A Dynamic Traffic Simulation Model on Planning Networks Qi Yang Caliper Corporation TRB Planning Application Conference Houston, May 20, 2009

Outline Motivation Model structure Input Output Case study Next step

Motivation: An engine for DTA Static traffic assignment failed to capture the temporal dimension of traffic flows Time variant travel times (links and paths between OD pairs) –Estimation of congestion –Travel time skimming for activity based models –Dynamic ODME Various DTA models available, and we need one which works in TransCAD

Requirements The need to represent: –Queues, shockwaves and spillbacks –Delay at intersections and bottlenecks –Traffic signal controls at intersections Why not microscopic traffic simulation? –Data is often not adequate to calibrate the model –Computational requirement is extensive, especially for large networks –Modelers need a cheap and fast solution because of time and budget constraints

Proposed Model: Transportation Dynamic Network Analyzer TransDNA is a procedure which runs as a thread in TransCAD A path-based traffic simulator for moving individual vehicles between OD pairs “Completely” compatible with existing planning networks Reuse (begin from) the trip matrices in planning models Produce time-dependent travel times by links, paths, and trips Complementary to traditional 4-step model and a tool for new activity based models

Model Structure: Work Flow 4-Step Planning Model Turn Movement Counts TransCAD MMA Link Travel Times Intersection Traffic Control Plans Signal Timing Time-Variant Matrices TransDNA Traffic Simulation Speed & Travel Times Link and Turn Movement Counts Seed OD Matrices Dynamic ODME Path Choice Model Dynamic Map Themes Path Tables Trip Tables Capacities

Model Structure: Network Representation Travel lanes Added lanes on left and/or right Movements allowed and lane grouping

Traffic Models Delay at intersections (global or node specific) –Signalized –Unsignalized Vehicle movements in links modeled by: –Speed/Density, or –Volume/Delay

Traffic Dynamics in Mesoscopic Simulation In Real-world In TransDNA

Van Aerde Model Where: Greenshields Model Pipes Model Capacity Speed Free Flow Speed Capacity Jam Density k and q has linear relationship k and u has linear relationship c 1 = c 3 = 0 (1) If u c = ½ u f, k c = ½ k j (2) If u c = u f, c 1 = 1 / k j c 2 = 0, c 3 = 1 / q c – 1 / k j u f Source: Hesham Rakha and Brent Crowther

Input Network –Road classification (capacity, free flow speed, etc) –Number of lanes and their length –Travel time variability Travel time tables –Historical –Updated Time-dependent OD matrices –By access control (HOV, trucks, etc) –By value of time (tolls and HOT) Intersection Signal Controls –Green splits –Delay by movements –Saturation flows by lane groups Model parameters

Output Trip table –Ori., Des., Path –Dep. Time, Arr. Time –Mileage, Delay Link passage –Vehicle ID, Time Enter/Leave Link statistics –Vehicle Count, Speed, Entry Queue Movement counts and delay

Case Study: I-270 Corridor, MD Subarea from PG/WashCOG –2371 links and 928 connectors –100,688 ODs Simulation –6-9:00 AM peak –571,000 trips –Runs times faster than real time w/ data recording on an i7 desktop (8 cores)

I-270/I-495 – Density 6:30 AM8:30 AM 9:00 AM

Case Studies – Columbus, IN Full Planning Model –8811 links and 984 connectors –7225 OD pairs (85x85) Simulation Result –8-10 AM peak –824,000 trips (not much congestion) –Runs times faster than real-time on i7 desktop (8 cores)

Link labels - where are the vehicles?

Next Step Complete the DTA and ODME loop Model calibration & validation based on field data Support user defined SD and VD functions Testing and more testing … …

Thank You!

Van Aerde Model