Dynamic Methods of Traffic Control that Impact Quality of Life in Smart Cities
pdf
Abstract:
Project Description
In the recent past the term Smart Cities was introduced to mainly characterize the integration into our daily lives of the latest advancements in technology and information. Although there is no standardized definition of ”Smart Cities”, what is certain is that it touches upon many different domains that affect a city’s physical and social capital. Our vision of a smart city resembles that of a living organism which automatically responds and continuously adapts to new situations by regulating its internal functions. Smart cities are intertwined with traffic control systems that use advanced infrastructures to mitigate congestion and improve safety. Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. We propose to introduce a novel hierarchical adaptive controls paradigm to urban network traffic control that will adapt to changing movement and interaction behaviors from multiple entities (vehicles, public transport modes, bicyclists, and pedestrians). Such a paradigm will leverage several key ideas of cyberphysical systems to rapidly and automatically pin-point and respond to urban arterial congestion thereby improving travel time and reliability for all modes. Safety will also be improved since advanced warnings actuated by the proposed cyber-physical system will alert drivers to congested areas thereby allowing them to avoid these areas, or to adapt their driving habits. Such findings have a tangible effect on the well-being, productivity, and health of the traveling public. A proposed heterogeneous multi-sensor network to monitor arterial congestion and incident events will also provide a wealth of rich detailed data (traffic flow and densities, trajectories, etc.) that can be used by traffic engineers, scientists, and students to improve their understanding of traffic behaviors under such conditions, as well as formulate new methodologies to mitigate them.
Technical Approach
The primary goal is to create a Cyber-Control Network (CCN) that will integrate seamlessly across heterogeneous sensory data in order to create effective control schemes and actuation sequences. Accordingly, the proposal introduces a Cyber-Physical architecture that will then integrate 1) a sub-network of heterogeneous sensors, 2) a decision control substrate and 3) a sub-actuation network that carries out the decisions of the control substrate (traffic control signals, changeable message signs). This is a major departure from more prevalent centralized Supervisory Control And Data Acquisition (SCADA), in that the CCN will use a hierarchical architecture that will dynamically instantiate the sub-networks together to respond rapidly to changing cyber-physical interactions. Such an approach allows the cyber-physical system to adapt in real-time to salient traffic events occurring at different scales of time and space. We will consequently introduce a ControlWare module to realize such dynamic sub-network reconfiguration and provide decision signal outputs to the actuation network. A secondary, complementary goal is to develop a heterogeneous sensor network to reliably and accurately monitor and identify salient arterial traffic events. Such sensors measure the same traffic from different facets – for example counts and occupancy from in pavement loop detectors, visual saliency and general object tracking from fixed or actuated pan-tilt cameras, vehicle and traffic flow speeds from roadside radars, individual vehicle speeds and locations from on-board GPS systems. Methodologies will be developed to understand optimal placement and utilization of such sensors to monitor and identify salient events and also to provide signals to the ControlWare component. Extensive micro-traffic simulation, formulated by real-world heterogeneous traffic sensor data, will provide tangible results that will be used to evaluate CCN performance, modeling, and design formulations. Novel real-time computer graphics visualization methodologies will be also evaluated as an intuitive human interface to understand and extract salient traffic behaviors and events. In addition to novel CPS theoretical constructs and visualization human interfaces, the project will also deploy an actual roadside sensor sub-network consisting of camera sensors, loop detection interfaces, and radar sensors within an urban corridor to monitor and detect salient traffic events. This will then be used to exercise the CNN both in real-time and within the micro-traffic simulation. Significant systems engineering and integration efforts will be required in order for the sensor output to be conformant with the ITS national architecture at the physical and logical layers. Experimentation will require the team to address challenges in the deployment of unique sensor components and communications protocols to be interoperable with existing regional traffic management systems.
Submitted by Theodore Morris
on
Abstract:
Project Description
In the recent past the term Smart Cities was introduced to mainly characterize the integration into our daily lives of the latest advancements in technology and information. Although there is no standardized definition of ”Smart Cities”, what is certain is that it touches upon many different domains that affect a city’s physical and social capital. Our vision of a smart city resembles that of a living organism which automatically responds and continuously adapts to new situations by regulating its internal functions. Smart cities are intertwined with traffic control systems that use advanced infrastructures to mitigate congestion and improve safety. Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. We propose to introduce a novel hierarchical adaptive controls paradigm to urban network traffic control that will adapt to changing movement and interaction behaviors from multiple entities (vehicles, public transport modes, bicyclists, and pedestrians). Such a paradigm will leverage several key ideas of cyberphysical systems to rapidly and automatically pin-point and respond to urban arterial congestion thereby improving travel time and reliability for all modes. Safety will also be improved since advanced warnings actuated by the proposed cyber-physical system will alert drivers to congested areas thereby allowing them to avoid these areas, or to adapt their driving habits. Such findings have a tangible effect on the well-being, productivity, and health of the traveling public. A proposed heterogeneous multi-sensor network to monitor arterial congestion and incident events will also provide a wealth of rich detailed data (traffic flow and densities, trajectories, etc.) that can be used by traffic engineers, scientists, and students to improve their understanding of traffic behaviors under such conditions, as well as formulate new methodologies to mitigate them.
Technical Approach
The primary goal is to create a Cyber-Control Network (CCN) that will integrate seamlessly across heterogeneous sensory data in order to create effective control schemes and actuation sequences. Accordingly, the proposal introduces a Cyber-Physical architecture that will then integrate 1) a sub-network of heterogeneous sensors, 2) a decision control substrate and 3) a sub-actuation network that carries out the decisions of the control substrate (traffic control signals, changeable message signs). This is a major departure from more prevalent centralized Supervisory Control And Data Acquisition (SCADA), in that the CCN will use a hierarchical architecture that will dynamically instantiate the sub-networks together to respond rapidly to changing cyber-physical interactions. Such an approach allows the cyber-physical system to adapt in real-time to salient traffic events occurring at different scales of time and space. We will consequently introduce a ControlWare module to realize such dynamic sub-network reconfiguration and provide decision signal outputs to the actuation network. A secondary, complementary goal is to develop a heterogeneous sensor network to reliably and accurately monitor and identify salient arterial traffic events. Such sensors measure the same traffic from different facets – for example counts and occupancy from in pavement loop detectors, visual saliency and general object tracking from fixed or actuated pan-tilt cameras, vehicle and traffic flow speeds from roadside radars, individual vehicle speeds and locations from on-board GPS systems. Methodologies will be developed to understand optimal placement and utilization of such sensors to monitor and identify salient events and also to provide signals to the ControlWare component. Extensive micro-traffic simulation, formulated by real-world heterogeneous traffic sensor data, will provide tangible results that will be used to evaluate CCN performance, modeling, and design formulations. Novel real-time computer graphics visualization methodologies will be also evaluated as an intuitive human interface to understand and extract salient traffic behaviors and events. In addition to novel CPS theoretical constructs and visualization human interfaces, the project will also deploy an actual roadside sensor sub-network consisting of camera sensors, loop detection interfaces, and radar sensors within an urban corridor to monitor and detect salient traffic events. This will then be used to exercise the CNN both in real-time and within the micro-traffic simulation. Significant systems engineering and integration efforts will be required in order for the sensor output to be conformant with the ITS national architecture at the physical and logical layers. Experimentation will require the team to address challenges in the deployment of unique sensor components and communications protocols to be interoperable with existing regional traffic management systems.