U.S. patent application number 15/288333 was filed with the patent office on 2017-11-09 for prediction for lane guidance assist.
The applicant listed for this patent is Robert Gordon. Invention is credited to Robert Gordon.
Application Number | 20170323564 15/288333 |
Document ID | / |
Family ID | 60082247 |
Filed Date | 2017-11-09 |
United States Patent
Application |
20170323564 |
Kind Code |
A1 |
Gordon; Robert |
November 9, 2017 |
PREDICTION FOR LANE GUIDANCE ASSIST
Abstract
The automated lane management assist method, data structure and
system receive unprocessed lane-specific limited-access highway
information, including lane use and speed limits, from traffic
detectors in the roadway or from other sources, process and develop
predicted information from these sources and substitute the
predicted information for the current information where
appropriate, thus improving the timeliness of the information in a
form that assists in the selection of driving lanes and target
speeds for vehicles, including in partially and fully automated
vehicles, and communicate the processed predicted information to
the vehicles by suitable means.
Inventors: |
Gordon; Robert; (Plainview,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Gordon; Robert |
Plainview |
NY |
US |
|
|
Family ID: |
60082247 |
Appl. No.: |
15/288333 |
Filed: |
October 7, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62333352 |
May 9, 2016 |
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62342532 |
May 27, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 2552/05 20200201;
B60W 30/143 20130101; B60W 30/18163 20130101; B60W 30/12 20130101;
G08G 1/0145 20130101; G08G 1/096725 20130101; G08G 1/096775
20130101; B60W 40/08 20130101; G05D 1/0088 20130101; B60W 2556/45
20200201; G08G 1/167 20130101; G05D 1/0285 20130101; B60W 2040/0881
20130101; G01C 21/3658 20130101; G08G 1/0129 20130101; G08G 1/09
20130101 |
International
Class: |
G08G 1/09 20060101
G08G001/09; G08G 1/16 20060101 G08G001/16; G08G 1/0967 20060101
G08G001/0967 |
Claims
1. A method of assisting in selection of driving lanes and target
speeds for vehicles, comprising the steps of: a. receiving
unprocessed lane-specific limited-access highway data from a
traffic management center (TMC), or another data source; b.
combining the unprocessed lane-specific limited-access highway data
with data from a static database to create intermediate
lane-specific limited-access highway data; c. generating processed
predicted lane-specific limited-access highway data from the
intermediate lane-specific limited-access highway data, said
processed predicted lane-specific limited-access highway data
conforming to a temporal and/or spatial data structure comprising
barrels divided into zones, wherein boundaries of the barrels are
defined by physical roadway configuration changes and permanent
changes in regulatory use of the limited-access highway lanes and
wherein boundaries of the zones are defined by changes in traffic
conditions along the limited-access highway resulting from entry
ramps and exit ramps and locations of motorist information devices
and regulatory devices that provide changeable information and
active traffic management control of the limited-access highway;
and d. providing the processed predicted lane-specific
limited-access highway data to one or more vehicles, wherein said
processed predicted lane-specific limited-access highway data is
visually or audibly presented in the one or more vehicles, said
processed predicted lane-specific limited-access highway data
enabling an in-vehicle guidance assist vehicle module of the one or
more vehicles to select a preferred lane and target speed for the
preferred lane using a copy or a subset of the static database.
2. The method of claim 1, wherein the step of providing the
processed predicted lane-specific limited-access highway data to
the one or more vehicles is dynamic.
3. The method of claim 1, wherein the in-vehicle guidance assist
vehicle module uses the processed predicted lane-specific
limited-access highway data to develop lane selection and target
speed selection.
4. The method of claim 1, wherein the processed predicted
lane-specific limited-access highway data provided to the
in-vehicle guidance assist vehicle module includes one or more of
lane volume, lane passenger car equivalent volume, lane average
headway, lane density, lane speed, vehicle length by lane, static
and dynamic regulatory lane-use data.
5. The method of claim 1, wherein appropriate information decision
zones relating to roadway geometrics and roadway traffic
information devices are established, and wherein the processed
predicted lane-specific limited-access highway data corresponding
to the information decision zones is provided to the one or more
vehicles sufficiently in advance of an action required by the one
or more vehicles or a vehicle operator of the one or more vehicles
to facilitate safe lane changes and speed adjustments in
conformance with individual motorist driving preferences.
