Non-Motorized Traffic Exploratory Analysis --------------------------------------------------------------- Chao Wu Advisor: Professor Greg Lindsey Humphrey School of Public Affairs University of Minnesota April 28, 2014
Abstract In 2012, Minnesota Department of Transportation (MnDOT) implemented the Minnesota Bicycle and Pedestrian Counting Initiative, a non-motorized transportation (bicycle and pedestrian) count project. A voluntary statewide count was repeated in spring 2013, and fall 2013. Between September 10 th, 2013 and September 19 th, 2013, 14 communities conducted counts in their municipalities using a standard count form provided by MnDOT. Counting from September 2013 has been analyzed in the report. The first objective of the report is to summarize results of non-motorized traffic counts. In addition, the second objective of the report is to explore potential relationship between the non-motorized counts and several dependent variables as followed: Anural average daily traffic (AADT) counts Population density Median household income Education Unemployment rate City class
Introduction Nowadays, there are more and more pressures for government to encourage and foster both recreational and utilitarian non-motorized transportation. It becomes more and more important to understand non-motorized transportation patterns. Understanding how does bicycle and pedestrian volume react with the factors listed above is important for the planning and management of non-motorized transportation. Understand these relationships are important because it could prioritize the investments, and help to make more efficient investments. (Lonetta, Angela, Jenny, Jason, 2013). The following report is divided into five parts. First of all, the background part will introduce the data collecting method, and provide summary results by municipality. Followed by that, the methodology part will introduce how the assumptions have been made. Then, during the techniques part, different methods will be introduced. Followed by that, the results part will present the major findings of the research. In addition, conclusion and recommendation will be presented to discuss the possible outcomes. In the end, appendix A through X will summarize count for each municipality. Background In September 2013, 14 Minnesota communities completed 238.25 hours of short-term manual bicycle and pedestrian counts at 55 locations during peak weekday and peak weekend traffic hours, following MnDOT procedures. Participating communities
included International Falls, Carltons, Grand Rapids, La Prairie, Coleraine, St. Cloud, St. Joseph, Sartell, Sauk Rapids, Waite Park, Fargo, Moorhead, West Fargo, and Dilworth. However, Fargo, and West Fargo are excluded in the following analysis due to insufficient AADT information. Table 1 below summarizes community counts by city size, number of locations monitored, and total hours of counts. Minnesota divides their cities into four classes. Class 4 city has population of 10,000 or less, class 3 city has population from 10,000 to 20,000, class 2 city has population from 20,000 to 100,000, and class 1 city has population over 100,000. Since this report focuses on cities of Minnesota, so Fargo, and West Fargo are excluded during the analysis. Six communities, or 43%, counted in only one location. Also, MnDOT encouraged communities to count on Tuesdays, Wednesdays, and Thursdays in September during evening peak hours, especially from 4:00PM to 6:00PM. Counting on Saturdays and/or other times identified as providing value to local initiatives were also encouraged. In the end, last two columns showed average bike count and average pedestrian count for each municipality.
Table 1: Location and Hours Counted in Minnesota/North Dakota Municipalities Class Population Number of Total Mean Hourly Mean Hourly Locations Hours Bike Count Pedestrian Count International Falls 4 6424 3 14.25 8 16 Carltons 4 854 2 8 10 12 Grand Rapids 3 10916 4 40 3 5 La Prairie 4 663 1 10 2 1 Coleraine 4 1983 1 10 2 2 St. Cloud 2 65986 6 12 12 83 St. Joseph 4 6646 1 2 3 3 Sartell 3 16183 1 2 12 9 Sauk Rapids 3 12965 1 2 7 4 Waite Park 4 6680 1 2 3 5 Fargo 1 223490 20 80 12 31 Moorhead 2 39039 9 36 23 35 West Fargo 2 27478 3 12 6 19 Dilworth 4 4091 2 8 2 2 In addition, Appendix A through L showed a detailed count for each municipality. It included brief background information for each municipality, a summary of average bikes and pedestrian count of all sites in each municipality, a conclusion of maximum bikes and pedestrians count per hour of all sites in each municipality. Also, tables of each count location and graphs of each count location attached at end of each Appendix. Methodology for Exploratory Analysis Table 2 below included dependent variables, independent variables and assumption of how independent variables and dependent variables correlated with each other.
