CN114613139B - Travel generation prediction method suitable for large-scale sports activity traffic prediction - Google Patents
Travel generation prediction method suitable for large-scale sports activity traffic prediction Download PDFInfo
- Publication number
- CN114613139B CN114613139B CN202210256967.0A CN202210256967A CN114613139B CN 114613139 B CN114613139 B CN 114613139B CN 202210256967 A CN202210256967 A CN 202210256967A CN 114613139 B CN114613139 B CN 114613139B
- Authority
- CN
- China
- Prior art keywords
- activity
- traffic
- travel
- cell
- scale
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000000694 effects Effects 0.000 title claims abstract description 210
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000012937 correction Methods 0.000 claims description 34
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000005259 measurement Methods 0.000 claims description 13
- 230000000737 periodic effect Effects 0.000 claims description 6
- 230000037081 physical activity Effects 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000011835 investigation Methods 0.000 abstract description 11
- 230000004308 accommodation Effects 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008520 organization Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Along with increasing of large-scale sports holding frequency, the accurate and efficient prediction of traffic demands of the large-scale sports is a precondition for safe and smooth holding of the large-scale sports. And the traditional resident generation prediction method and the activity demand prediction method based on large-scale investigation have the determination of weak pertinence, large workload and the like. According to the method, the travel generation prediction method suitable for the large-scale sports activities is provided, namely, the traffic generation prediction in the traffic demand prediction four-stage method is used as a theoretical basis, and the total travel quantity base number range of the activities and the attraction degree of different areas or groups are determined according to the scale, the type and other attributes of the large-scale sports activities, so that the travel generation quantity of each area is predicted and calculated. The prediction method can predict the traffic generation amount in the large-scale sports holding process without large-scale investigation, and has the advantages of simplicity, convenience, rapidness, strong applicability, high accuracy and the like.
Description
Technical Field
The invention relates to the field of traffic travel prediction, in particular to a travel generation prediction method suitable for traffic prediction of large-scale sports activities.
Background
The large-scale sports are held, so that the mass cultural life of the national is enriched, the urban development process is accelerated, and the frequency of holding the large-scale sports is increased in recent years. Experience with numerous large-scale events shows that traffic problems are a key factor in success or failure of large-scale events. The reasonable planning and arrangement of a large amount of people and vehicles collected in a short time is a premise that the activities can be safely and orderly held.
Traffic demand prediction is also a reference basis for the core research content of traffic planning and traffic organization control. The internationally more common model for traffic demand prediction is a "four-stage" traffic demand prediction model. Traffic volume prediction is used as the first stage of four stages in the model, and is the first step of whole demand prediction and the most critical step. The traffic generation quantity prediction directly determines the whole scale of traffic demands and plays a role in whole control. Once a large error occurs in the production prediction, the whole demand prediction will also deviate directly from the actual situation. Therefore, a more accurate traffic generation prediction method needs to be proposed to improve the traffic demand prediction accuracy.
The traditional resident trip generation prediction is calculated according to resident population, land utilization condition and resident trip investigation data. In the prior patents related to travel generation prediction, a travel generation amount prediction method and a travel generation amount prediction system screen travel generation amounts and travel attraction amounts at different time points from travel software data, and an autoregressive moving average model is established to predict future travel amounts; in the urban traffic demand prediction method based on the POI, the travel generation capacity index is calculated based on the map interest point attribute, so that the travel amount is predicted. The two methods greatly make up for the defects in the traditional calculation method.
However, the travel purpose is clear and single in the traffic demand of large-scale sports activities, so that the prediction method for daily travel of residents is not applicable. In the prior art, demand prediction of large-scale sports usually adopts an intention investigation method to master the audience passenger flow of each region. However, the prediction mode based on the large-scale investigation result has the defects of large workload, long time consumption, poor generalization and the like. Not only is large-scale investigation required to ensure the prediction accuracy, but also each investigation result can only be applied to the prediction and cannot be popularized and applied to future activities.
