problem: how to get from a to b many paths each with a different value to the decision maker

WWS 527a – Transportation Policy and Planning Analysis Fall 2009/10

Week 8

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PROBLEM: How to get from A to B•Many Paths

•Each with a Different Value to the Decision Maker•Each Segment Changing with Uncertainty over Time

Addressing the Real-time Aspects In Turn-by-turn Navigation

4


Week 8

Link Travel TimesHistoric, Actual & Forecast During Day One week-day on one link

Things change!


Week 8

The Measurement Problem• How to collect the real time Speed Data?

– Incremental Infrastructure• In pavement loop detectors (single point)

• radar/laser/video signpost systems (single point)

• EZ Pass readers (2 point span measurement, Excellent)

– CrowdSourced Data• Map data: NYT article• Wireless Location Technology (Cellular Probes, see Fontaine, et al)

– Cell-tower trilateration» Yet to demonstrate sufficient accuracy

– Cell-handoff processing» maybe OK for simple networks

• Floating Car (Vehicle Probe) data processing (see Demers et al)


Week 8

Cell Probe Technology• Practical success requires more than cell phones• Cell phone movement based on cell location and “hand-offs”

from one cell to another• Pattern recognition techniques filter out data from those not

on the highway• Then traffic algorithms generate travel times and speeds on

roadway links• Cell phones need to be turned on, but not necessarily in use• Full regional systems in place in Baltimore, Antwerp, and Tel

Aviv = 4,600 miles, Shanghai


Week 8

Cell Probe Technology

GSM

SampleObserverSampleSample

ObserverObserver

Cell

Cell

Cell

Directionof travel

GSMGSM

SampleObserverSampleSample

ObserverObserver

Cell

Cell

Cell

Directionof travel


Week 8

Cell Probe Privacy

Speeds on road links

Personal cellular

position data

Estimotion sample

observer

Estimotion traffic

situation

ITIS publishing systems

Cellular network operator

Cellular network operator ITISITIS

Firewall

Speeds on road links

Personal cellular

position data

Estimotion sample

observer

Estimotion traffic

situation

ITIS publishing systems

Cellular network operator

Cellular network operator ITISITIS

Firewall


04/22/2311/9/2008 Week 8

Handset 49, part 1


04/22/2311/9/2008 Week 8

Handset 49, part 2


04/22/2311/9/2008 Week 8

Handset 49, part 3


04/22/2311/9/2008 Week 8

Handset 49, part 4


04/22/2311/9/2008 Week 8

Handset 49, part 5


04/22/2311/9/2008 Week 8

Handset 49, part6


04/22/2311/9/2008 Week 8

Handset 49, full trip


Week 8

Path-Finding Drive Tests

Handset 47Handset 47 Handset 52Handset 52

Handset 49Handset 49 GPS TrackGPS Track

(b)(a)

(c) (d)


Week 8

Baltimore MMTIS• At one point this was the first regional deployment of

commercial-quality cellular traffic probes in North America

• Mutually profitable public-private partnership– Test commercial markets during project– Integrate with existing public data – including transit and E-911– Encourage public applications beyond traditional ITS

• Contract signed September 2004; data flow to Maryland DOT began April 2005


Week 8

Baltimore MMTIS – Private Firms• Delcan-NET

– Transportation and technology consultants– Fifty plus years in business– Profitable every year; staff = 500 plus

• ITIS Holdings– Leader in traffic probes; staff = 100– Commercial customers – 16 automobile firms, for-profit 511– Profitable!– Publicly traded on London exchange

• National cellular firms (Verizon and AT&T)


04/22/2311/9/2008 Week 8


04/22/2311/9/2008 Week 8

MARYLAND DOT CAMERAS SHOW ACCURACY OF TRAFFIC INFORMATION BEING CAPTURED USING CELL PROBES

I-695 at HARTFORD ROADMonday, June 6th 2005

9:02:18 am


04/22/2311/9/2008 Week 8

I-695 at HARTFORD ROADMonday, June 6th 2005

9:33:06 am

CELL PROBES ACCURATELY UPDATETRAFFIC CONDITIONS AS CHANGES OCCUR


04/22/2311/9/2008 Week 8

Produced by Dr Hillel Bar Gerd, Associate Professor, Ben Gurion Negev University, Israel


Week 8

Baltimore Comparison with RTMS DataTraffic Situation reported by ITIS CFVD™ Technology and RTMS equipment

Baltimore I-695 @ I-70 Inner loop - Friday, August 12

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ITIS CFVD™ data RTMS data


Week 8

Analysis Route Overview

alon

This slide gives an overview of the Baltimore ring, and the north-western part which was analysed for this presentation


Week 8

Performance data I-695 – July 2005


Week 8

Baltimore I-695 Weekday Patterns

DistanceTime

CongestionStatus

06:00

12:00

19:00

24:00


Week 8

Baltimore I-695 Saturday Patterns

Distance

CongestionStatus

06:00

12:00

Time


Week 8

Baltimore I-695 Route Travel Time

Journey Time (sec)

Time

Day of week

08:00

18:00May

June

July


Week 8

Travel time comparisons over a common road sectionRoad section of 1.225 miles on I-695 Baltimore Beltway - junction 22 to 23

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04-Jul 11-Jul

04th July public holiday profile - no congestion throughout the day11th July normal Monday congestion profile - increased travel times at peak times


Week 8


04/22/2311/9/2008 Week 8


Week 8

What about Travel Time Variability?

