Jiaqi Zeng

Traffic congestion is a major factor constraining sustainable urban development and degrading residents’ travel experience, while green travel and shared mobility offer effective means of mitigating traffic congestion, and improving residents’ travel experience and living environment. However, it remains unclear how individualized guidance can be provided for green travel and shared mobility and how the benefits resulting from green travel guidance and shared mobility can be quantitatively evaluated at the city scale. Based on citywide license plate recognition data, all commute travelers were first identified, and their commute travel during the morning and evening peak periods was extracted. For all commute travel, algorithms were developed to quantify the green travel mode shift potential and carpooling potential of commute travelers. On this basis, the reductions in peak period travel demand and the congestion mitigation effects that could be achieved through green travel guidance and shared mobility under different scenarios were analyzed. The empirical results for Xiaoshan, China, showed that, under the illustrative upper-bound combined scenario constructed in this study, individualized guidance could reduce the peak number of vehicles traveling on the road by 28.73% in the morning peak and 25.96% in the evening peak, while the minimum network speed could be increased by 8.48% and 7.66%, respectively. Overall, an algorithmic framework for individualized travel guidance based on individual trajectory big data is proposed, and the congestion mitigation benefits achievable through green travel guidance and shared mobility are quantified.

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.

Gap-acceptance at roundabout entries is a safety-critical driver behavior with direct implications for intersection capacity analysis, microscopic traffic simulation, and the development of autonomous driving systems. Existing models either treat the decision as a static threshold comparison, limiting their ability to capture behavioral heterogeneity and context dependence, or rely on data-driven methods that achieve strong predictive performance at the cost of interpretability and mechanistic insight. This study proposes a unified behavioral framework that couples cumulative prospect theory (CPT) with the drift diffusion model (DDM) for gap-acceptance decisions at roundabout entries. The CPT module translates observable traffic states into a time-varying subjective value difference between entering and waiting, capturing loss aversion and probability distortion as systematic features of driver risk perception. This value difference drives the drift rate of the DDM module, in which entry commitment accumulates stochastically over time until accumulated evidence reaches the decision boundary. A simulation-based maximum likelihood (SML) estimator combined with a differential evolution (DE) optimizer jointly identifies all five model parameters from naturalistic trajectory data, with gaps that expire without entry treated as right-censored observations from a survival perspective. The framework is evaluated on the Citysim trajectory dataset against five baseline models spanning four established modelling paradigms. Results show that the proposed framework achieves superior performance in both gap decision behavior prediction and entry timing prediction across all distance windows, with an accuracy of 87.83% and a Hit@1.0s of 88.0% at the full 30–0 m window. The estimated parameters reveal that drivers at roundabout entries exhibit substantially higher loss aversion and stronger probability distortion than general behavioral economics benchmarks, and the framework reproduces the hesitation-then-commit kinematic signature observed in naturalistic driving data, providing mechanistic insights that purely predictive models cannot offer.

Discretionary lane changing is one of the most important behaviors in vehicle dynamics. The analysis of discretionary lane changing can provide support for human-like autonomous driving system and microscopic traffic simulation. The rule based discretionary lane change decision-making model has the advantages of good interpretability and being in line with human intuitiveness, but the performance of the rule based models are relatively poor. The learning-based discretionary lane changing decision-making models perform better in lane changing analysis than that of the rule based models, while they require a large amount of training data and have poor interpretability. This study proposes a framework for discretionary lane changing decision-making based on cumulative prospect theory and decision tree model, and uses genetic algorithm to calibrate the parameters of the framework. The framework proposed in this study has good interpretability for discretionary lane changing behavior similar to rule-based models, and it can achieve good lane changing prediction performance. The Next Generation Simulation (NGSIM) dataset is used to validate the framework proposed in this study. The results show that the framework proposed in this study can achieve better performance of discretionary lane change prediction than the analysis model based on decision tree and the analysis model based on cumulative prospect theory. When predicting discretionary lane changing behavior 2–0 seconds in advance, the accuracy of the framework in the test set can reach 85.6%.

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.

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-Sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model’s generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.