U.S. patent application number 13/088375 was filed with the patent office on 2011-08-04 for device, system and method for traffic prediction.
Invention is credited to Josef Mintz.
Application Number | 20110191012 13/088375 |
Document ID | / |
Family ID | 27401991 |
Filed Date | 2011-08-04 |
United States Patent
Application |
20110191012 |
Kind Code |
A1 |
Mintz; Josef |
August 4, 2011 |
Device, System and Method for Traffic Prediction
Abstract
A method is provided for predicting load of traffic of vehicles
that are travelling according to non reference route plan, provided
with Dynamic Route Guidance capability of their PMMS, in a Forward
Time Interval related Route Segment and according to a
predetermined protocol between mobile systems and a non mobile
system of a SODMS. Using mobile units, a traffic prediction query
is receiving according to a predetermined differential traffic load
match process. A match process is performed by each of the mobile
units and, according to a match, a predetermined response procedure
is enabled, wherein a response procedure in each mobile unit uses a
predetermined random process to select an allocated slot in which
to transmit a predetermined signal, which provides an improved way
to predict traffic in conjunction with off line database
statistics, preferably with such that are being adaptively
corrected by prior data and method to predict traffic which do not
include, or lack sufficient erratic traffic information.
Inventors: |
Mintz; Josef; (Jerusalem,
IL) |
Family ID: |
27401991 |
Appl. No.: |
13/088375 |
Filed: |
April 17, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11500472 |
Aug 8, 2006 |
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13088375 |
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10467661 |
Aug 11, 2003 |
7103470 |
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PCT/IB02/01996 |
Feb 8, 2002 |
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11500472 |
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60267693 |
Feb 9, 2001 |
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60274323 |
Mar 8, 2001 |
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60289083 |
May 7, 2001 |
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Current U.S.
Class: |
701/117 |
Current CPC
Class: |
G08G 1/0141 20130101;
G08G 1/096811 20130101; G08G 1/0962 20130101; G08G 1/096844
20130101; G08G 1/096827 20130101; G06Q 50/30 20130101; G08G 1/0129
20130101; G06Q 10/047 20130101; G08G 1/0112 20130101; G06Q 30/02
20130101; G01C 21/3492 20130101 |
Class at
Publication: |
701/117 |
International
Class: |
G08G 1/00 20060101
G08G001/00 |
Claims
1-4. (canceled)
5. A method to adjust traffic prediction, the method comprising a
plurality of iterations, wherein at least one iteration of the
plurality of iterations comprises: providing a traffic prediction
update for route planning associated with a plurality of vehicles,
wherein said traffic prediction update is based, at least in part,
on route plan modifications resulting from give-up processes
performed in a prior iteration, wherein said give-up process
comprises modification of a route plan, in accordance with a
give-up criterion, to exclude from the route plan a congested route
segment, which is expected to have traffic congestion, and wherein
said traffic prediction update includes a prediction, in a forward
time interval, of congested traffic on a route segment that is
still congested with respect to traffic predicted on said route
segment in said prior iteration; and in response to the traffic
prediction update, receiving an indication that a vehicle of said
vehicles has modified its route plan from a planned route that
includes said still congested route segment to a modified route
that does not include said still congested route segment.
6. The method of claim 5, wherein the traffic prediction update is
further based on statistical traffic data.
7. The method of claim 6 including updating a statistical database
according to the indication.
8. The method of claim 5, wherein the give-up process comprises a
give-up process utilizing one or more give-up criterion levels.
9. The method of claim 8, wherein a give-up criterion level of the
give-up criterion levels includes a threshold to modify a route
plan to a route having lower priority than the route plan of the
prior iteration.
10. The method of claim 8, wherein a give-up criterion level of the
give-up criterion levels includes a threshold to modify a route
plan to a route plan having a higher travel time than the route
plan of the prior iteration.
11. The method of claim 8, wherein a give-up criterion level of the
give-up criterion levels includes a threshold to modify a route
plan to an earlier, more preferable, route.
12. The method of claim 8, wherein a give-up criterion level of the
give-up criterion levels includes a threshold to modify a route
plan to a route having a-priori lower priority than the route plan
of the prior iteration.
13. The method of claim 5, wherein one or more of the iterations is
based on one or more random parameters.
14. The method of claim 5, wherein the give-up process in a
successive iteration to a prior iteration modifies planned routes
at a higher grade of give-up criterion level than the route plan of
the prior iteration.
15. The method of claim 5, wherein a length of the modified route
is greater than a length of the planned route.
16. The method of claim 5 comprising: if the traffic prediction
update indicates that too many mobile units gave up on said still
congested route segment in the prior iteration, receiving one or
more signals from one or more of said vehicles, indicating that
said one or more vehicles modify their current routes to routes
previously-used in the previous iteration.
17. The method of claim 16 comprising: receiving a signal
indicating that at least one of said vehicles returns to a
previously-used route by re-making a prior give-up process with a
lower grade of give-up level criterion.
18. The method of claim 16, wherein the previously-used routes are
shorter than the current routes.
19. The method of claim 16, wherein modification of said current
routes to previously-used routes comprises performing the
modification based on randomly selected values of at least one
parameter.
20. The method of claim 5 comprising: performing a plurality of
iterations until reaching a sufficient level of convergence.
21. The method of claim 20 comprising: performing said plurality of
iterations by taking into account a trade-off between low levels of
give-up grades and high levels of give-up grades.
22. The method of claim 5 including broadcasting said traffic
prediction update to the plurality of vehicles.
23. The method of claim 5, including, at a vehicle of the vehicles:
receiving positioning signals, planning a route according to
traffic updates and according to a respective give-up process,
transmitting radio signals to update a mapping system about route
modification, and receiving radio signals of traffic updates.
24. The method of claim 5, wherein said route modification
comprises modification to exclude from route plans one or more
route segments affected by predicted traffic congestion.
Description
RELATED APPLICATIONS
[0001] The present application claims priority of U.S. Provisional
Application No. 60/267,693, filed Feb. 9, 2001, U.S. Provisional
Applications No. 60/274,323, filed Mar. 8, 2001 and U.S.
Provisional Application No. 60/269,083 filed May 7, 2001 which are
incorporated by reference.
FIELD OF THE INVENTION
[0002] This invention relates generally to a method and system for
mapping potential traffic loads in forward time intervals,
according to various criteria which might indicate erratic traffic,
as a result of expected increase in the number of Mobile Telematics
Units (MTU) and In-Car Navigation Systems (CNS) users that use
Dynamic Route Guidance (DRG). In particular, the method and system
aims to provide an efficient means to estimate the potential
increase or decrease in the number of vehicles in selected places
(inconsistent traffic load), by using a radio system, in order to
help in determining levels of a potential erratic behavior in the
traffic due to the use of DRG by a significant percentage of
vehicles. This system and method may further help to investigate
sources of causes of erratic traffic and their level of effect,
including the use of traffic information and reactions of drivers
to telematics applications. This could help to improve traffic
predictions for the use of traffic control and DRG. In particular,
this method provides the ability to make use of a mapping system
platform which has the capability to allocate pre-assigned slots or
groups of slots for the detection of signal responses from mobiles
that have probe response capability. The above identified system is
mainly characterized by the ability of the mobiles to select
time/frequency slots for response signals according to a mapping
system query and according to a predetermined protocol. The
detection of mobile transmission signals is mainly characterized by
energy detection of mobile transmitted signals in allocated slots
and hence there is no need for a repeat in mobile transmission as a
result of signal collisions in the same slot. The non mobile
platform of such a mapping system, which may be referred to
hereinafter as Slot Oriented Discrimination Mapping System (SODMS),
or as otherwise referred to, as well as the mobile (probe) response
capability are described in U.S. application Ser. Nos. 09/945,257
and 09/998,061 filed Nov. 30, 2001 and PCT/IB00/00239 and their own
references.
DESCRIPTION OF RELATED ART
[0003] For example PCT publication WO 96/14586, published 17 May
1996, the disclosure of which is incorporated herein by reference,
describes, inter alia, a system for mapping of vehicles in
congestion. In one embodiment applicable to the mapping system
platform, described in the above publication, a central station
broadcasts a call to the vehicles which requests for example those
vehicles which are stopped or which have an average velocity below
a given value to broadcast a signal indicative of their position.
Such signals are broadcast in slots, each of which represent one
bit (yes or no) which relates to a position. Preferably, only one
logical slot (that may be represented by more than one actual slot)
is used to define the related position. Such signals are then used
to generate a map of those regions for which traffic is delayed or
otherwise moving slowly.
[0004] In the above-identified prior art, the possible construction
of consistent traffic database for possible use with traffic
predictions have been described. Such database could be constructed
by traffic mapping of queues, when quasi-stationary (temporary
stationary) statistics of traffic flow in a mapped road, at certain
periods of time of a day, and for days in which traffic conditions,
are considered to be repetitive. Such collected information, e.g.,
average arrival rates, could be used as off line database to
predict traffic in conjunction with real time updates of mapped
queues using statistical methods known in the art. By using the
mapping method in this embodiment for mapping the potential effects
of erratic traffic, either when produced as part of the current
traffic mapping application of the mapping system platform
(described by the above identified prior art) or by a separate
platform with similar communication capabilities, it is possible to
update the consistent traffic database by incorporating
inconsistent traffic predictions.
BACKGROUND TO THE INVENTION
[0005] The expected increase in the number of Telematics
applications by MTUs used with off-board or on-board route guidance
as well as the increase in the number of CNS users would increase
the percentage of vehicles that would use Dynamic Route Guidance
and would hence result in unpredicted changes in traffic load which
has the potential to cause erratic traffic.
[0006] Traditional traffic predictions could use a database of
consistent traffic in order to predict traffic according to
expected traffic loads, possibly also according to prior knowledge
about the behavior of the traffic and the current conditions of
traffic. However DRG effects on traffic might mostly be
unpredictable by such a database. This could be the result even
though there is a priori information about off board DRG (routs
plans provided by common service centers), since deviations in the
schedule of routes and possible use of alternative routes could in
a short time make prior knowledge to become irrelevant to traffic
prediction. Thus it would be valuable to have a means to update a
traffic database that would be used in conjunction with consistent
traffic information and possibly with other prior knowledge
including current traffic information in order to improve the
capability to predict potential to changes in traffic.
[0007] Consistent Traffic is defined as such traffic that has a
repetitive characteristic, with respect to specific time periods
and places, (e.g. certain hour in a certain day of the week in a
certain road). Consistent Traffic is a result of behavior patterns
that from a statistical point of view usually and in general may be
characterized. Such traffic characteristics may be stored in an
off-line data base which may contribute to traffic predictions.
[0008] Inconsistent Traffic is defined as such traffic that has a
non repetitive and erratic characteristic with respect to specific
time periods and places. Such traffic may for example be the result
of the ability by the individual driver to change routes according
to current traffic loads. As the number of drivers that have access
to detailed information on currently changing traffic increases,
and as the number of drivers that possess in-car sophisticated
capability to individually vary their previous route plans, and the
less coordination if any exists amongst various drivers, the more
inconsistent would become such traffic. Inconsistent Traffic is
difficult if at all possible to be characterized on a statistical
basis. Such traffic tends to be in general unpredictable, and leads
to unpredictable traffic loads.
[0009] The inconsistent traffic is expected to become a significant
issue in the control of the traffic when a significant percentage
of cars will be using dynamic route guidance and as a result might
probably, in themselves cause unexpected traffic loads at certain
places that would affect the traffic and reduce the efficiency of
dynamic route guidance. Traffic information used with Dynamic Route
Guidance (DRG) could be one reason for the inconsistency in the
traffic due to changes in planned routes, while driver preferences,
deviation from schedule, or reaction to local based services could
be other causes for an inconsistency in the conditions of the
traffic.
[0010] One general approach to resolve the problem of predicting
inconsistent traffic is to centralize the control of the individual
driver routes. This is not the approach which is considered in the
following embodiment of the invention as it leads to centralized
DRG which has many disadvantages beside feasibility problems with
large scale implementation.
[0011] As further explained, apart from the contribution of traffic
predictions of inconsistent traffic to traffic control the
predictions could further lead to a relatively low cost
implementation of an anonymous predictive DRG approach based on
distributed intelligence of the in car computers and also to
contribute to the implementation of more efficient telematics
applications.
[0012] Predictions for inconsistent traffic is based on a process
of traffic load estimation for predetermined place and time
interval, (for example, estimating the number of vehicles that use
in-car navigation computers which are expected to pass in a certain
road in a certain forward time interval). However when the source
of such information is limited to car navigation units that use
dynamic route guidance only, and the estimation process is the only
means for such predictions, it would be required that most of the
cars should use car navigation systems. In practice such a
situation would doubtfully be viable. However, the situation when a
significant percentage of vehicular systems would most probably be
using Dynamic Route Guidance (DRG) may be considered realistic in
the not too distant future, and hence inconsistent traffic would
begin to appear at an early stage, whereas reliable traffic
prediction for this situation would not yet be available. With the
lack of traffic predictions, the problems that would be encountered
at such stages could lead to a significant dilemma by the
individual drivers, about the efficiency of Dynamic Route Guidance.
The dilemma would be whether to consider recommended DRG according
to current traffic, while ignoring unpredictable traffic that might
result due to the significant number of DRG users, or ignoring the
recommended DRG. For such early stages of inconsistent traffic the
following embodiment suggests a modified method of traffic
predictions in order to enable reliable prediction at such early
stages. Traffic load predictions would preferably refer mostly to
sensitive roads that encounter recurrent traffic jams.
SUMMARY OF THE INVENTION
[0013] The present invention provides a preferred method and system
for differential mapping of potential traffic loads in forward time
intervals in selected places, which could be a result of DRG, in
order to provide rapid and effective means for traffic prediction.
The mapping system, in which slots are allocated to probe
responses, and mobile units that are equipped with route guidance
with probe response capability in allocated slots, could be used as
a platform for the following modified prediction method. The mobile
unit would be referred to as Potential Mobile Mapping System
(PMMS). The route guidance capability of a PMMS could be based on
either on board or off board route guidance. The prediction method
described in the following could be implemented with such
platforms, either with or without the implementation of the
application of mapping of current traffic as part of this platform.
