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System-Level Analysis of Austin Arterials Presented by the University of Texas at Austin Center for Transportation Research for TxDOT Dr. S. Travis Waller Dec 15th, 2008 Traditional Transportation Planning § Trip Generation § Based on demographic data and land use, develop the number of trips originating or ending in a region § Trip Distribution § Assigns specific origins and destinations to trips § Mode Split § Determines mode of travel for each trip § Traffic Assignment § Assigns trips onto specific roadway segments Traffic in Traditional Planning § Traditional planning only addresses vehicular traffic through Static Network Assignment § Major benefit of static assignment § Static assignment is relatively easy to solve making regional models possible in previous decades § Major detriment of static assignment § Static assignment treats traffic as a single steady state value (time insensitive) and neglects critical traffic behavior such as queue formation and traffic spillover from one street onto another. § Dynamic traffic assignment has been developed to maintain regional network modeling with correct traffic behavior § Including other operational considerations such as transit service, pricing, ITS, etc. Expanding Traditional Planning Comparison: Static vs. Dynamic • Volume/ Capacity can be • Volume/Capacity is always less greater than 1 than 1 – Inconsistency with real – Consistent with real world world traffic counts counts • Inflow >= Outflow • Inflow must = Outflow – Models queue formation, – Does not adequately shockwave propagation, model congestion where congestion formation and inflow > outflow such as dissipation queue formation • Ideal for transit operations, • Cannot be used for signal signal optimization, ITS optimization, ITS devices, devices, ramp metering, transit precise transit modeling, ramp operations, dynamic pricing metering and any time-varying traffic consideration Impact of Traffic Dynamics One example: queue formation and dissipation due to signals DTA captures precise second-by-second vehicle movements throughout the network Static assignment can not capture traffic signalization (due to the omission of time evolution). Signals cause specific non-uniform impact on traffic however (as shown) Queues form and dissipate Scope and Objective § Implement cutting-edge Dynamic Traffic Assignment (DTA) techniques in regional traffic network modeling § Develop methods for incorporating the new modeling approach into the conventional 4-step travel demand modeling process § Develop a reliable, flexible, well-calibrated network traffic model for both transportation planning and operations § Conduct multi-modal, multi-level network-wide analysis and scenario evaluations § Outcomes: § Calibrated model for system-wide evaluation in central Texas § Integrated modeling process applicable to other regions Example Analysis Options: Traffic Humps (hypothetical) Setting: Models reduction in speed by reducing the free flow speed and Saturation Capacity Metrics: Changes in travel times, level of service in the affected street and surrounding neighborhoods Capabilities: DTA can track individual vehicle trajectory, and observe the path change due to the introduction of speed humps & alternate routes selected by drivers plus the change in link flows Traffic Humps (Cont’d) •About 1,200 vehicles during the peak hours An example trip Before choose alternative routes, which accounts for to Red River After 56% reduction of the traffic volume •Examples of changes in vehicle trajectories •To Red River (5%) •To I-35 (4%) •To Guadalupe (13%) •To Speedway (30%) An example trip to I-35 Change Two-Way Street to One-Way (hypothetical) Location: Set Guadalupe St (between W 29th St and FM 969 (MLK Blvd)) a southbound-only street Setting: Modeled as a direction elimination and capacity reallocation of the corresponding link Metrics: Changes in travel times, level of service & link flows in the target street and its vicinity; changes in vehicle trajectories; changes in O-D travel time Change Two-Way Street to One-Way (Cont’d) Before After •About 1200 vehicles detour during the peak hours •Examples of changes in vehicle trajectories An example trip •To Lamar (30%) to Lamar Blvd •To Rio Grande (9%) •To I-35 (5%) Change One-Way Street to Two-Way (hypothetical) Location: Set W 6th St from I-35 to MO-Pac Express to be a two-way street Capabilities: Can track individual vehicle trajectory which can be aggregated to obtain changes in link flows Metrics: Changes in travel times, level of service and link flows in the target street and its vicinity and system-wide traffic; changes in vehicle trajectories Change One-Way