporting challenges due to confidentiality requirements (Zhang, 2003; Richard Paling Consulting, 2008; Harris and Anderson, 2011). In addition, surveys are often once‐off endeavors with limited repeatability due to time lapses and changes in scope, and fail to provide systemic logistics

> 1 Very few models conclude with converting tonnages flows to vehicle‐units and assigning this to the transport network. Currently this falls outside the scope of the India FDM.

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observations, impacting their macroeconomic effectiveness (Havenga and Pienaar, 2012b; Bergquist et al., 2016).

There are numerous national and international freight transportation models discussed in the literature, for example the Italian national model, the REGARD model in Norway, the Walloon Region freight model in Belgium, the SCENES European model system for passengers and freight, the Dutch TEM‐II and SMILE models, as well as the Swedish SAMGODS model (De Jong et al., 2004; Müller et al., 2012; Ivanova, 2014). The lack of a standardized modeling approach is attributable to limited methodological and calibration details being made available publicly, the challenges involved in the estimation of freight demand given that reliable data on freight volumes are scarce and fragmented, and data availability is different for each geography modeled, as well as the variety of objectives driving freight demand modeling which give rise to models that are context‐specific, hampering the transferability of models (Mommens et al., 2017).

To improve the continuity and application of its country‐level FDM, the chosen approach for India’s model is a demand‐side model due to the comprehensive nature and resulting application possibilities of demand‐side modeling. The spatial and sectoral disaggregation is enabled through utilization of hybrid input data i.e. actual and modeled data, to improve accuracy. The model outline is discussed below.

# **Methodology for developing the India Freight Demand Model (FDM)**

The underlying driver of the India FDM is sufficient disaggregation of the national transportable economy on a spatial and sectoral level, followed by flow generation via gravity modeling, to enable detailed analysis on all the core components of the national freight system.

## **<u>Data collection</u>**

The depth of data available in India is immense yet dispersed, therefore giving rise to the typical onerous data cleaning and data integration challenges demanded by heterogeneous data sources. The following data was received from various sources and agencies in India:

1. Economic data from the Prowess Database that specified intermediate demand, mining and agricultural production, and beneficiation of products at specific locations throughout India over multiple years.

2. Handbook of horticultural statistics from the Ministry of Agricultural Statistics, and mining statistics from the Ministry of Mines.

3. Import and export data per commodity for all ports in India. This included import and export data per port for major commodities. For some ports import and export data ‐ from ports such as the Kolkata Dock System (KDS) – were more detailed on a commodity level, described origin‐ destination states and specified whether freight was containerized or not. Having access to hinterland ODs is not common, and this provided valuable input into modeling of some corridors.

4. Freight rail movements on commodity and OD level 5. Production volumes for agricultural and mining commodities per state or district 6. Industry reports for some key commodities 19
# **<u>Supply and Demand per Commodity on a Geographical basis</u>**

Supply and demand on a geographical basis per commodity is determined as per equations 1 and 2 below.

����� ������������������� �������� ������������ �������� �������������� (1) ����� ������������������������� (2) Total supply equals total demand which corresponds to the market equilibrium condition (Ivanova, 2014). The modeling of total supply and demand addresses a concern raised by Lyk‐Jensen (2011) that freight traffic forecasting typically does not adequately incorporate international trade flows.

An official spatially disaggregated I‐O table was not available for the Indian economy, thus final supply and demand tables had to be constructed (the tables are therefore referred to as supply and demand tables, not I‐O tables, as the upstream and downstream interdependencies are not automated). The model is a hybrid, dependent on actual and modeled data, but differs from many other models in that the main inputs are actual data, while the overall supply and demand balance is maintained, implying modeling of unknown data points. In the case of India, the latter refers mainly to final regional consumption data, as well as the regionalization of aggregate data. Actual data is hardcoded in the supply and demand tables, which is much more reliable and useful than an assumption‐based disaggregation. For example, if new data on totals or regional supply and/or demand elements become known, the supply and demand tables are updated and rebalanced (the same holds true for district‐ level data, import and export data). Discussions with key players were also conducted for gathering spatially disaggregated actual data for larger commodities. The key was to rebalance both the aggregate and regional supply and demand tables when actual data replaced modeled data, resulting in improved disaggregated supply and demand tables. The same process is followed for flows where known flows (such as rail data) are hardcoded and the remainder modeled.

