CN110968909A - Non-motor vehicle simulation method based on discrete element simulation platform - Google Patents
Non-motor vehicle simulation method based on discrete element simulation platform Download PDFInfo
- Publication number
- CN110968909A CN110968909A CN201811154215.3A CN201811154215A CN110968909A CN 110968909 A CN110968909 A CN 110968909A CN 201811154215 A CN201811154215 A CN 201811154215A CN 110968909 A CN110968909 A CN 110968909A
- Authority
- CN
- China
- Prior art keywords
- motor vehicle
- rider
- force
- model
- contact
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000033001 locomotion Effects 0.000 claims abstract description 29
- 239000002245 particle Substances 0.000 claims abstract description 14
- 230000006399 behavior Effects 0.000 claims description 29
- 238000013016 damping Methods 0.000 claims description 20
- 239000000126 substance Substances 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 10
- 230000009471 action Effects 0.000 claims description 8
- 230000000694 effects Effects 0.000 claims description 7
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000010998 test method Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 24
- 230000006872 improvement Effects 0.000 description 18
- 230000008569 process Effects 0.000 description 13
- 230000008859 change Effects 0.000 description 12
- 238000011835 investigation Methods 0.000 description 9
- 238000011160 research Methods 0.000 description 8
- 230000001133 acceleration Effects 0.000 description 7
- 230000018109 developmental process Effects 0.000 description 7
- 238000011161 development Methods 0.000 description 6
- 230000015572 biosynthetic process Effects 0.000 description 5
- 238000002955 isolation Methods 0.000 description 5
- 230000008447 perception Effects 0.000 description 5
- 238000003786 synthesis reaction Methods 0.000 description 5
- 230000000007 visual effect Effects 0.000 description 5
- 230000001413 cellular effect Effects 0.000 description 4
- 239000003795 chemical substances by application Substances 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 4
- 230000007547 defect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 4
- 238000000691 measurement method Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 3
- 238000001125 extrusion Methods 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000035484 reaction time Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000032823 cell division Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000008140 language development Effects 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Landscapes
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention relates to a non-motor vehicle simulation method based on a discrete element simulation platform, which comprises the following steps: step 1) obtaining arrival data of the non-motor vehicle, and inputting the arrival data into a discrete element simulation platform to construct a non-motor vehicle arrival model; step 2) acquiring road data of the non-motor vehicle lane, and establishing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform; step 3) establishing a physiological sensing space of the non-motor vehicle on the basis of the non-motor vehicle individual consisting of the non-motor vehicle and a rider, thereby constructing a non-motor vehicle model; on the basis of a social force model, introducing a contact model of a particle discrete element theory, calculating a driving force, a repulsive force, a contact force and the resultant force of the driving force, the repulsive force and the contact force, and constructing a combined model for non-motor vehicle simulation; and 4) placing the non-motor vehicle arrival model in the non-motor vehicle lane model, updating the motion state of the non-motor vehicle in real time by using the combined model, and simulating the non-motor vehicle flow.
Description
Technical Field
The invention belongs to the technical field of non-motor vehicle simulation, and particularly relates to a non-motor vehicle simulation method based on a discrete element simulation platform.
Background
Urban road traffic is the basis of urban development and has been paid attention to all the circles of society all the time, and urban traffic development planning is closely related to urban economic level, resident travel demand and other factors. However, in recent years, motorized traffic degree development reveals many urban problems, and according to a 2016 intelligent travel big data report, Beijing traffic jam people all cost as high as about 8000 yuan, and the urban traffic problems cause waste of a large amount of social capital, so that the urban development is greatly influenced. With the change of the urban development center of gravity from high speed to high quality, people gradually change the urban traffic development concept from 'thought of mobility' to 'people oriented', and advocate green travel.
The non-motor vehicle has the characteristics of flexibility, environmental protection, high efficiency and the like during the trip of the non-motor vehicle in slow traffic, and has great advantages during the short-distance trip. The non-motor vehicle travelling is an important component of slow traffic, and the travelling proportion of the non-motor vehicle is increased along with the high-speed development of the shared bicycle in the recent years. The non-motor vehicles mainly refer to non-motor vehicle groups formed by two types of bicycles and electric bicycles.
The conventional study method for the traffic capacity of the non-motor vehicle lane by the learner mainly comprises three types of actual measurement and investigation of actual traffic capacity, calculation of theoretical traffic capacity according to safety clearance or headway and the like, and establishment of simulation traffic capacity of a non-motor vehicle simulation model. In the early research on the traffic capacity of the non-motor vehicle lane, the actual traffic capacity of the non-motor vehicle lane is mainly obtained by an actual measurement investigation method, the actual measurement method has extremely high similarity with the actual situation, but the obtained result is often not representative due to the influence of factors such as weather, places, traffic conditions, road conditions and the like, and the actual measurement method needs to consume large manpower and financial resources. Therefore, in order to improve the data reliability and the research accuracy, besides the actual measurement method, part of researchers use a theoretical calculation method to research the traffic capacity of the non-motor lane, and find that the calculated value of the traffic capacity of the non-motor lane can be better obtained by combining the actual measurement method with the theoretical calculation method, but the calculated value is mostly obtained based on the flow-density relationship, is too ideal, does not consider the influence of factors such as the micro-motion characteristics and the psychological characteristics of the non-motor vehicle on the traffic capacity, and cannot verify the calculated value in an actual scene. Therefore, in order to better describe the motion characteristics of the non-motor vehicles and accurately reproduce the traffic behaviors of the non-motor vehicles, a computer simulation model establishing method becomes the leading direction of the non-motor vehicle related research.
At present, the establishment of a non-motor vehicle micro traffic simulation model mainly comprises two main methods based on rules and force; the former is mainly represented by a following model, a cellular automaton model and an Agent model; the latter is mainly represented by a social force model and a vector field model.
The study of the following model in the aspect of non-motor vehicle traffic modeling is based on the following behavior of the non-motor vehicle, the assumption that the non-motor vehicle lane is virtually divided is generally adopted, if the distance between the front non-motor vehicle and the rear non-motor vehicle is close enough, the movement of the rear non-motor vehicle is represented as the following behavior and is influenced by the movement state of the front vehicle, and the modeling is summarized through the stimulation-reaction rule between the front vehicle and the rear vehicle. However, the following model focuses on the influence of the front vehicle on the rear vehicle, and has poor description effect on behaviors such as lane changing and overtaking of non-motor vehicles. And the non-motor vehicles have larger difference with the motor vehicles, and uncertainty still exists in the definition of receiving the stimulation in the riding process. Finally, the model firstly divides the whole non-motor vehicle lane to keep similarity with the motor vehicle lane, and treats the difference between the motor vehicle and the non-motor vehicle by regarding the non-motor vehicle as a smart car, and the essence of the model does not completely accord with the motion characteristics of the non-motor vehicle.
The cellular automata model discretizes the non-motor vehicle lane according to the essence of cell division, and the situation that the lane is divided and the non-motor vehicle moves according to the divided lane exists, which is not completely consistent with the actual movement behavior of the non-motor vehicle. Meanwhile, the cellular automaton defines the non-motor vehicles as cells, the expression of individual behavior characteristics is insufficient, and the micro-motion behavior characteristics of the non-motor vehicles are difficult to characterize.
The Agent model mainly utilizes the calculation intelligence similar to human provided by the Agent to describe the dynamic structure of the traffic behavior, is mostly combined with other models such as a cellular automaton model and the like, is essentially to add an Agent intelligent calculation method to an individual in traffic simulation, and mostly emphasizes the influence research among motor vehicles, pedestrians and bicycles under the condition of mixed traffic flow.
