CN116071912A - Road traffic distribution determining method and device - Google Patents
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Abstract
The disclosure provides a method and a device for determining road traffic distribution, and relates to the technical field of computers. The specific implementation scheme is as follows: acquiring initial traffic volume values corresponding to N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; the traffic volume is used for representing the traffic volume on the candidate road; respectively determining first predicted passing times corresponding to N candidate roads by using traffic initial values corresponding to the candidate roads to obtain N first predicted passing times; and determining a first traffic distribution optimization value of a target road by utilizing the traffic prediction value and N first prediction transit times between the first place and the second place, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer which is more than or equal to 0 and less than or equal to N.
Description
Technical Field
The present disclosure relates to the field of computer technology, and in particular, to the field of road traffic distribution prediction.
Background
In the technical field of traffic planning, in order to predict traffic distribution of a target road between two places, only the influence of the traffic time of a single road section on traffic is often considered, and the traffic pattern of a global transportation road network in an equilibrium state is ignored. In addition, the influence of other travel modes on traffic diversion is not considered in the traditional prediction method, so that the accuracy of the determined road traffic distribution is low and the effect is poor.
Disclosure of Invention
The disclosure provides a method and a device for determining road traffic distribution.
According to an aspect of the present disclosure, there is provided a method of determining road traffic distribution, the method may include the steps of:
acquiring initial traffic volume values corresponding to N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; the traffic volume is used for representing the traffic volume on the candidate road;
respectively determining first predicted passing times corresponding to the N candidate roads by using traffic initial values corresponding to the candidate roads to obtain N first predicted passing times;
and determining a first traffic distribution optimization value of a target road by utilizing the traffic prediction value between the first location and the second location and the N first prediction transit times, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer greater than or equal to 0 and less than or equal to N.
According to another aspect of the present disclosure, there is provided a road traffic distribution determining apparatus, including:
the traffic volume initial value acquisition module is used for acquiring traffic volume initial values corresponding to the N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; the traffic volume is used for representing the traffic volume on the road;
The first predicted passing time determining module is used for determining first predicted passing times corresponding to the N candidate roads by utilizing traffic initial values corresponding to the candidate roads to obtain N first predicted passing times;
the first traffic distribution optimizing value determining module is used for determining a first traffic distribution optimizing value of a target road by utilizing the traffic prediction value between the first place and the second place and the N first prediction transit times, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer which is more than or equal to 0 and less than or equal to N.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.
Through the process, on the basis of the original traffic volume prediction model, the corresponding target road traffic allocation amount when the generalized transit time of the user of the whole road network is minimum is determined through the user balance model. Meanwhile, the accuracy of the determined traffic distribution amount of the target road is improved by calculating the influence value of other travel modes on the traffic amount of the target road.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a traffic prediction method according to the present disclosure;
FIG. 2 is a flow chart of determining first traffic data according to the present disclosure;
FIG. 3 is a flow chart of determining second traffic data according to the present disclosure;
FIG. 4 is a flow chart for constructing a traffic prediction model according to the present disclosure;
FIG. 5 is a flow chart of determining predicted outflow data for a target area according to the present disclosure;
FIG. 6 is a flow chart of a method of traffic prediction according to the present disclosure;
FIG. 7 is a flow chart of a road traffic prediction method according to the present disclosure;
FIG. 8 is a flow chart of determining a second traffic distribution optimization value according to the present disclosure;
FIG. 9 is a schematic diagram of a traffic prediction device according to the present disclosure;
FIG. 10 is a schematic illustration of a traffic flow prediction device according to the present disclosure;
FIG. 11 is a schematic diagram of a road traffic prediction device according to the present disclosure;
fig. 12 is a block diagram of an electronic device for implementing a route planning method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, the present disclosure relates to a method of traffic prediction, which may include the steps of:
S101: determining first traffic data of a target area;
s102: predicting second traffic volume data of the target area based on the history related data of the target area; the history-related data includes data affecting a change in traffic volume of the target area;
s103: determining a traffic increase rate of the target area according to the first traffic data and the second traffic data;
s104: based on the traffic growth rate, predicted inflow data and predicted outflow data of the target area are determined.
