Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

/pdf/review-of-data-fusion-methods-for-real-time-and-multi-sensor-3jy63u90va.pdf

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

In this paper, we present a systematic literature review concerning 3D object recognition and classification. We cover articles published between 2006 and 2016 available in three scientific databases (ScienceDirect, IEEE Xplore and ACM), using the methodology for systematic review proposed by Kitchenham. 
Based on this methodology, we used tags and exclusion criteria to select papers about the topic under study. 
After the works selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the tests and evaluation performed and the databases used. 
Lastly, we compressed all the obtained information in a general overview and presented future prospects for the area.

3D object recognition and classification: a systematic literature review

With the rapid development of sensing technology, Cyber-Physical Systems (CPS) are connecting our real-world and cyber spaces by real-time situation awareness and intelligent control. In this process, one of the major challenge is how to make fast, accurate and intelligent decisions based on high-dimension, speed and volume sensing data stream. In this paper, we put human into the traditional CPS data process model and formulate a closed-loop computing paradigm for CPS data and decision intelligence. We propose a human-in-the-loop reference model for CPS, which extends the traditional cyber-physical interaction into a closed-loop process based on cyber, physical and human factors. We define the key features of human-in-the-loop CPS, summarize it as three aspects: semantic, interactive, iterative and analyze the major challenges from the perspective of data characteristics. Recent progresses in three typical application domains are reviewed and examined for their decision models and whether they have solved the target issues of human-in-the-loop CPS. According to the review and comparison, the paper finally summarizes several key future opportunities to establish an intelligent human-in-the-loop CPS.

Data and Decision Intelligence for Human-in-the-Loop Cyber-Physical Systems: Reference Model, Recent Progresses and Challenges

Community-based health services have risen as important online resources for resolving users health concerns. Despite the value, the gap between what health seekers with specific health needs and what busy physicians with specific attitudes and expertise can offer is being widened. To bridge this gap, we present a question routing scheme that is able to connect health seekers to the right physicians. In this scheme, we first bridge the expertise matching gap via a probabilistic fusion of the physician-expertise distribution and the expertise-question distribution. The distributions are calculated by hypergraph-based learning and kernel density estimation. We then measure physicians attitudes toward answering general questions from the perspectives of activity, responsibility, reputation, and willingness. At last, we adaptively fuse the expertise modeling and attitude modeling by considering the personal needs of the health seekers. Extensive experiments have been conducted on a real-world dataset to validate our proposed scheme.

/pdf/multiview-physician-specific-attributes-fusion-for-health-4dj3zkze8k.pdf

Multiview Physician-Specific Attributes Fusion for Health Seeking

Automated Microfossil Identification and Segmentation using a Deep Learning Approach

Recognizing multi-view objects with occlusions using a deep architecture

Heterogeneous traffic data collected from different types of sensors are fused for estimating traffic states more accurately. Data quality and fusion method are two key issues required to be solved in the traffic state estimation. In this paper, the authors propose a fusion method of heterogeneous traffic data based on the Kalman filters (KF) and Gaussian mixture models (GMM). The noise in collected raw data is reduced by the KF in order to improve the quality of input data for fusion. The vectors of historical data from global positioning system (GPS) and remote traffic microwave sensors (RTMS) in different traffic states are modeled with multiple multi-variate GMM respectively. Finally, the estimated traffic state can be obtained by computing the posterior probabilities with the vector data and GMM. Performance of the authors work is examined by series of experiments, and the results show that the proposed method is effective for improving the precision of traffic state estimation.

Fusing heterogeneous traffic data by Kalman filters and Gaussian mixture models

Nowadays, processing traffic flows has become an important part in intelligent transportation system (ITS). Prediction and estimation of flows, as a main application in this field, has gradually developed. Moreover, there exist some inherent relationships among various traffic flows, and the mining of related information can provide a platform for traffic flow prediction and estimation, and it can supply some guidance to layout traffic sensors. This paper presents a method of cluster by k-medoids to mine related information of traffic flows from spatial dimension. From spatial dimension, road lanes are clustered by k-medoids to constitute a table of related information. In order to make the mining of related information of flows more accurate, degree of saturation is also used to cluster related information. The results indicate that cluster through combination of flow and degree of saturation has a higher efficiency, and cluster by k-medoids outperforms that by k-means in all experiments.

Mining related information of traffic flows on lanes by k-medoids

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Recognizing multi-view objects with occlusions using a deep architecture

Fusing heterogeneous traffic data by Kalman filters and Gaussian mixture models

Mining related information of traffic flows on lanes by k-medoids