U.S. patent application number 15/077880 was filed with the patent office on 2016-09-15 for estimating time travel distributions on signalized arterials.
The applicant listed for this patent is PELMOREX CANADA INC.. Invention is credited to Kevin Adda, Andre Gueziec, J.D. Margulici, Edgar Rojas.
Application Number | 20160267788 15/077880 |
Document ID | / |
Family ID | 48874007 |
Filed Date | 2016-09-15 |
United States Patent
Application |
20160267788 |
Kind Code |
A1 |
Margulici; J.D. ; et
al. |
September 15, 2016 |
ESTIMATING TIME TRAVEL DISTRIBUTIONS ON SIGNALIZED ARTERIALS
Abstract
A system is provided for estimating time travel distributions on
signalized arterials. The system may be implemented as a network
service. Traffic data regarding a plurality of travel times on a
signalized arterial may be received. A present distribution of the
travel times on the signalized arterial may be determined. A prior
distribution based on one or more travel time observations may also
be determined. The present distribution may be calibrated based on
the prior distribution.
Inventors: |
Margulici; J.D.; (Oakland,
CA) ; Adda; Kevin; (Santa Clara, CA) ;
Gueziec; Andre; (Sunnyvale, CA) ; Rojas; Edgar;
(Santa Clara, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PELMOREX CANADA INC. |
OAKVILLE |
|
CA |
|
|
Family ID: |
48874007 |
Appl. No.: |
15/077880 |
Filed: |
March 22, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14323352 |
Jul 3, 2014 |
9293039 |
|
|
15077880 |
|
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|
13752351 |
Jan 28, 2013 |
8781718 |
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14323352 |
|
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61591758 |
Jan 27, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08G 1/0116 20130101;
G08G 1/0129 20130101; G08G 1/00 20130101; G08G 1/0112 20130101 |
International
Class: |
G08G 1/01 20060101
G08G001/01 |
Claims
1. A method for estimating time travel distributions on signalized
arterials, the method comprising: receiving travel data about a
signalized arterial collected by one or more reidentification
devices, the travel data corresponding to data collected within a
common time segment in each of a plurality of different days;
receiving real-time travel data about the signalized arterial
collected by one or more reidentification devices; and executing
instructions stored in memory, wherein execution of the
instructions by a processor: normalizes the travel data into a
plurality of individual pace values, the pace values expressed as a
ratio of time per distance, calculates an average pace value for
the signalized arterial as a linear combination of the individual
pace values weighted by distance traveled across the signalized
arterial, estimates a distribution based on the average pace value,
travel data, and store the estimated distribution in memory,
calibrates the distribution based on the real-time travel data, and
generates a real-time prediction of the traffic conditions of the
signalized arterial based on the calibrated distribution.
2. The method of claim 1, wherein the normalized travel data is
expressed in seconds per mile.
3. The method of claim 1, wherein the normalized travel data uses a
base unit of time of 15 minutes.
4. The method of claim 1, wherein the normalized travel data uses a
base unit of space corresponding to standard Traffic Message
Channel (TMC) location codes.
5. The method of claim 1, wherein the estimated distributions
include variations in pace throughout different time periods in a
day.
6. The method of claim 5, wherein the estimated distributions are
calibrated using patterns of increase and decrease in travel
times.
7. The method of claim 1, wherein the received travel data for a
particular signalized arterial also includes observations from
neighboring streets.
8. The method of claim 1, wherein the received travel data for a
particular signalized arterial also includes contextual
evidence.
9. The method of claim 8, wherein the contextual evidence includes
local weather, incidents, and special events.
10. A non-transitory computer-readable storage medium, having
embodied thereon a program executable by a processor to perform a
method for estimating time travel distributions on signalized
arterials, the method comprising: receiving travel data about a
signalized arterial collected by one or more reidentification
devices, the travel data corresponding to data collected within a
common time segment in each of a plurality of different days;
receiving real-time travel data about the signalized arterial
collected by one or more reidentification devices; normalizing the
travel data into a plurality of individual pace values, the pace
values expressed as a ratio of time per distance; calculating an
average pace value for the signalized arterial as a linear
combination of the individual pace values weighted by distance
traveled across the signalized arterial; estimating a distribution
based on the average pace value, travel data, and store the
estimated distribution in memory; calibrating the distribution
based on the real-time travel data; and generating a real-time
prediction of the traffic conditions of the signalized arterial
based on the calibrated distribution.
11. The non-transitory computer-readable storage medium of claim
10, wherein the normalized travel data is expressed in seconds per
mile.
12. The non-transitory computer-readable storage medium of claim
10, wherein the normalized travel data uses a base unit of time of
15 minutes.
13. The non-transitory computer-readable storage medium of claim
10, wherein the normalized travel data uses a base unit of space
corresponding to standard Traffic Message Channel (TMC) location
codes.
14. The non-transitory computer-readable storage medium of claim
10, wherein the estimated distributions include variations in pace
throughout different time periods in a day.
15. The non-transitory computer-readable storage medium of claim
14, wherein the estimated distributions are calibrated using
patterns of increase and decrease in travel times.
16. The non-transitory computer-readable storage medium of claim
10, wherein the received travel data for a particular signalized
arterial also includes observations from neighboring streets.
17. The non-transitory computer-readable storage medium of claim
10, wherein the received travel data for a particular signalized
arterial also includes contextual evidence.
18. The non-transitory computer-readable storage medium of claim
17, wherein the contextual evidence includes local weather,
incidents, and special events.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application is a continuation and claims the
priority benefit of U.S. patent application Ser. No. 14/323,352
filed Jul. 3, 2014, which claims the priority benefit of U.S.
patent application Ser. No. 13/752,351 filed Jan. 28, 2013, which
issued as U.S. Pat. No. 8,781,718 on Jul. 15, 2014, which claims
the priority benefit of U.S. provisional application No. 61/591,758
filed on Jan. 27, 2012, the disclosures of which are incorporated
herein by reference.
BACKGROUND
[0002] 1. Field of the Invention
[0003] The present invention generally concerns traffic management.
More specifically, the present invention concerns estimating time
travel distributions on signalized arterials and thoroughfares.
[0004] 2. Description of the Related Art
[0005] Systems for estimating traffic conditions have historically
focused on highways. Highways carry a majority of all vehicle-miles
traveled on roads and are instrumented with traffic detectors.
Notably, highways lack traffic signals (i.e., they are not
"signalized"). Estimating traffic conditions on signalized streets
represents a far greater challenge for two main reasons. First,
traffic flows are interrupted because vehicles must stop at
signalized intersections. These interruptions generate complex
traffic patterns. Second, instrumentation amongst signalized
arterials is sparse because the low traffic volumes make such
instrumentation difficult to justify economically.
[0006] In recent years, however, global positioning system (GPS)
connected devices have become a viable alternative to traditional
traffic detectors for collecting data. As a result of the
permeation of GPS connected devices, travel information services
now commonly offer information related to arterial conditions.
Although such information is frequently available, the actual
quality of the traffic estimations provided remains dubious.
[0007] Even the most cursory of comparisons between information
from multiple service providers reveals glaring differences in
approximated signalized arterial traffic conditions. The low
quality of such estimations is usually a result of having been
produced from a limited set of observations. Recent efforts,
however, have sought to increase data collection by using
re-identification technologies.
[0008] Such techniques have been based on be based on magnetic
signatures, toll tags, license plates, or embedded devices. The
sampling sizes obtained from such technologies are orders of
magnitude greater than those obtained from mobile GPS units. Sensys
Networks, Inc. of Berkeley, Calif., for example, collects arterial
travel time data using magnetic re-identification and yields
sampling rates of up to 50%. Notwithstanding these recently
improved observation techniques, there remains a need to provide
more accurate estimates of traffic conditions on signalized
arterials.
SUMMARY OF THE PRESENTLY CLAIMED INVENTION
[0009] A system for estimating time travel distributions on
signalized arterials includes a processor, memory, and an
application stored in memory. The application is executable by the
processor to receive data regarding travel times on a signalized
arterial, estimate a present distribution of the travel times,
estimate a prior distribution based on one or more travel time
observations, and calibrate the present distribution based on the
prior distribution.
BRIEF DESCRIPTION OF DRAWINGS
[0010] FIG. 1 is a block diagram of a system for estimating time
travel distributions on signalized arterials.
[0011] FIG. 2 is a series of graphs showing distributions of pace
on a signalized arterial segment at the same time on over three
consecutive days.
[0012] FIG. 3 is a graph showing variations in pace throughout
different times periods in a day.
[0013] FIG. 4 is a block diagram of a device for implementing an
embodiment of the presently disclosed invention.
DETAILED DESCRIPTION
[0014] FIG. 1 is a block diagram of a system for estimating time
travel distributions on signalized arterials. The system of FIG. 1
includes a client computer 110, network 120, and a server 130.
Client computer 110 and server 130 may communicate with one another
over network 120. Client computer 110 may be implemented as a
desktop, laptop, work station, notebook, tablet computer, smart
phones, mobile device or other computing device. Network 120 may be
implemented as one or more of a private network, public network,
WAN, LAN, an intranet, the Internet, a cellular network or a
combination of these networks.
[0015] Client computer 110 may implement all or a portion of the
functionality described herein, including receive traffic data and
other data or and information from devices using re-identification
technologies. Such technologies may be based on magnetic
signatures, toll tags, license plates, or embedded devices. Server
130 may receive probe data from GPS-connected mobile devices.
Server 130 may communicate data directly with such data collection
devices. Server 130 may also communicate, such as by sending and
receiving data, with a third-party server, such as the one
maintained by Sensys Networks, Inc. of Berkeley and accessible
through the Internet at www.sensysresearch.com.
[0016] Server computer 130 may communicate with client computer 110
over network 120. Server computer may perform all or a portion of
the functionality discussed herein, which may alternatively be
distributed between client computer 110 and server 130, or may be
provided by server 130 as a network service for client 110. Each of
client 110 and server computer 130 are listed as a single block,
but it is envisioned that either be implemented using one or more
actual or logical machines.
[0017] In one embodiment, the system may utilize Bayesian Inference
principles to update a prior belief based on new data. In such an
embodiment, the system may determine the distribution of travel
times y on a given signalized arterial at the present time T. The
prior beliefs may include the shape of the travel time distribution
and the range of its possible parameters .theta..sub.T (e.g., mean
and standard deviation) that are typical of a given time of day,
such that y follows a probability function p(y|.theta..sub.T) These
parameters themselves may follow a probability distribution
p(.theta..sub.T|.alpha..sub.T) called the prior distribution. The
prior distribution may comprise its own set of parameters
.alpha..sub.T, which are referred to as hyper-parameters.
[0018] The system may estimate the current parameters using a
recent travel time observation of the arterial of interest. The
system may also account for observations on neighboring streets. In
still further embodiments, the system may consider contextual
evidence such as local weather, incidents, and special events such
as sporting events, one off road closures, or other intermittent
traffic diversions. In one embodiment, y* may designate the current
travel time observations. The system may determine the likeliest
.theta..sub.T using a known y* and .alpha..sub.T.
[0019] The system 100 may account for one or more travel time
variability components. First, there may be individual variations
between vehicles traveling at the same time of day. These
variations stem from diverse driving profiles among drivers and
their varying luck with traffic signals. Second, there may be
recurring time-of-day variations that stem from fluctuating traffic
demand patterns and signal timing. Third, there may be daily
variations in the distributions of travel times over a given time
slot. System 100 may account for other time travel variability
components.
[0020] In one exemplary embodiment, the system 100 may employ
standard Traffic Message Channel (TMC) location codes as base units
of space, and fifteen-minute periods as base units of time. In such
an embodiment, the system approximates that traffic conditions
remain homogeneous across a given TMC location code over each
fifteen-minute period. The system 100 may also use other spatial or
temporal time units depending on the degree of precision desired.
For example, the system 100 may normalize travel time data into a
unit of pace that is expressed in seconds per mile. The system 100
may also calculate the average pace as a linear combination of
individual paces weighted by distance traveled. Such calculations
may be more convenient than using speed values.
[0021] FIG. 2 is a series of graphs showing distributions of pace
on a signalized arterial segment at the same time on over three
consecutive days. More specifically, FIG. 2 shows an exemplary
distribution of pace on a 2-km arterial segment in Seattle, Wash.
for the same fifteen-minute time period on three consecutive days.
As suggested in FIG. 2, determining an exact distribution shape for
a given fifteen minute period on any given day may pose a difficult
realistic objective. The presently described system can, however,
directly observe three different states of an arterial segment and
then calibrate the prior probabilities of being in either state
from archived data. The system may also use real-time data to help
refine a given brief regarding which of the multiple state applies
to the real-time prediction.
[0022] FIG. 3 is a graph showing variations in pace throughout
different times periods in a day. As shown in FIG. 3, the presently
disclosed system may account for time-of-day variations. Notably,
the box indicates the 25.sup.th, 50.sup.th, and 75.sup.th
percentile value while the dotted lines extend to extreme values.
In such embodiments, the system may use data regarding regular
patterns of increase and decrease in travel times to calibrate
prior distributions by time of day.
[0023] FIG. 4 is a block diagram of a device 400 for implementing
an embodiment of the presently disclosed invention. System 400 of
FIG. 4 may be implemented in the contexts of the likes of client
computer 110 and server computer 130. The computing system 400 of
FIG. 4 includes one or more processors 410 and memory 420. Main
memory 420 may store, in part, instructions and data for execution
by processor 410. Main memory can store the executable code when in
operation. The system 400 of FIG. 4 further includes a storage 420,
which may include mass storage and portable storage, antenna 440,
output devices 450, user input devices 460, a display system 470,
and peripheral devices 480.
[0024] The components shown in FIG. 4 are depicted as being
connected via a single bus 490. The components may, however, be
connected through one or more means of data transport. For example,
processor unit 410 and main memory 420 may be connected via a local
microprocessor bus, and the storage 430, peripheral device(s) 480
and display system 470 may be connected via one or more
input/output (I/O) buses. In this regard, the exemplary computing
device of FIG. 4 should not be considered limiting as to
implementation of the presently disclosed invention. Embodiments
may utilize one or more of the components illustrated in FIG. 4 as
might be necessary and otherwise understood to one of ordinary
skill in the art.
[0025] Storage device 430, which may include mass storage
implemented with a magnetic disk drive or an optical disk drive,
may be a non-volatile storage device for storing data and
instructions for use by processor unit 410. Storage device 430 can
store the system software for implementing embodiments of the
present invention for purposes of loading that software into main
memory 410.
[0026] Portable storage device of storage 430 operates in
conjunction with a portable non-volatile storage medium, such as a
floppy disk, compact disk or Digital video disc, to input and
output data and code to and from the computer system 400 of FIG. 4.
The system software for implementing embodiments of the present
invention may be stored on such a portable medium and input to the
computer system 400 via the portable storage device.
[0027] Antenna 440 may include one or more antennas for
communicating wirelessly with another device. Antenna 440 may be
used, for example, to communicate wirelessly via Wi-Fi, Bluetooth,
with a cellular network, or with other wireless protocols and
systems including but not limited to GPS, A-GPS, or other location
based service technologies. The one or more antennas may be
controlled by a processor 410, which may include a controller, to
transmit and receive wireless signals. For example, processor 410
execute programs stored in memory 412 to control antenna 440
transmit a wireless signal to a cellular network and receive a
wireless signal from a cellular network.
[0028] The system 400 as shown in FIG. 4 includes output devices
450 and input device 460. Examples of suitable output devices
include speakers, printers, network interfaces, and monitors. Input
devices 460 may include a touch screen, microphone, accelerometers,
a camera, and other device. Input devices 460 may include an
alpha-numeric keypad, such as a keyboard, for inputting
alpha-numeric and other information, or a pointing device, such as
a mouse, a trackball, stylus, or cursor direction keys.
[0029] Display system 470 may include a liquid crystal display
(LCD), LED display, or other suitable display device. Display
system 470 receives textual and graphical information, and
processes the information for output to the display device.
[0030] Peripherals 480 may include any type of computer support
device to add additional functionality to the computer system. For
example, peripheral device(s) 480 may include a modem or a
router.
[0031] The components contained in the computer system 400 of FIG.
4 are those typically found in computing system, such as but not
limited to a desk top computer, lap top computer, notebook
computer, net book computer, tablet computer, smart phone, personal
data assistant (PDA), or other computer that may be suitable for
use with embodiments of the present invention and are intended to
represent a broad category of such computer components that are
well known in the art. Thus, the computer system 400 of FIG. 4 can
be a personal computer, hand held computing device, telephone,
mobile computing device, workstation, server, minicomputer,
mainframe computer, or any other computing device. The computer can
also include different bus configurations, networked platforms,
multi-processor platforms, etc. Various operating systems can be
used including Unix, Linux, Windows, Macintosh OS, Palm OS, and
other suitable operating systems.
[0032] The foregoing detailed description of the technology herein
has been presented for purposes of illustration and description. It
is not intended to be exhaustive or to limit the technology to the
precise form disclosed. Many modifications and variations are
possible in light of the above teaching. The described embodiments
were chosen in order to best explain the principles of the
technology and its practical application to thereby enable others
skilled in the art to best utilize the technology in various
embodiments and with various modifications as are suited to the
particular use contemplated. It is intended that the scope of the
technology be defined by the claims appended hereto.
* * * * *
References