The Use of IPASS to Improve The Use of IPASS to Improve Mobility and Safety Through Mobility and Safety Through

Traveler InformationTraveler InformationApril 2004April 2004

BackgroundBackground

• The Illinois State Toll Highway Authority (ISTHA) has 274 Miles of Toll Highways

• 24 Mainline and 43 Entrance / Exit Ramp Plazas

• IPASS Automatic Toll Collection

• Over 2 million transactions per day 50% of which are IPASS

• Incident Management• Fully Integrated 2 Way CAD • CCTV• DMS

• Incident Response Plans• Travel Times• Safety Messages

• Weather Data• Queue Detection• Center to Center Interface• Open Architecture • UNIX Servers / PC Workstations• IPASS Travel Times

Traffic & Incident Management System (TIMS)Traffic & Incident Management System (TIMS) 2004 Best of ITS Public Safety Award Winner2004 Best of ITS Public Safety Award Winner

Travel TimesTravel Times

Point Travel Time AlgorithmsPoint Travel Time Algorithms

ExtrapolationTT = L/S

Detector Station (s)Provides

Speed Data

InterpolationTT = 2L/(S1+S2)

IPASS Travel Time DataIPASS Travel Time Data

• When a vehicle arrives at a Plaza the following origin and destination data are transmitted to the TIMS

• Date

• Time

• Plaza ID

• Lane

IPASS Basic TT AlgorithmIPASS Basic TT Algorithm

Traffic Flow

A B C D E FRequired Travel Time LinksAB {AE-BE, AF-BF, %AF}

BC {%BE, %BF, %AF}CD {%BE, %BF, %AF}DE {%BE, %BF, %AF)

EF {BF-BE, AF-AE, %AF}

TDLsAEAFBEBF TollTag Reader

AFAEBEBFAB

182

RAWTOLLTAG

READS

PARSEDTOLLTAG

READS

TDL 001 TDL 002 TDL N

VDS 1000 VDS MVDS 1003VDS 1002VDS 1001

IPASSSERVER

MAP GUIs GCMDMS

CongestionManagement

Data FlowData Flow

Lessons LearnedLessons Learned

Traffic Flow

A B C D E FOriginal Travel Time EqsAB {AE-BE, AF-BF, %AF}

BC {%BE, %BF, %AF}CD {%BE, %BF, %AF}DE {%BE, %BF, %AF)

EF {BF-BE, AF-AE, %AF}

TDLsAEAFBEBF

EF

Revised Link EquationsWhen two TDLS are usedVi = num Tag Reads TDLiSi = Speed TDLiAvg Speed = V1S1+V2S2/(V1+V2)TT = SegLen/(Avg Speed)

Lessons LearnedLessons Learned

Standard Deviation Method All Data Near Minimum Method

Algorithm ValidationAlgorithm Validation

• Floating Car Runs Throughout the System during Peak and Off Peak Periods

• Overall - Expected Results were Closely Correlated to Actual

5 6 7 8 9 10 11

Time of Day

20

30

40

50

60

70

Spe

ed ActualMeasured

Theoretical Tag Speed Measurement Lagging

Validation ResultsValidation Results

• The calculated travel times for long sections of roadway are less accurate during times of unstable flow.

• The availability of data for the segment travel times vary significantly.

• The impact that variable delays at exit ramp plazas have on computations can be significant.

• The sample size for computations and the number of actual tag reads affect the overall quality of the data.

Travel Time UsagesTravel Time Usages

User DialogsUser Dialogs

GCM Travel Web PageGCM Travel Web Page

DMSDMS

ConclusionsConclusions

• PROS• Uses Existing Infrastructure

• Actual data is very accurate

• Over one million daily tag reads

• CONS• Existing Infrastructure

Optimized for Toll Collections not Traffic Data

• Where plazas are widely separated (up to 30 miles) TT can lag significantly

• Only 25% pass the tests for validity

Future EffortsFuture Efforts

• Addition IPASS Readers are planned at the endpoints in conjunction with IDOT

• 34 new DMS are being added• Additional IPASS Readers are being planed at more

frequent intervals to further improve data resolution • AVL for maintenance, police, and money trucks may be

added • Currently through a Federally Funded Program RTMS

data is being added in the urban areas to improve data resolution and to add a new incident detection algorithm