U.S. patent application number 13/756107 was filed with the patent office on 2013-08-01 for method and system for traffic performance analysis, network reconfiguration, and real-time traffic monitoring.
This patent application is currently assigned to Taif University. The applicant listed for this patent is Taif University. Invention is credited to Sultan Hamadi Aljahdali, Mohammed Ouali.
Application Number | 20130197790 13/756107 |
Document ID | / |
Family ID | 48869894 |
Filed Date | 2013-08-01 |
United States Patent
Application |
20130197790 |
Kind Code |
A1 |
Ouali; Mohammed ; et
al. |
August 1, 2013 |
METHOD AND SYSTEM FOR TRAFFIC PERFORMANCE ANALYSIS, NETWORK
RECONFIGURATION, AND REAL-TIME TRAFFIC MONITORING
Abstract
A method and system for traffic performance analysis, network
reconfiguration, and real-time traffic monitoring and surveillance
is described. Traffic performance analysis and congestion detection
is achieved through discrete event simulation. The road network is
translated to a graph. The translation is the result of the
processing of a high resolution satellite or aerial image
consisting of road extraction. In the resulting graph, road
intersections are represented by vertices and road sections by
edges. Edges have several properties such as the capacity of the
section, presence of traffic signs and traffic lights, rate of
generation (vehicles leaving parking), and rate of absorption
(vehicles going to parking) Temporal simulation allows detecting
congestions as well as congestion propagation. Real-time traffic
monitoring consists of detecting abnormal traffic slowdowns. This
is achieved by observing traffic with a camera. The video is
processed to compute the optical flow which allows the computation
of the traffic speed. Individual vehicle speed is also computed to
detect speeding vehicles by comparing their speed to the nominal
speed limit in the road section. The whole system can be grouped in
a traffic control room or a traffic information system.
Inventors: |
Ouali; Mohammed; (Taif,
SA) ; Aljahdali; Sultan Hamadi; (Taif, SA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Taif University; |
Taif |
|
SA |
|
|
Assignee: |
Taif University
Taif
SA
|
Family ID: |
48869894 |
Appl. No.: |
13/756107 |
Filed: |
January 31, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61593298 |
Jan 31, 2012 |
|
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Current U.S.
Class: |
701/118 |
Current CPC
Class: |
H04N 19/97 20141101;
G08G 1/0133 20130101 |
Class at
Publication: |
701/118 |
International
Class: |
G08G 1/01 20060101
G08G001/01 |
Claims
1. An intelligent transportation system, comprising: an augmented
graph data depicting an urban road network; a memory for storing
the augmented graph data; a discrete event simulator using the
augmented graph data to check if a congestion has been solved; a
plurality of cameras placed on critical road sections; a plurality
of displays showing videos captured by the cameras; a device for
controlling traffic lights and variable-message signs; and a server
running the discrete event simulator and processing the camera
video streams in real time for real time surveillance.
2. The intelligent transportation system according to claim 1,
wherein the augmented graph data is originated from a high
resolution satellite or an aerial image, the high resolution
satellite or the aerial image being used for road extraction.
3. The intelligent transportation system according to claim 2,
wherein information of the augmented graph data comprising
signaling, road capacity, and speed limits.
4. The intelligent transportation system according to claim 1,
wherein the device controls traffic lights and variable-message
signs is an automatically or a manually controlling device.
5. A method for detecting congestions, enhancing traffic
performances, and real-time traffic surveillance, comprising:
obtaining an augmented graph data depicting an urban road network;
detecting congestion by the augmented graph data; updating locally
road signs and signaling to alleviate congestion; verifying if the
congestion has been solved by using a discrete event simulator;
installing a plurality of cameras along the road; monitoring
traffic flows by videos recorded by the cameras; and changing the
timing of traffic lights or modifying electronic traffic signs to
solve traffic congestion.
6. The method for detecting congestions, enhancing traffic
performances, and real-time traffic surveillance according to claim
5, wherein the augmented graph data is originated from a high
resolution satellite or an aerial image, the high resolution
satellite or the aerial image being used for road extraction.
7. The method for detecting congestions, enhancing traffic
performances, and real-time traffic surveillance according to claim
5, wherein detecting congestion comprising: processing videos to
compute optical flows; and computing traffic speed from the optical
flow.
8. The method for detecting congestions and enhancing traffic
performances according to claim 6, wherein information of the
augmented graph data comprising signaling, road capacity, and speed
limits.
9. The method for real-time traffic surveillance according to claim
5, wherein changing the timing of traffic lights or modifying
electronic traffic signs to solve traffic congestion is operated
manually or automatically.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of the filing date of
provisional application No. 61/593,291, filed on Jan. 31, 2012. The
contents of the provisional application are incorporated by
reference in its entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of Invention
[0003] The present invention relates to intelligent transportation
systems (ITS) and road traffic control, and more particularly to a
method and system detecting congestions, enhancing traffic
performances, and real-time traffic surveillance.
