Cycling in London

Jane Silber

on 12 September 2014

This article is more than 11 years old.


As the CEO of Canonical, I am proud of the growth of the team in London.  From a team of 5 around a kitchen table in London 10 years ago, the business has grown to 650 employees globally of which over 100 are based in London.

Like many businesses in London, one of the most popular modes of transport to the office is cycling and an even larger proportion of the team would cycle to the office if they felt it was safer than it is now.

We value employee satisfaction, health and freedom and firmly endorse the Mayor’s Vision for Cycling in London. We specifically support the cross London plans from City Hall to create new segregated routes through the heart of the city.

These plans are good for London and Londoners, making it a more attractive and productive city in which we can build a business and serve customers.

Proposed Farringdon Road route. Image from Transport For London 2014.

 

I encourage everyone to respond directly to TFL about these proposals. This particularly applies to businesses whose support for cycling is often not registered.

I know that there many business leaders like me who feel the same and will be speaking up over the coming days.

Talk to us today

Interested in running Ubuntu in your organisation?

Newsletter signup

Get the latest Ubuntu news and updates in your inbox.

By submitting this form, I confirm that I have read and agree to Canonical's Privacy Policy.

Related posts

Native integration available between Canonical LXD and HPE Alletra MP B10000

Native integration available between Canonical LXD and HPE Alletra MP B10000. The integration combines efficient open source virtualization with high...

Why you should retire your Microsoft Azure Consumption Commitment (MACC) with Ubuntu Pro

Fulfilling your Microsoft Azure Consumption Commitment (MACC) requires efficient planning. Discover how allocating your MACC to Ubuntu Pro allows you to meet...

How to launch a Deep Learning VM on Google Cloud

Setting up a local Deep Learning environment can be a headache. Between managing CUDA drivers, resolving Python library conflicts, and ensuring you have...