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Measuring multimodal transportation

It has also partnered with Inrix and announced that the Arizona Department of Transportation has selected its traffic monitoring and managing services 

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Initiative covers all trips including those made by delivery drivers and commercial trucks
Initiative covers all trips including those made by delivery drivers and commercial trucks

Mobility analytics company, StreetLight Data, has announced a new scheme to assess the way all modes of travel interact.

 

According to the company, which aims to bring “real-world travel patterns to light”, as transportation behaviour becomes more complex, analytics that focus on one mode alone are no longer sufficient.

 

Its Multimodal Measurement Initiative (M2Initiative) will factor in trips made by personal vehicles, public transit, walking, biking, commercial trucks, and even “gig economy” trips made by on-demand ride-share and delivery drivers.

 

As part of the M2Initiative, StreetLight Data is building a working group to collaborate on the issue of multimodal travel interactions.

 

The company said it is already engaged with select early-adopter partners and clients to deliver analytics for new modes. The M2Initiative partners include providers of mode-specific data sets, public agencies which want to lead in measuring new modes, academic institutions and private transportation firms which are exploring new business models.

 

“Interaction is what we all truly care about,” said Laura Schewel, CEO of StreetLight Data. “As a timely example, not many people care about electric scooters in the abstract. We care if they have a positive or negative impact on driving, pedestrian activity, or transit ridership.

 

“As we move forward into a world where you might expect to take six modes of transportation in a single day, and where new modes, including autonomous vehicles and aerial urban travel are on the horizon, we must measure all of these new variables separately and together, in order to effectively manage them.”

 

In the first iteration of the M2Initiative, StreetLight focused on bicycle, pedestrian, bus, train, and “gig economy” driving (which covers ride-hailing apps like Uber and Lyft, plus delivery applications like GrubHub and Postmates). It has already delivered several editions of these analytics to pilot clients.

 

StreetLight reports that it is accelerating the development of new analytics that describe the mobility behaviour of each of these transportation modes individually. It is also “prioritising” the creation of new tools that describe how these modes interact.

 

“We must ensure that we maximise the social and environmental gains that can come from all of these [transit] modes, but not fall prey to the potential dangers – dangers that can be exacerbated if we treat modes as unrelated,” added Schewel.

 

StreetLight Data said it is accepting a limited number of additional partners and is actively seeking organisations that can contribute calibration or validation data.

 

In a separate announcement, StreetLight Data has partnered with Inrix and that the Arizona Department of Transportation (ADOT) has selected its traffic services to monitor, measure and manage the state’s road network.

 

StreetLight Data will enable ADOT to transform Inrix and other geospatial data into travel pattern analytics for state-wide modelling. Arizona DOT will have greater insight into road performance and problem areas, enabling more intelligent infrastructure planning and transportation investments.

 

If you like this, you might be interested in reading the following:

 

Shining a light on mobility behaviour

StreetLight Data is on a mission to use big data to highlight real-world traffic patterns and make cities better places to live and work, by Sue Weekes

Read more

 

Tapping into StreetLight data

MnDOT will use StreetLight data to bring new traffic intelligence such as origin-destination and select link studies

Read more

 

Ohio DOT strengthens mobility intelligence

StreetLight Data will enable ODOT to transform Inrix data into actionable intelligence

Read more

 

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