Powerpoint Sl from San Diego ITE Mtg

Information about Powerpoint Sl from San Diego ITE Mtg

Published on October 30, 2007

Author: funnyside

Source: authorstream.com

Content

Predicting the Duration of Congestion:  Predicting the Duration of Congestion on Bay Area Freeways Steven B. Colman, AICP Dowling Associates, Inc. Motivation:  Motivation Develop a Simple Model to Predict the Duration of Congestion: Discern important variables Practical– use available data Performance measure usable when peak hour volume > physical capacity Freeway Congestion:  Freeway Congestion Definition and Characteristics: When speeds < 35 mph for >15 minutes (Caltrans) Collected using floating cars (2002) 369 route miles (almost 60%) “congested” San Francisco Bay Area Overview:  San Francisco Bay Area Overview 9 counties (rural to very urban) 7 million residents, 3.5 million jobs ~620 route miles of freeway Among worst congestion of any metro area in USA (72 h/resident/year) Topographic constraints PM Congestion 2002:  PM Congestion 2002 San Francisco Why Use Congestion Duration as a Performance Measure?:  Why Use Congestion Duration as a Performance Measure? Readily understood by public/ decisionmakers Measures “convenience” of travel (must I engage in time-shifting behavior?) May be a better metric than LOS to measure improvement to congested urban freeway Works where peak demand>capacity Excluded Areas:  Excluded Areas Toll plazas Tunnels Single-lane freeways Freeway-freeway connectors (immediate vicinity) San Francisco County Variable lane configurations Uncongested segments Variables Used:  Variables Used Dependent variable: typical weekday hours congested at 46 locations Independent data set: AADT per lane Interchange spacing Percent heavy trucks Grade (flat/hilly/mountainous) Percent of weekday trips that are HBW HOV lane (dummy) Simple Congestion Duration at a Bottleneck:  Simple Congestion Duration at a Bottleneck Congestion persists even after V<C Shape (slope) of demand curve will affect congestion duration The Data Set:  The Data Set Regression Approaches:  Regression Approaches Linear model Log-log model Linear vs. Log-log model:  Linear vs. Log-log model Linear model says ADTLN, PCTTRK, & GRADE most significant Congestion begins at 13,600 ADT/LN Log model more intuitive Log model says ADT, Lanes, PCTTRK, GRADE, and HOV dummy significant Residuals somewhat better with logs Residuals Comparison:  Residuals Comparison Rowena to prepare Conclusions:  Conclusions Variable signs all intuitive– more traffic= longer congestion; more lanes= shorter Percent of HBW trips & interchange spacing don’t appear significant Reliability of method ~ +/- 1 hour Cautious application advisable Limitations & Sources of Error:  Limitations & Sources of Error Method based on AADT, not AWDT Traffic volumes vary within sections Congestion duration measured on only a few days Auxiliary lanes ignored Directionality (D) ignored Estimation of truck percentages Availability of Data:  Availability of Data Go to www.dowlinginc.com

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