CN116071912A - Method and device for determining road traffic distribution - Google Patents

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

A method and device for determining road traffic volume distribution

Technical Field

The present disclosure relates to the field of computer technology, and in particular to the field of road traffic volume distribution prediction.

Background Art

In the field of traffic planning technology, in order to predict the traffic distribution of a target road between two places, only the impact of the travel time of a single section on the traffic volume is often considered, while the traffic pattern of the global transportation road network in a balanced state is ignored. In addition, traditional prediction methods do not consider the impact of other travel modes on traffic diversion, resulting in low accuracy and poor results in determining road traffic distribution.

Summary of the invention

The present disclosure provides a method and device for determining road traffic volume distribution.

According to one aspect of the present disclosure, a method for determining road traffic volume distribution is provided, and the method may include the following steps:

Obtaining initial traffic volume values corresponding to N candidate roads; the candidate roads are roads between the first location and the second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the vehicle flow on the candidate roads;

Using the initial traffic volume values corresponding to the candidate roads, respectively determine the first predicted travel times corresponding to the N candidate roads to obtain N first predicted travel times;

Using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times, a first traffic volume distribution optimization value of the target road is determined, and the target road is the i-th candidate road among the N candidate roads, where 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 device for determining road traffic volume distribution, comprising:

A traffic volume initial value acquisition module is used to acquire traffic volume initial values corresponding to N candidate roads; the candidate roads are roads between the first location and the second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the traffic volume on the road;

A first predicted travel time determination module, configured to determine the first predicted travel times corresponding to the N candidate roads by using the initial traffic volume values corresponding to the candidate roads, and obtain N first predicted travel times;

The first traffic volume distribution optimization value determination module is used to determine the first traffic volume distribution optimization value of the target road by using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times, wherein the target road is the i-th candidate road among the 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 an electronic device, comprising:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in any embodiment of the present disclosure.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to enable a computer to execute the method in any embodiment of the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method in any embodiment of the present disclosure is implemented.

Through the above process, on the basis of the original traffic volume prediction model, the target road traffic allocation corresponding to the minimum generalized travel time of users in the entire highway network is determined through the user equilibrium model. At the same time, by calculating the impact of other travel modes on the target road traffic volume, the accuracy of the determined target road traffic allocation is improved.

It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

FIG1 is a flow chart of a traffic volume prediction method according to the present disclosure;

FIG2 is a flow chart of determining first traffic volume data according to the present disclosure;

FIG3 is a flow chart of determining second traffic volume data according to the present disclosure;

FIG4 is a flow chart of constructing a traffic volume prediction model according to the present disclosure;

5 is a flow chart of determining predicted outflow data for a target area according to the present disclosure;

FIG6 is a flow chart of a vehicle flow prediction method according to the present disclosure;

FIG7 is a flow chart of a road traffic volume prediction method according to the present disclosure;

8 is a flow chart of determining a second traffic volume distribution optimization value according to the present disclosure;

FIG9 is a schematic diagram of a traffic volume prediction device according to the present disclosure;

FIG10 is a schematic diagram of a vehicle flow prediction device according to the present disclosure;

FIG11 is a schematic diagram of a road traffic volume prediction device according to the present disclosure;

FIG. 12 is a block diagram of an electronic device for implementing the route planning method according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

As shown in FIG1 , the present disclosure relates to a method for traffic volume prediction, which may include the following steps:

S101: Determine first traffic volume data of a target area;

S102: predicting second traffic volume data of the target area based on historical relevant data of the target area; the historical relevant data includes data that affects changes in traffic volume of the target area;

S103: Determine a traffic volume growth rate of the target area according to the first traffic volume data and the second traffic volume data;

S104: Determine predicted inflow data and predicted outflow data of the target area based on the traffic volume growth rate.

The solution provided in this embodiment can be applied to electronic devices, such as servers, computers, tablet computers, notebooks, etc. More specifically, this embodiment can perform the above-mentioned S101-S104 processing through a target application in the electronic device. Among them, the target application can be determined according to actual conditions, and the target application needs to have certain data processing functions.

