Travel Time Estimation on Arterial Streets
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Travel Time Estimation on Arterial Streets By Heng Wang, Transportation Analyst Houston-Galveston Area Council Dr. Antoine G Hobeika, Professor Virginia Tech. Outline. Objective and background Focusing methodology development Methodology validation Conclusion and future study Q & A.

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Travel Time Estimation on Arterial Streets

By

Heng Wang, Transportation Analyst Houston-Galveston Area Council

Dr. Antoine G Hobeika, Professor Virginia Tech


Outline
Outline

  • Objective and background

  • Focusing methodology development

  • Methodology validation

  • Conclusion and future study

  • Q & A


Objective
Objective

Methodologies were prepared for the proposal for real-time travel time estimation on major arterial streets.

Requirements:

  • Short time interval update for real-time estimation

  • Simple-computation time

  • Make good use of real time detected traffic information

  • Well behaved


About the methodology
About the Methodology

The developed methodology is presented into two sections:

1. Travel time estimation on an isolated arterial link;

2. Travel time estimation on a signalized arterial link that also considers the traffic situation on the upstream and downstream links(Network Algorithms).


Section 1 travel time estimation on an isolated arterial link travel time components
Section 1- Travel time estimation on an isolated arterial link --Travel Time Components

  • Travel time(HCM)=link travel time + intersection control delay

  • Components of intersection control delay:

    1) Uniform delay

    2) Incremental delay (over-saturation delay)

    3) Initial delay


Intersection control delay hcm2000 and its weakness in short time period update situation
Intersection Control Delay (HCM2000) and its weakness in short time period update situation

  • Uniform Delay:

  • Incremental Delay:

  • Initial Delay:


Developed algorithms intersection control delay observed vehicle group identification
Developed Algorithms--Intersection Control Delay short time period update situation-Observed Vehicle Group Identification


Developed intersection control delay algorithms
Developed Intersection Control Delay Algorithms short time period update situation

  • Case 1-where there is no initial queue for the observed vehicle group;

  • Case 2-there is an initial queue for the observed vehicle group and its clearance time is less than a cycle length;

  • Case 3- where initial queue clearance time (d3) is greater than a cycle length.


Intersection control delay case 1 no initial queue
Intersection Control Delay short time period update situation- Case 1 no initial queue


Intersection Control Delay short time period update situation-Case 2 an initial queue exists and it is smaller than one cycle length( 0<d3<CL)

g1=d3-r

situation


Intersection Control Delay short time period update situation-Case 3 -Initial Queue clearance time d3 is greater than one cycle length (d3>CL)


Validation of intersection control delay algorithms
Validation of Intersection Control Delay Algorithms short time period update situation

An intersection at N Franklin St/Peppers Ferry RD in Christiansburg, Virginia was selected to initially conduct control delay analyses based on traffic volume and the arrival of vehicles in the observed group.


Validation of intersection control delay algorithms1
Validation of Intersection Control Delay Algorithms short time period update situation

MAE for developed algorithm result with real control delay: 10.85sec

MAE for HCM2000 algorithm result with real control delay:14.28sec


Validation of intersection control delay algorithms2
Validation of Intersection Control Delay Algorithms short time period update situation

ANOVA Table for Actual Delay vs HCM2000 results

ANOVA Table for Actual Delay vs Developed Algorithm results


Total travel time computation
Total Travel Time Computation short time period update situation

  • Travel Time Without initial Queue:

  • Travel time with an initial queue but without blackout:

  • Travel time with blackout (i.e. QL> LTD) :


Section 2 network algorithms
Section 2- Network Algorithms short time period update situation

Network conditions that influence input parameters:

  • Bottleneck on the downstream link:

    Change intersection capacity;

  • Blackout Situation:

    Change the identification of the observed vehicle group.


Algorithm 1(No blackout) short time period update situation

Is departing rate from link i smaller

than downstream link’s capacity?

Yes

No

Use intersection capacity of link i

Use downstream lane capacity as

the intersection capacity of link i


Algorithm 2(Determining the intersection capacity of link i when blackout is on the downstream link i+1)

Is Li+1 -QLi+1<100ft?

(High congestion downstream?)

Yes

No

Use the detected flow rate from

downstream detector as

the intersection capacity of link i

Algorithm 1


Algorithm 3(Determining incoming volume when blackout is on link i)

Is Li –Qli>100ft

High congestion on link i?)

Yes

No

Use the dissipated volume from link i-1

as the incoming volume to link i

Use the smaller of the following two values:

a) the dissipating rate from link i-1

b) the intersection capacity of link i

which is the maximum dissipating rate of link i




Results
Results link)


Conclusion and future study
Conclusion and future study link)

  • Algorithms in section 1 provide accurate results when compared with HCM2000 by using real world data;

  • Algorithms in section 2 are robust when compared with CORSIM simulation results;

  • Real world data would be collected to validate the section 2 of the developed methodology.


Questions
Questions? link)


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