Mining urban mobility trajectory is critical for understanding individual travel behavior and exploring the mechanism of city evolution. In the era of big data, with the proliferation of ubiquitous mobile sensors such as cellphones and GPS devices, a wide range of spatio-temporal urban trajectories can be collected with fine resolutions, which provides comprehensive data support for the task of urban mobility trajectory mining. However, there are still many challenges in this task. First, the urban trajectories are with large volumes and their related semantic annotations are absent. Second, it is difficult to achieve impressive results when simulating complex human activities by mathematical models, while the use of real trajectories for simulation may involve risks such as privacy leakage. Finally, macroscopic and microscopic simulation results often conflict with each other. The parallel transportation systems (PTS) can effectively tackle these challenges by introducing artificial transportation systems that correspond to the real transportation systems. Through the virtual-reality interactions, PTS can realize the management and control for the real transportation systems. Oriented to the PTS, this dissertation carries out the mining of urban mobility trajectory step by step around three stages: travel semantic information awareness, travel trajectory generation, and scenario-assisted decision-making. The contents and contributions of this dissertation are as follows.
1. As one of the research components of travel semantic information awareness, transportation mode detection is important for applications such as transportation service recommendations. The conventional "segmentation-inference" two-stage pipeline uses heuristic rules to identify the change points in trajectories, which is not only inefficient but also cannot ensure the reliability of segmentation. In addition, the available information in unlabeled trajectories is difficult to be utilized effectively. This dissertation improves the conventional two-stage pipeline and re-models the transportation mode detection task as an end-to-end dense classification problem. Then, a semi-supervised end-to-end model with similarity entropy-based regularization is presented for point-wise detection. The proposed model applies an encoder-decoder network with shortcut connections as a backbone to fuse the shallow motion features with deep semantic features for high-precision detection. Further, a semi-supervised module based on similarity entropy regularization is designed to mine the underlying information in unlabeled trajectories. The experimental results on the GeoLife trajectory dataset show that the proposed model achieves 79.3% accuracy and about 5% improvement in the recall metric compared to baseline models.
2. As the second research component of travel semantic information awareness, inferring the travel intentions of residents is significant for understanding the current situation of city movements. The lack of related semantic labels in the available data leads to the inability of supervised models. In addition, there is a lack of unified measures for travel-related attributes, such as spatio-temporal patterns and geographic information, and it is thus difficult to map them into the same feature space, leading to inefficiency in travel pattern extraction. This dissertation proposes a collaborative travel intention inference approach based on empirical rules and a topic model. First, empirical rules are designed to infer and then filter out users' trips with job and homing intentions. Then, a continuous term aware latent Dirichlet allocation model is presented, in which the trip chains of each user and each trip are treated as a document and a high-dimensional word with heterogeneous attributes, respectively. The travel intentions for the remaining trips can be inferred in such a soft clustering way. Experiments on Beijing cellphone signaling data show that the proposed model achieves 7.7% improvement in the perplexity metric compared to the baseline models, and the inferred results are highly consistent with the results from the household travel survey at a macro level.
3. The travel trajectory generation can effectively solve the problems of limited real-scenario coverage and privacy leakage. Existing trajectory generation models ignore the spatial characteristics and the temporal order of the visited locations, which leads to a large difference between the generated trajectories and the real ones. In addition, the lack of semantic information limits the further applications of the generated results. This dissertation proposes a semantic-guiding adversarial network for urban trajectory generation, in which the transportation modes and travel intentions are set as semantic items. The proposed model consists of masked multi-headed attention modules and yields the visits in a sequence-to-sequence manner. To learn the spatial characteristics implied by real trajectories, this dissertation uses a Monte Carlo search to complete the unfinished trajectories and converts them into images. Then a discriminator is adopted to signify how "real" the trajectory image looks, and its output is regarded as a reward signal to update the generator. The experiments on GPS trajectory data show that, compared to the baseline models, the proposed model can reduce the difference between the generated trajectories and real ones in two spatial characteristics (i.e., the radius of gyration and travel distance) by 10% and 27%, respectively, which indicates that the generated results are more realistic.
4. Under the "experimental evaluation" operation mode in PTS, this dissertation takes the epidemic outbreak scenario as a demonstration to perform the scenario-assisted decision-making task. The dynamic of virus propagation among individuals is modeled, and then an agent-based microsimulation environment is constructed as an artificial transportation system to conduct the epidemic spread simulation. After that, the trajectories are loaded into the agents as the basis for their interactions, and the macroscopic changes under different preventative measures can be revealed from the microscopic individual level. Unlike existing studies, there are no other requirements such as the complex topological networks of individuals in the trajectory data-driven simulation, in which the interactions between agents can be derived by the intersections of their trajectories. Additionally, this dissertation applies generated trajectories to explore the correlations of population densities and preventative measures with the epidemic spreading process. All of those demonstrate the decision-making value of urban trajectories.
To sum up, this dissertation studies urban mobility trajectory mining under the framework of the parallel transportation systems and improves the accuracy of tasks such as travel semantic information awareness and travel trajectory generation. By conducting computational experiments in a data-driven artificial transportation system, the propagation dynamics mechanism of urban travel is explored from macro and micro views. The achievements of the study help to understand urban mobility and bring into full play the multiplier effect of data on the productivity of smart cities, which is significant for understanding urban behavior and optimizing urban decision-making.
|Keyword||平行交通系统 城市出行轨迹挖掘 出行语义感知 出行轨迹生成 疫情传播模拟|
|李志帅. 面向平行交通系统的城市出行轨迹挖掘方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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