Smart Infrastructure for Urban Mobility Stephen F. Smith TRAFFIC - - PowerPoint PPT Presentation

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Smart Infrastructure for Urban Mobility Stephen F. Smith TRAFFIC - - PowerPoint PPT Presentation

Technologies for Safe and Efficient Transportation Smart Infrastructure for Urban Mobility Stephen F. Smith TRAFFIC CONGESTION costs $121 billion per year in the US produces 56 billion pounds of CO 2 annually 40% 40% of of time ime spent


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SLIDE 1

Smart Infrastructure for Urban Mobility

Stephen F. Smith

Technologies for Safe and Efficient Transportation

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SLIDE 2

Technologies for Safe and Efficient Transportation

TRAFFIC CONGESTION

costs $121 billion per year in the US produces 56 billion pounds of CO2 annually 40% 40% of

  • f time

ime spent pent on

  • n sur

urface ace street eets in in ur urban ban ar areas eas is is spent pent idling idling

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SLIDE 3

Technologies for Safe and Efficient Transportation

POORLY TIMED SIGNALS

are one big reason why

  • Those signal systems with adaptive

capabilities have inherent limitations

– Only detect and respond to traffic that is already stopped and waiting – Unable to respond to real-time events – Are designed strictly for arterials

  • Vast majority of intersections run fixed plans

– Designed for average traffic conditions – Begin “aging” the moment they are installed

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SLIDE 4

Technologies for Safe and Efficient Transportation

Goal: Real-'me op'miza'on of traffic

flows for urban (grid) road networks

Technical Approach:

– Decentralized control – Coordinated ac/on

Research Progress

  • 2010-11: Development of core

approach

  • 2011-12: East Liberty pilot deployment
  • 2013-15: Expansion of pilot test site
  • 2015: Integra'on with connected

vehicle technology Benefits

  • True real-'me response
  • Mul'ple dominant flows
  • Scalable, incremental

deployment

  • Mul'-modal op'miza'on

www.surtrac.net

Penn Ave./Penn Circle

Scalable Urban Traffic Control

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SLIDE 5

Technologies for Safe and Efficient Transportation

Signal Control as a Distributed Online Planning Problem

Intersec'on Scheduler

  • 1. Current traffic condi'ons are

extracted from sensor data streams Controller

  • 2. System computes phase

schedule that op'mizes flow at intersec'on and sends commands to the controller when it is 'me to change phases

Controller

Intersec'on Scheduler

Sensors

  • 3. Schedule is communicated

to downstream neighbors to indicate what is coming

  • 4. Rolling Horizon:

Scheduling cycle is repeated every few seconds

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SLIDE 6

Technologies for Safe and Efficient Transportation

Surtrac in East Liberty

Penn Circle Test Site (Jun 2012):

% Improv. Travel Time # of Stops Wait Time Emissions AM rush 30% 29% 48% 24% Mid Day 33% 53% 50% 29% PM rush 23% 9% 36% 18% Evening 18% 35% 28% 14% Overall 26% 31% 41% 21%

Bakery Square Expansion (Nov 2013):

% Improv. Travel Time # of Stops Wait Time Emissions AM rush 17% 34% 33% 16% Mid Day 21% 37% 38% 18% PM rush 29% 45% 46% 25% Overall 24% 40% 42% 21%

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SLIDE 7

Technologies for Safe and Efficient Transportation

Baum Boulevard C e n t r e A v e n u e Penn Avenue Highland Ave Fifth Avenue

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SLIDE 8

Technologies for Safe and Efficient Transportation

Pittsburgh Smart City Challenge Vision

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SLIDE 9

Technologies for Safe and Efficient Transportation

Integration with Connected Vehicle Technology

B a u m B

  • u

l e v a r d C e n t r e A v e n u e Penn Avenue Highland Ave F i f t h A v e n u e

24 intersec=ons are equipped with DSRC Radio Communica=ons

  • Better sensing
  • Use of mode, route info.
  • Incident detection and

real-time re-routing

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SLIDE 10

Technologies for Safe and Efficient Transportation

Sharing Vehicle Route Information

Basic Concept

– Connected Vehicle (CV) shares its route with the network – Intersections incorporate this

  • info. into local optimization

Initial Results

– Average delay is significantly improved – CVs receive immediate benefit – Little adverse effect and overall flow efficiency improves with level of penetration

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SLIDE 11

Technologies for Safe and Efficient Transportation

The Longer View

  • As CV technology emerges the signal

network will provide the gateway to real- time control information

  • AI planning and machine learning

techniques will be a key enabler