Qinglong Lu (卢庆龙)

Qinglong Lu (卢庆龙)

Learning, thinking, and practicing.

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 2

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Blog Post number 1

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conferences

Gao, J., Lu, Q. L. & Cai, M. (2020). Quantifying privacy vulnerability under linkage attack across multi-source individual mobility data. In 99th Transportation Research Board (TRB) Annual Meeting.

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With the advances in detector and sensor technologies, identity detection-based intelligent transportation systems—such as license plate recognition (LPR) system and parking electronic toll collection (ETC) system—have been widely deployed in urban transportation, generating large quantities of multi-source individual-based mobility data set (e.g., LPR data and parking data). Given the high frequency, precision and wide coverage, these individual-based mobility data can be used in many transportation research areas, such as transportation planning, traffic prediction and individual mobility pattern profiling. With the increasing demand for publishing and sharing these individual-based data sets to researchers and practitioners, the privacy issue of data publishing has been a major concern since true identities of individuals can be revealed by linkage attack. In this paper, we quantitatively measure the privacy disclosure risk caused by linkage attack across multi-source individual-based mobility data sets. Taking an example of LPR data and parking data, a traffic-knowledge-driven adversary model is proposed for linkage attack conducting among LPR data and parking data. Two common modes of LPR data publishing are examined and two quantitative criteria are introduced to present the risk of privacy leakage under linkage attack. The experimental results demonstrate that anonymized individual still under high risk of being linked successfully (71.63% under mode 1 and 36.55% under mode 2). This study serves as a wake-up call for relevant agencies and data owners about the privacy vulnerability caused by linkage attack across multi-source individual-based mobility data.

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Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2020). PC-SPSA: Implementation assessment and exploration of different historical data-set generation methods for enhanced DTA model calibration. In 3rd Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS).

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Calibrating DTA models is complex due to the involved undeterminedness, non-linearity, and dimensionality, restricting calibration approaches especially when calibrating larger networks. Simultaneous perturbation stochastic approximation (SPSA) has been proposed for the DTA model calibration, with encouraging results, for more than a decade with multiple variants trying to improve its application scalability on larger networks. Recently, PC-SPSA has been proposed, combining Principal Component Analysis (PCA) with SPSA to reduce the problem dimensions and non-linearity by limiting the search space in lower dimension space based on orthogonal Principal Components evaluated upon a set of historical estimates. In this paper, we further explore PC-SPSA implementation by assessing its sensitivity towards SPSA parameters definition, its performance in calibrating synthetic problems of different dimensions and non-linearity, and formulating multiple OD historical data–set generation methods for improved calibration (in case of non-existent or irrelevant historical estimates). The performance of each method is compared calibrating an urban network of Munich with similar PC-SPSA settings, depicting more correlated generation techniques perform better consistently than simplified ones.

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Qurashi, M., Lu, Q., Cantelmo, G., & Antoniou, C. (2020) PC-SPSA: Exploration and assessment of different historical data–set generation methods for enhanced DTA model calibration. In 9th Symposium of the European Association for Research in Transportation (hEART2020).

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This paper explores multiple historical data-set estimation methods which are crucial for the calibration performance for principal component analysis (PCA) based algorithms. We first propose multiple sets of historical data-set generation methods with probable calibration scenarios (which replicate more realistic changes within the structure of the demand) and later explore the performance of all the proposed historical data-sets with PC-SPSA to understand the importance of different historical data-set generation parameters. As per the current results, more correlatedly generated historical estimates (i.e. method 3 and 6) outperform other simplified techniques but it will be further interesting to explore and analyze their performance calibrating other different sets of scenarios. Next steps, to be shown in hEART2020 conference, will include, the exploration of all the proposed methods on the possible demand scenarios to identify the best most generically wellperforming data-set generation technique, and later validating that technique on a larger network of Munich city (with a network of 8689 links, 706 detector location and demand of OD matrix [73 × 73] or 5329 OD pairs) with different demand scenarios and also other network information e.g. travel times etc. Finally, results proposed in this study are still based on synthetic experiments. This is a limitation, as we aim to test PCA based algorithms when historical data sets are not available (or information is not reliable). To do so, we will use real traffic data from Munich to generate a benchmark scenario that is assumption free - e.g. the “true” network state is derived from real data and not from syntetic functions. This will allow us to validate our probability function against real data in an assumption free scenario.

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Lu, Q.L., Yang, K., & Antoniou, C. (2021). Crash risk analysis for the mixed traffic flow with human-driven and connected and autonomous vehicles. In 24th IEEE International Conference on Intelligent Transportation Systems (ITSC).

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In the near future, traditional or low-automation vehicles will share the roads with Connected and Autonomous Vehicles (CAVs) over many years. Yet, this complexity may impose new unknowns on the real-time crash risk evaluation. Consequently, it is important to explore crash risk analysis in such kind of mixed traffic flow environments. This paper constructed several special traffic variables in mixed traffic flow environments and proposed the kernel logistic regression (KLR) model to evaluate the crash risk in real-time. A simulated urban expressway corridor based on the North-South Elevated Road in Shanghai, China, is developed in SUMO, for the purpose of collecting the traffic safety data and traffic data (i.e., virtual detector data and Global Navigation Satellite System (GNSS) data) in mixed traffic flow environments. The prediction performance of KLR models was tested and analyzed with the simulated data, and is also compared with that of support vector machines (SVM) models. The results show that KLR has a good prediction performance like SVM. Considering KLR can provide probability estimates directly and can naturally extend to multi-class classification, priority should be given to KLR in similar problems, especially when crash risk is classified into multiple levels. The proposed KLR model is therefore recommended and has the potential to evaluate the real-time crash risk in the mixed traffic flow environment.

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Lu, Q.L., Qurashi, M., & Antoniou, C. (2022). A Stochastic Programming Method for OD Estimation Using LBSN Check-In Data. In 4th Symposium on Management of Future Motorway and Urban Traffic Systems.

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Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e. check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.

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Lu, Q.L., Qurashi, M., & Antoniou, C. (2023). A Two-Stage Stochastic Programming approach for Dynamic OD Estimation. In 102th TRB Annual Meeting 2023.

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Estimating origin-destination (OD) demand is indispensable for urban transport management and traffic control systems. While the existing estimation methods rely on data sources like household travel surveys and traffic network detection, they incur very high costs and are still either less frequent or low in coverage density triggering lower observability and indeterminacy issues for OD estimation. With ubiquity of smartphones, Location based social networks (LSBN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. However, thus far, most LSBN-based estimation models only focus on static (day-level) OD estimation. This paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level mobility flows using LBSN data. The first stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. A generalized Benders decomposition algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments employing generalized least squares (GLS) estimator on the case study of Tokyo city. The results depict that the algorithm convergence can be guaranteed within several steps. The algorithm shows satisfactory performance in check-in pattern estimation, OD flows estimation, and activity share estimation. Further, the implementation of the model in practical applications is also specifically discussed.

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news

publications

Liu, Z. J., Lu, Q. L., & Gao, J. (2024). A similarity-based data-driven car-following model considering driver heterogeneity. Transportation Research Procedia, 78, 611-618.

Published in Transportation research procedia, 2024

Human drivers usually have distinct driving patterns and preferences. Driver heterogeneity is crucial for modeling driving behaviors. This paper incorporates driver heterogeneity with data-driven approaches to predict car-following behaviors. A bi-level similarity-based car-following model is proposed to predict the vehicle’s moving distance. In the upper level, drivers with similar driving patterns as the ego vehicle are identified using k-nearest neighboring (kNN) search. In the lower level, leveraging kNN model, candidate records are selected from the identified vehicles’ trajectories and applied to predict the ego vehicle’s moving distance, combining the driving pattern similarity measured in the upper level. By taking into account the driver heterogeneity, the proposed model is capable of identifying the most relevant driving situations, which leads to an improvement of prediction accuracy. Furthermore, the established bi-level structure largely shrinks the searching space of candidate records, which reduces the searching complexity and enhances computational efficiency. We quantitatively evaluate and compare the performance of the proposed model in terms of both prediction accuracy and computational efficiency using real-world vehicle trajectory data.

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Yang, N., Lu, Q. L., Lyu, C., & Antoniou, C. (2024). Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data. Transportation Research Record, 03611981241245681.

Published in Transportation research record, 2024

Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.

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Yang, N., Lu, Q. L., Yamnenko, I., & Antoniou, C. (2024). Efficient Cloud-Sourced Transport Mode Detection Using Trajectory Data: A Semi-Supervised Asynchronous Federated Learning Approach. IEEE Internet of Things Journal.

Published in IEEE Internet of Things Journal, 2025

The Internet of Things enables collaborative efforts in pattern recognition tasks within intelligent transportation systems, such as transport mode detection (TMD). However, collecting individual trajectories, like GPS records, typically involves privacy issues. To address this, federated learning frameworks have recently been applied. In such frameworks, users retain their private client datasets and are responsible for training local models. Only the updated client model parameters are sent to a central server, where these parameters are aggregated to update a global model. Then, the server broadcasts the updated global model to all users for the next round of local training. In this way, users can contribute to the global model without sharing private data. However, traditional federated learning frameworks are inefficient as the server has to wait for multiple users to upload their model parameters for synchronous parameter aggregation and consistency updates. This process also faces risks from unreliable clients. Furthermore, private client datasets are often unlabeled, posing challenges for local model training. Therefore, this paper proposes a semi-supervised asynchronous federated learning framework for both point-level and segment-level TMDs. Specifically, the proposed framework incorporates model splitting techniques, model shift penalties, and entropy-based aggregation strategies to address model complexity, model drift, and data imbalance, respectively. Moreover, a CNN-based deep learning model with multiple encoders is proposed, and a pseudo-labelingbased approach is applied to utilize unlabeled datasets. The case study demonstrates that the proposed model achieves satisfactory performance on a real-world dataset, and the proposed federated learning framework is robust under varying hyperparameter configurations.

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Deng, Z., Liu, Y., Zhu, M., Xiang, D., Yu, H., Su, Z., Lu, Q., Schreck, T., & Cai, Y. (2025). TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning. IEEE Transactions on Visualization and Computer Graphics.

Published in IEEE Transactions on Visualization and Computer Graphics, 2025

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network’s structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess’s paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews.

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Deng, Z., Chen, H., Lu, Q. L., Su, Z., Schreck, T., Bao, J., & Cai, Y. (2025). Visual comparative analytics of multimodal transportation. Visual Informatics, 9(1), 18-30.

Published in Visual Informatics, 2025

Contemporary urban transportation systems frequently depend on a variety of modes to provide residents with travel services. Understanding a multimodal transportation system is pivotal for devising well-informed planning; however, it is also inherently challenging for traffic analysts and planners. This challenge stems from the necessity of evaluating and contrasting the quality of transportation services across multiple modes. Existing methods are constrained in offering comprehensive insights into the system, primarily due to the inadequacy of multimodal traffic data necessary for fair comparisons and their inability to equip analysts and planners with the means for exploration and reasoned analysis within the urban spatial context. To this end, we first acquire sufficient multimodal trips leveraging well-established navigation platforms that can estimate the routes with the least travel time given an origin and a destination (an OD pair). We also propose TraDyssey, a visual analytics system that enables analysts and planners to evaluate and compare multiple modes by exploring acquired massive multimodal trips. TraDyssey follows a streamlined query-and-explore workflow supported by user-friendly and effective interactive visualizations. Specifically, a revisited difference-aware parallel coordinate plot (PCP) is designed for overall mode comparisons based on multimodal trips. Trip groups can be flexibly queried on the PCP based on differential features across modes. The queried trips are then organized and presented on a geographic map by OD pairs, forming a group-OD-trip hierarchy of visual exploration. Domain experts gained valuable insights into transportation planning through real-world case studies using TraDyssey.

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Dai, J., Sun, W., Lu, Q. L., Schmöcker, J. D., & Antoniou, C. (2025). Railway-station-area vitality in response to COVID-19: A case study of diverse Japanese cities. Cities, 105970.

Published in Cities, 2025

This study examines the impact of COVID-19 on the number of visitors weighted by their time spent at facilities within and nearby railway stations, analyzing short-term demand losses and long-term recovery trends at 69 major stations in diverse Japanese cities using aggregated mobile phone data. We refer to this as “station area vitality”. We extend previous research by integrating external variables, such as land use and Points of Interest (POIs), to explain vitality drops and forecast recovery over two years. The findings reveal that multifunctional station areas—those combining leisure shopping, daily-needs shopping, and transport purposes—showed greater resilience during the pandemic. This underscores the value of mixed-use development and flexible zoning for enhancing station resilience. Furthermore, our forecasting models, particularly ARIMAX and LSTM, can to some degree predict long-term recovery trends during or after the pandemic when external variables and extended learning periods are included. We hence suggest that this can offer critical insights for urban planners and policymakers to build more resilient station areas and to forecast their performance during a new pandemic.

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Lu, Q. L., Sun, W., Lyu, C., Schmöcker, J. D., & Antoniou, C. (2025). Post-disruption lane reversal optimization with surrogate modeling to improve urban traffic resilience. Transportation Research Part B: Methodological, 197, 103237.

Published in Transportation Research Part B: Methodological, 2025

Rapid post-disruption recovery is essential but challenging, given the complex interactions between vehicular flows and the network supply. Simulation-based methods are widely used to assist the planner with realistic user-system interactions in the recovery measure optimization, though the application to large-scale transportation networks remains computationally expensive. This study explores the feasibility of using surrogate models as a time-efficient alternative to resource-intensive simulations. Lane reversal control is employed as a novel recovery measure and an optimization framework prioritizing systematic recovery is developed. A resilience loss indicator based on Macroscopic Fundamental Diagram dynamics is used to evaluate the real-time performance of the transportation system. The proposed surrogate model, therefore, also focuses on approximating recovery evaluation indicators, i.e., the resilience loss, other than link flows and density. The surrogate model contains a dynamic analytical network model and a Gaussian Process Regression model. The former provides the analytical resilience loss and considers the temporal correlation of network changes resulting from time-dependent lane reversal decisions. The latter captures the difference between simulated and analytical resilience losses. Experiments are conducted on a large real-world road network in Kyoto City. The proposed approach demonstrates its efficacy by mitigating traffic resilience loss by about 6% under scenarios of 15 and 20 controllable links with a mere five algorithm iterations, requiring only 150 simulation runs. We also illustrate a trade-off between recovery performance and control resources that more controllable links unnecessarily offer better resilience improvement given the short decision-making duration and the very tight computational budget.

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Lu, Q. L., Stern, S., Sadrani, M., & Antoniou, C. (2025). Dynamic network capacity allocation using model predictive control with sparse identification of nonlinear dynamics. IEEE Transactions on Intelligent Transportation Systems, 3564934.

Published in IEEE Transactions on Intelligent Transportation Systems, 2025

Demand variations throughout the day and area popularity differences across the city result in spatiotemporal changes in traffic flow. One of the well-known phenomena arising from these changes is tidal traffic, characterized by an imbalance between inbound and outbound traffic on a given road. It reflects the fluctuation in the alignment between transportation system supply and demand. Lane reversal control has been a common supply-side measure for dealing with this urban traffic ``sickness’’ by adapting road capacity allocation to the demand imbalance between two directions of a road. This study investigates the dynamic network capacity allocation control problem in the era of connected and autonomous vehicles (CAVs), which integrates dynamic traffic signal splits and lane reversal controls. Considering the high dimensionality and non-linearity of urban transportation systems, we apply the sparse identification of nonlinear dynamics (SINDy) technique to construct a sparse yet sufficiently accurate surrogate model. This model estimates the forthcoming network traffic state based on the current state and implemented control decisions. The surrogate model is integrated into a model predictive control (MPC) method, forming a SINDy-MPC framework to assist in optimal decision-making in real time. The experiments show that the system identified by SINDy exhibits stability in the presence of Gaussian noise disturbances. The proposed dynamic network allocation control scheme can effectively reduce traffic imbalance, improve traffic efficiency, and enhance traffic resilience against cyberattacks.

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Wu, H., Zame, S. I., Guo, T., Lu, Q. L., & Antoniou, C. (2025). A sustainable multi-objective framework for multi-phased, capacitated vertiport siting with land use integration. Communications in Transportation Research, 5, 100186.

Published in Communications in Transportation Research, 2025

This research presents a multi-objective optimization framework for incremental siting of capacitated vertiports that integrates real land use data and aims to maximize generalized cost savings while minimizing infrastructure costs and emissions. The multi-phased siting framework uniquely facilitates the gradual evolution of Urban Air Mobility (UAM) operations from initial electric vertical takeoff and landing vehicles (eVTOLs) to more advanced modular flying vehicles (MFVs). This phased technological progression provides a practical pathway toward fully operational flying cars while ensuring feasible infrastructure adaptability across these transitions. Applied to the Munich metropolitan area, the framework demonstrates that multi-phased siting, particularly a 4-phased strategy, yielding about $1.315 × 10^5$ euros higher daily net profits. Specifically, compared to base single-phased approach, the 4-phased strategy delivers substantial marginal improvements across key metrics for an exemplary operating day: $1.3 \times 10^4$ euros in generalized travel cost savings, 15 ​t in emissions reductions, and a 0.9% increase in UAM mode share. Beyond four phases, the benefits diminish relative to increased complexity. A full factorial analysis examining capacity constraints and infrastructure costs reveals that ignoring either factor leads to impractical outcomes-unconstrained capacity results in demand exceeding 60-fold capacity, while disregarding infrastructure costs generates negative net profits due to overinvestment. The analysis identifies an optimal infrastructure cost subsidy range of 20%–40%, balancing performance gains with economic sustainability. These findings enable integrated planning that effectively balances operational efficiency, system-wide environmental externalities, and economic viability through optimized cost allocation and phased investment strategies.

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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