[0004] 2. Related Art
[0005] The size and complexity of transport problems continue to
increase with the growth of cities, road networks, and the number
of motor vehicles. The main issue in urban transportations is
traffic congestion and poor traffic performances. Traffic
congestion is due to several factors such as the infrastructure,
the ratio of number of vehicles with respect to the capacity of the
road network, and the traffic signaling to name a few. Moreover,
road traffic depends heavily on the time of the day. Rush hours
generally occur at the time people commute to and from work, 8 am
and 4 pm, and around lunch time, 12 pm. This pattern makes road
traffic non ergodic. Despite this problem, a decent amount of
research effort was devoted to traffic flow modeling and
simulation.
[0006] The demand in terms of road space continues to grow for the
reasons mentioned above. If the status quo persists--no new roads
are built or no structural nor organizational changes are made,
congestions are unavoidable. Their impacts are important and
multiple. They result in economic, social, and environmental costs.
It is thus necessary to limit, or at least, to manage road
congestions. This can be done either by limiting the request for
traffic or by managing the flow of vehicles.
[0007] When dealing with road traffic analysis, both modeling and
simulation are viable alternatives. However, depending on the
nature of problems at hand, one alternative may overtake over the
other.
[0008] Vehicular traffic problems are usually treated in the
literature, and much research has focused on methodologies for the
optimization and evaluation of transportation systems (S. Chen et
al., A Multimodal Hierarchical-Based Model for Integrated
Transportation Networks, Journal of Transportation Systems
Engineering and Information Techonology, 9(6):130-135, 2009).
Lozano et al. (An algorithm for the recognition of levels of
congestion in road traffic problems", Mathematics and Computers in
Simulation, 79(6):1926-1934, 2009) present an algorithm for
identifying levels of congestion in traffic problems. D'Ambrogio et
al. (Simulation model building of traffic intersections",
Simulation Modeling: Practice and Theory, 17(4):625-640, 2009)
propose a model for an urban road network made up of traffic
intersections.
[0009] Other research presented an analytical queuing model that
preserves finite capacity queues and uses parameters to investigate
the correlation between the queues (C. Osorio et al., An analytic
finite capacity queuing network model capturing the propagation of
congestion and blocking; European Journal of Operational Research
196(3):996-1007, 2009). This model can be validated by mathematical
methods and existing simulation results. Finally, some studies
measure the size of the queues of road intersections in order to
find points of congestion in urban networks (Liu et al., Real-time
queue length estimation for congested signalized intersections,
Transportations Research Part C, 17(4):412-427).
[0010] US Patent application No. 2011/0115648 A1 and WPO
2009/122107 A1 patent application, which are applied by Laurgeau et
al., provide a method for computing actual travel times using
vehicles in the road network. Their method is based on devices on
board of vehicles and relays disposed across the road network. In
the preferred embodiment of their invention, a set of relays are
positioned in points known by their GPS coordinates; a vehicle
containing a transmitting device and passing by a relay drops a
message with the vehicle ID; the messages are aggregated and
processed in a data processing center and actual run times are
computed. While this method is good in computing actual vehicles
run times, it has several drawbacks: it is very expensive to
implement as it requires vehicles to carry transmitting devices; a
set of relays has to be deployed to allow for message reception.
Several vehicles traveling between two given points in the network
may have variable actual run time according to the experience,
state of mind, and mood of the driver. This leads to fluctuating
measurements that may need to be smoothed or expressed as an
average and standard deviation couple of data. While this method is
better than other methods using magnetic loops, as it does neither
need road works nor significant maintenance, it is still subject to
the deployment of specific equipment, namely relays, and the
acquisition of transmitting devices that should be mounted on all
vehicles.
[0011] Therefore, there is a need for a method and apparatus which
does not present the drawbacks of the mentioned conventional
methods.
SUMMARY OF THE INVENTION
[0012] This invention is directed to a method and system to analyze
traffic performance, to detect potential congestions and to analyze
the propagation of congestions, to reconfigure the network in terms
of road signs and markings, and to monitor the traffic in
real-time.
[0013] In accordance with an aspect of this invention, traffic
performance analysis requires representing the road network as a
graph, where the vertices are road intersections and the edges are
road sections connecting intersections. Satellite images can be
used as a source data to build the graph. Image processing
techniques such as road extraction may be applied to extract the
roads. This intermediate data may be converted to a suitable format
such as Open Street Map or any other format known to the one
skilled in the art. The road map is then augmented with road signs
and markings as well as information regarding the capacity of the
road section. The mathematical representation of the road network
can be extracted semi-automatically or automatically to build a
graph--in the sense of graph theory. It is then easy to apply the
maximum flow or the max flow min cut algorithms to detect
congestions. Although the same procedure allows tracking down the
congestion propagation, other methods are known to those skilled in
the art. Indeed, other techniques based on queuing systems also
allow for the road traffic analysis (Ouali et al., A Multiclass
BCMP Queuing Modeling and Simulation-Based Road Traffic Flow
Analysis, ACM Simultech, The Netherlands, 2011; Boris S. Kerner,
Introduction to Modern Traffic Flow Theory and Control: the long
road to three-phase traffic theory, Springer-Verlag Berlin
Heidelberg, 2009).
[0014] In accordance with a further aspect of the invention, it is
also desirable to process an existing urban road network that is
subject to congestion. This is achieved through network
reconfiguration. The operations described in this stage are
semi-automatic, although they could be automatic. In this stage, a
human traffic expert or an operator suggests to update locally the
road signs and signaling to alleviate congestion. However, one
needs to verify that this solution is viable.
[0015] This is done through discrete event simulation. The
simulator is built in such a way that it takes into account the
actual graph--with capacities, road signs, and signaling. The
detection of congestions is confirmed when the actual flow reaches
the capacity of the edge.
[0016] In accordance with another aspect of the invention,
real-time traffic monitoring concerns the surveillance of the road
traffic. A set of cameras is installed along the main boulevards,
avenues, and expressways in the road network. The average speed of
the traffic is measured by measuring the optical flow in the video.
The traffic speed is compared to the locally defined speed limit:
if the traffic speed is below a certain ratio of the speed limit, a
human operator who is monitoring the traffic in control rooms or
traffic information systems is requested to find the glitch
downstream. The operator may change the timing of traffic lights or
modify electronic traffic signs to solve the problem. The measured
traffic speeds are aggregated to compute the estimated run times
between two points of the road network according to the current
traffic conditions. Estimated run times are displayed on
variable-message signs. It should also be noted that it is possible
to count the number of vehicles
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The accompanying drawings show an embodiment having no
limiting character. The invention will be described with reference
to the accompanying drawings, wherein:
[0018] FIG. 1 is a top-level flowchart representation of the
present invention;
[0019] FIG. 2 is a flowchart representation of an embodiment of the
congestion detection and propagation stage of the present
invention;
[0020] FIG. 3 is a flowchart representation of an embodiment of the
real-time traffic monitoring stage of the present invention; in
particular, the flow chart depicts the traffic speed estimation and
the individual vehicle speed estimation stages;
[0021] FIG. 4 shows an information system in accordance with the
invention in a particular embodiment.
DETAILED DESCRIPTION
[0022] For purposes of explanation, specific embodiments are set
forth to provide a thorough understanding of the present invention.
However, it will be understood by one skilled in the art, from
reading this disclosure, that the invention may be practiced
without these specific details. Moreover, well-known elements,
devices, process steps and the like are not set forth in detail in
order to avoid obscuring the scope of the invention described.
[0023] The present invention provides a method and system for
traffic performance analysis, congestion detection and network
reconfiguration, and finally, real-time traffic monitoring and
surveillance. The source data structure is in the form of a fully
described graph. The graph is built for a particular road network
or a city. To build the graph, satellite or aerial images of the
city are processed in order to extract the road network. Other
methods known to those skilled in the art are also available. Graph
theory algorithms such as maximum flow or max-flow min-cut may be
used to find critical points or hot spots in the graph. Hot spots
are areas where the flow tends towards the capacity of an edge,
which characterizes congestions. This analysis might be cumbersome
for large graphs. Furthermore, the max flow algorithms do not allow
considering the rate of generation and the rate of absorption in
each edge. Discrete event simulation, however, is a convenient tool
that may be used.
[0024] Once the congestions are found in the graph, a traffic
expert suggests some changes to road signs and markings in order to
overcome the congestions. For every suggested change, the simulator
confirms whether or not the congestion has been definitely overcome
or if it still persists.
[0025] It should be noted that the steps of locally updating the
road signs and traffic light durations and verifying that these
changes overcome congestions may also be performed automatically
and algorithmically without departing from the scope of the
invention. When all congestions are solved, the augmented graph
data structure is saved. This data structure is used during traffic
monitoring and surveillance so that when the traffic flow slows
down and reaches a critical limit, the operator uses the graph data
structure along with the simulator to determine the propagation of
the congestion upstream. Consequently, the operator, who ideally is
a traffic expert, changes road signs and/or traffic light durations
temporarily to help improve the traffic conditions.
