Predictor elevator for traffic during peak conditions

A computer controlled elevator system (FIG. 1) including signal processing means for dynamically computing the population spread or density of the buildings, i.e., the number of elevator users in a building on a floor-by-floor basis, including the lobby, and to use such information to compensate for traffic shifts occuring in connection with the up-peak period in which dynamic channeling is used for an elevator car assignment scheme based on prediction methodology, all in accordance with an algorithm (FIG. 3). If, for example, the prediction methodology predicts that the up-peak dynamic channeling scheme should begin but the real time data has not detected any beginnings of an up-peak traffic pattern, the prediction methodology is over-ridden until the real time data finally picks up such a pattern. Additionally, if the floor population spread which is derived from real time data indicates that one or more floors individually have received all of their expected floor population, those floors are devalued to a nominal "priority" basis of "1" in the dynamic channeling scheme, even though the prediction methodology predicts the arrival of additional people for those floor(s) in the remaining up-peak time set by the system. Thus, "too early start" (FIG. 2A) and "too late end" (FIG. 2B) of dynamic channeling are avoided.

REFERENCE TO RELATED APPLICATIONS 
This application relates to some of the same subject matter as the 
co-pending patents/applications listed below owned by the assignee hereof, 
the disclosures of which are incorporated herein by reference: 
Ser. No. 07/580,888 of the inventor hereof entitled "Behavior Based Cyclic 
Predictions for an Elevator System with Data Certainty Checks" filed on 
even date herewith and the applications cited therein including-- 
Ser. No. 07/508,312 of the inventor hereof entitled "Elevator Dynamic 
Channeling Dispatching for Up-Peak Period" filed on Apr. 12, 1990; 
Ser. No. 07/508,313 of the inventor hereof entitled "Elevator Dynamic 
Channeling Dispatching Optimized Based on Car Capacity" filed on Apr. 12, 
1990; 
Ser. No. 07/508,318 of the inventor hereof entitled "Elevator Dynamic 
Channeling Dispatching Optimized Based on Population Density of the 
Channel" filed on Apr. 12, 1990; 
U.S. Pat. No. 5,024,296 issued Jun. 18, 1991; 
Ser. No. 07/580,887 of the inventor hereof entitled "Floor Population 
Detection for an Elevator System" also filed on even date herewith; as 
well as-- 
U.S. Pat. No. 5,022,497 issued Jun. 11, 1991; and 
U.S. Pat. No. 5,035,302, issued Jul. 30, 1991. 
TECHNICAL FIELD 
The present invention relates to elevator systems and more particularly to 
computer controlled systems which use predictions of future traffic 
conditions based on past or historic data, as well as real time events, as 
a guide to, for example, assigning elevator cars to certain floors for 
"channeling" for the operation of the system during up-peak periods. More 
particularly, the present invention relates to the timing of when and how 
the historic and real time data are combined for making the predictions 
and even more particularly to techniques for compensating for significant 
traffic shifts which impact on a peak period, with the up-peak period 
being the particularly preferred application of the invention. 
BACKGROUND ART 
Dynamic channeling capability is an important feature in elevator systems 
to enhance system efficiency during up-peak periods. For further 
background information, note, for example, U.S. Pat. No. 4,846,311 of 
Kandasamy Thangavelu entitled "Optimized `Up-Peak` Elevator Channeling 
System with Predicted Traffic Volume Equalized Sector Assignments" of Otis 
Elevator Company, the assignee hereof, the disclosure of which is 
incorporated herein by reference, as well as others of assignee's patents. 
Additionally, note is made of this inventor's application Ser. No. 
07/508,312 entitled "Elevator Dynamic Channeling Dispatching for Up-Peak 
Period" filed on Apr. 12, 1990, Ser. No. 07/508,313 entitled "Elevator 
Dynamic Channeling Dispatching Optimized Based on Car Capacity" filed on 
Apr. 12, 1990, and Ser. No. 07/508,318 entitled "Elevator Dynamic 
Channeling Dispatching Optimized Based on Population Density of the 
Channel" filed on Apr. 12, 1990. 
