CN111524345B - Induction control method for multi-objective optimization under constraint of real-time queuing length of vehicle - Google Patents
Induction control method for multi-objective optimization under constraint of real-time queuing length of vehicle Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
Abstract
The invention discloses a multi-objective optimization induction control method under the constraint of real-time queuing length of vehicles, which comprises the following steps: 1) collecting traffic data of the intersection in real time; 2) preprocessing data; 3) establishing an optimization model by using the targets of minimum total delay, minimum parking times and maximum traffic capacity; 4) determining a model constraint condition and calibrating parameters; 5) and acquiring a real-time optimal period, and distributing the maximum green time of the induction signal control model according to the real-time requirement of each phase. According to the method, under different traffic demands, the established multi-objective optimization model can better improve the running efficiency of the intersection, and especially the improvement effect is most obvious when the saturation is higher. The optimization model provided breaks through the limitation that the maximum green time of the traditional sensing signal is fixed and unchanged, can better improve the control efficiency of sensing signal control, better adapts to traffic changes at intersections in different time periods, and meets the traffic demands of random changes.
Description
Technical Field
The invention relates to a traffic signal control technology, in particular to a multi-objective optimization induction control method under the constraint of real-time queuing length of vehicles.
Background
Transportation has been an important component of human civilization all the time, and with the continuous forward development of social productivity, transportation plays more and more obvious roles in the whole national economy, and the good and bad traffic conditions concern the economic benefits of the whole society. The intersection is the key of urban traffic, most of daily traffic jam is caused by insufficient traffic capacity of the intersection, and the traffic running state of the urban road intersection is closely related to the traffic running state of the whole city. The key of the urban road traffic capacity is the traffic capacity of the intersection, and if the intersection signal control system of the urban traffic network is continuously optimized and improved, the traffic pressure of a congested area can be effectively relieved, so that the distribution of traffic flow in the whole urban area is reasonable, the bottleneck influence on the road is reduced or eliminated, and the traffic capacity and the service level of the road are improved. At the present stage, a signal control system of the domestic intersection mainly comprises timing control and induction signal control. The timing control is based on historical traffic flow data, and a cyclic timing control timing scheme is preset in the signal controller. Timing control is single and cannot adapt to traffic flow changing in real time. The induction signal control can well overcome the limitation of timing control, and compared with the timing control, the induction signal control has better adaptability to the randomness of the arrival of the vehicles, can reduce the queuing and delay of the vehicles at the intersection, and improves the traffic capacity and the running efficiency of the intersection. The inductive signal control is applied to a traffic signal control system because it can better satisfy a real-time changing traffic flow.
The existing vehicle real-time queuing length data are mostly applied to signal lamp self-adaptive control, fuzzy control and intelligent control, and the research on induction signal control based on the vehicle real-time queuing length is less. There is a research on providing an induction signal control model with a phase jump function based on the queuing length data, that is, after a certain phase is finished, the next phase is not executed, but the lane with the maximum vehicle queuing length is selected as the next release phase according to the vehicle queuing lengths of all lanes. However, in an actual intersection, the traffic flow is complex, and the jumping of the phase is easy to cause wrong judgment of pedestrians and drivers and form long-time waiting. In the existing research of multi-objective optimization signal control, although the established model can better improve the traffic condition of the intersection, the model belongs to a static fixed timing model, the real-time change situation of the traffic flow is not considered, the real-time change traffic demand cannot be met, and the model does not belong to the category of induction signal control.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-objective optimization induction control method under the constraint of real-time queuing length of vehicles aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a multi-objective optimization induction control method under the constraint of real-time queuing length of vehicles comprises the following steps:
1) real-time collection of intersection traffic data
The data collected includes: real-time traffic flow and vehicle queuing length;
2) preprocessing data;
removing abnormal values, filling missing values and denoising the acquired data by using a data analysis tool, and establishing a corresponding traffic flow database after data processing is finished;
3) the method comprises the steps that vehicle queuing lengths of all road entrances are obtained in real time and serve as input data, and a multi-objective optimization model is constructed by the aid of the goals of minimum total delay at the road intersections, minimum parking times and maximum traffic capacity;
3.1) counting the traffic flow of motor vehicles at each entrance of the intersection
All-weather on-site sampling is carried out on the traffic flow of motor vehicles at the intersection, and the traffic flow statistical data of each hour are obtained;
3.2) calculation of the traffic capacity of the individual Motor vehicle lanes
Calculating the traffic capacity of each motor lane at the intersection;
the calculation formula of the actual traffic capacity S of the motor lane is as follows:
S＝N0·γ·η·C·n′
wherein N is0The theoretical traffic capacity of the road is shown, gamma is a correction coefficient influenced by non-motor vehicles, eta represents a correction coefficient influenced by lane width, C is a correction coefficient influenced by intersections, and n' is a correction coefficient of the number of lanes;
3.3) acquisition of latency and ratio of flow to phase
Acquiring the waiting time of each phase and the flow ratio of each phase;
3.4) constructing an induction signal control model based on multi-objective optimization under the constraint of real-time vehicle queuing length;
establishing a multi-objective optimized induction signal control model based on real-time vehicle queuing length constraint by combining traffic flow statistical data, the traffic capacity of each lane and the real-time queuing length, waiting time and flow ratio of each lane in each phase;
if n phases exist at the intersection, the induction signal control model is as follows:
in the formula: d is total delay of the intersection; h is the total number of parking times; q is the traffic capacity; q. q.siThe traffic arrival rate of the phase i; c is a signal period; diAverage delay for phase i; h isiThe stopping rate is the phase i; siObtaining the saturated flow of the phase i according to the traffic capacity of each motor lane; giAn effective green time for phase i; liIs the loss time of phase i; y isiIs the flow ratio of phase i; queiThe real-time queuing length for phase i; t is tiLatency for phase i;
3.5) determining constraints of the model
After the signal optimization multi-objective model is established, constraint conditions need to be established on the established model, and constraint is mainly carried out on two aspects of a signal period and an effective green light time.
The minimum effective green time is mainly the minimum time for ensuring that the pedestrian can safely cross the street, and is determined according to the safety region of the pedestrian crossing distance and the pedestrian walking speed
The minimum signal control period means that vehicles arriving at an intersection can be just completely released in one signal period under the ideal condition, and is mainly determined according to the sum of green light loss time and the total traffic flow ratio;
the maximum effective green time and the maximum signal control period are mainly determined according to the queuing length, the traffic flow and the flow ratio of the vehicles at the signal control intersection.
Wherein: giminMinimum effective green time, s, for phase i; gimaxMaximum effective green time, s, for phase i; cminIs the minimum signal control period, s; cmaxControlling the period for the maximum signal;
4) model parameter calibration
3.1) determination of the minimum effective Green time
The minimum effective green light time is mainly the shortest time for ensuring that the pedestrian can safely cross the street and is determined according to the safety region of the pedestrian crossing distance and the pedestrian walking speed, and the calculation method comprises the following steps:
in the formula: l ispThe distance, m, from the pedestrian crossing to the safety zone; v. ofpThe walking speed of the pedestrian is m/s; i is the green light interval, s.
3.2) determining the minimum Signal control period
The shortest signal control period means that vehicles arriving at an intersection can just completely pass in one signal period under the ideal condition, and is mainly determined according to the sum of green light loss time and the total traffic flow ratio, and the calculation method is as follows:
in the formula: l is the sum of the green light loss time s; and Y is the total traffic flow ratio.
3.3) determining the maximum effective green time and the maximum signal control period
The maximum effective green time and the maximum signal control period are mainly set according to the queuing length, the traffic flow and the flow ratio of the vehicles at the signal control intersection, and the value range of the general maximum signal control period is 180-200 s. The maximum effective green light time value range is as follows: greater than the minimum effective green time and not more than 60 s.
5) Calculating the optimal control period of the signal control model
And solving the model to obtain the effective green time of each phase, further obtaining the optimal control period of the signal, obtaining the green time according to the real-time traffic flow demand, and multiplying the green time by a correction coefficient of 1.25-1.5 to obtain the maximum green time controlled by the induction signal.
The invention has the following beneficial effects: compared with the traditional induction signal control model, the established multi-objective optimization model can better improve the running efficiency of the intersection under different traffic demands, particularly has the most obvious improvement effect when the saturation is higher, breaks through the limitation of fixed maximum green time of the traditional induction signal, can better improve the control efficiency of induction signal control, better adapts to traffic changes of the intersection in different time periods, and meets the traffic demand of random change.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a sensing control method for multi-objective optimization under the constraint of real-time queuing length of a vehicle includes the following steps: the method comprises the following steps of research data collection and processing, multi-objective optimization model construction, model parameter calibration and model solution, and specifically comprises the following steps:
1 Collection and processing of research data
1.1) collecting traffic data at intersections
The main data include: real-time traffic flow and all-day vehicle queuing length; (real-time traffic flow is the detection data executed by the induction signal control, when the vehicle is detected to pass through, the induction signal control can prolong the green light time of one unit, otherwise, the current phase is adjusted to the next phase)
1.2) data processing and database building
Removing abnormal values, filling missing values and denoising the acquired data by using a data analysis tool, and establishing a corresponding traffic flow database after data processing is finished;
1.3) measuring the geometrical dimensions of the road section and the intersection
The parameters to be acquired here include: the road section length (km), the lane width (m) of the main road and the secondary road, the number of lanes, the slope gradient, the intersection oblique crossing angle, the intersection signal lamp control scheme and the like.
2, constructing a multi-objective optimization model, which mainly comprises the following steps:
2.1) investigating the traffic flow of motor vehicles at each entrance of the intersection
Carrying out all-weather field investigation on the traffic flow of the motor vehicles on the researched road section to obtain the traffic flow (pcu/h) per hour;
2.2) calculation of the traffic capacity of the individual Motor vehicle lanes
The parameters required for the acquisition here are: the theoretical traffic capacity N0(pcu/h) of the road, the correction coefficient gamma of the influence of non-motor vehicles, the correction coefficient eta of the influence of lane width, the correction coefficient C of the influence of intersections, the correction coefficient N' of the number of lanes and the actual traffic capacity S of motor vehicles have the following calculation formula:
S＝N0·γ·η·C·n′
2.3) obtaining the real-time queuing length, waiting time and flow ratio of each phase of the vehicle
Obtaining the real-time queuing length Que (pcu), the waiting time t(s) and the flow ratio y of the vehicles of each lane of each phase according to a video detector;
2.4) constructing an induction signal control model based on multi-objective optimization under the constraint of real-time vehicle queuing length
Establishing an induction signal control model based on multi-objective optimization under the constraint of real-time vehicle queuing length by combining the traffic flow of field investigation, the traffic capacity of each lane and the real-time queuing length, waiting time and flow ratio of each lane in each phase;
if there are n phases at the intersection, the total delay minimum can be expressed as:
the minimum value of the total number of stops can be expressed as:
the traffic capacity maximum can be expressed as:
in the formula: d is total delay of the intersection, s; h is the total number of parking times; q is the traffic capacity, pcu/h; q. q.siThe traffic flow arrival rate of the phase i is vehicle/h; c is the signal period, s; diIs the average delay of phase i, s/pcu; h isiThe stopping rate is the phase i; siSaturated flow for phase i, pcu/h; giEffective green time, s, for phase i; liIs the loss time of phase i, s; y isiIs the flow ratio of phase i; queiThe real-time queuing length of the phase i is vehicle; t is tiIs waiting for phase iM, s.
qi、C、di、hiFor process variables in formula conversion, the final model requires the data: gi、li、yi、Quei、ti、Si. The data used for model calculation is li、yi、Quei、ti、Si。SiAccording to the traffic capacity of each motor vehicle lane, liAccording to the actual situation of the intersection, 3s can be taken as usual. The model output data is giFinally, the effective green time g of each phase can be determinediAnd loss time liThe sum of (1) is obtained as a signal control period.
2.5) determining constraints of the model
After the signal optimization multi-objective model is established, constraint conditions need to be established on the established model, and the constraint conditions are mainly constrained in two aspects of a signal period and an effective green light time, as follows:
in the formula: giminMinimum effective green time, s, for phase i; gimaxMaximum effective green time, s, for phase i; cminThe shortest signal control period, s; cmaxIs the maximum signal control period, s.
3, calibrating model parameters, mainly comprising the following steps:
3.1) determination of the minimum effective Green time
The minimum effective green light time is mainly the shortest time for ensuring that the pedestrian can safely cross the street and is determined according to the safety region of the pedestrian crossing distance and the pedestrian walking speed, and the calculation method comprises the following steps:
in the formula: l ispThe distance of the crosswalk from the safe area,m；vpthe walking speed of the pedestrian is m/s; i is the green light interval, s.
3.2) determining the minimum Signal control period
The shortest signal control period means that vehicles arriving at an intersection can just completely pass in one signal period under the ideal condition, and is mainly determined according to the sum of green light loss time and the total traffic flow ratio, and the calculation method is as follows:
in the formula: l is the sum of the green light loss time s; and Y is the total traffic flow ratio.
3.3) determining the maximum effective green time and the maximum signal control period
The maximum effective green time and the maximum signal control period are mainly determined according to the queuing length, the traffic flow and the flow ratio of the vehicles at the signal control intersection, and the value range of the general maximum signal control period is 180-200 s. The maximum effective green light time value range is as follows: greater than the minimum effective green time and not more than 60 s.
4, solving the model, mainly comprising the following steps:
4.1) fast non-dominated ranking Algorithm for the NSGA-II Algorithm
The purpose of the rapid non-dominant sorting is to layer the population and select cross and variant offspring populations; assuming the population is P, n of each individual P in P needs to be calculatedpAnd SP。npIs the number of individuals in the population that dominate the individual p, SPIs the set of individuals within the population that are dominated by individual p. The algorithm mainly comprises the following steps:
a) looking for all n in the populationp0 and stored in the current set F1Performing the following steps;
b) at F1Is S, the set of individuals governed by it isiGo through SiFor each individual l, perform nl＝nl1, if n islIndividual i is stored in set H when 0.
c) Note F1The individuals obtained in (1) are the individuals of the first non-dominant layer, and H is taken as the current set.
And repeating the steps until the whole population is layered.
4.2) Congestion calculation for NSGA-II Algorithm
Degree of congestion (n)d) The population density is the density of surrounding individuals of a given individual in the population, and can be visually expressed as the individual, and the calculation of the crowding degree is an important link for ensuring the diversity of the population.
The specific calculation steps are as follows:
a) let n bed＝0，n＝1，2，…，N；
b) For each optimization objective function, the following operations are performed:
(b1) sorting the populations based on an optimized objective function;
(b2) order 1d＝Nd＝∞；
(b3) Calculating nd＝nd+(fm(i+1)-fm(i-1))，n＝2，3，…，N-1。
4.3) Congestion comparison operator for NSGA-II Algorithm
After the operations (4.1) and (4.2), each individual i in the population has a non-dominant sequence i determined by the non-dominant sequencerankAnd degree of congestion nd. A congestion degree comparison operator may be defined according to these two attributes: comparing the individual i with the individual j, and determining that the individual i is better than the individual j as long as any one of the following conditions is met.
a) The non-dominant layer of the individual i is better than the non-dominant layer of the individual j, i.e. irank<jrank；
b) If the same rank is present, but individual i has a greater crowding distance than individual j, i.e., irank＝jrankAnd i isd>jd；
4.4) calculating the maximum green time of the Signal control model
And (4) obtaining the optimal control period of the signal according to the steps (4.1), (4.2) and (4.3), and distributing the maximum green light time controlled by the induction signal according to the real-time traffic demands of each phase.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (2)
1. A multi-objective optimization induction control method under the constraint of real-time queuing length of a vehicle is characterized by comprising the following steps:
1) real-time collection of intersection traffic data
The data collected includes: real-time traffic flow and vehicle queuing length;
2) preprocessing data;
removing abnormal values, filling missing values and denoising the acquired data by using a data analysis tool, and establishing a corresponding traffic flow database after data processing is finished;
3) the method comprises the steps that vehicle queuing lengths of all road entrances are obtained in real time and serve as input data, and a multi-objective optimization model is constructed by the aid of the goals of minimum total delay at the road intersections, minimum parking times and maximum traffic capacity;
3.1) counting the traffic flow of motor vehicles at each entrance of the intersection
All-weather on-site sampling is carried out on the traffic flow of motor vehicles at the intersection, and the traffic flow statistical data of each hour are obtained;
3.2) calculation of the traffic capacity of the individual Motor vehicle lanes
Calculating the traffic capacity of each motor lane at the intersection;
the calculation formula of the actual traffic capacity S of the motor lane is as follows:
S＝N0·γ·η·C·n′
wherein N is0The theoretical traffic capacity of the road is shown, gamma is a correction coefficient influenced by non-motor vehicles, eta represents a correction coefficient influenced by lane width, C is a correction coefficient influenced by intersections, and n' is a correction coefficient of the number of lanes;
3.3) acquisition of latency and ratio of flow to phase
Acquiring the waiting time of each phase and the flow ratio of each phase;
3.4) constructing an induction signal control model based on multi-objective optimization under the constraint of real-time vehicle queuing length;
establishing a multi-objective optimized induction signal control model based on real-time vehicle queuing length constraint by combining traffic flow statistical data, the traffic capacity of each lane and the real-time queuing length, waiting time and flow ratio of each lane in each phase;
if n phases exist at the intersection, the induction signal control model is as follows:
in the formula: d is total delay of the intersection; h is the total number of parking times; q is the traffic capacity; q. q.siThe traffic arrival rate of the phase i; c is a signal period; diAverage delay for phase i; h isiThe stopping rate is the phase i; siObtaining the saturated flow of the phase i according to the traffic capacity of each motor lane; giAn effective green time for phase i; liIs the loss time of phase i; y isiIs the flow ratio of phase i; queiThe real-time queuing length for phase i; t is tiLatency for phase i;
4) determining constraints of a model
And establishing constraint conditions for the constructed model, which are specifically as follows:
wherein: giminA minimum effective green time for phase i; gimaxMaximum effective green time for phase i; cminControlling the period for the minimum signal; cmaxControlling the period for the maximum signal;
5) calculating the maximum green time of the signal control model
And solving the model to obtain the effective green time of each phase, further obtaining the optimal control period of the signal, and distributing the maximum green time controlled by the induction signal according to the real-time traffic demand of each phase.
2. The induction control method for multi-objective optimization under the constraint of the real-time queuing length of the vehicles according to claim 1, wherein parameters in the constraint conditions of the model in the step 4) are calibrated as follows:
4.1) determination of the minimum effective Green time
The minimum effective green light time is the shortest time for ensuring that the pedestrian can safely cross the street and is determined according to the safety region of the pedestrian crossing distance and the pedestrian walking speed, and the calculation method comprises the following steps:
in the formula: l ispDistance, v, of crosswalk from safety zonepThe walking speed of the pedestrian is shown, and I is the green light interval time;
4.2) determining the minimum Signal control period
The shortest signal control period refers to that vehicles arriving at the intersection can be just completely released in one signal period, and is determined according to the ratio of the sum of green light loss time to the total traffic flow, and the calculation method is as follows:
in the formula: l is the sum of green light loss time, and Y is the total traffic flow ratio;
4.3) determining the maximum effective green time and the maximum signal control period
The maximum effective green time and the maximum signal control period are set according to the queuing length, the traffic flow and the flow ratio of the vehicles at the signal control intersection, the value range of the maximum signal control period is 180-plus-200 s, and the value range of the maximum effective green time is as follows: greater than the minimum effective green time and not more than 60 s.
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