CN117690301B - Expressway diversion induction method considering induction compliance rate - Google Patents
Expressway diversion induction method considering induction compliance rate Download PDFInfo
- Publication number
- CN117690301B CN117690301B CN202410157068.4A CN202410157068A CN117690301B CN 117690301 B CN117690301 B CN 117690301B CN 202410157068 A CN202410157068 A CN 202410157068A CN 117690301 B CN117690301 B CN 117690301B
- Authority
- CN
- China
- Prior art keywords
- induction
- affected
- accident
- compliance rate
- traveler
- 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.)
- Active
Links
- 230000006698 induction Effects 0.000 title claims abstract description 93
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000013461 design Methods 0.000 claims abstract description 47
- 238000004088 simulation Methods 0.000 claims abstract description 32
- 238000011835 investigation Methods 0.000 claims abstract description 25
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 230000001939 inductive effect Effects 0.000 claims abstract description 14
- 238000009826 distribution Methods 0.000 claims abstract description 7
- 239000003795 chemical substances by application Substances 0.000 claims description 50
- 238000012360 testing method Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000011160 research Methods 0.000 claims description 7
- 238000005315 distribution function Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000006399 behavior Effects 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 230000001351 cycling effect Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 2
- 229910052709 silver Inorganic materials 0.000 description 2
- 239000004332 silver Substances 0.000 description 2
- 244000101724 Apium graveolens Dulce Group Species 0.000 description 1
- 235000015849 Apium graveolens Dulce Group Nutrition 0.000 description 1
- 235000010591 Appio Nutrition 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003090 exacerbative effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of intelligent high speed, in particular to a highway diversion induction method considering induction compliance rate, which comprises the following steps: s1, accident scene design, namely designing a highway diversion induction scene; s2, inducing compliance rate investigation, namely selecting factors which refer to accident scenes and influence the inducing compliance rate, designing options of affected travelers, and carrying out SP questionnaire investigation design and distribution; s3, designing an agent, calibrating an induction compliance rate model, and generating an affected traveler agent according to historical passenger flow OD and survey sample data; s4, accident simulation, namely performing accident flow simulation, inputting a shunt induction strategy, and calculating the induction compliance rate of the individual affected travelers; s5, scheme comparison and selection are carried out, scheme evaluation indexes are designed, and an optimal strategy scheme is generated. According to the method, the induction compliance rate of the affected travelers is considered, the expressway diversion induction strategy combinations under different scenes are evaluated, compared and selected, and important references can be provided for the design of the expressway diversion induction scheme.
Description
Technical Field
The invention relates to the technical field of intelligent high speed, in particular to a highway diversion induction method considering induction compliance rate.
Background
With the continuous increase of the traffic of the expressway, the problems of traffic jam and accidents are increasingly prominent. Therefore, the reasonable highway diversion induction strategy is very important for emergency treatment of highway congestion and accidents. With the increasing population urbanization and the increasing amount of vehicles kept, the traffic jam problem of the expressway is increasingly prominent. The existing diversion strategy can disperse the jammed vehicles to other roads by guiding the vehicles to bypass accident points, limiting the main line traffic flow of the toll station, setting dynamic speed limit and the like, thereby effectively reducing the jam, improving the road traffic capacity and shortening the driving time. In addition, the diversion induction strategy can also help accident handling units and emergency vehicles to arrive at the site more quickly to rescue and handle work, and reduce the time of casualties and road traffic blockage. Therefore, research and design of efficient diversion induction strategies is of great importance for modern urban traffic management.
However, existing highway diversion strategies often lack consideration of the induction compliance of the affected travelers. Induced compliance refers to the extent to which the affected traveler follows the shunt strategy. During the implementation of the diversion strategy, people may choose not to follow the inducement due to a number of factors, such as personal behavior preferences, attitudes, habits, etc., personal characteristics, and lack of trust, detour time, money, etc. The lack of design of a plan that considers the induction compliance rate will result in the application of the diversion strategy in actual situations not achieving the desired effect, even further exacerbating the traffic congestion problem.
In conclusion, the expressway diversion induction method considering the induction compliance rate has important theoretical and practical significance for solving the problem of traffic jam. By further researching the induction compliance rate of the affected traveler under different conditions and pertinently carrying out the design and optimization of the induction strategy, the operability and feasibility of the diversion strategy can be improved, and the effective relief of the traffic jam can be realized.
Disclosure of Invention
The invention aims to provide a highway diversion induction method considering induction compliance rate so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a highway diversion induction method considering induction compliance rate comprises the following steps:
Step one: accident scene design: the accident scene design comprises the steps of designing a highway diversion induction scene;
Step two: induction compliance rate investigation: the induction compliance rate survey selects factors which refer to accident scenes and influence the induction compliance rate, designs options of affected travelers, and develops SP questionnaire survey design and distribution;
Step three: and (3) intelligent body design: the intelligent agent designs a calibration induction compliance rate model, and generates an intelligent agent of the affected traveler according to the historical passenger flow OD and survey sample data;
Step four: accident simulation: accident flow simulation is carried out by accident simulation, a shunt induction strategy is input, and the induction compliance rate of the individual affected travelers is calculated;
Step five: the scheme is selected: the scheme comprises the following specific operations: and (5) designing a scheme evaluation index and generating an optimal strategy scheme.
