Xiaoguang Wang, Assistant Professor, Department of Geography, Central Michigan University Chao Liu,

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Education, University of Maryland Lidia Kostyniuk, Email: lidakost@umich.

Institute Qing Shen, Email: qs@u.washington.

1 Xiaoguang Wang, Assistant Professor, Department of Geography, Central Michigan University Chao Liu, Research Associate, National Center for Smart Growth, Research and Education, University of Maryland Lidia Kostyniuk, Research Scientist, University of Michigan Transportation Research Institute Qing Shen, Professor and Chair, Department of Urban Design and Planning College of Built Environments, University of Washington Shan Bao, Research Fellow, University of Michigan Transportation Research Institute

2 Complete Street Sprawl Street Birmingham, MI Whittaker Rd, MI

3 Whether and to what extent does the street environment influence fuel efficiency? More specifically Whether and to what extent do complete streets decrease fuel efficiency, and whether there might be countermeasures to improve fuel efficiency of such complete streets?

4 Roadway Environment On road (lanes, lane widths, pavements, intersections, speed limit,traffic lights, etc.) Off road (business patterns, land uses, parking, density) Trip Trip purpose Trip length Time of day Drivers Gender Age Weather Temperature Rain/Snow Traffic condition Volume (congestion or not) Driving behavior Speed acceleration/decelera tion Energy consumption rate Vehicle Technology Age Size

6 GPS Traces Integrated Vehicle-Based Safety Systems (IVBSS) collected by University Michigan Transportation Research Institute (UMTRI), automobile drivers driving Honda Accord LX sedans over 40 days with a data collection frequency of 10 Hz (26 GB data) Will be used to derive fuel rate, trip characteristics, driver characteristics, traffic condition, weather, and driving behaviours Road environments Road network data provided by Southeast Michigan Council of Governments (SEMCOG), 2011 Business pattern data from InfoUSA, 2006 Population data from Census 2010

7 Fuel efficiency for the m th trip travelling on the n th segment* * Segments which have been travelled by at least 5 different drivers were selected Road segment 1 Road segment 2 Road segment 3 Driver 2 Trip 3 Driver 2 Trip 2 Driver 2 Trip 1 Driver 1 Trip 2 Driver 1 Trip 1

8 Road segment 1 Road segment 2 Road segment 3 Off road environment (buffer area) Big box density Small retail density Population density Employment density On road environment Speed limit # of lanes Intersection density

Roadway Environment On road (lanes, lane widths,

density) Trip Trip purpose* Trip length Time of day

Traffic condition Volume (congestion or not) Driving

9 Roadway Environment On road (lanes, lane widths, pavements, intersections, speed limit, stop signs,traffic lights, etc.) Off road (business patterns, land uses, parking, density) Trip Trip purpose* Trip length Time of day Drivers Gender Age Weather Temperature Rain/Snow Traffic condition Volume (congestion or not) Driving behavior Speed acceleration/decelera tion Energy consumption rate Vehicle (controlled) Technology Age Size

10 108 drivers 16,886 trips 4,711 road segments 197,652 trip segments Average fuel economy: miles per gallon

Mean Std. Minimu Deviation m Maximum Women 0.42 0.49 0.00 1.00 Young 0.35 0.48 0.00 1.00 Middle 0.37 0.48 0.00 1.00 Old 0.28 0.45 0.00 1.00 Highway 0.52 0.50 0.00 1.00 City 0.48 0.50 0.00 1.00 Speed Limit 52.

11 Mean Std. Minimu Deviation m Maximum Women Young Middle Old Highway City Speed Limit # of Lanes Intersection density at 0.1 mile Employment density at 0.1 mile , , Population density at 0.1 mile , , Big box density at 0.1 mile Small retail density at 0.1 mile Intersection density at 0.25 mile Employment density at 0.25 mile 1, , , Population density at 0.25 mile 1, , , Big box density at 0.25 mile Small retail density at 0.25 mile

12 Young Middle Old Total Young Middle Old Total Young Middle Old Total Middle age women has the highest average fuel economy; older women has the lowest. Young drivers have lower fuel economy Mean Fuel Economy (mpg) Male Female Total *Drivers are equally divided by gender and age groups

Fuel Economy (mpg) 35 30 25 20 15 10 5 0 interstate other freeways other principal arterial

13 Fuel Economy (mpg) interstate other freeways other principal arterial minor arterials major collectors 40% 35% 30% 25% 20% 15% 10% 5% 0% % of trip segments Fueleconomy.gov

Coefficients Sig. (Constant) 17.9071 0.00 Speed limit 0.0996 0.00 # of Lanes 0.0716 0.00 Intersection Density 0.0449 0.00 Employment Density -0.0001 0.00 Population Density 0.0001 0.00 Big box density -0.

14 Coefficients Sig. (Constant) Speed limit # of Lanes Intersection Density Employment Density Population Density Big box density Small retail density Women Young Old Highway Higher speed limit, higher number of lanes, higher intersection density -> higher fuel economy Both big box and small retails decrease fuel economy Women has better fuel economy

15 refine sprawl and complete streets typology testing structural equation models Use traffic and driving behaviors (speed, acceleration, cruising, etc.) as intermediate variables Refine road segment selections

16 A U shape

Coefficients Sig. (Constant) 23.2822 0.00 Speed Limit 0.0968 0.00 # of Lanes 0.1208 0.00 Intersection Density 0.0426 0.00 Employment Density 0.0000 0.00 Population Density -0.0001 0.

17 Coefficients Sig. (Constant) Speed Limit # of Lanes Intersection Density Employment Density Population Density Big box density Small retail density Women Young Old City Roads Higher speed limit, higher number of lanes, higher intersection density -> higher fuel economy Both big box and small retails decrease fuel economy Women has better fuel economy

APPENDIX I: Traffic Forecasting Model and Assumptions

APPENDIX I: Traffic Forecasting Model and Assumptions Appendix I reports on the assumptions and traffic model specifications that were developed to support the Reaffirmation of the 2040 Long Range Plan.