Street to Two-Way (Cont’d) •The total link flow is reduced by 1,388 vehicles, which accounts for 53% of the traffic flow •The largest part of the detouring vehicles (37%) switch to a parallel route, 5th St. Street Widening (hypothetical) Dynamic assignment allows for system-wide routing decisions to be explained as well as precise time evolving traffic representation. Metrics: Changes in travel times, level of service and link flows in the affected street and surrounding neighborhoods; changes in vehicle trajectories and O-D travel times Network Improvement: (hypothetical) Street Widening • About 1,400 vehicles during the peak hours are attracted from other streets, which accounts An example trip from S for 30% increase of the traffic volume 1St Ave •7mph speed improvement Before •Examples of changes in vehicles trajectories After •From I-35 (1%) •From S 1st (10%) An example trip from I-35 Critical Link Analysis made possible from Vehicle Path Analysis Traffic flow that uses a southbound MO-Pac link Traffic flow uses a northbound MO-Pac link Other Example Analysis Options § ITS Deployment § Dynamic pricing § More efficient corridor studies § Transit simulation § Improved mode choice analysis § Etc. Overview of the Study Area The Greater Austin Area, Central Texas •Approach boundaries are the CAMPO region: Travis, Williamson and Hays Counties •Easily expandable to include other counties as well (Bastrop, Caldwell, Burnett) •STILL NEED UPDATED FIGURE Modeling Technique: Dynamic Traffic Assignment § Regional model scope § Captures time-varying network & traffic movements § Correctly models street capacities on traffic § Compatible with both regional models and localized traffic models § Capable of handling large- scale networks while modeling traffic control, ITS and transit while tracking individual vehicle movements § DTA output: time stamped individual vehicle movements through network Model Development Procedure Network Profile (Updated) § Source -CAMPO § Nodes (9,622) § Links (19,519) § O-D demand pairs (234,729) § Source - City of Austin § Signals (580) • Pretimed: 456 • Self-optimized: 124 § Source - CAPMetro § Transit lines (316) • Covered road segments: 3,613 • Bus stops: 4,202 Highway Network Calibration § Calibration components § Supply: Road capacity and free-flow speed, signal phasing and timing settings, bus dwell times § Demand: O-D table and profile (from CAMPO: unmodified) § Calibration criteria § Road Network: traffic volume § Transit Network: GPS data on bus routes § Calibration data § Approximately 1,000 traffic counts of major highways and arterials (CAMPO/TxDOT) § Multiple days of GPS data for 14 bus routes (CAP Metro) Network Calibration Results •RMSEs by the functional classification are: •18% (freeways) •22% (major arterials) •23% (minor arterials) •Models substantially exceed typical planning standards for arterial street calibration The Calibrated Network Flow Pattern (NEED TO COMPARE WITH OTHER MAP STILL) •Traffic flow rate at the peak period •The color scheme shows traffic congestion conditions •Many bottlenecks in the vicinity of interchanges and intersections •Congested roadway segments on I-35, TX-1 (MO-Pac), TX- 685, FM-620 Example Corridor Errors I-35 IH 35 @ FM 969 5% (211 veh.) 3% 1% 1% 4% Example Corridor Errors - MoPac MoPac between US-183 and Cesar Chavez Transit Network Calibration § Calibration components § Transit route-link calibration § Transit schedule calibration § Calibration criteria § Schedule adherence § Headway variation § Calibration data § GPS-measured timepoint-based bus run time § Data collected January-June 2008 § 20-50 whole-route run time samples available for each surveyed route during morning peak hours on weekdays Transit Service Area Calibrated Bus Routes (GPS Data) • Local service routes • 001 1l/1M, 003 Burnet & Manchaca • Limited & Flyer Routes • 135 Dell Limited, 174 North Burnet Limited • Feeder Routes • 214 Lago Vista Feeder, 240 Parkfield • Crosstown Bus Routes • 350 Airport Blvd, 383 Research • ‘Dillo & Special Services • 412 Main Campus, 450 Congress ‘Dillo • Express Routes • 982 Pavilion Express, 983 Noth 183 Express Local Service Route Example: Route 1 SB Local Service Route Example: Route 1 SB Cross Town Route Example: Route 350 NB Cross Town Route Example: Route 350 NB Express Route Example: Route 982 SB Express Route Example: Route 982 SB Transit System Calibration Result § Schedule adherence compares the GPS-measured arrival times and the simulated arrival times against the scheduled arrival times over the transit system. § Arrival times are evaluated at the time points along each bus route, as defined by Capital Metro. 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