The sectoral disaggregation is informed by existing classification protocols. Following a number of iterations in the 1970s and 1980s to standardize data collection and reporting, the Annual Survey of Industries Commodity Classification scheme (ASICC) was developed in India in the late 1990s while maintaining linkages with the Standard International Trade Classification (SITC) which is maintained by the United Nations Statistics Division, and the Harmonized System (HS) which is maintained by the World Customs Organization (South Asian Association for Regional Cooperation, 2006). The final commodity group list for the India FDM maintains the link with the HS classification, informed by the practicality of data availability. As shown in Table 2, the model contains data for 31 commodity groups (to identify specific supply chains and to understand cost drivers) grouped into 8 cargo types (to identify logistic solution criteria and to identify required infrastructure, policy and operations).

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<!-- Start of picture text --> break bulk Palltisable<br>Refrigerated<br>SE ———<br><!-- End of picture text -->
<!-- Start of picture text --> Lz<br>0.5 +<br>E<br>a<br>ov<br>8<br>@ Correlation coefficient of freight<br>0.5 +— i—_ aa —— with distance R<br><!-- End of picture text --> <!-- Start of picture text --> @ Coefficient of determination (R2)<br>14<br>g 2 B = v 2 rH 2<br>$ 38 3s # 2 8 =€ 8<br>|s883 £gs =£© =388 £8§ 8€ B&<br>= 8 S g 2 z 8 =<br>= 8 bp ££ & = 8<br>g g 3 ¢€ #8<br>= = 3 = =<br>& 2 =<br><!-- End of picture text -->
<!-- Start of picture text --> a) Supply of vehicles b) Demand of vehicles G} Freight flaw of vehicies !<br>La,<br>x * foe<br>—_—<br><!-- End of picture text -->
Tij = the estimated volume of freight flows between origin i and destination j Ai = the balancing factor for origin i that ensures compliance to Oi Bj = the balancing factor for destination j that ensures compliance to Dj Oi = the constraint value for origin i (i.e. total supply) Dj = the constraint value for destination j (i.e. total demand) ƒ is the decay function where: ƒ(Cij,ß) = exp(‐ß.Cij) in case of an exponential function ƒ(Cij,ß) = Cij<sup>‐ß</sup> in case of a power function where: Cij = the distance between origin i and destination j (the resistance measure) ß = the decay parameter The availability of both supply and demand data enables the use of a doubly‐constrained gravity model (de Jong and Van der Vaart, 2010) where total flows from a district (the origin) equal the total supply from that district, while flows to a district (the destination) equal the total demand at that district. This ensures adherence to the market equilibrium condition where total supply equals total demand (Ivanova, 2014).

Equations (4) and (5) above hold for a doubly‐constrained gravity model if the constraint equations (6) and (7) below are satisfied through an iterative procedure: For the India FDM, the road distance matrix was used to determine the flow data for the non‐rail component of freight. A detailed national road network was constructed. This allowed road travel times to be estimated between the various origins and destinations, penalized for the type of road. A lower resistance was given to national roads, so freight collates towards these highways, the logic being assumed improved travel time on highways. This refers to Cij in Equations 3 to 5, which typically refers to distance, but can be adjusted based on estimated travel time or costs. For the India FDM, it is adjusted by ranking roads through reducing the travel distance for highways and increasing the travel distance for rural roads.

In a doubly‐constrained spatial interaction model where both the origins and destinations are known but the derived freight flows over the transport network are unknown, the problem is essentially confined to the estimation of a suitable decay parameter. In terms of actual flows, only rail freight‐ flow information is available (which in most instances accounted for only a small market share); the distance decay parameter could therefore not be derived (for all commodities) from actual data. Decay parameters utilized in other gravity models are also typically not published, these could therefore not be used as a starting point, as commodities tend to exhibit similar distribution characteristics. Decay parameters for the South African FDM were however available. At the outset of the South African gravity‐modeling exercise, distance‐decay parameters were developed informed by the decay parameter principles discussed above as well as known flows such as rail flows and large industry flows.

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The ‘best‐fitting’ distance decay parameters were subsequently selected. These decay factors have been fine‐tuned over a period of 10 years. Annual application and interaction with industry have proven the accuracy of these decay factors to model commodity‐flow behavior. These decay factors were utilized as a starting point, informed by known rail and port flows in India, and fine‐tuned through iterative application of the gravity model. However, the freight flow behavior of some commodities in South Africa is completely different to in India, such as sugar cane, which is transported very short distances in South Africa to one region, but long distances across the country in India. The best‐fitting decay factors were used based upon multiple tests of various decay factors to determine the best overall fit in line with partially known data and inputs from local stakeholders.

The UK Department for Transport (2002) also stated that, lacking freight‐flow data from which to estimate the parameters, developers iteratively adjust the coefficients to match observed counts and vehicle miles of travel estimates by truck type and if not available, using analyst judgement. A distance‐ decay parameter is developed for each commodity group individually to account for the varying nature and utility of the commodity, as discussed above.

The gravity modeling is done using software called FlowMap® which was developed in 1990 at Utrecht University. The cost‐effective and user‐friendly software was initially targeted towards use in developing countries’ spatial planning and has been applied successfully in South Africa for various spatial planning purposes since 2000, when the Professional Edition was released. FlowMap® expands typical GIS functionality to allow for the management and analysis of data that depict spatial relations such as distances, flows, travel times and travel costs (Utrecht University, 2013).

The convergence criterion must also be set in FlowMap®. This determines how well the number of trips that to be estimated from the origins and to the destinations has to equal the set variables for the origin and destination constraints respectively. The convergence criterion also determines how well the MTL that is to be estimated has to equal the distance as was set above (de Jong and Van der Vaart, 2010). This is the required accuracy of fit between the modeled data results and the input parameters.

# **<u>Typologies</u>**

Once freight flows have been modeled, they are aggregated to facilitate analysis and recommendations. A succinct view of national freight flows can be obtained through aggregation into two key freight‐flow typologies for India, each with unique transportation characteristics. Firstly, corridor flows occur over long distances between major cities. Due to the high freight density on corridors, these flows provide ideal opportunities for economies of scale and consolidation. Rail transportation is generally the most efficient and suited mode for this type of flow, although countries with navigable river systems (such as some countries in Europe) have had major successes in returning high‐volume freight to inland waterways (Inland Navigation Europe, 2016). Secondly, there are those flows that are neither urban nor corridor flows and are labeled ‘other’. Typified by broad distributions of origins and destinations outside the cities, medium to long distances and low freight densities, flows of this nature are the most difficult to optimize and can be suited to either road or rail (these flows are mainly in rural areas).<sup>2</sup>

> 2 A third typology, urban freight flows (freight traveling within major cities), typically travel short distances, and are more affected by congestion and therefore better suited to smaller road vehicles. Due to the nature of demand‐side modeling, the latter is not modeled, as urban freight mostly refers to the ”last leg” of transport, in effect a double‐count of traffic that has already been measured as a supply‐and‐demand flow, and includes collections, deliveries, distributions and redistributions.

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To define the corridors used during the aggregation of OD‐level flows, a graphical depiction of the total road network was superimposed with a simple map of the highways, and roads classified as being either highways or regional roads. For the corridor aggregation, the major cities and the catchment areas surrounding the cities were identified as the core catchment due to the high densities involved, while outlying areas were identified as the wider catchment due to lower densities: flows from a core to a core were defined as corridor Class 1 and are flows from a single origin to a single destination, flows from a core to a wider catchment areas are defined as corridor Class 2, and flows from wider catchment areas to wider catchment areas are defined as corridor Class 3. The difference between Class 3 and the ‘other’ typology is that Class 3 flows still flow along the corridor and can therefore be easily consolidated with other corridor flows.

# **<u>Modeling Transport Costs</u>**

With a complete database of freight flows, transport costs are calculated in a similarly disaggregated fashion. Actual rail and waterway tariff data were received from the respective operators. Road tariffs (costs) were calculated as per equation (8).

<!-- Start of picture text --> L �∑ni�1 ∑pj�1 ∑sk�1 xyijk �� d �c �l �q �e �f �m �z�ijk �tk� (8)<br>L = road line haul cost d = depreciation rate per tonne‐km<br>n = number of commodity groups c = cost of capital per tonne‐km<br>i = commodity grouping l = license fee and road tax per tonne‐km<br>p = number of typologies q = insurance per tonne‐km<br>j = typology e = driver fees per tonne‐km<br>s = number of routes f = fuel cost per tonne‐km<br>k = route m = maintenance and repair costs per tonne‐km<br>x = tonnes transported z = tire wear cost per tonne‐km<br>y = distance in kilometers t = toll fees per tonne‐km<br><!-- End of picture text --> This equation involves the summation of all the different cost elements of road transport within a typology on a specific route. The different cost elements of road transport are determined by vehicle type; this, in turn, is determined by the commodity type, typology and route of travel. The commodity’s ‘preferred’ vehicle type will change with changes in each of these variables. Once the vehicle type and volume are known, the cost elements can be assigned according to equation (8), above. The core drivers of transport costs, i.e. weight in tonnes (x) and distance traveled (y), describe the base of the formula.

