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,Presentation Outline,Bicycle transportation planning in Milwaukee Is there a problem? Research objectives Methods Res
2、ults Conclusions,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,Bicycle Planning in Wisconsin,WIDOT Bicycl
3、e 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
4、possible Utilize a Bicycle Level of Service (BLOS) or Bicycle Compatibility Index,(Huber, 2005 and Wisconsin Department of Transportation-September, 1993),Research Objectives:,Implement a Multi-Criteria Evaluation (MCE) and Simple Additive Weighting (SAW) methodology towards bicycle facility plannin
5、g 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,Methodology,Determine BLOS for each road segment in the study area Collect all performance data
6、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,Milwaukee, Wisconsin, B
7、ayview Neighborhood,Constraint Map and Aggregated Criteria,Performance criteria Crime Bicycle Crashes Population Parks Schools Recreation areas BusinessesThrough query process reduced road network to existing and viable roads Summarized criteria per road segment,Wisconsin Department of Transportatio
8、n-September, 1993,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,(Malczewski, 1999)
9、,1.0,Value Function Decision Rule,(Malczewski, 1999),j Vi = wjvj(xij)j = 1Vi = Total value of each road segment wjvj = Criterion value function and weighted summation xij= Criterion attribute value from i to j,Trade-off Analysis,Value function applied to summarized criteria Attractiveness (ATTR) and
10、 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
11、 .7 BLOS x .2 + ATTR x .8 BLOS x .1 + ATTR x .9 ATTR x 1.0,+,Bayview Neighborhood Route Analysis,Bayview Criteria Analysis,Bayview Criteria Analysis Cont.,Bayview Neighborhood Results,Optimum bicycle facility placement combines BLOS and social factors! As ATTR increases crime is reduced and # of bus
12、inesses 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,Conclusions,Multi-Criteria Evaluation in a GIS environment can quantify several competing bicycling planning criteria
13、 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,
