CN109409584A

CN109409584A - Tourism trip method and system for planning based on traffic flow forecasting

Info

Publication number: CN109409584A
Application number: CN201811166612.2A
Authority: CN
Inventors: 不公告发明人
Original assignee: Hangzhou City Science And Technology Co Ltd
Current assignee: Hangzhou City Science And Technology Co Ltd
Priority date: 2018-10-08
Filing date: 2018-10-08
Publication date: 2019-03-01

Abstract

The present invention provides the tourism trip method and system for planning based on traffic flow forecasting, this method comprises: determining the plan trip period comprising the plan travel time, determine period corresponding to trip period in corresponding former years are planned, determine the target area comprising trip purpose ground, obtain the traffic flow histories data of the target area within corresponding period in former years, difference degree according to each sampling area data on flows in traffic flow histories data divides target area, obtain multiple traffic zones, the traffic flow histories data of distribution and traffic zone within corresponding period in former years according to each traffic zone, planning trip period and/or traffic path.This method and system can satisfy the demand that user formulates plan of travel at a specified future date in advance, influence route experience and progress because catching up with period and the section of traffic peak on the way to avoid in route.

Description

Travel planning method and system based on traffic flow prediction

Technical Field

The invention relates to the technical field of traffic prediction, in particular to a travel planning method and system based on traffic flow prediction.

Background

With the improvement of living standard of people, people who choose to go out to travel more and more, the travel becomes a main choice for people to relax, relax and entertain in holidays or leisure time. Whether the whole tourism process is smooth or not and whether the tourism experience is good or bad are all closely related to the traffic condition, if the conditions such as traffic jam and the like occur in the process of going out or returning, the tourism mood of people can be greatly influenced, and even the subsequent whole tourism planning is disturbed. Therefore, people want to be able to avoid rush hour and section by adjusting the planned travel time and route when planning an outgoing travel in advance, especially a self-driving travel or a travel by car.

Currently, a related method for predicting traffic flow is mainly near-term prediction, such as predicting traffic conditions after several hours, and a travel plan such as travel planning generally needs to be made several days, weeks or even months ahead, so that the existing way for predicting near-term traffic conditions cannot meet the demand of people for predicting far-term traffic conditions.

Disclosure of Invention

Objects of the invention

In order to overcome at least one defect in the prior art and meet the requirement of a user for making a forward trip plan in advance, the invention provides the following technical scheme.

(II) technical scheme

As a first aspect of the present invention, the present invention discloses a travel planning method based on traffic flow prediction, comprising:

determining a planned travel time period containing planned travel time;

determining a corresponding period of the past year corresponding to the planned travel time period;

determining a target region containing the travel destination, and acquiring historical traffic flow data of the target region in the corresponding period of the past year;

dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions;

and planning a travel time period and/or a travel route according to the distribution of each traffic area and the historical data of the traffic flow of the traffic area in the corresponding period of the past year.

In one possible embodiment, the planned travel period is one natural week in duration.

In one possible embodiment, the determining the corresponding time period of the past year corresponding to the planned trip period includes:

judging whether the planned travel time period contains a holiday or a holiday due to the holiday, and taking the previous year period containing the holiday or the holiday as the corresponding previous year period under the condition that the planned travel time period contains the holiday or the holiday; and under the condition that the planned travel time interval does not contain holidays and rest dates, determining the corresponding period of the next year according to the climate condition of the planned travel time interval.

In a possible implementation, the determining the corresponding time period of the past year according to the climate condition of the planned trip period includes:

acquiring the average value of the meteorological element predicted values of the planned trip time period;

determining a first time period of the past year in synchronization with the planned travel time period, and determining a second time period of the past year, wherein the second time period of the past year comprises the first time period of the past year and has a time span larger than the first time period of the past year;

determining a corresponding time period closest to the average value of the meteorological element predicted values in the planned travel time period in the second time period of the past year, and determining the corresponding time period as a corresponding time period of the past year; wherein the meteorological elements comprise at least one of: air temperature, humidity, wind power, ultraviolet index and air pollution index.

In a possible embodiment, the starting date of the second period of the past year is the same as or prior to the starting date of the first period of the past year, and in the case of the starting date prior to the starting date of the first period of the past year, the date deviation value between the two does not exceed a preset starting date deviation threshold value; and the ending date of the second time period of the previous year is the same as or later than the ending date of the first time period of the previous year, and under the condition that the ending date of the first time period of the previous year is later than the ending date of the first time period of the previous year, the date deviation value between the second time period of the previous year and the ending date does not exceed a preset ending date deviation threshold value.

In a possible implementation, one of the following is selected as the range of the target area: the system comprises an urban loop range, an urban rail transit loop range and a range with the radius taking the trip destination as the center as R, wherein R is a preset sampling radius.

In one possible embodiment, the traffic flow history data includes data collected at each traffic flow monitoring point in the target region during the corresponding period of the past year.

In a possible implementation manner, the dividing the target area according to the difference degree of the flow data of each sampling area in the traffic flow historical data includes:

selecting one sampling area from the target area as a reference area;

determining sampling areas which have space distances from the reference area not exceeding a preset distance threshold value and have same-ratio difference degrees from the flow data of the reference area not exceeding a preset flow difference threshold value in other sampling areas;

classifying the determined sampling area and the reference area into the same traffic area;

and repeating the steps for the sampling areas which are not classified into the traffic areas, and classifying the determined sampling areas and the newly selected reference areas into another traffic area until all the sampling areas are classified into the traffic areas.

In a possible embodiment, the corresponding time period of the past year is a time period of a year before the planned trip time period, or a time period of each year of N years before the planned trip time period, where N > 1.