6. The method of claim 1, further comprising using the processed
predicted lane-specific limited-accessed highway data in
conjunction with software in the one or more vehicles.
7. The method of claim 1, further comprising using the processed
predicted lane-specific limited-accessed highway data in
conjunction with data provided by the one or more vehicles.
8. The method of claim 1, further comprising using the processed
predicted lane-specific limited-accessed highway data in
conjunction with data provided by an occupant of the one or more
vehicles.
9. The method of claim 8, wherein the data provided by the occupant
of the one or more vehicles includes one or more of vehicle
characteristics, vehicle passenger occupancy, highway use
preferences, and toll preferences.
10. The method of claim 1, further comprising periodically
providing the data from the static database according to the
roadway zone based data structures to the one or more vehicles to
update the copy or the subset of the static database in the one or
more vehicles.
11. The method of claim 1, wherein the processed predicted
lane-specific limited-access highway data includes one or more of
lane based traffic parameter data, TMC traffic incident report
data, lane blockage information, lane closure information,
limitations on lane use, shoulder information, regulatory lane use
data, scheduled roadway closures, dynamic speed limits, current
lane speed, volume and occupancy vehicle detector data, camera
data, vehicle class based lane restrictions, vehicle overheight
restrictions, vehicle overweight restrictions, vehicle occupant
requirements, and toll information to assist in the generation of
lane selection information and the target speed information for
vehicles.
12. The method of claim 1, wherein the processed predicted
lane-specific limited-access highway data is generated based on one
or more factors selected from the group consisting of traffic
information, limitations on lane use, shoulder information,
regulatory lane use data, scheduled roadway closures, toll
information, dynamic speed limits, current lane speed, volume and
occupancy vehicle detector data, camera data, vehicle class,
vehicle overheight restrictions, vehicle overweight restrictions,
vehicle occupant calls, and toll information.
13. The method of claim 1, further comprising performing one or
more quality check of the processed predicted lane-specific
limited-access highway data and substituting current lane-specific
limited-access highway data for the predicted lane-specific
limited-access highway data if the processed predicted
lane-specific limited-access highway data is not of sufficient
quality.
14. A non-transitory computer-implemented roadway zone based data
structure for expressing predicted traffic parameters, incident
data, regulatory data and toll information in geographical segments
that are appropriate for limited-access highway lane selection and
target speed selection for the chosen lanes, said data structure
comprising: a. at least one interface for receiving unprocessed
lane-specific limited-access highway data from a traffic management
center; and b. a processor coupled to the at least one interface,
wherein the processor receives the unprocessed lane-specific
limited-access highway data through the at least one interface,
processes the unprocessed lane-specific limited-access highway
data, generates processed predicted lane-specific limited-access
highway data conforming to a temporal and/or spatial data structure
comprising barrels divided into zones, wherein boundaries of the
barrels are defined by physical roadway configuration changes and
permanent changes in regulatory use of the limited-access highway
lanes and wherein boundaries of the zones are defined by changes in
traffic conditions along the limited-access highway resulting from
entry ramps and exit ramps and locations of motorist information
devices and regulatory devices that provide changeable information
and active traffic management control of the limited-access highway
and transmits processed predicted lane-specific limited-access
highway data to at least one vehicle in a form appropriate for
limited-access highway lane selection and target speed selection
for the chosen lanes, wherein the processed predicted lane-specific
limited-access highway data is visually or audibly presented in the
at least one vehicle.
15. The data structure of claim 14, further comprising a computer
storage for storing processed predicted lane-specific
limited-access highway data, wherein the processor receives the
unprocessed lane-specific limited-access highway data through the
at least one interface, processes the unprocessed lane-specific
limited-access highway data, and outputs processed predicted
lane-specific limited-access highway data to the computer storage
in a form appropriate for limited-access highway lane selection and
target speed selection for the chosen lanes.
16. The data structure of claim 15, wherein the processed predicted
lane-specific limited-access highway data is transmitted to the at
least one vehicle from the computer storage.
