Scalable system for smart urban transport management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city

Real-time sensor data integration in vertical transport systems

In this project, mobile connectivity and an innovative approach to sensor data gathering and integration have been employed to automate maintenance inspection, performance monitoring and ride quality measurement in vertical transportation systems. An Inertial Navigation System (INS) has been proposed, implemented and tested to track lift car movement profile. The inherent characteristics of vertical motion have been used to minimize errors and obtain higher accuracy in the integration results. The measurement of a correlation between kinematic profiles constructed from lift-car tracking data compared to its nominal values provides key information on the lift condition at any time. A frequency analysis was applied to processing vibrations and noise data, effectively adding another dimension to the lift ride quality measurement. This approach enabled lift performance profiles to be compiled automatically and transmitted in real time, which significantly rationalized and improved the process of maintenance inspection and monitoring. An advanced prototype, AdInspect, has been produced, with the full set of described features. Industry partners are currently evaluating it

Navigating within the urban environment using Location and Orientation-based Services

Up to now most attempts to develop pedestrian navigation tools for the urban environment have used GPS technologies to display position on two-dimensional digital maps (as in the classic 'satnav ' systems on the market). Although GPS is the key technology for location-based services (LBS), it cannot currently meet all the requirements for navigation in urban environments. Specifically, GPS technologies suffer from multipath signal degradation and they cannot provide orientation information at low or zero speed, which is an essential component of navigation. It has also been demonstrated in research that maps are not always the most effective interfaces to pedestrian navigation applications on mobile devices. This paper will explore solutions to the orientation and interface challenges in pedestrian navigation on mobile devices. Orientation information is necessary to help the user self-localise in an unknown environment and can be provided by calibrated digital compass integrated with the GPS positioning. Further orientation assistance can be provided b

Self-adaptive service driven architecture for intelligent transport system

Modern transport systems rely on an increasing number of sensors to control their operation including orientation, speed, arrival and departure times, fuel consumption, passenger count etc. These have become important parts of the echo system with their availability and performance playing a key role in daily commuting. However, they produce data in high volume and frequency that need to be processed fast enough so that it can be shared across the network in real-time. Traditional system architectures have been unable to meet transport systems quality requirements scuh as scalability, adaptability, sustainability, extendibility and high availability. Thus, in this work, we propose a novel architecture to acquire, store, manipulate and integrate information from heterogeneous data sources to produce a reliable prediction. It will help transport managers and bus companies to optimize, in real-time, scheduling, stops usage and passenger’s time