Xiao Luo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiao Luo has a total of 57 co-authors. Here are the top co-authors by number of shared publications:
- Chonghao Zhang
Urban Mobility Institute, Tongji University, Shanghai, China
4 shared publications Hao Yu
4 shared publications- Liang Dong
City University of Hong Kong
2 shared publications - Xi Guo
University of Science and Technology Beijing, Beijing, China
2 shared publications Xingyu Liang
2 shared publicationsXuanyu Liu
2 shared publications
Latest Publications
- 9 September 2026
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 278
Dynamic risk propagation and resilience in the global semiconductor trade network: A dual-modulated SIR model
- 9 September 2026
- Published by Elsevier in Reliability Engineering & System Safety
- Vol. 278
Dynamic risk propagation and resilience in the global semiconductor trade network: A dual-modulated SIR model
- 5 September 2026
- Published by Elsevier in Journal of Cleaner Production
- Vol. 576
Who bears the burden? Tracing carbon emission responsibilities and flows across socioeconomic strata for sustainable development
- 5 September 2026
- Published by Elsevier in Journal of Cleaner Production
- Vol. 576
Who bears the burden? Tracing carbon emission responsibilities and flows across socioeconomic strata for sustainable development
- 16 October 2025
- Published by Springer Nature in International Journal of Control, Automation, and Systems
- Vol. 23 (10) , 3182-3196
Traffic Accident Prediction Considering Imbalanced Data: An Interpretable Machine Learning Model
- 16 October 2025
- Published by Springer Nature in International Journal of Control, Automation, and Systems
- Vol. 23 (10) , 3182-3196
Traffic Accident Prediction Considering Imbalanced Data: An Interpretable Machine Learning Model
- 5 November 2023
- Published by Springer Nature
- p. 569-583
M2MTR: Reposition Idle Taxis in the Many-to-Many Manner with Multi-agent Reinforcement Learning
- 5 November 2023
- Published by Springer Nature
- p. 569-583
M2MTR: Reposition Idle Taxis in the Many-to-Many Manner with Multi-agent Reinforcement Learning
- 21 September 2023
- Published by Springer Nature in Applied Spatial Analysis and Policy
- Vol. 17 (1) , 269-300
Urban Night Vitality Measurements and Related Factors Based on Multisource Data: a Case Study of Central Shanghai
- 21 September 2023
- Published by Springer Nature in Applied Spatial Analysis and Policy
- Vol. 17 (1) , 269-300
Urban Night Vitality Measurements and Related Factors Based on Multisource Data: a Case Study of Central Shanghai
- Open Access
- 18 February 2023
- Published by Springer Nature in Data Science and Engineering
- Vol. 8 (1) , 1-22
Construct Trip Graphs by Using Taxi Trajectory Data
The trip graph, which can model the residents’ taxi demands, consists of vertices used to indicate trips and edges used to indicate the follow-up relationships between trips. A trip $$v_j$$ is a tight follow-up trip of another trip $$v_i$$ , if a taxi can arrive at the departure location of $$v_j$$ within a time threshold $$\delta$$ after it finishes $$v_i$$ . However, for big cities, there are a large amount of trips every day and it is time consuming to construct a trip graph. In this paper, we propose efficient algorithms to construct trip graphs for big cities. When constructing a trip graph, the most expensive step is to connect the vertices if the tight follow-up relationships exist. To find out the tight follow-up trips fast, we design an index considering both spatial and temporal constraints. To designate appropriate search areas, we propose efficient methods to determine the distance-based search areas and the traffic-based search areas. We conduct experiments on real datasets, i.e., the taxi trajectories of Shanghai in 2015. The experimental results show that our algorithm can construct the trip graph about 40 times faster than the straightforward method. We also demonstrate the usages of the trip graph in green transportation applications, i.e., the minimum fleet analysis and the minimum total “idle” mileage analysis.- 18 February 2023
- Published by Springer Nature in Data Science and Engineering
- Vol. 8 (1) , 1-22
Open AccessConstruct Trip Graphs by Using Taxi Trajectory Data
The trip graph, which can model the residents’ taxi demands, consists of vertices used to indicate trips and edges used to indicate the follow-up relationships between trips. A trip $$v_j$$ is a tight follow-up trip of another trip $$v_i$$ , if a taxi can arrive at the departure location of $$v_j$$ within a time threshold $$\delta$$ after it finishes $$v_i$$ . However, for big cities, there are a large amount of trips every day and it is time consuming to construct a trip graph. In this paper, we propose efficient algorithms to construct trip graphs for big cities. When constructing a trip graph, the most expensive step is to connect the vertices if the tight follow-up relationships exist. To find out the tight follow-up trips fast, we design an index considering both spatial and temporal constraints. To designate appropriate search areas, we propose efficient methods to determine the distance-based search areas and the traffic-based search areas. We conduct experiments on real datasets, i.e., the taxi trajectories of Shanghai in 2015. The experimental results show that our algorithm can construct the trip graph about 40 times faster than the straightforward method. We also demonstrate the usages of the trip graph in green transportation applications, i.e., the minimum fleet analysis and the minimum total “idle” mileage analysis. - Open Access
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation
The complex driving environment brings great challenges to the visual perception of autonomous vehicles. It's essential to extract clear and explainable information from the complex road and traffic scenarios and offer clues to decision and control. However, the previous scene explanation had been implemented as a separate model. The black box model makes it difficult to interpret the driving environment. It cannot detect comprehensive textual information and requires a high computational load and time consumption. Thus, this study proposed a comprehensive and efficient textual explanation model. From 336k video frames of the driving environment, critical images of complex road and traffic scenarios were selected into a dataset. Through transfer learning, this study established an accurate and efficient segmentation model to obtain the critical traffic elements in the environment. Based on the XGBoost algorithm, a comprehensive model was developed. The model provided textual information about states of traffic elements, the motion of conflict objects, and scenario complexity. The approach was verified on the real-world road. It improved the perception accuracy of critical traffic elements to 78.8%. The time consumption reached 13 minutes for each epoch, which was 11.5 times more efficient than the pre-trained network. The textual information analyzed from the model was also accordant with reality. The findings offer clear and explainable information about the complex driving environment, which lays a foundation for subsequent decision and control. It can improve the visual perception ability and enrich the prior knowledge and judgments of complex traffic situations.
Open AccessEfficient textual explanations for complex road and traffic scenarios based on semantic segmentation
The complex driving environment brings great challenges to the visual perception of autonomous vehicles. It's essential to extract clear and explainable information from the complex road and traffic scenarios and offer clues to decision and control. However, the previous scene explanation had been implemented as a separate model. The black box model makes it difficult to interpret the driving environment. It cannot detect comprehensive textual information and requires a high computational load and time consumption. Thus, this study proposed a comprehensive and efficient textual explanation model. From 336k video frames of the driving environment, critical images of complex road and traffic scenarios were selected into a dataset. Through transfer learning, this study established an accurate and efficient segmentation model to obtain the critical traffic elements in the environment. Based on the XGBoost algorithm, a comprehensive model was developed. The model provided textual information about states of traffic elements, the motion of conflict objects, and scenario complexity. The approach was verified on the real-world road. It improved the perception accuracy of critical traffic elements to 78.8%. The time consumption reached 13 minutes for each epoch, which was 11.5 times more efficient than the pre-trained network. The textual information analyzed from the model was also accordant with reality. The findings offer clear and explainable information about the complex driving environment, which lays a foundation for subsequent decision and control. It can improve the visual perception ability and enrich the prior knowledge and judgments of complex traffic situations.