17. A system for assisting in selection of driving lanes and target
speeds for vehicles, comprising: a. an interface for receiving
unprocessed lane-specific limited-access highway data from a
traffic management center (TMC), advanced lane management assist
management center (ALMAMC), or another data source; b. a processor
coupled to the interface, wherein the processor receives the
unprocessed lane-specific limited-access highway data through the
interface, processes the unprocessed lane-specific limited-access
highway data, generates processed predicted lane-specific
limited-access highway data conforming to a temporal and/or spatial
data structure comprising barrels divided into zones, wherein
boundaries of the barrels are defined by physical roadway
configuration changes and permanent changes in regulatory use of
the limited-access highway lanes and wherein boundaries of the
zones are defined by changes in traffic conditions along the
limited-access highway resulting from entry ramps and exit ramps
and locations of motorist information devices and regulatory
devices that provide changeable information and active traffic
management control of the limited-access highway, and transmits
processed predicted lane-specific limited-access highway data to
one or more vehicles, wherein the processed predicted lane-specific
limited-access highway data is visually or audibly presented in the
at the one or more vehicles; and c. one or more of a lane closure
guidance module, lane and speed limit requirements module, dynamic
lane use requirements module, toll information module, module for
checking detector values for accuracy, module for formatting
traffic data, miscellaneous data module, and static database
module, said one or more module operatively coupled to the
processor for developing driving lane and target speed
selection.
18. A system of claim 17, further comprising a computer storage for
storing the processed predicted lane-specific limited-access
highway data, said computer storage being coupled to the processor,
wherein the processor receives the unprocessed lane-specific
limited-access highway data through the interface, processes the
unprocessed lane-specific limited-access highway data, and outputs
the processed predicted lane-specific limited-access highway data
to the computer storage.
19. A system of claim 18, further comprising a transmitter
operatively coupled to the computer storage for transmitting the
processed predicted lane-specific limited-access highway data to
the one or more vehicles.
20. A system of claim 17, wherein the processor performs one or
more quality check of the processed predicted lane-specific
limited-access highway data and substitutes current lane-specific
limited-access highway data for the processed predicted
lane-specific limited-access highway data if the processed
predicted lane-specific limited-access highway data is not of
sufficient quality.
Description
CROSS REFERENCE OF RELATED APPLICATIONS
[0001] This patent application is a nonprovisional patent
application of and claims priority from the provisional patent
application Ser. No. 62/333,352 filed on May 9, 2016, and this
patent application also claims the benefit of the provisional
patent application Ser. No. 62/342,532 filed on May 27, 2016, both
of which are hereby incorporated by reference in their
entirety.
FIELD OF THE INVENTION
[0002] This invention was not made pursuant to any
federally-sponsored research and/or development.
[0003] A prior invention (Management Center Module for Advanced
lane Management Assist--U.S. Pat. No. 9,053,636) provides a method
and system for assisting the drivers of vehicles, and the
intelligent in-vehicle systems in partially and fully automated
vehicles to select a specific lane for vehicle travel on limited
access highways as well as a recommended speed. That patent employs
current lane specific information from traffic detectors in the
roadway or from other sources. The present invention develops
predicted information from these sources and substitutes it for the
current information where appropriate, thus improving the
timeliness of the information.
BACKGROUND
[0004] This patent application extends the usefulness of the
following two prior patents, the disclosures of which are hereby
incorporated by reference in their entirety, as if fully set forth
herein: [0005] U.S. Pat. No. 9,053,636 Management Center Module for
Advanced Lane Management Assist for Automated Vehicles and
Conventionally Driven Vehicles (ALMAMC) [0006] U.S. Pat. No.
9,286,800 Guidance Assist Vehicle Module (ALMAVM)
[0007] These patents describe a methodology (ALMA) for using
traffic management center (TMC) information to select a most
appropriate freeway lane for a driver or automated vehicle and to
provide a target speed for that lane. The TMC traffic condition
information, is essentially current information on traffic speed
and other variables for each through traffic lane. The information
is organized according to a data structure described in in the
ALMAMC patent that considers the physical and functional features
of the freeway as well as traffic information devices. The
information is transmitted to the vehicle where it is further
processed (ALMAVM). This additional processing develops guidance on
the best lane and target speed by looking at traffic speeds for
several miles ahead (downstream) of the vehicle's current
position.
[0008] Since the vehicle may not reach the look-ahead distance for
a few minutes, the current patent improves the performance of the
prior patents by using predicted traffic speed in place of current
traffic speed for lane selection and target speed recommendations.
To obtain lane based speed information, TMCs may use sources such
as roadway based traffic detectors and reports from connected
vehicles that include position, speed and lane identification.
Prediction for other key parameters provided by the ALMAMC patent
is provided.
[0009] Pan et al.sup.1 provide a review of traffic prediction
techniques. Examples of prediction techniques include: .sup.1 BEI
PAN, UGUR DEMIRYUREK, and CYRUS SHAHABI, Utilizing Real-World
Transportation Data for Accurate Traffic Prediction, Integrated
Media System Center, University of Southern California. [0010] 1.
Simulation Models--Traffic prediction using microscopic simulation
models in conjunction with traffic detector measurements.
References include Gehrke and J. Wojtusiak.sup.2 and Ben Akiva et
al.sup.3. .sup.2 JAN D. GEHRKE and JANUSZ WOJTUSIAK, A Natural
Induction Approach to Traffic Prediction for Autonomous Agent-based
Vehicle Route Planning, Machine Learning and Inference Laboratory,
MLI 08-1, George Mason University, Feb. 17, 2008..sup.3 MOSHE
BEN-AKIVA, MICHEL BIERLAIRE, HARTS KOUTSOPOULOS, and RABI
MISHALANI, DynaMIT: a simulation-based system for traffic
prediction, Massachusetts Institute of Technology Intelligent
Transportation Systems Program, Presented Paper presented at the
DACCORD Short Term Forecasting Workshop February, 1998. [0011] 2.
Data Mining Techniques--This class analyzes the data collected by
traffic detectors. Various analysis approaches include: [0012] A.
Auto-Regressive Integrated Moving Average (ARIMA) Model..sup.4 The
Exponential Smoothing model is a special case of ARIMA that has
been extensively used for traffic data applications. Strictly
speaking these models are just estimators of current conditions
that are used as predictors. Although not discussed by Pan, The
ALMAMC software uses a Kalman Filter in this manner to process lane
specific information developed by the TMC from traffic detectors
and other sources. .sup.4 G. BOX and G. JENKINS, Time series
analysis: Forecasting and control. San Francisco: Holden-Day, 1970
(book). [0013] B. Neural Network Models have been used for traffic
prediction.sup.5 as have genetic algorithms. .sup.5 SHERIF ISHAK
and CIPRIAN ALECSANDRU, Optimizing Traffic Prediction Performance
of Neural Networks under Various Topological, Input, and Traffic
Condition Settings, JTE'04, Volume 130. [0014] C. Historical
Models--These models process historical data and provide prediction
by reference to a future time period.
[0015] Pan concludes that he ARIMA/Exponential Smoothing models ae
best for short term prediction (our interest) and that historical
models are best for long term prediction. Pan provides an algorithm
based on error characteristics to select between them.
SUMMARY OF THE INVENTION
[0016] The ALMAMC patent provides traffic speeds and other traffic
variables according to a geographically related data structure
described in the patent. The current patent replaces these current
variables with predicted values when the prediction is estimated to
be sufficiently accurate. Prediction periods are typically two
minutes in duration, and typically predictions for three such
periods may be provided, for a total prediction time of typically
up to six minutes.
[0017] Prediction is provided in the current patent only when
historic and current traffic conditions and estimated errors
indicate that the prediction is likely to be accurate. When these
conditions are not present, current traffic variables as provided
by the processes in the ALMAMC patent are used.
[0018] The ALMAMC patent provides for filtering of the TMC traffic
detector lane speed data. Occupancy and volume data if available
are similarly processed. As an example, that patent describes the
filter process using Kalman Filters. In ALMAMC, only the current
values of these quantities are employed.