The target area may be a target community, a target administrative district, a target city, or an area defined by specific longitude and latitude in a map, etc., which is not limited here.

Traffic volume refers to the number of participants in the two-way traffic passing through a certain location or section on the road in a unit of time. Traffic participants can be pedestrians, non-motor vehicles, motor vehicles, etc.

The first traffic volume data may be base year traffic volume data collected from multiple locations or multiple sections selected in the target area. Specifically, it may be the traffic volume data corresponding to the target area in the base year, wherein the base year may refer to the current year or a specified year in history. Taking Kunming as the target area as an example, multiple stations are selected as motor vehicle traffic volume data collection points in the periphery of Kunming city, and the current year is taken as the base year. The sum of the motor vehicle traffic volume data collected from each station in the current year can be used as the first traffic volume data.

The second traffic volume data may be the traffic volume data of the target area in the target year, and specifically may be the traffic volume data of a certain year in the future. Among them, the future traffic volume data of the target area has an obvious correlation with social and economic development. In the process of predicting future traffic volume, based on the future social and economic development trend of the target area, the correlation between traffic travel and social and economic development can be analyzed, and the second traffic volume data can be predicted by predicting the economic development indicators of the target area in various dimensions. The historical relevant data of the target area may be the economic development data related to the target area before the base year.

The traffic volume growth rate of the target area is determined according to the first traffic volume data and the second traffic volume data. Specifically, the ratio of the second traffic volume data to the first traffic volume data can be used as the traffic volume growth rate of the target area.

The predicted inflow data and predicted outflow data of the target area are determined based on the traffic volume growth rate and the pre-acquired traffic inflow data and traffic outflow data of the target area in the base year.

Through the above process, the traffic growth rate of the target area can be obtained through the overall traffic volume data of the target area, and the predicted inflow data and predicted outflow data of the target area can be determined with only a small amount of manpower and material resources.

As shown in FIG. 2 , in one implementation, S101 may include the following sub-steps:

S201: Acquire reference data of a target area, where the reference data includes at least one of traffic control station data, toll station data, and mobile phone signaling data of the target area;

S202: Determine first traffic volume data of the target area using reference data of the target area.

The reference data of the target area may be one of traffic station data, toll station data and mobile phone signaling data. For example, the toll station data is used as reference data to determine the first traffic volume data of the target area. The reference data of the target area may also be two or three of the above three types of data, which will not be described in detail here.

Traffic station data refers to traffic volume data collected at one or more locations by setting up traffic stations, which may include collecting routes, collecting longitude and latitude, and vehicle flow, etc. Toll data can be based on highway toll information records, integrating toll station traffic volume data; mobile phone signaling data can be collected from mobile phone communication devices carried by individuals, and the data generated by tracking mobile phone positioning signals within a unit of time.

The first traffic volume data of the target area is determined by using the reference data of the target area. The multi-source traffic data may be preprocessed first. The preprocessing may include data cleaning, data elimination, data conversion, etc. After the data is preprocessed, the multi-source data is aggregated according to the predetermined collection sites to obtain the site-based traffic volume data. Finally, the traffic data of each site is corrected 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 traffic station data, toll station data and mobile phone signaling data of the target area, and the data source is reliable and has high accuracy.

As shown in FIG. 3 , in one implementation, step S102 may include the following sub-steps:

S301: predicting relevant data of the target area in a target year based on historical relevant data of the target area;

S302: Inputting relevant data of the target year into a pre-built traffic volume prediction model to obtain second traffic volume data.

The historical relevant data of the target area may be the historical relevant data of the target area in the base year and several years before the base year, and the relevant data of the target area in the target year may be the relevant data corresponding to a certain year in the future. For example, in order to determine the relevant data corresponding to Kunming in 2030 (the target year), a forecast is made based on the historical relevant data corresponding to 2001-2020 (the base year).

In one embodiment, the historical related data includes at least one of population data, GDP data, highway mileage data, and passenger data of the target area.

For example, to predict the population data of the target area in the target year, a combination of quantitative and directional methods can be used to conduct statistical analysis on the population of the target area. According to its historical development law, a preset mathematical model is used to analyze the future development trend of the population data, and then the population of the target area in the target year is determined.

