CN113868874A - Method for predicting traffic around tourist attractions, and method and system for controlling congestion - Google Patents
Method for predicting traffic around tourist attractions, and method and system for controlling congestion Download PDFInfo
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Abstract
The invention relates to a method for predicting traffic around tourist attractions, a method for controlling congestion and a system thereof, which comprises the steps of firstly constructing a traffic environment simulation model of a simulation road section around the attractions, then determining the number of tourist intelligent bodies in the current simulation period by using a poisson process according to the historical traffic state of the simulation road section based on the interaction between the tourists and the traffic state, determining the traffic volume of the vehicle intelligent bodies in the current simulation period according to the number of the tourist intelligent bodies in the current simulation period and the trip mode of the tourist intelligent bodies, further simulating the behavior of the vehicle intelligent bodies in the traffic environment simulation model by using a following model which is discretely and uniformly distributed and based on time-varying safe distance to obtain the traffic state of the current simulation period, finally predicting and determining a congestion threshold value according to the traffic state under different total passenger flow rates, and limiting the reservation number of the tourists at the attractions in the simulation period to be lower than the congestion threshold value, the traffic jam condition around the tourist attractions is effectively relieved by controlling the intelligent traffic of the tourists.
Description
Technical Field
The invention relates to the field of urban traffic planning, in particular to a method for predicting traffic around tourist attractions, a method and a system for controlling congestion.
Background
With the development of economy, the quantity of vehicles kept is larger and larger, the contradiction between traffic supply and demand is prominent, and large-scale traffic jam often appears in some tourist cities during holidays, especially in roads around tourist attractions in the cities, and serious traffic jam often appears during the holidays. At present, the scheme for solving the problem of traffic jam with high popularity has the following two types: one is a congestion fee policy that addresses the problem of traffic congestion by regulating the excessive demand for scarce road space. The congestion fee can effectively reduce the traffic volume at first, but the inelasticity of the price enables the proportion of the congestion fee in the expensive overall operation cost of the automobile to be relatively small, only the part of people sensitive to the charge at first can be excluded, and the implementation of the congestion fee is often obstructed due to the doubtful attitude held by the public, so that the timeliness problem exists. Another approach is to enforce a low-cost restriction policy directly. However, the traffic restriction policy has a limited effect on the traffic because the policy stimulates the use of a substitute vehicle or causes an increase in the rate of illegal trips and an increase in the intensity of legal vehicle trips. In addition, vehicle restriction causes deterioration of public service level, and public travel time cost rises; some people may circumvent the restriction policy by purchasing more vehicles. Therefore, the two schemes cannot effectively solve the problem of traffic jam around tourist attractions.
Disclosure of Invention
The invention aims to provide a method for predicting traffic around tourist attractions, a method and a system for controlling congestion, so as to effectively relieve the traffic congestion situation around the tourist attractions by controlling the flow of an intelligent agent of tourists.
In order to achieve the purpose, the invention provides the following scheme:
a method of predicting traffic around a tourist attraction, the method comprising:
constructing a traffic environment simulation model of a simulation road section around the scenic spot;
determining the number of tourist agents in the current simulation period by utilizing a poisson process according to the historical traffic state of the simulation road section; the traffic states comprise smooth running, slow running, congestion and severe congestion;
determining the traffic volume of the vehicle intelligent agent in the current simulation period according to the number of the tourist intelligent agents in the current simulation period and the trip mode of the tourist intelligent agents;
and simulating the behavior of the vehicle intelligent body in the traffic environment simulation model by using a tracking model which is discretely and uniformly distributed and based on a time-varying safety distance according to the traffic volume of the vehicle intelligent body in the current simulation period by using the simulation result of the previous simulation period as the initial state of the current simulation period, so as to obtain the traffic state in the current simulation period.
Optionally, the traffic environment simulation model includes: a scenic spot gate intelligent body, a scenic spot road section intelligent body and a signal lamp intelligent body;
the sight spot gate agent comprises: the name and the position of the sight spot gate;
the intelligent agent for the road section where the scenic spot is located comprises: road segment length, lane number, and lane width;
the signal lamp agent includes: signal location and signal split.
Optionally, the determining, according to the historical traffic state of the simulation road segment, the number of the guest agents in the current simulation cycle by using a poisson process specifically includes:
determining the historical traffic state of each historical observation day according to the historical floating car data of a plurality of historical observation days;
taking the historical traffic state with the highest occurrence probability of the historical traffic state in the same time period as the current simulation cycle in a plurality of historical observation days as the reference traffic state of the current simulation cycle;
acquiring a tourist intelligent agent reference base number of a current simulation period in a reference traffic state according to historical floating car data and historical tourist flow data;
determining a conversion coefficient of the number of the tourist intelligent agents in the current simulation period according to the traffic state of the time period after the average time for the tourist intelligent agents to go to the scenic spots in the current simulation period;
and taking the product of the reference base number of the tourist intelligent bodies in the current simulation period and the conversion coefficient of the number of the tourist intelligent bodies in the current simulation period in the standard traffic state as a poisson parameter, and determining the number of the tourist intelligent bodies in the current simulation period by utilizing a poisson process.
Optionally, the obtaining of the reference base number of the guest intelligent agent in the current simulation cycle in the reference traffic state according to the historical floating car data and the historical guest traffic data specifically includes:
if the number of days of the historical observation day is larger than or equal to the number of days threshold, extracting the historical observation day corresponding to the historical traffic state which is the same as the traffic state of the current simulation cycle;
taking the extracted average value of the number of the tourists in the time period corresponding to the standard traffic state in all historical observation days as the reference base number of the tourist intelligent agent in the current simulation cycle in the standard traffic state;
if the number of days of the historical observation day is smaller than the threshold value of the number of days, the number of tourists and the traffic state at the historical moment in any one historical observation day are obtained; the historical time is the time after the average time of the tourist intelligent agent going to the scenic spots is pushed back from the starting time of the current simulation period;
calculating the result of dividing the conversion coefficient of the number of tourists and the number of the downstream passenger agents in the traffic state in the same period of time as the current simulation cycle in any historical observation day, and taking the result as converted passenger flow data;
and performing smooth fitting on the converted passenger flow data to obtain the reference base number of the tourist intelligent agent in the current simulation period in the reference traffic state.