Table 2: Dependent Variables vs. Independent Variables Dependent variables Independent variables Assumption Bicycle counts Average Annual Daily Traffic Positively (AADT) correlated Pedestrian counts Population Density Positively correlated Median household income Positively correlated Education Positively correlated Unemployment rate Positively correlated Job Density Positively correlated City Class Positively correlated In terms of dependent variables, bicycle counts and pedestrian counts are targeted dependent variables. In terms of independent variables, there are Average Annual Daily Traffic (AADT), population density, median household income, education, unemployment rate, job density and city class. The assumption was that all independent variables were positively correlated with dependent variables. It means if there is an increase of independent variable, there is an increase of dependent variable. Techniques Generating Hourly Vehicular AADT The following part will discuss two different methods been used in the analysis. The method is to reverse extrapolate of AADT into a two-hour traffic count. Since the AADT
data from MnDOT website is the average daily count, but we only have a two hour count for bicycle and pedestrian. The initial thought was to extrapolate two-hour count into daily counts; then daily counts to weekly counts; finally to and annual daily traffic count. Since we have limited non-motorized adjustment factor, the new approach is to reverse extrapolate AADT into a 2-hour short duration count. MnDOT has provided an AADT factor for weekdays and weekends in September. With this factor, it is possible to reverse extrapolate AADT counts into September daily counts. In terms of hours of day factor, MnDOT provided a spreadsheet that has automatic traffic recorder (ATF) information (MnDOT, 2013). The ATF report has 230 randomly picked roads in Minnesota with every hour traffic count throughout 2013. Among all roads information, five random selected roads have been chosen. Then calculate the average hours of day factor. In this way, we will able to match motorized traffic with non-motorized traffic. Generating Census Related Variables The second methodology is to use 2010 census data on other dependent variables at block group level. Considering access distance between bicycles and pedestrians are different, 1 mile buffer or access zone was created for bicycles, and 1/2 mile buffer or access zone was created for pedestrians. Within the buffer zone, 2010 data for median household income, education level, employment distribution, and population were generated from Census Bureau. In order to create the buffer zones, GIS maps with road names are used from Census Bureau website. First of all, buffer the count location by 0.5 mile and 1 mile
respectively. Then clip the block group information into each buffer zone. In addition, information about median household income, education level, employment distribution, and population are also found from Census Bureau website. After clip the block group, manually match the above data coordinated with block group numbers. Results The following part will show several multiple regressions analysis. There are two sets of data are been analyzed. The first set of data has 19 observations without AADT. The second set of data has 11 observations with AADT. The reason is that MnDOT only provides AADT for limited numbers of roads. In addition, since participated communities do not have class one city, the multiple regressions use class two city and class three city as oppose to class four city. During the analysis, we want the absolute value of t-value to be greater than 1.95 or 2.58 to be significant at confidence level of 95% and 99% respectively, meanwhile the absolute value of p-value to be smaller than 0.05 or 0.01 to be significant at confidence level of 95% and 99%. Table 1 below showed the multiple regressions for 0.5 Mile pedestrian count. Based on t-value, there is no independent variable showing significance. Similarly, in terms of p-value, there is also no independent variable showing significance. Also, the adjusted R square is as low as -0.03, it indicates all independent variables combine explaining 3% of the dependent variables.
Table 1: 0.5 Mile Pedestrian Count Coefficients Standard Error t Stat dp-value Intercept 15.48 20.01 0.77 0.46 Population Density 0.00 0.00-1.03 0.32 Median income 0.00 0.00 0.37 0.72 College degree or higher -41.24 54.70-0.75 0.47 Unemployment rate -59.64 123.57-0.48 0.64 Jobs Density 0.00 0.00 1.41 0.19 Class II 3.49 9.96 0.35 0.73 Class III -0.62 6.72-0.09 0.93 Adjusted R Square -0.03 Observations 19.00 Table 2 below showed the multiple regressions for 0.5-mile bicycle count. There is no significance of t-value, due to all t-values are smaller than 1.96. Similarly, there is no significance of p-value, due to all p-values are greater than 0.05. On the other hand, the adjusted R square indicates all independent variables combine explaining 25.8% of the dependent variables. Table 2: 0.5 Mile Bike Count Coefficients Standard Error t Stat P-value Intercept -1.28 10.96-0.12 0.91 Population Density 0.00 0.00 0.57 0.58 Median income 0.00 0.00 0.96 0.36 College degree or higher -3.00 29.97-0.10 0.92 Unemployment rate -3.45 67.71-0.05 0.96 Jobs Density 0.00 0.00 0.95 0.36 Class II -4.95 5.46-0.91 0.38 Class III -2.84 3.68-0.77 0.46 Adjusted R Square -0.258 Observations 19.00
Table 3 below showed the multiple regressions for 1 Mile pedestrian count. According to the data, only unemployment rate is showing potential relationship with pedestrian count, due to higher t-value. However, in terms of p-value there is no dependent variables showing significance because all p-values are greater than 0.05. In terms of adjusted R square, it indicates only 16% of dependent variables can be explained by independent variables. Table 3: 1 Mile Pedestrian Count Coefficients Standard Error t Stat P-value Intercept 60.50 25.22 2.40 0.04 Population Density 0.00 0.01-0.39 0.71 Median income 0.00 0.00-0.23 0.83 College degree or higher -100.89 71.78-1.41 0.19 Unemployment rate -441.20 209.98-2.10 0.06 Jobs Density 0.00 0.00 0.69 0.50 Class II 6.80 12.98 0.52 0.61 Class III -1.25 6.48-0.19 0.85 Adjusted R Square 0.1607 Observations 19 Table 4 below showed the multiple regression for 1 Mile bicycle count. According to the data, job density and city class are showing higher t-value but not enough to be significant. In terms of p-value, there is no significance showing due to no smaller than 0.05 p-values. In terms of adjusted R square value, it indicates all independent variables combine explaining 14% of the dependent variables.
