Assessment of Commuter Preference for E-Bike as a Last-Mile Connectivity Mode – A Case Study on Electronic City, Noida

Abstract

Urban centers worldwide face growing transportation challenges driven by rapid urbanization and increasing travel demand, leading to congestion, pollution, and strain on existing infrastructure. Addressing the critical issue of "last-mile connectivity"—the efficient movement between public transport hubs and final destinations—is paramount for enhancing the effectiveness of mass transit systems and promoting sustainable urban mobility. Electric bike (e-bike) sharing systems have emerged as a promising, sustainable, and flexible solution to bridge this last-mile gap, offering benefits such as reduced travel costs, improved accessibility, environmental sustainability, and health co-benefits. However, the widespread adoption of e-bike sharing, particularly in rapidly developing urban areas like Electronic City, Noida, is hindered by a complex interplay of factors including limited understanding of commuter preferences, infrastructure deficiencies, operational challenges, and policy ambiguity. Electronic City, a major IT and commercial hub in Noida, relies heavily on metro connectivity, yet commuters frequently encounter last-mile challenges, incurring additional costs and time. This highlights a critical need for focused research to understand the factors influencing commuter preferences for e-bikes in this specific context and to identify strategies for promoting their adoption. The overarching aim of this research was to comprehensively assess and analyze commuter preferences for e-bikes as a viable and effective last-mile connectivity mode within Electronic City, Noida, Uttar Pradesh. To achieve this aim, the study pursued three specific objectives: to identify the factors affecting the choice of e-bike as a last-mile connectivity mode, to analyze the impact of different factors on user preference of e-bike, and to recommend strategies to promote the usage of e-bike as a last-mile connectivity mode. The research employed a case study approach focusing on Electronic City (Sector 62), Noida, and its adjacent sectors. Primary data was collected through structured surveys administered to a sample of commuters at selected metro stations near the study area. A stratified sampling technique was used to ensure a representative sample of the commuting population. A total of 160 samples were collected, with a gender distribution of 60% male and 40% female, and 75% of respondents in the 18-45 age group. The data analysis involved a comprehensive framework including Mode's Utility Analysis, Passenger Behavior Analysis using a Binomial Logit Regression model, and Mode Specific Service Quality Analysis using an Ordinal Regression model. A key analytical method employed was Binary Logistic Regression to assess the factors influencing the likelihood of commuters switching to e-bikes. Heterogeneity analysis was also conducted to examine e-bike usage patterns across different gender and occupation groups. The key findings from the data analysis revealed several significant factors influencing the likelihood of commuters switching to e-bikes. The Binary Logistic Regression model indicated that gender, specific occupation categories, current last-mile mode used, and distance from public transport significantly influenced the likelihood of switching. Notably, individuals in one gender category (Gender (1)) showed significantly higher odds of switching compared to the reference category. Certain occupation categories (Occupation (1) and Occupation (2)) also demonstrated a significantly increased likelihood to switch. Commuters using specific existing last-mile modes (Last Mile Mode (1) and Last Mile Mode (2)) were also significantly more likely to switch. Conversely, being in a particular distance category from public transport (Distance from PT (1)) significantly decreased the odds of switching. The heterogeneity analysis highlighted variations in e-bike usage across gender and occupation, with male students showing notably higher usage than female students. The study also identified critical challenges faced by users, with limited availability of e-bikes and lack of docking spaces being the most prominent issues, followed by difficulty in booking and safety concerns. In conclusion, the research identified key socio-economic, trip-related, and service-related factors that significantly influence commuter preferences and the propensity to adopt e-bikes for last-mile connectivity in Electronic City, Noida. The findings underscore the importance of addressing infrastructure gaps, improving service reliability, and implementing targeted strategies to promote e-bike adoption. Based on these insights, the thesis proposes several key recommendations, including improving e-bike infrastructure and availability, enhancing mobile app integration and access, implementing affordable pricing models, integrating e-bikes into multi-modal connectivity planning, and strengthening government policy support and integration. These strategies aim to create a more conducive environment for e-bike adoption, contributing to a more sustainable, efficient, and inclusive urban transportation system in Noida and similar urban contexts.