Zhengyi Cai

The impact of traffic incidents in urban road networks often propagates beyond the affected segment, causing performance degradation at adjacent intersections and leading to network-level congestion. To address this challenge, this paper proposes a novel method for real-time incident detection and impact quantification using license plate recognition (LPR) data. The key ideas of this method is: 1) Incident-induced queue spillback often results in a Green Phase Failure (GPF) at upstream intersections, and the delays of vehicles experiencing this failure inherently contain information about the GPF duration. 2) By leveraging LPR data to reconstruct vehicle trajectories and establish a baseline of historical queuing information, the GPF duration can be accurately derived from the delay of a subsequent vehicle. 3) The proposed Delay-Discrepancy-Detection (3D) method performs a vehicle-by-vehicle analysis of delay discrepancies to achieve real-time identification of GPFs and calculation of their duration, achieving automated incident detection and real-time impact analysis. The proposed method was validated using both simulation data and field data collected in Hangzhou, China. Qualitatively, the results demonstrate that our method can promptly and accurately detect the occurrence of incident-induced GPFs. Quantitatively, the method exhibits high precision, with calculated GPF duration errors not exceeding 5.5 seconds in field tests and 8.5 seconds in simulation scenarios, confirming its capability for precise incident impact quantification.

Mobile phone signalling data (MPSD) has become a valuable resource for driving numerous urban applications. However, due to high collection costs and strict privacy constraints, the scarcity of labelled datasets limits its broader application. To address this challenge, we reframe the problem from pure generation to a reality‐preserving simulation. This study proposes a deep reinforcement learning framework that synthesises MPSD trajectories by learning a policy to translate high‐fidelity GPS data into realistic cell tower connection sequences. The framework employs proximal policy optimisation (PPO) to learn a sophisticated connection policy, guided by a composite reward function that jointly optimises for point‐level accuracy and trajectory‐level distributional similarity, effectively capturing physical phenomena like connection hysteresis. A comprehensive evaluation across micro‐ (continuity) and macro‐ (distributional) levels demonstrates that our model outperforms state‐of‐the‐art baselines. The synthetic trajectories achieve high fidelity, with an effective point ratio of 0.97 (ground truth: 1.0), an average temporal sampling rate nearly identical to real data (79s vs. 77s), and low divergence in spatiotemporal distributions. Crucially, our approach enables lossless transfer of semantic labels (e.g., travel mode and purpose) from the source GPS data, directly addressing the label scarcity problem. The practical utility of the synthetic data is validated on two downstream tasks—map matching and travel mode identification—where models trained on our data achieve performance comparable to those trained on real data. This work presents a scalable solution to the data and label scarcity problem in mobility research, providing a robust method for generating semantically enriched, application‐ready MPSD for advancing large‐scale urban mobility analysis.

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks.

Mobile Phone Signaling (MPS) data record the daily traces of urban residents, offering a cost-effective means to obtain travel information for urban traffic management and planning at low cost. However, despite the vast amount of data available, there remains a lag in the development of techniques for identifying fine-grained information such as travel modes. On the one hand, the high positioning error and irregular collection frequency make the identification of fine-grained modes challenging. On the other hand, the difficulties in collecting real labeled data limit the training and evaluation of advanced models. In this paper, we present an advanced Travel Mode Identification (TMI) framework and collect real labeled MPS data for evaluation. Specifically, a fast smoothing method is proposed to enhance noise reduction in large-scale trajectories while effectively mitigating positioning errors. We propose novel point-level bus route alignment features for advanced deep-learning models to improve differentiation between motorized modes. Furthermore, a deep learning model with ensembled feature encoding modules is designed to overcome training instability due to limited data amount. Our proposed framework achieves an accuracy of 83.06% in identifying fine-grained travel modes, including walking, riding, bus, car, and metro, with recall rates exceeding 78% for all modes except walking. We analyze the relationship between accuracy and the spatiotemporal characteristics of trajectories, revealing a significant impact from the collection frequency, while showing insensitivity to distance gaps and positioning errors. This study demonstrates the potential of MPS for TMI and promotes its application in intelligent transportation systems.