Orf 467 – Transportation Systems Analysis Fall 2007/8 11/9/2007Week 8 16 1 1 1 2 2 2 3 3 3 4 3 55...

11/9/2007 Week 8

Orf 467 – Transportation Systems Analysis Fall 2007/8

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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

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

Things change!

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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)

– Processing “Existing” Data• 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)

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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

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Cell Probe Technology

GSM

SampleObserverSampleSample

ObserverObserver

Cell

Cell

Cell

Directionof travel

GSMGSM

SampleObserverSampleSample

ObserverObserver

Cell

Cell

Cell

Directionof travel

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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

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Orf 467 – Transportation Systems Analysis Fall 2007/8Handset 49, part 1

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Orf 467 – Transportation Systems Analysis Fall 2007/8Handset 49, part6

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Orf 467 – Transportation Systems Analysis Fall 2007/8Handset 49, full trip

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Path-Finding Drive Tests

Handset 47Handset 47 Handset 52Handset 52

Handset 49Handset 49 GPS TrackGPS Track

(b)(a)

(c) (d)

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Baltimore MMTIS• Provides 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

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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

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11/9/2007 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

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I-695 at HARTFORD ROADMonday, June 6th 2005

9:33:06 am

CELL PROBES ACCURATELY UPDATETRAFFIC CONDITIONS AS CHANGES OCCUR

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

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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

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Analysis Route Overview

alon

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

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Performance data I-695 – July 2005

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Baltimore I-695 Weekday Patterns

DistanceTime

CongestionStatus

06:00

12:00

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Baltimore I-695 Saturday Patterns

Distance

CongestionStatus

06:00

12:00

Time

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Baltimore I-695 Route Travel Time

Journey Time (sec)

Time

Day of week

08:00

18:00May

June

July

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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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Travel time (seconds)

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

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Vehicle Probes

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

• Maintain travel times between strategically located virtual monuments

11/9/2007 Week 8

Orf 467 – Transportation Systems Analysis Fall 2007/8 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

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Median Speed (by direction) on National Highway Network 1:30pm 11/14/07

> 40 mph < 40 mph 1:30pm

11/14/07

height ~ speed

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Average Speed (by direction) on National Highway Network 1:30pm 11/14/07

> 40 mph < 40 mph

1:30pm 11/14/07

height ~ speed

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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

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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

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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

STATE

HW

Y 32

STATE HWY 2

STATE HWY 151US HW

Y 9

STATE HWY 378

2ND

ST

STATE HWY 155

WASHINGTON AVE

WINTER ST

15T

H S

T

STATE HWY 43

8TH

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STA

TE

HW

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77B

RO

AD

WAY

AV

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CAMPBELL AVE

ACCESS RD

1ST ST

STATE HWY 136

COUNTY HWY 130

TIBBITS AVE

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CO

LN A

VE

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MP

LOU

DO

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STATE HWY 43

RAMP

RA

MP

RA

MP

RAMP

RA

MP

HVCC

Rensselaer Technology

Park

TroyColonie

North Greenbush

Albany

Experiment Details

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Orf 467 – Transportation Systems Analysis Fall 2007/8 Basic Operational Architecture

Two-way cellular data communications between

Customized Live|Server

at ALK

Customized CoPilot|Live

In vehicles

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Destination

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Orf 467 – Transportation Systems Analysis Fall 2007/8 Every Second

CoPilot|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

11/9/2007 Week 8

Orf 467 – Transportation Systems Analysis Fall 2007/8 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

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When Available

ALK Server …Receives: Other congestion information from various

source, blends them in TT(mi, mj )

ALK Server Updates:

TT(mi, mj )

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Orf 467 – Transportation Systems Analysis Fall 2007/8 What We Heard

I 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.

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also Can Watch Vehicles

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Forecasting Travel Times Using Exponential Smoothig

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Historical Expectation: Concepts

•Patterns Differ over Days & Time of Day

•Most Significant Difference is Between Weekdays and Weekends

Zoo Interchange – Hale Interchange (All Days)

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Historical Expectation: Concepts

•Two Peak Periods

•Each appears to be Bell Shaped

•Afternoon Peak Period Appears to have “Extra Hump”

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Historical Expectation: Solution

estimatedbetoparametersareCKand

Where

CCCKtfTT

TimeTravelWeekday

iii

te

,,,2

1),(

:

),(),(),()(

22/2)(

2

333222111

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Historical Expectation: Application

Minimize the SSE between Historical Estimation Function and actual data points

nsobservatio

estimatenobservatioSSE 2)(

Downtown – Zoo Interchange

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Historical Expectation: Application

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Using Real-Time Information to Improve our Estimate

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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.

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Peak Hour Characteristics & Return to

Normalcy250

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Function

Real Data

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Series2

Series1

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

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•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(

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111

ISy

I

bSSb

bSIy

S

Ltt

tt

tttt

ttLt

tt

Single

Double

Triple

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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

t

where

tt

Forecast

tt

Smoothing

SS

SXS

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•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

t

where

cc

Forecast

Smoothing

SS

SXS

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0 10000 20000 30000 40000 50000 60000 70000 80000

Smoothing

Function

Real Data

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Smoothing

Function

Real Data

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1.0

0 10000 20000 30000 40000 50000 60000 70000 80000

Weights


Burleigh – Zoo (June 14)

11/9/2007 Week 8

Orf 467 – Transportation Systems Analysis Fall 2007/8 Including Real-Time Information:

Application

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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

Orf 467 – Transportation Systems Analysis Fall 2007/8 11/9/2007Week 8 16 1 1 1 2 2 2 3 3 3 4 3 55...

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Transcript of Orf 467 – Transportation Systems Analysis Fall 2007/8 11/9/2007Week 8 16 1 1 1 2 2 2 3 3 3 4 3 55...