Scalable System for Smart Urban Transport Management

Abstract

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.

Dissipation Characteristics of Vehicle Queue in V2X Environment Based on Improved Car-Following Model

“…In V2V environment, drivers have access to such real-time information as the release status of forward signal lights and the motion status of neighboring vehicles. e driver gets a real-time status of the traffic flow ahead through the V2V device and is given an advance reaction time before the car in front initiate [26]. In this case, the process of vehicle queue dissipation at the intersection is shown as Figure 1.…”

Internet of Things Data Visualization for Business Intelligence

“…The authors applied their proposal to a real city to approve its efficiency and feasibility. Furthermore, in order to achieve smart transportation management,Khan et al (2020) presented an architecture capable of integrating heterogeneous dynamic Big Data gathered from various sources in urban transportation systems. The authors employed data mining and machine learning models and covered all the necessary steps to deal with Big Data, that is, data acquisition, storage, analysis and visualization, together with realtime monitoring and forecasting, with the aim of assisting decision-makers.…”