CN106991824B - Toll station vehicle queuing prediction method - Google Patents

Toll station vehicle queuing prediction method Download PDF

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CN106991824B
CN106991824B CN201710368012.3A CN201710368012A CN106991824B CN 106991824 B CN106991824 B CN 106991824B CN 201710368012 A CN201710368012 A CN 201710368012A CN 106991824 B CN106991824 B CN 106991824B
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toll
lane
vehicles
queuing
average
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CN106991824A (en
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韩直
岳海亮
余晓南
王振科
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China Merchants Chongqing Communications Research and Design Institute Co Ltd
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    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

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Abstract

The invention provides a toll station vehicle queuing prediction method, which comprises the following steps: s1, collecting toll lane information of a toll station and traffic information of a toll lane; s2, acquiring real-time traffic flow information Qi and real-time queuing length information of the toll station at the moment Ti in real time; and S3, predicting the average queuing length and the average number of queued vehicles at the moment of Ti + TT according to the toll lane information, the traffic volume information, the real-time traffic flow information Qi and the real-time queuing length information, and predicting the blocking degree according to the average queuing length and the average number of queued vehicles at the moment of Ti + TT.

Description

Toll station vehicle queuing prediction method
Technical Field
The invention relates to a traffic prediction method, in particular to a toll station vehicle queuing prediction method.
Background
The toll station is generally used for toll management of high-speed access roads, the toll station is used as a part of a road network, and the passing state of the toll station also needs to be detected and managed, such as queuing length and congestion state, an effective method for predicting the queuing state and the congestion state of the toll station is not available in the prior art, the existing vehicle queuing prediction is generally used for intersections, the traffic condition of the intersections is collected through an image collecting device, then the vehicle queuing length is detected by adopting a method combining gray level and edge detection, although the queuing state of the intersections can be detected, the queuing state of the intersections within the future set time can not be detected, so that accurate traffic coordination processing measures are not facilitated, and the existing vehicle queuing prediction method for the intersections is not suitable for the toll station because the toll station carries out different toll channels for different types of vehicles, moreover, toll stations have the influence of human factors, so that the existing method cannot be applied to toll station vehicle queuing prediction.
Therefore, in order to solve the above technical problems, it is necessary to provide a new method.
Disclosure of Invention
In view of the above, the present invention provides a method for predicting vehicle queuing at a toll station, which can accurately predict queuing states and traffic states of the toll station within a period of time in the future, so as to facilitate a traffic management department to make accurate coordination management measures according to prediction results, and improve traffic efficiency of the toll station.
The invention provides a toll station vehicle queuing prediction method, which comprises the following steps:
s1, collecting toll lane information of a toll station and traffic information of a toll lane;
s2, acquiring real-time traffic flow information Qi and real-time queuing length information of the toll station at the moment Ti in real time;
s3, predicting the average queuing length and the average number of queued vehicles at the moment of Ti + TT according to the toll lane information, the traffic volume information, the real-time traffic flow information Qi and the real-time queuing length information, and predicting the blocking degree according to the average queuing length and the average number of queued vehicles at the moment of Ti + TT.
Further, the toll lane information includes a toll lane type and a toll lane service rate;
wherein:
x denotes a lane type, and X ═ (X1, X2, …, xr, …, xn), xr denotes an r-th class toll lane, and r ═ 1,2, …, n;
y denotes a toll lane service rate, and Y ═ Y1, Y2, …, yr, …, yn), yr denotes a service rate of the r-th class of toll lanes, and r ═ 1,2, …, n.
Further, the traffic information of the toll lane comprises the average head space of the queuing vehicles of the r-th toll lane
Figure BDA0001302104230000021
Proportion alpha of r-type toll lane traffic volume to total traffic volumerAnd average vehicle occupancy in toll station transitions
Figure BDA0001302104230000022
αrRepresenting the proportion of the traffic volume of the r-th toll lane to the total traffic volume of the toll station.
Further, the average headway distance of the vehicles queued in the toll lane
Figure BDA0001302104230000023
Obtained by the following formula:
Figure BDA0001302104230000024
wherein n isrRepresenting the number of r-th type lanes, i representing the i-th r-type lane, m representing the number of monitoring times, j representing the j-th measurement on the i r-th type toll lanes, qijDenotes the vehicle queue length, V, at the j-th measurement of the i-th type of toll laneijIndicating a queue length of qijThe number of vehicles in time.
Further, the average occupied area of the vehicles in the transition section of the toll station
Figure BDA0001302104230000025
Calculated by the following formula:
Figure BDA0001302104230000031
where m represents the number of measurements, Si represents the total area occupied by the vehicle at the i-th measurement, and Ni represents the number of vehicles at the i-th measurement.
Further, step S3 includes the following steps:
and predicting the average queuing length and the average number of queued vehicles within the TT moment by the following formulas:
Figure BDA0001302104230000032
Figure BDA0001302104230000033
wherein the content of the first and second substances,
Figure BDA0001302104230000034
represents the average number of queued vehicles for the class r toll lane,
Figure BDA0001302104230000035
represents the average queuing length of the r-th toll lane, BirRepresents the number of vehicles conforming to the r-type toll lane in the transition section at the time of Ti,
Figure BDA0001302104230000036
and when there are no vehicles in the transition that meet the r-class lane,
Figure BDA0001302104230000037
nrrepresenting the number of r-th type lanes, S being the area occupied by the vehicle in the transition section at time Ti, LirThe length of the queue of the ith r-th toll lane at the time of Ti is shown.
Further, the blocking degree of the toll station is judged according to the following method:
if it is
Figure BDA0001302104230000038
The r-th toll lane is in a smooth state;
if it is
Figure BDA0001302104230000039
The r-th toll lane is in a blocking state, wherein N is the number of vehicles which can be accommodated in a single lane in the r-th toll lane;
if it is
Figure BDA00013021042300000310
The toll station is in a congestion state, wherein M is the sum of the total number of vehicles which can be accommodated by the primary toll lane and the total number of vehicles which can be accommodated by the transition section;
if it is
Figure BDA0001302104230000041
The toll booth is in a congested state.
Further, the method also comprises the following steps:
calculating service intensity of class r toll lane
Figure BDA0001302104230000042
Figure BDA0001302104230000043
Judging the expected change state of the queuing length according to the service intensity of the r-th toll lane of the toll station:
when in use
Figure BDA0001302104230000044
The current toll station is in an unstable state, and the queuing length is increased;
when in use
Figure BDA0001302104230000045
It indicates that the current toll station is in a stable state and the queuing length will be reduced. .
The invention has the beneficial effects that: the invention can realize accurate prediction of the queuing state and the traffic state of the toll station within a period of time in the future, thereby facilitating the traffic management department to make accurate coordination management measures according to the prediction result and improving the passing efficiency of the toll station.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of the toll booth model of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings of the specification:
the invention provides a toll station vehicle queuing prediction method, which comprises the following steps:
s1, collecting toll lane information of a toll station and traffic information of a toll lane;
s2, acquiring real-time traffic flow information Qi and real-time queuing length information of the toll station at the moment Ti in real time;
and S3, predicting the average queuing length and the average number of queued vehicles at the moment of Ti + TT according to the toll lane information, the traffic volume information, the real-time traffic flow information Qi and the real-time queuing length information, and predicting the blocking degree according to the average queuing length and the average number of queued vehicles at the moment of Ti + TT.
In this embodiment, the toll lane information includes a toll lane type and a toll lane service rate;
wherein:
x denotes a lane type, and X ═ (X1, X2, …, xr, …, xn), xr denotes an r-th toll lane, and r ═ 1,2, …, n, wherein the toll lane types include man-way, ETC-way, bus-way, truck-way, and the like, and the number of lanes of each type is different, as shown in fig. 2, both the 1 st type lane and the 2 nd type toll vehicle have two lanes, and the other type toll lane has one lane;
y denotes a toll lane service rate, and Y ═ Y1, Y2, …, yr, …, yn), yr denotes a service rate of the r-th class of toll lanes, and r ═ 1,2, …, n, where the service rate is the number of vehicles served per unit time.
In this embodiment, the traffic information of the toll lane includes an average headway distance of vehicles queued in the r-th toll lane
Figure BDA0001302104230000051
Proportion alpha of r-type toll lane traffic volume to total traffic volumerAnd average vehicle occupancy in toll station transitions
Figure BDA0001302104230000052
αrIndicating class r chargingThe proportion of the traffic volume of the lane to the total traffic volume of the toll station, wherein the proportion alpha of the traffic volume of the r-type toll lane to the total traffic volumerThe historical monitoring data of the toll station is counted, although the historical monitoring data changes at a certain moment, in the prediction process of the toll station, the predicted time period TT is long enough, for example, TT is 1 hour, and then the actual alpha in the TT time periodrThe value of (a) and the historical data statistic value tend to be consistent, therefore, the error of the (a) and the historical data statistic value can be basically ignored, the accuracy of the final queuing prediction cannot be influenced, and certainly, the alpha value is alpharThe value of (b) may be different in some time periods, or may be directly counted by historical data, for example, the value of (b) may be different in commuting rush hour period, day time period, night time period, holiday, etc. every day.
Wherein the average head space of the vehicles in line in the toll lane
Figure BDA0001302104230000061
Obtained by the following formula:
Figure BDA0001302104230000062
