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

Greg Rybarczyk September 5, 2006 Presentation Outline  Bicycle transportation planning in Milwaukee  Is there a problem?  Research objectives  Methods  Results  Conclusions

Greg Rybarczyk September 5, 2006 Statistics Source: U.S. Census, 2000 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

Greg Rybarczyk September 5, 2006 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)

Greg Rybarczyk September 5, 2006 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

Greg Rybarczyk September 5, 2006 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

Greg Rybarczyk September 5, 2006 Milwaukee, Wisconsin, Bayview Neighborhood Lake Michigan N

Greg Rybarczyk September 5, 2006 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

Greg Rybarczyk September 5, 2006 Criteria Ranking and Normalized Weighting CriterionRank Normalized Weight Population Parks Recreation Areas Schools Businesses Crime Crashes  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 (Malczewski, 1999 ) 1.0

Greg Rybarczyk September 5, 2006 Value Function Decision Rule (Malczewski, 1999) j V i = ∑ w j v j (x ij ) j = 1 V i = Total value of each road segment w j v j = Criterion value function and weighted summation x ij = Criterion attribute value from i to j

Greg Rybarczyk September 5, 2006 Trade-off Analysis  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 = w j 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 +

Greg Rybarczyk September 5, 2006 Bayview Neighborhood Route Analysis Lake Michigan Lake Michigan Lake Michigan

Greg Rybarczyk September 5, 2006 Bayview Criteria Analysis “A” “C” BLOS

Greg Rybarczyk September 5, 2006 Bayview Criteria Analysis Cont.

Greg Rybarczyk September 5, 2006 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

Greg Rybarczyk September 5, 2006 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?

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