Multimodal Mobility Modeling and Traffic Profiling in Cyber-Physical Systems

这项研究工作的主要目标是调查使用移动的设备沿着蜂窝无线网络和其他传感技术来估计移动性，并建立强大的时空移动模型。运输系统将被视为目标应用程序。提取个人的移动模型可以分析交通状况，因此可以促进广泛的交通应用：车辆路线，优化交通信号灯，道路维护规划和拥堵管理。软计算推理和过滤方法为建模移动性提供了一系列功能。然而，由于流动性通常是一个非线性的动态过程，一个小的非线性可以导致困难的表示后验知识。为了调查这些挑战，将采用贝叶斯滤波和粒子滤波等顺序蒙特-卡罗方法。贝叶斯方法将被用来解决两个开放的问题：a）无线定位的基础上的信号分析（例如，指纹和RSS; B）同时定位和映射。综合传感方法进行了探索，以解决稀疏信号的问题，在这样的系统。各种各样的数据源，如蜂窝网络，手机，道路摄像机，车载GPS设备，环路探测器，和移动的增强现实模型，将作为补充的流动性线索进行研究。为了最大限度地发挥多模态的潜力，拟议的研究工作将调查几种数据融合技术，用于推断和估计移动性和交通趋势。在这里，拟议的研究项目将采用软计算作为推理方法，其中概率技术用于从移动性数据中捕获趋势，模糊和Dempster-Shafer技术为捕获的移动性和交通数据中的不确定性推理提供语言范例。粒子，卡尔曼，GMF，和HMM滤波器将采用在各个层次的拟议建模方案，以解决稀疏的测量压缩传感技术将进行研究。

The main objective of this research work is to investigate the use of mobile devices along with cellular wireless networks and other sensing technology to estimate mobility and to build robust tempro-spatial mobility models. Transportation systems will be considered as a target application. Extracting a mobility model for individuals enables the profiling of traffic conditions and hence can facilitate a wide range of transportation applications: vehicle routing, optimized traffic lights signalling, road maintenance planning, and congestion management. Soft-Computing inferencing and filtering methods offer a range of capabilities for modeling mobility. However, since mobility is generally a non-linear dynamic process, a small non-linearity can lead to difficulties to represent the posterior knowledge. To investigate such challenges, Bayesian filtering and sequential Monte-Carlo methods such as particle filters will be employed. The Bayesian methods will be employed to tackle two open issues: a) wireless localization based on signal profiling (e.g, fingerprints and RSS; b) simultaneous localization and mapping. Comprehensive Sensing methods are explored to address the sparse signals issue present in such systems. A diverse range of data sources, such as cellular networks, cell-phones, road cameras, on-board GPS devices, loop detectors, and mobile augmented reality models, will be studied as complementary mobility cues. To exploit multi-modality to its utmost potential, the proposed research work will investigate several data fusion techniques for inferencing and estimating mobility and traffic trends. Here, the proposed research project will adopt a soft-computing as reasoning approach, where probabilistic techniques are used to capture trends from mobility data and fuzzy and Dempster-Shafer techniques offer linguistic paradigms for reasoning about uncertainty in the captured mobility and traffic data. Particles, Kalman, GMF, and HMM filters will be employed at various levels of the proposed modeling scheme; to address sparsity in the measurements Compressing Sensing techniques will be investigated.