The scheme provided by the embodiment can be applied to electronic equipment, such as a server, a computer, a tablet computer, a notebook computer and the like. Still more specifically, the present embodiment can execute the processing of S101 to S104 described above by the target application in the electronic apparatus. The target application may be determined according to actual situations, and the target application needs to have a certain data processing function.
The target area may be a target cell, a target administrative area, a target city, or an area defined in a map with a specific longitude and latitude, etc., which is not limited herein.
Traffic refers to the number of participants actually involved in traffic through a certain point or section on the aisle in both directions per unit time. Wherein the traffic participant may be a pedestrian, a non-motor vehicle, a motor vehicle, or the like.
The first traffic data may be annual traffic data collected in a plurality of locations or a plurality of sections selected in the target area. Specifically, it may be traffic data corresponding to the target area within the year of the year, where the year of the year may refer to the current year or the history-specified year. Taking Kunming city as a target area for illustration, selecting a plurality of stations at the periphery of the Kunming city as motor vehicle traffic volume data acquisition points, taking the current year as the base year, and taking the sum of motor vehicle traffic volume data acquired by all stations in the current year as first traffic volume data.
The second traffic data may be traffic data of the target area in the target year, and may specifically be traffic data of a future year. In the process of predicting the future traffic volume, the correlation between the traffic trip and the social economic development can be obtained by analyzing based on the future social economic development trend of the target area, so that the second traffic volume data is predicted by predicting the economic development index of the target area in each dimension. The historical relevant data for the target area may be economic development data related to the target area prior to the base year.
And determining the traffic increase rate of the target area according to the first traffic data and the second traffic data. Specifically, the ratio of the second traffic volume data to the first traffic volume may be taken as the traffic volume increase rate of the target area.
And determining predicted inflow data and predicted outflow data of the target area according to the traffic volume increase rate and the traffic inflow data and traffic outflow data of the target area in the base year, which are acquired in advance.
Through the process, the traffic growth rate of the target area can be obtained through the whole traffic volume data of the target area, and the predicted inflow data and the predicted outflow data of the target area can be determined with less consumption of manpower and material resources.
As shown in fig. 2, in one embodiment, S101 may include the sub-steps of:
s201: acquiring reference data of a target area, wherein the reference data comprises at least one of intermodulation station data, toll station data and mobile phone signaling data of the target area;
s202: first traffic volume data of the target area is determined using the reference data of the target area.
The reference data of the target area may be one of inter-station data, toll station data, and cell phone signaling data, for example, the first traffic volume data of the target area is determined using the toll station data as the reference data. The reference data of the target area may be two or three of the above three data, which will not be described here.
The intermodulation station data refers to traffic volume data of one or more positions acquired by arranging the intermodulation station at the position, and specifically may include acquisition lines, acquisition longitude and latitude, traffic volume and the like. The charging data can be based on the highway charging information record, and the obtained charging station traffic volume data are integrated; the mobile phone signaling data can be data generated by tracking mobile phone positioning signals in unit time by taking mobile phone communication equipment carried by a person as an acquisition object.
The first traffic data of the target area may be determined by using reference data of the target area, and the preprocessing may include data cleaning, data rejection, data conversion, and the like. And after preprocessing the data, performing set counting processing on the multi-source data according to a predetermined acquisition site to acquire traffic data based on the site. And finally, correcting traffic data of each site based on the mobile phone signaling data.
Through the above process, the first traffic volume data of the target area is determined based on at least one of the inter-station data, the toll station data and the mobile phone signaling data of the target area, and the data source is reliable and the accuracy is high.
As shown in fig. 3, in one embodiment, step S102 may include the sub-steps of:
s301: predicting relevant data of the target area in the target year based on historical relevant data of the target area;
s302: and inputting the related data of the target year into a pre-constructed traffic volume prediction model to obtain second traffic volume data.
The historical related data of the target area may be historical related data of the target area in the base year and years before the base year, and the related data of the target area in the target year may be related data corresponding to the target area in a future year. For example, in order to determine relevant data corresponding to year 2030 (the target year) in Kunming, prediction is made based on historical relevant data corresponding to year 2001-2020 (the base year).
In one embodiment, the history-related data includes at least one of demographic data, GDP data, road mileage data, and passenger data for the target area.