The invention provides a travel generation prediction method suitable for large-scale sports activities. The invention is characterized in that: and determining the total trip amount base range of the activity attraction and the attraction degree of different areas or groups according to the attributes such as the scale, the type and the like of the large-scale sports activity, and accordingly carrying out prediction calculation on trip generation amounts of the areas. The travel generation prediction does not need large-scale traffic investigation, and can be used for accurately predicting different large-scale sports activities by analyzing activity characteristics, so that the travel generation prediction has the advantages of simplicity, convenience, rapidness, strong applicability, high accuracy and the like.
Disclosure of Invention
The invention provides a travel generation prediction method suitable for large-scale sports activities, namely a demand prediction model for predicting traffic generation according to various attribute characteristics of the large-scale sports activities based on traffic generation prediction in a traffic demand prediction four-stage method. The prediction method can predict the traffic generation amount in the large-scale sports holding process without large-scale investigation, and effectively overcomes the defects of large workload, long time consumption, multiple repeated work and the like caused by large-scale investigation.
The method is a travel generation prediction method suitable for large-scale sports activities. The method comprises the following steps: activity attribute feature analysis, total generated traffic prediction, and traffic prediction for each cell.
The activity attribute characteristic analysis part determines the features of the large-scale sports activities such as the holding scale, the holding place, the duration, the activity type and the like and classifies the levels or types;
and the total generated traffic prediction is that a series of coefficient correction is carried out on the basis of an original unit method according to the activity attribute characteristics so as to calculate the generated traffic prediction of the target activity.
And the generated traffic quantity prediction of each cell considers the related influence factors with the activity as the trip purpose, further determines the trip generation right of each traffic cell, and finally calculates the generated traffic quantity of each traffic cell.
The travel generation prediction method suitable for large-scale sports activities comprises the following steps:
step 1: analyzing activity attribute features
A. collecting data information of a target activity and related information related to the holding of a large-scale sports activity;
b. and sorting out all attribute characteristics related to the travel of the large-scale sports activity and collecting relevant data of the representative large-scale sports activity under all the attribute characteristics.
The step b in the step 1 is characterized in that the attribute related to the travel traffic prediction of the large-scale sports activities is as follows: activity type, activity location, activity venue can accommodate rated persons, duration of activity, activity infrastructure configuration, activity related crowd territory
The specific description of each attribute feature, including category or level, is shown in the following table:
step 2: establishing total generated traffic predictions
In traffic demand prediction for large-scale sports activities, the purpose of resident travel is to go to the activity site only to participate in the activity, so the activity attribute feature is a dominant factor affecting travel generation. The large sports activities are divided into periodic comprehensive events, periodic single events, tournaments, temporary events and main participation events according to the types of the activities. The data of the past activities of each type of activity are collected and counted respectively, the activities of the middles of the generated traffic values in each type of activity are used as standard activities, the generated traffic is used as a reference to generate traffic G *, and each attribute value of the traffic G * is the standard value of the activity.
G * —benchmark generation traffic volume for activity;
a 1, activity site correction coefficients;
a 2, activity participation number correction coefficient;
a 3 -activity duration correction factor;
a 4, configuring correction coefficients for parking lots of the movable field;
the determination method of the travel generation correction coefficient comprises the following steps:
correction coefficient a of activity location 1
The place where the event is held reflects the traffic convenience degree of arriving at the event, the scale of the event, etc. to a certain extent, thereby affecting the travel generation amount of the event to a certain extent, and thus introducing an event place correction coefficient reflecting the area where the event is located. According to the cities of the activities, the activities are divided into six types of cities, namely a first-line city, a new first-line city, a second-line city, a third-line city, a four-line city and a five-line city. By comparing the actual generation amount of the event held in different level cities with the travel generation amount of the standard event.
Correction coefficient a of number of participants in activity 2
The number of participants involved in an activity has a direct impact on the travel production of the activity. The number of event participants refers to the maximum number of event accommodations, and the maximum number of event accommodations can be the ratio of the maximum accommodations of the target event to the maximum accommodations of the same type of standard event.
Activity duration correction factor a 3
The duration of the activity refers to the total duration of the whole field of activity from beginning to end, and the value of the duration of the activity can be the ratio of the duration of the target activity to the duration of the similar standard activity.
Correction coefficient a for parking lot configuration of movable field 4
According to the large-scale sports venue periphery and inside can be equipped with the parking area in order to satisfy the parking demand of driving the crowd that goes to the venue, the configuration condition in parking area has influenced the convenience degree of driving trip to a certain extent, and then influences the trip selection of the crowd who tends to drive trip. And determining the correction coefficient of the parking lot configuration of the movable field by comparing the total capacity of the parking lot of the target activity with the total capacity of the parking lots of the similar standard activities.
Step 3: establishing per-cell generated traffic predictions
And in the aspect of travel generation amount of each traffic cell, determining travel generation rights of each cell according to the influence of different activity types on factors such as land utilization, population quantity, participation willingness and the like of each traffic cell.
kj＝KjQjCj
Wherein k j is the travel generation right of the jth traffic cell; k j generates basic weight for travel of the jth traffic cell; q j is the land utilization coefficient of the jth traffic cell; c j is the degree of interest of the jth traffic cell resident in the ith category of sports activity. The travel generated G j of the j-th traffic cell is:
Gj＝G·kj/∑jkj
Determination method of travel generation right influence factors of traffic cell
Travel generation basic weight K j
The travel generation basic weight of a traffic cell is related to the population quantity of residents of the traffic cell, and generally the trend of larger cell travel generation quantity is shown as the population base is larger. Here, the ratio of the total population of the traffic cell to the total population in the study range may be taken.
Land utilization coefficient Q of traffic district j
In the process of resident transportation trip, the land utilization scale and the property are dominant factors for determining trip generation. According to the urban land classification and planning construction land standard (GBJ 137-90), urban land in China is divided into the following 10 types: residential, public, industrial, warehouse, off-road, road square, municipal public, green, special, water and other land. The land type related to the travel of residents participating in sports activities is mainly living land and public facilities, so that the land utilization coefficient is calculated according to the proportion of the scale of the two types of land in the total land scale of the cell.
Coefficient of interest level C for residents in traffic cells in sports activities j
This factor is introduced because travel is aimed at viewing an event and the different traffic cell residents are of different interest for different large events. Since the degree of interest is not a quantitative indicator, the coefficient is calculated here from the number of stadiums in the cell. The greater the number of venues for a certain type of sports activity within a cell, the greater the level of interest of the residents of the cell in such activity. The specific calculation formula is shown below.
Cj＝SjAj
C j -a measure coefficient of interest degree of the jth traffic cell resident to the activity;
S j, the number measurement coefficient of the stadium in the j traffic district and the surrounding stadium;
A j -attention scale factor of the j-th traffic cell resident to the sports activity.
Method for determining measurement coefficient
Stadium number measurement coefficient S j
The coefficient is determined by simultaneously considering two factors of the number of venues and the accessibility of the reached venues, namely, the coefficient is obtained by adding up the number of the venues which are reachable by residents in different time ranges after a certain weight is given. In terms of the value of the time ranges, three travel time ranges of 10 minutes, 30 minutes and 60 minutes are selected, and the number of relevant stadiums which can be reached when the travel time is within 10 minutes, 10 minutes to 30 minutes and 30 minutes to 60 minutes from the centroid of the cell is counted. The specific calculation formula is as follows.
S 1j —starting from the jth traffic cell, the number of stadiums related to the activity that can be reached in 10 minutes for travel time;
s 2j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 10 minutes to 30 minutes;
s 3j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 30 to 60 minutes;
i-refers to the ith traffic cell within the research scope;
Alpha 1、α2、α3 -weight coefficient.
The weight coefficient is determined by a decay function of the acceptable time for the cell population to reach the stadium. The specific calculation method is as follows.