Table 4: 1Mile Bike Count Coefficients Standard Error t Stat P-value Intercept 0.12 14.56 0.01 0.99 Population Density 0.00 0.01-0.34 0.74 Median income 0.00 0.00 0.18 0.86 College degree or higher 31.16 41.45 0.75 0.47 Unemployment rate -71.67 121.25-0.59 0.57 Jobs Density 0.00 0.00 1.21 0.25 Class II -9.23 7.49-1.23 0.24 Class III -5.03 3.74-1.34 0.21 Adjusted R Square -0.141 Observations 19.00 Summary of Modeling Results According to the results above, there is no single dependent variable showing significant relationship with independent variables. However, it seems only population density; job density and city class are showing significance during the analysis. The following multiple regressions will only use the above dependent variables and exclude the rest. Table 5 below showed the multiple regressions for 0.5 Mile pedestrian count. According to the data, population density and job density are showing significant t-value. In terms of p-value, both population density and job density are showing a small p-value. In terms of adjusted R square value, it indicates all independent variables combine explaining 14.85% of the dependent variables. Even though population density and job density are showing significance, but it is difficult to determine whether those dependent variables correlate
with each other or they both correlate with pedestrian count. As can be seen in table 6, a correlation analysis is presented. According to the table, job density and population density are showing the highest correlation number, which is 0.79. It means denser the area is more jobs there will be. Also, population density and job density are also showing relative high correlation with city class. It indicates larger city is intent to have denser population and more jobs. However, all dependent variables are showing less correlation with pedestrian count. To sum up, the tables above reflect land use pattern more than anything else. There is no significant evidence showing pedestrian and bicycle counts are correlated with those dependent variables. Table 5: 0.5 Mile Pedestrian Count Redo Coefficients Standard Error t Stat P-value Intercept 7.62 3.33 2.29 0.04 Population Density -0.01 0.00-1.90 0.08 Job Density 0.00 0.00 2.15 0.05 Class II 0.99 6.28 0.16 0.88 Class III -2.98 4.89-0.61 0.55 Adjusted R Square 0.1485 Observations 19
Table 6: 0.5 Mile Pedestrian Count Redo Ped Count Population Density Jobs Density Class II Class III Ped Count 1.0000 Population Density 0.0500 1.0000 Jobs Density 0.3781 0.7954 1.0000 Class II 0.2689 0.6280 0.6979 1.0000 Class III -0.2511-0.2348-0.2911-0.4060 1.0000 Conclusion According to results above, there are no significant relationship between dependent variables and independent variables. There are several reasons causing those results: Too few observations Other independent variables o Time of day o Weather Randomness Location There are too few observations causing data extremely unstable. In addition, there are other independent variables. For example, different communities recorded on different time of day. Also, normally, we expect to see more bicycles and pedestrian during morning or evening. Similarly non-motorized traffic is heavily relying on the weather. More pedestrians and bikes are expected during good weather days. Furthermore, non-motorized traffic has a large proportion of randomness. For example, some bicycle
users occasionally use bikes to exercise. There is only a small portion of the users use bike as a transportation tool. Last but not least, location is one of the most important issues. For example, more pedestrians and bikes are expected in locations such as schools or trails. Similarly, almost no pedestrians or bikes are expected near highway intersections. In addition, Suggestion Based on the analysis above, even though the figures show a less relative correlation, a few recommendations are also provided. First of all, MnDOT should continue to encourage communities to participate in annual statewide bicycle and pedestrian counts. More counts will provide more adequate local non-motorized transportation patterns. Communities should be encouraged to complete several counts at each location in order to develop patterns for more thorough analysis. In addition, the analysis results would be better if each location is organized by the street type. Also, recording the weather is also important in order to avoid any useless data.
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