logging in or signing up Powerpoint Sl from San Diego ITE Mtg funnyside Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 112 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: October 30, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript 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 capacityFreeway 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 constraintsPM Congestion 2002: PM Congestion 2002 San FranciscoWhy 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 segmentsVariables 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 SetRegression Approaches: Regression Approaches Linear model Log-log modelLinear 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 prepareConclusions: 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 advisableLimitations & 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 percentagesAvailability of Data: Availability of Data Go to www.dowlinginc.com You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Powerpoint Sl from San Diego ITE Mtg funnyside Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 112 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: October 30, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript 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 capacityFreeway 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 constraintsPM Congestion 2002: PM Congestion 2002 San FranciscoWhy 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 segmentsVariables 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 SetRegression Approaches: Regression Approaches Linear model Log-log modelLinear 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 prepareConclusions: 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 advisableLimitations & 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 percentagesAvailability of Data: Availability of Data Go to www.dowlinginc.com