As a second aspect of the present invention, the present invention discloses a travel planning system based on traffic flow prediction, comprising:

the trip time period determining module is used for determining a planned trip time period containing planned trip time;

a corresponding period determining module for determining a corresponding period of the past year corresponding to the planned trip period;

a historical data acquisition module, configured to determine a target region including the travel destination, and acquire historical traffic flow data of the target region in the corresponding period of the past year;

the target region dividing module is used for dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions;

and the travel planning module is used for planning a travel time interval and/or a travel route according to the distribution of each traffic area and the historical data of the traffic flow of the traffic areas in the corresponding period of the past year.

In a possible implementation, the duration of the planned travel period determined by the travel period determination module is a natural week.

In one possible implementation, the respective period determination module includes:

a first determining unit, configured to determine whether the planned travel time period includes a holiday or a holiday due to the holiday, and if the planned travel time period includes the holiday or the holiday, take a previous year period including the holiday or the holiday as a previous year corresponding period;

and the second determining unit is used for determining the corresponding period of the next year according to the climate condition of the planned travel period under the condition that the planned travel period does not contain holidays and rest dates.

In one possible implementation, the second determining unit includes:

the acquiring subunit is used for acquiring the average value of the meteorological element predicted values of the planned trip time period;

the time interval determining subunit is used for determining a first time interval of the past year which is synchronous with the planned travel time interval, and determining a second time interval of the past year which contains the first time interval of the past year and has a time interval span larger than the first time interval of the past year;

a period determining subunit, configured to determine, in the second period of the past year, a corresponding period closest to the average value of the predicted meteorological element values in the planned travel period, and determine the corresponding period as a corresponding period of the past year; wherein,

for the meteorological elements to include at least one of: air temperature, humidity, wind power, ultraviolet index and air pollution index.

In one possible implementation, the historical data acquisition module includes:

and the monitoring point acquisition unit is used for acquiring data acquired by each traffic flow monitoring point in the target region in the corresponding period of the past year as the historical traffic flow data.

In a possible implementation, the target zone partitioning module includes:

a reference selection unit configured to select one of the sampling areas from the target region as a reference area;

the area determining unit is used for determining sampling areas which have a spatial distance with the reference area not exceeding a preset distance threshold value and have the same-ratio difference degree with the flow data of the reference area not exceeding a preset flow difference threshold value in other sampling areas;

the area classification unit is used for classifying the determined sampling area and the reference area into the same traffic area;

and the circulating unit is used for repeatedly executing the steps on the sampling areas which are not classified into the traffic areas, and classifying the determined sampling areas and the newly selected reference area into another traffic area until all the sampling areas are classified into the traffic areas.

In a possible implementation, the corresponding period of the past year determined by the corresponding period determination module is a period of the year before the planned travel period, or a period of each year of the year N before the planned travel period, where N > 1.

(III) advantageous effects

The travel planning method and the travel planning system based on traffic flow prediction can serve users needing to make a travel plan in advance, particularly users needing to make a long-term travel plan, meet the requirement that the users make the travel plan several days, weeks or even months in advance, and avoid influence on travel experience and progress caused by overtaking the time period and the road section of a traffic peak in the journey.

Drawings

The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining and illustrating the present invention and should not be construed as limiting the scope of the present invention.

Fig. 1 is a schematic flow chart of a travel planning method based on traffic flow prediction according to a first embodiment of the present invention.

Fig. 2 is a plan view of a target zone divided into traffic zones.

Fig. 3 is a schematic diagram of the distribution of sampling regions included in a target region.

Fig. 4 is a dotted schematic of two sampling areas versus flow data for each time period.

Fig. 5 is a schematic plan view of a traffic area obtained by performing similarity classification on a sampling area of a target region.

Fig. 6 is a block diagram of a travel planning system based on traffic flow prediction according to a third embodiment of the present invention.

Detailed Description

In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention.

It should be noted that: in the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described are some embodiments of the present invention, not all embodiments, and features in embodiments and embodiments in the present application may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In this document, "first", "second", and the like are used only for distinguishing one from another, and do not indicate their degree of importance, order, and the like.

The division of modules, units or components herein is merely a logical division, and other divisions may be possible in an actual implementation, for example, a plurality of modules and/or units may be combined or integrated in another system. Modules, units, or components described as separate parts may or may not be physically separate. The components displayed as cells may or may not be physical cells, and may be located in a specific place or distributed in grid cells. Therefore, some or all of the units can be selected according to actual needs to implement the scheme of the embodiment.

An embodiment, namely a first embodiment, of a travel planning method based on traffic flow prediction according to the present invention is described in detail with reference to fig. 1 to 5. The embodiment is mainly applied to users needing to make a travel plan in advance, particularly users needing to make a long-term travel plan, and can meet the requirement that the users make the travel plan several days, weeks or even months in advance, so that the influence on travel experience and progress caused by overtaking up to the time and road sections of traffic peaks in the journey is avoided.

As shown in fig. 1, the travel planning method based on traffic flow prediction according to this embodiment includes the following steps:

step 100, determining a planned travel time period including planned travel time.

Before a user makes a recent trip plan or a forward trip plan, a planned trip time is determined, and the planned trip time is the time expected by the user to trip. The planned travel time may be a date, such as 10/1/2018, or an approximate time period of a day, such as 10/1/2018 morning. Taking the trip as an example, if a user desires to travel to a place on a certain day after several weeks, the user needs to determine the planned trip time according to the time arrangement of the user.

After the planned travel time is determined, a planned travel time period needs to be determined according to the planned travel time so as to facilitate subsequent travel planning. It will be appreciated that the planned travel time is necessarily included within the planned travel period. The planned travel time interval is a preset time interval with a certain date span, and the size of the date span is set according to needs. For example, every 5 days is set as a period, the first of which is the first day of the first period of the year on 1 month and 1 day of the year, and so on, each average year is divided into 73 periods, and the last period of the year (i.e., the 73 rd period) for leap years includes a time span of 6 days. It is understood that, in the present application, a time period refers to a time period or time interval in days, for example, a time span of 7 days from 10 months 1 days in 2018 to 10 months 7 days in 2018.

Step 200, determining the corresponding period of the next year corresponding to the planned trip time period.

After the planned travel time period is determined, in order to predict the traffic condition of the planned travel time period, a period corresponding to the planned travel time period is determined as a prediction reference in the next year, that is, a period corresponding to the planned travel time period in the next year is determined. The corresponding period of the past year may be a current year synchronization period, for example, the planned trip period is 2018, 10, month 1 to 7, the corresponding period of the past year may be directly set to 2017, 10, month 1 to 7, or the corresponding period of the past year corresponding to the planned trip period may be determined by other conditions that can actually affect the trip of people, which will be described in detail in the following.

It should be noted that, in this embodiment, the previous year corresponding period is a period of the previous year of the year in which the planned trip period is located, that is, the previous year corresponding period.

Step 300, determining a target region containing a trip destination, and acquiring historical traffic flow data of the target region in a corresponding period of the past year.

After the corresponding period of the previous year (i.e., the corresponding period of the previous year) is determined, the time element required for predicting the traffic condition is determined, and the regional element, i.e., the target region, is also determined. The target zone is an area that contains the travel destination and has a geographic extent greater than the travel destination. For example, if the travel destination is an Imperial palace, the target area may be set to be an area within two rings.

After the time elements and the area elements required for predicting the traffic condition are determined, the traffic condition prediction can be started, and the traffic condition prediction is performed by acquiring historical traffic flow data of a target area in a corresponding period of the previous year. The traffic flow history data can reflect the size of the traffic flow in the target region, i.e. the congestion degree of the road condition at different time periods (different time points in a day). By analyzing the traffic flow of the target region containing the travel destination in the corresponding period of the previous year corresponding to the planned travel period, the user can reasonably arrange the travel plan of the current year by referring to the traffic jam time and the place of the previous year, and avoid the jam time and the jam place.

It should be noted that, because a target region usually includes facilities such as multiple intersections and viaducts, a target region includes multiple sampling regions, each sampling region is a unit region, each sampling region may be a region including an intersection or another region capable of collecting traffic flow, each sampling region contributes a set of traffic flow data including data of different time periods, and the traffic flow data of the target region is composed of the traffic flow data collected in each sampling region in the target region.

And 400, dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions.

After obtaining the historical traffic flow data of the target region in the corresponding period of the previous year (namely the corresponding period of the previous year), starting to analyze the historical traffic flow data. The analysis process is roughly as follows: analyzing traffic flow data of each sampling area contained in the target area in a corresponding period of the previous year, and dividing the sampling areas with relatively small data difference degree into the same traffic area, so that the sampling areas with relatively large data difference degree are inevitably divided into different traffic areas, and finally obtaining a plurality of traffic areas. If the data difference degrees of all the sampling areas are small, the standard of the difference degrees is redefined, so that the data difference degrees between the sampling areas can be distinguished to be relatively small and relatively large.

A traffic zone is a zone that contributes one or more sets of data, consisting of one or more sampling zones, to indicate how similar the traffic flows in each zone within a target area are. If one traffic area comprises a plurality of sampling areas, the traffic flow data of the plurality of sampling areas have higher consistency; if a traffic area only comprises one sampling area, the traffic flow data of the sampling area has a larger difference with other sampling areas in the target area, and therefore, the traffic flow data of the sampling area is independently classified into a traffic area only comprising the traffic area.

And 500, planning a travel time period and/or a travel route according to the distribution of each traffic area and the historical data of the traffic flow of the traffic areas in the corresponding period of the past year.

After the sampling area is classified and the target region is divided into a plurality of traffic regions, a trip time period is planned according to the position distribution of each traffic region and the traffic flow historical data of the traffic region in the corresponding period of the past year (namely the corresponding period of the previous year), as shown in fig. 2, D is a trip destination, R is the target region containing the trip destination D, and the target region R is divided into three traffic regions, namely a traffic region a, a traffic region B and a traffic region C. If the communication flow of the traffic area A in the corresponding period of the previous year (namely the corresponding date in the previous year of the planned travel year) is smaller than that of the traffic area B and the traffic area C on the whole, the user is advised to enter the travel destination D through the traffic area A; if the traffic areas A, B, C differ from each other in peak traffic flow periods, the traffic area with the smallest traffic flow in the user's expected travel period is selected.

In one embodiment, the planned travel period is one natural week in duration.

The planned travel period is in weeks, one for each week. The first natural week in each year begins at the first monday (or sunday) of the year, sunday (or saturday) ends, and so on for subsequent natural weeks. Taking the natural week as a monday start and a sunday end as an example, if the last day of the last natural week in a year (i.e., the last sunday of the year) is not 12-month 31-day, and the time span between the first day of the last natural week and 12-month 31-day is less than the time of one natural week (i.e., 7-day), the last day of the last natural week is counted as the sunday of the week. Taking 2018 as an example, the first day of the last natural week of 2018 is 12 and 31 months, so the time span of the natural week is from 12 and 31 months of 2018 to 1 and 6 months of 2019, and the first day of the first natural week of 2019 is the first monday of 2019, i.e. 1 and 7 months.

In one embodiment, the step 200 of determining the corresponding time period of the next year corresponding to the planned travel period includes the following steps:

step 210, judging whether the planned travel time interval contains holidays or holidays generated due to the holidays, and taking the previous year period containing the holidays or the holidays as the corresponding previous year period under the condition that the planned travel time interval contains the holidays or the holidays; and under the condition that the planned travel time interval does not contain holidays and holidays, determining the corresponding time period of the next year according to the climate condition of the planned travel time interval.

Holidays are a common term for holidays and holidays. The current legal festival and holiday in China is 11 days, including 3 days in spring festival, 3 days in national day, 1 day each of Yuan Dan, Qingming, Wuyi, Bingmi and mid-autumn. It is understood that, according to the related arrangement of domestic vacation, if the legal holiday day is a working day, there may be a case of a retirement, and therefore, a holiday due to the retirement is also included in the determination conditions at the time of the above determination.

Since there are many traveling persons during holidays compared to non-holiday periods, holidays are one of the factors that have the greatest influence on traffic flow. When the corresponding period of the previous year (namely the corresponding period of the previous year) is determined, if the planned travel period comprises a holiday or a holiday, the previous year period comprising the holiday or the holiday is directly used as the corresponding period of the previous year, and the traffic condition of the current travel is predicted by referring to the traffic condition of the corresponding period of the previous year. For example, if the planned travel time is 2018, 2 and 15 days, the planned travel period is 2018, 2 and 12 days to 18 days, wherein the planned travel period includes the first four days of spring festival holidays (except for early three), so the corresponding period of the previous year includes the first four days of spring festival holidays 2017 and the three days before sunset, i.e., 24 days to 30 days in 2017, 1 and 24 days to 30 days in month 2017 (wherein 27 days are except for sunset). The time span of the corresponding period of the previous year is the same as the time span of the planned trip period, when the planned trip period is a natural week, the corresponding period of the previous year is 7 days, and the position of the holiday in the corresponding period of the previous year is the same as the position of the holiday in the planned trip period.

It should be noted that the holidays can also include foreign holidays which are accepted by the public in China and have a large influence on the public life, such as valentine's day, christmas day, and the like. When the planned travel period does not include the domestic legal holiday but includes the foreign holiday, the planned travel period may be determined to include the holiday, and the previous year period including the foreign holiday may be used as the previous year corresponding period. However, it should be noted that when the planned travel time interval only includes the holiday of the foreign festival and not the holiday of the domestic festival, if the corresponding period of the previous year includes not only the holiday of the foreign festival but also the holiday of the domestic festival, the data need to be further considered to be usable. For example, the planned travel time is 2 and 13 days in 2019, the natural week in which the planned travel time is located includes valentine's day but does not include any domestic holiday, so the corresponding period of the previous year is 11 to 17 days in 2 and 2018, but the corresponding period of the previous year includes spring festival (except for 15 days), so the traffic flow data is not accurate, and at this time, the situation needs to be judged as the planned travel time period does not include any holiday, and the corresponding period of the previous year is determined according to the climate condition of the planned travel time period.

In one embodiment, the determining the corresponding time period of the next year according to the climate conditions of the planned trip period in step 210 comprises the following steps:

step 221, obtaining an average value of the meteorological element predicted values in the planned travel time period. Wherein the meteorological elements comprise at least one of: air temperature, humidity, wind power, ultraviolet index and air pollution index.

When the corresponding period of the previous year (namely the corresponding period of the previous year) corresponding to the planned trip period needs to be determined according to the climate conditions, the predicted value of the meteorological elements of the planned trip period is firstly obtained through medium-long term meteorological forecasting or other manners, and the meteorological elements can comprise one or more of air temperature, humidity, wind power, ultraviolet index and air pollution index. For example, the planned trip time period is from 8/10/8/2018, and the meteorological elements include air temperature, wind power, ultraviolet index and air pollution index, and the predicted average air temperature, the predicted average wind power, the predicted average ultraviolet index and the predicted average air pollution index of each day from 8/10/8/2018/14/2018 are obtained through meteorological forecasting, so that the predicted average air temperature, the predicted average wind power, the predicted average ultraviolet index and the predicted average air pollution index of the whole day from 8/10/2018/14/2018 are obtained, that is, the average value of predicted values of the meteorological elements in the planned trip time period is obtained.

Step 222, determining a first time interval of the past year in synchronization with the planned travel time interval, and determining a second time interval of the past year, wherein the second time interval of the past year comprises the first time interval of the past year and the time interval span is larger than the first time interval of the past year.

The first time period of the previous year (i.e., the first time period of the previous year) and the planned trip time period are contemporaneous time periods, for example, the planned trip time period is from 10 months and 8 days to 14 days in 2018, and the first time period of the previous year is from 10 months and 8 days to 14 days in 2017. The second period of the previous year (i.e., the second period of the previous year) is a period including the first period of the previous year and having a time span greater than the first period of the previous year, for example, the second period of the previous year may be 3 days to 19 days in 10 months in 2017, or 8 days to 23 days in 10 months in 2017.

And 223, determining a corresponding time interval closest to the average value of the meteorological element predicted values in the planned travel time interval in the second time interval of the previous year, and determining the corresponding time interval as the corresponding time interval of the previous year.

For example, the meteorological parameters are only one item, the predicted average temperature T of the planned trip period from 2018, 10, 8 and 14 days is 22.5 ℃, the second period of the previous year is determined as 2017, 10, 3 and 19 days, the average temperatures T1 from 3 to 9 days, the average temperatures T2 from 4 to 10 days and the average temperatures T11 from 13 to 19 days in the second period of the previous year are obtained, 11 different period average temperatures in the second period of the previous year are obtained, the predicted average temperature T of the planned trip period is compared with the 11 different period average temperatures to obtain the average temperature closest to the predicted average temperature, the average temperature T3 of the period which is the period which should be the closest to the average temperature is determined, for example, the average temperature T3 from 5 to 11 days, and the period from 10, 5 to 11 days in 2017 is the corresponding period of the previous year.

If there are a plurality of meteorological elements, for example, wind power, ultraviolet index, and air pollution index, in addition to air temperature, the corresponding period of time determined to be closest to the planned travel period in the second period of the previous year is determined in the same manner as the corresponding period of time determined to be closest to the planned average air temperature. If the corresponding time periods determined by the four items of air temperature, wind power, ultraviolet index and air pollution index are not identical, the corresponding time period with the most nearest items can be selected as the corresponding time period of the previous year. If the corresponding time periods determined by the above four items are different, the determination may be made according to the weights assigned to the four items in advance and the time intervals between the corresponding time periods, for example, the closest corresponding time periods corresponding to the air temperature, the wind power, the ultraviolet index, and the air pollution index are respectively 5 days to 11 days, 4 days to 10 days, 6 days to 12 days, and 11 days to 17 days, in the above four items, the air temperature has the largest weight, the air pollution index has the smallest weight, the corresponding time period of the air temperature is located between the wind power and the ultraviolet index, and the time interval between the corresponding time period of the air pollution index and the other three items is larger, so that the corresponding time period of the air temperature (5 days to 11 days) is selected as the corresponding time period of the previous year.

In one embodiment, the starting date of the second period of the past year is the same as or prior to the starting date of the first period of the past year, and in the case that the starting date of the second period of the past year is prior to the starting date of the first period of the past year, the date deviation value between the starting date of the second period of the past year and the starting date of the first period of the past year does not exceed the preset starting date deviation threshold; the ending date of the second time interval in the past year is the same as or later than the ending date of the first time interval in the past year, and under the condition that the ending date of the second time interval in the past year is later than the ending date of the first time interval in the past year, the date deviation value between the ending date of the second time interval in the past year and the ending date of the first time interval in the past year does not exceed the preset ending date deviation threshold value.

If the start date deviation threshold and the end date deviation threshold are preset, for example, if the start date deviation threshold Ds and the end date deviation threshold De are both 5 days, then the second period of the previous year from 10, month 8 and day 14 of the planned trip period 2018 is from 10, month 3 and day 19 of the previous year, and then a period closest to the average value of the predicted values of the meteorological elements is determined in the second period of the previous year, which has the same time span as the planned trip period. It is understood that the start date deviation threshold and the end date deviation threshold may be set to different values, and the setting of the specific values may be set according to the situation.

In one embodiment, one of the following is selected as the range of the target area: the urban loop range, the urban rail transit loop range, and the range with the radius taking the trip destination as the center as R, wherein R is a preset sampling radius.

And determining the corresponding period of the previous year by judging the holidays or determining the corresponding period of the previous year by the weather conditions, namely determining the target region. The range of the target zone is necessarily larger than and fully encompasses the range of the travel destination. When setting the target area, the range of the target area may be set in one of the following three ways:

first, a loop range of a city where a travel destination is located is set as a range of a target region. A loop is a road looped around the central area of a city, typically a motorway around the city. For example, if the destination is Tiananmen, the target area may be the second loop in Beijing, and the range of the target area is the range of the second loop.

And secondly, taking the track traffic loop range of the city where the travel destination is located as the range of the target region. The loop line is a full-circle surrounding route, which refers to a circular transportation system formed by roads, railways and vehicles inside the roads and railways, and is mainly characterized in that the traffic route is in a ring shape. For example, if the travel destination is the beijing scenic mountain park, the target area may be set as the loop line of the beijing subway No. 2, and the range of the target area is the range within the No. 2 line.

Third, the radius with the destination as the center is a range of R, where R is the preset sampling radius. For example, when the travel destination is the shikah sea park in beijing, the range of the target region is set to a range of one circle having a radius of 5 km and the center of the shikah sea park. It is understood that the preset sampling radius R may be adjusted as required, for example, if the travel destination is far and the road traffic is inconvenient, the radius R may be enlarged appropriately, for example, R is set to 10 km, so as to ensure that the traffic flow history data is sufficient and representative.

In one embodiment, the traffic flow history data includes data collected at each traffic flow monitoring point in the target territory during a corresponding time period of the year.

When the traffic flow historical data of the target region in the corresponding period of the previous year (namely the corresponding period of the previous year) is obtained, the traffic flow historical data can be obtained by obtaining the data collected by each traffic flow monitoring point in the target region. With the development of the internet of things technology, a lot of traffic monitoring points are generally arranged on a highway at present, each monitoring point is provided with a sensor or a detection device for detecting traffic flow, such as a geomagnetic sensor, a video monitor and the like, the traffic flow data in each time period can be obtained and uploaded to a platform for storage, and therefore traffic flow data is formed.

Specifically, according to the content in step 300, a target area includes a plurality of sampling areas, and the sampling areas may be divided according to traffic flow monitoring points, that is, one traffic flow monitoring point represents one sampling area, and the target area includes 10 monitoring points, which means that the target area is divided into 10 sampling areas.

If the monitoring points sample the traffic flow data once every half hour, when the planned travel time period is 7 days, each monitoring point in the target region can collect 48 pieces of traffic flow data every day, and if the target region comprises 20 pieces of traffic flow monitoring points, 10 x 48 x 7=3360 pieces of data are totally collected in the target region in the corresponding time period (same as 7 days) in the previous year.

In one embodiment, the step 400 of dividing the target area according to the difference degree of the flow data of each sampling area in the traffic flow historical data comprises the following steps:

step 410, a sampling area is selected from the target region as a reference area.

As shown in fig. 3, assuming that the target region R includes 10 sampling regions { s1, s2, s3, … …, s10}, that is, 10 sampling points, and assuming that the corresponding period of the previous year is 7 days, taking the case where the traffic flow history data is obtained through monitoring points as an example, assuming that the monitoring points sample the traffic flow data every half hour, and the target region includes 10 monitoring points (that is, the target region is divided into 10 sampling regions), one of the sampling regions s1 is arbitrarily selected as a reference sampling region.

And step 420, determining sampling areas which have a spatial distance from the reference area not exceeding a preset distance threshold value and have the same-ratio difference degree from the flow data of the reference area not exceeding a preset flow difference threshold value in other sampling areas.

Among the other sampling regions { s2, s3, s4, … …, s10}, a sampling region which is spatially distant from the reference region s1 by no more than a preset distance threshold Dl and which is different from the flow data of the reference region s1 by no more than a preset flow difference threshold Df is determined.

As shown in fig. 3 and 4, taking the sampling region s2 as an example, the actual spatial distance d12 between the sampling region s2 and the reference region s1 is smaller than the preset distance threshold Dl, but the difference { r21, r22, r23, … … } between the flow rate data collected at each time point by the sampling region s2 and the reference region s1 exceeds the preset flow rate difference threshold Df.

Taking the sampling region s3 as an example, the actual spatial distance d13 between the sampling region s3 and the reference region s1 is smaller than the preset distance threshold Dl, and the difference { r31, r32, r33, … … } between the flow rate data collected by the sampling region s3 and the reference region s1 at each time point does not exceed the preset flow rate difference threshold Df. The subsequent sampling regions s 4-s 10 and so on determine all the sampling regions that meet the preset distance criterion and meet the degree of difference criterion.

And step 430, classifying the determined sampling area and the reference area into the same traffic area.

Assuming that the sampling regions s3, s4, s6 were determined in step 420, the three sampling regions are classified as the same region as the reference region s1, which is named traffic region a.

And step 440, repeating the steps for the sampling areas which are not classified into the traffic areas, and classifying the determined sampling areas and the newly selected reference area into another traffic area until all the sampling areas are classified into the traffic areas.

After the sampling areas s1, s3, s4 and s6 are classified into the same traffic area, the steps 410 to 430 are repeated for the remaining sampling areas s2, s5 and s7-s10, and when the reference area is selected in the step 410, the sampling areas are selected from the remaining sampling areas which are not classified into any traffic area, and the other sampling areas in the step 420 are the remaining sampling areas which are not classified into any traffic area. Until all the sampled regions are classified into the same or different traffic regions, for example, the final result is that as shown in fig. 5, sampled regions s1, s3, s4, s6 belong to traffic region a, sampled regions s7, s9, s10 belong to traffic region B, and sampled regions s2, s5, s8 belong to traffic region C. Travel time periods and travel routes may then be planned based on the distribution of traffic zones A, B, C and the traffic flow data, such as in fig. 5 where traffic flow for traffic zone a is generally lower than traffic zones B and C, suggesting that the user travel via traffic zone a to travel destination D. On the other hand, of the four sampling areas s1, s3, s4 and s6 included in the traffic area a, if the traffic flow of the sampling area s1 in the daytime is lower than that of the sampling areas s3, s4 and s6 as a whole, the user is recommended to pass through the sampling area s1 of the traffic area a to reach the travel destination D.

One embodiment, namely a second embodiment, of the travel planning method based on traffic flow prediction according to the present invention is described in detail with reference to fig. 1 to 5. The embodiment is mainly applied to users needing to make a travel plan in advance, particularly users needing to make a long-term travel plan, and can meet the requirement that the users make the travel plan several days, weeks or even months in advance, so that the influence on travel experience and progress caused by overtaking up to the time and road sections of traffic peaks in the journey is avoided.

It should be noted that, in this embodiment, the previous year corresponding period is the corresponding period of the previous N years of the year in which the planned trip period is located, and N > 1. For example, N =2, the corresponding period of the previous year is the corresponding period of the previous year and the previous two years of the year in which the planned trip period is located.

It is to be understood that the history data of the traffic flow acquired when the corresponding period of the previous year is the corresponding period of the previous two years is history data of two years.

And after obtaining the historical traffic flow data of the target region in the corresponding period of the past year, starting to analyze the historical traffic flow data. The analysis process is roughly as follows: analyzing traffic flow data of each sampling area contained in the target area in a corresponding period of the past year, and dividing the sampling areas with relatively small data difference degree into the same traffic area, so that the sampling areas with relatively large data difference degree are inevitably divided into different traffic areas, and finally obtaining a plurality of traffic areas. If the data difference degrees of all the sampling areas are small, the standard of the difference degrees is redefined, so that the data difference degrees between the sampling areas can be distinguished to be relatively small and relatively large.

It can be understood that when the corresponding period of the previous year is the corresponding period of the previous two years, two different division results may be obtained by dividing the target region according to the difference degree of the traffic data of the corresponding period of each year.

After the sampling area is classified and the target region is divided into a plurality of traffic regions, a travel time interval is planned according to the position distribution of each traffic region and the historical data of the traffic flow of the traffic region in the corresponding period of the past year, as shown in fig. 2, D is a travel destination, R is the target region containing the travel destination D, and the target region R is divided into three traffic regions, namely a traffic region a, a traffic region B and a traffic region C. If the communication flow of the traffic area A in the corresponding period of the past year (namely the corresponding date in the previous year of the planned travel year) is smaller than that of the traffic area B and the traffic area C on the whole, the user is advised to enter the travel destination D through the traffic area A; if the traffic areas A, B, C differ from each other in peak traffic flow periods, the traffic area with the smallest traffic flow in the user's expected travel period is selected.

It can be understood that when the corresponding period of the previous year is the corresponding period of the previous two years, two different target region division results may be obtained, and at this time, a more reasonable result is selected from the two different division results. For example, a serious traffic accident occurs near the travel destination in the corresponding period of the last year, or a large-scale road construction is performed in the travel destination in the corresponding period of the last year, but no traffic accident or road construction is performed in the corresponding period of the previous year, at this time, the traffic flow data in the corresponding period of the last year is not representative, and the traffic area distribution map divided according to the traffic flow data in the corresponding period of the previous year is not representative, so that the traffic flow data in the corresponding period of the previous year is selected, and the traffic area distribution map divided according to the traffic flow data in the corresponding period of the previous year is selected.

In one embodiment, the planned travel period is one natural week in duration.

Since there are many traveling persons during holidays compared to non-holiday periods, holidays are one of the factors that have the greatest influence on traffic flow. When the corresponding period of the past year is determined, if the planned travel period comprises a holiday or a holiday, the planned travel period directly takes the period of the past year comprising the holiday or the holiday as the corresponding period of the past year, and the traffic condition of the current travel is predicted by referring to the traffic condition of the corresponding period of the past year. For example, if the planned travel time is 2 and 15 days in 2018, the planned travel time period is 12 to 18 days in 2 and 2 months in 2018, wherein the planned travel time period includes the first four days of holidays in spring festival (except for early three days), and therefore, if the corresponding time period in the previous year is the corresponding time period in the previous two years, the corresponding time period in the previous two years includes the first four days of holidays in spring festival in 2017 and three days before the early sunset, that is, 24 to 30 days in 1 and 24 months in 2017 (wherein 27 days is except for sunset), and the corresponding time period in the previous year in the corresponding time period in the previous two years is 2016 2 and 4 to 10 days in 2 months in 2016 (wherein 7 days is except for sunset). The time span of the corresponding period of the past year is the same as that of the planned travel period, when the planned travel period is a natural week, the corresponding period of the past year is 7 days, and the position of the holiday in the corresponding period of the past year is the same as that in the planned travel period.

And when the corresponding period of the previous year is the corresponding period of the previous two years, determining a first period of the previous year and a second period of the previous year according to the corresponding period of the previous year in the corresponding period of the previous two years, and determining the first period of the previous year and the second period of the previous year according to the corresponding period of the previous year in the corresponding period of the previous two years. The first time interval of the last year and the first time interval of the previous year are synchronous time intervals with the planned trip time interval, for example, the planned trip time interval is 10 months and 8 days to 14 days in 2018 years, the first time interval of the last year is 10 months and 8 days to 14 days in 2017 years, and the first time interval of the previous year is 2016 months and 8 days to 14 days in 2016 years. The second period of the last year is a period that includes the first period of the last year and has a time span greater than the first period of the last year, for example, the second period of the last year may be between 3 and 19 days 10 and 10 months of 2017, or between 8 and 23 days 10 and 8 months of 2017. The first period of the previous year and the second period of the previous year are the same.

For example, the meteorological elements have only one air temperature item, the predicted average air temperature T of the planned trip period from 2018, 10, month, 8 and 14 is 22.5 ℃, the second period of the previous year is determined as 2017, 10, month, 3 and 19, the average air temperatures T1 of the previous year from 3 to 9, the average air temperatures T2 of the previous year from 4 to 10 and the average air temperatures T11 of the previous year from 13 to 19 are obtained, the average air temperatures of 11 different periods in the second period of the previous year are obtained in total, then the predicted average air temperature T of the planned trip period is compared with the average air temperatures of the 11 different periods to obtain the average air temperature closest to the predicted average air temperature, and the period corresponding to the closest average air temperature, for example, the average air temperature T3 of the previous year from 5 to 11, and the period from 5 to 11 in the previous year from 2017 corresponds to the previous year.

When the corresponding period of the previous year is the corresponding period of the previous two years, the corresponding period of the average value of the predicted meteorological element values is determined in the second period of the previous year, and the period is determined as the corresponding period of the previous year. The corresponding period of the previous year may not be the same as the corresponding period of the last year, for example, the corresponding period of the last year is 2017, 10 and 5 days to 11 days, and the corresponding period of the previous year may be 2016, 10 and 11 days to 17 days.

When the corresponding period of the previous year is the corresponding period of the previous two years, the historical data of the traffic flow comprises two parts, wherein one part is the data of the corresponding period of the last year in the corresponding period of the previous two years, and the other part is the data of the corresponding period of the previous year in the corresponding period of the previous two years.

As shown in fig. 3, assuming that the target region R includes 10 sampling regions { s1, s2, s3, … …, s10}, the corresponding period of the previous year is 7 days, taking the case where the traffic flow history data is acquired through monitoring points as an example, assuming that the monitoring points sample the traffic flow data once every half hour, and the target region includes 10 monitoring points (i.e., the target region is divided into 10 sampling regions), one of the sampling regions s1 is arbitrarily selected as a reference sampling region.

The steps of this embodiment can be executed by referring to the steps described in the first embodiment, and are not described in detail again.

An embodiment of a travel planning system based on traffic flow prediction according to the present invention, i.e., a third embodiment, is described in detail with reference to fig. 6. The embodiment is a system applying the travel planning method provided by the first embodiment and the second embodiment, and is mainly applied to users who need to make a travel plan in advance, especially users who need to make a long-term travel plan, and can meet the requirement that the users make the travel plan several days, weeks, or even months in advance, so as to avoid influencing the travel experience and progress due to overtaking up to a traffic peak time period and road segment in the journey.

As shown in fig. 6, the travel planning system based on traffic flow prediction according to this embodiment mainly includes:

and the trip time period determining module is used for determining a planned trip time period containing planned trip time.

And the corresponding period determining module is connected with the trip period determining module and is used for determining the corresponding period of the past year corresponding to the planned trip period.

And the historical data acquisition module is connected with the corresponding period determination module and used for determining a target region containing a trip destination and acquiring historical traffic flow data of the target region in a corresponding period of the past year.

And the target region dividing module is connected with the historical data acquisition module and is used for dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions.

And the travel planning module is connected with the target region dividing module and used for planning a travel time interval and/or a travel route according to the distribution of each traffic region and the historical data of the traffic flow of the traffic region in the corresponding period of the past year.

In one embodiment, the duration of the planned travel period determined by the travel period determination module is a natural week.

In one embodiment, the respective time period determination module includes:

the first determining unit is used for judging whether the planned travel time interval contains holidays or holidays generated due to the holidays, and taking the previous year period containing the holidays or the holidays as the corresponding previous year period under the condition that the planned travel time interval contains the holidays or the holidays.

And the second determining unit is connected with the first determining unit and is used for determining the corresponding period of the next year according to the climate condition of the planned travel period under the condition that the planned travel period does not contain holidays and holidays.

In one embodiment, the second determination unit comprises:

and the acquiring subunit is used for acquiring the average value of the meteorological element predicted values in the planned travel time period.

And the time interval determining subunit is connected with the acquiring subunit and is used for determining a first time interval of the past year which is synchronous with the planned travel time interval and determining a second time interval of the past year which contains the first time interval of the past year and has a time interval span larger than the first time interval of the past year.

And the period determining subunit is connected with the period determining subunit and used for determining the corresponding period closest to the average value of the meteorological element predicted values in the planned travel period in the second period of the previous year and determining the corresponding period as the corresponding period of the previous year. Wherein,

for meteorological elements including at least one of: air temperature, humidity, wind power, ultraviolet index and air pollution index.

In one embodiment, the start date of the second period of time of the past year is the same as or prior to the start date of the first period of time of the past year, and the date deviation value between the start date of the second period of time of the past year and the start date of the first period of time of the past year does not exceed the preset start date deviation threshold. The ending date of the second time interval in the past year is the same as or later than the ending date of the first time interval in the past year, and under the condition that the ending date of the first time interval in the past year is later than the ending date of the first time interval in the past year, the date deviation value between the two does not exceed the preset ending date deviation threshold value.

In one embodiment, one of the following is selected as the range of the target zone: the urban loop range, the urban rail transit loop range, and the range with the radius taking the trip destination as the center as R, wherein R is a preset sampling radius.

In one embodiment, the historical data acquisition module comprises:

and the monitoring point acquisition unit is used for acquiring data acquired by each traffic flow monitoring point in the target region in the corresponding period of the past year as historical traffic flow data.

In one embodiment, the target zone partitioning module includes:

and the reference selection unit is used for selecting one sampling area from the target area as the reference area.

And the area determining unit is connected with the reference selecting unit and is used for determining the sampling areas which have the spatial distance with the reference area not exceeding a preset distance threshold value and have the same-ratio difference degree with the flow data of the reference area not exceeding a preset flow difference threshold value in other sampling areas.

And the area classifying unit is connected with the area determining unit and is used for classifying the determined sampling area and the reference area into the same traffic area.

And the circulating unit is connected with the reference selection unit and the area classification unit and is used for repeatedly executing the steps on the sampling areas which are not classified into the traffic areas and classifying the determined sampling areas and the newly selected reference areas into another traffic area until all the sampling areas are classified into the traffic areas.

In one embodiment, the corresponding period of the past year determined by the corresponding period determination module is a period of the year before the planned travel period or a period of each year in the N years before the planned travel period, where N > 1.

The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. A travel planning method based on traffic flow prediction is characterized by comprising the following steps:

determining a planned travel time period containing planned travel time;

determining a target region containing the travel destination, and acquiring historical traffic flow data of the target region in the corresponding period of the past year; the historical traffic flow data reflects the size of traffic flow in the target region in the corresponding period of the past year;

dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions, wherein the traffic flow data in the traffic regions have higher consistency;

and planning a travel time period and/or a travel route passing through a traffic area with smaller traffic flow according to the distribution of each traffic area and the historical data of the traffic flow of the traffic area in the corresponding period of the past year.

2. The travel planning method of claim 1 wherein the planned travel period is one natural week in duration.

3. The method of travel planning for travel of claim 1 wherein said determining a corresponding time period of the past year corresponding to said planned travel time period comprises:

4. The method of travel planning of claim 3 wherein said determining the corresponding time period of the past year in accordance with the climate conditions of the planned travel time period comprises:

determining a corresponding time period closest to the average value of the meteorological element predicted values in the planned travel time period in the second time period of the past year, and determining the corresponding time period as a corresponding time period of the past year; wherein,

the meteorological elements include at least one of: air temperature, humidity, wind power, ultraviolet index and air pollution index.

5. The travel planning method according to claim 1, wherein the historical data of traffic flow comprises data collected from traffic flow monitoring points in the target area during the corresponding period of the past year.

6. The travel planning method according to claim 5, wherein the dividing the target area according to the difference degree of the flow data of each sampling area in the traffic flow historical data comprises:

selecting one sampling area from the target area as a reference area;

7. The travel planning method according to any one of claims 1-6 wherein the corresponding time period of the past year is a time period of a year one year prior to the year of the planned travel time period, or a time period of each year N years prior to the year of the planned travel time period, where N > 1.

8. A travel planning system based on traffic flow prediction is characterized by comprising:

a historical data acquisition module, configured to determine a target region including the travel destination, and acquire historical traffic flow data of the target region in the corresponding period of the past year; the historical traffic flow data reflects the size of traffic flow in the target region in the corresponding period of the past year;

the target region dividing module is used for dividing the target region according to the difference degree of the flow data of each sampling region in the traffic flow historical data to obtain a plurality of traffic regions, and the traffic flow data in the traffic regions have higher consistency;

and the travel planning module is used for planning a travel route of a travel time interval and/or a traffic area with a smaller passing traffic flow according to the distribution of each traffic area and the historical data of the traffic flow of the traffic area in the corresponding period of the past year.

9. The travel planning system of claim 8 wherein the planned travel period determined by the travel period determination module is one natural week in duration.

10. The travel planning system of claim 8 wherein said respective time period determination module comprises: