Yuanxuan Yang
Biographic Data
| ID | 6581508 |
|---|---|
| NAME | Yuanxuan Yang |
| GIVEN NAMES | Yuanxuan |
| FAMILY NAME | Yang |
| SIGNATURE | YANG Y |
| AFFILIATIONS | University of Leeds |
| ORCID | 0000-0002-7970-2544 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
Connected bikeability in London: Which localities are better connected by bike and does this matter
Bikeability, the extent to which a route network enables cycling for everyday travel, is a frequently cited theme for increasing and diversifying cycling uptake and therefore one that attracts much research attention. Indexes designed to quantify bikeability typically generate a single bikeability value for a single locality. Important to transport planners making and evaluating infrastructure decisions, however, is how well-connected by bike are…
Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes
Understanding the interactions between different travel modes is crucial for improving urban transport resilience, especially during times of disruption and transit failure. As a flexible and sustainable travel mode, bikeshare schemes are able to solve “first/last” mile problems in urban transit as well as provide an alternative to motorised traffic. This paper uses OD (origin and destination) trip data from the London Cycle Hire Scheme and tempo…
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems
Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning…
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study ana…
Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes
Understanding the interactions between different travel modes is crucial for improving urban transport resilience, especially during times of disruption and transit failure. As a flexible and sustainable travel mode, bikeshare schemes are able to solve “first/last” mile problems in urban transit as well as provide an alternative to motorised traffic. This paper uses OD (origin and destination) trip data from the London Cycle Hire Scheme and tempo…
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study ana…
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems
Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning…
Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: A case study of four London Tube strikes
Understanding the interactions between different travel modes is crucial for improving urban transport resilience, especially during times of disruption and transit failure. As a flexible and sustainable travel mode, bikeshare schemes are able to solve “first/last” mile problems in urban transit as well as provide an alternative to motorised traffic. This paper uses OD (origin and destination) trip data from the London Cycle Hire Scheme and tempo…
Connected bikeability in London: Which localities are better connected by bike and does this matter
Bikeability, the extent to which a route network enables cycling for everyday travel, is a frequently cited theme for increasing and diversifying cycling uptake and therefore one that attracts much research attention. Indexes designed to quantify bikeability typically generate a single bikeability value for a single locality. Important to transport planners making and evaluating infrastructure decisions, however, is how well-connected by bike are…
Computer Science (4 works) · Engineering (4 works) · Geography (4 works) · Transport engineering (4 works) · Transportation Planning and Optimization (4 works) · Urban Transport and Accessibility (4 works) · Human Mobility and Location-Based Analysis (3 works) · Bike sharing (2 works) · Cartography (2 works) · Cycling (2 works)