In the vector field model, the individual unit emphasizes the influence of the front individual, and generally considers less interaction between the non-motor vehicle motion characteristic and the transverse action.
When the social force model simulates the riding process of the non-motor vehicle, based on the stress behavior of the individual riding process of the non-motor vehicle, the continuity of the model is better in time and space level compared with other models, the movement direction of the non-motor vehicle can also change in real time according to the stress without moving according to a fixed lane, and meanwhile, the individual movement process of the non-motor vehicle rider is carefully depicted by describing the mutual influence generated by the individual and other traffic participants, so that the actual road non-motor vehicle movement state is relatively met. However, the existing research has the defects that the distance of the contact acting force generated between the non-motor vehicles is judged to be too short, the calculation result of the contact acting force can be large enough to enable the individuals to be overlapped, so that the actual situation is not completely met, meanwhile, the required calculation capacity requirement of the social force model is relatively high, the calculation amount is large, and the calculation time is relatively long.
Disclosure of Invention
The invention aims to solve the defects of the existing non-motor vehicle simulation method, and provides a non-motor vehicle simulation method based on a discrete element simulation platform, wherein a social force model is used as a modeling basis, a particle discrete element contact model is introduced, the contact force of a non-motor vehicle workshop in the social force model is corrected, and a non-motor vehicle combination model is established; and calibrating the model according to the actually measured data, integrating environment Intellij IDEA software based on java language on the basis, performing simulation realization work of the non-motor vehicle combination model, and performing validity verification on the non-motor vehicle combination model.
In order to achieve the above object, the present invention provides a non-motor vehicle simulation method based on a discrete element simulation platform, which specifically comprises:
step 1) obtaining arrival data of the non-motor vehicle, and inputting the arrival data into a discrete element simulation platform to construct a non-motor vehicle arrival model;
step 2) acquiring road data of the non-motor vehicle lane, and inputting the road data into a discrete element simulation platform to construct a non-motor vehicle lane model;
step 3) establishing a physiological sensing space of the non-motor vehicle on the basis of the non-motor vehicle individual consisting of the non-motor vehicle and a rider, thereby constructing a non-motor vehicle model; introducing a contact model of a particle discrete element theory based on a social force model, calculating a driving force, a repulsive force, a contact force and the resultant force of the driving force, the repulsive force and the contact force, and constructing a combined model for non-motor vehicle simulation;
and 4) placing the non-motor vehicle arrival model in the non-motor vehicle lane model, updating the motion state of the non-motor vehicle in real time by using the combined model, and simulating the non-motor vehicle flow.
As an improvement of the above technical solution, the acquiring of the arrival data of the non-motor vehicle in step 1) specifically includes:
and continuously counting the arrival data of the non-motor vehicles for 1 hour by taking 10 seconds as a time interval, and fitting the arrival distribution condition of the non-motor vehicles by adopting discrete distribution in a mathematical statistical function. The method specifically comprises the following steps:
wherein the content of the first and second substances,
is the mean value of the samples, S2Taking the sample variance as the sample variance, and taking p and k as two characteristic parameters of a negative binomial distribution function;
when the sample variance S2And the mean value of the samples
When the ratio of (A) is obviously more than 1, the requirement of negative binomial distribution is met, and the arrival condition of the non-motor vehicle can be better described by using the negative binomial distribution;
after obtaining the negative two-term distribution, according to chi2The test method comprises performing goodness-of-fit test on negative binomial distribution with confidence level α of 0.052Calculated value is less than or equal to
Namely, the non-motor vehicle arrival data is considered to conform to negative binomial distribution, and the probability distribution is shown as a formula (3);
wherein, χ2The testing method is a fitting degree testing method;
at 0.05 significant level χ2Checking a critical value; p (X ═ X) is the probability of reaching X non-motor vehicles in 10 seconds; x is the number of non-motor vehicles arriving;
acquiring arrival data of the non-motor vehicles according to the negative binomial distribution, wherein the arrival data is a non-motor vehicle arrival rule; the non-motor vehicle comprises: bicycles and electric bicycles.
As an improvement of the above technical solution, the step 2) specifically includes:
initializing a non-motor vehicle running environment, determining traffic flow conditions of the left side and the right side of a road, acquiring road data of the non-motor vehicle lane according to the boundary strength, the width of the non-motor vehicle lane and the length of the non-motor vehicle lane, and constructing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform.
As one improvement of the above technical solution, in the step 3), firstly, a non-motor vehicle model is constructed; calculating a driving force and a repulsive force based on the social force model; by introducing a contact model of a particle discrete element theory, virtual contact behavior between non-motor vehicle individuals is described as mutual extrusion of a physiological sensing space, so that the defect that the non-motor vehicles cannot contact and cannot analyze contact force between the non-motor vehicles is overcome; and further researching the driving force, the repulsive force, the contact force and the resultant force of the non-motor vehicle units to construct a non-motor vehicle simulation combined model.
The step 3) specifically comprises the following steps:
step 3-1), the non-motor vehicle individual is an elliptical model formed by the non-motor vehicle and the rider, wherein the center of a handlebar of the non-motor vehicle is located at the focus of the elliptical model, and the nose tip of the rider is located at the center of the elliptical model; establishing a physiological sensing space of the non-motor vehicle surrounding the periphery of the non-motor vehicle by taking the length of a long shaft of the non-motor vehicle as the diameter, thereby constructing a non-motor vehicle model; wherein the non-motor vehicle model is a non-motor vehicle unit comprising a plurality of non-motor vehicle individuals and corresponding non-motor vehicle physiological sensing spaces;
step 3-2) introducing a contact model of a particle discrete element theory based on the social force model, and calculating the resultant force borne by the non-motor vehicle, namely the vector sum of the driving force, the repulsive force and the contact force, so as to establish a combined model for simulating the non-motor vehicle and calibrate the parameters of the combined model; wherein the parameters of the combined model include: a driving force parameter, a repulsive force parameter, a contact force parameter, and a boundary action parameter.
As one improvement of the above technical solution, before calculating the contact force in step 3-2), determining whether the contact force is generated specifically includes:
before calculating the contact force, whether the contact force is generated or not needs to be judged in order to reduce the calculation amount, and a grid cell method is selected to judge the contact behavior between the non-motor individual bodies. Firstly, abstracting a non-motor vehicle lane section into a rectangular grid to divide the rectangular grid into a plurality of grids, and selecting 1-2 times of the maximum diameter of a non-motor vehicle physiological sensing space as the side length of the grids to divide the non-motor vehicle lane. Under the division mode, the non-motor vehicle unit occupies at least one grid in the non-motor vehicle lane and at most four grids simultaneously;
judging whether other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs,
if no other non-motor vehicle units exist in the grid to which the non-motor vehicle units belong, determining that no contact force is generated between the non-motor vehicle units;
if other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs, whether the physiological sensing space in the grid to which the non-motor vehicle unit belongs is contacted with other physiological sensing spaces is judged;
if the physiological sensing space in the grid does not contact with other physiological sensing spaces; determining that no contact force is generated between the individual non-motor vehicles;
if the physiological sensing space in the grid is contacted with other physiological sensing spaces; it is determined that a contact force is generated between the individual non-motor vehicles, and the contact force applied to the non-motor vehicle unit is calculated.
Wherein, calculating the contact force that the non-motor vehicle unit receives specifically includes:
if the non-motor vehicle units have contact behaviors, the contact force applied to the non-motor vehicle unit where the rider i is located is divided into a normal component force and a tangential component force; the normal component of the contact force represents the tendency that non-motor vehicles contacting with the physiological sensing space of the non-motor vehicles tend to bounce away from each other when the non-motor vehicles want to recover the original state; the tangential component of the contact force represents the friction effect generated in the longitudinal direction of the non-motor vehicle when the non-motor vehicle is contacted with the physiological sensing space;
suppose that the normal elastic coefficient and the normal damping coefficient of the rider i are respectively kn、cnAccording to equation (4), then, the normal component of the contact force is at α with a normal overlap of α
Comprises the following steps:
wherein the content of the first and second substances,
is the speed of rider i relative to rider j;
is a unit vector from the center of rider i to the center of rider j;
assuming that the tangential elastic coefficient and the tangential damping coefficient of the rider i are respectively kt、ctTangential component of contact force, according to equation (5)
Comprises the following steps:
wherein the content of the first and second substances,
is the tangential displacement at contact point a;
is the slip velocity at contact point a;
normal component of contact force
And tangential component force
Vector composition is carried out to obtain contact force
As one improvement of the above technical solution, in the step 3-2), calculating the driving force applied to the non-motor vehicle unit specifically includes:
the rider i is always driven by the driving force, and at any time t, the driving force borne by the non-motor vehicle unit is calculated according to a formula (7);
wherein the content of the first and second substances,
is the driving force experienced by the non-motor vehicle unit; m isiThe mass of the individual non-motor vehicle in which the rider i is located;
a desired direction vector for rider i, pointing to the target point;
is the desired speed of rider i;
is the vector velocity of i at time t; tau isiIs the duration.
As an improvement of the above technical solution, in the step 3-2), calculating a repulsive force borne by the non-motor vehicle unit specifically includes:
to simplify the calculation, it is determined whether the non-motor vehicle unit is subjected to a repulsive force of another non-motor vehicle unit before the repulsive force to the non-motor vehicle unit is calculated.
Determining that the non-motor vehicle unit is subjected to a repulsive force of the other non-motor vehicle units if the safety distance between the non-motor vehicle unit and the other non-motor vehicle units is less than or equal to 5 m;
and if the safety distance between the non-motor vehicle individual and the other non-motor vehicle individuals is more than 5m, determining that the non-motor vehicle is not subjected to the repulsive force of the other non-motor vehicles.
Wherein, the obtaining of the repulsive force suffered by the non-motor vehicle unit specifically comprises:
the individual non-motor vehicle on which the rider i is located is subjected to a repulsive force as calculated by the following formula (9);
wherein A isiThe strength of the repulsive force among the repulsive forces; b isiA safe distance for creating a repulsion relationship between rider i and rider j;
is a direction vector directed by rider j to rider i; dijThe individual boundary distance from the rider i to the rider j.
As an improvement of the above technical solution, in the step 3-2), calculating the total force applied to the non-motor vehicle specifically includes:
and (3) carrying out vector synthesis on the formula (4) and the formula (5) to obtain the contact force borne by the non-motor vehicle unit, and then carrying out vector synthesis on the contact force, the formula (7) and the formula (9) to obtain the resultant force borne by the non-motor vehicle unit.
The invention has the advantages that: the method comprises the steps of constructing a non-motor vehicle model based on a physiological sensing space of a non-motor vehicle rider, establishing a non-motor vehicle simulation combination model based on a contact force model introduced into a particle discrete element theory by a social force model, and considering the driving force, the repulsive force and the contact force of the physiological sensing space when the non-motor vehicle unit rides, so as to research the resultant force of the non-motor vehicle unit riding. And optimizing the calculation workload of the simulation model by introducing a contact judgment rule and a repulsive force judgment rule. The combined model has the advantages that the non-motor vehicle model is established based on the physiological sensing space of the non-motor vehicle, so that the stress analysis of the driving force, the repulsive force and the contact force of the non-motor vehicle is accurate and reasonable, the simulation calculation efficiency is high, and the motion behavior description of the non-motor vehicle is more real.
Drawings
FIG. 1 is a flow chart of a non-motor vehicle simulation method based on a discrete element simulation platform according to the present invention;
FIG. 2 is a schematic illustration of the physiological sensing of spatial contact behavior of a non-motorized vehicle of the present invention;
FIG. 3 is a schematic illustration of the normal component of the contact force experienced by the rider of the present invention;
FIG. 4 is a schematic view of the tangential component of the contact force experienced by the rider of the present invention;
FIG. 5 is a schematic illustration of the driving force experienced by the non-motorized vehicle of the present invention;
FIG. 6 is a schematic illustration of the repulsive forces experienced by the non-motorized vehicle of the present invention;
FIG. 7 is a schematic representation of the non-motor vehicle contact determination rule of the present invention;
FIG. 8 is a schematic illustration of a non-motor vehicle simulated rider trajectory in a simulated scenario analysis of the present invention;
FIG. 9 is a non-motor vehicle density-speed relationship graph obtained using a non-motor vehicle model in accordance with the present invention and actual investigation.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a non-motor vehicle simulation method based on a discrete element simulation platform, which specifically includes:
step 1) obtaining arrival data of the non-motor vehicle, and inputting the arrival data into a discrete element simulation platform to construct a non-motor vehicle arrival model;
step 2) acquiring road data of the non-motor vehicle lane, and establishing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform;
step 3) establishing a physiological sensing space of the non-motor vehicle on the basis of the non-motor vehicle individual consisting of the non-motor vehicle and a rider, thereby constructing a non-motor vehicle model; introducing a contact model of a particle discrete element theory based on a social force model, calculating a driving force, a repulsive force, a contact force and the resultant force of the driving force, the repulsive force and the contact force, and constructing a combined model for non-motor vehicle simulation;
and 4) placing the non-motor vehicle arrival model in the non-motor vehicle lane model, updating the motion state of the non-motor vehicle in real time by using the combined model, and simulating the non-motor vehicle flow.
As one improvement of the above technical solution, the step 1) specifically includes:
and continuously counting the arrival data of the non-motor vehicles for 1 hour by taking 10 seconds as a time interval, and fitting the arrival distribution condition of the non-motor vehicles by adopting discrete distribution in a mathematical statistical function. The method specifically comprises the following steps:
wherein the content of the first and second substances,
is the mean value of the samples, S2Taking the sample variance as the sample variance, and taking p and k as two characteristic parameters of a negative binomial distribution function;
when the sample variance S2And the mean value of the samples
When the ratio of (A) is obviously more than 1, the requirement of negative binomial distribution is met, and the arrival condition of the non-motor vehicle can be better described by using the negative binomial distribution;
after obtaining the negative two-term distribution, according to chi2The test method comprises performing goodness-of-fit test on negative binomial distribution with confidence level α of 0.052Calculated value is less than or equal to
Namely, the non-motor vehicle arrival data is considered to conform to negative binomial distribution, and the probability distribution is shown as a formula (3);
wherein, χ2The testing method is a fitting degree testing method;
at 0.05 significant level χ2Checking a critical value; p (X ═ X) is the probability of reaching X non-motor vehicles in 10 seconds; x is the number of non-motor vehicles arriving;
acquiring arrival data of the non-motor vehicle according to the negative binomial distribution; wherein the arrival data is the arrival rule of the non-motor vehicle; the non-motor vehicle comprises: bicycles and electric bicycles;
as an improvement of the above technical solution, the step 2) specifically includes:
initializing a non-motor vehicle running environment, determining traffic flow conditions of the left side and the right side of a road, acquiring road data of the non-motor vehicle lane according to the boundary strength, the width of the non-motor vehicle lane and the length of the non-motor vehicle lane, and constructing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform.
As one improvement of the above technical solution, in the step 3), firstly, a non-motor vehicle model is constructed; calculating a driving force and a repulsive force based on the social force model; by introducing a contact model of a particle discrete element theory, virtual contact behavior between non-motor vehicle individuals is described as mutual extrusion of a physiological sensing space, so that the defect that the non-motor vehicles cannot contact and cannot analyze contact force between the non-motor vehicles is overcome; and further researching the driving force, the repulsive force, the contact force and the resultant force of the non-motor vehicle units to construct a non-motor vehicle simulation combined model.
