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
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 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 • 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 • Uniform Delay: • Incremental Delay: • Initial Delay:
Developed Algorithms--Intersection Control Delay -Observed Vehicle Group Identification
Developed Intersection Control Delay Algorithms • 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 2 an initial queue exists and it is smaller than one cycle length( 0<d3<CL) g1=d3-r situation
Intersection Control Delay-Case 3 -Initial Queue clearance time d3 is greater than one cycle length (d3>CL)
Validation of Intersection Control Delay Algorithms 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 Algorithms 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 Algorithms ANOVA Table for Actual Delay vs HCM2000 results ANOVA Table for Actual Delay vs Developed Algorithm results
Total Travel Time Computation • 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 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) 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
Conclusion and future study • 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.