Dynamic channeling provides a way of balancing the building traffic density 
evenly among the elevator cars in a building. In channeling generally, the 
floors above the main floor or lobby are grouped into sectors, with each 
sector consisting of a set of contiguous floors and with each sector 
assigned to a car, with such an approach being used during up-peak 
conditions. For dynamic channeling, rather than merely assigning an equal 
number of floors to each sector, prediction methodology is used for 
estimating the future traffic flow levels for the various floors every 
short time interval, for example, every five (5) minutes based on past 
events or traffic conditions. These traffic predictors are then used to 
more intelligently and dynamically assign the floors to more appropriately 
configured sectors, having possibly varying numbers of floors to optimize 
the effects of up-peak channeling. 
Thus, a modern day, computerized elevator system for an office building 
continuously monitors and records elevator-related, significant events 
occurring in the building, preferably for every minute or short interval 
of the day, at least during the normal business day, and every day of the 
year, at least for every business day. Based on the data resulting from 
the building's elevator usage, a series of predictions are performed to 
estimate the traffic density during the next few upcoming intervals, each 
of which intervals usually is a relatively short period of time, typically 
of the order of some few minutes, e.g., as noted above, five (5) minutes. 
The predictions used are in turn based on two major factor 
types--"historic" and "real time" based prediction. 
Historic prediction typically is done based on the information collected 
over the past several days relevant to the same instant or period of time. 
For example, at 9:15 AM, the historic prediction will predict the traffic 
arrival count at the lobby for, for example, the next two (2) minute 
interval consisting of 9:15 AM to 9:17 AM. The prediction is based on the 
data that was collected and maintained during the same 9:15 AM to 9:17 AM 
interval on, for example, every regular business day, for the last several 
days, prior to the day of the prediction. 
On the other hand, real time prediction is a prediction based on much more 
recent data collected over a sufficiently short period of time, usually 
involving some minutes, to effectively be considered "real time" for the 
time period for which the prediction is being made. It thus predicts 
traffic based on the events or data of only the past some minutes, rather 
than the past few days. 
Depending on the number of intervals being "looked ahead" and the type of 
prediction(s) involved, typically a real time prediction uses a number 
(one or more) of the past intervals prior to the current interval. For 
example, at 9:15 AM, the real time prediction might use the data collected 
during the last three, five (5) minute intervals of, e.g., 9:00 AM to 9:05 
AM, 9:05 AM to 9:10 AM, and 9:10 AM to 9:15 AM. Based on these three sets 
of collected data, the real time prediction predicts the expected traffic 
for the next five (5) minute interval in a way that matches or at least 
approximates the current traffic arrival curve. 
Single exponential smoothing is preferably used in the historic based 
predictions, while linear exponential smoothing preferably is used in the 
real time predictions. These smoothing techniques are discussed in general 
(but not in any elevator context or in any context analogous thereto) in 
Forecasting Methods and Applications by Spyros Makridakis and Steven C. 
Wheelwright (John Wiley & Sons, Inc., 1978), particularly in Section 3.3: 
"Single Exponential Smoothing" and Section 3.6: "Linear Exponential 
Smoothing." 
A linear combination of these two prediction factors, namely historic 
(x.sub.h) and real time (x.sub.r), with equal weight being given to the 
two factors, typically provides the final prediction to be used in having 
the elevator system initiate or terminate certain elevator dispatching 
schemes or operations, particularly the initiation and termination of 
up-peak channeling. This is described in some detail in, for example, the 
exemplary embodiment of the '311 patent, although variants other than 
equality of the factors is disclosed in the patent as being possible. 
Thus, in accordance with the '311 patent's exemplary embodiment: 
EQU Final Prediction (X)=ax.sub.h +bx.sub.r 
where "a" and "b" are weighing "constants," in which a+b=1 and preferably 
are equal to each other, namely, a=b=0.5. 
This exemplary prediction methodology works perfectly if people keep up the 
same schedule every day of the week down to the second. 
