An official website of the United States government
The impact of mobility decisions not only shapes urban traffic patterns and planning, but also its associated effects, such as greenhouse gas (GHG) emissions. Although e-bike sharing is not a new concept, it has shown significant strides in technological progress in recent years due to the ongoing process of digitalization, specifically towards decarbonization effects. Past studies have shown that e-bike sharing shows a potential as a fast, mobile, and environmentally friendly alternative to cars and public transport. Although e-bikes represent a viable alternative to traditional means of transportation, there is a lack of quantification in understanding the impact and acceptance of e-bikes towards social contexts as well as its adoption as a type of sharing concept. In this paper, we employ the Unified Theory of Acceptance and Use of Technology (UTAUT) model as an analytical framework to discern the use and acceptance of e-bike sharing as an emerging technological concept across different cities and social contexts. Our findings reveal that the e-bike sharing system's utilization is skewed towards a small percentage of "frequent users", and overall usage is biased towards younger, more-educated, and higher-income populations who live in bike-friendly areas. Our work contributes to the feasibility of embedding the e-bike sharing concept in the scope of the energy transition.
more »
« less
This content will become publicly available on November 3, 2026
A Spatially-Adapted SHAP Approach for Interpreting Deep Bike Usage Learning and Prediction
Understanding the spatial dynamics of bike-sharing usage is critical for effective urban planning and mobility resource management. In this study, we propose an interpretable deep learning approach to uncover spatial relationships embedded in bike-sharing activities. Specifically, we develop a spatially-adapted SHapley Additive exPlanations (SHAP)-based method to quantify the spatial dependencies between locations in bike-sharing activities and apply it to interpret the predictions of a bike-sharing model. Extensive experiments upon Citi Bike data from New York City in December 2023 reveal that spatial influence does not strictly follow geographic proximity and is anisotropic. Additionally, non-member users exhibit weaker spatial dependencies in their bike usage behavior, resulting in lower short-term predictability compared to member users. Our studies shed deep insights into the spatial dynamics of bike-sharing systems and provide guidance for more effective service deployment and system design.
more »
« less
- Award ID(s):
- 2303575
- PAR ID:
- 10653508
- Publisher / Repository:
- Proceedings of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2025 (SIGSPATIAL 2025)
- Date Published:
- Page Range / eLocation ID:
- 1114 to 1117
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Bike sharing systems have been in place for several years in many urban areas as alternative and sustainable means of transportation. Bicycle usage heavily depends on the available infrastructure (e.g., protected bike lanes), but other—mutable or immutable—environmental characteristics of a city can influence the adoption of the system from its dwellers. Hence, it is important to understand how these factors influence people’s decisions of whether to use a bike system or not. In this this paper, we first investigate how altitude variation influences the usage of the bike sharing system in Pittsburgh. Using trip data from the system, and controlling fora number of other potential confounding factors, we formulate the problem as a classification problem, develop a framework to enable prediction using Poisson regression, and find that there is a negative correlation between the altitude difference and the number of trips between two stations (fewer trips between stations with larger altitude difference). We further, discuss how the results of our analysis can be used to inform decision making during the design and operation of bike sharing systems.more » « less
-
We report results of an experiment in applying deep Q-learning for dynamic spectrum sharing (DSS) in the Alleys of Austin scenario from the DARPA Spectrum Collaboration Challenge. This scenario mimics mobile operations in an urban environment by up to five squads (teams) of soldiers. Each team operates its own wireless network. We consider teamwise– distributed DSS, where there is no central agent to coordinate spectrum usage across teams, but spectrum usage within each team is coordinated by a single member of that team. The spatial distributions of the soldiers creates opportunities for spatial reuse by certain subsets of the teams, and our experiment is set up to evaluate whether the deep Q-learning algorithm can discover and take advantage of these opportunities. The results show that deep Q-learning is able to take advantage of spatial reuse and that doing so results in better performance than a fair-share, disjoint spectrum allocation among the teams.more » « less
-
Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system’s efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness.more » « less
-
Micromobility usage has increased significantly in the last several years as exemplified by shared escooters and privately owned bicycles. In this study, we use traffic camera footage to observe the behavior of over 700 shared e-scooters and privately owned bicycles in Asbury Park, New Jersey. We address the following questions: (1) What are the behavioral differences between bicycle and e-scooter usage in terms of helmet use, bike lane / sidewalk use, gender split, group riding, and by time of day? (2) Are more protective conditions associated with helmet use and bike lane / sidewalk use? And (3) what is the gender split between e-scooter users and cyclists? We find notable differences in safety precautions: around one third of cyclists but no shared e-scooter users were observed wearing a helmet. Among cyclists, helmet use was more prominent among men than women. However, men were more likely to ride on the road than women. We also found that the gender split was narrower among e-scooter users, with a nearly even gender split – as opposed to cyclists, where only 21% of cyclists were observed to be women. Our findings suggest that e-scooter users take fewer safety precautions, in that they are less likely to use a bike lane and to wear a helmet. We conclude with policy implications with regards to safety and gender differences between these two modes.more » « less
