A Methodology with Distributed Algorithms for Large-Scale Human Mobility Prediction

In today’s era of big data, huge amounts of spatial-temporal data related to human mobility, e.g., vehicle trajectories, are generated daily from all kinds of city-wide infrastructures. Understanding and accurately predicting such a large amount of spatial-temporal data could benefit many real-world applications, e.g., efficient transportation resource relocation. However, the mix of spatial and temporal patterns among these activities and the scale of the data (in a city level) pose great challenges for accurate predictions under real-time constraints. To bridge the gap, this dissertation proposes a methodology for the prediction of large-scale human mobility, especially a city level’s vehicle trajectory distribution across the road network. The thesis has several major components: (1) a novel model for the prediction of spatial-temporal activities such as people’s outflow/inflow movements combining the latent and explicit features; (2) different models for the simulation of corresponding flow trajectory distributions in the road network, from which hot road segments and their formation can be predicted and identified in advance; (3) different MapReduce-based distributed algorithms for the simulation and analysis of large-scale trajectory distributions under real-time constraints. First, our proposed methodology quantifies the latent features of spatial environments and temporal factors through tensor factorization, given existing mobility datasets. We model the relationship between spatial-temporal activities and the latent and other explicit features as a Gaussian process, which can be viewed as a distribution over the possible functions to predict human mobility. After the prediction of overall inflow/outflow, we further model these movements’ trajectory distributions in the road network, from which the corresponding hot road segments and its possible causes, among other things, can be predicted in advance. For example, based on our prediction, in the next half hour, a high percentage of vehicles that travel from region A/B toward region C/D might pass through the same road segment, which indicates that a possible traffic jam or bottleneck could form there later. This process is computationally intensive and would require efficient algorithms for real-time response because the scale of a city’s road network and the possible number of trajectories that people might choose to take during certain time periods could be very large. Efficient distributed algorithms are proposed and validated