The prediction methods for other related data are similar to those for population data and will not be elaborated here.

After obtaining the relevant data of the target area in the target year, the second traffic volume data can be determined based on the relevant data. Specifically, the relevant data of the target year are standardized respectively, and the standardized results are input into the pre-built traffic volume prediction model to obtain the second traffic volume data.

Through the above process, the second traffic volume data of the target area is determined based on the pre-built traffic volume prediction model, and the method of obtaining the second traffic volume data is simple, efficient and has high accuracy.

As shown in FIG4 , in one embodiment, the construction method of the pre-traffic volume prediction model includes:

S401: extracting corresponding annual traffic impact factors based on historical relevant data of the target area;

S402: Calculate based on the annual traffic impact factor and the corresponding annual traffic volume data to obtain a traffic volume prediction model.

Specifically, factor analysis is performed on the historical data of the target area to extract a few annual traffic impact factors from more original variables. For example, the annual population factor, annual GDP factor, annual highway mileage factor, etc. of the target area can be extracted from the historical data.

Based on the above multiple annual traffic impact factors and the corresponding annual traffic volume data, the traffic volume prediction model can be obtained by performing nonlinear regression based on multiple annual traffic impact factors and the corresponding annual traffic volume data. As the input data of the prediction model grows, the corresponding traffic volume output data shows an S-shaped change, that is, in the early stage of development, the data or scale grows slowly, and the growth trend slows down after reaching a certain scale. At the end of the development, the output value no longer grows with the growth of the traffic factor value, and reaches a stable value.

As shown in FIG. 5 , in one implementation, step S104 may include the following sub-steps:

S501: Determine initial inflow data and initial outflow data of a target area;

S502: Determine predicted inflow data for the target area using the initial inflow data and the traffic volume growth rate;

S503: Determine the predicted outflow data of the target area using the initial outflow data and the traffic volume growth rate.

The initial inflow data and initial outflow data of the target area in the base year can be determined by obtaining relevant data from the base year OD table, or calculated based on the base year traffic volume data of the target area. For example, based on the toll station data of the target area, the vehicle inflow data and vehicle outflow data of the target area in the base year can be determined. Then, the predicted inflow data and predicted outflow data of the target area can be determined respectively using the traffic volume growth rate.

As shown in FIG6 , the present disclosure relates to a method for predicting vehicle flow, which may include the following steps:

S601: Acquire first vehicle data leaving a first location and second vehicle data arriving at a second location;

S602: Determine, based on the first vehicle data and the second vehicle data, an initial value of vehicle flow prediction between the first location and the second location, wherein the initial value of vehicle flow prediction includes a first initial value of vehicle flow prediction and a second initial value of vehicle flow prediction affected by different factors;

S603: Determine an optimized value of the traffic flow prediction 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.

The first vehicle data includes the traffic volume data leaving the first location in the base year and the traffic volume data leaving the first location in the target year. The second vehicle data includes the traffic volume data arriving at the second location in the base year and the traffic volume data arriving at the second location in the target year. The first vehicle data and the second vehicle data can be obtained according to steps S101-S104, which will not be repeated here.

According to the first vehicle data and the second vehicle data, an initial value of the predicted traffic volume between the first location and the second location is determined, that is, an initial value of the predicted traffic volume starting from the first location and arriving at the second location.

Different factors may include a first factor and a second factor, wherein the first factor may be a macro factor such as economy and politics that affects regional development. The first vehicle flow forecast initial value affected by the first factor may be a vehicle flow forecast initial value obtained by comprehensively considering the economic development and policy impact of each region while keeping the road network structure unchanged.

The second factor may be a traffic factor corresponding to a newly built traffic project, and the second traffic flow prediction initial value affected by the second factor may be a traffic flow prediction initial value obtained by the construction of a new highway project and the increase in transportation supply capacity.