For example, to predict demographics of a target area in a target year, a combination of quantification and targeting may be used by performing a statistical analysis of the demographics of the target area. According to the historical development rule, a preset mathematical model is adopted to analyze the future development trend of population data, and then the population number of the target area in the target year is determined.
Other relevant data prediction methods are similar to population data prediction methods, and will not be described in detail herein.
After obtaining each relevant data of the target area in the target year, the second traffic volume data can be determined based on each relevant data. Specifically, the related data of the target year are respectively subjected to standardization processing, and the standardized result is input into a pre-constructed traffic volume prediction model to obtain second traffic volume data.
Through the process, the second traffic volume data of the target area is determined based on the pre-constructed traffic volume prediction model, and the mode of acquiring the second traffic volume data is simple, convenient and efficient and has higher accuracy.
As shown in fig. 4, in one embodiment, the method for constructing the pre-traffic prediction model includes:
s401: extracting corresponding annual traffic influence factors based on historical related data of the target area;
s402: and calculating based on the annual traffic impact factors and corresponding annual traffic data to obtain a traffic prediction model.
Specifically, the historical related data of the target area is subjected to factor analysis, and fewer annual traffic influence factors are extracted from more original variables. For example, annual population factors, annual GDP factors, annual highway mileage factors, and the like of the target area may be extracted from the history-related data.
The calculating based on the plurality of annual traffic influencing factors and the corresponding annual traffic data may be a non-linear regression based on the plurality of annual traffic influencing factors and the corresponding annual traffic data to obtain a traffic prediction model. The prediction model has the advantages that along with the increase of input data, corresponding traffic output data changes in an S shape, namely, in the initial development stage, the data or the scale increases slowly, the increasing trend becomes slow after a certain scale is reached, and in the final development stage, the output value does not increase along with the increase of the traffic factor value any more, and a stable value is reached.
As shown in fig. 5, in one embodiment, step S104 may include the sub-steps of:
s501: determining initial inflow data and initial outflow data of a target area;
s502: determining predicted inflow data of the target area by using the initial inflow data and the traffic volume increase rate;
s503: and determining the predicted outflow data of the target area by using the initial outflow data and the traffic volume increase rate.
The initial inflow data and the initial outflow data of the target area in the base year can be obtained after related data is acquired according to the base year OD table or calculated based on the base year traffic volume data of the target area. For example, from the toll gate data of the target area, the vehicle inflow data and the vehicle outflow data of the target area in the year of the year can be determined. The traffic growth rate can then be used to determine predicted inflow data and predicted outflow data, respectively, for the target area.
As shown in fig. 6, the present disclosure relates to a method of predicting traffic flow, which may include the steps of:
s601: acquiring first vehicle data leaving a first location and second vehicle data arriving at a second location;
s602: determining a vehicle flow prediction initial value between a first place and a second place according to the first vehicle data and the second vehicle data, wherein the vehicle flow prediction initial value comprises a first vehicle flow prediction initial value and a second vehicle flow prediction initial value which are influenced by different factors;
s603: a traffic flow prediction optimization value between the first location and the second location is determined using at least one of the first traffic flow prediction initial value and the second traffic flow prediction initial value.
The first vehicle data includes traffic data of a base year leaving the first location and traffic data of a target year leaving the first location. The second vehicle data includes traffic data of the base year reaching the second location and traffic data of the target year reaching the second location. The first vehicle data and the second vehicle data may be acquired according to steps S101-S104, which are not described herein.
A traffic flow prediction initial value between the first location and the second location, that is, a traffic flow prediction initial value that starts from the first location and reaches the second location, is determined based on the first vehicle data and the second vehicle data.
The different factors may include a first factor and a second factor, wherein the first factor may be a macroscopic factor affecting regional development of economy, politics, and the like. The first traffic flow prediction initial value affected by the first factor may be a traffic flow prediction initial value obtained by comprehensively considering economic development and policy influence of each region under the condition that the road network structure remains unchanged.
The second factor may be a traffic factor corresponding to the newly-built traffic project, and the second initial value of the traffic prediction affected by the second factor may be an initial value of the traffic prediction affected by the newly-built road project and the increased transportation supply capacity.