wherein n isrRepresenting the number of r-th type lanes, i representing the i-th r-type lane, m representing the number of monitoring times, j representing the j-th measurement on the i r-th type toll lanes, qijDenotes the vehicle queue length, V, at the j-th measurement of the i-th type of toll laneijIndicating a queue length of qijThe number of vehicles in time; in order to obtain the average distance between the car heads, readable markers such as marker posts are required to be arranged on the queuing section of the toll station and are arranged along the lane direction at equal intervals of 1 meter, as shown in fig. 2, and the transition section of the toll station is a gradual change area in fig. 2; the method comprises the steps that image information is collected through image collection equipment for detecting a transition section and a toll lane, then the number of real-time vehicles in a queuing area of the toll lane and the number of vehicles in the queuing area are identified according to the image information, and the number of vehicles in the transition section and the total area S occupied by the vehicles are identified; the image acquisition equipment is arranged at the toll station and can be adjustedIt is possible to identify the entire queuing area as well as the transition segment.
Average occupied area of vehicles on transition section of toll station
Figure BDA0001302104230000063
Calculated by the following formula:
Figure BDA0001302104230000064
where m represents the number of measurements, Si represents the total area occupied by the vehicle at the i-th measurement, and Ni represents the number of vehicles at the i-th measurement.
In this embodiment, step S3 includes the following steps:
and predicting the average queuing length and the average number of queued vehicles within the TT moment by the following formulas:
and predicting the average queuing length and the average number of queued vehicles within the TT moment by the following formulas:
Figure BDA0001302104230000071
Figure BDA0001302104230000072
wherein the content of the first and second substances,
Figure BDA0001302104230000073
represents the average number of queued vehicles for the class r toll lane,
Figure BDA0001302104230000074
represents the average queuing length of the r-th toll lane, BirRepresents the number of vehicles conforming to the r-type toll lane in the transition section at the time of Ti,
Figure BDA0001302104230000075
and when there are no vehicles in the transition that meet the r-class lane,
Figure BDA0001302104230000076
nrrepresenting the number of r-th type lanes, S being the area occupied by the vehicle in the transition section at time Ti, LirThe method comprises the steps of representing the queuing length of the ith r-th toll lane at the Ti moment, obtaining the current image state of the real-time traffic parameter acquired at the Ti moment through a camera device, and then analyzing the number of vehicles in the ith r-th toll lane and the queuing length q through the existing methodijThe number of vehicles in time.
The traffic flow information Qi can be obtained by setting a ground induction coil or a vehicle arrival rate detector such as an RFID reader-writer at a road section in front of a toll station to acquire the traffic volume Mi at the time of Ti, and then obtaining the traffic volume Mi by a formula
Figure BDA0001302104230000077
Obtaining traffic flow information Qi, wherein t represents the time interval acquired by the acquisition equipment;
further, the blocking degree of the toll station is judged according to the following method:
if it is
Figure BDA0001302104230000078
The r-th toll lane is in a smooth state;
if it is
Figure BDA0001302104230000079
The r-th toll lane is in a blocking state, wherein N is the number of vehicles which can be accommodated in a single lane in the r-th toll lane;
if it is
Figure BDA00013021042300000710
The toll station is in a congestion state, wherein M is the sum of the total number of vehicles which can be accommodated by the primary toll lane and the total number of vehicles which can be accommodated by the transition section;
if it is
Figure BDA0001302104230000081
The toll station is in a congestion state; in the method, the queuing length of the current toll station is calculated, but the queuing length is longThe expected change state of the degree is obtained by:
computing service strength
Figure BDA0001302104230000082
Figure BDA0001302104230000083
Judging the expected change state of the queuing length according to the service intensity of the r-th toll lane of the toll station:
when in use
Figure BDA0001302104230000084
The current toll station is in an unstable state, the queuing length is increased, that is, the traffic jam of the toll station is intensified, so that traffic prompt can be performed at the intersection in front of the toll station or a road section which is beneficial to changing the driving direction, and subsequent vehicles change the driving direction and enter the highway from other toll stations, thereby being beneficial to relieving the traffic pressure of the current toll station and being beneficial to the coordination of traffic;
when in use
Figure BDA0001302104230000085
The current toll station is in a stable state, the queuing length is reduced, and the queuing of a single channel of the toll station is gradually dissipated; by the method, lane factors (namely the number and the type of lanes and the like) and human factors (service efficiency, service intensity factors and the like) of the toll station are comprehensively considered, so that the queuing state and the vehicle number state of the toll station and the expected change state of vehicle queuing of the toll station can be accurately predicted, and the traffic management department can make accurate coordination measures, such as informing other unreachable vehicles to change the driving route and the like.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (5)

1. A toll station vehicle queuing prediction method is characterized in that: the method comprises the following steps:
s1, collecting toll lane information of a toll station and traffic information of a toll lane;
s2, collecting real-time traffic flow information Q of toll station at Ti moment in real timeTiAnd real-time queuing length information;
s3, according to the information of the toll lane, the traffic information of the toll lane and the real-time traffic flow information QTiPredicting the average queuing length and the average number of queued vehicles at the moment of Ti + TT according to the real-time queuing length information, and predicting the blocking degree according to the average queuing length and the average number of queued vehicles at the moment of Ti + TT;
the toll lane information comprises a toll lane type and a toll lane service rate;
wherein:
x denotes a lane type, and X ═ (X1, X2, …, xr, …, xn), xr denotes an r-th class toll lane, and r ═ 1,2, …, n;
y represents the toll lane service rate, and Y ═ Y1, Y2, …, yr, …, yn), yr represents the service rate of the r-th class of toll lanes, and r ═ 1,2, …, n;
the traffic information of the toll lane comprises the average head space of the queuing vehicles of the r-th toll lane
Figure FDA0002682638270000011
Proportion alpha of r-type toll lane traffic volume to total traffic volumerAnd average vehicle occupancy in toll station transitions
Figure FDA0002682638270000012
In step S3, the method includes the steps of:
and predicting the average queuing length and the average number of queued vehicles within the moment of Ti + TT according to the following formulas:
Figure FDA0002682638270000013
Figure FDA0002682638270000014
wherein the content of the first and second substances,
Figure FDA0002682638270000015
represents the average number of queued vehicles for the class r toll lane,
Figure FDA0002682638270000016
represents the average queuing length of the r-th toll lane,
Figure FDA0002682638270000021
and when there are no vehicles in the transition that meet the r-class lane,
Figure FDA0002682638270000022
nrrepresenting the number of r-th type lanes, S being the area occupied by the vehicle in the transition section at time Ti, LfrThe length of the queue of the f-th class charging lane at the time of Ti is shown.
2. The toll station vehicle queuing prediction method as claimed in claim 1, wherein: average headway distance of queuing vehicles in r-type toll lane
Figure FDA0002682638270000023
Obtained by the following formula:
Figure FDA0002682638270000024
wherein n isrRepresenting the number of r-th type lanes, f representing the f-th r-type lane, m representing the number of monitoring, j representing the number of times of toll collection for the f r-th type lanesJ measurement, qfjDenotes the vehicle queue length, V, at the j-th measurement for the f r-th type toll lanesfjIndicating a queue length of qfjThe number of vehicles in time.
3. The toll station vehicle queuing prediction method as claimed in claim 1, wherein: average occupied area of vehicles in transition section of toll station
Figure FDA0002682638270000025
Calculated by the following formula:
Figure FDA0002682638270000026
wherein m represents the number of measurements, Sp represents the total area occupied by the vehicles in the transition section at the p-th measurement, and Np represents the number of vehicles in the transition section at the p-th measurement.
4. The toll station vehicle queuing prediction method as claimed in claim 1, wherein: judging the blocking degree of r-type lanes of the toll station according to the following method:
if it is
Figure FDA0002682638270000031
The r-th toll lane is in a smooth state;
if it is
Figure FDA0002682638270000032
The r-th toll lane is in a blocking state, wherein N is the number of vehicles which can be accommodated in a single lane in the r-th toll lane;
if it is
Figure FDA0002682638270000033
The toll station is in a congestion state, wherein M is the sum of the total number of vehicles which can be accommodated by the primary toll lane and the total number of vehicles which can be accommodated by the transition section;
if it is
Figure FDA0002682638270000034
The toll booth is in a congested state.
5. The toll station vehicle queuing prediction method as claimed in claim 4, wherein: also comprises the following steps:
calculating service intensity rho of r-type toll laner：
Figure FDA0002682638270000035
Judging the expected change state of the queuing length according to the service intensity of the r-th toll lane of the toll station:
when rhorIf the number of the toll stations is more than or equal to 1, the current toll station is in an unstable state, and the queuing length is increased;
when rhor<1, the current toll station is in a stable state, and the queuing length is reduced.
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