The four principles are designed to act as a starting point for how cities can re-imagine and re-think their streets for pedestrians, bikes and transit.
Sidewalk Labs has published a document detailing four ‘principles’ which seek to serve as a starting point for how cities can completely ‘reimagine’ streets – and who they are for.
As inspiration, the urban innovation organisation points to the “complete streets” movement which has previously pushed forward important design solutions for cities to improve safety and sidewalk activity – while still making sure people can get where they need to go.
Cities like Boston and Toronto, for example, have published complete streets guidelines to promote design standards that allow pedestrians, bicycles, and public space to co-exist with cars safely.
In 2017, National Association of City Transportation Officials (Nacto) released its Blueprint for Autonomous Urbanism to “proactively guide the [self-driving car] technology to prioritise people-first design.”
But according to Willa Ng, mobility lead at Sidewalk Labs, writing in a blog post on Medium, there is an opportunity to push this critical thinking even further as cities are on the “cusp of a new era” where new technological capabilities are about to converge.
These include: autonomous vehicles, which can be programmed to follow speed limits or take certain routes; new mobility services like ride-hail and bike-share services, which reduce the need for parking space; and flexible infrastructure, which makes it possible to use street spaces in a variety of ways.
These advances have put cities in a position to completely re-imagine streets – and who they are for, said Ng.
In response, Sidewalk Labs has released a set of new street design principles to help cities make streets more efficient and safe, while giving space back to people. These are:
Principles 1 and 2 go hand in hand: different street types could prioritise different modes – and adjust their width and speed limit accordingly. So do principles 3 and 4: using technology to make lanes more flexible – and give space back to people whenever possible.
To illustrate these concepts, Sidewalk Labs has provided examples of how they might play out in practice on different street types.
On streets that prioritise pedestrians. It has called these streets laneways – they’re very narrow and limited to 4mph. Pedestrians would feel comfortable to stroll or linger here.
Streets that prioritise cyclists – accessways – are slightly wider and have a speed limit of 14mph; here, cyclists can travel naturally, without being hemmed into “safe zones”, since the majority of space on that street type is dedicated to cyclists’ use.
Another street type – called transitways – are wider still. They allow for all modes except cars and always give priority to public transit through dedicated lanes and signal priority.
"At rush hour, street space on a boulevard could be reserved for high person-throughput modes, such as transit"
Finally, streets that allow for all modes, including cars, are called boulevards. Consistent with “complete streets” principles, they include barriers and have a speed limit of 25mph.
Although cars would only be allowed on boulevards, emergency, disabled access, and connected autonomous vehicles would be allowed on all street types (more on that last point below).
These four street types, with their corresponding designs and speed limit interventions, would already go a long way to improving the safety and vibrancy of the public realm. But with new technologies, even more could be achieved.
Raised concrete curbs could be removed in favor of curbless, LED-embedded pavement that can – with the flick of a switch – signal a change in the number of lanes, the width of the “sidewalk,” or even the direction of the street. At rush hour, street space on a boulevard could be reserved for high person-throughput modes, such as transit.
At off-peak hours, or perhaps on a designated holiday, it could be easily converted into public space. With these dynamic curbs, boulevards could be narrower overall and still serve multiple uses, based on demand.
“Managing this new curbside demand would require a system that understands congestion patterns in real-time,” said Ng. “Low-cost sensors and machine-learning simulation models can together inform a mobility management system that adapts to new and predicted conditions by reallocating lanes, changing speed limits or pricing, or adjusting signal timings to keep all modes moving – and safe.”
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