Determining a traffic flow prediction optimization value between a first location and a second location using at least one of a first traffic flow prediction initial value and a second traffic flow prediction initial value includes:

The optimized value of the traffic flow prediction between the first location and the second location can be determined by using only the first traffic flow prediction initial value or the second traffic flow prediction initial value. Alternatively, the first traffic flow prediction initial value and the second traffic flow prediction initial value can be weighted and summed, and the summation result can be used as the optimized value of the traffic flow prediction between the first location and the second location. The weights of the first traffic flow prediction initial value and the second traffic flow prediction initial value can be set as needed. For example, both weights can be set to 0.5. When the construction time of the new highway project is far from the current time, the weight of the second traffic flow prediction initial value can be appropriately lowered. The specific value can be set as needed and is not limited here.

In one implementation, a method for determining the first vehicle flow prediction initial value includes:

Obtaining a first vehicle flow initial value, where the first vehicle flow initial value is the vehicle flow starting from the first location and arriving at the second location;

The first vehicle data, the second vehicle data and the first vehicle flow initial value are used to determine the first vehicle flow prediction initial value.

Among them, the initial value of the first traffic flow can be the traffic flow from the first location to the second location in the base year. The base year can be 2020, 2019, etc., and there is no limitation here.

The first vehicle data, the second vehicle data and the first vehicle flow initial value are used to determine the first vehicle flow prediction initial value. Specifically, the following formula (1) can be used for calculation:

表示第一车流量初始值，O_i表示离开第i个第一地点的第一车辆数据，d_j表示到达第j个第二地点的第二车辆数据，

表示离开第i个第一地点的第一车辆预初始数据，

表示到达第j个第二地点的第二车辆初始数据，f()表示计算式。Where, _Qij represents the initial value of the first vehicle flow prediction,

represents the initial value of the first vehicle flow, O _i represents the first vehicle data leaving the i-th first location, d _j represents the second vehicle data arriving at the j-th second location,

represents the initial data of the first vehicle leaving the i-th first location,

represents the initial data of the second vehicle arriving at the j-th second location, and f() represents a calculation formula.

The calculation formula f() is based on selection methods including constant growth coefficient method, average growth coefficient method, Detroit method, Follett method and Furniss method. The expressions f() corresponding to different selection methods are different and are not limited here.

The calculation according to formula (1) must meet the conservation constraints of regional traffic inflow and regional traffic outflow, that is, the total amount of traffic flowing into each target area in the region is equal to the total amount of traffic flowing out of each target area. Specifically, the following constraints should be met:

Among them, o _i represents the predicted data of the first vehicle leaving place i, d _j represents the predicted data of the second vehicle arriving at place j, and Q represents the total traffic volume in the area.

In one embodiment, the second traffic flow prediction initial value can be determined based on the travel time before and after the completion of the new highway project. When the first traffic flow prediction initial value is not 0, the second traffic flow prediction initial value is determined using the first traffic flow prediction initial value and the travel time between the first location and the second location. Specifically, the following formula (2) can be used for calculation:

Among them, _Qij ′ represents the initial value of the second traffic flow prediction, _Qij represents the initial value of the first traffic flow prediction, _tij represents the first travel time from the i-th first location to the j-th second location, _tij ′ represents the second travel time from the i-th first location to the j-th second location, and γ represents the gravity model parameter.

Before and after the completion of the new highway project, the travel efficiency from i to j is improved and the travel time is shortened, that is, t _ij ′<t _ij . The difference between t _ij ′ and t _ij reflects the degree of improvement in the travel efficiency from i to j. The larger the difference, the more vehicles will be attracted to choose the target area to pass, and the larger the initial value of the second traffic flow prediction.

When the initial value of the first vehicle flow prediction is 0, the initial value of the second vehicle flow prediction is determined by using the first vehicle data, the second vehicle data and the travel time between the first location and the second location. Specifically, the following formula (3) can be used for calculation:

Among them, _Qij ′ represents the initial value of the second vehicle flow prediction, _Oi represents the first vehicle data leaving the i-th first location, _dj represents the second vehicle data arriving at the j-th second location, _tij represents the first travel time from the i-th first location to the j-th second location, _tij ′ represents the second travel time from the i-th first location to the j-th second location, and k, α, β, and γ all represent gravity model parameters.