However, in reality, there sometimes will be relatively abnormal variations 
in people's behavior from day to day, producing passenger traffic shifts. 
Thus, for example, even though a person or a group of people usually come 
to work every day at 8:00 AM, some days they are late and some days they 
are early. This abnormal variance from normal behavior or pattern can 
produce some out of sync conditions, particularly on the days of the 
variances, using the previously disclosed, exemplary prediction 
methodology of the '311 patent, which is based on normal behavior or 
traffic patterns, which is what exists for most days. Hence, although the 
'311 patent provided a very substantial advance in the art, it can be 
further optimized under certain operating conditions. 
Thus, if there are any such abnormal or unusual shifts in the traffic 
pattern from the historic pattern(s) in either direction, i.e., early or 
late arrival of the passengers from the predicted conditions or events, 
the prior standard methodology could cause on these some few "abnormal" 
days the initiation of up-peak channeling at a time not in sync with the 
actual traffic pattern and/or maintain such up-peak channeling beyond the 
need for such channeling. 
For example, if the system expected the arrival of a group of people at 
8:10 AM, historic prediction would start anticipating and tuning the 
system to the expected destination of the people in the group. However, if 
this group of people were late for some reason (e.g., a traffic accident 
or other traffic delay, etc.), causing a temporal shift, to a later time, 
the system effectively would be "unaware" of the variance. 
Hence, even though the real time prediction was then showing, for example, 
a traffic density of zero, the historic prediction factor would still 
affect the dynamic channeling to accommodate the historically expected 
group. However, since in fact there were no people to be served under the 
postulated conditions, the system operation under those circumstances 
would not be operating as effectively and efficiently as possible, and a 
later start of up-peak channeling would be desirable under these 
circumstances. 
Additionally, at the other, terminating end of the channeling time 
spectrum, further inaccurate predictions could occur when a significant 
group of people would arrive early with respect to their normal (historic) 
arrival time. This would introduce another significant temporal shift in 
time (in this instance to an earlier time) of the real passenger traffic 
in comparison to the final prediction, when it was based in significant 
part on the historic factor. 
For example, if people on, e.g., floor "ten" of a building historically 
came to work every day from 7:52 AM to 8:03 AM in the past, then the 
historic prediction factor would expect and predict the same behavior for 
today. However, if in fact some, relatively few people changed their 
habits, permanently or temporarily, then the equally weighted, final 
prediction methodology could again be out of sync. So, if every one on 
floor "ten" is on the job by, for example, 7:59 AM, the preferred, 
exemplary methodology of, for example, the '311 patent did not immediately 
detect the change(s) and would continue predicting and giving some weight 
to floor "ten" in continuing to creating dynamic channel(s), even though 
in fact there was then on that day no further need to do so for that 
floor. Thus, an earlier finish or end of the up-peak channeling operation 
would be desirable under these particular circumstances. 
It should be noted that, although the '311 patent discussed initially using 
equally the two prediction factors, it also discussed varying the relative 
weights to be given to them over time based on the following methodology. 
As noted in the '311 patent, as a general statement, the relative values 
of the two prediction factors could be selected in a way which would cause 
the two types of predictors to be relatively weighted in favor of one or 
the other, or given equal weight if the "constants" are equal, as desired. 
However, the relative values for "a" and "b" preferably were determined as 
follows. When the up-peak period started, the initial final predictions 
preferably assumed that a=b=0.5, namely the factors were at least 
initially to be treated equally. Further predictions were then made at the 
end of each minute, using the past several minutes data for the real time 
prediction, as well as using the historic prediction data. 
The final predicted data for, for example, six (6) minutes was compared 
against the actual observations at those minutes. If at least, for 
example, four observations were either positive or negative and the error 
was more than, for example, twenty (20%) percent of the combined 
predictions, then the values of "a" and "b" were adjusted. This adjustment 
was preferably made using a "look-up" table generated, for example, based 
on past experience and experimentation in such situations. The look-up 
table provided relative values, so that, when the error was large, the 
real time predictions were given increasingly more weight. 
An exemplary, typical look-up table suggested in the '311 patent is 
presented below: 
______________________________________ 
VALUES For 
ERROR a b 
______________________________________ 
20% 0.40 0.60 
30% 0.33 0.67 
40% 0.25 0.75 
50% 0.15 0.85 
60% 0.00 1.00 
______________________________________ 
These values were further described as typically varying from building to 
building and could be "learned" by the system by experimenting with 
different values and comparing the resulting combined prediction against 
the actual, so that, for example, the sum of the square of the error was 
minimized. Thus, the prediction factors "a" and "b" were adaptively 
controlled or selected. 
However, in the above, detailed, "look-up table" example of the preferred 
embodiment(s) of the '311 patent, if there was a significant late arrival 
of enough people that otherwise would have been sufficient with that day's 
actual beginning traffic to initiate up-peak channeling based on the 
historic data, up-peak channeling would be initiated and dynamic 
channeling assignments made to the cars for at least six (6) minutes, even 
though, for example, no significant traffic had yet arrived justifying the 
initiation of dynamic channeling operation of the elevator system. 
This situation, which might be termed "late arrival" from the standpoint of 
the delayed arrival of the passengers causing the abnormal traffic shift 
or "too early start" from the standpoint of the pattern being designed to 
start based on the normal traffic flow, is graphically illustrated in FIG. 
2A and could actually delay the service for at least some of the 
passengers that had in fact arrived, depending on their destination floors 
and the specific car assignments made as to the assigned floors in the 
dynamic channeling algorithm. 
A like delay in response time for the proper termination of the up-peak 
channeling operation could occur, if, for example, most, if not 
substantially all, of the people going to one or more of the floors had in 
fact arrived earlier than historically had occurred in the past, resulting 
in these floors still being considered as having traffic to be 
accommodated under the dynamic channeling algorithm in use, when in fact 
such was not the case. This situation, which might be termed an "early 
finish" from the standpoint of the relatively early arrival of the 
passengers causing the abnormal traffic shift or a "too late finish" from 
the standpoint of the pattern being designed to end based on the normal 
traffic flow, is graphically illustrated in FIG. 2B and again would be 
less than ideal under these specific, relative unique, somewhat abnormal 
circumstances. 
Thus, although, the adaptive approach of the invention of the '311 patent 
represented a very significant advance in the art, it did not cover all 
possible variances and in particular did not immediately adjust the 
initiation and termination of dynamic channeling to fit the currently 
existing traffic conditions, resulting in less than total optimization 
under these particular unusual variances. 
DISCLOSURE OF INVENTION 
The present invention is designed to provide an alternative or supplemental 
adaptive methodology for further optimizing the prior methodology and 
compensating for these types of potential "out of sync" problems by 
monitoring more closely different aspects of the building's activities, 
particularly its population density aspects, and/or otherwise qualify the 
use of the historic prediction factor in making the final prediction. It 
accordingly fine tunes the operation of the elevator system and its 
algorithms to reduce some out of sync conditions which might arise due to 
abnormal conditions under the system's previous exemplary prediction 
methodology, in different optimizing ways, all as explained more fully 
below. 
The present invention thus originated from the need to optimize elevator 
system performance using further optimizing peak car assignments 
procedures to compensate for certain abnormal types of variances in 
traffic patterns in connection with, for example, up-peak dynamic 
channeling or other up-peak or other peak car assignment schemes. 
To prevent the "too early start" problem or variance alluded to above, 
i.e., to compensate for the abnormal "late start temporal shift", the 
system predictor related subsystem in the preferred embodiment of the 
invention monitors the real time prediction component, and a contingency 
or qualification is placed upon the use of historic predictions. 
In accordance with the preferred methodology of the invention, the historic 
prediction component(s) (x.sub.h) are not allowed to affect the overall or 
final prediction (X) until the start of a traffic pattern is indicated by 
the real time predictions (x.sub.r). However, preferably, once the 
historic prediction factor is activated, it remains active until the end 
of the peak period. 