Preferably, in the first step, the design of the diversion induction scene of the expressway is specifically: and designing a high-speed accident scene case by combining the characteristics of accident types, accident duration time, accident influence lane number and the like.
Preferably, in the second step, the design and distribution of the SP questionnaire are specifically:
Based on the surrounding information of the accident scene in the step S1, taking five factors of detour distance, detour potential cost, detour comfort level, delay time and destination distance into consideration, and constructing an orthogonal design model;
designing options of the affected travelers;
forming an SP survey of the affected traveler's selections in the highway accident scenario;
And issuing a questionnaire, obtaining an SP investigation data set selected by the affected traveler, then cleaning the data, selecting a discrete variable to perform 0-1 coding, creating a coding variable, mapping each option to a corresponding binary variable, and finally converting the original questionnaire data into data of 0-1 coding.
Preferably, the third step specifically operates as: and modeling and researching the behaviors of the affected travelers in the accident scene by utilizing the SP investigation data set selected by the affected travelers and utilizing a plurality of logic models, comparing the optimal variable combination, and generating the affected traveler agent according to the historical passenger flow OD and the investigation sample data.
Preferably, the modeling research comprises the following specific steps:
In a plurality of logic models, let Representing an affected travelerIn a sceneLower selection optionIs used as a function of the utility function of (a),To represent a vector of personal attributes of the affected traveler in the utility function,To represent the vector of travel characteristics of the affected traveler in the utility function,To represent variables of surrounding information of an accident scene in the utility function,、、To represent a vector of individual variable coefficients in the utility function,To obey the independently identical gummel distributed error terms,The calculation formula of (2) is as follows:
Order the For the total branch number, each affected traveler will select the corresponding selection item according to the maximum selection utility, and calculate the probability of the selection item being selectedThe method comprises the following steps:
Order the Is a 0-1 variable, when the traveler is affectedSelection optionsIn the time-course of which the first and second contact surfaces,And 1, C is the total number of affected travelers, otherwise 0, and the log likelihood function is as follows:
Solving the log-likelihood function by adopting a maximum likelihood estimation method to obtain parameters Is set, and T-test parameters: first, a negative log likelihood function value is calculated for each parameterIs a gradient of (2); second, parameters are estimated by Newton's methodIs a value of (2); third, parameters are calculated using a Hessian matrixStandard error of (2); fourthly, performing T test and calculating T test parameters; fifthly, calculating a model pseudo R party;
the variable combination which is more optimal is specifically as follows: and comprehensively testing different variable combinations, and selecting an optimal model by using T test parameters and a pseudo R party.
Preferably, in the fourth step, the specific operation of calculating the induction compliance rate of the individual affected travelers is as follows:
a. scene initialization, namely setting shunt induction starting time based on the accident scene in the step one, wherein the specific operation is as follows: make the road traffic capacity under normal condition be Determining the residual traffic capacity of the road according to the accident typeSetting the starting time of the emergency plan according to daily experience；
B. generating an agent, namely generating an affected traveler agent according to historical passenger flow OD and survey sample data, and calculatingDelay time of timeThe specific operation is as follows: according to the historical passenger flow OD data, an interchange portal frame in a research section range and a toll station are used as starting and ending points, and a historical OD matrix of passenger flow travel is generated; setting random number seeds according to the passenger personal attribute information obtained by investigation and the historical OD (on demand) ratio of the passenger flow trip and the personal attribute composition, and generating the OD and the personal attribute of each intelligent agent;
c. inducing compliance rate calculation, circularly updating simulation time according to simulation step length, and adding affected traveler agent into the system According to delay timeCalculating the induction compliance rate of the affected travelers;
d. generating a road queuing system Selecting options according to the inducing compliance rate of the affected travelerSubsequently, the agent information is transmitted into the road queuing system；
E. Queuing systemService is carried out, the time of the affected crowd leaving the system is determined, and a new delay time is generated according to the queuing length；
F. cycling steps c, d, e until the simulation time is greater than the upper boundary of the simulation time。
Preferably, the OD and personal attribute of each agent are generated specifically as follows: assuming random number seedFor some random number between 0 and 1, for numerical variablesAssuming that the distribution function isThe inverse function of the distribution function isClass-variantThe values of (2) are as follows:
For bisection type variables The variables are assumed to includeEach category has the probability ofThe sum of the probabilities is 1, the classification variablesThe values of (2) are as follows:
, 。
Preferably, in the fifth step, the generating an optimal policy scheme specifically includes: inputting different strategy schemes, including different parameters such as shunt induction strategy starting time, designing scheme evaluation indexes, and generating an optimal strategy scheme by taking the evaluation indexes as standards; wherein:
the design scheme evaluation index specifically comprises: to give passengers Is to wait for a period ofAverage waiting time of passengersMaximum waiting time of passengersThe calculation formula of (2) is as follows:
=
=
The generation of the optimal strategy scheme is specifically as follows: and selecting a shunt induction strategy scheme according to the standard that the average waiting time of the passengers is shortest than the longest waiting time of the passengers.
