Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Traffic management has become more complicated because increasing road networks and vehicles day by day due to population increasing. Monitoring and tracking of movable vehicles is one of the most comprehensive issues. Decision support system for traffic management is the key points of intelligent transportation system. In this paper we propose the decision support system for handling traffic by using the concept of sensor and wireless networking. It is easier to identify the traffic at routes and provide feasible decisions to the traffic system, which makes the decision support intelligent by using the wireless sensor technologies.

http://ieeexplore.ieee.org/iel7/7430173/7435618/07435818.pdf

An algorithm for accessing traffic database using wireless technologies

The direction of a moving target is an important piece of information in many wireless sensor network (WSN) applications, such as in boundary security, traffic flow control, etc. Due to its robustness, the magnetic sensor can be used to detect a passing ferromagnetic object. By using the orthogonality of two perpendicularly placed sensing units in a monomer magnetic sensor, a linear algorithm based on a magnetic dipole model to identify the ferromagnetic object's moving direction is introduced in this paper. It has been successfully applied in real WSN applications to reduce the numbers of nodes, and prolong the lifetime of the network. Both simulation and field experiments show it has strong noise immunity and more than 95% correction rate in direction detecting.

A Practicable Method for Ferromagnetic Object Moving Direction Identification

Real-time road traffic monitoring is widely considered to be a promising traffic management approach in urban environments. In the smart cities scenario, traffic trajectory sensor data streams are constantly produced in real time from probe vehicles, which include taxis and buses. By exploiting the mass sensor data streams, we can effectively predict and prevent traffic jams in a timely manner. However, there are two urgent challenges to processing the massive amounts of continuously generated trajectory sensor data: (1) the inhomogeneous sparseness in both spatial and temporal dimensions that is introduced by probe vehicles moving at their own will, and (2) processing stream data in real time manner with low latency. In this study, we aim to ameliorate the aforementioned two issues. We propose an online approach to addresses the major defect of inhomogeneous sparseness, which focuses on employing only real-time data rather than mining historical data. Furthermore, we set up a real-time system to process trajectory data with low latency. Our tests are performed using field test data sets derived from taxis in an urban environment; the results show that our proposed method lends validity and efficiency advantages for tackling the sparseness, and our real-time system is viable for low latency applications such as trafficmonitoring.

Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream

This article presents a new platform called PCIV (intelligent platform for vehicular control) for traffic monitoring, based on radio frequency Identification (RFID) and cloud computing, applied to road traffic monitoring in public transportation systems. This paper shows the design approach and the experimental validation of the platform in two real scenarios: a university campus and a small city. Experiments demonstrated RFID technology is viable to be implemented to monitor traffic in smart cities.

PCIV, an RFID-Based Platform for Intelligent Vehicle Monitoring

A new value is reaching new heights in the InformationSociety, Road Safety. In this situation, strategic trafficmanagement is a key factor in intelligent transport systemsapplications (ITS), even though it also involves different sectors such as economic (oil dependency), environmental (pollution, noise. . . ), safety (both drivers and pedestrians) and health (breath problems, circulation. . . ). In urban areas, the trafficcontrol centre (TCC) gathers parameters and informationabout real time traffic which are used to implement trafficpolicies and legislation. This paper presents an intelligent system able to provide information in real-time about: detection (presence), flow rate, counting and vehicle classification. The classification has been made in five groups (5+1): powered two-wheeler, passenger car, van, truck and bus. The detection system used is a laser scanner sensor.

Traffic Monitoring: Improving Road Safety Using a Laser Scanner Sensor

The present article is a continuation of our previous works on the anomalies in the decay rates and the capacitance measurements inside a modified Faraday cage. Here we present the anomalous variations in the measurements of inductance when a coil is placed inside an interleaving structure of metal and organic material. The fluctuation in the inductance values was found to be in the range - 0.007 to 0.018 mH. Additionally, it was observed that the temperature coefficient which was 0.0062 mH/°C initially jumped to two distinct levels, 0.0095 mH/°C and 0.0145 mH/°C, respectively. A multiple factor analysis of our results revealed a very strong correlation (R2 = 95.2%) between the inductance and the combination of the temperature and the relative humidity both measured inside the cage, next to the inductor. 

/pdf/variability-of-coil-inductance-measurements-inside-an-12wx3sph.pdf

Variability of coil inductance measurements inside an interleaving structure

/pdf/reply-to-role-of-ambient-humidity-underestimated-in-research-3k5y440h.pdf

Antonio Mocholi

Papers

Traffic Monitoring: Improving Road Safety Using a Laser Scanner Sensor

Variability of coil inductance measurements inside an interleaving structure

Reply to: Role of ambient humidity underestimated in research on correlation between radioactive decay rates and space weather