Wherein t is the time of the cell residents to reach the stadium, and a and b are parameters. a. b, obtaining the obtained data after calibrating the attenuation function f (t) by investigating the acceptable time for the residential community residents to reach the stadium.
Attention measurement coefficient A for physical activity j
The interest in sports activities is also a manifestation of the interest level of the cell residents in the activities. The attention here is mainly expressed in that residents search or view information or contents related to sports activities through a network. The quantization mode of the coefficient is to count the click rate or search rate of residents in each cell on the information related to the physical activities on the network so as to calculate the proportion of the residents in the whole statistical area. The method comprises the steps of counting the number of times that residents browse web pages containing related keywords of sports activities in the range of each traffic cell according to the current network big data and the mobile phone positioning or IP address as a standard for judging the traffic cell, wherein the counted time range is within one month from the fact that the advertisement is issued to the outside of the activity. The specific calculation formula is as follows.
N j -number of times the jth traffic cell resident browses a web page containing sports activity related keywords;
Σ iNi —number of times all traffic cell residents browse web pages containing sports activity related keywords.
The invention has the beneficial effects that:
The travel generation prediction method suitable for large-scale sports activities is improved according to various attributes and characteristics of the sports activities on the basis of traffic generation prediction in a traditional traffic demand prediction four-stage method. The method effectively overcomes the defects of large workload, long time consumption, multiple repeated work and the like caused by large-scale investigation in the traditional traffic demand prediction, improves the accuracy of model prediction, and provides a basis for the design of traffic organization and management and control schemes in future large-scale sports activities.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The flow chart of the invention is shown in fig. 1, and the specific embodiment of the invention adopts a travel generation prediction method suitable for large-scale sports activities. The method comprises the following steps: activity attribute feature analysis, total generated traffic prediction, and traffic prediction for each cell.
The activity attribute characteristic analysis part determines the features of the large-scale sports activities such as the holding scale, the holding place, the duration, the activity type and the like and classifies the levels or types;
and the total generated traffic prediction is that a series of coefficient correction is carried out on the basis of an original unit method according to the activity attribute characteristics so as to calculate the generated traffic prediction of the target activity.
And the generated traffic quantity prediction of each cell considers the related influence factors with the activity as the trip purpose, further determines the trip generation right of each traffic cell, and finally calculates the generated traffic quantity of each traffic cell.
The specific steps of each part of the invention are described in detail as follows:
step 1: analyzing activity attribute features
A. collecting data information of a target activity and related information related to the holding of a large-scale sports activity;
b. and sorting out all attribute characteristics related to the travel of the large-scale sports activity and collecting relevant data of the representative large-scale sports activity under all the attribute characteristics.
The step b in the step 1 is characterized in that the attribute related to the travel traffic prediction of the large-scale sports activities is as follows: activity type, activity location, activity venue can accommodate rated persons, duration of activity, activity infrastructure configuration, activity related crowd territory
The specific description of each attribute feature, including category or level, is shown in the following table:
step 2: establishing total generated traffic predictions
In traffic demand prediction for large-scale sports activities, the purpose of resident travel is to go to the activity site only to participate in the activity, so the activity attribute feature is a dominant factor affecting travel generation. The large sports activities are divided into periodic comprehensive events, periodic single events, tournaments, temporary events and main participation events according to the types of the activities. The data of the past activities of each type of activity are collected and counted respectively, the activities of the middles of the generated traffic values in each type of activity are used as standard activities, the generated traffic is used as a reference to generate traffic G *, and each attribute value of the traffic G * is the standard value of the activity.
G * —benchmark generation traffic volume for activity;
a 1, activity site correction coefficients;
a 2, activity participation number correction coefficient;
a 3 -activity duration correction factor;
a 4, configuring correction coefficients for parking lots of the movable field;
the determination method of the travel generation correction coefficient comprises the following steps:
correction coefficient a of activity location 1
The place where the event is held reflects the traffic convenience degree of arriving at the event, the scale of the event, etc. to a certain extent, thereby affecting the travel generation amount of the event to a certain extent, and thus introducing an event place correction coefficient reflecting the area where the event is located. According to the cities of the activities, the activities are divided into six types of cities, namely a first-line city, a new first-line city, a second-line city, a third-line city, a four-line city and a five-line city. By comparing the actual generation amount of the event held in different level cities with the travel generation amount of the standard event.
Correction coefficient a of number of participants in activity 2
The number of participants involved in an activity has a direct impact on the travel production of the activity. The number of event participants refers to the maximum number of event accommodations, and the maximum number of event accommodations can be the ratio of the maximum accommodations of the target event to the maximum accommodations of the same type of standard event.
Activity duration correction factor a 3
The duration of the activity refers to the total duration of the whole field of activity from beginning to end, and the value of the duration of the activity can be the ratio of the duration of the target activity to the duration of the similar standard activity.
Correction coefficient a for parking lot configuration of movable field 4
According to the large-scale sports venue periphery and inside can be equipped with the parking area in order to satisfy the parking demand of driving the crowd that goes to the venue, the configuration condition in parking area has influenced the convenience degree of driving trip to a certain extent, and then influences the trip selection of the crowd who tends to drive trip. And determining the correction coefficient of the parking lot configuration of the movable field by comparing the total capacity of the parking lot of the target activity with the total capacity of the parking lots of the similar standard activities.
Step 3: establishing per-cell generated traffic predictions
And in the aspect of travel generation amount of each traffic cell, determining travel generation rights of each cell according to the influence of different activity types on factors such as land utilization, population quantity, participation willingness and the like of each traffic cell.
kj＝KjQjCj
Wherein k j is the travel generation right of the jth traffic cell; k j generates basic weight for travel of the jth traffic cell; q j is the land utilization coefficient of the jth traffic cell; c j is the degree of interest of the jth traffic cell resident in the ith category of sports activity. The travel generated G j of the j-th traffic cell is:
Gj＝G·kj/∑jkj
Determination method of travel generation right influence factors of traffic cell
Travel generation basic weight K j
The travel generation basic weight of a traffic cell is related to the population quantity of residents of the traffic cell, and generally the trend of larger cell travel generation quantity is shown as the population base is larger. Here, the ratio of the total population of the traffic cell to the total population in the study range may be taken.
Land utilization coefficient Q of traffic district j
In the process of resident transportation trip, the land utilization scale and the property are dominant factors for determining trip generation. According to the urban land classification and planning construction land standard (GBJ 137-90), urban land in China is divided into the following 10 types: residential, public, industrial, warehouse, off-road, road square, municipal public, green, special, water and other land. The land type related to the travel of residents participating in sports activities is mainly living land and public facilities, so that the land utilization coefficient is calculated according to the proportion of the scale of the two types of land in the total land scale of the cell.
Coefficient of interest level C for residents in traffic cells in sports activities j
This factor is introduced because travel is aimed at viewing an event and the different traffic cell residents are of different interest for different large events. Since the degree of interest is not a quantitative indicator, the coefficient is calculated here from the number of stadiums in the cell. The greater the number of venues for a certain type of sports activity within a cell, the greater the level of interest of the residents of the cell in such activity. The specific calculation formula is shown below.
Cj＝SjAj
C j -a measure coefficient of interest degree of the jth traffic cell resident to the activity;
S j, the number measurement coefficient of the stadium in the j traffic district and the surrounding stadium;
A j -attention scale factor of the j-th traffic cell resident to the sports activity.
Method for determining measurement coefficient
Stadium number measurement coefficient S j
The coefficient is determined by simultaneously considering two factors of the number of venues and the accessibility of the reached venues, namely, the coefficient is obtained by adding up the number of the venues which are reachable by residents in different time ranges after a certain weight is given. In terms of the value of the time ranges, three travel time ranges of 10 minutes, 30 minutes and 60 minutes are selected, and the number of relevant stadiums which can be reached when the travel time is within 10 minutes, 10 minutes to 30 minutes and 30 minutes to 60 minutes from the centroid of the cell is counted. The specific calculation formula is as follows.
S 1j —starting from the jth traffic cell, the number of stadiums related to the activity that can be reached in 10 minutes for travel time;
s 2j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 10 minutes to 30 minutes;
s 3j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 30 to 60 minutes;
i-refers to the ith traffic cell within the research scope;
Alpha 1、α2、α3 -weight coefficient.
The weight coefficient is determined by a decay function of the acceptable time for the cell population to reach the stadium. The specific calculation method is as follows.
Wherein t is the time of the cell residents to reach the stadium, and a and b are parameters. a. b, obtaining the obtained data after calibrating the attenuation function f (t) by investigating the acceptable time for the residential community residents to reach the stadium.
Attention measurement coefficient A for physical activity j
The interest in sports activities is also a manifestation of the interest level of the cell residents in the activities. The attention here is mainly expressed in that residents search or view information or contents related to sports activities through a network. The quantization mode of the coefficient is to count the click rate or search rate of residents in each cell on the information related to the physical activities on the network so as to calculate the proportion of the residents in the whole statistical area. The method comprises the steps of counting the number of times that residents browse web pages containing related keywords of sports activities in the range of each traffic cell according to the current network big data and the mobile phone positioning or IP address as a standard for judging the traffic cell, wherein the counted time range is within one month from the fact that the advertisement is issued to the outside of the activity. The specific calculation formula is as follows.
N j -number of times the jth traffic cell resident browses a web page containing sports activity related keywords;
Σ iNi —number of times all traffic cell residents browse web pages containing sports activity related keywords.
Specific embodiments of the present invention are described in detail above. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (1)
1. A trip generation prediction method suitable for large sports activities, the method comprising the steps of:
step 1: analyzing activity attribute features
A. collecting data information of a target activity and related information related to the holding of a large-scale sports activity;
b. sorting out all attribute characteristics related to the travel of the large-scale sports activity and collecting relevant data of the representative large-scale sports activity under all the attribute characteristics;
The step b in the step 1 is characterized in that the attribute related to the travel traffic prediction of the large-scale sports activities is as follows: the activity type, the activity place and the activity venue can accommodate the rated number of people, the duration of the activity, the configuration of an activity infrastructure and the regional scope of the crowd involved in the activity;
the specific description of each attribute feature, including category or level, is shown in the following table:
step 2: establishing total generated traffic predictions
Dividing large-scale sports activities into periodic comprehensive events, periodic single events, tournaments, temporary events and main participation events according to the types of the activities; collecting the past activity data of each type of activity, respectively counting, sorting according to the generated traffic volume values from large to small, wherein the activity of the type of activity, to which the median of the generated traffic volume values belongs, is used as a standard activity, the generated traffic volume is used as a reference to generate traffic volume G *, and each attribute value is the standard value of the type of activity;
G * —benchmark generation traffic volume for activity;
a 1, activity site correction coefficients;
a 2, activity participation number correction coefficient;
a 3 -activity duration correction factor;
a 4, configuring correction coefficients for parking lots of the movable field;
the determination method of the travel generation correction coefficient comprises the following steps:
correction coefficient a of activity location 1
Determining by comparing the actual generation amount of the activities held in the city with the travel generation amount of the standard activities;
Correction coefficient a of number of participants in activity 2
The number of the activity participants refers to the maximum number of the people which can be accommodated in the activity, and the maximum number of the activity participants is the ratio of the maximum number of the accommodated people of the target activity to the maximum number of the accommodated people of the similar standard activity;
Activity duration correction factor a 3
The duration of the activity refers to the total duration of the whole field of activity from the beginning to the end, and the value of the total duration of the whole field of activity is the ratio of the duration of the target activity to the duration of the similar standard activity;
Correction coefficient a for parking lot configuration of movable field 4
Determining a parking lot configuration correction coefficient of the movable field by comparing the total capacity of the parking lot of the target activity with the total capacity of the parking lot of the similar standard activity;
step 3: establishing per-cell generated traffic predictions
kj＝KjQjCj
Wherein k j is the travel generation right of the jth traffic cell; k j generates basic weight for travel of the jth traffic cell; q j is the land utilization coefficient of the jth traffic cell; c j is the interested degree of the type of sports activities of the jth traffic district resident; the travel generated G j of the j-th traffic cell is:
Determination method of travel generation right influence factors of traffic cell
The travel generation basic weight K j takes the ratio of the population of the traffic cell to the population of the population in the research range;
Land utilization coefficient Q of traffic district j
According to the GBJ 137-90 of the urban land classification and planning construction land standard, urban land in China is divided into the following 10 types: residential, public, industrial, warehouse, off-road, road plaza, municipal public, green, special, water and other land; the land type related to the travel of residents participating in the sports activities is living land and public facilities land, and the land utilization coefficient is calculated according to the proportion of the scale of the two types of land in the total land scale of the cell;
Coefficient of interest level C for residents in traffic cells in sports activities j
The specific calculation formula is shown as follows;
Cj＝SjAj
C j -a measure coefficient of interest degree of the jth traffic cell resident to the sports activity;
S j, the number measurement coefficient of the stadium in the j traffic district and the surrounding stadium;
a j -attention degree measurement coefficient of the jth traffic district resident to the sports activity;
Method for determining measurement coefficient
Stadium number measurement coefficient S j
In terms of the value of the time ranges, three travel time ranges of 10 minutes, 30 minutes and 60 minutes are selected, and the number of relevant stadiums which can be reached when the travel time is within 10 minutes, 10 minutes to 30 minutes and 30 minutes to 60 minutes from the centroid of the cell is counted respectively; the specific calculation formula is as follows;
s 1j —starting from the jth traffic cell, the number of stadiums related to the activity that can be reached in 10 minutes for travel time;
s 2j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 10 minutes to 30 minutes;
s 3j —starting from the jth traffic cell, the number of stadiums relating to activity reachable at a travel time of 30 to 60 minutes;
i-refers to the ith traffic cell within the research scope;
alpha 1、α2、α3 -weight coefficient;
The weight coefficient is determined through an attenuation function of the acceptable time for the residential community to reach the stadium; the specific calculation method is as follows;
Wherein t is the time of cell residents reaching a stadium, and a and b are parameters; a. b, obtaining the attenuation function f (t) after calibrating the attenuation function f (t) by using the obtained data by investigating the acceptable time of the residential community to reach the stadium;
Attention measurement coefficient A for physical activity j
The quantization mode of the coefficient is to count the click rate or search rate of residents in each cell on the information related to the physical activities on the network so as to calculate the proportion of the residents in the whole research statistical area; the method comprises the steps that through current network big data, according to the mobile phone positioning or the IP address as a standard for judging the affiliated traffic cells, the times of browsing the webpage containing the related keywords of the sports activity by residents in the range of each traffic cell are counted, and the counted time range is within one month from the fact that the advertisement is issued to the outside of the activity; the specific calculation formula is as follows;
N j -number of times the jth traffic cell resident browses a web page containing sports activity related keywords;
-number of times all traffic cell residents browse web pages containing sports related keywords.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210256967.0A CN114613139B (en) | 2022-03-16 | 2022-03-16 | Travel generation prediction method suitable for large-scale sports activity traffic prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210256967.0A CN114613139B (en) | 2022-03-16 | 2022-03-16 | Travel generation prediction method suitable for large-scale sports activity traffic prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114613139A CN114613139A (en) | 2022-06-10 |
| CN114613139B true CN114613139B (en) | 2024-04-19 |
Family
ID=81862372
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210256967.0A Active CN114613139B (en) | 2022-03-16 | 2022-03-16 | Travel generation prediction method suitable for large-scale sports activity traffic prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114613139B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182196A (en) * | 2017-11-27 | 2018-06-19 | 东南大学 | A kind of Urban traffic demand Forecasting Methodology based on POI |
| CN112184281A (en) * | 2020-04-03 | 2021-01-05 | 广州市交通规划研究院 | Railway junction passenger demand prediction method based on travel space classification |
| WO2021237812A1 (en) * | 2020-05-29 | 2021-12-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban travel mode comprehensive identification method based on mobile phone signaling data and including personal attribute correction |
-
2022
- 2022-03-16 CN CN202210256967.0A patent/CN114613139B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182196A (en) * | 2017-11-27 | 2018-06-19 | 东南大学 | A kind of Urban traffic demand Forecasting Methodology based on POI |
| CN112184281A (en) * | 2020-04-03 | 2021-01-05 | 广州市交通规划研究院 | Railway junction passenger demand prediction method based on travel space classification |
| WO2021237812A1 (en) * | 2020-05-29 | 2021-12-02 | 南京瑞栖智能交通技术产业研究院有限公司 | Urban travel mode comprehensive identification method based on mobile phone signaling data and including personal attribute correction |
Non-Patent Citations (3)
| Title |
|---|
| 出行类别生成模型对某市客运出行生成量的预测;余丹丹;;硅谷(第19期);全文 * |
| 基于土地利用的交通需求预测分析;陈大鹏;孙瑞;王栋;;交通科技与经济(第02期);全文 * |
| 新城区居民出行生成和分布预测模型研究;余继东, 吴瑞麟;武汉城市建设学院学报(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114613139A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109327841B (en) | Multi-dimensional evaluation and accurate construction evaluation method based on coverage and value association | |
| WO2022198963A1 (en) | Big data-based commercial space quality evaluation method and system, device, and medium | |
| CN109978224B (en) | Method for analyzing and acquiring traffic trip rates of buildings with different properties | |
| CN106909692A (en) | It is a kind of to calculate the method that urban public utilities cover radiation index | |
| CN108717676A (en) | Evaluation space method and system are lived in duty under different scale based on multi-data fusion | |
| Qian et al. | Quantify city-level dynamic functions across China using social media and POIs data | |
| CN109003128A (en) | Based on improved random forest public bicycles website Demand Forecast method | |
| CN111754135A (en) | Regional development index calculation method applied to rural planning | |
| Meng et al. | Did highways cause the urban polycentric spatial structure in the Shanghai metropolitan area? | |
| CN111612223B (en) | Population employment distribution prediction method and device based on land and traffic multisource data | |
| CN110956089B (en) | ICT technology-based historical block walking measurement method | |
| Wang et al. | Spatial evaluation of the accessibility of public service facilities in Shanghai: A community differentiation perspective | |
| Xuan et al. | Research on Correlation between Spatial Quality of Urban Streets and Pedestrian Walking Characteristics in China Based on Street View Big Data | |
| CN114662774A (en) | City block vitality prediction method, storage medium and terminal | |
| Nagy et al. | First results in modelling objective well-being in Hungary at lower territorial level | |
| CN114613139B (en) | Travel generation prediction method suitable for large-scale sports activity traffic prediction | |
| CN115730763B (en) | Method and device for calculating reachability of workday facilities based on terminal signaling data | |
| Yin | Forecast without historical data: objective tourist volume forecast model for newly developed rural tourism areas of China | |
| CN115146990B (en) | Urban activity quantitative evaluation method integrating multisource geographic big data | |
| CN107480222B (en) | Group of cities space relationship strength measure system based on microblog data | |
| CN115829811A (en) | County public service facility configuration evaluation method and system | |
| CN113744525A (en) | Traffic distribution prediction method based on feature extraction and deep learning | |
| Wang | Eco-tourism benefit evaluation of Yellow River based on principal component analysis | |
| CN106485921A (en) | A kind of region highway network wagon flow state analysis method based on social data | |
| Liu et al. | A Dynamic Prediction Framework for Vitality of Urban Public Space-From hypothesis to algorithm and verification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |