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The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility Planning

The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility Planning. Greg Rybarczyk, M.S. Department of Geography University of Wisconsin-Milwaukee. Presentation Outline. Bicycle transportation planning in Milwaukee Is there a problem? Research objectives

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The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility Planning

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  1. The Integration of Multi-Criteria Evaluation and Least Cost Path Analysis for Bicycle Facility Planning Greg Rybarczyk, M.S. Department of Geography University of Wisconsin-Milwaukee

  2. Presentation Outline • Bicycle transportation planning in Milwaukee • Is there a problem? • Research objectives • Methods • Results • Conclusions

  3. Statistics Milwaukee is listed as one of the top ten worst cities for utilitarian walking and bicycling, and in the top ten for recreational bicycling and walking, as stated by Medical News Today, February 28, 2005 Source: U.S. Census, 2000

  4. Bicycle Planning in Wisconsin • WIDOT Bicycle Facility Planning Guidelines • Bicycling origins-destinations should be located near parks, commercial facilities, employment centers, and, recreational facilities • Safety should be minimized • Bicycle Planning in Wisconsin follows 2 paradigms • “Ad=hoc” planning-constructing bicycle facilities wherever possible • Utilize a Bicycle Level of Service (BLOS) or Bicycle Compatibility Index (Huber, 2005 and Wisconsin Department of Transportation-September, 1993)

  5. Research Objectives: • Implement a Multi-Criteria Evaluation (MCE) and Simple Additive Weighting (SAW) methodology towards bicycle facility planning in the City of Milwaukee • Utilize a value function to relate attribute worth for the criteria under consideration • Produce a neighborhood level optimum bicycle network analysis • Conduct trade-off analysis

  6. Methodology • Determine BLOS for each road segment in the study area • Collect all performance data for each road segment • Conduct an inverse ranking and weighting of performance criteria • Establish a decision rule for each criterion under consideration • Assess aggregated performance of each road segment via shortest path analysis • Utilize GIS for display and trade-off analysis

  7. Lake Michigan N Milwaukee, Wisconsin, Bayview Neighborhood

  8. Constraint Map and Aggregated Criteria • Performance criteria • Crime • Bicycle Crashes • Population • Parks • Schools • Recreation areas • Businesses • Through query process reduced road network to existing and viable roads • Summarized criteria per road segment Wisconsin Department of Transportation-September, 1993

  9. Criteria Ranking and Normalized Weighting • Utilized a reversed rank and sum method • Assigned the most weight to negative criteria • Multiplied weight by criteria values then summed all criteria • Goal is to derive the lowest cost (maximum benefit) for each road segment 1.0 (Malczewski, 1999)

  10. Value Function Decision Rule j Vi = ∑ wjvj(xij) j=1 Vi = Total value of each road segment wjvj =Criterion value function and weighted summation xij= Criterion attribute value from i to j (Malczewski, 1999)

  11. Value function applied to summarized criteria Attractiveness (ATTR) and BLOS BLOS and ATTR were weighted to equal 1 Weighting schemes were re-assigned as “cost” for shortest path analysis Weighting Scheme = wj BLOS x 1.0 BLOS x .9 + ATTR x .1 BLOS x .8 + ATTR x .2 BLOS x .7 + ATTR x .3 BLOS x .6 + ATTR x .4 BLOS x .5 + ATTR x .5 BLOS x .4 + ATTR x .6 BLOS x .3 + ATTR x .7 BLOS x .2 + ATTR x .8 BLOS x .1 + ATTR x .9 ATTR x 1.0 Trade-off Analysis +

  12. Lake Michigan Lake Michigan Lake Michigan Bayview Neighborhood Route Analysis

  13. “A” “A” “A” “C” BLOS Bayview Criteria Analysis

  14. Bayview Criteria Analysis Cont.

  15. Bayview Neighborhood Results • Optimum bicycle facility placement combines BLOS and social factors! • As ATTR increases crime is reduced and # of businesses increase • BLOS paths only contain elevated # of all negative criteria • Trade-off analysis reveals that an acceptable BLOS can be reached when incorporating “other” bicycle data

  16. Conclusions • Multi-Criteria Evaluation in a GIS environment can quantify several competing bicycling planning criteria • Careful analysis is needed by the decision maker during the trade-off analysis • A combination of supply-side and demand-side bicycle transportation criteria can be assimilated • Interdependency between criteria may justify other criteria to measure road performance • Further inclusion of directness, slope, weather?

  17. Thank You Special Thanks to: University of Wisconsin-Milwaukee Bicycle Federation of Wisconsin

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