This "threshold" qualifying prevents the historic prediction factor from 
affecting the initiating of the dynamic channeling procedure or other 
up-peak car assignment scheme when there is a significant traffic shift to 
a later time, from what otherwise would have been the first or initial 
portion of a "too early start" of dynamic channeling, particularly when 
there is in fact really no traffic to move. 
To prevent the "late finish" problem or variance alluded to above, i.e., to 
compensate for the abnormal "early finish temporal shift, " the system 
predictor related subsystem in the preferred embodiment of the present 
invention preferably relies on the pertinent floor(s)' population data 
accumulated up to that day based on real time de-boarding and boarding 
count data. This data can be evolved for the system using, for example, 
the methodology of application Ser. No. 07/580,887 entitled "Floor 
Population Detection for an Elevator System" referred to above. 
The floor's population is monitored and analyzed while, for example, 
up-peak dynamic channeling or some other up-peak elevator car assignment 
scheme is in operation, to determine if up-peak operation should be 
terminated. 
Since the purpose of channeling is to take people to their destination more 
effectively, the need for the system's channeling or other assignment 
scheme is completed once everyone (or most everyone) has arrived at their 
respective destinations. Therefore, when the system detects an 
inconsistency between the real count and the predictions, the invention 
gives greater, if not total, weight to the real count. 
The use of these two optimizers in the present invention significantly 
improves the performance of the dynamic channel generation of the system 
by reducing out of sync conditions which otherwise might have occurred in 
the timing of the creation and termination times of dynamic channeling or 
other up-peak assignment scheme, thereby avoiding any too early starting 
or too late finishing of the scheme. 
Although the particularly preferred application of the principles of the 
present invention is for the up-peak periods of time, these principles 
with some modification can also be applied to other peak periods, such as, 
for example, the down-peak period. 
Additionally, the principles of the invention can likewise be applied to 
other pertinent situations in which real and historic prediction factors 
are combined to make up a final or used prediction in an elevator system 
to vary car dispatching or assignments. 
The invention may also be practiced in a wide variety of elevator systems, 
utilizing known technology, in the light of the teachings of the 
invention, which are discussed below in some further detail. 
Other features and advantages will be apparent from the specification and 
claims and from the accompanying drawings, which illustrate an exemplary 
embodiment of the invention.

BEST MODE 
First Exemplary Elevator Application 
For the purposes of detailing a first, exemplary elevator system, reference 
is had to the disclosures of U.S. Pat. No. 4,363,381 of Bittar entitled 
"Relative System Response Elevator Car Assignments" (issued Dec. 14, 1982) 
and Bittar's subsequent U.S. Pat. No. 4,815,568 entitled "Weighted 
Relative System Response Elevator Car Assignment With Variable Bonuses and 
Penalties" (issued Mar. 28, 1989), supplemented by U.S. Pat. No. 5,024,295 
issued Jun. 18, 1992, as well as of the commonly owned U.S. Pat. No. 
4,330,836 entitled "Elevator Cab Load Measuring System" of Donofrio & 
Games issued May 18, 1982, the disclosures of which are incorporated 
herein by reference. 
One application for the present invention is in an elevator control system 
employing microprocessor-based group and car controllers using signal 
processing means, which through generated signals communicates with the 
cars of the elevator system to determine the conditions of the cars and 
responds to, for example, hall calls registered at a plurality of landings 
in the building serviced by the cars under the control of the group and 
car controllers, to provide, for example, assignments of the hall calls to 
the cars, or, during up-peak conditions assigning the cars to various 
floor sectors using, for example, dynamic channeling in which the 
assignment is based at least in part on combined prediction values 
including real time and historic data. An exemplary elevator system with 
an exemplary group controller and associated car controllers (in block 
diagram form) are illustrated in FIGS. 1 and 2, respectively, of the '381 
patent and described in detail therein, as well as in some of the related 
applications referred to above. 
The makeup of micro-computer systems, such as may be used in the 
implementation of the elevator car controllers, the group controller, and 
the cab controllers can be selected from readily available components or 
families thereof, in accordance with known technology as described in 
various commercial and technical publications. The microcomputer for the 
group controller typically will have appropriate input and output (I/O) 
channels, an appropriate address, data and control buss and sufficient 
random access memory (RAM) and appropriate read-only memory (ROM), as well 
as other associated circuitry, as is well known to those of skill in the 
art. The software structures for implementing the present invention, and 
the peripheral features which are disclosed herein, may be organized in a 
wide variety of fashions. 
Additionally, for further example, the invention could be implemented in 
connection with the advanced dispatcher subsystem (ADSS) and the 
operational control subsystems (OCSSs) and their related subsystems of the 
ring communication system of FIG. 1 hereof as described below. 
Examplary Ring System (FIG. 1) 
As a variant to the group controller elements of the system generally 
described above and as a more current application, in certain elevator 
systems, as described in co-pending application Ser. No. 07/029,495, 
entitled "Two-Way Ring Communication System for Elevator Group Control" 
(filed Mar. 23, 1987), the disclosure of which is incorporated herein by 
reference, the elevator group control may be distributed to separate 
microprocessors, one per car. These microprocessors, known as operational 
control subsystems (OCSS) 100, 101, are all connected together in a 
two-way ring communication (102, 103). Each OCSS 100, 101 has a number of 
other subsystems and signaling devices 104-112A, etc., associated with it, 
as is described more fully below, but basically only one such collection 
of subsystems and signaling devices is illustrated in FIG. 1 for the sake 
of simplicity. 
The hall buttons and lights are connected with remote stations 104 and 
remote serial communication links 105 to the OCSS 101 via a switch-over 
module 106. The car buttons, lights and switches are connected through 
similar remote stations 107 and serial links 108 to the OCSS 101. The car 
specific hall features, such as car direction and position indicators, are 
connected through remote stations 109 and remote serial link 110 to the 
OCSS 101. 
The car load measurement is periodically read by the door control subsystem 
(DCSS) 111, which is part of the car controller. This load measurement is 
sent to the motion control subsystem (MCSS) 112, which is also part of the 
car controller. This load measurement in turn is sent to the OCSS 101. 
DCSS 111 and MCSS 112 are micro-processors controlling door operation and 
car motion under the control of the OCSS 101, with the MCSS 112 working in 
conjunction with the drive and brake subsystem (DBSS) 112A. 
The dispatching function is executed by the OCSS 100, under the control of 
the advanced dispatcher subsystem (ADSS) 113, which communicates with each 
OCSS 101 via the information control subsystem (ICSS) 114. The car load 
measured may be converted into boarding and de-boarding passenger counts 
using the average weight of a passenger by the MCSS 112 and sent to the 
OCSS 101. The OCSS sends this data to the ADSS 113 via ICSS 114. 
The ADSS 113 through signal processing inter alia collects the passenger 
boarding and de-boarding counts at the various floors and car arrival and 
departure counts, so that, in accordance with its programming, it can 
analyze the traffic conditions at each floor, as described below. The ADSS 
113 can also collect other data for use in making predictions, etc., if so 
desired. 
For further background information reference is also had to the magazine 
article entitled "Intelligent Elevator Dispatching Systems" of Nader 
Kameli and Kandasamy Thangavelu (AI Expert, September 1989; pp. 32-37), 
the disclosure of which is also incorporated herein by reference. 
Owing to the computing capability of the "CPUs," the system can collect 
data on individual and group demands throughout the day to arrive at a 
historical record of traffic demands for each day of the week and compare 
it to actual demand to adjust the overall dispatching sequences to achieve 
a prescribed level of system and individual car performance. Following 
such an approach, car loading and floor traffic may also be analyzed 
through signals from each car that indicates for each car the car's load. 
Alternatively, passenger sensors, which sense the number of passengers 
passing through each elevator's doors, using for example, infra-red 
sensors, can be used to get car boarding and de-boarding counts for car 
stop at floors other than the lobby and for each combined car arrival and 
departure at the lobby. 
