CN111401643B - Urban rail transit passenger flow loop self-adaptive intelligent train scheduling method - Google Patents
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Abstract
The invention relates to an urban rail transit passenger flow loop self-adaptive intelligent train dispatching method, which comprises the following steps: step 1), predicting the off-train passenger capacity; step 2), obtaining the station passenger outbound capacity; and 3) carrying out train operation capacity adjustment according to the information in the step 1) and the step 2. Compared with the prior art, the method has the advantages of complete support information, high informatization and intellectualization levels, high dispatching and commanding efficiency and the like.
Description
Technical Field
The invention relates to an intelligent train dispatching method, in particular to an intelligent train dispatching method for self-adaption of a passenger flow loop of urban rail transit.
Background
Urban rail transit, as a high-speed and large-capacity public transport, is becoming increasingly important in mass travel, and especially when large-scale activities of several consecutive days exist, it is responsible for indelible.
When a large passenger flow occurs on a line due to equipment failure and the like, at present, a dispatcher can only check CCTV pictures by looking up the CCTV pictures, the number of the CCTV pictures which can be checked by a control center at the same time is limited, the dispatcher cannot conveniently master passenger flow information in time, and the dispatcher selects an operation control strategy according to ordinary experience.
With the continuous enrichment of passenger flow detection means and the rapid development of AI and big data technologies, how to effectively apply the technologies to the accurate acquisition of passenger flow information so as to adopt a matching strategy to perform emergency handling and passenger flow guidance at the right time becomes a technical problem to be solved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an intelligent train scheduling method for the urban rail transit passenger flow loop self-adaptation, which has the advantages of complete support information, high informatization and intelligentization levels, high scheduling command efficiency and the like and can assist a dispatcher in making a train operation scheduling decision when holding continuous large-scale activities for days.
The purpose of the invention can be realized by the following technical scheme:
an urban rail transit passenger flow loop self-adaptive intelligent train dispatching method comprises the following steps:
step 1), predicting the off-train passenger capacity;
step 2), obtaining the bus station passenger outbound capacity;
and 3) carrying out train operation capacity adjustment according to the information in the step 1) and the step 2.
Preferably, the under-train passenger capacity prediction specifically includes:
step 1.1), carrying out big data analysis based on ticket service clearing data, train weighing and other data, and establishing an OD model of train departure load, passenger number of getting-on and getting-off and passenger flow of each station aiming at a current schedule;
step 1.2), if a certain station C i The exit is connected with a large-scale event place, large-scale events are held within a plurality of days continuously, safety check needs to be carried out on personnel going out of the station and entering the place, partial exits are closed according to the place reception capacity, and at the moment, the passenger getting-off amount needs to be predicted in order to control the passenger arrival rate within the passenger going-out capacity range of the station.
Preferably, the predicting the passenger capacity under the train specifically comprises:
step 1.2.1) passenger arrival rate prediction preconditions: station C in the day i Other stations have no similar large-scale activities;
step 1.2.2) under normal conditions, at C i Within the range of n stations at the upstream, if the arrival intervals of the trains and the front vehicle at the same platform accord with the planned interval, and the arrival intervals of the trains at the upstream stations in the current direction and the front vehicle at the same platform also accord with the planned interval, the departure load of the trains and the arrival intervals of the trains at C can be determined according to the departure load of the trains i-n+1 To C i-1 The number of passengers getting on and off each station among stations and the passenger flow model are compared with the train at C i If the number of passengers is predicted, if the station C i The up and down trains are counted if the terminal station is not the terminal station;
step 1.2.3) if C j A station has a shield door fault, j belongs to [ i-n +1, i-1 ]]Train at C j The station adopts the over-station adjustment strategy, then C j The number of passengers getting on and off the station is 0, and the passengers on the vehicle can select to be in the position C j-1 Station and C j+1 Getting off the bus at the station, if j = i-1, the bus needs to be checked at the station C i Make a notice that the newly-added number of visitors may be in C i Standing out;
step 1.2.4) if the fault treatment results in that the threshold value F (actual departure interval-planned departure interval) of the train A and the previous train are on the same platform, the part of the train on the upstream of the train A is buckled and stopped at a late point, the part of the train on the upstream of the train A and the previous train is inserted for reducing the large interval and is adjacent to the passenger train, the passenger carrying conditions of the adjacent passenger train, the train A and the train at the late point on the upstream are different from those of the normal alignment point, and the part of the train is required to be at the station C i The number of visitors is specifically predicted.
Preferably, the statistics of the number of people in the step 1.2.2) are specifically as follows:
step 1.2.2.1) R j,j+1 For train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]The passenger flow is obtained by a multi-element passenger flow detection mode such as train weighing;
step 1.2.2.2) X k For train A at station C k The number of passengers, k, belongs to [ j +1, i-1 ]]The model value or the number of passengers getting off the train at the station can be referred;
step 1.2.2.3) S k For train A at station C k The number of passengers is [ j +1, i-1 ]]The number of passengers getting on the train can be predicted according to the model value or the number of the recent passengers getting on the train stopping at the station; train A at C k The number of the passengers on the station is the smaller value of the predicted number of the passengers on the train A and the number of the passengers on the train A;
step 1.2.2.4) according to train at C i Predicted arrival load, model arrival load and model passenger number predictionTrain at C i The number of passengers getting off, the train is at C k K is [ i-n +1,i ]]The detection can be carried out by a multi-element passenger flow detection mode such as train weighing;
step 1.2.2.5) station C i-n To C i-1 Each train leaving the station of updating C i The number of visitors is predicted.
