Assessing Bicycle Infrastructure Using a Multidimensional Approach: Integrating Vibration Analysis, Sensor Technologies, and Cyclist Preferences

Abstract

As urban populations continue to rise, the need for efficient and sustainable transportation options has become increasingly critical. Bicycling is being promoted worldwide as a solution to alleviate traffic congestion, reduce environmental impacts, and improve public health. However, the growth of cycling mode share depends on the availability of appropriate infrastructure. While considerable efforts have been made to create safe and accessible cycling paths, a comprehensive evaluation of bicycle infrastructure remains necessary to understand its influence on comfort, safety, and overall user experience. This dissertation focuses on assessing bicycle infrastructure using a multidimensional approach that integrates vibration analysis, sensor technologies, and cyclist preferences. Specifically, it seeks to address how different methods can evaluate the condition of bicycle paths and offer insights into improving urban cycling environments. Key questions arise: How can we accurately measure cyclist comfort, safety, and overall bikeability of different bicycle facilities? What are the critical factors in evaluating bicycle infrastructure? Can we develop and compare new techniques with traditional assessment methods to better understand cyclist needs? The research investigates several dimensions of bicycle infrastructure, including vibration levels and surface quality. It introduces innovative approaches to gathering data using smart devices, such as portable bicycle lights and smartphones. These tools are tested across different pavement types to determine their reliability in assessing cyclists' comfort. The research contributes to developing a new framework for bikeability, incorporating cyclist feedback and advanced metrics for comfort assessment. The dissertation also explores cyclists' preferences towards bicycle facility indicators and heterogeneity in various cycling groups, including age, gender, bicycling frequency, weekly bicycling use, and trip purpose. This dissertation makes several contributions to the field of bicycle infrastructure evaluation. First, it presents a new vibration analysis technique to assess the comfort of cycling surfaces. Second, it introduces smart bicycle lights for data collection, highlighting their effectiveness in measuring road vibrations. Third, the research proposes a bikeability index integrating cyclist perceptions and measurable infrastructure indicators. Finally, the study provides a comparative analysis of different assessment methods, offering valuable insights into how cycling infrastructure can be improved for safety and comfort in urban environments. Chapter 1 highlights the rapid growth of urban populations and the corresponding increase in transportation demands. It underscores the environmental, health, and economic benefits of cycling, framing the need for improved infrastructure assessment. The research objectives, problem statement, questions guiding the dissertation, and expected contributions are outlined. Chapter 2 presents a review of existing bicycle infrastructure assessment methods. Various approaches, such as the Vibration Index, Bicycle Level of Service, Bikeability Index (BI), and Bicycle Safety Index, are discussed, along with tools like questionnaires, GIS systems, and accelerometers. The strengths and limitations of these assessment techniques are critically evaluated, setting the foundation for the new approaches introduced in subsequent chapters. Chapter 3 explores the use of vibration analysis as a method to assess the comfort level of different bicycle surfaces. It introduces advanced tools such as accelerometers and smartphones equipped with sensors to collect data on road vibrations experienced by cyclists. The chapter emphasizes the relationship between road surface quality and cycling comfort. The chapter also explores the methods for quantifying comfort using metrics such as the Dynamic Comfort Index (DCI), Root Mean Square (RMS), Cycling Comfort Index, and International Roughness Index. Chapter 4 presents the use of smart portable bicycle lights to collect vibration data from various cycling surfaces. These lights, which contain embedded sensors, allow for assessing road conditions. The reliability of the smart bicycle lights is also discussed in the chapter, and the test is done on two different pavements. Chapter 9 assesses the ride quality of cyclists on different pavement types in the case study area. Using a combination of cyclist feedback and vibration data, a linear mixed model reveals a strong correlation between surface type and ride quality. The results emphasize the importance of smooth and well-maintained pavements in enhancing cyclists’ comfort, particularly in urban settings. Chapter 10 investigates the varying perceptions of cyclists regarding the importance of different infrastructure elements based on demographic factors such as age, gender, and cycling frequency. Using the Technique for Order of Preference by Similarity to Ideal Solution method, the research highlights significant priority differences, with safety and pavement quality ranking highly among most groups. The findings help city authorities to consider infrastructure improvements based on the specific area population and activity pattern to cater to diverse cycling populations' needs. Chapter 11 summarizes the research's key findings and implications for urban planning and policy. It emphasizes the need for cities to adopt multidimensional assessment methods that incorporate objective measurements (like vibration analysis) and subjective cyclist feedback. Recommendations are made for future research, including expanding these methodologies to other geographic regions and further refining the BI. The study's findings are particularly relevant for urban planners and policymakers designing and maintaining bicycle infrastructure. By integrating advanced sensor technologies and cyclist feedback, the quality of bicycle paths can be evaluated, and data-driven decisions can be made to improve cycling conditions in cities. Future research directions include expanding the scope of assessment to cover diverse geographical areas and exploring additional factors, such as cyclist demographics, that may influence infrastructure design.