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages that a plurality of logic models are used for calibrating the induction compliance rate, and the intelligent agent is used for carrying out simulation evaluation on the shunt induction strategy, so that the method has wider applicability compared with the traditional design method of the shunt induction strategy.
Drawings
FIG. 1 is a schematic diagram of an algorithm structure according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an agent simulation in an embodiment of the present application;
FIG. 3 is a schematic diagram of an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and the specific embodiments.
Examples:
referring to fig. 1-3, the present embodiment provides a technical solution:
a highway diversion induction method considering induction compliance rate comprises the following steps:
S1, accident scene design, namely designing a highway diversion induction scene;
S2, inducing compliance rate investigation, namely selecting factors which refer to accident scenes and influence the inducing compliance rate, designing options of affected travelers, and carrying out SP questionnaire investigation design and distribution;
s3, designing an agent, calibrating an induction compliance rate model, and generating an affected traveler agent according to historical passenger flow OD and survey sample data;
S4, accident simulation, namely performing accident flow simulation, inputting a shunt induction strategy, and calculating the induction compliance rate of the individual affected travelers;
s5, scheme comparison and selection are carried out, scheme evaluation indexes are designed, and an optimal strategy scheme is generated.
Further, in step S1, accident scene design is performed, and the design of the diversion induction scene of the expressway is specifically as follows: the high-speed accident scene case is designed by combining the characteristics of accident type, accident duration time, accident influence lane number and the like;
Further, in the step S2, the inducing compliance rate investigation is performed, and the factors for referring to the accident scene and influencing the inducing compliance rate are selected, so that the SP questionnaire investigation design and distribution are specifically: s2-1, constructing an orthogonal design model based on surrounding information of an accident scene in the step S1, taking into consideration five factors of detour distance, detour potential cost, detour comfort, delay time and destination distance, S2-2, designing options of affected travelers, S2-3, forming an SP survey about the choices of the affected travelers in the expressway accident scene, S2-4, and issuing questionnaires;
Further, based on the surrounding information of the accident scene in the step S1, the five factors of the detour distance, the detour potential cost, the detour comfort level, the delay time and the destination distance are considered, and the construction of the orthogonal design model is specifically as follows: for each factor, three levels (low, medium and high) are designed from small to large according to the actual condition of the surrounding road detouring of a certain expressway accident, and a 5-factor 3-level uniform design table comprising 9 scenes is used Performing scene design to form a uniform design table; as shown in table 1:
Further, three levels (low, medium, high) are specifically designed from small to large: the low, medium and high correspond to the actual scene detour index to be reduced by 50%, and the actual scene detour index to be improved by 50% respectively.
Further, the S2-2, the selection items of the design affected travelers are specifically: the options for the affected traveler include at least two or more of the following options: four options of induction, detour, waiting to the service area, and abandoning travel are not obeyed.
Further, S2-3, forming an SP survey about the selection of affected travelers in the accident scene specifically comprises: one item of design includes the personal attributes of the affected traveler including income level, family population, academic, gender and age surveys and SP surveys of the affected traveler selection in 9 big scenes, the options of the personal attributes of the affected traveler are designed according to the common classification, and the options of the affected traveler selection in 9 big scenes are designed according to the classification in S2-2.
Further, S2-4, issuing questionnaires specifically comprises: a questionnaire is issued and the SP survey data set selected by the affected traveler is obtained. And (3) cleaning data, selecting discrete variables to perform 0-1 coding, creating coding variables, mapping each option to a corresponding binary variable, and finally converting the original questionnaire data into 0-1 coded data.
Further, in step S3, designing the agent, calibrating the inducing compliance rate model, and generating the affected traveler agent according to the historical passenger flow OD and the survey sample data specifically includes: and modeling and researching the behaviors of the affected travelers in the accident scene by utilizing the SP investigation data set selected by the affected travelers and utilizing a plurality of logic models, comparing the optimal variable combination, and generating the affected traveler agent according to the historical passenger flow OD and the investigation sample data.
Further, the specific steps of modeling and researching the behaviors of the affected travelers in the accident scene by utilizing the SP investigation data set selected by the affected travelers and utilizing a plurality of logic models are as follows:
In a plurality of logic models, let Representing an affected travelerIn a sceneLower selection optionIs used as a function of the utility function of (a),To represent a vector of personal attributes of the affected traveler in the utility function,To represent the vector of travel characteristics of the affected traveler in the utility function,To represent variables of surrounding information of an accident scene in the utility function,、、To represent a vector of individual variable coefficients in the utility function,To obey the independently identical gummel distributed error terms,The calculation formula of (2) is as follows:
Order the For the total branch number, each affected traveler will select the corresponding selection item according to the maximum selection utility, and calculate the probability of the selection item being selectedThe method comprises the following steps:
Order the Is a 0-1 variable, when the traveler is affectedSelection optionsIn the time-course of which the first and second contact surfaces,And 1, C is the total number of affected travelers, otherwise 0, and the log likelihood function is as follows:
Solving the log-likelihood function by adopting a maximum likelihood estimation method to obtain parameters Is set, and T-test parameters: first, a negative log likelihood function value is calculated for each parameterIs a gradient of (2); second, parameters are estimated by Newton's methodIs a value of (2); third, parameters are calculated using a Hessian matrixStandard error of (2); fourthly, performing T test and calculating T test parameters; fifthly, calculating a model pseudo R party;
Further, the more preferable and optimal variable combinations are specifically: and comprehensively testing different variable combinations, and selecting an optimal model by using T test parameters and a pseudo R party.
