By Himanshu Raj and Dana Vigran

Have you ever thought about the entire journey of an item you order online or buy from a supermarket? Where it came from, how was it transported from the factory, what kind of vehicle was used, how many people were involved, how was it packaged, how many kilometers it travelled, and the emissions and air pollution that journey caused?

Global freight demand is estimated to triple between 2015 and 2050 and the ability to move goods efficiently has become the lifeblood of economic development, particularly in cities which generate over 80 percent of global GDP and an estimated 75 percent of global emissions.

Freight makes up an increasing percentage of transport operations and emissions, but there is no common methodology to capture the data we need on urban freight to optimize deliveries and operations for sustainable cities. Urban freight operations are run by a complex web of private and public sector actors from shippers and carriers to retailers and residents. Often, the most comprehensive data available lies with the private sector and rarely are there agreements in place for that data to be shared.

City logistics solutions should be evidence-based and designed to address the needs of these multiple stakeholders. For city planners and designers to identify these solutions, correct and complete data on urban freight transport is required. Only with comprehensive data from across the whole supply chain can local governments properly include freight transport in urban planning and better cater to freight vehicles through improved design and use of facilities and infrastructure.

However, for local governments, collecting and updating urban freight data can be expensive and often they do not have the resources to source high quality data, limiting their ability to implement data-driven policy.

The data we have and the data we need

Traditionally traffic counts are the most common survey method for urban freight policy making support, since policy makers have the habit and experience on their techniques, already popular in personal trip planning. 

According to Allen et al (2012), urban goods movement data can be collected using three main techniques: general commodity flow and commercial transport survey, specific stakeholder surveys of shippers, transporters, retailers, and vehicle specific surveys on vehicle usage and driver practices.

However, the lack of an established and accepted methodology means that the data gaps on urban freight are not only large but also diverse and variable. 

In a recent collection of responses from two projects, freight transport data experts in 10 EU countries and three developing countries identified a range of urban freight data gaps. These gaps have implications both for understanding urban freight transport activity patterns and also for developing urban freight models.

According to experts, here are some common data gaps and challenges:

• • There is little data about the activities of light goods vehicles (Gross vehicle weight < 3.5 tons)
  • The link and relationship between urban freight activity and freight activity further up in the supply chain is not well documented
  • Data about logistics infrastructure from which urban freight deliveries depart is lacking
  • There is insufficient geographical detail regarding goods vehicle trips in urban areas
  • There is a lack of data collection concerning the trips carried out by consumers for the purpose of shopping (which is a form of urban freight transport but which is often not defined as such for the purpose of urban freight data collection exercise).
  • There is insufficient data regarding non-road freight delivery modes
  • Collected data is not always reliable
  • Often in developing countries, the majority of urban freight transport is informal and hard to capture

Bridging the gap

There are emerging techniques and ideas on how to bridge these gaps. New technology offers the possibility to collect significant quantities of urban freight data at relatively low cost compared to previous techniques. Technology can also improve visibility of freight traffic flows and improve how and when goods are delivered. Companies are embracing technology to improve efficient deliveries. Collecting and sharing data also offers opportunities to reduce freight trips. Services such as route optimization and telematics for vehicles can be used to aid scheme design and inform local delivery plans.

However, stronger collaboration between the public sector and freight operators is needed to make vital datasets more widely and freely available.

And more than anything, common methodologies have the potential to make data more accessible for local governments and policy makers. Through the EcoLogistics project, ICLEI is working with local and regional governments in Argentina, Colombia and India to identify the primary data gaps on urban freight  and develop a common methodology for data collection which will address these gaps. The results will be used as a baseline to understand the flow of goods in project cities and accurately quantify the impact of urban freight.

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Himanshu works as an Officer in EcoMobility team, he is the project Officer of the IKI funded project EcoLogistics and supports the EcoMobility Alliance city network. He has a background in Infrastructure Planning (M.Sc.) with a focus on Climate Resilience.