It is however important for logistics infrastructure and efficiency planning and will be modeled based on freight flows developed from truck counts in subsequent phases of the project.

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The cost elements are primarily influenced by:

- **Asset type:** Vehicle type, condition and fuel‐efficiency.

- **Utilization:** Load factor, workdays per year, kilometers per year, driver hours and other labor regulation

- **Nature of trip:** Long‐haul or short haul, road condition, altitude and terrain.

- **Financing terms:** Interest rate, depreciation parameters.

Data regarding the cost elements and their drivers were sourced from multiple industry reports and workshops with organized trucking bodies in Delhi and Kolkata.

Adding a transport cost layer to the India FDM enables robust cost‐benefit modeling of various scenarios investigated to improve the performance of the Eastern Corridor. (This could be expanded to total logistics costs in future revisions of the model.)

# **<u>Final Model Parameters</u>**

The resulting parameters of the India FDM are:

- Freight flows between 672 geographical areas: 637 districts within India, 30 of India’s ports and 5 neighboring countries (Bangladesh, Bhutan, Myanmar, Nepal and Pakistan);

- For 31 commodity groups grouped into 8 cargo types

- On the three inland modes (road, rail and IWT), as well as crude oil in pipelines;

- Aggregated per typology namely corridor and rural freight;

- Identifying mode suitability for rail‐friendly freight and waterway‐friendly freight; and

- Adding actual railway and waterway transport costs and estimating costs for nine road cost drivers to enable cost‐benefit analysis.

The result of this process is a database of about 4 million unique freight flows, to which transport costs of known flows are added and estimated for modeled flows. Each unique flow specifies how many tonnes of a specific commodity were transported between a specific origin‐destination district pair during a year and on which mode (road/rail). Each record further differentiates whether this was domestic, import or export freight and calculates the tonne‐km associated with that freight flow.

The modeling framework and outputs of the India FDM addresses the unique characteristics and requirements of freight demand modeling, discussed in the literature survey as follows:

- The heterogeneity of freight transport is addressed through commodity and spatial disaggregation and allows for policy‐relevant conclusions for specific industries and geographical areas;

- Mapping the national freight flow landscape while enabling mesoscopic applications (due to the level of disaggregation), aids prioritization towards collaborative long‐term goals, aiding the diversity of stakeholders to align conflicting requirements;

- The linkages between freight transport, the broader economy and final consumption  studied through the development of disaggregated supply and demand tables;

- The challenges with the conversion of trade data in monetary terms to trade volumes  largely circumvented through developing the bottom‐up supply and demand tables in volume terms from all available sources;

- The choice of vehicle type is included in the transport cost model to account for cost sensitivities and the requirements of specific commodity groups;

- The modal interaction between rail, road and waterways is a core component of the model;

- The regionalization of supply and demand is facilitated through the development of disaggregated supply and demand tables commencing from the most granular spatial disaggregation at which

27
national data is available, namely districts. The bottom‐up data gathering process facilitates the correlation of the disaggregated data with available aggregates.

# **<u>Model Validation</u>**

- Given the lack of an established I‐O model in India, available data, desktop research and interviews with industry experts and logistics service providers were utilized to construct and refine supply and demand tables.

- Truck counts are a relatively cost‐effective tool to estimate aggregate freight flows and are therefore often used as a validation method for verification of modelled flows (Zhang et al., 2003; Richard Paling Consulting, 2008). In South Africa, for example, a robust national freight flow model (NFFM) was developed that translates the truck counts by the South African National Roads Agency into tonnages (Havenga and Pienaar, 2012a), and the NFFM and South Africa’s spatial freight demand model are used for cross‐validation purposes. Truck flow data for India was used on a case‐ by‐case basis, such as comparing traffic around Varanasi on the Delhi‐Kolkata corridor with the FDM results, and these comparisons were favorable. A next step would be the translation of total truck count data into freight flows for cross‐validation purposes.