[0019] An example of the prediction methodology (ALMAPR) that may
be used in the current patent and described in some detail is to
use the predictive capability of Kalman Filters to predict the
future lane based values of speed, volume and occupancy for the
first prediction period and to extrapolate the rate of speed,
volume and occupancy change into subsequent prediction periods. The
estimate of the error in the current traffic variables is provided
by the Kalman Filter and is used by the ALMAPR patent to assist in
determining when the use of prediction is appropriate. Other data
characteristics are also used for this purpose.
[0020] It is an object of the present invention to achieve, provide
and facilitate Prediction for Lane Guidance Assist to supplement
and/or replace modules in the ALMAMC and ALMAVM patents as follows:
[0021] Short term prediction of lane-specific traffic parameters
including speed and others for providing guidance for the selection
of a preferred lane and target speed for that lane; Such data is
used by the ALMAVM module but may be used for other lane selection
processes. [0022] Utilizing traffic management centers (TMCs), an
ALMA Management Center and/or other information sources as possible
data sources for the prediction model. [0023] Prediction traffic
parameters may conform to a data structure described in the ALMAMC
patent. [0024] Process checks may be performed for the quality of
the predicted data; if the predicted data is not of sufficient
quality, current data is substituted. Quality checks may include
periods when changes in speed are high, data is erratic, estimation
error is excessive and data that may be affected by traffic
incidents. The predicted data may be an improvement over the
current data because the predicted data is available in advance.
[0025] Predictions are provided in prediction time intervals of
appropriate duration. [0026] Provides average predicted lane speeds
for vehicles or drivers for a look ahead (downstream) distance from
the vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The features, aspects and advantages of the novel Prediction
for Lane Guidance Assist will become further understood with
reference to the following description and accompanying drawings
where
[0028] FIG. 1 is the flowchart of the ALMA simplified data
flow;
[0029] FIG. 2 is the graphical representation of Key Time Sequence
Relationships;
[0030] FIG. 3. Is ALMA Management Center Flow Chart for Additions
Required by ALMAPR; and
[0031] FIG. 4 is Example of Look Ahead Time Development
Process.
DESCRIPTION
[0032] Introduction. ALMA as described in U.S. Pat. Nos. 9,053,636
(ALMAMC) and 9,286,800 (ALMAVM) provides information to
conventional and partly or fully automated vehicles to enable them
to respond to freeway lane selection and target speed selection
information originating from a freeway traffic management center in
a way that is superior to the way that an unaided human driver or
automated vehicle would respond to that information. The
disclosures of these patents describe how speed, volume and
occupancy information collected at a traffic management center
(TMC) is processed by the ALMA Management Center (ALMAMC) and
transmitted to the vehicle. Using this information in conjunction
with routing, speed and location information supplied by the
vehicle and information from the vehicle operator, the ALMA Vehicle
Module (ALMAVM) recommends the most appropriate freeway lane and a
target speed for that lane.
[0033] FIG. 2 of the existing ALMAMC patent illustrates the ALMA
architecture. FIG. 1 shows a simplified view of this architecture.
Using roadway traffic sensors or other means to detect individual
vehicles, a traffic management center (TMC) 102 develops traffic
data such as speed, volume and occupancy. TMCs also detect and
manage traffic incidents and provide traffic information to
motorists by means of roadway devices such as dynamic message signs
(DMS) and other means. The ALMA traffic management center 103
further processes this information, organizes it and transforms it
using a prescribed data structure as described in the ALMAMC
patent. Processed traffic parameters suitable for the presentation
of lane conditions and other information are the transmitted by
means of a communication system 104 such as a smartphone with an
appropriate vehicle head unit. This information is used by the ALMA
Vehicle Module 107 in conjunction with vehicle position, speed and
routing information from the vehicle 105 and driving preferences
and other information from the motorist 106 to provide recommended
lane and target speed information to the motorist display and/or
the partially or fully automated vehicle management system 101.
[0034] The ALMAMC patent also describes the geometric data
structure to which the data is referenced. In summary, the freeway
is divided into barrels and zones. A barrel represents a set of
travel lanes in a roadway. It is physically or functionally
separated from other parallel lane sets. Barrel boundaries are
determined by changes in the physical roadway configuration and by
permanent changes along the roadway in the regulatory use of the
roadway or its lanes. A barrel is divided into zones. Zone
boundaries are determined by a number of factors including traffic
conditions, placement of motorist information devices and
regulatory devices that provide changeable information.
[0035] The existing ALMAMC patent provides information based on its
estimate of current traffic conditions. The current patent provides
this information based on short term predictions of traffic
conditions. This will improve performance under some traffic
conditions. The geometric data structure of the ALMAMC patent is
preserved in the current patent application.
[0036] The current ALMAPR patent application describes a set of
modules that replace certain ALMAMC and ALMAVM modules when
confidence in the prediction accuracy is high. These are described
in the following sections. The ALMAMC modules to be replaced
include five outputs in Table 3 of the ALMAMC patent. These are
described in Appendix B. Appendix A identifies the symbols used in
this patent. Essentially the new functions use the prediction
feature of the Kalman Filter for the prediction period that follows
the current period. An extrapolation process provides prediction
for subsequent prediction periods.
[0037] For predictive purposes, the computation of the number of
look-ahead zones and the look ahead speed is more complex that for
Modules 4.3R.2.5 and 4.3R.2.6 of the ALMAVM patent. These
modifications are described in subsequent sections of this
patent.
[0038] Temporal Relationships. The current patent (ALMAPR) predicts
traffic variables for several future time periods. FIG. 2 shows the
relationship among the key time sequences employed. Horizontal axis
D 204 is a time scale that relates the other horizontal axes to
clock time.
[0039] The top horizontal axis (A) 201 represents the intervals (n)
after the current interval for which the data is received by the
ALMAMC from the TMC. It is represented as one half minute in the
figure although some TMCs may provide different intervals. The
second horizontal axis (B) 202 represents the periods (r) for which
ALMAPR will predict the speed. The duration of these periods is
shown as two minutes in the figure.
[0040] As will be subsequently described, ALMAPR employs historic
traffic speed data. The third horizontal axis (C) 203 represents
the time periods (j) for which the historic data is compiled from
TMC data. Five minute intervals as shown are typically
employed.
[0041] The current patent ALMAPR provides a major addition to the
modules in the ALMAMC and ALMAVM patents. This addition provides:
[0042] The type of traffic parameter outputs shown in Table 3 of
the ALMAMC patent in the form of predicted parameters. The current
patent's outputs are shown in Appendix B. [0043] Modifications to
the spatial-temporal data structure in the ALMAVM patent to enable
the predicted traffic parameters to be effectively employed in the
vehicle.
[0044] Example of Prediction Process. As described earlier, a
number of prediction concepts may be used. An example of the
prediction process, and the constraints on its use, is described in
the following discussion with the assistance of FIG. 3.
[0045] The speed data SPINT(Z,L,) from the TMC 301 is averaged into
stored historic data reference periods (j) 203.
[0046] In the Historic Speed Data process 302, averages for the
most recent K3 days are obtained for each time period for weekdays,
and weekend days. Exception days (days excluded from the averaging
period) may be identified by the ALMAMC manager.
[0047] The historic data is processed for the purpose of
eliminating Kalman prediction data 305 for periods that history has
shown to be unsuitable. This includes: [0048] Periods when changes
in the speed in a zone averaged over all lanes exceed a threshold
value (K2) render Kalman prediction unsuitable 303. [0049] Detector
stations that frequently provide erratic data 304. These stations
can be identified when the total number of stored daily periods (j)
for which the average zone speed change exceeds K5 for
representative days is greater than K4.
[0050] Similar to the process described for the ALMAMC patent the
prediction process uses a Kalman Filter process 305 to estimate the
current speed (SPFIL(Z,L,r)). See, for example Welch, G and T R
Bishop, "An Introduction to the Kalman Filter", University of North
Carolina Department of Computer Science, TR 95-041, 2006.
[0051] The prediction process in this patent modifies the
estimation process in the ALMAMC patent as follows. Prediction for
the first prediction interval r.sub.0+1 205 and designated as
SPFILPR(B,Z,L,r.sub.0+1) is available from the Kalman iteration
that follows the estimate for the current interval. Prediction for
subsequent prediction intervals (r>r.sub.0+1) will be performed
by using the rate of change of speed for interval r+1 for the
subsequent intervals. The rate of speed change is computed as
RSC=(SPFIL(B,L,Z,r)-SPFIL(B,L,Z,r-1))/LR [0052] where LR is the
duration of the prediction interval (r) [0053] The predicted speed
when converted to space-mean-speed is designated as SPSPPR(B,Z,L)
for all prediction intervals.
[0054] The estimation error for speed (SPE(L,Z,r)) for the current
interval is used in module 307.
[0055] Module 306 interprets incident related information from the
TMC to identify the presence of an incident. The module identifies
zones affected by the incident. This information is sent to Module
307.
[0056] Under certain conditions the predicted value for speed may
be unreliable. Module 307 substitutes the current value of zone
speed (SPFILTOT(Z,r)) for each lane for the values for the
prediction intervals when the following conditions are present:
[0057] Speed estimation error (SPE(L,Z,r) exceeds an acceptable
threshold K6. [0058] Zone employs a detector station that provides
erratic data (304). [0059] Zones affected by an incident have been
identified (306).
[0060] As described in the ALMAMC patent, lane based data that
originates from point detectors can be processed to provide key
fundamental traffic parameters such as volume and occupancy (which
may be further processed to provide density.) TMC data that
originates from such sources as infrastructure based probes or
vehicle based sensors working in conjunction with vehicle to
infrastructure communications cannot be effectively processed in a
similar manner because all of the necessary variables cannot be
measured by these techniques. Module 308 controls the steps that
implement this distinction.
[0061] If the TMC speed data originated from point detectors,
Module 308 directs the computation to a path that will provide the
additional parameters described in the ALMAMC patent (Module 6 and
Table 3).
[0062] Module 309 converts the predicted time mean speed
originating from point detectors to predicted space mean speed
using the relationship shown in Equation 8 of the ALMAMC
patent.
[0063] Predicted volume and occupancy 310 are computed by Kalman
Filters in the prediction mode similar to that used to compute time
mean speed. Predicted density 310 is computed using the
relationships in Appendix B and predicted compensated occupancy 310
is computed using Equation 10 of the ALMAMC patent in conjunction
with the prediction processes described for speed in the current
patent.
[0064] The relationships required to compute the remaining
parameters 311 (predicted average headway, predicted average
vehicle length, predicted passenger car equivalent volume)
identified in Table 3 of the ALMAMC patent are computed as shown in
Appendix B.
[0065] When point detectors are used as the data source, the full
parameter set is provided to the ALMAVM module in the vehicle 313.
When other types of information (such as probe based information)
are used as the data source 312, only predicted space mean speed is
provided to the ALMAVM module 313.
[0066] Spatial--Temporal Relationships. The prior discussion
describes the additions to the ALMA Management Center required to
support predictive capability. The following discussion describes
additions and modifications to the ALMA Vehicle Module required to
support prediction.
[0067] ALMAVM identifies the number of downstream zones that should
be employed to estimate a "look ahead" speed (ALMAVM Module
4.3R.2.5). In that module, the look ahead speed is calculated as
the current speed for each zone weighted by the length of each
zone.
[0068] Where predicted speeds are used in place of current speeds,
it becomes necessary to identify the appropriate time interval to
be used for traversing each zone. An approach for doing this is
described with the assistance of FIG. 4. The solid line represents
the time-space plot of a vehicle after it enters the first look
ahead zone. The slope of each segment of the trace represents the
average speed of the vehicle for that zone and prediction interval.
This slope changes as the vehicle traverses each zone in the look
ahead distance. The first segment shown in the FIG. 401 uses the
speed for Zone Z+1 for the current time interval. The line segment
402 for the first prediction interval (r+1) lies entirely within
zone Z+1. Table 1 shows the predicted speed to be used for each
trace segment to fully develop a similar figure for each vehicle.
In this way each line segment serves as the base for the next line
segment when this process is completed, the value for look ahead
time (LAT(L)) is obtained.
TABLE-US-00001 TABLE 1 Predicted Speed for Vehicle Look ahead zones
Time interval Z + 1 Z + 2 Z + 3 Current interval r.sub.0 Slope of
segment 401 Prediction Interval r.sub.0 + 1 Slope of segment 402
Prediction Interval r.sub.0 + 2 Slope of Slope of Slope of segment
403 segment 404 segment 405 Prediction Interval r.sub.0 + 3 Slope
of segment 406
[0069] These concepts will be used to replace Module 4.3R.2.6 in
the GAVM program. In that module ZWAS(L) represents the look ahead
speed for each lane using current zone speeds. The predictive
replacement is provided by the expression:
ZPWAS(L)=DLA/LAT(L) [0070] where DLA is the look ahead distance 407
shown in FIG. 4 and LAT(L) is the look ahead time 408 for lane
L.
APPENDIX A
TABLE-US-00002 [0071] Definition of Symbols Symbol Definition
AHWPR(B, Z, L) Predicted average headway AVL Average vehicle length
d Detection zone DENFILPR (B, Z, L) Predicted lane density DLA Look
ahead distance F Scaling coefficient j Stored historical period K2
Threshold for excessive changes in speed K3 Number of averaging
days K4 Limit on number of prediction intervals for which speed
change is unsuitable K5 Threshold for speed change unsuitability K6
Acceptable estimated error for speed LAT(L) Predicted look ahead
time LL Length of detector sensing area in lane LR Duration of
prediction interval NOPREDDAYCLASS(d) Detection zones to be
eliminated because of erratic data OCCFILPR (Det, L) Predicted
occupancy PCE Passenger car equivalent volume PCEPR (B, L, Z)
Predicted passenger car equivalent volume r Computation and
prediction interval r.sub.0 Current interval RSC Rate of speed
change SPE(L, Z, r) Estimated error in speed SPFIL(L, Z, r) Kalman
filtered lane speed SPFILTOT(Z, r) Filtered speed for all through
lanes in zone SPINT(Z, L) Speed by lane from TMC SPSPPR(B, Z, L)
Predicted lane speed VOLFIL(BZL) Filtered detector volume in TMC
reference VOLFILPR(B, Z, L) Predicted lane volume Z Zone ZPWAS(L)
Predicted look ahead speed ZWAS(L) Look ahead speed using current
speed
APPENDIX B
ALMAPR Output Parameters
[0072] When appropriate as described in this patent the parameters
shown inn Table B1 are provided using the equations that follow the
table. The background for these equations is the same as that
provided for Equations 8, 9, 18, 19 and 20 in the ALMAMC patent.
Symbol definitions are provided in Appendix A.
TABLE-US-00003 TABLE B1 ALMAPR output parameters from the ALMA
Management Center Detectors with Accurate Volume and Speed Data
(may Detectors with Accurate or May Not Include Accurate Traffic
Parameter Volume and Occupancy Data Occupancy Data) Predicted Lane
Volume VOLFILPR(B, Z, L)--Volume VOLFILPR(B, Z, L)--Volume
(vehicles/hr) prediction process output prediction process output
converted to ALMA data converted to ALMA data structure structure
Predicted Average Headway AHWPR(B, Z, L) = AHWPR(B, Z, L) =
(hours/vehicle) 1/VOLFILPR(B, Z, L) converted 1/VOLFILPR(B, Z, L)
converted to ALMA data structure to ALMA data structure Predicted
Passenger Car PCEPR (B, L, Z)--Equation PCEPR (B, L, Z)--Equation
Equivalent Volume B1 converted to ALMA data B1 converted to ALMA
data structure structure Predicted Lane Speed SPSPPR(B, Z,
L)--Equation SPSPPR(B, Z, L)--Speed B3 converted to ALMA data
prediction process (307, 308, structure 309) converted to ALMA data
structure Predicted Lane Density DENFILPR (B, Z, L)--Equation
DENFILPR (B, Z, L)--Equation B4 converted to ALMA data B2
structure
Equations
[0073] PCEPR(Det,L)=PCE*VOLFILPR(B,Z,L)/VOLFIL(B,Z,L) B1
DENFILPR(B,Z,L)=VOLFILPR(B,Z,L)/SPSPPR(B,Z,L) B2
SPSPPR(B,Z,L)=VOLFILPR(B,Z,L)/DENFILPR(B,Z,L) B3
DENFILPR(Det,L)=(F*OCCFILPR(Det,L))/(LL+AVL(Det,L)) B4
* * * * *