Zhenliang Ma
Biographic Data
| ID | 6580969 |
|---|---|
| NAME | Zhenliang Ma |
| GIVEN NAMES | Zhenliang |
| FAMILY NAME | Ma |
| SIGNATURE | MA Z |
| AFFILIATIONS | KTH Royal Institute of Technology |
| ORCID | 0000-0002-2141-0389 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Real-time bus arrival delays analysis using seemingly unrelated regression model
To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors,…
Dynamic interlining in bus operations
The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes with a terminal station at a common hub, allowing a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-demand basis to serve scheduled trips on any route to avoid delays and regulate services. The paper examines …
Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport
Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal beh…
Mobility knowledge graph
Understanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various d…
Understanding bus network delay propagation
Bus transport, characterized by a complex network of routes and stops, frequently experiences delays that can affect the entire system's reliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation predominantly focuses on the route level. They lack a broader network-level perspective, which is essential for fully understanding the complex interactions and delay propagation. Additionally, previous st…
Demand management of congested public transport systems
Monitoring transit-served areas with smartcard data
Monitoring transit-served areas with smartcard data
Demand management of congested public transport systems
Understanding bus network delay propagation
Bus transport, characterized by a complex network of routes and stops, frequently experiences delays that can affect the entire system's reliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation predominantly focuses on the route level. They lack a broader network-level perspective, which is essential for fully understanding the complex interactions and delay propagation. Additionally, previous st…
Dynamic interlining in bus operations
The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes with a terminal station at a common hub, allowing a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-demand basis to serve scheduled trips on any route to avoid delays and regulate services. The paper examines …
Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport
Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal beh…
Mobility knowledge graph
Understanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various d…
Real-time bus arrival delays analysis using seemingly unrelated regression model
To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors,…
Computer Science (6 works) · Engineering (6 works) · Transport engineering (6 works) · Transportation Planning and Optimization (5 works) · Human Mobility and Location-Based Analysis (4 works) · Public transport (4 works) · Computer security (3 works) · Econometrics (3 works) · Economics (3 works) · Smart card (3 works)