SHRP2 C05: Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs Freeway Data Freeway data has been collected at several locations to test freeway capacity. The team has determined breakdown from two characteristics: - Critical Speed (SC) - LOS C/D Density boundary Stochastic Capacity Table 2. Traffic parameters of study sites Site # 99% flow rateCapacityCritical SpeedCritical DensityDensity (C/D) Table 1. Basic information of the study sites Site # HighwayDirection# LanesDetector ID Dis. to downstream Bottleneck (KM) Dis. to Detector (KM) 1I-680S I-35N I-280N N.A*0.36 4I-580W N.A0.25 5I-680N I-880N *Notes: “N.A” indicates the distance to downstream bottleneck is very long. Table 3. Summary of pre-breakdown headway distribution # Lane SiteArea Log-Normal Parameter Max. Breakdown Flow (pc/h/ln) Mean of Breakdown Flow (pc/h/ln) Shift (sec.) 3 1Bay SAT Bay Bay Bay Bay Arterial Data To best determine the stochastic capacity of arterials stochastic variation will be determined Arterial data has not yet been collected so there is nothing to present at this time. Dynasmart-P Computer Simulation Once these methods for determining stochastic capacities are developed, the methods will be implement into the computer simulation model Dynasmart-P. The overall goal will be to test and quantify capacity improving strategies by determining how well then improve breakdown periods. Freeway Capacity Distribution - Defined at bottlenecks - Flow prior to breakdown - Lognormal distribution provides best fit The conceptual framework provides a dynamic set of methodologies consistent with the projects goals and objectives. In the framework performance measure and potential factors that affect sustainable service rates on both arterials and freeways are being examined and tested how to best represent and implement the affects of these factors. Using Dynasmart-P computer simulation program we can better model network level travel time and effects of individual strategies. Figure X shows a predictions of travel time on a network level Conceptual Framework Plan Stochastic Capacity Expected Products Research Objectives (1) quantify capacity benefits of improvements at the network level (2) provide information and tools to analyze operational improvements as an alternative to traditional construction (3) develop guidelines for “sustained service rates” to be used in planning networks Strategy FreewayArterial Lane Treatments - Narrow lanes - Reversible lanes - HOV lanes - Variable lanes - On-street parking restrictions Signal Timing - Signal retiming - Adaptive traffic control - Queue management - Transit/truck signal priority Demand Metering - Ramp metering - Mainline metering - Ramp closures - Arterial demand metering Congestion Pricing - Pre-set pricing - Dynamic pricing - Distance/vehicle class tolls - High occupancy tolls - Central area pricing Traveler Information - Pre-trip information - In-vehicle information - Roadside messages - GPS navigation devices Variable Speed Limits Access Management - Raised medians - Accessconsolidation - Right turn channelization - Frontage roads Geometric Treatments - Flyovers - Improving weaving sections - Alt. left-turn treatments - Interchange modifications - Alignment changes Truck Treatments - Truck-only lanes - Truck restrictions/prohibitions Increases Capacity Decreases Prob. of Breakdown Increases Capacity Project Schedule Relationship to Other SHRP2 Projects Decreases Prob. of Breakdown Dynamsart- PStrategies for Improving Roadway Capacity ADD TEAM LOGOS HERE C05 Meeting Highway Capacity Needs C02 Systems-Based Performance Measures C01 and C07 Collaborative Decision Making Process C10 Integrated Travel Demand Model L03 Reliability Mitigation Strategies