- Following project approval of the India FDM based on initial estimates of freight logistics cost reduction opportunities on the Eastern Corridor, twelve months’ subsequent research and fine‐ tuning of the model shows remarkable alignment with initial estimates on an aggregate level. This is in line with experiences for example in Norway where, following numerous model refinements, the overall macro distribution in the new model version was quite similar to that obtained in the former version (Hovi, et al., 2013). However, the refined disaggregated models enable a better description and fit to mesoscopic applications, therefore improving validity and applicability to industry, regional or typology‐level transport challenges, as well as enabling targeted infrastructure investments. This confirms the usefulness of two different levels of freight transport modeling to increase the accuracy of the forecasts and the range of policy applications of the models:

- A fast (low‐resolution) policy analysis model which includes a wide range of factors but limited detail, to aid with initial screening and comparison of policy alternatives (‘first order approximations’); and

- A detailed (high‐resolution) forecasting model which analyzes limited factors in a lot of depth for estimates at a mesoscopic level (e.g. industry, region, typology or network) and to provide inputs for project evaluation.

The reasoning behind this is to address the diverse information needs of the numerous stakeholders, to minimize cost by using the policy‐analysis model for prioritizing options, and to improve accuracy by using the detailed model when long‐term, high‐cost decisions are required (De Jong, Gunn, & Walker, 2004). These principles can be utilized going forward.

# **<u>Research Constraints</u>**

- A national I‐O table for India has been developed as part of the World I‐O database (funded by the European Commission).<sup>3</sup> In the absence of average $/ton values this table cannot be converted to tonnage terms. The project team is endeavoring to develop such values for the I‐O table to serve as a macroscopic validation since the World I‐O database is not regionally disaggregated.

> 3 Refer http://www.wiod.org/new_site/database/niots.htm.

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Three primary factors favor road as a mode‐choice compared to rail, namely efficiency, infrastructure and lack of integration. Operational efficiency and a lack of high‐performing infrastructure reduce the operational capacity of the rail line, making it difficult to compete with roads. Integration poses a more complex problem. Although rail is cheaper per tonne‐km (refer Figure 18), the total supply chain cost may be higher when using rail if the user has to account for longer lead times and (current) unreliability. Rail is seldom the only mode of transport from origin to destination (the exception being pit‐to‐port operations). Therefore, for rail transport to be incorporated into an end‐to‐end transport solution requires the capability to switch freight at efficient intermodal terminals to a reliable rail service (both of which currently do not exist) to mitigate the additional handling costs and longer lead times.

<!-- Start of picture text --> 0.060<br> 0.050<br> 0.040<br> 0.030<br> 0.020<br> 0.010<br> ‐<br>Road Rail Waterway<br>Rate per tonkm ($US)<br><!-- End of picture text --> _Figure 18: Rate comparisons for inland modes (Indian national average rates per mode for 2015)_ Fuel is the major cost driver of road transport, contributing 51% of the overall road transport costs (Figure 19). The aging and under‐maintained vehicle fleet is highly fuel‐inefficient, a situation exacerbated by poor road conditions and severe congestion. Poor road conditions also wear tires quicker, adversely influencing this second largest road cost element. The fact that maintenance and repair and insurance are relatively low echoes the assessment of Kumar (2014) that the fragmented and informal nature of the trucking industry results in many operators who “drive under the radar” with unroadworthy, uninsured vehicles that are most likely also overloaded and unlicensed causing further damage to infrastructure. Apart from normalizing the road‐rail modal split, improved road transport efficiency is high on the agenda.

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<!-- Start of picture text --> 70,000<br> 60,000<br> 50,000<br> 40,000<br> 30,000<br> 20,000<br> 10,000<br> ‐<br>Million US$<br><!-- End of picture text --> _Figure 19: Road freight transport’s cost drivers in India_

# **<u>The Eastern Corridor</u>**

The Eastern Corridor was identified as a key focus area of the study, representing 19% of total corridor tonne‐km in India, and 24% of total tonnes transported. In the context of the national freight flow picture, this represents 6% of tonnage, 10% of transport activity and 8% of transport costs.

Similar to the modal split in the rest of India, most this freight is on roads (refer Table 3). In contrast to aggregated national freight flows, the rail market share of tonnes transported and the transport task (tonne‐km) is almost equal, indicating long road transport distances and a relatively undeveloped rail sector, also illustrated by a relatively low‐cost market share, i.e. mainly low‐value commodities are transported on rail.

_Table 3: Road and rail freight on the Eastern Corridor_

|**Mode**|**Tonne**<br>**Millions**|**s**<br>**%**|**Tonne‐k**<br>**Billions**|**ms**<br>**%**|**Average**<br>**distance**|**Transport c**<br>**US$ billion**|**ost**<br>**%**|
|---|---|---|---|---|---|---|---|
|**Road**|206.6|71%|224.3|70%|1,086|9.1|79%|
|**Rail**|83.6|29%|97.6|30%|1,167|2.5|21%|
|**Total**|**290.2**||**321.8**||**1,108**|**11.6**||

Almost none of the freight identified on the Eastern Corridor is ExIm traffic (refer Table 5:). It is, in its current form, mostly a domestic corridor, mainly since a large portion of ExIm freight that should use the Kolkata port system rather uses remote ports towards the West due to efficiency and capacity challenges.

_Table 5: ExIm and domestic split of freight for the Eastern Corridor_ 33
|**Flow types**|**Tonn**|**es**|**Tonne‐**|**kms**|**Average**|**Transport**|**cost**|
|---|---|---|---|---|---|---|---|
||||||**distance**|||
||Millions|%|Billions|%||US$billion|%|
|Export|2.5|0.9%|3.8|1.2%|1,518|0.15|1.3%|
|Import|6.2|2.1%|8.5|2.6%|1,358|0.34|2.9%|
|Domestic|281.5|97.0%|309.6|96.2%|1,099|11.11|95.8%|
|**Total**|**290.2**||**321.8**||**1,108**|**11.60**||

The Kolkata port system does not operate as a hinterland port for the states along the corridor. The port is mainly a gateway port for West Bengal, with 66% of imports destined for West Bengal and 69% of exports originating in West Bengal. Capacity enhancements and improved connectivity should however provide a cheaper alternative for other states along the corridor.

<!-- Start of picture text --> Importing state using Kolkata Port Exporting state using Kolkata Port<br>Uttar<br>Tamil Nadu Karnataka2% Other states8% Tamil Nadu Pradesh2%<br>2% Gujarat 2%<br>Maharashtra 2% Gujarat Other states<br>3%<br>2% 10%<br>Haryana<br>Jharkhand<br>3%<br>3%<br>Punjab<br>Bihar<br>3%<br>4%<br>Jharkhand<br>Uttar 4%<br>Pradesh<br>11% West Bengal Odisha West Bengal<br>66% 4% 69%<br><!-- End of picture text --> _Figure 20: Imports and exports at the port of Kolkata, by state utilization_ This is confirmed by the ATD of imports and exports through the Port of Kolkata which are on the lower end when compared to the other commercial ports (refer Figure 21).

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<!-- Start of picture text --> Port Karaikal<br>Port Dhamra<br>Port Paradip<br>Port Haldia<br>Port Mormugao<br>Port Kolkata<br>Port Krishnapatnam<br>Port Visakhapatnam<br>Port Kakinada Bay<br>Port Gangavaram<br>Port Ennore<br>Port Mumbai<br>Port Hazira<br>Port JNPT<br>Port Dahej<br>Port Mangalore<br>Port Chennai<br>Port Kandla<br>Port Mundra<br>Port Cochin<br>Port V.O.Chidambaranar<br>Port Pipavav<br> ‐ 200 400 600 800 1,000 1,200 1,400 1,600<br>Export Import<br><!-- End of picture text --> _Figure 21: ATD of Kolkata port compared to India’s other major ports_ Table 4 compares the ATD of imports and exports for the seven states surrounding the Eastern Corridor compared to the average distance of that state to the Port of Kolkata. From the table, it is clear that from a distance point of view, most imports to and exports from the seven states should come through the Port of Kolkata. Furthermore, a large proportion of domestic freight between the states should also ideally make use of the Eastern Corridor as the most direct and efficient route.

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<!-- Start of picture text --> atecv Beam je neon break bl oe a ivy Bai<br>ZY al Se<br><!-- End of picture text -->
# **<u>Applications of the FDM ‐ Potential for modal shift on the Eastern Corridor</u>**

The current transport cost for the Eastern Corridor is US$11.6 billion (refer Table 5:). Initial results show that this can be reduced by US$1.6 billion if rail‐friendly freight along the corridor is returned to rail, according to the current design, i.e. freight with origin‐destination pairs along the corridor (refer Table 7:). Many of the freight flows currently using roads on the corridor are better suited to rail transportation (refer Figure 22). These flows are typically high‐density, long‐haul flows of less time‐ sensitive commodities that originate or are destined to locations close to the rail line. Specific reasons why these flows are not currently on rail relate to issues of efficiency and capacity on the Eastern Corridor rail line. Attracting these flows would require the rail line to commit and invest in the upgrade of its services and the design of a targeted commercial strategy. It has however become clear that a dedicated corridor focus can solve corridor capacity problems, but does not _per se_ address problems with port limitations, connectivity or integrated logistics planning.

The Eastern Corridor has the potential to play a far more prominent role in unlocking logistics efficiencies and trade potential in the landlocked states north of West Bengal. It can also become a robust and prolific hinterland corridor for the two terminals of the Port of Kolkata to enable port‐led development in the surrounding states. A key element of the proposal is to redesign the Eastern Corridor where it terminates in Kolkata by routing it via a logistics hub. Feasibility studies are required to determine the optimal location of a logistics hub. The logistics hub will require a high‐volume, high‐ speed connection to the rail line at Dankuni, a high‐volume, accessible truck terminal, and efficient intermodal transfer facilities. Capacity for other supply chain services such as storage, customs clearance and value‐added services would also be beneficial but is not required right from the start. The logistics hub also needs to be connected to KDS directly, circumventing the city‐center of Kolkata completely. A rail or inland waterways shuttle is suggested as a high‐speed, high‐volume solution. An accessible, high‐speed connection from the logistics hub to HDC will also be necessary, but this may very well remain a road solution. If these conditions are met, a further US$2.1 billion of costs can be saved (these savings are detailed in Table 7:). Modal shift on the Eastern Corridor will therefore only contribute 44% of the total potential of $3.7 billion saving that can be achieved by an integrated corridor design.

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_Table 7: Eastern Corridor savings potential in US$ million – mainly related to improved utilization of rail and improved port system_

|**Strategic**<br>**objective**|**Intervention**|**Description**|**Dedicated**<br>**freight**<br>**corridor**<br>**(US$**<br>**millions)**|**Logistics hu**<br>**dedicated li**<br>**(US$**<br>**millions)**|**b and**<br>**nk**<br>**%**|
|---|---|---|---|---|---|
|Rail corridor<br>solution|Modal shift within<br>corridor|Rail‐friendly freight on road shifts to<br>rail (origin and destination are<br>within the corridor)|**1,647**||**44%**|
|Kolkata as<br>gateway for<br>|Hinterland port shift of<br>rail‐friendlyfreight|Freight that can use rail will shift to<br>the closerport of Kolkata||**496**|**13%**|
|Uttar Pradesh,<br>Jharkhand and<br>Bihar|Hinterland port shift of<br>freight on road|Freight that cannot use rail (due to<br>location of load points), but will shift<br>exports away from Western ports<br>due to the improved link and<br>shorter distance||**868**|**23%**|
|Improve access<br>and reduce<br>congestion|Corridor‐city link|Freight shifts from road to rail<br>because it can more easily reach the<br>port on rail||**549**|**15%**|
||Kolkata city logistics<br>improvement|The hub and link will also have a<br>concomitant positive alleviation<br>effect on inner‐citycongestion||**146**|**4%**|
||Terminal‐port link|Reduce current ExIm costs due to<br>reduced congestion||**39**|**4%**|
|**Total**|||**1,647**|**2,097**||

# **‐ ‐** **<u>Applications of the FDM Examples of Cost Benefit analysis on the Eastern Corridor</u>**

The six interventions discussed above all relate to modal shifts, attracting freight to the port, and reducing congestion in and around Kolkata. The fact is that, despite these interventions most freight remains on road and exactly because of these interventions the road traffic on the Eastern Corridor will increase as more ExIm freight uses the corridor. Therefore, the systemic issues that plague road transport in India in general need also to be addressed. The versatility and detail of the India FDM allow the assessment of what the potential benefit could be on the Eastern Corridor resulting from road efficiency interventions. Although this study is only a prefeasibility analysis, it does not detail the cost/investment side of the cost‐benefit analysis. The results, however, are real and illustrate how the model can be used.

In total, US$ 1.7 billion per annum could be saved through six systemic interventions listed in Table 8 Many of these interventions require industry‐wide collaboration and/or costly infrastructure development – the cost and practicality of which also need to be taken into account.

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_Table 5: Examples of cost-benefit analysis – mostly relates to more efficient road transport, as well as road-rail collaboration_

|**Intervention**|**Mode**|**Rationale**|**Savings**<br>**per**<br>**annum**<br>**(US$ millions)**|
|---|---|---|---|
|Improved training of truck<br>drivers|Road|Will improve distance that trucks can be<br>driven and fuel consumption|**271**|
|Add lanes to highways|Road|Will reduce congestion and therefore<br>increase distances that trucks can be driven|**437**|
|Replace vehicles with a<br>newer fleet<sup>4</sup>|Road|Improved fuel consumption|**‐111**|
|Improve truck turnaround<br>times inports|Road|Trucks drive further|**25**|
|Eradicate state border post<br>delays|Road and rail|Trucks drive further|**547**|
|Consolidate flows through<br>improved collaboration|Road and rail|Reduce empty haul|**509**|

# **Implications for Other Emerging Economies**

The implications of this work for others emerging economies are important. Understanding freight flows in these spaces where efforts to increase exports and to gain access to underdeveloped areas is urgent, and facilitating the movement of goods has a major impact on economic development (Ortuzer and Willumsen, 2011). Understanding freight flows, specifically:

- Assists in the prioritization of infrastructure investments in fiscal‐scarce environments;

- Assists in modal optimization, therefore reducing freight logistics costs;

- Assists with policy formulation, direction and prioritization to facilitate modal shift, private sector investments and spatial planning; and

- Aids identification of key development nodes.

Examples of these are developing in India, such as outlined in this report, and illustrated for South Africa and Sub‐Saharan Africa.

# **Concluding remarks**

The objective of this working paper is to report on the development of a disaggregated macroscopic FDM for India. This model is a sectorally and regionally disaggregated quantification of total national freight flows and related costs to (1) improve the understanding of the national freight‐flow landscape and facilitate evidence‐based policy formulation and investment prioritization, (2) establish intermodal freight potential along the country’s most dense freight corridor, the Eastern Corridor and (3) identify

> 4 This saving will depend on the cost of new vehicles. With current information, it does not result in a positive answer to shift to newer vehicles. Some truckers have suggested that only the engines could be replaced and not the full bodies.

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and prioritize logistics improvement‐ and cost saving interventions for rail, road and ports on the Eastern Corridor.

The Indian economy generates 4.6 billion tonnes of freight shipments, translating into a transport task of 3.1 trillion tonne‐km at a cost of US$1,043.0 billion. When expressing tonne‐km requirements in terms of GDP, India as one of the least productive of 41 countries for which this measure could be calculated, with a conversion ratio of around one US$ of GDP/tonne‐km, compared to the top 12 countries, which generate above US$10 of GDP/tonne‐km. Spatially challenged economies (i.e. economies with long transport distances between supply and demand areas, as well as to and from ports, such as the United States, the Russian Federation, China, India and South Africa) will work towards low costs per tonne‐km to offset the reality of high demand.

Similar to many emerging economies with an infrastructure backlog, the majority (70%) of India’s total transport task in tonne‐km is on roads, while 60% of tonne‐km is on long‐distance corridors, underscoring the importance of a strategic corridor focus. Initial indications are that potential savings of US$3.7 billion can be achieved through the development of a dedicated freight corridor on the dense Eastern Corridor. This includes engineering a more optimal modal balance between road and rail and increasing port efficiencies, both of which will be supported by connections to a freight logistics hub.

The research and view of supply, demand and flows support the quadrilateral view of India’s freight flows. It also, however, identifies new opportunities and insights. ExIm freight is important, but many of the freight logistics challenges relate to domestic freight. Both domestic and ExIm freight is poorly organized, however, the sheer volume of domestic freight causes major problems. Addressing ExIm issues in isolation will therefore have little effect, because the domestic freight will still create congestion. The Eastern Corridor is a case in point. Addressing the domestic freight challenges will not only create capacity for more ExIm freight along the corridor, it could also lead to ExIm freight on other corridors switching to the Eastern Corridor, reducing costs and relieving congestion at major ports and cities.

The benefits of the India FDM methodology to address freight transport related questions are:

- It is **comprehensive and allows for** quantitative analyses of an entire industry, commodity group, mode, state or country.

- It is **disaggregated and detailed** enough to execute targeted analyses on specific corridors and for single commodities and to conduct market share break points for different modes.

- It is **accurate** and be used as an input to calculate national logistics costs – with possibility of identifying the drivers ‐ and even external costs in subsequent phases.

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