The non mobile part of the mapping system (non mobile systems),
including the radio system and the mapping system, will be referred
to as the non mobile system platform. All applicable terms used in
the above identified prior art, in connection with traffic mapping,
and which are applicable and would contribute to the implementation
of the following embodiment of the invention, will hold also for
this application.
[0014] The aim of the differential mapping method for determining
potential traffic loads is to update a traffic information database
with information about deviation from expected traffic loads in
forward time intervals for selected road segments in order to
enable more accurate and prediction capability of the use of a
traffic information database. Based on the inherent limitations of
the database prediction capability (before deviation updates),
prediction criteria are formulated and could be transmitted by
means of the non mobile platform to the PMMS units. Such criteria
are intended to enable the prediction of expected potential
deviations from schedule and previously planned routes, at the
level of the database requirements. The PMMS units could determine
if they match the transmitted criteria, and if a match exists,
would respond accordingly. This could also be considered as a
method to improve accuracy levels of information in database that
could help to predict traffic according to pre-investigation of
local potential loads affected by DRG in selected forward time
intervals. The level of basic information in such database could
for example include consistent traffic, or higher level prediction
capabilities.
[0015] For example, if the use of the database is based on
prediction capabilities according to consistent traffic, then cars
that change their planned route according to traffic information,
most probably from the shortest route according to time and
distance to one that most probably is shortest according to time,
or other dynamic preference, could be used to indicate on possibly
expected inconsistent traffic that is not taken into account within
consistent traffic statistics. Thus it would be worth to first
isolate this group of cars in order to estimate their contribution
to the inconsistent traffic loads in specific road segments.
Preferably, this information would then be taken into account in
conjunction with a database of consistent traffic statistics,
preferably updated with current real time updates of traffic, to
determine current and predicted traffic information that would be
currently updated accordingly. The isolation process would use
prediction queries that would selectively target cars that made a
change to their route or deviated from schedule, according to
traffic information or other predetermined possible reasons such as
a response of drivers to a telematics application. The queries
determine the response criteria which will include but not be
limited to the following--a) vehicles that are planning to pass in
a certain road at a certain forward time interval according to
their modified route plan or schedule, and which did not plan to do
so according to a reference route (e.g., a default route or any
other route that could be referred by the PMMS as a reference that
may be determined according to criteria as part of a predetermined
protocol), and b) vehicles that did plan to pass in this road
according to the reference route, and are not planning to do so
according to the modified route plan or schedule, at the above
forward time interval.
[0016] Vehicles which are using their reference (e.g. default)
route will not respond to queries.
[0017] Criteria for determining whether a route is within reference
conditions (e.g., default) or not, could be provided from a common
external source, which considers the investigated level of possible
effect on the traffic statistics. The reference (e.g., default)
route information may be formed either in the in-car (on board)
systems, or received from external (off board) sources, and would
preferably be determined by route plan and schedule. Thus,
according to a predetermined protocol, a deviation in route or
schedule would exclude the route from being referred to as a
reference route and would determine it to be a non reference route.
The protocol would preferably include threshold levels of
deviation.
[0018] Typical default routes are such which could be considered
but not limited to conform with consistent traffic. Default routes
could be determined according to common criteria (e.g. the shortest
route, preferably with time schedules), for mobile units
participating in the following processes. Non default routes are
such that have some significant effect on known traffic statistics
as a result of deviation from schedule or from original route plan
that could be considered as default routes.
[0019] The in-car system will incorporate a predetermined decision
procedure, described in the following.
[0020] In principle, a Differential Traffic Load Prediction (DTLP)
process with respect to a Forward Time Interval related Route
Segment (FTIRS refers to a time interval with respect to a route
segment, usually a road segment) under investigation, could be
implemented by means of two types of traffic prediction queries
which would be transmitted by a mapping system to the PMMS units.
The prediction queries include the prediction criteria, and are
aimed at targeting groups of cars that are either expected to pass
through the FTIRS under investigation and were not expected to do
so, according to database information, (non expected
vehicles--NEV), or are not expected to pass through the FTIRS under
investigation, and were expected to do so, according to the
database information (expected vehicles--EV); --
Query-A):--type of a query with the aim of estimating the number of
vehicles which on their reference route are not expected to pass
through the investigated FTIRS, and on their non reference route
are expected to pass through the investigated FTIRS, (non expected
vehicles--NEV), and Query-B):--type of a query with the aim of
estimating the number of vehicles which on their reference route
are expected to pass through the investigated FTIRS and on their
non reference route are not expected to pass through the
investigated FTIRS, (expected vehicles--EV).
[0021] In order to enable responses in relation to forward time
intervals, it is required that the PMMS units would be equipped
with the means of reference or mean to calculate reference to
segments of planned routes and estimated travel time intervals
along respective route segments. Preferably, an estimated time
interval will be provided with respective confidence intervals.
[0022] Vehicles which are using a non reference planned route, will
enable the response procedure according to the following decision
procedure;
[0023] If the received query is identified as Query A, then,
according to the following differential traffic load match process
result, if there is a match between FTIRS in the query and the
planned non reference (e.g., default) route (route in use), and
there is no match between FTIRS in the query and the reference
route, then enable the response procedure.
[0024] If the received query is identified as Query B, then,
according to the following differential traffic load match process
result, if there is a match between FTIRS in the query and their
reference route, and there is no match between the FTIRS in the
query and non reference route (route in use), then enable the
response procedure.
[0025] Enabling the response procedure, in the predetermined
decision procedure, would preferably be expanded to include
additional criteria, for targeting vehicles. For example, with
respect to Query A, additional criteria in checking an interval
estimate for the probability to arrive within the investigated
FTIRS, would preferably be taken into account as part of the
decision procedure.
[0026] In order to alleviate the computation load in the in-car
system, involved in frequent matching in response to above queries,
it would be preferable to refer routes to predetermined area zones,
and by a preliminary predetermined screening procedure, preceding
the above decision procedure, vehicles whose planned (reference and
non reference) routes do not cross area zones in which the FTIRS is
included, will not continue with the more detailed matching process
in the above decision procedure.
[0027] A number of communication slots will be preferably allocated
for responders (cars which transmit in the allocated slots) in the
response procedure, separately, with respect to each Query. Each of
the targeted vehicles, (responders), in which the response
procedure is enabled, will use a predetermined response procedure
to select a slot in which to respond. This predetermined procedure
would preferably use a uniformly distributed random selection of a
slot out of all the allocated slots, to transmit a signal.
[0028] In accordance with an embodiment of the invention, there is
thus provided a method of predicting load of traffic of vehicles
that are traveling according to non reference route plan, provided
with Dynamic Route Guidance capability of their PMMS, in a Forward
Time Interval related Route Segment and according to a
predetermined protocol between mobile systems and a non mobile
system platform of a SODMS, the method comprising:
(a) receiving by mobile units a traffic prediction query and
according to a predetermined differential traffic load match
process, (b) performing a match process by each of the mobile units
and, according to a match, (c) enabling a predetermined response
procedure wherein a response procedure in each mobile unit uses a
predetermined random process to select an allocated slot in which
to transmit a predetermined signal, which provides an improved way
to predict traffic in conjunction with off line database
statistics, preferably with such that are being adaptively
corrected by prior data and method to predict traffic which do not
include, or lack sufficient erratic traffic information.