[0026] FIG. 1 schematically illustrates a system for traffic
performance analysis and congestion detection and network
reconfiguration, which includes 3 distinct parts: computation of a
digital map augmented with traffic information such road signs and
markings 1; computation of a graph data structure 11 that is
ultimately stored in a memory for later use by a simulator, where
an embodiment is shown in FIG. 2; detection and propagation of
traffic congestions 13. The computation of the digital map 6 is
facilitated by the processing of satellite or aerial images 2 of
the urban area of interest. Road extraction algorithms 4 are well
known in the specialized literature of computer vision. Digital map
6 is augmented with traffic information and signaling to obtain a
digital augmented map 8. It should be noted by those skilled in the
art that several formats to store the digital augmented map are
available, and that one can use format other than Open Street Map
without departing from the scope of the invention.
[0027] The graph data structure computation 10 is based on the
usage of the digital augmented map 8, where roads intersections are
vertices in the graph, and road sections are edges. Further
information is appended to the graph data structure, such as the
road signs and markings, traffic lights, edge capacity, rate of
generation, and the rate of absorption. The graph data structure is
stored in a memory 12. The detection and propagation of congestions
14 and 18 is based on the discrete event simulator embedded in a
data processing system 16. The data processing system takes the
graph data structure 10 as input and processes discrete
events--vehicles traveling along road sections and negotiating
intersections. Whenever a traffic flow gets closer to the
corresponding edge capacity, there is a risk of congestion. The
edge flow is saturated and the congestion is propagated upstream in
the graph. At this stage we are only concerned by the analysis of
the current state of the traffic conditions.
[0028] When the initial evaluation of the road network reveals the
existence of potential or actual congestions, the data processing
system 16 is used. A traffic expert examines locally the road
signs, markings and traffic lights and makes local changes to
overcome the congestion. The data processing system 16 re-processes
the new changes within the graph data structure. This stage is
depicted in FIG. 2. The procedure 17 is repeated until no
congestions are found. It should also be noted by those skilled in
the art that the steps 15 and 19 of locally updating the road signs
and traffic light durations and verifying that these changes
overcome congestions may as well be performed automatically and
algorithmically without departing from the scope of the invention.
Finally, the graph data structure along with the new traffic
signaling are stored in a memory for later use and processing.
[0029] The last stage of the system is the traffic monitoring and
surveillance. The flow chart in FIG. 3 depicts the preferred
embodiment of the invention. FIG. 4 depicts the preferred
embodiment of the traffic monitoring and surveillance part of the
invention. Video cameras 42 are installed to monitor traffic in
road sections 40. The video streams are processed to extract two
types of information: traffic speed computation 29 and individual
vehicles speed 35. Subsystems 29 and 35 are implemented in the data
processing center 44. Traffic speed computation is based on the
processing of acquired videos 22. Optical flow algorithm 24 is used
for this purpose. Other methods for computing optical flow exist
and are known to those skilled in the art. The traffic speed is
determined from the computed optical flow. Whenever a the traffic
speed falls below a given threshold an alarm 46 (a congestion alarm
26 in FIG. 3) is triggered and sent to a human operator who is
actually monitoring the traffic on displays 48. The data processing
center switches the display to the particular videos stream where
the congestion is forming for visual confirmation. The human
operator uses the data processing system 16 to reveal the upstream
propagation of the congestion. The human operator can then change
the road signs and/or the traffic light durations 28 on critical
road sections to help improve the traffic conditions. The change of
road signs assumes the availability of electronic variable message
signs 50 and 52. Individual vehicles speed computation is also
based on the processing of acquired videos 22. The video is
processed by an algorithm 30 for detecting individual vehicles. The
output of the algorithm may be the knowledge of a bounding box
around each vehicle in the frame. The individual speeds may then be
computed 32 and compared 34 to the nominal speed limit in the road
section of interest. Plate numbers of speeding vehicles may either
be shown to the operator or extracted automatically by processing
the current frame.
[0030] The traffic speed is computed by processing videos acquired
by surveillance cameras 42. The video processing takes place in the
data processing center 44. If the traffic speed on road section 40
is found to fall below a threshold or a rate, the data processing
center triggers an alarm 46 to a human operator. The data
processing center 44 switches to display 48 the road section of
interest to the operator for visual confirmation. The operator
updates the road signs and the traffic light durations in the
upstream road sections by sending new signaling to specific smart
hardware 50 and 52 for example. Another embodiment of the invention
is the detection of the speeding vehicle 38 and the extraction of
plate number either automatically by processing the current frame
or by a human operator.
[0031] While the invention has been described according to what is
presently considered to be the most practical and preferred
embodiments, it must be understood that the invention is not
limited to the disclosed embodiments. Those ordinarily skilled in
the art will understand that various modifications and equivalent
structures and functions may be made without departing from the
scope of the invention as defined in the claims. Therefore, the
invention, as defined in the claims, must be accorded the broadest
possible interpretation so as to encompass all such modifications
and equivalent structures and functions.
* * * * *