Determining a traffic flow prediction optimal value between the first location and the second location using at least one of the first traffic flow prediction initial value and the second traffic flow prediction initial value, comprising:
the optimized value of the traffic flow prediction between the first location and the second location may be determined using only the first traffic flow prediction initial value or the second traffic flow prediction initial value. Alternatively, the first and second initial values of the vehicle flow rate prediction may be weighted and summed, and the result of the summation may be used as the optimal value of the vehicle flow rate prediction between the first and second points. The weights of the first traffic prediction initial value and the second traffic prediction initial value may be set according to needs, for example, both weights may be set to 0.5, when the construction time of the newly added road project is far from the current time, the weight occupied by the second traffic prediction initial value may be properly adjusted down, and specific values may be set according to needs, which is not limited herein.
In one embodiment, the method for determining the initial value of the first traffic prediction includes:
acquiring a first traffic flow initial value, wherein the first traffic flow initial value is the traffic flow from a first place to a second place;
a first vehicle flow prediction initial value is determined using the first vehicle data, the second vehicle data, and the first vehicle flow initial value.
The initial value of the first traffic flow may be a traffic flow from the first place to the second place in the base year, and the base year may be 2020, 2019, etc., which is not limited herein.
A first vehicle flow prediction initial value is determined using the first vehicle data, the second vehicle data, and the first vehicle flow initial value. Specifically, the calculation can be performed by using the following formula (1):
wherein Q is ij A first vehicle flow prediction initial value is represented,
represents a first initial value of the vehicle flow, O i Representing first vehicle data leaving the ith first location, d j Second vehicle data representing arrival at the jth second place,/second vehicle data representing arrival at the jth second place>
First vehicle pre-initiation data representing departure from the ith first location,/second vehicle pre-initiation data representing departure from the ith first location>
Representing second vehicle initial data arriving at the jth second place, f () represents a calculation formula.
The selection methods according to the calculation formula f () include a constant growth coefficient method, an average growth coefficient method, a detritus method, a flett method, and a fonis method, and the expression f () corresponding to the different selection methods is different and is not limited herein.
The constraint that the regional traffic inflow and the regional traffic outflow are kept is required to be met by calculation according to the formula (1), namely: the total amount of traffic flowing in each target area in the region is equal to the total amount of traffic flowing out of each target area, and the following constraint conditions are met specifically:
wherein o is i Representing first vehicle prediction data leaving i-land, d j And the second vehicle prediction data reaching the j-land is represented, and Q represents the total traffic quantity of the region.
In one embodiment, the second initial value of the vehicle flow prediction may be determined based on the transit time before and after the creation of the new road project. When the first traffic flow prediction initial value is not 0, a second traffic flow prediction initial value is determined using the first traffic flow prediction initial value and a transit time between the first location and the second location. Specifically, the calculation can be performed by using the following formula (2):
wherein Q is ij ' represents a second predicted initial value of the flow, Q ij Representing a first predicted initial value, t ij Representing a first transit time, t, from an ith first location to a jth second location ij ' represents a second transit time from the ith first location to the jth second location, and gamma represents a gravity model parameter.
Before and after the new road project is built, the passing efficiency from i land to j land is improved, and the passing time is shortened, namely t ij ′<t ij 。t ij ' and t ij The difference value reflects the improvement degree of the passing efficiency from i ground to j ground, and the larger the difference value is, more vehicles are attracted to select a target area to pass, and then the larger the second vehicle flow prediction initial value is.
In the case where the first vehicle flow rate prediction initial value is 0, the second vehicle flow rate prediction initial value is determined using the first vehicle data, the second vehicle data, and the transit time between the first location and the second location. Specifically, the calculation can be performed by using the following formula (3):
wherein Q is ij ' represents a second initial value of the flow prediction, O i Representing first vehicle data leaving the ith first location, d j Representing second vehicle data arriving at a jth second location, t ij Representing a first transit time, t, from an ith first location to a jth second location ij ' represents a second transit time from the ith first location to the jth second location, and k, alpha, beta, gamma each represent a gravity model parameter.
The values of the gravity model parameters k, alpha, beta and gamma can be calculated based on a base year road network to obtain a base year travel time matrix, and regression analysis is carried out on the base year traffic data, wherein the traffic data can be data in an OD table corresponding to a first place and a second place. For example, k, α, β, γ obtained by regression analysis may be 1.045,0.863,0.758 and 0.434, respectively, and the gravity model parameter may take other values, which are not limited herein.
