Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Visual exploratory data analysis of traffic volume

Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically

Visual Exploratory Data Analysis of Traffic Volume

Abstract. Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.