njfuture redevelopment forum 2015 bottigheimer
TRANSCRIPT
Big Data and Innovations in Transportation Planning
Nat Bottigheimer
NJ FutureRedevelopment Forum
March 13th, 2015
Overview
• Private sector players innovating rapidly• Public agencies aren’t evolving as fast, aren’t yet
taking maximum advantage • The reasons to leverage data and analysis tools
isn’t only “to have better data…”… It’s also to have better debates
• Data and technology tools can make analysis more grounded in the actual and less distracted by hopes, dreams, wants, fears, interests, etc.
Better Framed Debates
• Data to frame conversations accurately– Is our traffic through traffic or internally
generated?– Implication: if we want to reduce traffic impacts,
should be controlling their behavior or our behavior?
• Avoid debates between conflicting ideals (like buses or bikes versus cars), and develop targeted solutions that generate the most benefit for the least cost– Accurately identify sources of delay
Fulton Reliability Analysis
Fulton Reliability Analysis
Who’s Responsible for This?
• In many communities, the “outside” sources of traffic are often demonized…
…when often that traffic is local
• Getting the data right is the difference between ineffective approaches targeting outsiders versus “impactful” approaches applied to insiders
Who’s Driving on My Local Streets?
Who’s Driving in My County?
Where Should that Arena Go?
Where to Build a Bridge?
Other Potential Applications16th Street Corridor in Washington, DC
N
Data Source Examples
Miovision
StreetLightData
Mygistics
TomTom
INRIX
Navteq
AirSage
CitySourced
PTV/NuStats
ESRI
Technical Questions
• Route taken versus trip origin– Study purpose (funding responsibility? Making a
physical improvement? Measuring VMT/GHG?)– Cell phone (towers) versus GPS
• Sizes of grids that generate data– How precisely do you need to know origins and
destinations? Elementary school? HS? Arena?• Figuring out which mode is generating data
– Speed and route characteristics • Imputing trip purpose
• Commuting to work or going home to sleep? • Work-arounds, post-processing often needed
Some Pros and Cons of Big Data
• Traditional data is Irregularly Available, Expensive, and Often Sampled
• Big Data is Fast, Comprehensive, Frequent, and Relatively Cheap– But sometimes not exactly want you want…– Work-arounds, post-processing, “key
assumptions” often required• Sometimes sampled data is more statistically
accurate, but samples also can lack “real-ness”
Concluding Thoughts
• Data and technology can reframe debates helpfully for smart growth…but can also be uncomfortable– Smart-driving and Connected Vehicles may
increase roadway and intersection capacities• Less radical & more outpatient surgery• Target real issues instead of made-up,
conflictual issues• Focus on operational improvements, not just
planning…build support for more projects.
