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Information Technologies &
Evolving Transportation Services
University of Colorado
Denver
ITE Western District Meeting
July 21, 2015
Alejandro Henao PhD Student
Department of Civil Engineering
AGENDA
What to expect
• Motivation
• Information Technologies
• Evolving Transportation
– Terminology
– Impacts
• Travel Modeling
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
MOTIVATION
Journey: Home to Work (& Back)
In Reality:
25 – 60 minutes (each way)!
MOTIVATION
Journey: Home to Work (& Back)
MOTIVATION
Journey: Home to Work (& Back)
+
Drive + Transit
~ 44 minutes
MOTIVATION
INTERMODAL TRIP:
Travel Information Evolution
What about Transportation & Equity?
Specific Populations:
• Low-Income
• Minorities
• The elderly
• People with disabilities
• Behind in Technology
Student to Teacher:
“That’s not a cellphone, that’s a remote control”
Smartphone Apps
Information Technology:
• Google Maps
• MapQuest
• Apple Maps
• Waze
• OpenStreetMap
• RideScout
• Moovit, Transit App
Information Technologies
INFO TECHNOLOGIES
• 0 minutes for searching for parking, parking, paying, and walking to final destination?
• Is there a bias for driving?
Information Technologies
Hypothesis 1: Ho: Real Time = Google Time Estimate Ha: Real Time ≠ Google Time Estimate
INFO TECHNOLOGIES
Hypothesis 1: Ho: Real Time = Google Time Estimate Ha: Real Time ≠ Google Time Estimate
INFO TECHNOLOGIES
Google Maps vs. Actual Times
n=44 Time Difference: 3.84 mins. s.d. = 2.89 t-stat (paired): 3.52E-11 Reject Null Hypothesis. Ha: Actual Time ≠ Google Maps
INFO TECHNOLOGIES
18 minutes 14 minutes VERSUS
Information Technologies
Transport Options
Traditionally:
• Driving
• Transit
• Walking
• Biking
Evolving Transportation
• Bikesharing
• Carsharing
• Ridesharing
• Ridesourcing
• Taxi Hailing
• Ridesourcing + Ridesharing
• “Microtransit”
EVOLVING TRANSPORT
Large-scale public bike sharing systems with:
• Electronic card
• Stations to check out and return (depend upon availability)
• Short period of time
Bikesharing
EVOLVING TRANSPORT
ROUND-TRIP CARSHARING:
• Must return the vehicle to the same location that it was accessed
Examples:
Carsharing
EVOLVING TRANSPORT
ONE-WAY CARSHARING:
• Allows users to return the vehicle at a different location than the starting point.
Example:
Carsharing
EVOLVING TRANSPORT
PEER-TO-PEER CARSHARING:
• Vehicles are owned by private individuals making them available for use by others and receiving payment when booked.
Example:
Carsharing
EVOLVING TRANSPORT
Ridesharing is a common trip taking by two or more individuals in a single vehicle.
• Carpooling, vanpooling, slugging.
• Drivers do not intend to make financial profit
Examples:
• Zimride, Carma, RideJoy, Bandwagon,
City & Regional Programs
Ridesharing
EVOLVING TRANSPORT
Sourcing of rides from a ‘for-fare’ driver pool accessible through an app-based platform.
Other names:
“Transportation Network Companies (TNCs)”, “ride-hauling”, “ride-booking”, “ride-matching”, “on-demand-rides”, “app-based rides”
Examples:
• Lyft, Uber, Sidecar
Ridesourcing
EVOLVING TRANSPORT
Ridesourcing
RIDESOURCING
≠
RIDESHARING
Associated Press
EVOLVING TRANSPORT
Examples:
• Curb, Flywheel
Taxi Hailing
EVOLVING TRANSPORT
Ridesourcing + Ridesharing
• LyftLine: NYC, San Francisco, L.A.
• UberPool: NYC, San Francisco, Paris, L.A.
• Cab Corner: NYC
• Via: NYC
• Split: Washington D.C.
EVOLVING TRANSPORT
Ridesourcing + Ridesharing
“Microstransit”
• Bridj: Boston, Washington D.C.
• Chariot: San Francisco
• Leap: San Francisco.
– Regulatory Issue right now.
EVOLVING TRANSPORT
Impacts
• How do they compete and interact with more traditional modes?
• Why people shifted from a previous mode?
EVOLVING TRANSPORT
Transportation & Equity:
• Low-Income
• Minorities
• The elderly
• People with disabilities
• Behind in Technology
Impacts
Impacts
• New Trips (i.e. induced travel)
• Multimodality
• Intermodality (i.e. mixed-modes)
• What do cities actually gain (or lose)?
– Mode share, VMT, congestion
EVOLVING TRANSPORT
Door-to-door Travel Model
IMPROVEMENTS • More accurate times. • Add missing technology-
based options. • Intermodal. • Custom Specific.
GOALS • MAKE SMARTER DECISIONS! • SHIFT FROM DRIVING TO MORE SUSTAINABLE MODES OF TRANSPORTATION! • TECHNOLOGY OPPORTUNITIES FOR CITIES AND TRANSIT AGENCIES!
TRAVEL MODELING
Information Technologies &
Evolving Transportation Services
University of Colorado
Denver
Alejandro Henao PhD Student
Department of Civil Engineering
Contact:
ITE Western District Meeting
July 21, 2015