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Study identifies top US cities for driverless vehicles

As cities prepare for autonomous vehicles, planners need to prioritise deployment strategies to maximise potential benefits, report finds

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The heatmap showing the city of Austin (left) and downtown Austin (right)
The heatmap showing the city of Austin (left) and downtown Austin (right)
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Cities must prioritise deployment strategies as they prepare for autonomous vehicles

“Big data is a powerful tool that should be used as cities explore highly autonomous vehicles"

Transportation analytics firm Inrix has developed a scalable and customisable scoring system to prioritise ‘corridors’ for highly autonomous vehicles (HAV) deployment.

 

To see what urban areas could have the greatest proportion of vehicle travel replaced by HAVs, transportation analytics firm Inrix looked at one year’s worth of travel – nearly 1.3 billion trips – in and around the top 50 US cities by population density.

 

Leveraging aggregated trip data from millions of connected cars, parking availability and restrictions, and census demographic data, Inrix created a scalable and customisable scoring system to analyse and visualise priority corridors for HAV deployment.

 

Trips that began and ended within a 25-mile radius of each downtown were analysed and compared to aggregate regional trips (including outbound, inbound, and passing-through trips) to establish a percentage of intra-city travel. Inrix then looked at the percentage of a city’s intra-city trips, 10 miles or less, and combined these two metrics to score each city out of a possible 100 points.

 

New Orleans came out on top with a ‘city score’ of 90.33 out of a possible 100 points followed by Albuquerque, Tucson, Portland, Omaha, El Paso, Fresno, Wichita, Las Vegas, and Tulsa at 88.09.

 

As cities prepare for autonomous vehicles, planners need to prioritise deployment strategies to maximise potential benefits, the report stated. The mobility goals of cities vary, such as reducing congestion and emissions, expanding mobility to underserved and lower income populations, and reducing cost-intensive capital infrastructure improvements.

 

“Big data is a powerful tool that should be used as cities explore HAVs, and mobility data and analytics are more powerful when multiple layers are added into the equation,” said Bob Pishue, transportation analyst at Inrix.

 

“Using data-driven insights to inform public planning will allow city officials to proactively leverage HAVs to solve key mobility and societal challenges while mitigating potentially-negative side effects of this technology.”

 

To demonstrate a city’s readiness for highly autonomous vehicles, Inrix ran an analysis and constructed heat maps to score and visualise results for Austin, Texas.

 

Austin was chosen as it has established itself as a hub for HAV research and testing, including its place as one of the four cities where Google’s self-driving car project, recently renamed Waymo, is testing on public roads.

 

The two maps shown visualise millions of data points on travel patterns, parking scenarios and demographics, with darker shading denoting an area likely to benefit most from HAVs. The map on the left includes trip and demographics data only. The map on the right adds parking utilisation.

 

Looking at the full city map for Austin there is a clear concentration of high-scoring census block groups in and around downtown, where unsurprisingly the concentration of origin and destination trips per zone are high. However, there are several areas outside of downtown that would be worth further exploration for HAV deployment.

 

This includes Spicewood Springs Road from the North Capital of Texas Highway toward Crestview (Block Group Chain 1), and Rosedale, just north of Central Austin (Block Group Chain 2).

 

A closer look at the downtown civic district at the census block level, accounting for parking utilisation and supply, yields several areas (highlighted in yellow) where corridors of high-scoring zones are well positioned to benefit from HAVs.

 

This includes the southern boundary of the University of Texas Austin (Block Chain 1); West 15th Street (Block Chain 2); 11th Street south of the State Capitol (Block Chain 3); 5th and 6th Streets (Block Chain 4); and along the Colorado River in Downtown (Block Chain 5). As Austin city planners continue to prepare for HAV deployment for public use, leveraging big data will help to identify target corridors to maximise benefits for the city and its residents.

 

 

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