-
City of Norfolk
Page 14
Vision
Element
Alignment and Approach
Connected
Vehicles
(and other
users)
(one a 3-intersection tight-diamond with adjacent at-grade LRT crossing operated from
a single controller), Sensys volume counters, traffic monitoring cameras, all within a
contiguous 6.2 mile section of 4 to 10 lane roadways, with 36 Ethernet-connected
signals using 170 Type cabinets. The fiber channels throughout emanate from a Comm
field node located in our traffic maintenance building, in the corridor. The corridor is
identified in Figure 2. V2I RSU devices will be installed at locations as desired to
establish testing frameworks. After evaluating the literature and the lessons learned
from the CV test-beds and collaborating with USDOT, the team will identify numerous
CV applications that will be implemented and tested in the field (Red Light Violation
Warning, Pedestrian warnings and/or detection, SPaT messaging in complex urban
signal settings, etc.). Furthermore, the Norfolk team will coordinate with signal
controller/system vendors (e.g., Econolite) to expedite the implementation and testing
of Intelligent Traffic Signal Systems (I-SIGs) in the field where data from CV are
integrated and fused with data from traditional sensors for optimizing signal timing. CV
and remote data will allow predicting traffic patterns and enable proactive signal timing
to reduce congestion, improve transit service, and enhance overall intersection safety.
Given that DSRC-equipped vehicles are not yet common on the roads, the Norfolk team
suggests working with the industry leaders in wireless communications (e.g.,
Qualcomm) to test and implement alternative solutions.
Working with Qualcomm, equip smartphones with the DSRC capability as well as a
safety and mobility app. User interface and information provided to the user will
be optimized to minimize driver distraction.
Install after-market DSRC on test vehicles with the same safety app on cell phones
Install DSRC in signal cabinets and enable communication of SPaT with vehicles
and cell phones.
Lastly, to have a holistic system, the Norfolk team will make relevant data from CVs
available to the “cloud” to support other applications that do not necessarily rely on
DSRC. For example, an emergency vehicle (EV) with a DSRC can transmit its location,
route, and destination to vehicles in its path and to an RSU which then sends this
information to the cloud. CVs and others with a smartphone app in the immediate area
of the EV would receive a message with exact details about the EV’s route, which will
help vehicles and pedestrians clear paths for the safe passing of the EV.
The team would expect to advance the state of R&D in the CV arena, while also creating
current improvements for operations and safety in proximity to signalized intersections.
THREE
Intelligent,
Sensor-Based
Infrastructure
Consistent with our stated Vision, our approach definitively is designed for maximum
impact at reasonable life-cycle costs. In Norfolk’s Vision, smartphones, or more
generally mobile consumer devices, will play a central role as a sensing and
communication technology. There are numerous motivating factors for this: (i) The
proliferation of mobile devices with ever-increasing computing, sensing, and
communication capabilities; (ii) The ability for rapid deployment and ease of software
updating with the current App-store system; (iii) The ability to collect a large-amount of
data at a very low cost; (iv) The ability to collect travel data across all modes of
transportation;.and (v) the potential to extract rich traveler information from both GPS
-
City of Norfolk
Page 15
Vision
Element
Alignment and Approach
Intelligent,
Sensor-Based
Infrastructure
and low-energy inertial sensors (i.e., accelerometers, gyroscopes, compass) within the
smartphones. From the rich sensor data, beyond extracting trajectories of travelers, it is
possible to detect the mode of travel (see figure), vehicle operating mode (e.g., idling,
accelerating), and the amount of time spent within each mode. Such information, which
currently does not exist at a large scale, will be invaluable for accurately estimating GHG
and other emissions. Furthermore, by relating such data to links and nodes of a
transportation network, the system operators can determine areas with inefficient
operations and higher emissions.
In addition, with the recent advancements in chipsets technology provided by
Qualcomm – a partner on Norfolk’s team) it is now possible to have dedicated short-
range communications (DSRC) technology on smartphones. This will enable
smartphones to serve as a medium for communications among vehicles, pedestrians,
and infrastructure to support various safety and mobility applications.
Streaming video from traffic monitoring cameras (already the most powerful tool in an
operator’s arsenal) is currently an underutilized resource, usually providing no direct
value over 90% of the time. We propose a ”multi-purpose” camera system that uses
visible and thermal imaging; PTZ, detection and 360 degree cameras; and powerful
video analytics platforms, to leverage the already-supported systems into a data-
production monster. (of note: relatively inexpensive camera systems are readily
maintainable by existing traffic signal maintenance labor forces) This system could
produce in addition to standard traffic data, both real-time and historical, movement
data on the entire user cross-section, O-D/route data, security, road surface and
weather conditions. Norfolk’s efforts in establishing a system-wide high-capacity
Ethernet platform can support the data transmission requirements for this approach.
The nature of the system also is less “black-box” since performance can be observed at
any time, enhancing troubleshooting.
The City plans to develop a comprehensive set of tools to estimate water inundation
levels due to flooding through crowdsourced images, video surveillance images if
available, and a network of gauges or sensors. A custom smartphone app will be
developed to enable citizens to collect and upload inundation image data in real-time.
CV V2I sensing has been addressed.