Further, in step S4, accident flow simulation is performed, a shunt induction policy is input, and the induction compliance rate of the affected traveler individual is calculated specifically as follows: s4-1, initializing a scene, and setting shunt induction starting time based on an accident scene in the step S1; s4-2, generating an agent, namely generating an affected traveler agent according to the historical passenger flow OD and survey sample data, and calculatingDelay time of time; S4-3, calculating the induction compliance rate, circularly updating the simulation time according to the simulation step length, and adding the affected traveler intelligent agent into the systemAccording to delay timeCalculating the induction compliance rate of the affected travelers; s4-4, generating a road queuing systemSelecting options according to the inducing compliance rate of the affected travelerSubsequently, the agent information is transmitted into the road queuing system; S4-5 queuing systemService is carried out, the time of the affected crowd leaving the system is determined, and a new delay time is generated according to the queuing length; S4-6, and circulating the steps S4-3, S4-4 and S4-5 until the simulation time is greater than the upper limit of the simulation time；
Further, step S4-1, scene initialization, based on the accident scene in step S1, sets the shunt induction start time specifically as follows: make the road traffic capacity under normal condition beDetermining the residual traffic capacity of the road according to the accident typeSetting the starting time of the emergency plan according to daily experience；
Further, step S4-2, generating the agent, generating the affected traveler agent according to the historical passenger flow OD and the survey sample data, and calculatingDelay time of timeThe method comprises the following steps: according to the historical passenger flow OD data, an interchange portal frame in a research section range and a toll station are used as starting and ending points, and a historical OD matrix of passenger flow travel is generated; setting random number seeds according to the passenger personal attribute information obtained by investigation and the historical OD (on demand) ratio of the passenger flow trip and the personal attribute composition, and generating the OD and the personal attribute of each intelligent agent;
further, the OD and personal attribute of each agent are specifically generated as follows: assuming random number seed For some random number between 0 and 1, for numerical variablesAssuming that the distribution function isThe inverse function of the distribution function isClass-variantThe values of (2) are as follows:
For bisection type variables The variables are assumed to includeEach category has the probability of(The sum of probabilities is 1), then the classification variantThe values of (2) are as follows:
,
further, calculating delay time according to traffic wave theory The method comprises the following steps: assume thatThe affected traveler agent added at any time isCalculating initial delay time:
=
Further, in step S4-3, the induction compliance rate is calculated specifically as follows: circularly updating the simulation time according to the simulation step length, and adding the simulation time into the system according to the section flow Time-affected traveler agentAccording to delay timeAnd a probability calculation formula for selecting the options in step S3, calculating the traveler agentIs the induction compliance rate of (a)；
Further, step S4-4, generating a road queuing systemSelecting options according to the inducing compliance rate of the affected travelerSubsequently, the agent information is transmitted into the road queuing systemThe method comprises the following steps: traveler agent calculated according to step S4-3Is the induction compliance rate of (a)Generating corresponding induced selections according to corresponding probabilities by using random seedsAdding the traveler agent selecting the corresponding option into the road queuing system, and calculating aiming at the agent not subject to inductionNumber of time-of-day affected travelers not subject to induction；
Further, the random seeds are utilized to generate corresponding induced selection according to the corresponding probabilityThe method comprises the following steps: for the purpose ofInducing selection, wherein the selection probability of each category is respectively(The sum of probabilities is 1), let the random number seed beThenThe values of (2) are as follows:
,
further, for agents that are not subject to induction, calculate Number of time-of-day affected travelers not subject to inductionThe method comprises the following steps: calculation ofTime of dayIs the sum of agents that are not subject to induction.
Further, step S4-5, queuing systemService is carried out, the time of the affected crowd leaving the system is determined, and a new delay time is generated according to the queuing lengthThe method comprises the following steps: assuming that the duration of the accident isDelay time of time tThe calculation formula is as follows:
Restarting the cycle until the simulation time 。
Further, in step S5, the scheme is compared and the scheme evaluation index is designed, and the generation of the optimal strategy scheme is specifically: inputting different strategy schemes, including different parameters such as shunt induction strategy starting time, designing scheme evaluation indexes, and generating an optimal strategy scheme by taking the evaluation indexes as standards;
further, the design scheme evaluation index specifically includes: to give passengers Is to wait for a period ofAverage waiting time of passengersMaximum waiting time of passengersThe calculation formula of (2) is as follows:
=
=
Further, the generation of the optimal strategy scheme is specifically as follows: and selecting a shunt induction strategy scheme according to the standard that the average waiting time of the passengers is shortest than the longest waiting time of the passengers.
The invention will be further illustrated with reference to specific examples:
S1, accident scene design, namely designing a highway diversion induction scene; taking a Beijing platform high-speed Fuzhou section long tunnel group area as a case scene, wherein the total length of the tunnel group area is about 48 km, the tunnel length is about 25 km, the traffic at the peak time of holidays reaches 33000veh/d, and the method belongs to a highway easy-congestion section; in the accident scene design, taking the recent accident as an example, the case accident type is vehicle rear-end collision, the main lane is occupied, the accident duration is 60 minutes, the affected traveler can select to bypass the surrounding Fuyinfang high speed, the average bypass distance is 5km, the bypass potential increases the cost by 10 yuan, the bypass comfort level is middle, and the delay time is 20 minutes;
S2, inducing compliance rate investigation, namely selecting factors which refer to accident scenes and influence the inducing compliance rate, designing options of affected travelers, and carrying out SP questionnaire investigation design and distribution;
s2-1, based on the surrounding information of the accident scene in the step S1, five factors including a detour distance, detour potential cost, detour comfort level, delay time and destination distance are considered;
Specific considerations and level settings are shown in table 2:
using a 5 factor 3 horizontal uniformity design table comprising 9 scenes The scene design is carried out, and the influence factor design table of each scene is shown in table 3:
S2-2, designing options of affected travelers; firstly, a national provincial detour can be selected, a Zhou Bianbai sand toll station and a Jingxi toll station are communicated with 115 county detours, the Jingxi toll station is communicated with Fuzhou detours at a high speed, and a Beijing-table high-speed route and an X115 route are adopted as routes passing from Fuzhou city to ancient fields; secondly, going to the south plain can select the Fuyinfin to bypass at high speed; in addition, the tunnel can wait to a Guandong service area 3 km in front of the tunnel; the selection is mainly as follows 3 kinds:
Selection 1: X115-Beijing station high speed, a section of county roads needs to be driven, the average detour distance is 30km, the detour potential increases cost by-35 yuan, the detour comfort level is low, and the delay time is 60 minutes; line 2: draining to the Fuyin at a high speed, bypassing to a celery hub, and bypassing for an average bypass distance of 5km, wherein the bypass is of a comfort level of middle and 20 minutes, and the potential cost is increased by 10 yuan; selection 3: delay time is 60 minutes when waiting to the end of the accident in the Guandong service area, so five options of non-compliance induction, national provincial detour, foster high-speed detour, waiting to the service area and giving up trip are set;
S2-3, forming an SP survey about the selection of the affected traveler in the expressway accident scene, and designing the SP survey about the selection of the affected traveler, wherein the SP survey mainly comprises the following survey contents as shown in table 4:
Investigation options include age, gender, income level, identity type, driving style, zhou Jun expressway trip frequency, main expressway trip purpose and week average expressway trip distance, in terms of age, investigation options are divided into six stages from under 18 years old to over 56 years old, gender aspects include male and female, income level options include below 2000 yuan, 2000-5000 yuan, 5000-8000 yuan, 8000-12000 yuan, 12000-20000 yuan and over 20000 yuan, identity type options include students, office workers, free occupations, retired people and others; the driving vehicle type aspect relates to cars, buses, trucks and other types; in terms of highway trip frequency, options are divided into 2-3 times per day, 1 time per week, less than 1 time per month, and very few; the main expressway trip destination options comprise business trips, travel, visiting friends, business trips and others; the travel distance of the highway is selected from less than 50 km, 50-100 km, 100-200 km, 200-500 km and more than 500 km;
given 9 hypothetical scenes of a crowd on the basis of the above data, for example, "in scene 1, a rear-end collision accident occurs in a Tianlong mountain tunnel, so that vehicles cannot pass, in order to bypass the accident scene, the driver needs to bypass a road section with a distance of 5 km, this bypass needs to pay a potential cost of 15 yuan, and the comfort is evaluated as moderate, since the bypass increases in distance, the driver needs to take about 10 minutes to reach the destination, and the destination is about 150 km away from the bypass road section, and in addition, can wait for a service area along the way, in which case you choose? "to ask questions to the interviewee;
S2-4, issuing questionnaires, carrying out investigation by using a network questionnaire platform, and acquiring an original questionnaire through customizing sample service; carrying out data cleaning, selecting age, gender, income level, identity type, driving vehicle type, zhou Jun expressway trip frequency, main expressway trip purpose and week average expressway trip distance to carry out 0-1 coding, creating coding variables, mapping each option to corresponding binary variables, and finally converting original questionnaire data into data of 0-1 coding;
S3, designing an agent, calibrating an induction compliance rate model, and generating an affected traveler agent according to historical passenger flow OD and survey sample data; inputting data of 0-1 codes, modeling by using a plurality of logic models, eliminating variables with significance less than 0.05, and modeling results are shown in table 5:
road traffic capacity under normal conditions according to actual conditions around tunnels 3000 Pcu/h, road remaining traffic capacity1500 Pcu/h delay timeFor 5 minutes;
Generating personal attributes of the affected traveler intelligent agent according to the historical passenger flow OD and survey sample data, on the basis, enabling the simulation step length to be 1 minute, and generating the affected traveler intelligent agent according to the section flow of the front portal of the Tianlong mountain tunnel ToFor example, at time=1, the properties of some agents are shown in table 6:
will go out person's agent According to the simulation timeContinuously adding the model according to the delay timeAnd a probability calculation formula for selecting the options in step S3, calculating the traveler agentCalculating the number of non-compliance induction of the affected travelers, adding the non-compliance-induction traveler agents into a queuing system, and generating a new delay timeThe loop is restarted until the simulation timeOn this basis, the longest delay time and the average delay time of the following two shunt induction methods were tested:
Scheme 1: starting bypass Fu silver high-speed X111 county road diversion induction prompt at 10 minutes;
scheme 2: starting diversion induction prompt of detour Fu silver at high speed and X111 county in 10 minutes, and recommending the crowd to go to a Guandong service area for waiting;
Please refer to table 7:
Finally, since the longest delay times are the same, the average delay time scheme 1 is shorter and scheme 1 is selected as the optimal split-flow induction method.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The highway diversion induction method considering the induction compliance rate is characterized by comprising the following steps of: the method comprises the following steps:
step one: accident scene design: the accident scene design comprises the design of a highway diversion induction scene, and the design of the highway diversion induction scene is specifically as follows: the method comprises the steps of designing a high-speed accident scene case by combining the characteristics of accident types, accident duration and accident influence lane numbers;
step two: induction compliance rate investigation: the induction compliance rate survey selects factors which refer to accident scenes and influence the induction compliance rate, designs options of affected travelers, and develops SP questionnaire survey design and release, wherein the SP questionnaire survey design and release are specifically as follows:
based on the surrounding information of the accident scene in the first step, taking five factors of detour distance, detour potential cost, detour comfort level, delay time and destination distance into consideration, and constructing an orthogonal design model;
designing options of the affected travelers;
forming an SP survey of the affected traveler's selections in the highway accident scenario;
Issuing questionnaires, obtaining SP investigation data sets selected by affected travelers, then cleaning the data, selecting discrete variables to perform 0-1 coding, creating coding variables, mapping each option to a corresponding binary variable, and finally converting original questionnaire data into data of 0-1 coding;
Step three: and (3) intelligent body design: the intelligent agent designs a calibration induction compliance rate model, generates an affected traveler intelligent agent according to historical passenger flow OD and survey sample data, and specifically operates as follows: modeling research is carried out on behaviors of the affected travelers in an accident scene by utilizing an SP investigation data set selected by the affected travelers and utilizing a plurality of logic models, the optimal variable combination is selected, and an affected traveler agent is generated according to historical passenger flow OD and investigation sample data;
step four: accident simulation: the accident simulation carries out accident flow simulation, a shunt induction strategy is input, the induction compliance rate of the individual affected travelers is calculated, and the specific operation of calculating the induction compliance rate of the individual affected travelers is as follows:
a. scene initialization, namely setting shunt induction starting time based on the accident scene in the step one, wherein the specific operation is as follows: make the road traffic capacity under normal condition be Determining the residual traffic capacity/>, of a road according to the accident typeSetting the starting time/>, according to daily experience, of an emergency plan；
B. generating an agent, namely generating an affected traveler agent according to historical passenger flow OD and survey sample data, and calculatingDelay time of time/>The specific operation is as follows: according to the historical passenger flow OD data, an interchange portal frame in a research section range and a toll station are used as starting and ending points, and a historical OD matrix of passenger flow travel is generated; setting random number seeds according to the passenger personal attribute information obtained by investigation and the historical OD (on demand) ratio of the passenger flow trip and the personal attribute composition, and generating the OD and the personal attribute of each intelligent agent;
c. inducing compliance rate calculation, circularly updating simulation time according to simulation step length, and adding affected traveler agent into the system According to delay time/>Calculating the induction compliance rate of the affected travelers;
d. generating a road queuing system Selecting options/>, based on the induced compliance rate of the affected travelerSubsequently, the agent information is transferred into the road queuing system/>；
E. Queuing systemService is carried out, the time that the affected crowd leaves the system is determined, and a new delay time/>, according to the queuing length, is generated；
F. cycling steps c, d, e until the simulation time is greater than the upper boundary of the simulation time；
Step five: the scheme is selected: the scheme comprises the following specific operations: the method comprises the steps of designing a scheme evaluation index, generating an optimal strategy scheme, wherein the generation of the optimal strategy scheme is specifically as follows: inputting different strategy schemes, including parameters of different shunt induction strategy starting times, designing scheme evaluation indexes, and generating an optimal strategy scheme by taking the evaluation indexes as standards; wherein:
the design scheme evaluation index specifically comprises: to give passengers Is/>Passenger average waiting time/>Passenger longest waiting time/>The calculation formula of (2) is as follows:
=/>；
=/>；
The generation of the optimal strategy scheme is specifically as follows: and selecting a shunt induction strategy scheme according to the standard that the average waiting time of the passengers is shortest than the longest waiting time of the passengers.
