Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies (2012) / Chapter Skim
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17 C h a p t e r 2 Introduction The project was organized in three phases: foundational research, data collection and preliminary analyses, and reliability prediction models. Phase 1: Foundational Research The foundational research effort included • Conducing a literature review; • Identifying the reliability metrics to be used in the research; • Defining the improvement strategies that affect travel time reliability; • Specifying an experimental design for the research; • Identifying the types of data that were needed to conduct the research; and • Defining an analysis plan for conducting the research, including the model forms to be investigated.
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18 • Percentage of trips with space mean speeds ≤50 mph; and • Percentage of trips (section or origin–destination) with space mean speeds ≤30 mph (4)
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19 Freight Efforts In terms of economic value, reliability is probably more important to freight carriers and shippers than to personal travelers. With the rise in just-in-time deliveries (largely as a replacement to extensive warehousing)
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20 truck speed is updated monthly for the entire length of I-70 across Missouri as well as I-70 nationwide. This speed estimate is supplied as a monthly average to the Missouri DOT by ATRI and the freight performance measurement database described above.
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21 sources of congestion. Figure 2.2 shows how these seven sources interact to produce total congestion.
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22 Chapter 4) to identify the parameters necessary to conduct the primary analyses.
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23 Table 2.3. Congestion Strategy Effects Matrix: Operational Improvements Strategy Substrategies Included Effect on Congestion Sources Factors Affecting Reliability Strategy Implementation Existing Methodology to Calculate Effects Significance of Expected Effect on Reliability Operational Improvements -- Freeways TMC Operations Integrated real-time incident management, verification, detection, and traveler information Reduces delay due to incidents, weather, special events, work zones, and bottlenecks Geographic coverage, equipment density, congestion level, and program aggressiveness IDAS High Service patrols Must include incident scene management methods Reduces delay due to incidents Geographic coverage, vehicle route density, congestion level, and program aggressiveness IDAS High On-scene incident management improvements Response agency coordination and training Reduces delay due to incidents Program aggressiveness IDAS Medium Remote verification (CCTV)
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24 Table 2.3. Congestion Strategy Effects Matrix: Operational Improvements (continued)
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25 Access management Reduce driveways on arterials, provide interparcel access Reduces delay due to bottlenecks Geographic coverage and congestion level Travel demand models Medium Advanced signal systems Centrally controlled signals, advanced detection, and advanced signal control strategies Reduces delay due to poor signal timing Geographic coverage, equipment specifications, and program aggressiveness Simulation High Signal retiming and optimization Regularly scheduled signal optimization programs Reduces delay due to poor signal timing Geographic coverage, equipment specifications, and program aggressiveness Simulation High Changeable lane assignments Reversible lanes Reduces delay due to bottlenecks Geographic coverage and congestion level Simulation Medium HOV by-pass ramp Provide by-pass lanes for HOVs and buses at entrance ramps Reduces delay due to ramp bottlenecks Congestion level Simulation Medium Parking restrictions Restrict parking on arterial streets during peak hours Reduces delay due to bottlenecks Geographic coverage and congestion level Simulation Medium Incident management Incident management coordination among agencies focused on arterials Reduces delay due to incidents Geographic coverage, vehicle route density, congestion level, and program aggressiveness IDAS Medium Event management Incident management coordination among agencies and event ingress– egress planning and coordination Reduces delay due to special events Geographic coverage, equipment density, congestion level, and program aggressiveness IDAS Medium Road weather information systems Weather information supplied to TMCs from roadside weather stations Reduces delay due to incidents and weather Geographic coverage, equipment density, congestion level, and program aggressiveness IDAS High Remote verification (CCTV) Camera views available to multiple agencies and in TMC Reduces delay due to incidents Geographic coverage, equipment density, and program aggressiveness IDAS High Real-time traveler information Pretrip information by 511, web sites, subscription alerts; en route information on DMS, 511, and real-time navigation systems Reduces delay due to incidents, weather, special events, work zones, and bottlenecks Geographic coverage, equipment density, congestion level, and program aggressiveness IDAS High Note: TMC = traffic management center; IDAS = ITS deployment analysis system; HOT = high-occupancy toll; TOT = truck-only toll; DMS = dynamic message sign; HERS = Highway Economic Requirements System.
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26 2. Cross-sectional analysis -- Patterned after classical experimental design, this approach establishes a matrix of factors and their levels.
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27 Factors Considered The experimental design is detailed in Table 2.6. The top-level design in Table 2.6 shows the overarching factors that were studied.
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28 Table 2.6. Experimental Design Factors Levels Highway Type Urban Rural Freeways Signalized Arterials Freeways Area size Small, medium l l Large, very large l l Base congestion Low (AADT/Ca <7)
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29 (e.g., capacity) of a nearby downstream bottleneck.
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30 Journal of the Transportation Research Board, No. 1917, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp.
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