The values of the gravity model parameters k, α, β, and γ can be obtained by calculating the base year travel time matrix based on the base year road network, and by combining the base year traffic data through regression analysis, where the traffic data can be the data in the OD table corresponding to the first location and the second location. For example, k, α, β, and γ obtained by regression analysis can be 1.045, 0.863, 0.758, and 0.434, respectively. The gravity model parameters can also take other values, which are not limited here.

As shown in FIG. 7 , the present disclosure relates to a road traffic volume prediction method, which may include the following steps:

S701: Obtaining initial traffic volume values corresponding to N candidate roads; the candidate road is a road between a first location and a second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the traffic flow on the road;

S702: using the initial traffic volume values corresponding to the candidate roads, determining the first predicted travel times corresponding to the N candidate roads, and obtaining N first predicted travel times;

S703: Determine the first traffic volume distribution optimization value of the target road using the traffic volume prediction value between the first location and the second location and the N first predicted travel times acquired in advance, where the target road is the i-th candidate road among the N candidate roads, where i is a positive integer greater than or equal to 0 and less than or equal to N.

The candidate roads may be one or more of the multiple travel modes between the first location and the second location, such as roads, railways, etc. Preferably, the N candidate roads may be multiple roads between the first location and the second location, and N may be 1, 2, 3, etc., which is not limited here.

The initial traffic volume value is used to characterize the traffic volume on the candidate road, and specifically can be the number of motor vehicles per unit time. For example, the unit time can be set to 1 hour, 1 day or 1 year, etc., which is not limited here.

The traffic volume initial values corresponding to the N candidate roads obtained are N traffic volume initial values, where the N traffic volume initial values need to satisfy the traffic volume equilibrium model, that is, the sum of the N traffic volume initial values should be equal to the traffic volume prediction value starting from the first location and arriving at the second location. For example, there are a total of 3 candidate roads between the first location and the second location, and the traffic volume prediction value starting from the first location and arriving at the second location is 10,000 vehicles/day. When the traffic volume initial value of the first candidate road is 300 vehicles/day and the traffic volume initial value of the second candidate road is 600 vehicles/day, the traffic volume initial value of the third candidate road is 100 vehicles/day.

According to the initial value of the traffic volume corresponding to each candidate road, the first predicted travel time corresponding to each candidate road can be determined. When the capacity of N candidate roads is determined, according to the initial value of the traffic volume corresponding to each candidate road, the first predicted travel time corresponding to each candidate road can be determined. For example, all three candidate roads are two-way four-lane roads. Based on the initial values of the traffic volume of the above three candidate roads, three first predicted travel times can be calculated. For example, the first predicted travel time of the first candidate road is 2 hours, that is, starting from the first location, the travel time required to reach the second location along the first candidate road is 2 hours. Correspondingly, the first predicted travel time of the second candidate road is 4 hours, and the first predicted travel time of the third candidate road is 0.5 hours. The specific travel time can determine different results according to different calculation methods, which are not exhaustive here.

The first traffic volume distribution optimization value of the target road is determined by using the traffic volume prediction value between the first location and the second location and the N first predicted travel times obtained in advance. Specifically, a user equilibrium model can be constructed based on the traffic volume prediction value between the first location and the second location and the N first predicted travel times to minimize the total travel time of the entire highway network (N candidate roads), and the traffic volume of each candidate road is redistributed based on the user equilibrium model, thereby obtaining the first traffic volume distribution optimization value of the target road.

In one embodiment, the first predicted travel time corresponding to the N candidate roads is determined by using the initial traffic volume value corresponding to the candidate roads, including using the following formula (4) for calculation:

Wherein, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _cf represents the vehicle travel time of the a-th candidate road under predetermined conditions, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, C represents the travel capacity of the a-th candidate road, and α and β represent model parameters.

Among them, the vehicle travel time of the ath candidate road under predetermined conditions can be determined based on the vehicle traveling according to the speed limit standard corresponding to the ath candidate road, or it can be determined under ideal conditions where there are no passing vehicles on the road, and can be obtained through the highway operation management department of the corresponding section, which is not limited here.

The capacity of the candidate road section is related to the designed number of lanes of the road. For example, the capacity of a two-way four-lane highway is weaker than that of a two-way six-lane highway.