• An excellent empirical study: Black, I, T. K. Chin “Forecasting Travel Time variability in Urban Areas”Rept # 0010-GD01017-TCF-02 Hyder Consulting (UK) Nov, 2007


Week 8

Vehicle Probes

• INRIX a current leader• Google traffic; crowdSourcing• Assign Speed data to network segments of Digital

Map database, or• Maintain travel times between strategically

located virtual monuments


Week 8

Vehicle Probes

• Assign Speed data to network segments of Digital Map database, or

• Maintain travel times between strategically located virtual monuments


Week 8

North American Monument Network• ~125,000 North American “Monuments”• ~106 (mi, mj)• Can create Median travel Tims by Time-of-Day

– For Example: AM Peak, Midday, PM Peak, Night, Weekend day

(mi, mj) near Troy (mi, mj) larger area


04/22/2311/9/2008 Week 8

Median Speed (by direction) on National Highway Network 2:30pm 11/16/09

> 40 mph < 40 mph 2:30pm

11/15/09

height ~ speed


04/22/2311/9/2008 Week 8

Average Speed (by direction) on National Highway Network 2:30pm 11/19/09

> 40 mph < 40 mph

2:30pm 11/15/09

height ~ speed


04/22/2311/9/2008 Week 8

Real-Time Dynamic Minimum ETA Sat/Nav

•250 Volunteers using CoPilot|Live commuting to/from RPI

• CoPilot continuously shares real-time probe-based traffic data

• CoPilot continuously seeks a minimum ETA route

“Advance” project Illinois Universities

Transportation Research Consortium

The late 90s

Conducted its version of the abandoned “ADVANCE” (Advanced Driver and Vehicle Advisory Navigation ConcEpt )project

&

Won ITS America’s 2007“Best Innovative Research” Award


04/22/2311/9/2008 Week 8

Project Objectives• Create: real-time data collection from vehicles

and dissemination to vehicles of congestion avoidance information which is used to automatically reroute drivers onto the fastest paths to their destinations

• Target locations: small to medium-sized urban areas

• Aspects: operations, observability, controllability, users, information transfer to travelers


04/22/2311/9/2008 Week 8

3-month field test

Capital District (Albany), NY, USA

Journey-to-work

200 participants80 Tech Park employees120 HVCC staff & students“Techy” travelers

Network:Freeways & signalized arterialsCongested linksPath choices exist

I 90

I 787

US

HW

Y 4

STAT

E HW

Y 32

STATE HWY 2

STATE HWY 151US HW

Y 9

STATE HWY 378

2ND

ST

STATE HWY 155

WASHINGTON AVE

WINTER ST

15TH

ST

STATE HWY 43

8TH

ST

STA

TE H

WY

377

BRO

ADW

AY A

VE

CAMPBELL AVE

ACCESS RD

1ST ST

STATE HWY 136

COUNTY HWY 130

TIBBITS AVE

LIN

CO

LN A

VER

AM

P

LOU

DO

N R

D

STATE HWY 43

RAMP

RAM

P

RA

MP

RAMP

RAM

P

HVCC

Rensselaer Technology

Park

TroyColonie

North Greenbush

Albany

Experiment Details


04/22/2311/9/2008 Week 8

Basic Operational ArchitectureTwo-way cellular data communications between

Customized Live|Server

at ALK

Customized CoPilot|Live

In vehicles

6

Destination

12, 4

3 57

8


04/22/2311/9/2008 Week 8

Every SecondCoPilot|Live Determines “Where am I”,

Then…

CoPilot|Live “Where Am I”,

Then…

ALK Server Updates:

TT(mi, mj )

If Momument, mj , is passed

Send mi , mj , ttk(mi, mj )= t(mi) - t(mi) (52 bytes)

Set i=j


04/22/2311/9/2008 Week 8

Every “n” Minutes

ALK Server Builds: set Uk

Sends: TT(mi, mj ) for every (i,j) in Uk

CoPilot|Live … Send… Current Location & Destination,

Last update time (42 bytes)

ALK Server …Send… New TT(mi, mj ) for every (i,j) in Uk

(280 bytes/100arcs)

CoPilot|Live …Updates TT(mi, mj ) in Uk , ETA on current route,

Finds new MinETA route, if MinETA “substantially” better then… Adopt new route

ALK Server …Determines Uk : set of TT(mi, mj ) within “bounding polygon”

of (Location;Destination)k that have changed more than “y%” since last update.

CoPilot|Live Sends: “Where am I”, Dest., Last update Receives/Posts: updatesComputes: MinETA Updates route, if better


04/22/2311/9/2008 Week 8

When Available

ALK Server …Receives: Other congestion information from various

source, blends them in TT(mi, mj )

ALK Server Updates:

TT(mi, mj )


04/22/2311/9/2008 Week 8

What We HeardI find it interesting how willing I am to listen to a machine tell me

which route to take

I like using it for when I have no idea on how to get somewhere, and it is good for my normal

route because it keeps me out of traffic on route 4.

It is great, it took a while to trust it telling me where to go, but i like it because i

cant get lost! Thanks.

This thing is awesome. I was a little skeptical at first but once i got the hang

of it I don’t know how I went along without it. I think any student commuting

to school will benefit from this.

I'm very impressed with the CoPilot program thus far. The

directions are accurate and it adapts quickly to route changes.


04/22/2311/9/2008 Week 8

1

2

3

also Can Watch Vehicles


04/22/2311/9/2008 Week 8

Forecasting Travel Times Using Exponential Smoothig


04/22/2311/9/2008 Week 8

Historical Expectation: Concepts

•Patterns Differ over Days & Time of Day

•Most Significant Difference is Between Weekdays and Weekends

Zoo Interchange – Hale Interchange (All Days)


04/22/2311/9/2008 Week 8

Historical Expectation: Concepts

•Two Peak Periods•Each appears to be Bell Shaped•Afternoon Peak Period Appears to have “Extra Hump”


04/22/2311/9/2008 Week 8

Historical Expectation: Solution

estimatedbetoparametersareCKand

Where

CCCKtfTT

TimeTravelWeekday

iii

te

,,,2

1),(

:

),(),(),()(

22/2)(

2

333222111


04/22/2311/9/2008 Week 8

Historical Expectation: Application

Minimize the SSE between Historical Estimation Function and actual data points

nsobservatioestimatenobservatioSSE 2)(

Downtown – Zoo Interchange


04/22/2311/9/2008 Week 8

Historical Expectation: Application


04/22/2311/9/2008 Week 8

Using Real-Time Information to Improve our Estimate


04/22/2311/9/2008 Week 8

Including Real-Time Information: Concepts

Real-Time Information“Since a desirable route needs to be given when the driver asks for it, but the computation of such a route requires travel times which occur later, we need to be able to forecast such travel times.”DEFINITION: A real-time travel time is a data

point that can be received or constructed and measures the time it takes to traverse a specific route from one location to another location ending now.


04/22/2311/9/2008 Week 8


Peak Hour Characteristics & Return to

Normalcy250

350

450

550

650

750

850

950

1050

0 10000 20000 30000 40000 50000 60000 70000 80000

Function

Real Data

580

680

780

880

980

1080

1180

1280

0 10000 20000 30000 40000 50000 60000 70000 80000

Series2Series1

During Peak Hours, Traffic Patterns Remain at a relatively constant distance to Historical Estimate

There will be a time at which traffic patterns will return to free flow conditions

Moorland - Downtown

Burleigh - Zoo


04/22/2311/9/2008 Week 8

•Method of “smoothing” a time series of observations•Most recent observations are given a high weight and previous observations are given lower weights that decrease exponentially with the age of the observation


Exponential Smoothing

310)1( 11

tSyS ttt

10)1(

10)1(

11

111

bSSbbSyStttt

tttt

10)1(

10)1(

10)1(

11

111

ISy

I

bSSb

bSIy

S

Ltt

tt

tttt

ttLt

tt

Single

Double

Triple


04/22/2311/9/2008 Week 8

Including Real-Time Information: Solution

•During Peak Periods:•Adaptation of Double Exponential Smoothing•Trend is the Trend of the Historical Estimate•Observation weighted with Most Recent Estimate + Slope for Smoothed Estimate •Forecast done by adding trend to most recent estimate

}1,0{ parameters smoothing ~ }{functionparameter 10 Estimated)(

:

)()(:

)()()1(:

11

111

n

nnnn

nnnnn

twhere

ttForecast

ttSmoothing

SS

SXS


04/22/2311/9/2008 Week 8


•During Non-Peak Periods•Adaptation of Double Exponential Smoothing•Trend is decay to free flow Conditions

}1,0{ parameters smoothing ~ c},,{3Chapter in estimatedfunction parameter 10)(

:

)0()1(:

)0()1()1(:

1

11

n

nn

nnn

twhere

ccForecast

Smoothing

SS

SXS


04/22/2311/9/2008 Week 8

250

450

650

850

1050

1250

1450

0 10000 20000 30000 40000 50000 60000 70000 80000

Smoothing

Function

Real Data

250

450

650

850

1050

1250

1450

0 10000 20000 30000 40000 50000 60000 70000 80000

Smoothing

Function

Real Data

0.0

0.2

0.4

0.6

0.8

1.0

0 10000 20000 30000 40000 50000 60000 70000 80000

Weights


Burleigh – Zoo (June 14)


04/22/2311/9/2008 Week 8

Including Real-Time Information: Application


04/22/2311/9/2008 Week 8

THETA 0.3000 Time Step 0:03:00

PHE 0.8000

CAI 0.5000

C 0.85

Progression Through Sample Day: Moorland – DowntownJune 14, 2002

problem: how to get from a to b many paths each with a different value to the decision maker

Documents