SearcharxivSearch

arXiv subjects

Duanya Lyu

Publications and source records attributed to Duanya Lyu.

4 recordsLinked to original sources

How Infrastructure and Streetscape Shape E-Scooter Route Choice: Evidence from Washington, DC

E-scooters have emerged as an important micromobility mode for short urban trips, yet evidence on route choice behavior remains limited. This study examines e-scooter route choice in Washington, DC using GPS trajectory data and a Path Size Logit model. In addition to roadway and infrastructure characteristics, the model incorporates visual streetscape features extracted from Google Street View imagery using computer vision techniques. The results show that the effectiveness of cycling infrastructure depends strongly on roadway context. On major roads, only protected bicycle facilities significantly increase route attractiveness, whereas on minor roads both protected and designated lanes provide utility gains. Sidewalks constitute the most frequently used riding environment, yet only asphalt-paved sidewalks are associated with positive utility, suggesting that sidewalk riding may reflect the absence of attractive on-street alternatives rather than a preference for pedestrian infrastructure. Tree coverage, particularly during summer, as well as building and wall coverage, are positively associated with route choice. Likelihood ratio tests and value-of-distance analysis indicate that roadway infrastructure exerts a stronger influence on route choice than visual streetscape features, although the latter provide additional explanatory power. These findings support targeted infrastructure investment and the integration of streetscape improvements as a complementary strategy for enhancing micromobility route attractiveness.

stat.AP

StreetDesignAI: Broadening Designer Perspectives Through Multi-Persona Evaluation of Cycling Infrastructure

Designing cycling infrastructure requires balancing the competing needs of diverse user groups, yet designers often struggle to anticipate how different cyclists experience the same street environment. We investigate how persona-based evaluation can support cycling infrastructure design by making experiential conflicts explicit during the design process. Informed by a formative study with 12 domain experts and crowdsourced bikeability assessments from 427 cyclists, we present StreetDesignAI, an interactive system that enables designers to (1) ground evaluation in real street context through imagery and map data, (2) receive parallel feedback from simulated cyclist personas spanning confident to cautious users, and (3) iteratively modify designs while the system surfaces conflicts across perspectives. A within-subjects study with 26 transportation professionals comparing StreetDesignAI against a general-purpose AI chatbot demonstrates that structured multi-perspective feedback significantly Broaden designers' understanding of various cyclists' perspectives, ability to identify diverse persona needs, and confidence in translating those needs into design decisions. Participants also reported significantly higher overall satisfaction and stronger intention to use the system in professional practice. Qualitative findings further illuminate how explicit conflict surfacing transforms design exploration from single-perspective optimization toward deliberate trade-off reasoning. We discuss implications for AI-assisted tools that scaffold persona-aware design through disagreement as an interaction primitive.

cs.HC

Analyzing the Impact of Service Frequency and On-time Performance on Transit Ridership in Miami-Dade County

This study investigates the impact of transit service attributes, focusing on service frequency and on-time performance (OTP), on bus ridership in Miami-Dade County. We obtained route-level ridership from automated passenger counter (APC) data and service performance metrics from the General Transit Feed Specification Real Time (GTFS-RT) data. The panel dataset allows us to effectively isolate the independent effects of OTP and frequency on bus ridership, contributing to a better understanding of how to improve service quality and support ridership. Our analysis examines both a pre-COVID period (January 2018-December 2019) and a post-COVID recovery period (July 2021-July 2023). Descriptive analysis reveals a steady ridership decline before the pandemic, followed by a sharp drop during the pandemic and a gradual recovery to pre-pandemic levels by late 2022. This recovery, however, varied by route and was accompanied by significant temporal fluctuations in service performance measures. To analyze these dynamics, we developed two-way fixed effects (2FE) models. Model outputs show that while frequency consistently influences ridership over time, OTP becomes an increasingly important determinant in the post-COVID recovery period. The observed significant interaction between frequency and OTP in the recovery period further suggests that improving reliability is particularly effective on frequent routes. Our study also highlights the need for targeted ridership-boosting strategies that differ by time of day, with a focus on punctuality during AM-peak and frequency during midday and PM-peak, to better support ridership growth and improve network performance.

physics.soc-ph

Potentials and Limitations of Large-scale, Individual-level Mobile Location Data for Food Acquisition Analysis

Understanding food acquisition is crucial for developing strategies to combat food insecurity, a major public health concern. The emergence of large-scale mobile location data (typically exemplified by GPS data), which captures people's movement over time at high spatiotemporal resolutions, offer a new approach to study this topic. This paper evaluates the potential and limitations of large-scale GPS data for food acquisition analysis through a case study. Using a high-resolution dataset of 286 million GPS records from individuals in Jacksonville, Florida, we conduct a case study to assess the strengths of GPS data in capturing spatiotemporal patterns of food outlet visits while also discussing key limitations, such as potential data biases and algorithmic uncertainties. Our findings confirm that GPS data can generate valuable insights about food acquisition behavior but may significantly underestimate visitation frequency to food outlets. Robustness checks highlight how algorithmic choices-especially regarding food outlet classification and visit identification-can influence research results. Our research underscores the value of GPS data in place-based health studies while emphasizing the need for careful consideration of data coverage, representativeness, algorithmic choices, and the broader implications of study findings.

cs.CY