LinkedIn respects your privacy

LinkedIn and 3rd parties use essential and non-essential cookies to provide, secure, analyze and improve our Services, and to show you relevant ads (including professional and job ads) on and off LinkedIn. Learn more in our Cookie Policy.

Select Accept to consent or Reject to decline non-essential cookies for this use. You can update your choices at any time in your settings.

A new take on mixed mode traffic data collection that I thought you'd be interested in

Phil Silver

IoT & Robotics for Government l Navy Veteran (opinions expressed are my own)

Published Jul 6, 2016

+ Follow

Over the past few weeks I've had several conversations regarding advances in AI, IoT, and analytics in the context of urban transportation and smart cities, and thought that there might be some renewed interest in this post.   Enjoy!

While at Cubic I worked to find ways to use advanced analytics to understand the impact of pedestrians, cyclists, drivers and passengers on public transport use, road congestion and economic activity. The goal was to be able to manage intersection activity so that all road, sidewalk and cycle path users could share these mobility assets in safer, cheaper, faster and smarter ways.

The problem was not the lack of data. It was collecting relevant and accurate mixed traffic active transportation data from multiple sources that could be used to create reliable and useful inferences for planning, investment, safety, signal management and reporting.

I thought that you might be interested in this short presentation on Making Mixed Mode Traffic Collection Easier. It has been designed to address elements on this topic that you may already be aware of as well as hopefully introduce some new concepts that you may find worthy of further exploration.

Slide summary:

2-5: The goals and challenges associated with gathering appropriate, reliable, actionable data across multiple modes in order to build more accessible, safe and efficient transportation networks.
6: A visual depiction of the active transportation monitoring environment and tools to understand it.
7: The lost opportunity, costs, and risks associated with a disjointed active transportation monitoring and data collection strategy.
8: A new approach to collecting and using active transportation data to improve transportation network performance, safety and efficiency.
9-10: RoadSys’ suite of integrated mixed traffic real-time monitoring and reporting tools and example deployment.
11-12: Case study examples.
13: Contact details and on-line references of interest.

If some element of the material covered here speaks to you please share this post and reach out to Dennis LeBlanc at 480-289-0081 or dennis@roadsysllc.com to learn more.

Ken Kaplan

Proper assimilation of such data would be huge in terms of public safety, faster commutes, better targeted infrastructure spending, etc. & would have far ranging benefits.

Paolo Guarnieri

Interesting

Daniel Collins

Phil, a very precise presentation that talks about a problem you and I and others have wrestled with around the world. Even in a city that wants to solve these problems, the IOT end of this type of pursuit still leaves a lot of room for improvement. It's getting there though, as your last few slides imply.

Tom Webster

A key challenge for multi modal data is it is all too easy to miss particular traffic doing odd things and slipping through the gaps in sensors placed in fixed positions, e.g. cycles cycling in the traffic lanes rather than in the cycle lane. As this can often be exactly the data that the engineer/planner needs to take notice of - e.g. vehicles parked in cycle lane, overcrowded cycle lane. I am wondering what the latest advances in video counting are as this appears to be the way forward, there are now often high resolution cameras covering the required areas in city centres, clever enough software could pick up and classify all the traffic data and realtime this to common database, the data can conveniently be cross referenced against the video feed for validation, and there is no need to install and maintain equipment in the carriageway surface.

Greg Yova

Phil the concept seems clear. The issue as always is getting those who hold the purse strings to take the long view. The assessment of the cost of "not fixing it" is something that is a very tough sell even though it is spot on. It requires a visionary in management/leadership to understand the complex association of all the parts and the whole. But don't give up, these efforts are going to be part of bringing some order and flow back to cities.

Iain Blackmore

Absolutely relevant for multi modal cities. Throw in some bus data plus some train info for main stations and you start to get a live or even predictive picture of the whole network.

Richard Jasper

Very interesting and necessary situation to study. Especially with growing populations and increased use and public availability of cycles.

See more comments

LinkedIn respects your privacy

A new take on mixed mode traffic data collection that I thought you'd be interested in

Phil Silver

IoT & Robotics for Government l Navy Veteran (opinions expressed are my own)

More articles by this author

Others also viewed

5 Secrets To 10,000 Connections On LinkedIn

Today I reached 10,000 Connections, what does that really mean?

Thank You LinkedIn for Over 15,000 Connections.

TRICKY - HOW TO CELEBRATE MEN'S DAY IN OFFICE

Verified Chaos

Explore topics

An Open Thank You to My 10000 LinkedIn Connections and Followers

Feb 14, 2018

Tapping big data to fuel smart city development

Mar 5, 2016

Urbanomics Mobility Project: Opportunity to leverage transit analytics capabilities with MasterCard’s consumer reporting

Sep 14, 2015