[0029] In another embodiment of the invention it would be valuable
to use traffic predictions in conjunction with applications which
have a potential to cause erratic conditions of traffic. Such
applications could include local based services in telematics and
in particular position related commerce (p-commerce sometimes
referred to as I-commerce or m-commerce). There might be different
ways to implement p-commerce and hence to increase the level of
unpredicted traffic. For example in order to improve p-commerce
applications, it would be an advantage to large stock holders and
others to have a query tool that would help them to identify
sufficient demand, preferably according to prices and including non
solicited products, for special offers. This could create a hunting
trip environment. With such a tool, queries could be provided in a
way similar to an auction process, preferably by a broadcast
message to the telematics users, with respect to products with
possibly one or more ranges of prices. The user, usually a driver,
will have a stored list of preferences for products, in his
Telematics Computer (TC which could be the computer of a
Telematics-PMMS) that would be matched with broadcast messages
according to preferences in the list. For example, a stored product
list (SPL) which may include products with ranges of prices could
enable the TC to respond to a broadcast query. If such responses
would provide information about the estimated number of the
potential clients and possibly their position distribution it would
enable the vendor to determine a time window and price for a
special offer according to demand. The offer could then target the
potential clients. Most probably this would target the responders
who would contribute to the decision making. When considering a
system platform with capabilities such as suggested for a traffic
mapping system, both, in this embodiments of the invention and in
the reference prior art, together with telematics mobile unit with
PMMS capabilities, which enable to estimate the number of
responders to a query by random response in predetermined number of
slots, it would be possible to implement a hunting trip
application, efficiently.
[0030] Thus in accordance with this embodiment of the invention,
there is thus provided a method for estimating according to
criteria and a predetermined protocol local demand (e.g., for
products or services) according to SPL, preferably in conjunction
with predicting respective load of differential traffic in forward
time intervals for selected places which might result from a
hunting trip application, and according to a further predetermined
protocol between TCs and a non mobile system platform of a SODMS,
the method comprising:
(a) receiving by TC units a query of a hunting trip application and
according to a predetermined match process, (b) performing a match
process by each of the TC units and, according to a match, (c)
enabling a predetermined response procedure wherein a response
procedure in each TC unit uses a predetermined random process to
select an allocated slot in which to transmit a predetermined
signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1, describes an iterative estimation procedure that is
preferably used with more than a single iteration of estimation
(separate allocation of slots with each iteration). The iterative
estimation procedure is preferably aimed to obtain an estimated
result of the number of responders with a restricted acceptable
error level and to reduce biasness. The error level of the estimate
in a single iteration is a function of the ratio between the number
of slots in which responses are detected (responding slots) and the
given number of allocated slots. Since the ratio of responding
slots to a given number of allocated slots would be a result of the
number of responders, it is desirable to assess in advance a
realistic anticipated range of responders, in order to determine a
minimal number of initial allocated slots.
DETAILED DESCRIPTION OF THE DRAWINGS
[0032] FIG. 1, describes an iterative estimation procedure that is
preferably used with more than a single iteration of estimation
(separate allocation of slots provided with each performed
iteration). The iterative estimation procedure is preferably aimed
to obtain an estimated result of the number of responders with a
restricted acceptable error level, to reduce biasness and to check
consistency. The error level of the estimate in a single iteration
is a function of the ratio between the number of slots in which
responses are detected (responding slots) and the given number of
allocated slots. Since the ratio of responding slots to a given
number of allocated slots would be a result of the number of
responders, it is desirable to assess in advance a realistic
anticipated range of responders, in order to determine a minimal
number of initial allocated slots. Since such realistic ranges of
responders could be anticipated from statistical data, according to
time and place, then a data base of possible initial ranges would
preferably be evolved for any particular urban entity, preferably
as probability distribution from which ranges of confidence
intervals could be derived. Combined estimates that can use joint
probabilities and Bayesian methods as described above with respect
to FIG. 1 are described in more detail in the detailed description
of Preferred Embodiment of the invention.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT
[0033] The present invention provides a preferred method and system
for differential mapping of potential traffic loads in forward time
intervals in selected places, which could be a result of DRG, in
order to provide rapid and effective means for traffic prediction.
The mapping system, in which slots are allocated to probe
responses, and mobile units that are equipped with route guidance
with probe response capability in allocated slots, could be used as
a platform for the following modified prediction method. The mobile
unit would be referred to as Potential Mobile Mapping System
(PMMS). The route guidance capability of a PMMS could be based on
either on board or off board route guidance. The prediction method
described in the following could be implemented with such
platforms, either with or without the implementation of the
application of mapping of current traffic as part of this platform.
The non mobile part of the mapping system (non mobile systems),
including the radio system and the mapping system, will be referred
to as the non mobile system platform. All applicable terms used in
the above identified prior art, in connection with traffic mapping,
and which are applicable and would contribute to the implementation
of the following embodiment of the invention, will hold also for
this application.
[0034] The aim of the differential mapping method for determining
potential traffic loads is to update a traffic information database
with information about deviation from expected traffic loads in
forward time intervals for selected road segments in order to
enable more accurate and prediction capability of the use of a
traffic information database. Based on the inherent limitations of
the database prediction capability (before deviation updates),
prediction criteria are formulated and could be transmitted by
means of the non mobile platform to the PMMS units. Such criteria
are intended to enable the prediction of expected potential
deviations from schedule and previously planned routes, at the
level of the database requirements. The PMMS units could determine
if they match the transmitted criteria, and if a match exists,
would respond accordingly. This could also be considered as a
method to improve accuracy levels of information in database that
could help to predict traffic according to pre-investigation of
local potential loads affected by DRG in selected forward time
intervals. The level of basic information in such database could
for example include consistent traffic, or higher level prediction
capabilities.
[0035] For example, if the use of the database is based on
prediction capabilities according to consistent traffic, then cars
that change their planned route according to traffic information,
most probably from the shortest route according to time and
distance to one that most probably is shortest according to time,
or other dynamic preference, could be used to indicate on possibly
expected inconsistent traffic that is not taken into account within
consistent traffic statistics. Thus it would be worth to first
isolate this group of cars in order to estimate their contribution
to the inconsistent traffic loads in specific road segments.
Preferably, this information would then be taken into account in
conjunction with a database of consistent traffic statistics,
preferably updated with current real time updates of traffic, to
determine current and predicted traffic information that would be
currently updated accordingly. The isolation process would use
prediction queries that would selectively target cars that made a
change to their route or deviated from schedule, according to
traffic information or other predetermined possible reasons such as
a response of drivers to a telematics application. The queries
determine the response criteria which will include but not be
limited to the following--a) vehicles that are planning to pass in
a certain road at a certain forward time interval according to
their modified route plan or schedule, and which did not plan to do
so according to a reference route (e.g., a default route or any
other route that could be referred by the PMMS as a reference that
may be determined according to criteria as part of a predetermined
protocol), and b) vehicles that did plan to pass in this road
according to the reference route, and are not planning to do so
according to the modified route plan or schedule, at the above
forward time interval.
[0036] Vehicles which are using their reference (e.g. default)
route will not respond to queries.
[0037] Criteria for determining whether a route is within reference
conditions (e.g., default) or not, could be provided from a common
external source, which considers the investigated level of possible
effect on the traffic statistics. The reference (e.g., default)
route information may be formed either in the in-car (on board)
systems, or received from external (off board) sources, and would
preferably be determined by route plan and schedule. Thus,
according to a predetermined protocol, a deviation in route or
schedule would exclude the route from being referred to as a
reference route and would determine it to be a non reference route.
The protocol would preferably include threshold levels of
deviation.
[0038] Typical default routes are such which could be considered
but not limited to conform with consistent traffic. Default routes
could be determined according to common criteria (e.g. the shortest
route, preferably with time schedules), for mobile units
participating in the following processes. Non default routes are
such that have some significant effect on known traffic statistics
as a result of deviation from schedule or from original route plan
that could be considered as default routes.
[0039] The in-car system will incorporate a predetermined decision
procedure, described in the following.
[0040] In principle, a Differential Traffic Load Prediction (DTLP)
process with respect to a Forward Time Interval related Route
Segment (FTIRS refers to a time interval with respect to a route
segment, usually a road segment) under investigation, could be
implemented by means of two types of traffic prediction queries
which would be transmitted by a mapping system to the PMMS units.
The prediction queries include the prediction criteria, and are
aimed at targeting groups of cars that are either expected to pass
through the FTIRS under investigation and were not expected to do
so, according to database information, (non expected
vehicles--NEV), or are not expected to pass through the FTIRS under
investigation, and were expected to do so, according to the
database information (expected vehicles--EV); --
Query-A):--type of a query with the aim of estimating the number of
vehicles which on their reference route are not expected to pass
through the investigated FTIRS, and on their non reference route
are expected to pass through the investigated FTIRS, (non expected
vehicles--NEV), and Query-B):--type of a query with the aim of
estimating the number of vehicles which on their reference route
are expected to pass through the investigated FTIRS and on their
non reference route are not expected to pass through the
investigated FTIRS, (expected vehicles--EV).
[0041] In order to enable responses in relation to forward time
intervals, it is required that the PMMS units would be equipped
with the means of reference or mean to calculate reference to
segments of planned routes and estimated travel time intervals
along respective route segments. Preferably, an estimated time
interval will be provided with respective confidence intervals.
[0042] Vehicles which are using a non reference planned route, will
enable the response procedure according to the following decision
procedure;
[0043] If the received query is identified as Query A, then,
according to the following differential traffic load match process
result, if there is a match between FTIRS in the query and the
planned non reference (e.g., default) route (route in use), and
there is no match between FTIRS in the query and the reference
route, then enable the response procedure.
[0044] If the received query is identified as Query B, then,
according to the following differential traffic load match process
result, if there is a match between FTIRS in the query and their
reference route, and there is no match between the FTIRS in the
query and non reference route (route in use), then enable the
response procedure.
[0045] Enabling the response procedure, in the predetermined
decision procedure, would preferably be expanded to include
additional criteria, for targeting vehicles. For example, with
respect to Query A, additional criteria in checking an interval
estimate for the probability to arrive within the investigated
FTIRS, would preferably be taken into account as part of the
decision procedure.
[0046] In order to alleviate the computation load in the in-car
system, involved in frequent matching in response to above queries,
it would be preferable to refer routes to predetermined area zones,
and by a preliminary predetermined screening procedure, preceding
the above decision procedure, vehicles whose planned (reference and
non reference) routes do not cross area zones in which the FTIRS is
included, will not continue with the more detailed matching process
in the above decision procedure.
[0047] A number of communication slots will be preferably allocated
for responders (cars which transmit in the allocated slots) in the
response procedure, separately, with respect to each Query. Each of
the targeted vehicles, (responders), in which the response
procedure is enabled, will use a predetermined response procedure
to select a slot in which to respond. This predetermined procedure
would preferably use a uniformly distributed random selection of a
slot out of all the allocated slots, to transmit a signal.
[0048] A predetermined estimating procedure will be used in the non
mobile system platform, to determine estimated number of responders
according to the total number of slots in which responses are
detected in a given number of allocated slots. The estimating
procedure would preferably use a number of secondary procedures, as
described in the following and illustrated in FIG. 1. It is
preferably aimed to obtain the estimated number of responders with
an acceptable error level, however the error level is a function of
the ratio between the number of responders and the given number of
allocated slots. The greater the number of allocated slots in
proportion to the number of responders, the lower would be the
error level. The error level can be defined as the maximum
cumulative probability that could produce a similar result from a
number of responders which is either greater or lower than the
acceptable estimation interval of responders. The acceptable error
level would preferably be determined according to the sensitivity
of the estimation in the specific application. Since there is a
variation around the most frequent number of responding slots,
(slots in which responses are detected), which depends on the
number of allocated slots and the number of responders, it is
desirable to assess in advance a realistic anticipated range of
numbers of responders, in order to determine a minimal number of
initial allocated slots for an acceptable variance. Since such
realistic ranges of responders could be anticipated from
statistical data, according to time and place, then a database of
possible initial ranges would preferably be evolved for any
particular urban entity, (preferably as probability distribution
from which ranges of confidence intervals could be derived). The
database of ranges would be preferably evolved taking into account
conditions specific to such an entity, such as, (but not limited
to), characteristic traffic conditions, characteristic
infrastructure servicing traffic flow, and prevailing decision
processes used by route guidance procedures. The technique of
evolving a database of ranges for initial numbers of expected
responders would preferably be based on statistical and empirical
methods and computer simulations. In order to determine the
required initial number of allocated slots, based on the database
of ranges, it is also preferably required to take into account the
prevailing conditions in available radio communication spectrum,
limitations imposed by the need to investigate preferred number of
FTIRS in a reasonably meaningful short cycle time, and an
acceptable tolerable error in the resulting predictions. Since the
initial determined number of allocated slots might not achieve the
preferably acceptable error level, successive repetitive iterations
in allocation of slots and re-estimation of number of responders,
might be required. In order to determine the possible need for
adjustment of number of allocated slots in a minimal number of
iterations, an error estimating function, and an optimized
adjustment function, would preferably be evolved. The error
estimating function would preferably estimate the error, (e.g., by
confidence interval) in the resulting estimated number of
responders, as a function of the ratio between the number of
detected number of responding slots (responses) and number of
allocated slots (preferably considering the probability
distribution of responders). Based on the error estimating
function, the required preferred number of allocated slots may have
to be adjusted for a further iteration, and may also vary during a
possible series of iterations. The optimized adjustment process in
arriving at the preferred number of allocated slots with a minimal
number of iterations would preferably use earlier results (with a
non acceptable tolerable error), to predict according to
statistical combination the required improvement in the error level
(e.g., computing Maximum Likelihood Estimates or Estimates), and to
determine accordingly the preferred required number of allocated
slots to be used in the subsequent iteration, in order to save
further iterations. The significance in performing iterations is,
in addition to the potential in reducing the error level, in
checking consistency, particularly in cases where little, or no,
a-priori knowledge exists about the probability distribution of
responders that provide a certain number of responses. Thus, at
least two iterations would preferably be allowed even though the
first proportion between the number of responses and allocated
slots might be satisfying, i.e., indicating on an acceptable error
level.
[0049] The estimating procedure would preferably use statistical
methods which could produce acceptable estimation intervals (based
on interval estimation approach such as confidence and tolerance
intervals with upper and lower limits). A single point that is the
most frequent number of responses (responding slots) in a
pre-determined number of slots for pre-determined simulated (or
analytically calculated) number of responders could provide the
distribution of the number of responses around this point and could
determine a tolerance interval for the interval estimate. The most
frequent number of responses will be referred to in the following
as single point estimate for the number of responders in a
predetermined number of slots. One conservative way of determining
an acceptable estimation interval for decision making about the
possible range of responders that respond by a certain number of
responses in a predetermined number of allocated slots, is by first
determining a tolerance interval according to a respective single
point estimate, either produced by a simulation of responses
according to a certain repeated number of responders in certain
number of allocated slots or by analytical calculation, then, to
determine according to the response distribution of the responses
an acceptable tolerance interval. Based on the acceptable tolerance
interval it is enabled to determine, either by simulation or by
analytical calculation, two other response distributions for the
same number of allocated slots which indicate on the potential of
an upper and lower number of responders to produce responses within
the acceptable tolerance interval, by determining acceptable error
e.g., according to cumulative-probability of the overlap (analogous
to error type II in hypothesis testing, with respect to an
acceptance region). As a result of the single point estimates of
the upper and lower distributions of responses which overlap with
the tolerance interval within an acceptable error it would be
enabled to determine upper and lower numbers of responders which
could be used to further determine upper and lower limits to an
acceptable interval for the estimation of potential responders that
might produce the same number of responses in the allocated slots.
The upper and lower limits of this interval could be determined
with respect to the sensitivity of the decisions that have to be
taken accordingly. Such limits could also be interpreted as
determining the rejected regions of potential responders. From the
point of view of the acceptable estimation interval definition, for
a significantly wide range of different numbers of responses for a
sufficient number of slots, consistency in terms of percentage of
error would be expected around said single point estimates for a
respective range of responders due to close to linear relation
between said single point estimates and respective responders in
that range. An alternative approach to determine estimation
intervals is by producing probability distribution function (PDF)
of potential responders around a said single point estimate, either
analytically or by simulation, from which the acceptable estimation
interval could be derived e.g., according to the confidence
interval of this PDF. Such a PDF could be used for traffic behavior
analysis according to different criteria, e.g., criteria which
characterize reaction of mobile units to telematics applications,
which may cause erratic traffic. Each PDF could be derived for a
certain number of allocated slots by normalizing simulated
distributions of the relative frequency of a certain number of
responses, determined by a said single point estimate related to a
certain number of responders, which may be produced with other
(lower) relative frequency by responders which have a different
number from the number of responders which relates to the said
point estimate. A sufficiently high range of the number of
responders should be used to enable the normalization of the
relative frequencies of the responses to determine a said PDF. For
high accuracy of the relative frequencies that should be determined
also for high number of potential responders (theoretically
unlimited but practically limited by the application) a
sufficiently high number of repetitions of response procedures
should be used, to determine the relative number of the responses,
for the said number of responses determined by the said single
point estimate of responders (tested according to a number of
allocated slots). Repeating the simulation for a sufficient range
of numbers of responders to provide relative frequencies of the
same number of responses around relative frequency derived
according to the said single point estimate would determine a
distribution of the said number of responses according to the
(practical) range of numbers of the potential responders. According
to the accumulated number of responses that produce the relative
frequencies of responses (according to the said sufficiently high
number of repetitions to the same number of responders) a
normalization phase can be taken to produce a said PDF. The
simulation could be further expanded to determine such
distributions for different numbers of allocated slots around
different numbers of responders (determined by said single point
estimate). Such PDFs could be used to provide confidence intervals
for single estimate of responders with single allocation of slots.
For estimates that would use more than a single allocation of slots
it would be valuable to create joint PDFs for combinations between
different numbers of slots with different numbers of responders
related to the said single point estimates. Error estimating
functions could further be formulated according to statistical
methods and by simulations that could consider a-priori knowledge
about the probability distribution of responders (Bayesian
approach). The estimating process would count the number of the
slots that were detected to be used by at least one responder and
will use this number as an input to a predetermined estimating
function (e.g., based on pre stored table that includes PDFs,
confidence intervals, and upper and lower limits of said acceptable
estimation intervals, constructed according to simulations) which
could provide required estimates as a function of number of slots
detected to be used by responders in the allocated slots. The
estimate would be considered as the estimation of the number of
vehicles according to the query criteria. Estimating functions
(tables) could be predetermined preferably by using the described
method for simulation and other statistical methods known in the
art. Separate estimating functions would be preferably evolved for
various ranges of numbers of allocated slots. An increase in the
number of allocated slots ought to shorten the acceptable
estimation interval. In practice this would enable to use more
efficiently the allocated communication resources. Response and
detection procedures could further include a possible
discrimination between number of responders in each slot. However
this would require accurate power control on the transmitters of
the responders which for short burst transmissions could be more
costly to be implemented (e.g., CDMA). Non information signals
would be preferably used by the responders. However, if information
bearing signals are used by the responders capture effects also
could be considered to distinguish between slots. Nevertheless
short energy burst in slots could minimize time of detection and
hence preferably fit to the response procedure where responders use
allocated slots randomly by the responders and the detection
process of their transmitted signals could consider just energy
detection.
[0050] The estimations that may according to one type of query
selectively represent additional number of vehicles that were not
expected (preferably according to probabilistic levels) to arrive
to the FTIRS, (NEV), and according to a different type of query,
the number of vehicles that were expected (preferably according to
probabilistic levels) to arrive to the FTIRS and would not arrive
to the FTIRS, (EV), would indicate on change in expected load, in
the FTIRS. This could be used in conjunction with an off line
database of traffic statistics to determine according to the
expected traffic and the non expected traffic (predicted
differential traffic load) the weighted sum of the missing EVs and
the additional NEVs with the predictable traffic load in the
segment of road (e.g., by using statistical methods known in the
art such as convolution between PDF of the estimate of the expected
load in the database and the estimated number of NEVs, would
provide a PDF of the updated estimate to be used for the
computation of a new expected load due to NEVs).
[0051] For this purpose it would be useful to construct respective
PDF's in conjunction with the function tables that are produced to
provide estimation intervals, as further described in the detailed
description.
[0052] This is the basis for an improved way to predict traffic in
conjunction with off line database statistics, preferably with such
that are being adaptively corrected by mapping of the current
traffic.
[0053] In addition to the contribution potential of such
improvement to central control on traffic it would have the
potential to improve, and even enable, reliable dynamic route
guidance. However the way of how to use such predictions is a very
important issue when considering the extensive use of car
navigation systems, in which the planned routes are being
independently modified according to such predictions. The following
highlights a preferable method by which such predictions could
enable efficient distributed DRG.
[0054] In order to explain the benefit of this approach for
implementing distributed DRG it would be worth to describe
traditional approaches in comparison.
[0055] In order to overcome unpredictable traffic problems, in the
future, traditional approaches are considering a system that would
be almost fully controlled, i.e., in-car computers will not make
the decisions for their best route but rather a Big Brother
approach will do it by providing the recommended routes in order to
maintain predictive traffic. This approach would use a central
computation method that will have to maintain the knowledge on the
destination of each vehicle as well as its current position along
the road. Beside the numerous computations that it would require it
would need a communication platform that would have to accommodate
a huge volume of data that will connect the vehicles to the control
center. In practice, roadside beacons that have two way
communication capabilities are considered for this purpose. Apart
from the non privacy characteristic of such a system it will have a
tremendous cost and will require computation power that probably
makes the idea impractical for wide coverage implementation. This
problem increases when a significant number of drivers would not
obey the central route guidance, and hence it will reduce the
system efficiency and could even make it unreliable. For such
reasons a concept of predictive Dynamic Route Guidance based on
distributed intelligence should preferably be used whereby in-car
computers would be making decisions on their preferred routes.
However, with such an approach the traffic would probably become
even more unpredictable. To overcome this problem there would be a
need to cope with unpredicted traffic in a way such as proposed
above and to use periodical corrections to statistical traffic
databases. To realize such an approach, predicted traffic
information would have to be, periodically, estimated and then
provided to the car navigation computers so that a trial and fail
based process would be used to refine an equilibrium between the
individual needs and the offered traffic routes. This would
implement a system based on distributed intelligence in which, in
addition to taking into account current traffic information, the
car navigation computers would have to use a predetermined give-up
process which, according to the predicted traffic information and
their planned route, each car would try to identify if its planned
route is going to take part in a predicted traffic congestion or
traffic jam. The identification of such situation would result from
a comparison between the predicted traffic information and the
planned route. If the comparison would identify predicted traffic
congestion along the planned route it would automatically give up
on its planned route, if it would have a more reasonable
alternative route. The give up process would preferably be used
according to priorities and could consider various criteria levels.
For example, in a first iteration of such trial and fail cycle,
cars that would have an alternative route that might increase the
length of their planned route by, say 5 percent, but would not
significantly affect their traveling time, would automatically
change their planned route to the alternative route which a-priori
had a lower priority. A further cycle of prediction and update to
the cars, probably indicating on changes in traffic predictions
according to the reactions of cars to the previous give up
procedure cycle, could either result in additional cars, with a
higher grade of give up level (e.g., alternative route with say 10%
increase in length to remainder of planned route), to give up on
the planned route, if previously predicted traffic congestion still
predicted. Such procedures might, some times, allow cars to return
to an earlier, more preferable, route (reduced grade of give up
level), in the case that too many cars have given up on their
planned routes at a previous iteration, and accordingly traffic
loads are alleviated. In addition to predetermined give up process
based on parameters of increase and reduction of give up levels,
random parameters might preferably be used in order to refine, and
even to control the convergence iterative process. As a result of a
sufficient number of such iterations, this process could lead to a
convergence to equilibrium, with the grade of give up level and its
reduction tapering off. Trade off between low and high levels of
give up grades would preferably be taken into account, with the
parameters of the iterative process.
[0056] When Car Navigation System (CNS) with on board DRG
capability are considered as being used it would be easy to observe
the benefit of such approach since periodical process of such
prediction processes could help to refine the preferred route by on
board DRG of the CNS units. However one of the trends in telematics
is to provide off board DRG to Telematics Computers (TC) installed
in cars. Such TC would be provided with a recommended route and
according to in-car positioning means the TC could navigate the
driver along the route. Thus to enable handling the traffic
predictions in an environment that partially use TC with off board
DRG and another part uses CNS units with on board DRG it would be
necessary to provide enhanced capability to TC units. For example,
a TC will be provided with a few alternative routes, (e.g., bypass
segments of routes), in order to overcome possible traffic load
problems in predetermined segments investigated in the prediction
process. These alternatives, would be used, according to priorities
by the TC, that would be equipped with a radio interface, such as
used with the CNS having on board DRG, enabling it to participate
in prediction processes. Thus, by participating in the prediction
processes the route plan would be refined by using a give up
procedure, according to a balance between current and predicted
traffic.
[0057] The predicted information would be preferably provided
through a broadcast channel, e.g., RDS/TMC, to car navigation end
users and off board DRG service providers as well as to traffic
control centers.
[0058] Another embodiment of the implementation the differential
traffic prediction process deals with effects on traffic loads as a
result of telematics applications, such as Local Based Services.
One type of such telematics application is position related
commerce service, sometimes named as p-commerce, m-commerce or
I-commerce. With such a service application, a service user would
preferably initiate a request to locate points of interest
according to criteria. For example a request may ask for locations
where a certain product may be found, with possible restrictions to
some range of prices and possibly within a certain distance from
the user's position. Another application of telematics is more
advertisement oriented and could be initiated by a vendor who
wishes to provide ordinary or special offers to drivers possibly
for a short term. In order to enable the vendor to administer such
offers efficiently it would be valuable to have a priori knowledge
about the potential demand for an offer. One way to get such
information is to use recorded information of requests initiated by
the potential buyers to assess the demand potential for a certain
level(s) of price. A problem, involved with special offers, could
be the lack by vendors of a priori knowledge about potential buyers
who might otherwise show interest in many different products, other
than those, subject to a special offer.
[0059] Beside the effect of p-commerce on the traffic load there
might be different ways to implement p-commerce and hence to
increase the level of unpredicted traffic. For example in order to
improve p-commerce applications, it would be an advantage to large
stock holders and others to have a query tool that would help them
to identify sufficient demand, preferably according to prices and
including non solicited products, for special offers. This could
create a hunting trip environment. With such a tool, queries could
be provided in a way similar to an auction process, preferably by a
broadcast message to the telematics users, with respect to products
with possibly one or more ranges of prices. The user, usually a
driver, will have a stored list of preferences for products, in his
Telematics Computer (TC) that would be matched with broadcast
messages according to preferences in the list. For example, a
stored product list (SPL) which may include products with ranges of
prices could enable the TC to respond to a broadcast query. If such
responses would provide information about the estimated number of
the potential clients and possibly their position distribution it
would enable the vendor to determine a time window and price for a
special offer according to demand. The offer could then either
target the potential clients and possibly others. Most probably
this would target the responders who would contribute to the
decision making. When considering a system platform with
capabilities such as suggested for a traffic mapping system and
telematics mobile unit with PMMS capabilities, (that uses pre
assigned slots to determine position and other distributions of
responders according to queries, and possibly to estimate the
number of responders to a query by random response in predetermined
number of slots), it would be possible to implement a hunting trip
application, efficiently.