The step 3) specifically comprises the following steps:
step 3-1), the non-motor vehicle individual is an elliptical model formed by the non-motor vehicle and the rider, wherein the center of a handlebar of the non-motor vehicle is located at the focus of the elliptical model, and the nose tip of the rider is located at the center of the elliptical model; establishing a physiological sensing space of the non-motor vehicle surrounding the periphery of the non-motor vehicle by taking the length of a long shaft of the non-motor vehicle as the diameter, thereby constructing a non-motor vehicle model; wherein the non-motor vehicle model is a non-motor vehicle unit comprising a plurality of non-motor vehicle individuals and corresponding non-motor vehicle physiological sensing spaces;
step 3-2) introducing a contact model of a particle discrete element theory based on the social force model, and calculating the resultant force borne by the non-motor vehicle, namely the vector sum of the driving force, the repulsive force and the contact force, so as to establish a combined model for simulating the non-motor vehicle and calibrate the parameters of the combined model; wherein the parameters of the combined model include: a driving force parameter, a repulsive force parameter, a contact force parameter, and a boundary action parameter.
As one of the improvements of the above-mentioned technical solution,
before calculating the contact force in the step 3-2), judging whether the contact force is generated, specifically comprising:
before calculating the contact force, whether the contact force is generated or not needs to be judged in order to reduce the calculation amount, and a grid cell method is selected to judge the contact behavior between the non-motor vehicle cells. Firstly, abstracting a non-motor vehicle lane section into a rectangular grid to divide the rectangular grid into a plurality of grids, and selecting 1-2 times of the maximum diameter of a non-motor vehicle physiological sensing space as the side length of the grids to divide the non-motor vehicle lane. Under the division mode, the non-motor vehicle unit occupies at least one grid in the non-motor vehicle lane, and occupies at most four grids simultaneously;
judging whether other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs,
if no other non-motor vehicle units exist in the grid to which the non-motor vehicle units belong, determining that no contact force is generated between the non-motor vehicle units;
if other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs, whether the physiological sensing space in the grid to which the non-motor vehicle unit belongs is contacted with other physiological sensing spaces is judged;
if the physiological sensing space in the grid does not contact with other physiological sensing spaces; determining that no contact force is generated between the individual non-motor vehicles;
if the physiological sensing space in the grid is contacted with other physiological sensing spaces; it is determined that a contact force is generated between the individual non-motor vehicles, and the contact force applied to the non-motor vehicle unit is calculated.
If the non-motor vehicle units have contact behaviors, the contact force applied to the non-motor vehicle unit where the rider i is located is divided into a normal component force and a tangential component force; a soft ball contact model rule in a particle discrete element theory is introduced, and a contact process of a physiological sensing space of a non-motor vehicle is decomposed into normal motion and tangential motion. Wherein the normal movement is a requirement for the non-motor vehicle units in contact to be away from each other, and a normal component of the contact force is generated; the tangential motion is the requirement that the non-motor vehicle unit wants to decelerate and avoid, and generates the tangential component force of the contact force; the larger the contact area between the physiological sensing space of the non-motor vehicle and other physiological sensing spaces of the non-motor vehicle is, namely the larger the overlapping area of the physiological sensing space of the non-motor vehicle and other physiological sensing spaces of the non-motor vehicle is, the larger the contact force is; similar to the spring compression behavior. The contact behavior is thus simplified to a damped motion of the spring vibrator.
As shown in FIG. 2, when the physiological sensing space of the non-motor vehicle where the rider i is located contacts the physiological sensing space of the non-motor vehicle where the rider j is located at the point A, contact force is gradually generated between the non-motor vehicle units along with the relative movement of the two non-motor vehicle individuals under the influence of the initial movement state, wherein α represents the normal overlapping amount of the rider i and the rider j, and delta represents the tangential overlapping amount of the rider i and the rider j, and the contact force of the non-motor vehicle unit where the rider i is located can be calculated by setting a spring, a damper and a slider.
The analysis of the normal component force of the contact force is as follows:
the normal component force of the contact force represents the tendency that the non-motor vehicles contacted in the non-motor vehicle physiological sensing space want to recover the original state and bounce away each other, as shown in fig. 3, a rider i is taken as an analysis unit, the normal component force of the contact force is analyzed from a two-dimensional plane stress angle, and the normal component force of the rider i, which is received by other riders j, is the resultant force of a normal elastic force and a normal damping force acted on the rider i by a spring and a damper together. To describe the calculation formula, assume that the normal elastic coefficient and the normal damping coefficient of the rider i are kn、cnThe speed of rider i relative to rider j is
Then rider i receives a normal component of contact force from the other rider j with a normal overlap of α
Comprises the following steps:
wherein the speed of rider i relative to rider j is
Is a unit vector from the center of rider i to the center of rider j;
the tangential component analysis of the contact force is as follows:
the tangential component of the contact force represents the friction effect generated in the longitudinal direction of the non-motor vehicle contacted with the physiological sensing space of the non-motor vehicle, thereby influencing the riding speed of the non-motor vehicle. As shown in FIG. 4, with the rider i as an analysis unit, the tangential component of the contact force is the friction force, the tangential elastic force and the tangential damping force acted on the rider i by the spring, the slider and the damper together, which are analyzed from the force angle of a two-dimensional planeThe resultant of the forces. For describing the calculation formula, the tangential elasticity coefficient and the tangential damping coefficient of the rider i are respectively assumed to be kt、ctTangential displacement at contact point A of
The slip speed of contact A is
Assuming that the tangential elastic coefficient and the tangential damping coefficient of the rider i are respectively kt、ctAccording to equation (5), then rider i receives a tangential component of the contact force from rider j
Comprises the following steps:
wherein the content of the first and second substances,
is the tangential displacement at contact point a;
is the slip velocity at contact point a;
meanwhile, according to the coulomb-Mohr rule, when the non-motor individual is subjected to the tangential elastic force which is larger than the normal elastic force and the friction coefficient musIn this case, the tangential component of the force experienced by the individual non-motor vehicle can be modified as follows:
as one improvement of the above technical solution, in the step 3-2), calculating the driving force applied to the non-motor vehicle unit specifically includes:
the rider i is always driven by the driving force, and at any time t, the driving force borne by the non-motor vehicle unit is calculated according to a formula (7);
wherein the content of the first and second substances,
is the driving force experienced by the non-motor vehicle unit; m isiThe mass of the individual non-motor vehicle in which the rider i is located;
a desired direction vector for rider i, pointing to the target point;
is the desired speed of rider i;
is the vector velocity of i at time t; tau isiIs the duration.
When the non-motor vehicle is ridden, the rider i descends along the path at each moment
And riding until reaching the target point. Assuming any time t and t +1 in the riding process, the current path direction vector of the rider i is
Defining the expected direction vector of the rider i at the moment as shown in the formula (8);
wherein the content of the first and second substances,
is the direction vector expected to ride;
is the direction vector of the rider at the time t;
is the direction vector of the rider at the moment t + 1;
when the running speed of the rider i is lower than the expected speed, the driving force is expressed by pushing the rider to accelerate towards the target point, the process is carried out until the riding speed of the rider reaches the expected speed, the driving force is expressed as 0 in value, the effect of accelerating the rider is not provided, but the vector direction still points to the destination, and the riding direction of the rider is corrected in real time; when other obstacles are met, the rider is influenced by other external forces, the running speed value is reduced, and the driving force continues to provide power for accelerating the rider to the expected speed.
As an improvement of the above technical solution, in the step 3-2), the calculating of the repulsive force of the non-motor vehicle unit specifically includes:
the repulsive force determination is required to simplify the amount of calculation.
When a rider rides a vehicle, if the rider sees that other riders or obstacles are closer to the rider, and the rider thinks that unsafe conditions such as collision occur, the rider can change the current motion state under the action of repulsive force, so that whether the rider receives the repulsive force is closely related to the visual field range (namely the safety distance) of the rider. If the safety distance between the non-motor vehicle individual and the other non-motor vehicle individual is less than or equal to 5m, determining that the non-motor vehicle is subjected to the repulsive force of the other non-motor vehicle; and if the safety distance between the non-motor vehicle unit and other non-motor vehicle units is more than 5m, determining that the non-motor vehicle is not subjected to the repulsive force of other non-motor vehicles.
Calculating the repulsive force borne by the non-motor vehicle unit, specifically comprising:
as shown in fig. 6, the repulsive force applied to the non-motor vehicle unit is directed to the rider i by the rider j, and is two-dimensionally resolved, and the repulsive force applied to the non-motor vehicle unit is resolved into a collision avoidance force and a braking force; wherein, the direction of the avoiding force is always vertical to the direction of the driving force; the braking force direction is always opposite to the driving force direction. In addition, the repulsion behavior among the non-motor vehicle units is the synthesis of the transverse avoidance behavior and the longitudinal deceleration behavior of the rider i, when the transverse avoidance space is enough to enable the rider i to complete the overrunning behavior, the rider i with higher expected speed can complete the overrunning behavior under the driving force, and the non-motor vehicle individual where the rider i is located is subjected to repulsion force calculation as shown in the following formula (9);
wherein A isiThe intensity of the repulsive force in the repulsive force is in a linear relation with the acceleration; b isiSafe distance for creating a repulsion relationship between rider i and rider j, BiIs a constant value, and is related to the vision of the rider, etc.;
is a direction vector directed by rider j to rider i; dijThe individual boundary distance from the rider i to the rider j; wherein the boundary distance is obtained according to an ellipse calculation formula (10);
wherein d isijThe distance of a central point connecting line between the rider i and the rider j is defined;
the included angle between the rider j and the horizontal direction is shown; thetajiIs the included angle of the central lines between the rider i and the rider j; a isjIs the minor axis length of rider j; bjThe length of the long axis of the rider j;
the method for calculating the repulsive force when the repulsive force occurs between the riders is described above, because the present embodiment simulates the movement boundary of the non-motor vehicle by the elliptical model, D is calculated in the repulsive forceijGiving a specific calculation mode, if a fixed obstacle is met, directly obtaining the value, or subtracting the distance from the center of the obstacle from the distance from the rider to the center of the obstacle, and calculating the repulsive force in a mode of mutually calculatingAlso, the method of calculating the repulsive force with the fixed obstacle will not be described in detail.
As an improvement of the above technical solution, in the step 3-2), calculating the total force applied to the non-motor vehicle specifically includes:
and carrying out vector synthesis on the formula (4) and the formula (5) to obtain the contact force borne by the non-motor vehicle unit, and then carrying out vector synthesis on the contact force, the formula (7) and the formula (9) to obtain the resultant force borne by the non-motor vehicle unit.
As one improvement of the above technical solution, in the step 3-2), calibrating the driving force parameter specifically includes:
the driving force parameters include: desired speed and duration;
(1) the calibrating the desired speed specifically includes:
when the non-motor vehicle is in an unconstrained free-flow state, the non-motor vehicle can keep a relatively stable speed to ride towards a target point, and the speed is the riding speed expected by a rider. Theoretically, the desired speed is variable and is related to the physical condition of the rider, the sex, age, road grade, etc., and in this embodiment, the non-motor vehicle includes: bicycles and electric vehicles; in order to simplify modeling calculation, gradient change is not considered in the aspect of non-motor vehicle road environment, and the physical strength of a rider is assumed to be unchanged; on the basis, the expected speeds of various types of riders are calibrated based on the non-motor vehicle rider speed survey data, and the expected speeds of different types of riders in various road sections are summarized as shown in the following table 1.
TABLE 1 summary of the expected speeds of different rider classes in each road section
As can be seen from the above table, the larger the width of the non-motor vehicle lane is, the smaller the age is, the faster the speed of the non-motor vehicle is, the linear regression is performed on three-point coordinates with the width of the non-motor vehicle lane as the abscissa and the desired speed as the ordinate, so that the relationship between the desired speed of various types of riders and the width of the road can be obtained, the statistics of the linear regression parameters of the desired speed of various types of riders are shown in the following table 2, and it can be found that the variation of the desired speed of the bicycle along with the width of the road is not as obvious as that of the electric vehicle, and the linear regression equation in the following table is used as the.
TABLE 2 statistical table of linear regression parameters of each category of rider's expected speed
(2) The calibration of the duration specifically comprises
The duration represents the time required for the rider to change the current state of the rider aiming at the emergency event, make a perception of the current event and generate an acceleration operation, wherein the duration of the movement behavior of the rider in the process of accelerating from the current speed to the expected speed is the time required for reaching the expected speed from the current speed; assuming that the time required for the non-motor vehicle to accelerate from 0 to the desired speed is Δ t, the duration τiThe mathematical expression of (1) is given by the formula (11),
wherein, tPerceptionFor the reaction time from the rider sensing the emergency event to the corresponding action being taken, at is the time required for the non-motor vehicle to accelerate from 0 to the desired speed,
is the current speed of the rider and,
is the desired speed of the rider.
In the prior art, the response time of a motor vehicle driver is defined to be at least 0.4s perception-response time, the average response time of a non-motor vehicle rider is defined to be 0.54s, the response time of each gas type rider is calibrated to be 0.41-0.82s, the acceleration and deceleration duration is calibrated to be 0.68-1.94s, and the adaptive time of the non-motor vehicle is 0.5 s. Assuming that a rider keeps spiritual concentration when riding, the reaction time of the non-motor vehicle is a fixed value and takes 0.5s, in order to simplify the calculated amount, the acceleration time of the non-motor vehicle only considers the type of the non-motor vehicle, and the type of the rider is not considered, the time for accelerating the bicycle in the non-motor vehicle to the expected speed is calibrated to be 1.8s, and the duration of the bicycle is 2.3 s; the electric vehicle accelerates to the expected speed for 1.4s, and the duration time of the electric vehicle is 1.9 s.
As one improvement of the above technical scheme, in the step 3-2), the repulsive force parameter A is adjustedi、BiThe calibration specifically comprises the following steps:
when only the driving force and the repulsive force act between the riders, the corresponding accelerations are as follows:
ai(t)＝dvi/dt＝1/mi(fi+fij) (12)
the driving force and repulsive force are introduced into the mathematical calculation formula as follows:
through the derivation of a shift term and a logarithm:
wherein the content of the first and second substances,
then the optimal estimate for fitting the non-motor vehicle survey can be calculated based on the least squares method as follows:
when other riders or obstacles appear in the visual field range when the rider rides the bicycle, the repulsive force is gradually increased as the distance between the rider and the other riders or obstacles is gradually reduced. The repulsive force parameters mainly comprise repulsive force distance and repulsive force intensity; wherein the repulsion distance parameter should be related to the rider's own visual field range, riding speed, etc. In the prior art, a reaction field ellipse model is established according to the visual field range of a rider so as to determine a repulsive force distance parameter. In this embodiment, the optimal estimated value of the safe distance parameter for calibrating the repulsive force is about 5 meters according to survey data of the radar monitor and the visual field characteristics of the rider in the existing research.
The repulsive force strength determines the degree of repulsion of a non-motor vehicle rider to other riders or obstacles, the range of the optimal estimated value of the repulsive force strength obtained by actual investigation is concentrated in the interval of [520,550], and in order to simplify the calculated amount, the situation that the riding speed of the electric vehicle in the non-motor vehicle is higher and the strength of repulsion to the obstacles is higher is considered. Therefore, in the present embodiment, the value of the repulsive force intensity is calibrated to the maximum value 550.
As an improvement of the above technical solution, in the step 3-2), the calibrating the parameters of the contact model specifically includes:
the non-motor vehicle contact model parameters comprise an elastic coefficient, a damping coefficient and a friction coefficient; wherein the elastic coefficient is a parameter for representing the resistance of the physiological sensing space of the non-motor vehicle to extrusion deformation; the damping coefficient is used for absorbing energy dissipated by the non-motor individual and preventing the contact individual from separating too quickly to cause distortion of the description of the motion process; the friction coefficient is a parameter for describing a phenomenon that the non-motor vehicle physiologically senses the contact and shows friction tangentially.
(1) Calibration of elastic coefficient
According to the most common Hertz-Mindlin nonlinear contact model of particle discrete element theory, the normal elastic coefficient calculation is shown in the following formula (17), meanwhile, the fact that the non-motor vehicle physiological sensing space has the same particle material characteristics and the radius of the non-motor vehicle physiological sensing space is known is considered, the radius calculation can be replaced by a constant C, and therefore the following formula (18) is simplified.
Wherein E isiThe physiological perception space elastic modulus of the rider i; ejThe spatial elastic modulus is physiologically perceived by the rider j; upsilon isiThe physiological perception space Poisson's ratio is I of the rider; upsilon isjA physiological perceived spatial poisson's ratio for rider j; e is the physiological sensing space elastic modulus of the rider; upsilon is the poisson ratio of the physiological sensing space of the rider; k is a radical ofnIs a normal elastic coefficient;
similarly, according to the basic theory of contact model, the tangential elastic coefficient can be calculated as the following formula 19, and similarly simplified to the following formula 20.
Wherein G isiThe physiological perception space shear modulus of the non-motor vehicle i; gjJ physiological sensing space shear modulus of the non-motor vehicle, α contact normal overlap, G physiological sensing space shear modulus of the non-motor vehicle, and ktIs the tangential elastic coefficient; v is the physiological sensing space Poisson's ratio of the non-motor vehicle;
the physiological sensing space of the non-motor vehicle has homogeneity, thereby meeting the requirement
Relationship according to k in the Hertz-Mindlin nonlinear contact modelt/knThe normal and tangential elastic modulus relationship is established as in equation (21) where 1-upsilon is unlikely to be 1 because the poisson ratio is not likely to be 12Since it is not 0, the formula (21) is simplified to the following formula (22).
Thereby obtaining the relation lambda of the Poisson's ratio and the intensity conversion coefficient as
The poisson ratio is defined as the ratio of absolute values of transverse positive strain and axial positive strain when a material is unidirectionally pulled or pressed, the change ratio of transverse and longitudinal positions in a very small time change can be considered in non-motor vehicle investigation under the condition that the non-motor vehicle is in a physiological space contact state (the longitudinal change is an actual distance change and a theoretical distance difference calculated by an original speed), and the poisson ratio in the non-motor vehicle physiological space is determined to be 0.37 by the prior art. The compressible degree of the individual physiological sensing space of the non-motor vehicle is close to the idea of the physiological buffer range of the pedestrian, so that the normal elastic coefficient of the non-motor vehicle is determined to be 1900N/m according to the ergonomic index, and under the condition of the limit compression amount, the normal elastic coefficient is calibrated to be 1900N/m according to the formula (23):
the resulting tangential modulus of elasticity was calibrated to 1320N/m.
(2) Calibration of damping coefficient
In the Hertz-Mindlin nonlinear contact model, a proportional relation between a damping coefficient and an elastic coefficient is defined as the following formula (24), wherein c and k correspond to the damping coefficient and the elastic coefficient.
Decomposing the damping coefficient into a normal damping coefficient and a tangential damping coefficient; the normal damping coefficient and the tangential damping coefficient of each rider are calibrated and summarized as shown in the following table 3;
TABLE 3 damping coefficient calibration summary table for each rider
(3) Calibration of friction coefficient
According to the coulomb-moire rule, when the non-motor vehicle is subjected to a tangential elastic force greater than a normal elastic force and a friction coefficient musThe tangential force experienced by the individual non-motorized vehicle can be modified as follows:
wherein the content of the first and second substances,
is a unit vector of the tangential force applied to the individual non-motor vehicle where the rider i is located,
satisfies the requirements that when the non-motor individual is subjected to a tangential elastic force which is larger than a normal elastic force and a friction coefficient musThe tangential elastic force experienced by the individual non-motor vehicle can be determined by the coefficient of friction musThe correction was made to determine that the coefficient of friction value was nominally 0.3, since the physiologically perceived spatial coefficient of friction non-physical friction behavior was similar in nature to the pedestrian's psychological buffer.
As an improvement of the above technical solution, the calibrating the boundary action parameter in step 3-2) specifically includes:
through field investigation of three non-motor vehicle lanes with different separation modes on two sides of the non-motor vehicle lane, the fact that safe distances kept by non-motor vehicle riders and boundaries in different separation modes are greatly different is found, in order to show the characteristics in a simulation model, action parameters of the boundaries on the left side and the right side are considered in a distinguishing mode, relevant parameter calibration is carried out according to transverse distribution characteristics of the non-motor vehicles and transverse speed change characteristics of the non-motor vehicles in investigation, and a machine and non-motor vehicle separation zone is divided into two types of greening isolation and guardrail isolation; the roadside forms are divided into roadside parking and curb two types, and specific calibration parameter values are shown in a summary table 4.
TABLE 4 summary of boundary action parameters
The invention can adopt java language development environment Intellij IDEA software to compile a non-motor vehicle combination model based on a discrete element simulation platform, embed, construct a non-motor vehicle lane model and a non-motor vehicle model of a non-motor vehicle lane by combining actual test data, simulate the movement of the non-motor vehicle by utilizing the non-motor vehicle combination model and based on the discrete element simulation platform, and the specific simulation process comprises the following steps:
step one, initializing a non-motor vehicle running environment (playground). Determining the boundary conditions of two sides of a road, inputting boundary strength parameters, and simultaneously inputting the width and length parameters of a non-motor lane according to simulation requirements to create a non-motor lane model of the non-motor lane. In this embodiment, as shown in fig. 8, the riding direction of the individual non-motor vehicle is from left to right, there is no conflict in opposite directions, and when the non-motor vehicle reaches the right side boundary, the entire movement rule is consistent with the survey data environment.
And step two, generating the motion requirement of the non-motor vehicle. Determining the generation quantity of the non-motor vehicles within every 10 seconds according to the input arrival distribution characteristic parameters, judging whether the generation quantity of the non-motor vehicles within the current time reaches the required generation quantity of the non-motor vehicles, if not, judging that a certain type of non-motor vehicles need to be generated according to the type proportion of the non-motor vehicles, wherein the initial speed of the generated non-motor vehicles is 0, and the expected destination is a right-side finish line.
And step three, simulating the motion behavior of the non-motor vehicle according to the stress requirement. Whether a non-motorized vehicle exists in the scanning area or not is judged, whether the non-motorized vehicle reaches the end point or not is judged, and if the non-motorized vehicle reaches the end point, the non-motorized vehicle disappears; if the non-motor vehicle unit does not reach the end point, simulating the running process of the non-motor vehicle according to the requirements of driving force, repulsive force and contact force in the combined model, and calculating the resultant force borne by the non-motor vehicle unit.
And step four, generating a simulation result data table. Recording simulation time, the number, the speed, the acceleration and the coordinate information of the non-motor vehicle, storing the simulation time, the number, the speed, the acceleration and the coordinate information in historical data, generating an excel table under a specified catalogue, and recording the characteristic parameters.
In order to ensure the accuracy of the result sample and reduce errors, the simulation needs to be carried out for multiple times in the same non-motor vehicle lane environment.
In order to verify the description condition of the combined model on the stress situation of the non-motor vehicle rider, the riding track of the rider in the simulation result in the embodiment is analyzed. As shown in fig. 8, in the simulation scene, the non-isolated condition is guardrail isolation, the roadside is curb isolation and roadside parking is not, and only the following two electric vehicle riders are generated in the simulation time zone. Through analysis, the whole riding track of the two riders is close to the swing trend of the actual riding track of the non-motor vehicle. The distance between the rider 1 and the guardrail when initially generated is 0.46 m, the influence of the boundary effect is obvious, the riding process is gradually far away from the guardrail isolation belt, the distance between the rider 2 and the rider 1 is 0.75 m, physiological space contact exists, the rider 1 is gradually far away from the riding process, the two riders are close to the end point and are influenced by the driving force of the simulation target point, the riding track is a curve, the curvature of the rider 2 is obviously large due to the fact that the rider is far away from the target point in the transverse direction, and the description of the stress effect of the rider by the combined model is obvious.
In order to verify that the microscopic traffic simulation result of the non-motor vehicle combined model is consistent with the macroscopic traffic condition obtained by actual investigation, a traffic flow basic diagram verification method is commonly used, and the relationships of the flow, density and speed of non-motor vehicles obtained by simulating the model with the same input conditions of road environment, traffic volume generation and the like are compared with the traffic flow basic diagram obtained by actual investigation, which is an important step for verifying whether the traffic simulation model is effective and reasonable. The embodiment mainly comprises non-motor vehicle flows of the electric vehicle and the bicycle, so that the average speed change interval is larger according to the expected speed difference of different vehicle types under low density; as the density increases, the velocity decreases and the average velocity variation interval decreases. As shown in fig. 9, the actual survey data of the road density-speed relationship diagram obtained by the actual survey is compared with the simulation result of the road density-speed relationship diagram output by multiple simulations, and it can be found that the two have good consistency, and the simulation result using the non-motor vehicle combination model is effectively checked to be consistent with the actual survey data of the macroscopic traffic condition obtained by the actual survey.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A non-motor vehicle simulation method based on a discrete element simulation platform is characterized by specifically comprising the following steps:
step 1) obtaining arrival data of the non-motor vehicle, and inputting the arrival data into a discrete element simulation platform to construct a non-motor vehicle arrival model;
step 2) acquiring road data of the non-motor vehicle lane, and establishing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform;
step 3) establishing a physiological sensing space of the non-motor vehicle on the basis of the non-motor vehicle individual consisting of the non-motor vehicle and a rider, thereby constructing a non-motor vehicle model; on the basis of a social force model, introducing a contact model of a particle discrete element theory, calculating a driving force, a repulsive force, a contact force and the resultant force of the driving force, the repulsive force and the contact force, and constructing a combined model for non-motor vehicle simulation;
and 4) placing the non-motor vehicle arrival model in the non-motor vehicle lane model, updating the motion state of the non-motor vehicle in real time by using the combined model, and simulating the non-motor vehicle flow.
2. The method according to claim 1, wherein the step 1) of obtaining arrival data of a non-motor vehicle specifically comprises:
continuously counting the arrival data of the non-motor vehicles for 1 hour by taking 10 seconds as a time interval, and fitting the arrival distribution condition of the non-motor vehicles by adopting discrete distribution in a mathematical statistical function; the method specifically comprises the following steps:
wherein the content of the first and second substances,
is the mean value of the samples, S2Taking the sample variance as the sample variance, wherein p and k are two characteristic parameters of a negative binomial distribution function;
when the sample variance S2And the mean value of the samples
When the ratio of (1) is obviously greater than 1, the requirement of negative binomial distribution is met, and the arrival condition of the non-motor vehicle is described by adopting the negative binomial distribution;
after obtaining the negative two-term distribution, according to chi2The test method comprises performing goodness-of-fit test on negative binomial distribution with confidence level α of 0.052Calculated value is less than or equal to
Considering the non-motor vehicle arrival data to conform to a negative binomial distribution, wherein the probability distribution is shown as a formula (3);
wherein, χ2The testing method is a fitting degree testing method;
at 0.05 significant level χ2Checking a critical value; p (X ═ X) is the probability of reaching X non-motor vehicles in 10 seconds; x is the number of non-motor vehicles arriving;
acquiring arrival data of the non-motor vehicle according to the negative binomial distribution; wherein the non-motor vehicle comprises: bicycles and electric bicycles.
3. The method according to claim 1, wherein the step 2) specifically comprises:
initializing a non-motor vehicle running environment, determining traffic flow conditions of the left side and the right side of a road, acquiring road data of the non-motor vehicle lane according to the boundary strength, the width of the non-motor vehicle lane and the length of the non-motor vehicle lane, and constructing a non-motor vehicle lane model by inputting the road data into a discrete element simulation platform.
4. The method according to claim 1, wherein the step 3) comprises in particular:
step 3-1), the non-motor vehicle individual is an elliptical model formed by the non-motor vehicle and the rider, wherein the center of a handlebar of the non-motor vehicle is located at the focus of the elliptical model, and the nose tip of the rider is located at the center of the elliptical model; establishing a physiological sensing space of the non-motor vehicle surrounding the periphery of the non-motor vehicle by taking the length of a long shaft of the non-motor vehicle as the diameter, thereby constructing a non-motor vehicle model; wherein the non-motor vehicle model is a non-motor vehicle unit comprising a plurality of non-motor vehicle individuals and corresponding non-motor vehicle physiological sensing spaces;
step 3-2) introducing a contact model of a particle discrete element theory based on a social force model, and calculating resultant force borne by the non-motor vehicle, namely the vector sum of driving force, repulsive force and contact force, so as to establish a combined model for simulating the non-motor vehicle and calibrate parameters of the combined model; wherein the parameters of the combined model include: a driving force parameter, a repulsive force parameter, a contact force parameter, and a boundary action parameter.
5. The method according to claim 4, wherein before calculating the contact force in step 3-2), determining whether the contact force is generated specifically comprises:
introducing a contact judgment rule, and judging the contact behavior between the non-motor individual bodies by adopting a grid cell method; firstly, abstracting a non-motor vehicle lane section into a rectangular grid to divide the rectangular grid into a plurality of grids, and selecting 1-2 times of the maximum diameter of a non-motor vehicle physiological sensing space as the side length of the grids to divide the non-motor vehicle lane; under the division mode, the non-motor vehicle unit occupies at least one grid in the non-motor vehicle lane and at most four grids simultaneously;
judging whether other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs,
if no other non-motor vehicle units exist in the grid to which the non-motor vehicle units belong, determining that no contact force is generated between the non-motor vehicle units;
if other non-motor vehicle units exist in the grid to which the non-motor vehicle unit belongs, whether the physiological sensing space in the grid to which the non-motor vehicle unit belongs is contacted with other physiological sensing spaces is judged;
if the physiological sensing space in the grid does not contact with other physiological sensing spaces; determining that no contact force is generated between the individual non-motor vehicles;
if the physiological sensing space in the grid is contacted with other physiological sensing spaces; it is determined that a contact force is generated between the individual non-motor vehicles, and the contact force applied to the non-motor vehicle unit is calculated.
6. The method according to claim 5, wherein the step 3-2) of calculating the contact force to which the non-motor vehicle unit is subjected comprises in particular:
if the non-motor vehicle individuals have contact behaviors, the contact force applied to the non-motor vehicle unit where the rider i is located is divided into a normal component and a tangential component; the normal component force of the contact force represents the tendency that non-motor vehicles contacted with the physiological sensing space tend to bounce away from each other when trying to recover the original state; the tangential component of the contact force represents the friction effect generated by the non-motor vehicle in the longitudinal direction when the physiological sensing space is contacted;
suppose that the normal elastic coefficient and the normal damping coefficient of the rider i are respectively kn、cnAccording to equation (4), then, the normal component of the contact force is at α with a normal overlap of α
Comprises the following steps:
wherein the content of the first and second substances,
is the speed of rider i relative to rider j;
is a unit vector from the center of rider i to the center of rider j;
assuming that the tangential elastic coefficient and the tangential damping coefficient of the rider i are respectively kt、ctTangential component of contact force, according to equation (5)
Comprises the following steps:
wherein the content of the first and second substances,
is a tangent at the contact point ADisplacement;
is the slip velocity at contact point a;
normal component of contact force
And tangential component force
Vector composition is carried out to obtain contact force
7. The method according to claim 4, wherein the step 3-2) of calculating the driving force experienced by the non-motor vehicle unit comprises:
the rider i is always driven by the driving force, and at any time t, the driving force borne by the non-motor vehicle unit is calculated according to a formula (7);
wherein the content of the first and second substances,
is the driving force experienced by the non-motor vehicle unit; m isiThe mass of the individual non-motor vehicle where the rider i is located;
a desired direction vector for rider i, pointing to the target point; v. ofi 0(t) is the desired speed of rider i;
is the vector velocity of i at time t; tau isiTo last forTime.
8. The method according to claim 4, wherein the step 3-2) of determining whether the non-motor vehicle unit is subjected to the repulsive force of the other non-motor vehicle units before calculating the repulsive force to which the non-motor vehicle unit is subjected, specifically comprises:
determining that the non-motor vehicle unit is subjected to a repulsive force of the other non-motor vehicle units if the safety distance between the non-motor vehicle unit and the other non-motor vehicle units is less than or equal to 5 m;
and if the safety distance between the non-motor vehicle individual and the other non-motor vehicle individuals is more than 5m, determining that the non-motor vehicle is not subjected to the repulsive force of the other non-motor vehicles.
9. The method of claim 8, wherein calculating the repulsive force experienced by the non-motor vehicle unit comprises:
the non-motor vehicle unit on which the rider i is located is subjected to a repulsive force as calculated by the following equation (9);
wherein A isiThe strength of the repulsive force among the repulsive forces; b isiA safe distance for creating a repulsion relationship between rider i and rider j;
is a direction vector directed by rider j to rider i; dijThe individual boundary distance from the rider i to the rider j.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811154215.3A CN110968909A (en) | 2018-09-30 | 2018-09-30 | Non-motor vehicle simulation method based on discrete element simulation platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811154215.3A CN110968909A (en) | 2018-09-30 | 2018-09-30 | Non-motor vehicle simulation method based on discrete element simulation platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110968909A true CN110968909A (en) | 2020-04-07 |
Family
ID=70028581
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811154215.3A Pending CN110968909A (en) | 2018-09-30 | 2018-09-30 | Non-motor vehicle simulation method based on discrete element simulation platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110968909A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111462491A (en) * | 2019-12-10 | 2020-07-28 | 北京航空航天大学 | Expressway confluence area traffic conflict early warning method based on ramp control |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138773A (en) * | 2015-08-25 | 2015-12-09 | 北京建筑大学 | Pedestrian simulation method and device based on discrete element simulation platform |
| CN105205216A (en) * | 2015-08-25 | 2015-12-30 | 北京建筑大学 | Cross-domain simulation method and device for large-scale pedestrian motion in hub |
-
2018
- 2018-09-30 CN CN201811154215.3A patent/CN110968909A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138773A (en) * | 2015-08-25 | 2015-12-09 | 北京建筑大学 | Pedestrian simulation method and device based on discrete element simulation platform |
| CN105205216A (en) * | 2015-08-25 | 2015-12-30 | 北京建筑大学 | Cross-domain simulation method and device for large-scale pedestrian motion in hub |
Non-Patent Citations (4)
| Title |
|---|
| 张蕊;杨静;林霖;: "基于颗粒离散元法的枢纽内部行人流仿真研究", 公路 * |
| 杨静;张蕊;刘荣强;冯焕东;: "基于颗粒流离散元方法的行人动力学模型及仿真研究", 交通运输系统工程与信息 * |
| 梁肖: "自行车微观行为动力学建模及仿真研究", 《中国优秀博士学位论文全文数据库》 * |
| 赵春龙: "平面交叉口混合交通流自行车穿越机动车微观行为模型研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111462491A (en) * | 2019-12-10 | 2020-07-28 | 北京航空航天大学 | Expressway confluence area traffic conflict early warning method based on ramp control |
| CN111462491B (en) * | 2019-12-10 | 2022-03-01 | 北京航空航天大学 | Expressway confluence area traffic conflict early warning method based on ramp control |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Du et al. | Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning | |
| CN110531740B (en) | Intelligent degree quantitative evaluation method for intelligent vehicle | |
| CN112896170B (en) | Automatic driving transverse control method under vehicle-road cooperative environment | |
| CN111178402B (en) | Scene classification method and device for road test of automatic driving vehicle | |
| Sun et al. | A driver behavior-based lane-changing model for urban arterial streets | |
| CN105279966B (en) | A kind of congestion recognition methods of Campus transport | |
| Nguyen et al. | Traffic conflict assessment for non-lane-based movements of motorcycles under congested conditions | |
| CN110414803B (en) | Method and device for evaluating intelligent level of automatic driving system under different internet connection degrees | |
| Olstam et al. | A framework for simulation of surrounding vehicles in driving simulators | |
| Li et al. | A macroscopic model of heterogeneous traffic flow based on the safety potential field theory | |
| Yu et al. | Using 3D mobile mapping to evaluate intersection design through drivers’ visual perception | |
| Toledo et al. | Alternative definitions of passing critical gaps | |
| Arasan et al. | Microsimulation study of vehicular interactions in heterogeneous traffic flow on intercity roads | |
| CN110968909A (en) | Non-motor vehicle simulation method based on discrete element simulation platform | |
| CN115855531A (en) | Test scene construction method, device and medium for automatic driving automobile | |
| Chen et al. | Platoon separation strategy optimization method based on deep cognition of a driver’s behavior at signalized intersections | |
| Li et al. | A new method based on field strength for road infrastructure risk assessment | |
| Olstam | Simulation of vehicles in a driving simulator using microscopic traffic simulation | |
| Sroczyński et al. | Examining the impact of distance between VSL road signs on vehicle speed variance | |
| CN113361866A (en) | Two-dimensional simulation method for microscopic traffic flow of non-motor vehicle | |
| Zhang et al. | A method for evaluating the complexity of test scenarios for autonomous vehicles | |
| Jiang et al. | A review of traffic behaviour and intelligent driving at roundabouts based on a microscopic perspective | |
| CN112308171A (en) | Vehicle position prediction modeling method based on simulated driver | |
| Gremmelmaier et al. | Cyclist behavior: a survey on characteristics and trajectory modeling | |
| Arkatkar | Micro-simulation study of vehicular interactions on upgrades of intercity roads under heterogeneous traffic conditions in India |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200407 |