Preferably, the specific prediction in step 1.2.4) is as follows:
let C be m For the station when the part of the train recovers operation, m is the starting station for the insertion operation of the passenger train and belongs to [ i-1,1]；
Step 1.2.4.1) estimating the number of regular passengers who do not go to a large-scale activity place in the departure load when the train leaves the station and the train A and the upstream late point train and the number of passengers getting off the downstream station of the regular passenger flow;
step 1.2.4.2) prediction of C i Train in the range of upstream n stations is at C i The number of passengers getting off, if the station C i The up and down trains need to be counted if the terminal station is not the terminal station.
Preferably, the step 1.2.4.1) is specifically:
a) Estimating train A and upstream late train C m The conventional number of people who do not go to the large-scale activity place in the load of the upstream stations leaving the stations and the number of the passengers who leave the large-scale activity place in the conventional passenger flow at the respective downstream stations are as follows:
the conventional passenger flow OD model of the large-scale activity place in the current time period and the current direction is as follows: at station C x Among passengers getting on will have B x:x+y % of passengers in C x+y Standing passenger, y>＝1；
According to the OD model, the train is at the station C m Model number of guests on each station at upstream calculation station C m-1 Model off-station load D m-1 Number of visitors under model of downstream station in current direction X Dm-1:m-1+p ，p>＝1；
Suppose that the current train is at station C m The number of guests at each upstream station includes the number of specific guests going to the large event and the number of model guests not going to the large event, and C m-1 Model off-station loadingLotus D m-1 Number of visitors under model of downstream station in current direction X Dm-1:m-1+p As the current train C m-1 Actual off-station load D m-1 Conventional number of visitors X who do not go to large-scale activity place at downstream station in current direction Dm-1:m-1+p ，p>＝1；
b) Estimation of temporary passenger train C m The conventional number of persons who do not go to the large-scale event place in the load of the upstream station leaving the station and the number of persons who get off the large-scale event place in the part of conventional passenger flow at the respective downstream stations are as follows:
consider C m The number of regular persons who do not go to the large-scale activity place in the load of the upstream station leaving the station and the number of persons who get off the regular passenger flow at the respective downstream stations are both 0;
c) Estimating the number of regular passengers who do not go to a large-scale activity place in the load of each station leaving after the operation of the train A and the upstream late train is recovered and the passenger train is inserted into the station and operated and the number of passengers who leave the station in the part of regular passenger flow at each downstream station:
according to the nearest punctual train at C m-1+p Actual number of passengers at station, C m-1+p Number of passengers on model station, current train in C m-1+p Actual number of guests at station estimate current train at C m-1+p Conventional number of guests S who do not go to large event place among the number of guests actually standing m-1+ p: general of ，p>＝1；
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C m-1+p Number of actual passengers getting off at station X m-1+p 、C m-1+p The number S of regular guests estimated from the number of actual guests in the station m-1+ p: general rule Calculating C m-1+p The number of the conventional people in the large-scale activity places is not increased in the load of the station leaving the station;
according to S m-1+ p: general of And the OD model calculates the train is in C m-1+p Number of regular guests S of a station m-1+ p: general of Number of passengers getting off at downstream station X m-1+p:m-1+p+q ，p>＝1，q>＝1；
According to the number of passengers getting off the downstream station and X of the conventional number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station m-1+p:m-1+p+q Calculating train C m-1+p Load D from station to station m-1+p All without goingRegular number of visitors in large-scale activity place at downstream station X Dm-1+p:m-1+p+q ，p>＝1，q>＝1；
Train at C m-1+p Actual number of visitors, p>=1, detection can be performed by multivariate passenger flow detection methods such as train weighing.
Preferably, the step 1.2.4.2) is specifically:
a)R j,j+1 for train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]The passenger flow is obtained by a multi-element passenger flow detection mode such as train weighing;
b) Predicting train A at station C k The number of passengers leaving, the number of passengers getting on, the number of regular passengers not leaving the large activity place in the off-station load and the number of passengers leaving the downstream station of the regular passenger flow are all [ j +1, i-1 ]]：
Train A at station C k Forecast of number of visitors X k Is X Dk-1:k ；
Train A at station C k Predicted number of guests S k Comprises the following steps:
b1 If train a and lead car are at station C k Does not meet the planned interval, then:
calculating and predicting the number of passengers getting on the train according to the ratio of the actual interval to the planned interval and the number of the passengers getting on the train at the punctual point where the train stops at the station;
train A is at C k The number of the passengers on the station is the smaller value of the predicted number of the passengers on the train A and the number of the passengers on the train A;
b2 If train a and the lead car are at station C k The arrival intervals of (a) meet the planned intervals, then:
can refer to the front vehicle at C k The number of passengers on the station is calculated to predict the number of passengers on the station; train A is at C k The number of the passengers on the station is the smaller value of the predicted number of the passengers on the train A and the number of the passengers on the train A;
according to the nearest punctual train at C k Actual number of passengers at station, C k Number of passengers on model station, current train in C k Station prediction guest number estimation current train in C k Conventional number of guests S who do not go to large-scale event place among predicted number of guests at station S k: general of ；
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C k Station forecast number of passengers X k 、C k Conventional number of guests S estimated from predicted number of guests at station k: general rule Calculating C k The number of the conventional people in the large-scale activity place is not increased in the load of the station leaving the station;
according to S k: general of And the OD model calculates the train is in C k Number of regular guests S of station k: general rule The number of passengers getting off at the downstream station X k:k+q ，q>＝1；
According to the number of passengers getting off the downstream station and X of the conventional number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station k:k+q Calculating train C k Load D from station to station k Regular number of people who can not go to large-scale activity places X number of passengers getting off at downstream stations Dk:k+q ，q>＝1；
c) Predicting train A at station C i The number of underground cars:
if train A participates in the calculation of step 1.2.4.1), train A can be referred to as C i-1 Off-station load of station D i-1 Estimated D i-1 The number of persons who can not go to large-scale activity places and the part of conventional passenger flow at station C i The number of passengers getting off the train A at the station C i Estimating the number of getting-off people;
if train A is not involved in the calculation in step 1.2.4.1), then train A at C can be considered as a function of train A i Predicted arrival load, model arrival load and model passenger number of train A at C i The number of passengers getting off.