[0060] A possible scenario could start with an update of one or
more products in the SPL (in the TC) according to predetermined
criteria (for example a product name and range of prices of
interest). A driver who enables the hunting trip application of the
TC would enable the TC to listen to broadcast queries and to
participate in responses to such queries. Queries would be matched
with the SPL and would enable a response of the TC to an identified
match. If the query is a distribution related query then according
to a predetermined protocol the TC would initiate a response in a
communication slot which best indicates on its attribute according
to a characteristic value. For example, for a query which
investigates distribution of potential clients in a restricted
area, and determines responses to be activated in predetermined
slots, it would respond in a slot that would best indicate on its
position, in a range determined by the slot. In this case the
characteristic value corresponds directly to position. Another
possibility could be the use of a characteristic value that
corresponds to estimate of time of arrival, which would require
calculated travel time, in which case the query would possibly
relate to time of arrival distribution, rather than user position.
Another possibility could estimate statistically the number of
potential clients by responding, according to a predetermined
protocol, randomly in determined number of slots which could
provide, according to the proportion between the number of slots
that were used by the responders and the number of the allocated
slots to responses, an estimate to the number of the potential of
respectively hooked vehicles (such an estimation could use the
interval estimate approach described with the differential traffic
load prediction method of estimating traffic loads in FTIRS). An
assessment of the demand could help the vendor to determine whether
to make an offer and for what price. Above methods may be used
independently or in combination with each other in order to enable
a vendor to make a decision about presenting an offer.
[0061] Implementing an offer could possibly use a broadcast
message, which would refer to a specific previous investigation
query, and user vehicles which had previously responded to this
query, would be targeted by their matching with the record in the
TC of the response to the query, and which was stored according to
the predetermined protocol. The targeted user could then be invited
to respond by manual intervention and possibly confirm his wish to
accept the offer. At this stage the vendor could possibly initiate
an additional second broadcast query targeted to the users that
accepted the offer, according to the record stored in the TC, with
respect to the specific message, in order to finally assess the
demand. The user vehicles in which there is a match between the
second broadcast query and the stored record in the TC would
respond in slots according to the predetermined protocol with
respect to this query. The vendor could then confirm the offer, by
implementing a broadcast message to the responders to the second
query. At this stage it would be a preferable possibility to enable
a registration process, in order to ensure purchase. Any
communication method used with the TC may be used for this purpose.
However such processes and other telematics applications have the
potential to create unpredictable traffic due to changes in planned
routes. Thus a further process that would involve estimates of
deviations in traffic loads as a result of such processes could be
used. For example TC units which each could be part of a PMMS
(Telematics PMMS--TPPMS) that made a change to route plans
according to a telematics application such as a hunting trip could
be targeted by traffic prediction queries by criteria that include
recent change to the route plan according the telematics
application. Implementation of such traffic predictions would a)
help investigate the influences of such telematics applications
upon traffic, and b) enable possible processes of control of such
influences, for example, by controlling the scope of the offers, so
as to obviate resulting traffic congestions. (In hunting trip
applications this might take the form of limiting the scope of
offers to a given acceptable range, or to limit potential arrivals
from certain directions or through certain road segments).
[0062] The invention has been described herein using examples in
which the indication signals transmitted by the responders in the
allocated (transmission) slots are transmitted in time, frequency
or time and frequency slots, preferably as RF (radio frequency)
pulse. Other types of transmission slots are also useful in the
invention such as frequency hopping and other spread-spectrum
transmission slots. The term "transmission slots" or "slots" as
used herein includes all these types of slots.
[0063] In a case when there would be possibly a need to further map
traffic queues in the local area in order to complement or improve
the level of consistent type traffic information, possibly as a
result of the need to use in conjunction with the need to map
erratic traffic as a result of local based telematics services,
such as mentioned in an embodiment above. One method proposed by
above identified prior art was to map traffic queues. In this
respect a further embodiment, provided by the following, could
improve the radio communication efficiency for queue mapping for a
slot oriented discrimination mapping system (SODMS) described in
the above identified prior art.
[0064] When assigned slots are allocated to construct a mapping
sample according to a distance from a mapping focus, there is a way
which enables to save the number of allocated slots by considering
that in any subsequent mapping sample, in mapping a queue of
vehicles, it is just required to check if a new probe, arriving to
the queue after a previous mapping sample, is farther from the
mapping focus than the farthest probe in a previous mapping sample.
Thus, in a preferable implementation process of sampling, the
assignment of allocated slots in a mapping sample that is taken
subsequently, (to a mapping sample in which the farthest probe was
detected), can be limited for a segment in the road that starts at
a position which was identified as the position of the farthest
probe (from the mapping focus) in a previous mapping sample. The
subsequent sample would cover the mapped range in a direction
farther from the mapping focus, for a length which may preferably
be determined from statistical data. Additional slots may
preferably be allocated exclusively to the farthest identified
probe in a mapping sample, in order to determine the motion rate in
a queue according to the motion distance of the farthest probe in
between successive mapping samples. These slots could be used by
such probe for transmission of data in any one of two ways, either
by regular modulated data communication, or by constructing a
respective code by means of which such a probe may use more than
one of these exclusively assigned slots in order to determine its
motion distance.
[0065] By arranging the allocated slots in an opposed order to the
queue, i.e., an order in which the increase in time corresponds to
a decrease in distance from the mapping focus, (and thus the first
assigned slot would be allocated to the farthest position from the
mapping focus in the mapped road segment), and by using feedback to
the probe which enables to stop the process of sampling in any one
mapping sample, it is possible to save communication resources. The
feedback message that would be transmitted to the probes would
enable to stop the sampling process for a mapping sample when
detecting the first probe (in the opposed queue) which by
definition is farthest probe for the mapping sample. Furthermore,
the opposite order of allocated slots could also be assigned in
order to limit queue mapping to a minimum predetermined range of
interest from the mapping focus, in order to save assigning slots
for queues that are too short to be of interest. Any feedback
message, e.g. busy bits (used with DSMA) or other appropriate
message according to a predetermined protocol through the broadcast
channel can be used to stop further responses from probe in any
mapping sample.
[0066] Further saving of communication resources with respect to
slot allocation could preferably take benefit of allowing the
possibility of missing the detection of a probe in a situation
where it is expected that the probe, if it would be detected, would
not have significant effect on the determination of the length of
the queue. For example, if an a priori knowledge exists about the
probe percentage amongst the arriving vehicles in a segment of
road, then if for example the probability of successive arrival of
probes within a meaningful shorter distance (shorter period of
time), compared to the expectation, is not sufficiently high, then
an allocation of slots to such a segment of road would preferably
be saved. In such cases where there is low significance of effect,
rather than no significance, for the detection of probes, then the
slots could be allocated for a shorter time, in order to save time
at the cost of lowering probability to detect a probe.
[0067] When allocation of adjacent frequency slots are assigned
with respect to different areas it would preferably be worth to
allocate such slots to the respective areas so as to minimize the
expected difference in radio propagation path loss between the
respective paths from these area related slots and a common base
station. This would enable higher discrimination between signals
that might be received with a very large difference in received
signal strength between each other, while enabling the small signal
to be detected.
* * * * *