A Big Data Approach for Improving Nebraska Cycling Routes

US Transportation Collection2020PDFTech ReportAlsaleem, FadiAl-Ramini, AliTakallou, Mohammad AliPiatkowski, Daniel PUniversity of Nebraska--Lincoln. Charles W. Durham School of Architectural Engineering and ConstructionNebraska. Department of TransportationUnited States. Department of Transportation. Federal Highway AdministrationNebraska. Department of TransportationNebraskaUnited StatesBicycle countsBicycle facilitiesBicycle travelCyclistsMachine learningRegression analysisRoute signsTravel patternsWeather conditionsCyclingStravaCycling InfrastructureSigned RoutesBicycle SignageSharrowsM095SPR-P1(20) M095Final Report July 2019 \u2013 December 2020More people are becoming interested in creating healthy lifestyle habits for themselves. It has been proved that cycling has several health benefits. Therefore, governments are planning towards more cycling-friendly infrastructures and environments. To understand the cycling activities in Nebraska, we analyze Strava data for the last three years, providing valuable insight into cycling activities for urban planning purposes. We analyze commute and recreational cycling patterns varying between weekdays and weekends to identify peak hours. Moreover, we study the effect of weather on cycling activity patterns for the last three years. Also, we use the impact of the outside temperature as a parameter to model the monthly cycling activities in Nebraska using polynomial regression. Furthermore, we analyze the spatial and demographic factors that influence cycling activities in Nebraska. Finally, we use machine learning regression to understand the effect of adding signed routes on cycling activities. We found a strong association between Strava and counter data counting for all cyclists in a specific location. Also, we found a correlation between weather conditions and cycling, where the average outside temperature has the most significant effect. However, other factors influence cycling activities like rainfall, wind speed, thunderstorms, and fog in different extents. Weekends and weekdays showed different cycling patterns during peak hours, explained by the fact that most people cannot ride their bike in working hours. The spatial analysis showed that cycling is profoundly affected by the existence of cycling infrastructure. Also, it showed that trails are the most used for recreational activities. Additionally, including bike lanes and signed routes to the street infrastructure are the most used for commute purposes. Moreover, the added bike lanes in Lincoln between 2017 and 2019 showed a significant rise in cycling activities. In Omaha, we showed a significant increase in cycling activities because of the installation of signed bicycle routes.113