As shown in fig. 7, the present disclosure relates to a road traffic prediction method, which may include the steps of:
s701: acquiring initial traffic volume values corresponding to N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; traffic volume is used to characterize traffic volume on a road;
s702: determining first predicted passing times corresponding to N candidate roads by using traffic initial values corresponding to the candidate roads to obtain N first predicted passing times;
s703: and determining a first traffic distribution optimization value of a target road by utilizing the traffic prediction value and N first prediction transit times between the first place and the second place, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer which is more than or equal to 0 and less than or equal to N.
The candidate road may be one or more of a plurality of travel modes between the first location and the second location, such as a road, a railway, etc. Preferably, the N candidate roads may be a plurality of roads between the first location and the second location, N may be 1,2,3, etc., which is not limited herein.
The traffic volume initial value is used to characterize the traffic volume on the candidate road, and may specifically be the number of vehicles per unit time, for example, the unit time may be set to 1 hour, 1 day, 1 year, or the like, which is not limited herein.
And obtaining N traffic volume initial values corresponding to the N candidate roads, wherein the N traffic volume initial values are required to meet a traffic volume balancing model, namely the sum of the N traffic volume initial values is equal to a traffic volume predicted value which starts from a first place and reaches a second place. For example, a total of 3 candidate roads are provided between the first location and the second location, and the predicted traffic volume value obtained in advance from the first location and reaching the second location is 10000 times/day, and when the initial traffic volume value of the 1 st candidate road is 300 times/day and the initial traffic volume value of the 2 nd candidate road is 600 times/day, the initial traffic volume value of the 3 rd candidate road is 100 times/day.
According to the initial value of the traffic volume corresponding to each candidate road, the first predicted passing time corresponding to each candidate road can be determined. Under the condition that the traffic capacity of the N candidate roads is fixed, according to the traffic volume initial value corresponding to each candidate road, the first predicted traffic time corresponding to each candidate road can be determined. For example, the 3 candidate roads are all bidirectional 4-lane roads, and based on the traffic volume initial values of the 3 candidate roads, 3 first predicted traffic times can be calculated, for example, the first predicted traffic time of the 1 st candidate road is 2 hours, that is, the traffic time required to reach the second location along the 1 st candidate road from the first location is 2 hours. Accordingly, the first predicted traffic time of the 2 nd candidate road is 4 hours, the first predicted traffic time of the 3 rd candidate road is 0.5 hours, and the specific traffic time can determine different results according to different calculation modes, which is not exhaustive here.
And determining a first traffic distribution optimization value of the target road by utilizing the traffic prediction value between the first place and the second place which are acquired in advance and N first prediction transit times. Specifically, a user balancing model may be constructed based on the traffic prediction value between the first location and the second location and the N first predicted transit times so that the total transit time of the entire highway network (N candidate roads) is the shortest, and traffic of each candidate road is redistributed based on the user balancing model, thereby obtaining a first traffic distribution optimization value of the target road.
In one embodiment, determining the first predicted transit times for the N candidate roads using the initial traffic volume values for the candidate roads includes calculating using the following equation (4):
wherein t (x) a ) Representing a first predicted transit time corresponding to the a-th candidate road, c f Representing the vehicle travel time x of the a-th candidate road under the predetermined condition a The traffic volume initial value corresponding to the a candidate road is represented, C represents the traffic capacity of the a candidate road, and alpha and beta represent model parameters.
The traffic time of the a-th candidate road under the predetermined condition may be determined according to the running of the vehicle according to the speed limit standard corresponding to the a-th candidate road, or may be determined corresponding traffic time under the ideal condition that the road does not pass the vehicle, and may be obtained by a highway operation management department of the corresponding road section, which is not limited herein.
The traffic capacity of the candidate road section is related to the lane setting number of the road, for example, the traffic capacity of a bidirectional four-lane road is weaker than the traffic capacity of a bidirectional six-lane road.
The road resistance parameters alpha and beta of the model can be obtained after nonlinear regression is carried out on each parameter according to the relation between the vehicle speed and the passing time, and road resistance parameters corresponding to roads of different grades are different. For example, the α=0.25, β=2.2 for the highway determined by the parameter calibration, and the α=1.64, β=2.17 for the secondary highway.
For the selected candidate road, the vehicle passing time, passing capacity and road resistance parameters under the free flow condition are constant values, and the vehicle passing time is directly related to the traffic volume initial value of the candidate road.
In one embodiment, determining a first traffic distribution optimization value for the target link using the pre-acquired traffic prediction value between the first location and the second location and the N first predicted transit times includes calculating using the following formula (5):
wherein Z represents the total transit time of vehicles corresponding to N candidate roads, t (x) a ) Representing a first predicted transit time, x, corresponding to an a-th candidate road a The traffic volume initial value corresponding to the a-th candidate road is represented, and A represents the set of N candidate roads.
At this time, the constraint condition of traffic balance between the first location and the second location needs to be satisfied, that is:
wherein q ω Is the distributed traffic between the first location and the second location, i.e. the traffic that starts from the first location and reaches the second location.
And allocating traffic on the kth path between the first location and the second location.
In one embodiment, as shown in fig. 8, the method further comprises:
s801: acquiring a new travel mode between a first place and a second place;
s802: determining the quantity of influence of the newly added travel mode on the first traffic distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode;
s803: and determining a second traffic distribution optimized value of the target road by using the first traffic distribution optimized value and the influence quantity.
The newly added travel mode may be a travel mode different from the candidate road, for example, in the case where the original candidate road is a road, the newly added travel mode may be a railway travel mode, an aviation travel mode, a ship travel mode, or the like, and is not limited herein.
The newly added travel mode can transfer the traffic volume of part of the original target roads, and on the basis of determining the first traffic volume distribution optimized value, the second traffic volume distribution optimized value of the target roads is further determined according to the influence quantity of the newly added travel mode on the first traffic volume distribution optimized value of the target roads. Specifically, the magnitude of the influence quantity is related to the second predicted passing time corresponding to the newly added travel mode, and the shorter the second predicted passing time is, the larger the influence quantity is.
In one embodiment, determining the number of influences of the newly added travel mode on the first traffic distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode includes calculating by using the following formulas (6) and (7):
x′ a ＝x a ·P ijk formula (6)
Wherein x' a Representing the influence quantity of the newly added travel mode on the first traffic distribution optimization value of the a candidate road, and x a A first traffic distribution optimization value, P, representing the a-th candidate road ijk Representing the transfer ratio of the transport mode k from the ith first place to the jth second place, U ijk The transit time of the transportation form k from the ith first place to the jth second place is represented, n is a positive integer greater than or equal to 1, and e is a base of natural logarithm.
As shown in fig. 9, in another embodiment, the present invention further provides a traffic volume prediction device, including:
a traffic data determination module 901, configured to determine first traffic data of a target area;
a prediction module 902, configured to predict second traffic volume data of the target area based on the history related data of the target area; the history-related data includes data affecting a change in traffic volume of the target area;
a growth rate determining module 903, configured to determine a traffic growth rate of the target area according to the first traffic data and the second traffic data;
the inflow/outflow data determination module 904 determines predicted inflow data and predicted outflow data of the target area based on the traffic volume increase rate.
In one embodiment, the traffic data determination module includes:
the reference data acquisition sub-module is used for acquiring reference data of a target area, wherein the reference data comprises at least one of data acquired by a intermodulation station of the target area, data acquired by a toll station and mobile phone signaling data;
and the first traffic data determining sub-module is used for determining the first traffic data of the target area by using the reference data of the target area.
In one embodiment, a prediction module includes:
The target year related data determining sub-module is used for predicting the related data of the target area in the target year based on the historical related data of the target area;
and the second traffic data determining sub-module is used for inputting the related data of the target year into a pre-constructed traffic prediction model to obtain second traffic data.
In one embodiment, the method for constructing the traffic prediction model includes:
extracting corresponding annual traffic influence factors based on historical related data of the target area;
and calculating based on the annual traffic impact factors and corresponding annual traffic data to obtain a traffic prediction model.
In one embodiment, the ingress and egress data determination module includes:
the initial data determining module is used for determining initial inflow data and initial outflow data of the target area;
a predicted inflow data determining module for determining predicted inflow data of the target area using the initial inflow data and the traffic volume increase rate;
and the predicted outflow data determining module is used for determining the predicted outflow data of the target area by utilizing the initial outflow data and the traffic volume increase rate.
As shown in fig. 10, in another embodiment, the present invention further provides a traffic flow prediction device, including:
A vehicle data acquisition module 1001 for acquiring first vehicle data leaving a first location and second vehicle data arriving at a second location;
a traffic flow prediction initial value determining module 1002, configured to determine a traffic flow prediction initial value between a first location and a second location according to first vehicle data and second vehicle data, where the traffic flow prediction initial value includes a first traffic flow prediction initial value and a second traffic flow prediction initial value affected by different factors;
the traffic flow prediction optimization value determination module 1003 is configured to determine a traffic flow prediction optimization value between the first location and the second location using at least one of the first traffic flow prediction initial value and the second traffic flow prediction initial value.
In one embodiment, the method for determining the initial value of the first traffic prediction includes:
acquiring a first traffic flow initial value, wherein the first traffic flow initial value is the traffic flow from a first place to a second place;
a first vehicle flow prediction initial value is determined using the first vehicle data, the second vehicle data, and the first vehicle flow initial value.
As shown in fig. 11, in another embodiment, the present invention further provides a road traffic prediction apparatus, including:
The traffic volume initial value obtaining module 1101 is configured to obtain traffic volume initial values corresponding to the N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; traffic volume is used to characterize traffic volume on a road;
the first predicted traffic time determining module 1102 is configured to determine first predicted traffic times corresponding to N candidate roads by using traffic initial values corresponding to the candidate roads, so as to obtain N first predicted traffic times;
the first traffic distribution optimization value determining module 1103 is configured to determine a first traffic distribution optimization value of a target road by using a traffic prediction value between a first location and a second location and N first predicted transit times, where the target road is an ith candidate road in the N candidate roads, and i is a positive integer greater than or equal to 0 and less than or equal to N.
In one embodiment, the road traffic prediction apparatus further includes:
the newly added travel mode acquisition module is used for acquiring the newly added travel mode between the first place and the second place;
the influence quantity determining module is used for determining the influence quantity of the newly added travel mode on the first traffic distribution optimization value of the target road by utilizing the second predicted passing time corresponding to the newly added travel mode;
And the second traffic distribution optimizing value determining module is used for determining a second traffic distribution optimizing value of the target road by utilizing the first traffic distribution optimizing value and the influence quantity.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 12 shows a schematic block diagram of an example electronic device 1200 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 12, the apparatus 1200 includes a computing unit 1201, which may perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the device 1200 may also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. An input/output (I/O) interface 1205 is also connected to the bus 1204.
Various components in device 1200 are connected to I/O interface 1205, including: an input unit 1206 such as a keyboard, mouse, etc.; an output unit 1207 such as various types of displays, speakers, and the like; a storage unit 1208 such as a magnetic disk, an optical disk, or the like; and a communication unit 1209, such as a network card, modem, wireless communication transceiver, etc. The communication unit 1209 allows the device 1200 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.
The computing unit 1201 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1201 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above, such as the prediction method. For example, in some embodiments, the prediction method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1200 via ROM 1202 and/or communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the prediction method described above may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to perform the prediction method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (13)
1. A method of determining road traffic distribution, comprising:
acquiring initial traffic volume values corresponding to N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; the traffic volume is used for representing the traffic volume on the candidate road;
respectively determining first predicted passing times corresponding to the N candidate roads by using traffic initial values corresponding to the candidate roads to obtain N first predicted passing times;
And determining a first traffic distribution optimization value of a target road by utilizing the traffic prediction value between the first location and the second location and the N first prediction transit times, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer greater than or equal to 0 and less than or equal to N.
2. The method of claim 1, wherein determining a first predicted transit time for the N candidate roads using the initial traffic volume values for the candidate roads comprises calculating using the following formula:
wherein t (x) a ) Representing a first predicted transit time corresponding to the a-th candidate road, c f Representing the vehicle travel time x of the a-th candidate road under the predetermined condition a The traffic volume initial value corresponding to the a candidate road is represented, C represents the traffic capacity of the a candidate road, and alpha and beta represent model parameters.
3. The method of claim 2, wherein the determining a first traffic distribution optimization value for a target link using the pre-obtained traffic prediction value between the first location and the second location and the N first predicted transit times comprises calculating using the following formula:
Wherein Z represents the total transit time of vehicles corresponding to N candidate roads, t (x) a ) Representing a first predicted transit time, x, corresponding to an a-th candidate road a The traffic volume initial value corresponding to the a-th candidate road is represented, and A represents the set of N candidate roads.
4. A method according to any one of claims 1-3, further comprising:
acquiring a newly added travel mode between the first place and the second place;
determining the quantity of influence of the newly added travel mode on the first traffic distribution optimization value of the target road by using the second predicted transit time corresponding to the newly added travel mode;
and determining a second traffic distribution optimized value of the target road by using the first traffic distribution optimized value and the influence quantity.
5. The method of claim 4, wherein determining the number of impacts of the newly added travel pattern on the first traffic allocation optimization value of the target link using the second predicted transit time corresponding to the newly added travel pattern comprises calculating using the following formula:
x′ a ＝x a ·P ijk ，
wherein x' a Representing the influence quantity of the newly added travel mode on the first traffic distribution optimization value of the a candidate road, and x a A first traffic distribution optimization value, P, representing the a-th candidate road ijk Representing the transfer ratio of the kth transportation mode from the ith first place to the jth second place, U ijk The transit time of the kth transportation mode from the ith first place to the jth second place is represented, n is a positive integer greater than or equal to 1, and e is the base of natural logarithm.
6. A road traffic distribution determining apparatus comprising:
the traffic volume initial value acquisition module is used for acquiring traffic volume initial values corresponding to the N candidate roads; the candidate road is a road between a first place and a second place, and N is an integer greater than or equal to 1; the traffic volume is used for representing the traffic volume on the road;
the first predicted passing time determining module is used for determining first predicted passing times corresponding to the N candidate roads by utilizing traffic initial values corresponding to the candidate roads to obtain N first predicted passing times;
the first traffic distribution optimizing value determining module is used for determining a first traffic distribution optimizing value of a target road by utilizing the traffic prediction value between the first place and the second place and the N first prediction transit times, wherein the target road is an ith candidate road in N candidate roads, and i is a positive integer which is more than or equal to 0 and less than or equal to N.
7. The apparatus of claim 6, wherein determining a first predicted transit time for the N candidate roads using the initial traffic volume values for the candidate roads comprises calculating using the following formula:
wherein t (x) a ) Representing a first predicted transit time corresponding to the a-th candidate road, c f Representing the vehicle travel time x of the a-th candidate road under the predetermined condition a The traffic volume initial value corresponding to the a candidate road is represented, C represents the traffic capacity of the a candidate road, and alpha and beta represent model parameters.
8. The apparatus of claim 7, wherein the determining a first traffic distribution optimization value for a target link using the pre-obtained traffic prediction value between the first location and the second location and the N first predicted transit times comprises calculating using the following formula:
wherein Z represents the total transit time of vehicles corresponding to N candidate roads, t (x) a ) Representing a first predicted transit time, x, corresponding to an a-th candidate road a The traffic volume initial value corresponding to the a-th candidate road is represented, and A represents the set of N candidate roads.
9. The apparatus of any of claims 6-8, further comprising:
The newly added travel mode acquisition module is used for acquiring a newly added travel mode between the first place and the second place;
the influence quantity determining module is used for determining the influence quantity of the newly added travel mode on the first traffic distribution optimization value of the target road by utilizing the second predicted passing time corresponding to the newly added travel mode;
and the second traffic distribution optimizing value determining module is used for determining a second traffic distribution optimizing value of the target road by using the first traffic distribution optimizing value and the influence quantity.
10. The apparatus of claim 9, wherein the determining the number of impacts of the newly added travel pattern on the first traffic allocation optimization value of the target link using the second predicted transit time corresponding to the newly added travel pattern comprises calculating using the following formula:
x′ a ＝x a ·P ijk ，
wherein x' a Representing the influence quantity of the newly added travel mode on the first traffic distribution optimization value of the a candidate road, and x a A first traffic distribution optimization value, P, representing the a-th candidate road ijk Representing the transfer ratio of the kth transportation mode from the ith first place to the jth second place, U ijk The transit time of the kth transportation mode from the ith first place to the jth second place is represented, n is a positive integer greater than or equal to 1, and e is the base of natural logarithm.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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