2. The highway diversion induction method considering the induction compliance rate according to claim 1, wherein: the modeling research comprises the following specific steps:
In a plurality of logic models, let Representing the affected traveler/>In scene/>Lower selection option/>Utility function of/>To represent the vector of the personal attributes of the affected traveler in the utility function,/>To represent the vector of travel characteristics of the affected traveler in the utility function,/>To represent the variables of the surrounding information of the accident scene in the utility function,/>、/>、/>To represent the vector of the various variable coefficients in the utility function,/>To obey the error term of independent identical Gumbel distribution,/>The calculation formula of (2) is as follows:
；
Order the For the total branch number, each affected traveler will select the corresponding option according to the maximum selection utility, and calculate the probability/>, of the option being selectedThe method comprises the following steps:
；
Order the Is a 0-1 variable, when the traveler is affected/>Select options/>Time,/>And 1, C is the total number of affected travelers, otherwise 0, and the log likelihood function is as follows:
；
Solving the log-likelihood function by adopting a maximum likelihood estimation method to obtain parameters Is set, and T-test parameters: first, calculate the negative log likelihood function value for each parameter/>Is a gradient of (2); second, estimating parameters/>, using Newton's methodIs a value of (2); third, calculate parameters/>, using a Hessian matrixStandard error of (2); fourthly, performing T test and calculating T test parameters; fifthly, calculating a model pseudo R party;
the variable combination which is more optimal is specifically as follows: and comprehensively testing different variable combinations, and selecting an optimal model by using T test parameters and a pseudo R party.
3. The highway diversion induction method considering the induction compliance rate according to claim 1, wherein: the generating OD and personal attribute of each agent is specifically: assuming random number seedFor some random number between 0 and 1, for numerical variable/>Assuming that its distribution function is/>The inverse of its distribution function is/>Then category type variable/>The values of (2) are as follows:
；
For bisection type variables Let the variables include/>Each category has the probability of/>, respectivelyThe sum of probabilities is 1, the classification variant/>The values of (2) are as follows:
, />。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410157068.4A CN117690301B (en) | 2024-02-04 | 2024-02-04 | Expressway diversion induction method considering induction compliance rate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410157068.4A CN117690301B (en) | 2024-02-04 | 2024-02-04 | Expressway diversion induction method considering induction compliance rate |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117690301A CN117690301A (en) | 2024-03-12 |
| CN117690301B true CN117690301B (en) | 2024-04-23 |
Family
ID=90130556
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410157068.4A Active CN117690301B (en) | 2024-02-04 | 2024-02-04 | Expressway diversion induction method considering induction compliance rate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117690301B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05250594A (en) * | 1992-03-04 | 1993-09-28 | Hitachi Ltd | Road traffic simulation system |
| CN101383091A (en) * | 2008-10-14 | 2009-03-11 | 北京交通大学 | Network control system for traffic induction outdoor screen |
| CN102750826A (en) * | 2012-06-26 | 2012-10-24 | 浙江大学 | Identification method of driver response behaviors under group induction information |
| CN102750827A (en) * | 2012-06-26 | 2012-10-24 | 浙江大学 | System for sampling and identifying data of driver response behaviors under group guidance information |
| WO2016169290A1 (en) * | 2015-04-21 | 2016-10-27 | 华南理工大学 | Decision-making supporting system and method oriented towards emergency disposal of road traffic accidents |
| CN109409560A (en) * | 2018-08-16 | 2019-03-01 | 北京交通大学 | Urban track traffic for passenger flow abductive approach based on Multi-Agent simulation |
| CN110428117A (en) * | 2019-08-15 | 2019-11-08 | 北京交通大学 | The accurate abductive approach of passenger flow and system under the more scenes of urban track traffic |
| CN113160582A (en) * | 2020-10-12 | 2021-07-23 | 招商新智科技有限公司 | Highway flow management and control method and system |
| CN114141030A (en) * | 2021-12-27 | 2022-03-04 | 吉林大学 | Post-accident highway traffic flow induction method under heterogeneous driving requirements |
| WO2022240906A1 (en) * | 2021-05-11 | 2022-11-17 | Strong Force Vcn Portfolio 2019, Llc | Systems, methods, kits, and apparatuses for edge-distributed storage and querying in value chain networks |
| CN115964864A (en) * | 2022-12-09 | 2023-04-14 | 广州现代产业技术研究院 | Emergency control method for simulation-driven highway accident |
| CN116543552A (en) * | 2023-04-27 | 2023-08-04 | 北京交通大学 | Urban arterial road slow blocking strategy simulation evaluation method and system based on vehicle-road cooperation |