The road resistance parameters α and β of the model can be obtained by performing nonlinear regression on various parameters based on the relationship between vehicle speed and travel time. Road resistance parameters corresponding to different levels of roads are different. For example, the α of the expressway determined by parameter calibration is 0.25, β is 2.2, while the α of the secondary road is 1.64, β is 2.17.

For the selected candidate roads, the vehicle travel time, traffic capacity and road resistance parameters under free flow conditions are all fixed values, and the vehicle travel time is directly related to the initial traffic volume of the candidate roads.

In one embodiment, the first traffic volume distribution optimization value of the target road is determined by using the traffic volume prediction value between the first location and the second location and the N first predicted travel times obtained in advance, including using the following formula (5) for calculation:

Wherein, Z represents the total travel time of vehicles corresponding to N candidate roads, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, and A represents the set of N candidate roads.

At this time, the constraint condition of traffic flow balance between the first location and the second location must be met, namely:

为第一地点和第二地点间分配到第k条路径上的交通量。Among them, ^qω is the distributed traffic volume between the first location and the second location, that is, the traffic volume starting from the first location and arriving at the second location.

is the traffic volume allocated to the kth path between the first location and the second location.

In one embodiment, as shown in FIG8 , the method further includes:

S801: Acquire a new travel mode between a first location and a second location;

S802: Determine the impact of the new travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the new travel mode;

S803: Determine a second traffic volume distribution optimization value of the target road by using the first traffic volume distribution optimization value and the impact quantity.

Among them, the newly added travel mode can be a travel mode different from the candidate road. For example, when the original candidate road is a highway, the newly added travel mode can be a railway travel mode, an air travel mode, a ship travel mode, etc., which is not limited here.

The newly added travel mode can transfer part of the traffic volume of the original target road. On the basis of determining the first traffic volume allocation optimization value, the second traffic volume allocation optimization value of the target road is further determined according to the impact of the newly added travel mode on the first traffic volume allocation optimization value of the target road. Specifically, the size of the impact amount is related to the second predicted travel time corresponding to the newly added travel mode. The shorter the second predicted travel time, the greater the impact amount.

In one embodiment, the second predicted travel time corresponding to the newly added travel mode is used to determine the impact of the newly added travel mode on the first traffic volume distribution optimization value of the target road, including using the following formula (6) and formula (7) for calculation:

x′ _a = _xa · _Pijk , Formula (6)

Wherein, _x′a represents the impact of the new travel mode on the first traffic volume allocation optimization value of the a-th candidate road, _xa represents the first traffic volume allocation optimization value of the a-th candidate road, _Pijk represents the transfer ratio of transportation mode k from the i-th first location to the j-th second location, _Uijk represents the travel time of transportation mode k from the i-th first location to the j-th second location, n is a positive integer greater than or equal to 1, and e is the base of the natural logarithm.

As shown in FIG9 , in another embodiment, the present invention further provides a traffic volume prediction device, comprising:

Traffic volume data determination module 901, used to determine first traffic volume data of a target area;

A prediction module 902 is used to predict second traffic volume data of the target area based on historical relevant data of the target area; the historical relevant data includes data that affects changes in traffic volume of the target area;

A growth rate determination module 903, for determining a traffic volume growth rate of a target area according to the first traffic volume data and the second traffic volume data;

The inflow and outflow data determination module 904 determines the predicted inflow data and predicted outflow data of the target area based on the traffic volume growth rate.

In one embodiment, the traffic volume data determination module includes:

A reference data acquisition submodule, used to acquire reference data of a target area, the reference data including at least one of data collected by an exchange station of the target area, data collected by a toll station, and mobile phone signaling data;

The first traffic volume data determining submodule is used to determine the first traffic volume data of the target area by using the reference data of the target area.

In one embodiment, the prediction module includes:

A target year related data determination submodule is used to predict the target area's related data in the target year based on the target area's historical related data;

The second traffic volume data determination submodule is used to input the relevant data of the target year into a pre-built traffic volume prediction model to obtain the second traffic volume data.

In one embodiment, the traffic volume prediction model is constructed by:

Based on the historical relevant data of the target area, extract the corresponding annual traffic impact factors;

The traffic volume prediction model is obtained by calculation based on the annual traffic impact factors and the corresponding annual traffic volume data.

In one embodiment, the inflow and outflow data determination module includes:

An initial data determination module, used to determine initial inflow data and initial outflow data of a target area;

A predicted inflow data determination module, for determining predicted inflow data of a target area using initial inflow data and a traffic volume growth rate;

The predicted outflow data determination module is used to determine the predicted outflow data of the target area by using the initial outflow data and the traffic volume growth rate.

As shown in FIG. 10 , in another embodiment, the present invention further provides a vehicle flow prediction device, comprising:

The vehicle data acquisition module 1001 is used to acquire first vehicle data leaving a first location and second vehicle data arriving at a second location;

A vehicle flow prediction initial value determination module 1002 is used to determine a vehicle flow prediction initial value between a first location and a second location based on the first vehicle data and the second vehicle data, wherein the vehicle flow prediction initial value includes a first vehicle flow prediction initial value and a second vehicle flow prediction initial value affected by different factors;

The vehicle flow prediction optimization value determination module 1003 is used to determine the vehicle flow prediction optimization value between the first location and the second location by using at least one of the first vehicle flow prediction initial value and the second vehicle flow prediction initial value.

In one implementation, a method for determining the first vehicle flow prediction initial value includes:

Obtaining a first vehicle flow initial value, where the first vehicle flow initial value is the vehicle flow starting from the first location and arriving at the second location;

The first vehicle data, the second vehicle data and the first vehicle flow initial value are used to determine the first vehicle flow prediction initial value.

As shown in FIG. 11 , in another embodiment, the present invention further provides a road traffic volume prediction device, comprising:

The traffic volume initial value acquisition module 1101 is used to acquire the traffic volume initial values corresponding to N candidate roads; the candidate road is the road between the first location and the second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the traffic volume on the road;

The first predicted travel time determination module 1102 is used to determine the first predicted travel time corresponding to N candidate roads by using the initial traffic volume value corresponding to the candidate roads, and obtain N first predicted travel times;

The first traffic volume distribution optimization value determination module 1103 is used to determine the first traffic volume distribution optimization value of the target road by using the pre-acquired traffic volume prediction value between the first location and the second location and N first predicted travel times, where the target road is the i-th candidate road among 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 volume prediction device further includes:

A new travel mode acquisition module is added, used to acquire a new travel mode between a first location and a second location;

An impact quantity determination module, used to determine the impact quantity of the newly added travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode;

The second traffic volume distribution optimization value determination module is used to determine the second traffic volume distribution optimization value of the target road by using the first traffic volume distribution optimization value and the impact quantity.

According to an embodiment 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 an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and/or required herein.

As shown in FIG12 , the device 1200 includes a computing unit 1201, which can 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 can 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.

A number of components in the device 1200 are connected to the I/O interface 1205, including: an input unit 1206, such as a keyboard, a mouse, etc.; an output unit 1207, such as various types of displays, speakers, etc.; a storage unit 1208, such as a disk, an optical disk, etc.; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1209 allows the device 1200 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The computing unit 1201 may be a variety of general and/or special processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, 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, which is tangibly contained in a machine-readable medium, such as a storage unit 1208. In some embodiments, part or all of the computer program may be loaded and/or installed on the device 1200 via the ROM 1202 and/or the 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 in any other appropriate manner (e.g., by means of firmware).

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and/or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions/operations specified in the flow chart and/or block diagram. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims (13)

1. A method for determining road traffic volume distribution, comprising: Obtaining initial traffic volume values corresponding to N candidate roads; the candidate roads are roads between a first location and a second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the vehicle flow on the candidate roads; Using the initial traffic volume values corresponding to the candidate roads, respectively determine the first predicted travel times corresponding to the N candidate roads to obtain N first predicted travel times; Using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times, a first traffic volume distribution optimization value of the target road is determined, and the target road is the i-th candidate road among the N candidate roads, where i is a positive integer greater than or equal to 0 and less than or equal to N. 2. The method according to claim 1, wherein determining the first predicted travel time corresponding to the N candidate roads using the initial traffic volume value corresponding to the candidate roads comprises calculating using the following formula:

Wherein, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _cf represents the vehicle travel time of the a-th candidate road under predetermined conditions, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, C represents the travel capacity of the a-th candidate road, and α and β represent model parameters. 3. The method according to claim 2, wherein the determining the first traffic volume distribution optimization value of the target road by using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times comprises calculating by using the following formula:

Wherein, Z represents the total travel time of vehicles corresponding to N candidate roads, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, and A represents the set of N candidate roads. 4. The method according to any one of claims 1 to 3, further comprising: Acquire a new travel mode between the first location and the second location; Determine the impact of the newly added travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode; A second traffic volume distribution optimization value of the target road is determined by using the first traffic volume distribution optimization value and the impact quantity. 5. The method according to claim 4, wherein the determining the impact of the newly added travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode comprises calculating by using the following formula: x′ _a = _xa · _Pijk ,

Wherein, _x′a represents the impact of the new travel mode on the first traffic volume allocation optimization value of the a-th candidate road, _xa represents the first traffic volume allocation optimization value of the a-th candidate road, _Pijk represents the transfer ratio of the k-th transportation mode from the i-th first location to the j-th second location, _Uijk represents the travel time of the k-th transportation mode from the i-th first location to the j-th second location, n is a positive integer greater than or equal to 1, and e is the base of the natural logarithm. 6. A device for determining road traffic volume distribution, comprising: A traffic volume initial value acquisition module is used to acquire the traffic volume initial values corresponding to N candidate roads; the candidate roads are roads between the first location and the second location, and N is an integer greater than or equal to 1; the traffic volume is used to characterize the traffic volume on the road; A first predicted travel time determination module, configured to determine the first predicted travel times corresponding to the N candidate roads by using the initial traffic volume values corresponding to the candidate roads, and obtain N first predicted travel times; The first traffic volume distribution optimization value determination module is used to determine the first traffic volume distribution optimization value of the target road by using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times, wherein the target road is the i-th candidate road among the N candidate roads, and i is a positive integer greater than or equal to 0 and less than or equal to N. 7. The device according to claim 6, wherein determining the first predicted travel time corresponding to the N candidate roads using the initial traffic volume value corresponding to the candidate roads comprises calculating using the following formula:

Wherein, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _cf represents the vehicle travel time of the a-th candidate road under predetermined conditions, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, C represents the travel capacity of the a-th candidate road, and α and β represent model parameters. 8. The device according to claim 7, wherein the determining the first traffic volume distribution optimization value of the target road by using the pre-acquired traffic volume prediction value between the first location and the second location and the N first predicted travel times comprises calculating by using the following formula:

Wherein, Z represents the total travel time of vehicles corresponding to N candidate roads, t( _xa ) represents the first predicted travel time corresponding to the a-th candidate road, _xa represents the initial value of the traffic volume corresponding to the a-th candidate road, and A represents the set of N candidate roads. 9. The device according to any one of claims 6 to 8, further comprising: A newly added travel mode acquisition module, used to acquire a newly added travel mode between the first location and the second location; An impact quantity determination module, used to determine the impact quantity of the newly added travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode; The second traffic volume distribution optimization value determination module is used to determine the second traffic volume distribution optimization value of the target road by using the first traffic volume distribution optimization value and the impact quantity. 10. The device according to claim 9, wherein the determining the impact of the newly added travel mode on the first traffic volume distribution optimization value of the target road by using the second predicted travel time corresponding to the newly added travel mode comprises calculating by using the following formula: x′ _a = _xa · _Pijk ,

Wherein, _x′a represents the impact of the new travel mode on the first traffic volume allocation optimization value of the a-th candidate road, _xa represents the first traffic volume allocation optimization value of the a-th candidate road, _Pijk represents the transfer ratio of the k-th transportation mode from the i-th first location to the j-th second location, _Uijk represents the travel time of the k-th transportation mode from the i-th first location to the j-th second location, n is a positive integer greater than or equal to 1, and e is the base of the natural logarithm. 11. An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5. 12. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5. 13. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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