{"id":13271,"date":"2026-07-08T08:57:15","date_gmt":"2026-07-08T08:57:15","guid":{"rendered":"https:\/\/www.qargo.com\/?p=13271"},"modified":"2026-07-08T08:57:18","modified_gmt":"2026-07-08T08:57:18","slug":"empty-miles","status":"publish","type":"post","link":"https:\/\/www.qargo.com\/post\/empty-miles\/","title":{"rendered":"Reducing empty miles: Why it\u2019s a data problem, not a driver problem"},"content":{"rendered":"\n

Empty miles, or empty running, refers to the distance travelled by a truck running without cargo. In practice, it\u2019s mileage that isn\u2019t directly carrying revenue-generating cargo \u2013 which means fuel, driver time and vehicle wear without a load on board.<\/p>\n\n\n\n

As much as transport operators try to avoid their fleet from getting into this position, it can be difficult to prevent it from happening. <\/p>\n\n\n\n

Often not because of any decisions made by the planning team or driver, but because there\u2019s simply not the right flow of data. The information they’d need to make better decisions either doesn’t exist, arrives too late, or lives in a system no one else can see. It’s a data problem that gets disguised as a human one.<\/p>\n\n\n\n

In this blog, we show how empty miles are not usually a human efficiency problem, but rather, a data visibility problem. <\/p>\n\n\n\n

How empty miles actually happen<\/h2>\n\n\n\n

DfT’s Domestic Road Freight Statistics<\/a> show that empty running rose to 31% in 2025 (5,897m of 18,975m total HGV kilometres run empty) in the UK.\u00a0<\/p>\n\n\n\n

The UK also compares unfavourably to the rest of Europe: one in three UK lorry kilometres is empty versus roughly one in four (25.9%) across the EU average.<\/p>\n\n\n\n

As you can see, it\u2019s a significant issue across the UK and EU. However, in most cases, empty miles are not caused by negligence.\u00a0<\/p>\n\n\n\n

A breakdown across various data points is often the more likely culprit.  <\/p>\n\n\n\n

Here\u2019s a common sequence of events that creates an empty leg: <\/p>\n\n\n\n

    \n
  1. Shipper confirms a load late<\/li>\n\n\n\n
  2. Carrier can\u2019t find a backhaul in time<\/li>\n\n\n\n
  3. Driver departs empty rather than waiting and returning late<\/li>\n<\/ol>\n\n\n\n

    It\u2019s really easy to look at this series of events and think \u201cwell, why couldn\u2019t the driver just find a backhaul.\u201d But, it\u2019s often a lot harder than it sounds. <\/p>\n\n\n\n

    Some transport operators may look at load boards to see if there\u2019s anything relevant that the driver could haul back. Load boards can help, but they only show what has been posted to the market \u2013 not the full picture of future demand, timing, compatibility or network fit. And because a large share of freight moves under contract, many loads never appear as spot opportunities in the first place.<\/p>\n\n\n\n

    Finally, a lack of real-time visibility means that carriers don\u2019t know where the trucks are in relation to available loads, making it difficult to confirm jobs before a competitor swoops in. <\/p>\n\n\n\n

    An incomplete flow of data is the bottleneck across all these scenarios, not the driver. <\/p>\n\n\n\n

    Where the data breaks down<\/h2>\n\n\n\n

    Data across the transport cycle is often disjointed and hidden. Here are some of the key areas where the flow of data breaks down: <\/p>\n\n\n\n