d) Station C i-n To C i-1 Each train leaving the station of updating C i The number of passengers is predicted.
Preferably, the station passenger outbound capacity acquisition specifically includes:
2.1 Detecting passenger flows at platforms, key channels and exits by adopting a multi-element passenger flow detection means at stations;
2.2 Station periodically reports the outbound rate of the current bottleneck area, how many people the buffer upstream of the bottleneck area can also accommodate.
Preferably, the train transportation capacity adjustment is specifically as follows:
3.1 To reduce the amount of passengers getting off the train at station Ci), an upstream station may be selected for passenger clearance:
3.1.1 In C) i-n To C i Selecting 1 stations C with small passenger quantity from the stations in between r Clearing the visitor;
3.1.2 If station C i Is a terminal station, if station C r Connecting the train section or the parking lot, the train can go back to the train section or the parking lot for waiting after clearing passengers, and then goes out of the train section or the parking lot from the station C r Current service number slave station C for train starting to run i The next task after the turn-back; if the station C r Not connecting the train section or the parking lot, the train keeps going forward after clearing the passenger, and the train is at the station C i Continuing to carry passengers for operation after turning back;
3.1.3 If station C i If the train is not a terminal station, the train continues to move forward after clearing passengers and is at a station C i The next station continues to carry passengers;
3.1.4 Discontinuous customer clearing, adopting decision of setting the time for clearing the customers;
3.2 To reduce station C) i The passenger capacity under the train, if the station C i Instead of terminal stations, it is also possible to select a part of train stops C i ：
Station C i Reporting: station C i Has a passenger outbound rate V in a bottleneck region o-i The upstream buffer of the bottleneck region can accommodate R at most i:max People, at present, can also accommodate passengers R i A human;
station C r Platform to station C i The platform has k vehicles including ascending and descending, the last vehicle is train A and station C r The first train B in the upstream section still requires time T1 to arrive at the station C i After time period T1, station C i Delta O of the number of people waiting to go out of the station i Is (k trains are at C) i Station forecast leaving passenger total-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max Of where V is o-i Is station C i The passenger outbound rate in the bottleneck region;
if (R) i + train B at C i Predicted number of passengers getting off at station) threshold F, then train B needs to be at station C i Jumping and stopping;
if the second train upstream of train B is also at C i When the station stops, if the train and the train B are trains in the same direction, the first train on the upstream of the train B is considered to stop so as to avoid continuous stop in the same direction;
whenever station C is updated i When the number of passengers is predicted, jump-stop decision is carried out.
Preferably, the 3.1.4) passenger clearing opportunity decision is specifically as follows:
station C i Reporting: station C i Has a passenger outbound rate V in a bottleneck region o-i The upstream buffer of the bottleneck region can accommodate R at most i:max People, at present, can also accommodate passengers R i A human;
station C r-1 Platform to station C i The platform has k vehicles including up and down, the last vehicle is train A and station C r-1 The first train B in the upstream section still requires time T1 to arrive at the station C i After time period T1, station C i Delta O of the number of people waiting to go out of the station i Is (k trains are at C) i Forecast of station for total number of passengers-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max The smaller of these;
if (R) i + train B at C i Predicted number of passengers getting off at station) threshold F, train B needs to be at station C r Clearing the customers;
the number of the full passengers in the train is T max If the train A is full of passengers and arrives at the station C i After, R i Approaching the threshold value F if the station C i Cannot complete T within 2 train intervals max The number of people is out of the station, and if the upstream train of the train B is full of passengers, the train may also be cleared if the station C i Is a terminal station, two columns will appearThe situation that the vehicle is continuously clear of passengers;
if station C i Not at the terminal station, but also at C if the second train upstream of train B i When the train and the train B are trains in the same direction, the first train on the upstream of the train B is considered to avoid continuous passenger clearing in the same direction;
whenever station C is updated i When the number of the passengers is predicted, passenger clearing decisions are made.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, big data analysis is carried out based on ticket service clearing data, train weighing and other data, and a train departure load, passenger number of getting-on and getting-off and OD passenger flow model of each station is established aiming at the current schedule, so that model support is provided for passenger flow prediction.
2. The invention can carry out real-time train leaving load detection and train getting-on and getting-off number detection at each station based on the multi-passenger flow detection modes such as train weighing and the like, and can carry out train getting-off amount prediction at a specified station in time.
3. The invention detects the passenger flow at the station platform, the key channel, the exit and the like based on a multi-element passenger flow detection means, and can know in real time how many people can be accommodated in the upstream buffer zone of the outbound bottleneck area.
4. The invention can scientifically adjust the transportation capacity according to the station passenger outbound capacity based on the information.
Drawings
FIG. 1 is a data relationship diagram of the present invention;
FIG. 2 is a circuit diagram of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
A station route diagram of an embodiment of the invention is shown in fig. 2. An intelligent train dispatching method for urban rail transit passenger flow loop self-adaptation comprises the steps of predicting the passenger volume under a train, obtaining the station passenger outbound capacity and adjusting the train transport capacity.
The method for predicting the passenger capacity under the train comprises the following steps:
1) Big data analysis is carried out based on data such as ticket service clearing data and train weighing, and a train leaving load, passenger number of getting-on and getting-off and OD passenger flow model of each station are established according to the current schedule.
2) If a certain station C i Exit linkage large-scale activity place is holding large-scale exhibition, motion meeting etc. in a few consecutive days, need carry out safety inspection to the personnel that enter the place of leaving a station to according to the place ability of recepting closed partial export, for reaching the speed control with the passenger this moment within the station passenger ability scope of leaving a station, need predict the volume of getting off a train:
A. passenger arrival rate prediction preconditions: station C in the day i Except that there are no similar large activities near the remaining stations.
B. Under normal conditions, at C i Within the range of n upstream stations, if the arrival intervals of the trains and the front vehicle on the same platform accord with the planned interval and the arrival intervals of the trains and the front vehicle on the same platform on the upstream of the current direction also accord with the planned interval, the departure load of the trains and the arrival intervals of the trains on the C station can be determined according to the departure load of the trains i-n+1 To C i-1 The number of passengers getting on and off each station between stations and the passenger flow model are compared with the train at C i If the number of passengers is predicted, if the station C i Not a terminal station, the up and down trains need statistics:
a)R j,j+1 for train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]And the passenger flow is obtained by a multi-element passenger flow detection mode such as train weighing.
b)X k For train A at station C k The number of passengers in (k) is [ j +1, i-1 ]]The model value or the number of passengers getting off the train at the station can be referred to.
c)S k For train A at station C k Upper part ofThe number of passengers, k, is [ j +1, i-1 ]]The predicted number of passengers can be calculated by referring to the model value or the number of the most recent passengers on the train which stops at the station. Train A is at C k The number of passengers on the station is the smaller value of the predicted number of passengers on the train A and the number of passengers on the train A.
d) According to train at C i Predicted arrival load, model arrival load and model passenger number of train at C i The number of passengers getting off.
e) Train at C k K is [ i-n + 1,i ]]The system can detect passenger flow in multiple passenger flow detection modes such as train weighing.
f) Station C i-n To C i-1 Each train leaving the station of updating C i The number of visitors is predicted.
C. If C is j The station has shielding door fault, j belongs to [ i-n +1, i-1 ]]Train at C j The station adopts the over-station adjustment strategy, then C j The number of passengers getting on and off the station is 0, and the passengers on the vehicle are generally selected to be in the position C j-1 Station and C j+1 Getting off the bus at the station, if j = i-1, the bus needs to be checked at the station C i Make an announcement that the number of newly added visitors is likely to be in C i When the train leaves the station, the train can be transferred to the reverse train to return to the station C i-1 And (4) a station.
D. If the fault treatment results in that the threshold value F of the train A and the previous train are on the same platform (actual departure interval-planned departure interval), the upstream part of the train A is buckled and stopped at a late point, a part of the train is inserted between the train A and the previous train for reducing the large interval, the passenger carrying conditions of the train, the train A and the upstream late point train are different from those of a normal alignment point train, and the part of the train is required to be positioned at a station C i The number of passengers getting off the elevator is specifically predicted. Let C be m For the station when the train returns to operation, the next train is the starting station for inserting operation, and m is [ i-1,1 ]]：
a) Estimating the number of regular passengers who do not go to a large-scale activity place in the departure load when the train leaves the station and the train A and the train at the upstream late point and the number of passengers who get off the station in the regular passenger flow at the respective downstream station:
estimate train A and upstream late train C m Upstream station leaving stationThe number of regular persons in the load who do not go to the large-scale event and the number of persons who get off the part of regular passenger flow at the respective downstream stations are as follows:
conventional passenger flow OD model of current time period and current direction without going to large-scale activity place: at station C x Among passengers getting on will have B x:x+y % of passengers are in C x+y Standing passenger, y>＝1。
According to the OD model, the train is at the station C m Model number of guests on each station at upstream calculation station C m-1 Model off-station load D m-1 Number of passengers X under model of downstream station in current direction Dm-1:m-1+p ，p>＝1。
Suppose that the current train is at station C m The number of guests at each upstream station includes the number of specific guests going to the large event and the number of model guests not going to the large event, and C m-1 Model off-station load D m-1 Number of visitors under model of downstream station in current direction X Dm-1:m-1+p As the current train C m-1 Actual off-station load D m-1 Conventional number of visitors X who do not go to large-scale activity place at downstream station in current direction Dm-1:m-1+p ，p>＝1。
Estimate oncoming traffic C m The conventional number of persons who do not go to the large-scale event place in the load of the upstream station leaving the station and the number of persons who get off the large-scale event place in the part of conventional passenger flow at the respective downstream stations are as follows: the passenger car is generally empty, so C can be considered as m The number of regular persons who do not go to the large-scale event place in the load of the upstream station leaving the station and the number of persons who get off the regular passenger flow at the respective downstream stations are both 0.
Estimating the number of regular passengers who do not go to a large-scale activity place in the load of each station leaving after the train A and the train at the upstream late point are recovered to operate and the passenger flow of the regular passengers at each downstream station after the adjacent passenger train is inserted into the station to operate:
according to the nearest punctual train at C m-1+p Actual number of passengers at station, C m-1+p Number of passengers on model of station, current train at C m-1+p Estimating current train at C by actual number of guests at station m-1+p Conventional number of guests S who do not go to large event place among the number of guests actually standing m-1+p: general of ，p>＝1。
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C m-1+p Number of actual passengers getting off at station X m-1+p 、C m-1+p Number of regular guests S estimated from number of actual guests in station m-1+ p: general rule Calculating C m-1+p The load of the station leaving the station does not reach the normal number of people in the large-scale activity place.
According to S m-1+ p: general of And the OD model calculates the train is in C m-1+p Number of regular guests S of station m-1+ p: general of The number of passengers getting off at the downstream station X m-1+p:m-1+p+q ，p>＝1，q>＝1。
According to the number of passengers getting off the downstream station and X of the regular number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station m-1+p:m-1+p+q Calculating train C m-1+p Load D from station to station m-1+p Regular number of people who can not go to large-scale activity places X number of passengers getting off at downstream stations Dm-1+p:m-1+p+q ，p>＝1，q>＝1。
Train at C m-1+p Actual number of visitors, p>=1, detection can be performed by multivariate passenger flow detection methods such as train weighing.
b) Prediction C i The train in the range of the upstream n stations is at C i The number of passengers getting off, if the station C i Not a terminal station, the up and down trains need to be counted:
·R j,j+1 for train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]And the passenger flow is obtained by a multi-element passenger flow detection mode such as train weighing.
Prediction of train A at station C k The number of passengers getting off, the number of passengers getting on, the number of regular passengers who do not go to large-scale activity places in the load leaving the station and the number of passengers getting off at the downstream station of the regular passenger flow are [ j +1, i-1 ]]：
Train A at station C k Predicting the number of passengers getting off X k Is X Dk-1:k 。
Train A at station C k Predicted number of guests S k Comprises the following steps:
if train A and the front train areStation C k Does not meet the planned interval, then:
and calculating the predicted number of passengers one according to the ratio of the actual interval to the planned interval and the number of the last passengers on the quasi-point train at the stop station.
Train A is at C k The number of the passengers getting on the station is the smaller value of the predicted number of the passengers getting on the train A and the number of the passengers getting on the train A.
Check if the train A and the front train are at the station C k If the arrival intervals of (a) all meet the planned interval, then:
can refer to the front vehicle at C k The number of passengers on the station is calculated to predict the number of passengers on the station, namely the number of passengers on the station is one, and the number of front vehicles is C k The number of guests at a station may be an actual value or a predicted value.
Train A is at C k The number of passengers on the station is the smaller value of the predicted number of passengers on the train A and the number of passengers on the train A.
According to the nearest punctual train at C k Actual number of passengers at station, C k Number of passengers on model station, current train in C k Station prediction guest number estimation current train in C k The number S of regular visitors not going to large-scale activity places among the predicted visitors at the station k: general of 。
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C k Station forecast number of passengers X k 、C k Conventional number of guests S estimated from predicted number of guests at station k: general of Calculating C k The number of persons who normally go to large activity places in the load of leaving the station is not limited.
According to S k: general of And the OD model calculates the train is in C k Number of regular guests S of a station k ： General of The number of passengers getting off at the downstream station X k:k+q ，q>＝1。
According to the number of passengers getting off the downstream station and X of the conventional number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station k:k+q Calculating train C k Load D from station to station k Regular people who do not go to large-scale activity places can get off at downstream station by X number of passengers Dk:k+q ，q>＝1。
Prediction of train A at station C i The number of underground cars:
if train A participates in the calculation in a), reference can be made to train A at C i-1 Off-station loading of a station D i-1 Estimated D i-1 The number of persons who can not go to large-scale activity places and the part of conventional passenger flow at station C i The number of passengers getting off the train A at the station C i Estimating the number of alighting persons.
If train A does not participate in the calculation in a), it is possible to calculate at C according to train A i Predicted arrival load, model arrival load, and model number of passengers in train A at C i The number of visitors.
Station C i-n To C i-1 Each train leaving the station of updating C i The number of visitors is predicted. The method for acquiring the outbound capacity of the passenger in the station comprises the following steps:
1) The station adopts a multi-element passenger flow detection means to detect passenger flows at platforms, key channels, exits and the like.
2) The station periodically reports the outbound rate of the current bottleneck area, and the buffer area at the upstream of the bottleneck area can also accommodate a plurality of people.
The train capacity adjusting method comprises the following steps:
1) For reducing station C i The passenger capacity under the train can be selected to clear passengers at an upstream station:
A. at C i-n To C i Selecting 1 stations C with small passenger capacity from the stations between r And clearing the passengers to avoid the conflict of passenger flow caused by clearing the passengers.
B. If the station C i Is a terminal station, if station C r Connecting the train section/parking lot, the train can go back to the train section/parking lot for waiting after clearing passengers, and then goes out of the train section/parking lot from the station C r The current service number of the train which starts to run is from the station C i The next task after the turn-back; if station C r Not connecting the train section/parking lot, the train will go forward after clearing passenger, and at the station C i And continuing to carry passengers for operation after turning back.
C. If the station C i If the train is not a terminal station, the train is clear of passengersThen go forward at station C i The next station continues to carry passengers.
D. The customer is generally not cleared continuously.
E. And (3) deciding the opportunity of clearing the visitor:
a) Station C i Reporting: station C i Has a passenger outbound rate V in a bottleneck region o-i The upstream buffer of the bottleneck area can accommodate R at most i:max People, at present, can also accommodate passengers R i A human.
b) Station C r-1 Platform to station C i The platform has k vehicles including ascending and descending, the last vehicle is train A and station C r-1 The first train B in the upstream section still requires time T1 to arrive at the station C i After the T1 time period, the station C i Delta O of the number of people waiting to go out of the station i Is (k trains are at C) i Forecast of station for total number of passengers-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max The smaller of these.
c) If (R) i + train B at C i Predicted number of passengers getting off at station) threshold F, then train B needs to be at station C r Clearing away the visitor.
d) The number of the full passengers in the train is T max If the train A is full of passengers and arrives at the station C i After, R i Approaching the threshold value F if the station C is i Can not finish T within 2 train interval time max The number of people going out, at which time if the train on the upstream of the train B arrives full of people, the train may also be cleared of passengers, if the station C i The terminal station can be used for continuously clearing passengers for two trains.
e) If the station C i Not at the terminal station, but also at C if the second train upstream of train B i When the train is in the same direction as the train B, the first train at the upstream of the train B is considered to be cleared so as to avoid continuous clearing in the same direction.
f) Whenever station C is updated i When the number of passengers is predicted, passenger clearing decisions are made.
2) For reducing station C i The passenger capacity under the train, if the station C i Instead of being an end station,can also select part of train jumping stops C i ：
A. Station C i Reporting: station C i The outbound speed of passengers in the bottleneck region is V o-i The upstream buffer of the bottleneck area can accommodate R at most i:max People, at present, can also accommodate passengers R i A human.
B. Station C r Platform to station C i The platform has k vehicles including up and down, the last vehicle is train A and station C r The first train B in the upstream section still requires time T1 to arrive at the station C i After time period T1, station C i Delta O of the number of people waiting to go out of the station i Is (k trains are at C) i Station forecast leaving passenger total-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max The smaller of these.
C. If (R) i + train B at C i Predicted number of passengers getting off at station) threshold F, then train B needs to be at station C i And (6) jumping and stopping.
D. If the second train upstream of train B is also at C i And (4) when the station stops, and the train B are trains in the same direction, considering stopping the first train at the upstream of the train B so as to avoid continuous stopping in the same direction.
E. Whenever station C is updated i When the number of passengers is predicted, jump stop decision is made.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. An intelligent train dispatching method for urban rail transit passenger flow loop self-adaptation is characterized by comprising the following steps:
step 1), predicting the off-train passenger capacity;
step 2), obtaining the station passenger outbound capacity;
step 3), carrying out train operation capacity adjustment according to the information in the step 1) and the step 2;
the under-train passenger capacity prediction specifically comprises the following steps:
step 1.1), performing big data analysis based on the ticketing and counting data and the train weighing data, and establishing an OD model of train departure load, passenger number of getting-on and getting-off and passenger flow of each station aiming at the current schedule;
step 1.2), if a certain station C i The exit is connected with a large-scale event place, large-scale events are held within a plurality of continuous days, safety check is needed to be carried out on personnel going out of the station and entering the place, partial exits are closed according to the place reception capacity, and at the moment, the passenger getting-off amount needs to be predicted in order to control the passenger arrival rate within the passenger going-out capacity range of the station;
the method for predicting the passenger capacity under the train specifically comprises the following steps:
step 1.2.1) passenger arrival rate prediction preconditions: station C in the day i Other stations have no similar large-scale activities nearby;
step 1.2.2) under normal conditions, at C i Within the range of n upstream stations, if the arrival intervals of the trains and the front vehicle on the same platform accord with the planned interval and the arrival intervals of the trains and the front vehicle on the same platform on the upstream of the current direction also accord with the planned interval, the departure load of the trains and the arrival intervals of the trains on the C station can be determined according to the departure load of the trains i-n+1 To C i-1 The number of passengers getting on and off each station between stations and the passenger flow model are compared with the train at C i If the number of passengers is predicted, if the station C i Not a terminal station, and an uplink train and a downlink train are counted;
step 1.2.3) if C j A station has a shield door fault, j belongs to [ i-n +1, i-1 ]]Train at C j The station adopts the over-station adjustment strategy, then C j The number of passengers getting on and off the station is 0, and the passengers on the vehicle can select to be in the position C j-1 Station and C j+1 Getting off the bus at the station, if j = i-1, the bus needs to be checked at the station C i Make an announcement that the number of newly added visitors is likely to be in C i Standing out;
step 1.2.4) if the fault is processedThe train A and the front train are positioned on the same platform (actual departure interval-planned departure interval) and are larger than a threshold value F, the upstream part of the train A is buckled and stopped at a late point, a part of the adjacent trains are inserted between the train A and the front train for reducing the large interval, the passenger carrying conditions of the adjacent trains, the train A and the upstream late point train are different from those of the normal alignment train, and the part of the train is required to be positioned at a station C i The number of passengers getting off the house is specifically predicted;
the statistics of the number of people in the step 1.2.2) are specifically as follows:
step 1.2.2.1) R j,j+1 For train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]Obtaining the passenger flow through a multi-element passenger flow detection mode;
step 1.2.2.2) X k For train A at station C k The number of passengers, k, belongs to [ j +1, i-1 ]]The model value or the number of passengers getting off the train at the station can be referred;
step 1.2.2.3) S k For train A at station C k The number of passengers in (k) is [ j +1, i-1 ]]The number of passengers getting on the train can be predicted according to the model value or the number of the recent passengers getting on the train stopping at the station; train A is at C k The number of the passengers getting on the station is the smaller value of the predicted number of passengers getting on the train and the number of the passengers getting on the train A;
step 1.2.2.4) according to train at C i Predicted arrival load, model arrival load, and model number of passengers in train i The number of passengers getting off, the train is at C k K is [ i-n +1,i ]]The detection can be carried out by a multivariate passenger flow detection mode;
step 1.2.2.5) station C i-n To C i-1 Each train leaving the station of updating C i The number of passengers getting off is predicted;
the specific prediction in step 1.2.4) is as follows:
let C be m For the station when the train returns to operation, the next train is the starting station for inserting operation, and m is [1, i-1 ]]；
Step 1.2.4.1) estimating the number of regular passengers who do not go to a large-scale activity place in the departure load when the train leaves the station and the train A and the upstream late point train and the number of passengers getting off the downstream station of the regular passenger flow;
step 1.2.4.2) prediction of C i The train in the range of the upstream n stations is at C i Number of passengers getting off, if station C i The up and down trains are counted if the terminal station is not the terminal station;
the step 1.2.4.1) is specifically as follows:
a) Estimating train A and upstream late train C m The conventional number of persons who do not go to the large-scale event place in the load of the upstream station leaving the station and the number of persons who get off the large-scale event place in the part of conventional passenger flow at the respective downstream stations are as follows:
conventional passenger flow OD model of current time period and current direction without going to large-scale activity place: at station C x Among passengers getting on will have B x:x+y % of passengers in C x+y Standing guest, y>＝1；
According to the OD model, the train is at the station C m Model number of guests on each station at upstream calculation station C m-1 Model off-station load D m-1 Number of passengers X under model of downstream station in current direction Dm-1:m-1+p ，p>＝1；
Suppose that the current train is at station C m The number of guests at each upstream station includes the number of specific guests going to the large event and the number of model guests not going to the large event, and C m-1 Model off-station load D m-1 Number of visitors under model of downstream station in current direction X Dm-1:m-1+p As the current train C m-1 Actual off-station load D m-1 Regular number of visitors who do not go to large-scale activity place at downstream station in current direction X Dm-1:m-1+p ，p>＝1；
b) Estimation of temporary passenger train C m The conventional number of persons who do not go to the large-scale event place in the load of the upstream station leaving the station and the number of persons who get off the large-scale event place in the part of conventional passenger flow at the respective downstream stations are as follows:
consider C m The number of regular persons who do not go to the large-scale activity place in the load of the upstream station leaving the station and the number of persons who get off the regular passenger flow at the respective downstream stations are both 0;
c) Estimating the number of regular passengers who do not go to a large-scale activity place in the load of each station leaving after the operation of the train A and the upstream late train is recovered and the passenger train is inserted into the station and operated and the number of passengers who leave the station in the part of regular passenger flow at each downstream station:
according to the nearest punctual train at C m-1+p Actual number of passengers at station, C m-1+p Number of passengers on model station, current train in C m-1+p Actual number of guests at station estimate current train at C m-1+p Conventional number of guests S who do not go to large event place among the number of guests actually standing m-1+ p: general rule ，p>＝1；
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C m-1+p Number of passengers getting off at station m-1+p 、C m-1+p The number S of regular guests estimated from the number of actual guests in the station m-1+ p: general rule Calculating C m-1+p The number of the conventional people in the large-scale activity place is not increased in the load of the station leaving the station;
according to S m-1+ p: general of And the OD model calculates the train at C m-1+p Number of regular guests S of a station m-1+ p: general of Number of passengers getting off at downstream station X m-1+p:m-1+p+q ，p>＝1，q>＝1；
According to the number of passengers getting off the downstream station and X of the conventional number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station m-1+p:m-1+p+q Calculating train C m-1+p Load D from station to station m-1+p Regular people who do not go to large-scale activity places can get off at downstream station by X number of passengers Dm-1+p:m-1+p+q ，p>＝1，q>＝1；
Train at C m-1+p Actual number of visitors, p>=1, detection can be performed by a multivariate passenger flow detection mode;
the step 1.2.4.2) is specifically as follows:
a)R j,j+1 for train A at station C j To C j+1 Load of interval, j belongs to [ i-n, i-1 ]]Obtaining the passenger flow through a multi-element passenger flow detection mode;
b) Predicting train A at station C k The number of passengers getting off, the number of passengers getting on, the number of regular passengers who do not go to large-scale activity places in the load leaving the station and the number of passengers getting off at the downstream station of the regular passenger flow are [ j +1, i-1 ]]：
Train A is atStation C k Forecast of number of visitors X k Is X Dk-1:k ；
Train A at station C k Predicted number of guests S k Comprises the following steps:
b1 If train a and the lead car are at station C k Does not meet the planned interval, then:
calculating and predicting the number of passengers getting on the train according to the ratio of the actual interval to the planned interval and the number of the passengers getting on the train at the punctual point where the train stops at the station;
train A is at C k The number of the passengers on the station is the smaller value of the predicted number of the passengers on the train A and the number of the passengers on the train A;
b2 If train a and lead car are at station C k The arrival intervals of (a) meet the planned intervals, then:
can refer to the front vehicle at C k The number of the passengers getting on the bus at the station is calculated to predict the number of the passengers getting on the bus; train A is at C k The number of the passengers on the station is the smaller value of the predicted number of the passengers on the train A and the number of the passengers on the train A;
according to the nearest punctual train at C k Actual number of passengers at station, C k Number of passengers on model station, current train in C k Station forecast number of guests estimate current train at C k Conventional number of guests S who do not go to large-scale event place among predicted number of guests at station S k: general rule ；
According to the conventional number of people who do not go to large-scale activity places in the load of leaving the station at the upstream station, C k Station forecast number of passengers X k 、C k Conventional number of guests S estimated from predicted number of guests at station k: general rule Calculating C k The number of the conventional people in the large-scale activity place is not increased in the load of the station leaving the station;
according to S k: general rule And the OD model calculates the train at C k Number of regular guests S of station k: general rule The number of passengers getting off at the downstream station X k:k+q ，q>＝1；
According to the number of passengers getting off the downstream station and X of the conventional number of people who do not go to the large-scale activity place in the load of the upstream station leaving the station k:k+q Calculating train C k Load D from station to station k Middle and large sized activitiesRegular number of persons in place X number of visitors getting off at downstream station Dk:k+q ，q>＝1；
c) Predicting train A at station C i The number of people getting off:
if train A participates in the calculation of step 1.2.4.1), train A at C can be referred to i-1 Off-station loading of a station D i-1 Estimated D i-1 The number of persons who can not go to large-scale activity places and the part of conventional passenger flow at station C i The number of passengers getting off the train A at the station C i Estimating the number of getting-off people;
if train A does not participate in the calculation in step 1.2.4.1), then train A at C can be referenced i Predicted arrival load, model arrival load, and model number of passengers in train A at C i The number of passengers getting off;
d) Station C i-n To C i-1 Each train leaving the station of updating C i The number of passengers is predicted.
2. The urban rail transit passenger flow loop self-adaptive intelligent train dispatching method according to claim 1, wherein the station passenger outbound capacity acquisition specifically comprises:
2.1 ) a station adopts a multi-element passenger flow detection means to detect passenger flows at a platform, a key passage and an exit;
2.2 Station periodically reports the outbound rate of the current bottleneck area, how many people the upstream buffer area of the bottleneck area can also accommodate.
3. The urban rail transit passenger flow loop adaptive intelligent train dispatching method according to claim 1, wherein the train capacity adjustment specifically comprises:
3.1 To reduce the amount of passengers getting off the train at station Ci), an upstream station may be selected for passenger clearance:
3.1.1 In C) i-n To C i Selecting 1 stations C with small passenger quantity from the stations in between r Clearing the visitor;
3.1.2 If station C i Is a terminal station, if station C r Connecting the train section or the parking lot, the train can go back to the train section or the parking lot for waiting after clearing passengers, and then goes out of the train section or the parking lot from the station C r Current service number slave station C for train starting to run i The next task after the turn-back; if station C r Not connecting the train section or the parking lot, the train keeps going forward after clearing the passenger, and the train is at the station C i Continuing to carry passengers for operation after turning back;
3.1.3 If station C i If the train is not a terminal station, the train keeps going forward after clearing passengers and is at a station C i Continuing to carry passengers for operation;
3.1.4 Discontinuous customer clearing, adopting decision of setting the time for clearing the customers;
3.2 To reduce station C) i The passenger capacity under the train, if the station C i Instead of terminal stations, it is also possible to select a part of train stops C i ：
Station C i Reporting: station C i Has a passenger outbound rate V in a bottleneck region o-i The upstream buffer of the bottleneck region can accommodate R at most i:max People, at present, can also accommodate passengers R i A human;
station C r Platform to station C i The platform has k vehicles including ascending and descending, the last vehicle is train A and station C r The first train B in the upstream section still requires time T1 to arrive at the station C i After time period T1, station C i Increment of number of people waiting to exit Δ O i Is (k trains are at C) i Forecast of station for total number of passengers-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max Of, wherein V o-i Is station C i The passenger outbound rate in the bottleneck region;
if (R) i:max -R i + train B at C i Predicted number of passengers getting off at station) of threshold value F, train B needs to be at station C i Jumping and stopping;
if the second train upstream of train B is also at C i When the station stops, if the train and the train B are trains in the same direction, the first train on the upstream of the train B is considered to stop so as to avoid continuous stop in the same direction;
whenever station C is updated i When the number of passengers is predicted, jump-stop decision is carried out.
4. The urban rail transit passenger flow loop self-adaptive intelligent train dispatching method according to claim 3, wherein the 3.1.4) passenger clearing opportunity decision is specifically as follows:
station C i Reporting: station C i Has a passenger outbound rate V in a bottleneck region o-i The upstream buffer of the bottleneck region can accommodate R at most i:max People, now also able to accommodate passengers R i A human;
station C r-1 Platform to station C i The platform has k vehicles including up and down, the last vehicle is train A and station C r-1 The first train B in the upstream section still requires time T1 to arrive at the station C i After time period T1, station C i Delta O of the number of people waiting to go out of the station i Is (k trains are at C) i Station forecast leaving passenger total-V o-i *T1)，R i Taking (R) i -△O i ) And R i:max The smaller of the two;
if (R) i:max -R i + train B at C i Predicted number of passengers getting off at station) of threshold value F, train B needs to be at station C r Clearing the customers;
the number of the full passengers in the train is T max If the train A is full of passengers and arrives at the station C i After, R i:max -R i Approaching the threshold F if the station C i Can not finish T within 2 train interval time max The number of people going out, at which time if the train on the upstream of the train B arrives full of people, the train may also be cleared of passengers, if the station C i If the terminal station is used, the situation that two trains of passengers are cleared continuously can occur;
if the station C i Not at the terminal station, but also at C if the second train upstream of train B i When the train and the train B are trains in the same direction, the first train on the upstream of the train B is considered to avoid continuous passenger clearing in the same direction;
whenever station C is updated i To the guestIn counting, the decision for clearing the visitor is made.
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