SHRP2 C05:Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs

Freeway DataFreeway 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 rate Capacity Critical Speed Critical Density Density (C/D)

1 2012 2052 56 37 212 2024 2093 53 39 233 1939 1982 49 40 234 2075 2127 54 39 235 1843 1882 47 40 236 2141 2172 63 35 20

Table 1. Basic information of the study sites

Site # Highway Direction # Lanes Detector ID Dis. to downstreamBottleneck (KM)

Dis. to Detector(KM)

1 I-680 S 3 401121 2.6 0.12 I-35 N 3 162.482 3.6 0.183 I-280 N 4 400714 N.A* 0.364 I-580 W 4 400950 N.A 0.255 I-680 N 4 401104 6.1 0.126 I-880 N 4 401240 12.1 0.24*Notes: “N.A” indicates the distance to downstream bottleneck is very long.

Table 3. Summary of pre-breakdown headway distribution

# Lane Site AreaLog-Normal Parameter

Max. Breakdown Flow (pc/h/ln)

Mean of Breakdown Flow (pc/h/ln)Shift (sec.)

31 Bay 1.5642 -1.3570 0.5251 2269 19362 SAT 1.4987 -1.1468 0.6887 2371 1893

4

3 Bay 1.6693 -1.4567 0.6908 2157 18324 Bay 1.5310 -1.7139 0.7811 2351 20275 Bay 1.7293 -1.2618 0.4751 2082 17596 Bay 1.5686 -1.3703 0.6665 2295 1909

Arterial DataTo best determine the stochastic capacity of arterials stochastic variation will be determinedArterial 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

Freeway Arterial

Lane Treatments - Narrow lanes - Reversible lanes - HOV lanes - Variable lanes - On-street parking restrictions

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Signal Timing - Signal retiming - Adaptive traffic control - Queue management - Transit/truck signal priority

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Demand Metering - Ramp metering - Mainline metering - Ramp closures - Arterial demand metering

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Congestion Pricing - Pre-set pricing - Dynamic pricing - Distance/vehicle class tolls - High occupancy tolls - Central area pricing

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Traveler Information - Pre-trip information - In-vehicle information - Roadside messages - GPS navigation devices

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Variable Speed Limits • •

Access Management - Raised medians - Accessconsolidation - Right turn channelization - Frontage roads

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Geometric Treatments - Flyovers - Improving weaving sections - Alt. left-turn treatments - Interchange modifications - Alignment changes

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Truck Treatments - Truck-only lanes - Truck restrictions/prohibitions

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Relationship to Other SHRP2 Projects

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Dynamsart- PStrategies for Improving Roadway Capacity

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C05Meeting Highway Capacity

Needs

C02Systems-Based Performance

Measures

C01 and C07Collaborative Decision

Making Process

C10Integrated Travel Demand

Model

L03Reliability Mitigation Strategies