Using such data and correlating it with the floor involved and, if so 
desired, the time of day and preferably the day of the week, a meaningful, 
historically based, building and floor population or traffic measures can 
be obtained on a floor-by-floor basis based on boarding and de-boarding 
counts by using appropriate signal processing routines. 
Exemplary Algorithm for Compensating for Traffic Shifts (FIG. 3) 
As generally illustrated in FIG. 3, the logic of the present invention 
provides exemplary techniques or methodology for preventing: 
a "too early" implementation of dynamic channeling for the up-peak period, 
which would occur if there was a "later start" traffic shift, i.e., one in 
which the people which normally come during the beginning of the up-peak 
period come in late, as well as 
a "too late" continuation of the dynamic channeling scheme after the 
up-peak period has been at least partially completed, i.e., for at least 
one or more of the floors, which would occur if there was an "early 
finish" traffic shift, i.e., in which most, if not all, of the passengers 
for those floors came in earlier than usual. 
The exemplary algorithm of FIG. 3 will be separately discussed in 
connection with those two types of variances in the context of dynamic 
channeling. However, it should be understood that the invention can be 
applied to other forms of up-peak car assignment schemes and to schemes 
for other peak periods. 
"Later Start" Traffic Shift (FIG. 2A) 
To prevent the "too early start" problem or variance alluded to above (note 
FIG. 2A), the system predictor monitors the prediction of both the 
historic and real time types. In the preferred embodiment of the invention 
a contingency or qualification is placed upon the use of historic 
predictions. 
In accordance with the preferred methodology of the invention, historic 
predictions (x.sub.h) are not allowed to affect the overall or final 
prediction (X) until the start of a traffic pattern is indicated by the 
real time predictions (x.sub.r). However, preferably, once the historic 
prediction factor is activated, it remains active until the end of the 
peak traffic pattern is indicated. 
This "threshold" qualifying prevents the historic prediction factor from 
affecting the initiating of the dynamic channeling procedure or other 
up-peak car assignment scheme when there is a significant traffic shift to 
a later time, from what otherwise would have been the first or initial 
portion of a "too early start" of dynamic channeling, particularly when 
there is in fact really no traffic to move. 
"Earlier Finish" Traffic Shift (FIG. 2B) 
To also prevent the "late finish" problem or variance alluded to above 
(note FIG. 2B), the system predictor preferably relies on the pertinent 
floor(s)' population data accumulated prior to that day, which is 
monitored and analyzed while, for example, up-peak dynamic channeling 
scheme is in operation, to determine if the channeling scheme should be 
terminated or be altered in part before the historic data would indicate 
such would be appropriate. 
Since the purpose of channeling is to take people to their destination more 
effectively, the need for the system's channeling scheme is completed once 
every one (or most everyone) has arrived at their respective destination. 
Therefore, in the algorithm, when the system detects an inconsistency 
between the real count and the predictions, the greater, if not total, 
weight is given to the real count. 
For example, if a floor population has a total population of one hundred 
and twenty (125) people, and this population density has stayed the same 
during the past few days, then it is reasonable and logical to assume that 
the expected number of people arriving at this particular floor will 
remain the same. As a further example, if after the first few minutes the 
system has counted up a total of "125" people as having arrived at that 
floor, but the prediction indicates during the next interval the floor is 
expected to receive an additional twelve (12) people, the system has a 
contradiction or an inconsistency in its data. 
Since real data is used for the current floor density, the prediction may 
be discounted. This is done by inactivating the effect of that floor in 
the dynamic channel creation process. In other words, instead of giving 
that a higher priority due to the expected or predicted twelve people, it 
is given a normal "priority" or status for only one (1) person arriving. 
Thus this floor, like all the other floors, will receive service, but it 
will be regular service and not high priority service. This allows greater 
emphasis or higher priority service to be given to the other floors in 
which the pre-determined floor populations have not yet been satisfied. 
The algorithm of FIG. 3 summarizes the above procedures and considerations 
as follows. 
For every floor in the system, in step 1 the floor population for the floor 
is monitored. Additionally, the real time prediction component (x.sub.r) 
of deboarding counts at each floor (as in said '311 patent) is evaluated. 
In step 2, if the real time component indicates the presence of passengers 
destined for this floor, in step 3 the standard prediction methodology is 
used which combines the historic prediction component (x.sub.h) with the 
real time component to determine the "final" prediction value (X). On the 
other hand, if no such indication is present, in step 4, the "final" 
prediction value (X) is set equal to the real time prediction component. 
In step 5, if the currently calculated floor population for that floor is 
greater than or equal to (.gtoreq.) the historic based floor population, 
that indicates that all passengers destined for that floor during up-peak 
have already arrived at that floor, and the value of "X" assigned to that 
floor is a nominal "1". Step 7 is then executed. On the other hand, if 
step 5 shows that the currently calculated floor population has not yet 
reached the historic based floor population, the sub-routine has been 
completed, and step 7 is executed. In step 7, once the process has been 
completed for every floor, all of the values of "X" are submitted to the 
dynamic channel routine to create new, updated channels for the floors. 
Floor Population Spread Data 
Since population density or spread data based on real time data is used in 
the invention, some understanding and discussion of this aspect of the 
elevator system is desirable for the complete understanding of the present 
invention. However, it should be understood that the methodology for 
pre-determining a building's floor population spread is not directly part 
of the present invention, and any appropriate methodology, particularly 
one that uses real time data such, as for example, de-boarding and 
boarding counts, can be used in this respect. 
In addition to monitoring the arrival count of the people and their 
destinations in up-peak operation, the system of the invention also 
preferably monitors the building's total population density, as well as 
each floor's population. During the first few days (referred to as the 
system's "learning" period), the system learns the building density by 
counting up the number of people entering the building during the 
"up-peak" period, which is typically the morning arrival period of the 
office building's inhabitants, using, for example, the technology of 
application Ser. No. 07/580,882 entitled "Floor Population Detection for 
an Elevator System" referred to above. 
Once the up-peak period (as it is defined in the system) is over, the 
system preferably compares the total accumulated for the day against the 
ones collected from the previous days. It then corrects the accumulated 
sum based on the value collected. Thus, today's count of density has only 
a limited weight in relation to the accumulated sum. 
This approach prevents any one day's irregular count from drastically 
altering the actual sum. 
This learning method is designed so that, if there is a shift in population 
density and the shift persists for, for example, ten (10) days, the 
accumulated population sum will completely reflect it. In other words, 
each day has only (in the ten day example) a ten (10%) percent affect in 
the accumulated sum "learned" thus far. But if it continues, the 
accumulated sum over time "permanently" adopts the population shift as the 
norm, until further population shift(s) need to be accommodated. 
This same type of methodology preferably is applied to learning and 
cumulatively adjusting the population density for each floor. 
In summary, during the up-peak period, each floor's population is computed 
by monitoring the boarding and de-boarding counts and using those counts 
to update that floor's population figure throughout that period on an 
additive bases. After the period has been completed, the floor-by-floor 
information, which had been maintained in a table, is used to determine 
the "final" historic based floor population spread using also historic 
data based at least on the past several active days' of population spread 
using "exponential smoothing." As a verifying cross-check the lobby's 
figure, which typically should equal the total building population, is 
compared to the total of all of the upper floors' populations. 
The historically based derivation of the floor population is recorded on 
the hard disk of the microcomputer of the ADSS 113 and made available for 
use in other signal processing functions in the system, such as, for 
example, this invention's compensation of abnormal up-peak related traffic 
shifts, as well as for, prediction methodology for dynamic channeling of 
the elevator cars. 
The use of these two optimizers in the present invention significantly 
improves the performance of the dynamic channel generation of the system 
by reducing the variances which would otherwise have occurred in the 
timing of the creation and termination times of dynamic channeling, 
thereby avoiding any too early starting or too late finishing of the 
scheme. 
Although this invention has been shown and described with respect to at 
least one detailed, exemplary embodiment thereof, it should be understood 
by those skilled in the art that various changes in form, detail, 
methodology and/or approach may be made without departing from the spirit 
and scope of this invention.