Optionally, when the traffic state is smooth, the formula is used
Determining conversion coefficients under slow running, congestion and severe congestion traffic states;
wherein, c(1)、c(2)And c(3)Respectively representing the conversion coefficient g under the slow running, the congestion and the serious congestion traffic states1、g2And g3Respectively representing the proportion of the tourists giving up to go to the scenic spot for playing under the conditions of slow traffic, congestion and severe traffic congestion.
Optionally, the determining the traffic volume of the vehicle intelligent agent in the current simulation cycle according to the number of the tourist intelligent agents in the current simulation cycle and the trip mode of the tourist intelligent agents specifically includes:
according to the number of the tourist agents in the current simulation period, the riding transportation modes of the tourists arriving at the scenic spots and the passenger capacity of each transportation mode, the formula is utilized
Determining the traffic volume of the touring vehicle; wherein the content of the first and second substances,
in order to provide a traffic volume for the touring vehicle,
for a simulation period t1Real number of tourist agents, m1、m2And m3Respectively, the proportion of tourists arriving at scenic spots through cars, tourism buses and buses, n1、n2And n3Respectively the passenger capacities of cars, tourism buses and buses;
according to the number of the guest agents in the current simulation period, a formula is utilized
Determining a number of pathway vehicles; wherein the content of the first and second substances,
for the number of route vehicles, round (. cndot.) is a rounding function, m*In proportion to passing cars and touring cars,
m4the proportion of tourists arriving at scenic spots through non-motor vehicles;
the sum of the amount of travel vehicle traffic and the number of vehicles in the route is used as the vehicle intelligent agent traffic of the current simulation period.
Optionally, the simulating, according to the vehicle intelligent agent traffic volume of the current simulation cycle, the vehicle intelligent agent behavior in the traffic environment simulation model by using the discrete uniform distribution and the following model based on the time-varying safety distance, to obtain the traffic state of the current simulation cycle specifically includes:
according to the vehicle intelligent agent traffic volume of the current simulation period, generating the time of each vehicle reaching the simulation road section of the current simulation period by utilizing discrete uniform distribution;
after the vehicle reaches the simulation road section, depicting the following behavior of the vehicle based on the following model of the time-varying safety distance to obtain the average speed of the vehicle on the simulation road section;
and determining the traffic state of the current simulation period according to the average speed of all vehicles in the simulation road section in the current simulation period.
Optionally, the following model based on the time-varying safety distance is
Wherein v isi(n) and vi(n +1) represents the speed of the ith vehicle at the nth and (n +1) th times, respectively, αiAs a degree of driver's sensitivity, yi(n) and yi(n +1) represents the inter-vehicle distance of the ith vehicle at the nth and (n +1) T times, respectively, vi-1(n) represents the speed of the (i-1) th vehicle at time nT,
indicates the optimum speed of the ith vehicle,
representing the maximum travel speed, eta of the vehiclei(n) denotes the i-th vehicle isThe vehicle safety distance at the nth time; xiiRepresenting the adjustment coefficient, ηlimRepresents the shortest safe distance, T represents the sampling time, beta represents the weight, Hsat(x) The function of saturation is represented by the value of,
a method for controlling traffic congestion around tourist attractions based on the method for predicting traffic around tourist attractions comprises the following steps:
acquiring the proportion of the guest intelligent agent reference base number of each simulation period in the simulation period to the total guest intelligent agent reference base number in the simulation period;
according to the specific gravity, the number of the tourist agents in each simulation period in the simulation time period under different total passenger flow is obtained;
according to the number of the tourist agents of each simulation cycle in the simulation time period under different total passenger flow rates, the traffic state of each simulation cycle in the simulation time period under different total passenger flow rates is obtained by utilizing a peripheral traffic prediction method of the tourist attractions;
determining the minimum total passenger flow in the total passenger flow corresponding to the serious congestion traffic state as a congestion threshold;
and limiting the reservation number of the tourists of the scenic spots in the simulation time period to be lower than the congestion threshold value, and relieving traffic congestion.
A system for controlling traffic congestion around tourist attractions, the system comprising:
the proportion obtaining module is used for obtaining the proportion of the guest intelligent agent reference base number of each simulation period in the simulation period to the total guest intelligent agent reference base number in the simulation period;
the number obtaining module of the tourist agents is used for obtaining the number of the tourist agents in each simulation period under different total passenger flow rates according to the proportion;
the traffic state determining module is used for obtaining the traffic state of each simulation cycle in the simulation time periods under different total passenger flows by utilizing a peripheral traffic prediction method of the tourist attractions according to the number of the tourist agents in each simulation cycle in the simulation time periods under different total passenger flows;
the congestion threshold determination module is used for determining the minimum total passenger flow in the total passenger flow corresponding to the serious congestion traffic state as a congestion threshold;
and the reservation quantity limiting module is used for limiting the reservation quantity of the tourists of the scenic spots in the simulation time period to be lower than the congestion threshold value, and relieving traffic congestion.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method for predicting traffic around tourist attractions, a method and a system for controlling congestion, which are characterized by firstly constructing a traffic environment simulation model of a simulation road section around the attractions, then determining the number of tourist intelligent bodies in the current simulation period by using a Poisson process according to the historical traffic state of the simulation road section based on the interaction between tourists and the traffic state, determining the traffic volume of a vehicle intelligent body in the current simulation period according to the number of the tourist intelligent bodies in the current simulation period and the travel mode of the tourist intelligent bodies, further using the simulation result of the previous simulation period as the initial state of the current simulation period, simulating the behavior of the vehicle intelligent body in the traffic environment simulation model by using a tracking model which is distributed discretely and is based on safe distance in a time-varying manner according to the traffic volume of the vehicle intelligent body in the current simulation period to obtain the traffic state of the current simulation period, and finally determining a congestion threshold value according to the traffic state prediction under different total passenger flow rates, and limiting the reservation quantity of the tourists of the scenic spots in the simulation time period to be lower than a congestion threshold, and effectively relieving the traffic congestion condition around the scenic spots by controlling the intelligent traffic of the tourists.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for predicting traffic around tourist attractions according to the present invention;
FIG. 2 is a schematic structural diagram of a method for predicting traffic around tourist attractions according to the present invention;
FIG. 3 is a schematic diagram illustrating a method for predicting traffic around tourist attractions according to the present invention;
FIG. 4 is a flow chart of a method for controlling traffic congestion around tourist attractions according to the present invention;
FIG. 5 is a diagram of a simulation model of traffic around a city tourist attraction according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating traffic status prediction for different passenger flows according to an embodiment of the present invention;
fig. 7 is a smooth fit graph of passenger flow provided by the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method for predicting traffic around tourist attractions, a method and a system for controlling congestion, so as to effectively relieve the traffic congestion situation around the tourist attractions by controlling the flow of an intelligent agent of tourists.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a tourist attraction peripheral traffic prediction method, which is characterized by depicting interaction between tourists and traffic states based on a multi-agent model, adopting the multi-agent model for modeling, describing interaction processes of travel behaviors of the tourists and the traffic states around the attraction from micro to macro, and researching influences of the number change of the tourists on peripheral roads, so as to simulate corresponding traffic states, and as shown in figures 1-3, the method comprises the following steps:
The traffic environment simulation model comprises: the intelligent scenic spot gate agent, the intelligent scenic spot road section agent and the intelligent signal lamp agent. The sight spot gate agent includes: sight gate name and sight gate location. The intelligent agent of the road section where the scenic spot is located comprises: road segment length, number of lanes and lane width. The signal lamp intelligent agent includes: signal location and signal split.
102, determining the number of tourist agents in the current simulation period by using a poisson process according to the historical traffic state of the simulation road section; traffic conditions include clear, slow, congested and heavily congested.
The number of the guest agents (intelligent agents) in each simulation period is generated, the current number of the guest agents is influenced by the traffic state determined by the number and the behaviors of the previous vehicle agents, and the influence factor needs to be described when the guest agents are generated. The specific implementation process is as follows:
102-1, determining the historical traffic state of each historical observation day according to the historical floating car data of a plurality of historical observation days;
102-2, taking the historical traffic state with the highest occurrence probability of the historical traffic state in the same time period as the current simulation cycle in a plurality of historical observation days as the reference traffic state of the current simulation cycle;
102-3, acquiring a tourist intelligent agent reference base number of the current simulation period in a reference traffic state according to the historical floating car data and the historical passenger flow data;
the reference base of the tourist agent can be determined in two ways:
the first method comprises the following steps: if the number of days of the historical observation day is larger than or equal to the number of days threshold (under the condition of enough data quantity), extracting the historical observation day corresponding to the historical traffic state which is the same as the traffic state of the current simulation cycle;
taking the extracted average value of the number of the tourists in the time period corresponding to the standard traffic state in all historical observation days as the reference base number of the tourist intelligent agent in the current simulation cycle in the standard traffic state;
and the second method comprises the following steps: if the number of days of the historical observation day is smaller than a threshold value of days (the number is lack), acquiring the number of tourists and the traffic state at the historical moment in any one historical observation day; the historical time is the time after the average time of the tourist intelligent agent going to the scenic spots is pushed back from the starting time of the current simulation period;
calculating a result of dividing a conversion coefficient of the number of tourists in the same period as the current simulation cycle in any historical observation day by the number of the intelligent agents of the tourists in the traffic state, and taking the result as converted passenger flow data;
and performing smooth fitting on the converted passenger flow data to obtain the reference base number of the tourist intelligent agent in the current simulation period in the reference traffic state.
102-4, determining a conversion coefficient of the number of the tourist agents in the current simulation period according to the traffic state of the tourist agents in the time period after the average time of the tourist agents going to the scenic spots in the current simulation period;
the average time delta t of the tourist Agent going to the scenic spot is obtained through prior data, namely t1-Δt，t1Traffic conditions during +1- Δ t) periods infer [ t [1，t1+1) the conversion coefficient of the number of the guest agents in the time period, thereby obtaining the number of the guests in different traffic states.
The proportion of abandoning the tourist groups to play in scenic spots under the conditions of slow traffic, congestion and serious congestion of traffic and the proportion of abandoning the tourist groups to play in scenic spots under the conditions are respectively g1，g2，g3And g4Wherein g is1+g2+g3+g 41. Therefore, when the traffic state is smooth, the formula is utilized
Determining conversion coefficients under slow running, congestion and severe congestion traffic states;
according to the practical situation, the number of the tourist agents in different traffic states can be selected as the reference base number, but the corresponding conversion coefficient also needs to be adjusted correspondingly.
And 102-5, taking the product of the reference base number of the tourist intelligent agents in the current simulation period and the conversion coefficient of the number of the tourist intelligent agents in the current simulation period in the standard traffic state as a poisson parameter, and determining the number of the tourist intelligent agents in the current simulation period by utilizing a poisson process.
The number of agents arriving at the attraction over a period of time is described by the poisson process, with the parameters being the amount of traffic determined by the traffic state at the corresponding period of time. The poisson process { N (t), t ≧ 0} is a counting process, and the following three conditions are met: n (0) ═ 0; n (t) is an independent incremental process, i.e. for any integer n, and any real number 0 < t0＜t1＜...＜tnIncrement N (t)1)-N(t0)、N(t2)-N(t1)、…、N(tn)-N(tn-1) Are independent of each other; (iii) for any s < t e [0, ∞), the corresponding increment N (s, t) ═ N (t) -N(s) obedience parameter is λ (t ∈ N(s)2-t1) Poisson distribution of (a).
And 103, determining the traffic volume of the vehicle intelligent agent in the current simulation period according to the number of the tourist intelligent agents in the current simulation period and the travel mode of the tourist intelligent agents.
The traffic volume of the vehicle agents in the simulation period is determined by the number of the guest agents acquired in the process and the travel mode of the guest agents. Defining the occupation ratios of m for tourists to arrive at scenic spots through cars, tourist buses, buses and non-motor vehicles1、m2、m3And m4Wherein m is1+m2+m3+m 41. Since the non-motor vehicles do not affect the road traffic conditions, the traffic flow of tourists to the scenic spots is determined by the number of cars, touring buses and buses. Defining the average number of tourists carried by cars, tourism buses and buses as n1、n2And n3Assuming that the coefficient for converting the bus and the tourist bus into the equivalent car is 2, the traffic volume of the tourist vehicle is as follows:
where round (·) represents a rounding function. Suppose there are no touring buses and buses passing by, and the number of cars passing on the actual road is m of the number of buses0According to the travel proportion of the tourists, the proportion of the cars and the buses in the travel of the motor vehicles of the tourists is m1/(1-m4) And m3/(1-m4) So that the number of cars in the tourism vehicle is m of the number of buses13X, as shown by the formula:
the ratio of passing cars and touring cars is thus as follows:
i.e. the passing car is a m of a touring car*X wherein m0-m13Is greater than 0. Thus, the number of cars routed is as follows:
the available vehicle Agent traffic is shown as follows:
the above process is summarized as follows:
according to the number of the tourist agents in the current simulation period, the riding transportation modes of the tourists arriving at the scenic spots and the passenger capacity of each transportation mode, the formula is utilized
Determining the traffic volume of the touring vehicle; wherein the content of the first and second substances,
in order to provide a traffic volume for the touring vehicle,
for a simulation period t1Real number of tourist agents, m1、m2And m3Respectively, the proportion of tourists arriving at scenic spots through cars, tourism buses and buses, n1、n2And n3Respectively the passenger capacities of cars, tourism buses and buses;
according to the number of the guest agents in the current simulation period, a formula is utilized
Determining a number of pathway vehicles; wherein the content of the first and second substances,
for the number of route vehicles, round (. cndot.) is a rounding function, m*In proportion to passing cars and touring cars,
m4the proportion of tourists arriving at scenic spots through non-motor vehicles;
the sum of the amount of travel vehicle traffic and the number of vehicles in the route is used as the vehicle intelligent agent traffic of the current simulation period.
And 104, taking a simulation result of the previous simulation period as an initial state of the current simulation period, and simulating the behavior of the intelligent vehicle in the traffic environment simulation model by using a tracking model which is discretely and uniformly distributed and is based on the time-varying safety distance according to the traffic volume of the intelligent vehicle in the current simulation period to obtain the traffic state in the current simulation period.
The vehicle Agent behaviors include a vehicle Agent arrival behavior and a vehicle Agent following behavior. In the simulation model, the arrival time of each vehicle is generated according to discrete uniform distribution, and the following behavior of the vehicle Agent is characterized based on a following model of a time-varying safety distance. The following model based on the time-varying safety distance is
Wherein v isi(n) and vi(n +1) represents the speed of the ith vehicle at the nth and (n +1) th times, respectively, αiAs a degree of driver's sensitivity, yi(N) and yi(n +1) represents the inter-vehicle distance of the ith vehicle at the nth and (n +1) T times, respectively, vi-1(n) represents the speed of the (i-1) th vehicle at time nT,
indicates the optimum speed of the ith vehicle,
representing the maximum travel speed, eta of the vehiclei(n) represents a vehicle safety distance of the ith vehicle at the nth time; xiiRepresenting the adjustment coefficient, ηlimRepresents the shortest safe distance, T represents the sampling time, beta represents the weight, Hsat(x) The function of saturation is represented by the value of,
in addition, in order to prevent the vehicle from collision or reversing, the distance y between the vehicles is usedi(n) is less than the collision distance yminAnd when the vehicle is in a full braking action, the vehicle i is enabled to take a full braking action. And acquiring the average speed of the simulated vehicle according to the simulation of the vehicle behavior, and acquiring the corresponding traffic state according to the average speed. (Note that the specific traffic state categories may be divided according to different criteria depending on the actual traffic environment.)
According to the traffic volume of the intelligent vehicle in the current simulation period, simulating the behavior of the intelligent vehicle in a traffic environment simulation model by using a discrete uniform distribution and a time-varying safety distance-based following model to obtain the traffic state in the current simulation period, which specifically comprises the following steps:
according to the vehicle intelligent agent traffic volume of the current simulation period, generating the time of each vehicle reaching the simulation road section of the current simulation period by utilizing discrete uniform distribution;
after the vehicle reaches the simulation road section, depicting the following behavior of the vehicle based on the following model of the time-varying safety distance to obtain the average speed of the vehicle on the simulation road section;
and determining the traffic state of the current simulation period according to the average speed of all vehicles in the simulation road section in the current simulation period.
For a simulation period t0，t0+ nl), generating the passenger flow volume of each simulation period according to a generation method of the number of the tourists, obtaining the traffic state of the simulation period, and comparing the traffic state with the real floating car data to verify the effectiveness of the simulation model in the traffic environment.
In order to examine the change situation of the traffic condition under the change of the total passenger flow, the aim of relieving traffic jam through the passenger flow control is achieved. The method comprises the steps of firstly, apportioning the total passenger flow according to a proportion, wherein the proportion is set as the proportion of a passenger flow reference value of each simulation period to a passenger flow reference value of the whole simulation period, so that the passenger flow in each simulation period is obtained, then, simulating the traffic condition, obtaining the average speed of each simulation period and the total average speed of the simulation period, and further, determining the total passenger flow threshold value for changing the traffic condition. In addition, to reduce the randomness of the simulation, multiple simulations may be performed for each total passenger flow. Therefore, the time setting of the on-line reservation system of the scenic spot can be refined, and the number of reserved visitors in each time period is limited to be lower than the congestion threshold value, so that the traffic state of the road sections around the scenic spot can be managed and controlled better, and the purpose of relieving traffic congestion is achieved.
In contrast, the present invention provides a method for controlling traffic congestion around a scenic spot based on the aforementioned method for predicting traffic around a scenic spot, as shown in fig. 4, the method for controlling traffic congestion around a scenic spot includes:
and step 405, limiting the number of tourist reservations of the scenic spots in the simulation time period to be lower than a congestion threshold value, and relieving traffic congestion.
The technical scheme of the invention is explained in detail by taking a building university as a tourist scene.
in the embodiment, a simulation model layer is established and is mainly used for analyzing the influence of the number variation of tourists on the traffic condition of the road around the university department of Xiansu school at the time of 10:01:30 to 11:00:10 in a certain day.
The agents in the simulation model layer are shown as follows:
road: the total distance from the junction of the martial road and the university road to the section of the junction of the martial road and the south abridged road close to the side of the county school gate is 500 meters, as shown by the solid line part in fig. 5;
a docking point: a county school door of a building door university located at 242 meters 215 and 242 meters ((the department of FIG. 5)) and a bus station located at 327 meters 290 and 5 meters ((the department of FIG. 5));
signal points: the traffic signal lamp (part (r) in fig. 5) of 193-194 meters has a signal lamp period of 160 seconds, namely, the traffic signal lamp alternately flickers at the frequency of 30-second green lamp, 30-second red lamp, 70-second green lamp and 30-second red lamp;
a vehicle: buses, tourist buses and cars;
and (3) tourists: generated according to the poisson process.
The simulation model layer comprises three immovable main bodies, namely a road section agent, a gate agent and a signal lamp agent, and two movable main bodies, namely a tourist agent and a vehicle agent. To better simulate the motion and interaction of each agent, the attributes of each agent are listed in Table 1.
TABLE 1agent Attribute Table
Since the display period of the signal lamp is 160 seconds, the simulation is performed in the present example with the period of 160 seconds, i.e. the period l is 160, and 22 simulation periods, i.e. the initial time t, are counted from 10:01:30 to 11:00:100The ratio of n to n is 10:01:30, and 22 is taken.
And 2, generating the number of the guest agents as follows:
the number of the tourist agents is influenced by the traffic conditions of the sections around the scenic spots. Because the simulation road section belongs to a secondary trunk road, the traffic state is divided as follows by referring to the Beijing urban road traffic operation evaluation index system: when the vehicle speed is not less than 6.97 m/s, the traffic state is smooth; when the vehicle speed is 4.17 m/s to 6.97 m/s, the traffic state is slow running; when the speed of the vehicle is 2.78 m/s to 4.17 m/s, the traffic state is congestion; when the vehicle speed is less than 2.78 m/s, the traffic state is heavily congested. In the simulation period, the real traffic states obtained based on the floating car data only have three types of slow running, congestion and severe congestion, and therefore, only the three states are considered in the case simulation. Meanwhile, in the time period of 9:30-10:30, the proportions of the three traffic states are respectively 16.67%, 66.66% and 16.67%, and in order to control the loss degree of the data information to be minimum, the passenger flow under the congestion traffic state is selected as a reference amount, namely when the traffic state is slow running, the passenger flow is increased according to a corresponding conversion coefficient; when the traffic state is congestion, the passenger flow is the same as the reference amount; and when the traffic state is serious congestion, reducing the passenger flow according to the corresponding conversion coefficient. Specifically, when the number of visitors in a congested state is taken as a reference, the conversion coefficients under a slow walking state and a severe congestion state are respectively shown in formulas 1 and 2:
g is obtained from the results of questionnaires1、g2And g3Respectively takes the values of 7.87%, 17.60% and 25.67%, thereby obtaining the slow line conversion coefficient
And severe congestion reduction factor
1.24 and 0.66 respectively.
The past floating car data shows that the road section is mostly in a congestion state in a 9:00-9:30 time period, so that the traffic state of 9:00-9:30 is assumed to be in a congestion state so as to process the passenger flow data. In order to obtain the reference amount of visitors in a congested state, the present example first converts the investigated passenger flow data into the passenger flow amount in a congested traffic state. The passenger flow under the congestion state does not need to be processed; when the traffic state is serious congestion, in order to obtain the passenger flow under the congestion state, the conversion coefficient is required according to the serious congestion
Increasing the passenger flow volume; similarly, when the traffic state is slow traveling, in order to obtain the passenger flow under the congestion state, the slow traveling conversion coefficient is required
The passenger flow volume is reduced. And after the passenger flow under the congestion state is obtained, carrying out spline smooth fitting on the converted passenger flow data, and taking the data obtained after fitting as the passenger flow reference rate. Because the field research data takes 5 minutes as an observation period and the simulation period is 160 seconds, the passenger flow reference rate of 5 minutes can be converted according to the time proportion according to the property that the number of events in any interval with the length t of the poisson process obeys the poisson distribution with the rate lambda tThe passenger flow reference rate is 160 seconds, and the Poisson distribution parameter is obtained by combining the corresponding conversion coefficient, so that the passenger flow is generated
The results are shown in FIG. 7.
And 3, generating the traffic volume of the vehicle Agent as follows:
the traffic volume of the vehicle agents is mainly determined by the number of the tourist agents currently arriving at the scenic spot and the traveling modes of the tourist agents, and the passenger volume of the simulation period of 160 seconds is generated by the step 2, so that the traffic volume of the vehicle agents in the simulation period can be obtained by combining the traveling modes of the tourist agents and the passing vehicle behaviors in the vehicle agents. Other vehicles in the observation video are functional vehicles such as a sprinkler and the like, and the occupation ratio is extremely low, so that the vehicles are not considered in the simulation. M is available from questionnaire data1、m2And m3Respectively taking 15.01%, 32.56% and 42.07%; combining the data of investigation and questionnaire, and referring to the number of passengers of the mansion door Jinlong large-scale high-grade passenger car as 39 seats, n can be obtained1、n2And n3Respectively taking 3, 39 and 12; the number of passenger flows generated in step 2 is combined
And calculating the traffic volume obtained by the touring vehicle.
Since the probability of the occurrence of more than 200 persons in the simulation period is about 0 and the number of the tourist buses is 0.77 calculated by the passenger flow of 200 persons, the number of the tourist buses in one simulation period is approximately 1. From the video data, it is possible to pass approximately 1 travel bus every 160 seconds, and therefore, this simulation process does not consider travel buses being traversed. Meanwhile, as no passing buses exist in villages and west villages of buildings, the number of passing cars is only considered in the simulation. The calculation shows that the ratio of cars to buses in the motor vehicle trip of tourists is 36.32 percent and 49.93 percent, and the number of cars in the tourism vehicles is 3.1 times of the number of buses; and then the ratio of cars to buses is 8.2:1 obtained by combining the video data, namely the number of cars passing through the simulated road is 8.2 of the number of busesThe available passing car is 1.65 times that of the tourism car, namely m*1.65. Thus, the number of passenger flows generated according to step 2
The simulation time interval t can be calculated1，t1+160) traffic volume.
Step 4, vehicle Agent behavior simulation and simulation periodic traffic conditions:
and 4, generating the arrival time of each vehicle through discrete uniform distribution according to the traffic volume obtained in the step 3, determining the number of vehicles arriving at the simulation area per second, and simulating the traffic in the current simulation period through a following model based on the time-varying safety distance. Concretely, the maximum running speed of the vehicle is taken
(i.e., 25 km/h), minimum safe distance ηlim10 m, adjustment coefficient xii23.33, and the sampling time T is 1 second; while assuming that each driver is homogenous, i.e. driver's sensitivity ai0.5(i ═ 1, 2, 3 … n), weight β ═ 0; distance of collision ymin2.5 m. In order to avoid the influence of the setting of the initial state on the simulation result, the simulation is carried out according to the time period of 9:58:50-10:01:30 (namely [ t ] before the simulation is carried out in the time period of 10:01: 30-11: 00: 10)0-160，t0) ) is performed for one cycle, and the result obtained by the simulation is used as the initial state of the simulation of the whole target time period. Wherein, in the simulation period, the simulation road section in front of the traffic signal lamp is set to be free of motor vehicles, the position speed of the floating vehicle behind the traffic signal lamp is usually about 13 equivalent cars is 0, the simulation road section behind the traffic signal lamp is set to be provided with 13 equivalent cars in each lane, and the head vehicle is arranged at a speed v0And (3) driving at a constant speed of 3 m/s, and driving the other vehicles through the following model, so as to obtain the traffic condition of the simulation period.
In order to verify the validity of the above model, the present example collects and collates 9:30-11:00 floating car data recording four time attributes of the car number, longitude, latitude, and received GPS. According to the attributes, the corresponding average speed in the simulation time period is calculated and used as reference data to check the accuracy of the model result. Specifically, if the traffic state in the simulation and the traffic state represented by the floating car data are slow running, the simulation result is correct, otherwise, the simulation result is incorrect; the other traffic states are the same. The simulation in the whole period of 10:01:30 to 11:00:10 is implemented for 30 times, 160 seconds is taken as a simulation period, each simulation has 22 simulation periods, and the accuracy of each simulation can be obtained by judging whether the model is correct or not in each simulation period. Through statistics, the average value of the accuracy rates of 30 simulation results is 60.61%, and the standard deviation is 6.99%. Furthermore, if a severe congestion state occurs as a judgment standard, the average value of the model accuracy rate is increased to 78.64%, and the corresponding standard deviation is 4.16%. Therefore, the simulation result of the model is accurate, the result change is relatively stable, and the model is credible.
And 5, acquiring a tourist flow threshold value to realize tourist control:
according to the simulation models established in the steps 1 to 4, the step 5 is used for obtaining the passenger flow threshold value of the traffic state change by simulating the traffic conditions under different passenger flows, and realizing the tourist control by combining with the on-line reservation visiting system so as to relieve the traffic jam condition.
Specifically, the total passenger flow in the time period of 10:01:30 to 11:00:10 in step 2 is about 2500 people, and a serious congestion situation occurs in the passenger flow, so that the change of the traffic condition of the road section is considered when the total passenger flow is gradually reduced from 2400 people to 1700 people. To reduce the influence of the simulation randomness, 5 simulations were performed for each passenger flow.
In each simulation, the total passenger flow is proportionally distributed according to the passenger flow reference value obtained by calculation in the step 2, then the converted passenger flow data is smoothly fitted, as shown in fig. 7, the passenger flow in each simulation period of 160 seconds is obtained, and then the simulation is carried out according to the text model. Because the traffic flow in the initial state is large, the traffic state in the simulated 1 st 160 seconds is mostly serious congestion, but the traffic state is mainly determined by newly generated vehicles arriving at the passenger flow due to the reduction of the passenger flow, and the traffic capacity of the road is enough to dissipate the traffic flow caused by the initial state in the 2 nd period. In order to accurately reflect the influence of the total passenger flow on the traffic state, the 1 st 160-second simulation result is not considered in the model prediction analysis.
As can be seen from fig. 6, as the total passenger flow increases, the average speed obtained by the simulation gradually decreases, and when the total passenger flow is 2000 to 2400 persons, a severe congestion period occurs in the simulation result, and until the total passenger flow decreases to less than 2000 persons, the severe congestion state disappears, and the traffic condition corresponding to the average speed changes from slow traffic to congestion. Therefore, based on this model, a threshold value at which the total passenger flow rate is 2000 people whose traffic state has changed to congestion can be obtained.
The method of the present invention suggests in this example that if the visiting time period of the reservation system can be further refined, and the reservation time unit is reduced from day to hour or even shorter unit, the controllability of the traffic condition of the road around the scenic spot can be enhanced. For example, the number of reserved visitors per hour of the group sages school door is limited to be less than 2000, and the simulation result can be obtained, so that the traffic state of the road section around the group sages school door of the university of the building door can be greatly improved under the policy.
The invention changes the control of vehicles into the control of the flow of tourists, thereby relieving traffic jam. The tourists have basic characteristics of free tourism, high outgoing rate and high motorization, and influence the traffic state around the urban tourist attractions. In turn, unlike rigid trips on general commutes, tourism trips belong to elastic trips, and the traffic state around the scenic spot affects the trip decision of the tourists. Therefore, the travel behavior of the tourists and the traffic state around the scenic spot have an interactive process. The Agent technology used by the invention adopts a bottom-up design method, and obtains the traffic threshold value of traffic state change by simulating traffic change conditions under different passenger flow rates, thereby providing important parameters for realizing time-interval tourist flow limitation to relieve the traffic state around scenic spots.
The invention also provides a system for controlling the traffic jam around the tourist attraction, which comprises:
the proportion obtaining module is used for obtaining the proportion of the guest intelligent agent reference base number of each simulation period in the simulation period to the total guest intelligent agent reference base number in the simulation period;
the number obtaining module of the tourist agents is used for obtaining the number of the tourist agents in each simulation period under different total passenger flow rates according to the proportion;
the traffic state determining module is used for obtaining the traffic state of each simulation cycle in the simulation time periods under different total passenger flows by utilizing a peripheral traffic prediction method of the tourist attractions according to the number of the tourist agents in each simulation cycle in the simulation time periods under different total passenger flows;
the congestion threshold determination module is used for determining the minimum total passenger flow in the total passenger flow corresponding to the serious congestion traffic state as a congestion threshold;
and the reservation quantity limiting module is used for limiting the reservation quantity of the tourists of the scenic spots in the simulation time period to be lower than the congestion threshold value, and relieving traffic congestion.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for predicting traffic around tourist attractions, comprising:
constructing a traffic environment simulation model of a simulation road section around the scenic spot;
determining the number of tourist agents in the current simulation period by utilizing a poisson process according to the historical traffic state of the simulation road section; the traffic states comprise smooth running, slow running, congestion and severe congestion;
determining the traffic volume of the vehicle intelligent agent in the current simulation period according to the number of the tourist intelligent agents in the current simulation period and the trip mode of the tourist intelligent agents;
and simulating the behavior of the vehicle intelligent body in the traffic environment simulation model by using a tracking model which is discretely and uniformly distributed and based on a time-varying safety distance according to the traffic volume of the vehicle intelligent body in the current simulation period by using the simulation result of the previous simulation period as the initial state of the current simulation period, so as to obtain the traffic state in the current simulation period.
2. The method of predicting traffic around tourist attractions of claim 1 wherein the traffic environment simulation model comprises: a scenic spot gate intelligent body, a scenic spot road section intelligent body and a signal lamp intelligent body;
the sight spot gate agent comprises: the name and the position of the sight spot gate;
the intelligent agent for the road section where the scenic spot is located comprises: road segment length, lane number, and lane width;
the signal lamp agent includes: signal location and signal split.
3. The method of predicting traffic around tourist attractions of claim 1, wherein the determining the number of tourist agents in the current simulation cycle by using a poisson process according to the historical traffic state of the simulation road section specifically comprises:
determining the historical traffic state of each historical observation day according to the historical floating car data of a plurality of historical observation days;
taking the historical traffic state with the highest occurrence probability of the historical traffic state in the same time period as the current simulation cycle in a plurality of historical observation days as the reference traffic state of the current simulation cycle;
acquiring a tourist intelligent agent reference base number of a current simulation period in a reference traffic state according to historical floating car data and historical tourist flow data;
determining a conversion coefficient of the number of the tourist intelligent agents in the current simulation period according to the traffic state of the time period after the average time for the tourist intelligent agents to go to the scenic spots in the current simulation period;
and taking the product of the reference base number of the tourist intelligent bodies in the current simulation period and the conversion coefficient of the number of the tourist intelligent bodies in the current simulation period in the standard traffic state as a poisson parameter, and determining the number of the tourist intelligent bodies in the current simulation period by utilizing a poisson process.
4. The method for predicting traffic around tourist attractions of claim 3, wherein the obtaining of the reference base number of the tourist agent in the current simulation cycle in the reference traffic state according to the historical floating car data and the historical passenger flow data specifically comprises:
if the number of days of the historical observation day is larger than or equal to the number of days threshold, extracting the historical observation day corresponding to the historical traffic state which is the same as the traffic state of the current simulation cycle;
taking the extracted average value of the number of the tourists in the time period corresponding to the standard traffic state in all historical observation days as the reference base number of the tourist intelligent agent in the current simulation cycle in the standard traffic state;
if the number of days of the historical observation day is smaller than the threshold value of the number of days, the number of tourists and the traffic state at the historical moment in any one historical observation day are obtained; the historical time is the time after the average time of the tourist intelligent agent going to the scenic spots is pushed back from the starting time of the current simulation period;
calculating the result of dividing the conversion coefficient of the number of tourists and the number of the downstream passenger agents in the traffic state in the same period of time as the current simulation cycle in any historical observation day, and taking the result as converted passenger flow data;
and performing smooth fitting on the converted passenger flow data to obtain the reference base number of the tourist intelligent agent in the current simulation period in the reference traffic state.
5. The method of predicting traffic around tourist attractions of claim 4 wherein,
when the traffic state is smooth, the formula is utilized
Determining conversion coefficients under slow running, congestion and severe congestion traffic states;
wherein, c(1)、c(2)And c(3)Respectively representing the conversion coefficient g under the slow running, the congestion and the serious congestion traffic states1、g2And g3Respectively representing the proportion of the tourists giving up to go to the scenic spot for playing under the conditions of slow traffic, congestion and severe traffic congestion.
6. The method of predicting traffic around tourist attractions of claim 1, wherein the determining the amount of vehicle agent traffic in the current simulation cycle based on the number of agent agents in the current simulation cycle and the travel patterns of agent agents comprises:
according to the number of the tourist agents in the current simulation period, the riding transportation modes of the tourists arriving at the scenic spots and the passenger capacity of each transportation mode, the formula is utilized
Determining the traffic volume of the touring vehicle; wherein the content of the first and second substances,
in order to provide a traffic volume for the touring vehicle,
for a simulation period t1Real number of tourist agents, m1、m2And m3Respectively, the proportion of tourists arriving at scenic spots through cars, tourism buses and buses, n1、n2And n3Respectively the passenger capacities of cars, tourism buses and buses;
according to the number of the guest agents in the current simulation period, a formula is utilized
Determining a number of pathway vehicles; wherein the content of the first and second substances,
for the number of route vehicles, round (. cndot.) is a rounding function, m*In proportion to passing cars and touring cars,
m4the proportion of tourists arriving at scenic spots through non-motor vehicles;
the sum of the amount of travel vehicle traffic and the number of vehicles in the route is used as the vehicle intelligent agent traffic of the current simulation period.
7. The method for predicting traffic around tourist attractions of claim 1, wherein the step of simulating the behavior of the vehicle agent in the traffic environment simulation model by using a tracking model based on a time-varying safety distance and a discrete uniform distribution according to the traffic volume of the vehicle agent in the current simulation cycle to obtain the traffic state in the current simulation cycle specifically comprises the steps of:
according to the vehicle intelligent agent traffic volume of the current simulation period, generating the time of each vehicle reaching the simulation road section of the current simulation period by utilizing discrete uniform distribution;
after the vehicle reaches the simulation road section, depicting the following behavior of the vehicle based on the following model of the time-varying safety distance to obtain the average speed of the vehicle on the simulation road section;
and determining the traffic state of the current simulation period according to the average speed of all vehicles in the simulation road section in the current simulation period.
8. The method of predicting traffic around tourist attractions of claim 1 wherein the time varying safety distance based car tracking model is
Wherein v isi(n) and vi(n +1) represents the speed of the ith vehicle at the nth and (n +1) th times, respectively, αiAs a degree of driver's sensitivity, yi(n) and yi(n +1) represents the inter-vehicle distance of the ith vehicle at the nth and (n +1) T times, respectively, vi-1(n) represents the speed of the (i-1) th vehicle at time nT,
indicates the optimum speed of the ith vehicle,
representing the maximum travel speed, eta of the vehiclei(n) represents a vehicle safety distance of the ith vehicle at the nth time; xiiRepresenting the adjustment coefficient, ηlimRepresents the shortest safe distance, T represents the sampling time, beta represents the weight, Hsat(x) The function of saturation is represented by the value of,
9. a method for controlling traffic congestion around a tourist attraction based on the method for predicting traffic around a tourist attraction of any one of claims 1 to 8, wherein the method for controlling traffic congestion around a tourist attraction comprises:
acquiring the proportion of the guest intelligent agent reference base number of each simulation period in the simulation period to the total guest intelligent agent reference base number in the simulation period;
according to the specific gravity, the number of the tourist agents in each simulation period in the simulation time period under different total passenger flow is obtained;
according to the number of the tourist agents of each simulation cycle in the simulation time period under different total passenger flow rates, the traffic state of each simulation cycle in the simulation time period under different total passenger flow rates is obtained by utilizing a peripheral traffic prediction method of the tourist attractions;
determining the minimum total passenger flow in the total passenger flow corresponding to the serious congestion traffic state as a congestion threshold;
and limiting the reservation number of the tourists of the scenic spots in the simulation time period to be lower than the congestion threshold value, and relieving traffic congestion.
10. A system for controlling traffic jam around tourist attractions, the system comprising:
the proportion obtaining module is used for obtaining the proportion of the guest intelligent agent reference base number of each simulation period in the simulation period to the total guest intelligent agent reference base number in the simulation period;
the number obtaining module of the tourist agents is used for obtaining the number of the tourist agents in each simulation period under different total passenger flow rates according to the proportion;
the traffic state determining module is used for obtaining the traffic state of each simulation cycle in the simulation time periods under different total passenger flows by utilizing a peripheral traffic prediction method of the tourist attractions according to the number of the tourist agents in each simulation cycle in the simulation time periods under different total passenger flows;
the congestion threshold determination module is used for determining the minimum total passenger flow in the total passenger flow corresponding to the serious congestion traffic state as a congestion threshold;
and the reservation quantity limiting module is used for limiting the reservation quantity of the tourists of the scenic spots in the simulation time period to be lower than the congestion threshold value, and relieving traffic congestion.
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