Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Secure Mutual Self-Authenticable Mechanism for Wearable Devices

YesDue to the limited communication range of wearable devices, there is the need for wearable devices to communicate amongst themselves, supporting devices and the internet or to the internet. Most wearable devices are not internet enabled and most often need an internet enabled broker device or intermediate device in order to reach the internet. For a secure end to end communication between these devices security measures like authentication must be put in place in other to prevent unauthorised access to information given the sensitivity of the information collected and transmitted. Therefore, there are other existing authentication solutions for wearable devices but these solutions actively involve from time to time the user of the device which is prone to a lot of challenges. As a solution to these challenges, this paper proposes a secure point-to-point Self-authentication mechanism that involves device to device interaction. This work exploits existing standards and framework like NFC, PPP, EAP etc. in other to achieve a device compatible secure authentication protocol amongst wearable device and supporting devices.

Simulating Large-Scale Microscopic Traffic Data

Traffic situations are continuous, uncertain, highly dynamic and partially observable, and they affect the day-to-day lives of people in a society. A worthwhile endeavor is to develop algorithms that can predict abnormal traffic situations by exploiting data from the myriad of sensors on the streets, in vehicles and in smartphones, leading to smoother flow of traffic. Unfortunately, the large volumes of microscopic (i.e. individual vehicle-level) data required for developing statistical/machine learning algorithms cannot be collected from the field by the public. The data collected by transportation agencies is either macroscopic or not widely available. In this thesis, a framework is developed for simulating large-scale traffic data using a microscopic simulation model and limited real-world data. Five kinds of sensors are simulated: inductor loop detector, lane area detector, multi-entry multi-exit detector, Bluetooth, and edgebased traffic measure. Data is simulated using this framework from multiple sensors over an area covering Montgomery County and Prince George County in Washington DC for 720 hours (30 days). The synthesized data is validated with respect to real-world data for volume and speed. Widely-used classifiers are used to recognize eight traffic events, namely Collision, Disabled Vehicle, Emergency Roadwork, Injuries Involved, Obstructions, Road Maintenance Operations, Traffic Signal Not Working and with no events in the synthesized dataset with high accuracy. Given limited real-world microscopic traffic data from a particular area, this framework is the first of its kind that can simulate data from multiple kinds of sensors over a very long duration with high-fidelity to the given data

Modelling and Simulating Bluetooth-based Moving Observers

Bluetooth devices on board of traffic objects depict an easy way of detecting motions of persons and goods. However, the precondition is that one Bluetooth device (the observer) discovers another Bluetooth device - namely the on board device - within a very short time range. Device discovery in case of Bluetooth connection establishment is heavily dependent from the so called inquiry process which allows Bluetooth devices to look for each other and to exchange relevant information to build up a lasting connection. In this paper we give an analytical model of the time it takes a Bluetooth-based moving observer to discover a Bluetooth device within a floating traffic object by considering the specific behaviour of the inquiry procedure. Therefore, the analytical model refers to the inquiry process described in the official Bluetooth standard protocol version 1.2 and thus includes a specific mode - named interlaced scan mode-which was introduced to speed up discovery times. We show that, taking that kind of mode into account gives more reasonable results in terms of reflecting practical measurements by means of simulations. As a result, we can much better describe the observers behaviour concerning the probability of the first detection