| CN117373243A (en) * | 2023-10-16 | 2024-01-09 | 招商局重庆交通科研设计院有限公司 | Three-dimensional road network traffic guidance and emergency rescue collaborative management method for underground roads |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7379890B2 (en) * | 2003-10-17 | 2008-05-27 | Makor Issues And Rights Ltd. | System and method for profit maximization in retail industry |
| JP2024517412A (en) * | 2021-04-16 | 2024-04-22 | ストロング フォース ヴィーシーエヌ ポートフォリオ ２０１９，エルエルシー | Systems, methods, kits, and apparatus for digital product network systems and biology-based value chain networks |
-
2024
- 2024-02-04 CN CN202410157068.4A patent/CN117690301B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05250594A (en) * | 1992-03-04 | 1993-09-28 | Hitachi Ltd | Road traffic simulation system |
| CN101383091A (en) * | 2008-10-14 | 2009-03-11 | 北京交通大学 | Network control system for traffic induction outdoor screen |
| CN102750826A (en) * | 2012-06-26 | 2012-10-24 | 浙江大学 | Identification method of driver response behaviors under group induction information |
| CN102750827A (en) * | 2012-06-26 | 2012-10-24 | 浙江大学 | System for sampling and identifying data of driver response behaviors under group guidance information |
| WO2016169290A1 (en) * | 2015-04-21 | 2016-10-27 | 华南理工大学 | Decision-making supporting system and method oriented towards emergency disposal of road traffic accidents |
| CN109409560A (en) * | 2018-08-16 | 2019-03-01 | 北京交通大学 | Urban track traffic for passenger flow abductive approach based on Multi-Agent simulation |
| CN110428117A (en) * | 2019-08-15 | 2019-11-08 | 北京交通大学 | The accurate abductive approach of passenger flow and system under the more scenes of urban track traffic |
| CN113160582A (en) * | 2020-10-12 | 2021-07-23 | 招商新智科技有限公司 | Highway flow management and control method and system |
| WO2022240906A1 (en) * | 2021-05-11 | 2022-11-17 | Strong Force Vcn Portfolio 2019, Llc | Systems, methods, kits, and apparatuses for edge-distributed storage and querying in value chain networks |
| CN114141030A (en) * | 2021-12-27 | 2022-03-04 | 吉林大学 | Post-accident highway traffic flow induction method under heterogeneous driving requirements |
| CN115964864A (en) * | 2022-12-09 | 2023-04-14 | 广州现代产业技术研究院 | Emergency control method for simulation-driven highway accident |
| CN116543552A (en) * | 2023-04-27 | 2023-08-04 | 北京交通大学 | Urban arterial road slow blocking strategy simulation evaluation method and system based on vehicle-road cooperation |
| CN117373243A (en) * | 2023-10-16 | 2024-01-09 | 招商局重庆交通科研设计院有限公司 | Three-dimensional road network traffic guidance and emergency rescue collaborative management method for underground roads |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117690301A (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Multiagent spatial simulation of autonomous taxis for urban commute: Travel economics and environmental impacts | |
| Xu et al. | Fleet sizing for one-way electric carsharing services considering dynamic vehicle relocation and nonlinear charging profile | |
| Githui et al. | The structure of users' satisfaction on urban public transport service in developing country: the case of Nairobi | |
| Yang et al. | Equitable? Exploring ridesourcing waiting time and its determinants | |
| Yang et al. | How does the suspension of ride-sourcing affect the transportation system and environment? | |
| CN113327424B (en) | Traffic demand prediction method and device and electronic equipment | |
| CN106682765A (en) | Charging station optimization layout method and apparatus thereof | |
| CN108399462A (en) | A kind of shared vehicle reservation system based on Internet of Things | |
| CN117690301B (en) | Expressway diversion induction method considering induction compliance rate | |
| Jou et al. | ACCEPTANCE TENDENCIES AND COMMUTERS'BEHAVIOR UNDER DIFFERENT ROAD PRICING SCHEMES | |
| Vedagiri et al. | Modelling modal shift due to the enhanced level of bus service | |
| Qin et al. | Analysis of the travel intent for park and ride based on perception | |
| Hamadneh et al. | Evaluation of the impacts of autonomous vehicles on the mobility of user groups by using agent-based simulation | |
| Diderot et al. | Intelligent transportation systems in developing countries: Challenges and prospects | |
| Yi et al. | Research on commuters’ carpooling behavior in the mobile internet context | |
| Shi et al. | Reform Beijing to a public transit oriented city–from the view of transportation equity | |
| CN112632374A (en) | Resident travel mode selection analysis method considering customized bus | |
| Parady et al. | Hetereogenous travel activity patterns in Japan: accounting for inter-dependencies in mobility tool use | |
| Shi et al. | A second-pricing based incentive-compatible mechanism for matching and pricing in ride-sharing | |
| Guo et al. | Impacts of transportation external cost pricing and transit fare reductions on household mode/route choices and environmental improvements | |
| Agyeman et al. | Covid-19 Impact on Shared Transport Services: Perspectives from Eight African Countries | |
| CN115148027B (en) | Traffic demand management method for improving congestion charge | |
| Chari et al. | Study of work trip travel in India | |
| Chakraborty et al. | What Motivates Electric Vehicle Purchase Intention in India: Results from a Stated Preference Survey | |
| Islam et al. | DEVELOPMENT OF MODE CHOICE MODEL BY NESTED LOGIT METHOD IN DHAKA CITY, BANGLADESH. |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |