Planning system and method based on traffic space-time adjustment demand

Abstract

The invention discloses a planning system and method based on a traffic space-time adjustment demand, the system comprises a spatial database, a stream processing framework, a distributed search engine, a data processing module and a dispersion force scheduling module, and the system calculates a traffic dispersion required by a congested road section by calculating a congestion index Cit of each road section in a future period of time; inquiring the currently deployed traffic dispersion personnel of the congested or seriously congested road section; calculating a dredging force tension index, rendering the road section with multiple colors, and generating a dredging force tension index trend chart; the optimal dispersion force scheduling scheme is automatically calculated for the road section with insufficient dispersion force configuration, so that the operation efficiency of the existing traffic regulation system is greatly improved, and the travel efficiency of urban residents is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban traffic regulation, and in particular to a planning system and method based on traffic spatiotemporal regulation requirements. Background Art

[0002] With the continuous increase in the number of cars in my country, the problem of urban road traffic congestion has become increasingly serious, which not only reduces the travel efficiency of residents, but also restricts the healthy development of cities. Traffic diverters play an important role in alleviating traffic congestion. They will implement corresponding traffic management measures according to the traffic volume and road conditions to optimize vehicle traffic efficiency and reduce illegal driving behaviors. However, under the traditional congestion management model, traffic diverters often intervene to deal with it after the fact. At this time, the congestion may have spread, which will cause us to miss the best time for diversion and require a lot of extra manpower and material resources to participate in the disposal, thus affecting the normal operation of other traffic management work.

[0003] In view of the above application scenarios, the present invention provides a method for planning diversion force based on the demand for spatiotemporal traffic regulation. It can evaluate the congestion situation of each road section in the future based on factors such as real-time road conditions, historical congestion index, traffic events and upstream flow, and perform superposition analysis with the traffic diversion personnel currently planned for the road section to form a diversion force tension index for each road section. The index is rendered in red, yellow and green colors, analyzed once every minute, and a diversion force tension index map is generated to timely grasp the current status and future needs of diversion force configuration. Green means that there is no congestion risk on the road section or the configured traffic diversion personnel can fully cope with the possible congestion; yellow means that the road section may be congested or the configured diversion force is insufficient; red means that the road section is likely to be congested and the configured diversion force is seriously insufficient. For sections with insufficient diversion force configuration, the optimal diversion force resource scheduling plan can also be automatically calculated, which is conducive to solving the problems of diversion force resource tension and waste at the same time, and ensuring the efficiency and rationality of diversion force planning.

[0004] It is well in line with the needs of congestion control and traffic diversion capacity planning in the field of traffic management. By rendering the traffic diversion capacity tension index of each road section on the map, it is possible to accurately grasp the weak links and dispatch diversion forces in a timely manner, thereby realizing the forward-looking traffic management planning of the traffic regulation department, continuously improving the work efficiency of traffic diversion personnel, and ensuring the safety and smooth travel of residents. Summary of the invention

[0005] The purpose of the present invention is to provide a method for traffic diversion force planning based on the needs of traffic spatiotemporal regulation, so as to solve the problem raised in the above-mentioned background technology that in the traditional congestion management mode, traffic diversion personnel often intervene to deal with the problem after the fact. At this time, the congestion may have spread, resulting in missing the best time for diversion, requiring a large amount of additional manpower and material resources to participate in the handling, thus affecting the normal operation of the transportation system.

[0006] To achieve the above object, the present invention provides the following technical solution: a planning system based on traffic spatiotemporal regulation requirements, comprising:

[0007] Spatial database, used to store basic information of road sections and real-time location information of traffic controllers;

[0008] Stream processing framework, used to calculate the real-time traffic conditions of road sections;

[0009] A distributed search engine to store and quickly retrieve massive amounts of historical congestion data;

[0010] The data processing module calculates the congestion index C of each road section in the future through the data obtained and processed by the system it ; Based on the mobile positioning terminal equipped by the traffic controller, query the traffic control force within a certain buffer zone of the road section; superimpose the congestion situation of the road section and the traffic controller to generate a trend chart of the traffic control force tension index;

[0011] The drainage force scheduling module schedules the drainage force based on the data obtained by the data processing module.

[0012] As a preferred technical solution, the basic information of road sections stored in the spatial database includes road grade, length, width, speed limit, and maximum flow; the real-time location information of traffic controllers includes the terminal number of the service equipment, location upload time, longitude, and latitude.

[0013] As a preferred technical solution, Kafka message queue and Flink streaming computing framework are used to process traffic information; Elasticsearch is used as distributed storage and search engine to save and query traffic information.

[0014] A method for planning traffic flow capacity based on traffic spatiotemporal regulation requirements includes the following steps:

[0015] Step S1: Calculate the congestion index C of each road section in the future it ; Specifically include:

[0016] a) First, all real-time traffic information detected by various sensors and cameras installed on each road section is connected to the streaming computing framework through the message queue;

[0017] b) Then the traffic information is grouped and processed according to the device number;

[0018] c) Count the number of vehicles N detected by each device per unit time T, which is the current traffic flow Q = N / T of the road section where the device is located;

[0019] d) The ratio of the current flow rate to the maximum flow rate of the road section is the real-time congestion index. The real-time flow rate and the real-time congestion index are saved, and the current time, current road section, current flow rate, congestion index, whether it is a holiday, whether it is a morning or evening peak, and current weather information are stored in the spatial database for querying historical congestion indexes;

[0020] e) Use the curve fitting algorithm to query the congestion index of road section i in the past period of time, take time as the independent variable and congestion index as the dependent variable to predict the congestion index C of road section i at time t xit ;

[0021] f) Based on the real-time flow of the upstream section, the flow outflow to the downstream section is planned according to the section width to calculate the congestion index C of section i at time t based on flow prediction yit ;

[0022] g) Based on the historical congestion information of the road sections stored in the spatial database, the congestion situation of each road section at time t yesterday, the same period last week, the same period last month and the same period last year is traversed and queried, including historical congestion index, whether it is a holiday, weather and other information. Holidays and weather conditions with the same weight are given a larger weight, and then the weighted average algorithm is used to calculate the congestion index C of road section i at time t based on historical data. zit ;

[0023] Step S2: Calculate the traffic controllers required for the congested road section;

[0024] Step S3: Query the traffic controllers currently planned for the congested or severely congested road sections;

[0025] Step S4: Calculate the drainage force tension index, render the road section with multiple colors, and generate a drainage force tension index trend graph;

[0026] Step S5: Automatically calculate the optimal traffic control force scheduling plan for the road sections with insufficient traffic control force.

[0027] As a preferred technical solution, the calculation of the congestion index based on traffic prediction in step f) includes the following steps:

[0028] 1) Traverse all road sections and query the traffic flow Q of road section i at the current time i ;

[0029] 2) According to the current traffic flow and length of each road section, calculate the time required for the traffic flow of this road section to converge into the intersection, record it as time t, calculate it once every minute, and you will get a traffic flow curve of the intersection based on time distribution, and you can also get the flow Q of road section i flowing out to the intersection at time t ict ;

[0030] 3) Based on the road network data, query all downstream road segment data of the road segment, including road grade, road width, section length, number of lanes, and maximum flow fields;

[0031] 4) Adopting a proportional planning strategy, that is, planning the flow rate out of the intersection of the upstream section at time t according to the width ratio of all downstream sections;

[0032] Assuming that section i has three downstream sections a, b, and c, the flow calculation formula for section i flowing out to these three sections at time t is as follows:

[0033] Q at =Q ict W a / (W a +W b +W c )

[0034] Q bt =Q ict W b / (W a +W b +W c )

[0035] Q ct =Q ict W c / (W a +W b +W c )

[0036] Among them, W a is the width of section a, W b is the width of section b, W c is the width of section c, Q at is the flow from section i to section a at time t, Q bt is the flow from section i to section b at time t, Q ct is the flow from section i to section c at time t; and the upstream flow is planned using the weighted average strategy of road grade;

[0037] 5) Sum up the flow of all upstream sections entering the intersection at time t for section i, and then subtract the flow outflow of section i at time t to get the flow of section i at time t;

[0038] Assuming that section i has three upstream sections a, b, and c, the flow calculation formula for section i at time t is as follows:

[0039] Q it =(Q ait +Q bit +Q cit )-Q ict

[0040] Among them, Q it is the flow rate of road section i at time t, Q ait is the flow from section a to section i at time t, Q bit is the flow from section b to section i at time t, Q cit is the flow from section c to section i at time t, Q ict is the flow rate from section i to the downstream section at time t;

[0041] 6) The flow Q from upstream of section i at time t it Compared with the maximum flow of the road section, the congestion index C of road section i at time t based on flow prediction can be obtained. yit , the calculation formula is as follows:

[0042] C yit =Q it / Q imax

[0043] Among them, C yit is the congestion index based on upstream traffic prediction, Q it is the flow from upstream of section i at time t, Q imax is the maximum flow rate of road section i;

[0044] As a preferred technical solution, in step S1, the influence of other traffic interference factors needs to be superimposed to further evaluate the congestion index in the future period:

[0045] Firstly, the above real-time congestion index, the congestion index based on traffic prediction and the congestion index based on historical data prediction are combined through a weighted average algorithm to obtain a comprehensive congestion index;

[0046] Secondly, it is necessary to consider the continuous impact of traffic incidents, traffic adjustments, and bad weather factors reported by the traffic command center on the congestion situation, and assign an impact coefficient greater than 1 to each influencing factor. Multiplying this coefficient will increase the congestion index; the calculation formula for the impact coefficient of other traffic interference factors is as follows:

[0047]

[0048] That is W o =F1xF2x…xFn ,

[0049] Among them, W o is the superposition influence coefficient of all other traffic interference factors, F i is the influence coefficient of a certain type of interference factor.

[0050] As a preferred technical solution, the calculation formula for the influence coefficient of traffic events and traffic adjustment factors is as follows:

[0051] F1=T / U

[0052] Among them, F1 is the influence coefficient of traffic events and traffic adjustment factors, T is the total number of lanes, and U is the number of unavailable lanes. If all lanes are unavailable, the traffic command center needs to promptly release information to the public through traffic broadcasting, online platforms or on-site traffic guidance screens, and the road section will no longer participate in subsequent calculations.

[0053] As a preferred technical solution, the additional impact coefficient calculation formula of bad weather on traffic congestion is as follows:

[0054]

[0055] Among them, F2 is the influence coefficient of bad weather, m is the number of historical data samples of sunny weather, S ic is the speed of historical data sample i in sunny weather, n is the number of historical data samples in bad weather, S iw is the speed of severe weather historical data sample i.

[0056] As a preferred technical solution, in step S3, the congestion level is first divided according to the congestion situation index. A congestion situation index less than 1.5 is considered unobstructed, greater than or equal to 1.5 and less than 1.8 is considered slow, greater than or equal to 1.8 and less than 2.0 is considered congested, and 2.0 and above is considered severe congestion. For congested and severely congested sections, we need to deploy certain traffic counselors, and the calculation formula is as follows:

[0057] P it =C it x L i x W i

[0058] Among them, P it is the diversion force required for section i at time t, C it is the congestion index of road section i at time t, L i is the length of road section i, W i It is the road grade coefficient, where the coefficient of expressway is set to 3, the coefficient of main road is set to 2, and the coefficient of other roads is set to 1.

[0059] As a preferred technical solution, in step S4, the calculation formula of the drainage force tension index is as follows:

[0060] T it =(P it -P i ) / P it

[0061] Among them, T it is the traffic flow tension index of section i at time t, with a value range of [0,1]; P it is the diversion force required for section i at time t; P i is the number of diversion forces currently planned for road section i; if the diversion force tension index of the road section is less than 0.3, it is considered that the diversion force configuration is sufficient and can meet the work needs of handling congestion risks, and green rendering is used; if the diversion force tension index is greater than or equal to 0.3 and less than 0.6, it is considered that the diversion force configuration is insufficient, traffic management work may have loopholes, and congestion conditions may not be adjusted in a timely and effective manner, and yellow rendering is used; if the diversion force tension index is greater than 0.6, it is considered that the diversion force configuration is seriously insufficient, which may lead to some traffic violations not being corrected, causing further congestion, and red rendering is used; for sections without congestion risks, green rendering is also used. Compared with the prior art, the beneficial effects of the present invention are:

[0062] The planning system and method based on the spatiotemporal traffic regulation demand of the present invention comprehensively considers factors such as real-time road conditions, historical congestion index, traffic events and upstream flow, predicts the congestion situation of road sections in the future, and combines the planned traffic controllers in the current road section to accurately grasp which road sections have weak traffic control capabilities, so as to dispatch resources in time and plan in advance to deal with possible traffic congestion, thereby greatly improving the operating efficiency of the existing traffic regulation system and the travel efficiency of urban residents. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A method flow chart of a method for planning traffic diversion force based on traffic spatiotemporal regulation requirements of the present invention;

[0064] Figure 2 This is a flow chart for evaluating the future congestion situation of a road section according to the present invention;

[0065] Figure 3 It is a schematic diagram of the calculation process of the congestion index predicted based on the upstream road network flow in the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] In order to achieve the above invention objectives, the embodiment of the present invention provides a method for planning traffic diversion force based on the demand for spatiotemporal traffic regulation, which is implemented by the following technical solutions:

[0068] A spatial database to store basic information of road sections (including road grade, length, width, speed limit, maximum flow rate, etc.) and real-time location information of traffic controllers (including terminal number of service equipment, location upload time, longitude, latitude, etc.);

[0069] A stream processing framework to calculate the real-time traffic conditions of road sections;

[0070] A distributed search engine to store and quickly retrieve massive amounts of historical congestion data;

[0071] A method for calculating the congestion situation of a road section in real time, taking into account factors such as real-time road conditions, historical congestion index, traffic events and upstream flow, to predict congestion in the future;

[0072] A method for real-time calculation of traffic controllers on a road section, based on the mobile positioning terminal equipped by the traffic controller, to query the traffic controller force within a certain buffer zone of the road section;

[0073] A method for calculating the traffic control tension index in real time, which overlays the congestion situation of the road section and the traffic controller to generate a trend chart of the traffic control tension index;

[0074] A set of diversion force scheduling methods to quickly schedule the "gravity" of diversion force based on congested road sections.

[0075] At the same time, a set of diversion force planning methods for use with the above system is provided. Please refer to Figure 1 , a traffic congestion planning method based on the demand for spatiotemporal traffic regulation, step S1: calculate the congestion index C of each road section in the future it ; Specifically include:

[0076] a) First, all real-time traffic information detected by various sensors and cameras installed on each road section is connected to the streaming computing framework through the message queue;

[0077] b) Then the traffic information is grouped and processed according to the device number;

[0078] c) Count the number of vehicles N detected by each device per unit time T, which is the current traffic flow Q = N / T of the road section where the device is located;

[0079] d) The ratio of the current flow rate to the maximum flow rate of the road section is the real-time congestion index. The real-time flow rate and the real-time congestion index are saved, and the current time, current road section, current flow rate, congestion index, whether it is a holiday, whether it is a morning or evening peak, and current weather information are stored in the spatial database for querying historical congestion indexes;

[0080] e) Use the curve fitting algorithm to query the congestion index of road section i in the past period of time, take time as the independent variable and congestion index as the dependent variable to predict the congestion index C of road section i at time t xit ;

[0081] f) Based on the real-time traffic of the upstream section, the traffic entering the downstream section is planned according to the width of the section to calculate the congestion index C of section i at time t based on traffic prediction yit ;

[0082] g) Based on the historical congestion information of the road sections stored in the spatial database, the congestion situation of each road section at time t yesterday, the same period last week, the same period last month and the same period last year is traversed and queried, including historical congestion index, whether it is a holiday, weather and other information. Holidays and weather conditions with the same weight are given a larger weight, and then the weighted average algorithm is used to calculate the congestion index C of road section i at time t based on historical data. zit ;

[0083] Step S2: Calculate the traffic controllers required for the congested road section;

[0084] Step S3: Query the traffic controllers currently planned for the congested or severely congested road sections;

[0085] Step S4: Calculate the drainage force tension index, render the road section with multiple colors, and generate a drainage force tension index trend graph;

[0086] Step S5: Automatically calculate the optimal traffic control force scheduling plan for the road sections with insufficient traffic control force.

[0087] Specifically,

[0088] The first step is to calculate the congestion situation of each road section in the future.

[0089] Since road traffic is a complex and open system, there are many reasons for traffic congestion, including road conditions, traffic flow, traffic incidents, illegal driving, etc. We will select some objective evaluation factors to make a prediction on future congestion trends.

[0090] The embodiment of the present invention uses the real-time congestion index, the congestion index based on traffic prediction and the congestion index based on historical data prediction as basic evaluation factors, and comprehensively considers the impact of other traffic interference factors on traffic congestion, and calculates the congestion index of a specific road section at a given time in the future.

[0091] The calculation formula is as follows:

[0092] C it =(C xit W x +C yit W y +C zit W z )W o

[0093] Among them, C it is the congestion index of road section i at time t, C xit is the real-time congestion index of road section i at time t, W x is the weight of the real-time congestion index, C yit is the congestion index of road section i at time t based on traffic prediction, W y is the weight of the congestion index based on traffic prediction, C zit is the congestion index of road section i at time t predicted based on historical data, W z is the weight of the congestion index predicted based on historical data, W o is the influence coefficient of other traffic interference factors.

[0094] The value range of the congestion index is [1,5], and the value range of the influence coefficient of other traffic interference factors is (1,5).

[0095] According to the above formula, the future congestion situation of each road section can be calculated as follows: Figure 2 As shown:

[0096] (i) Obtain real-time traffic information, calculate and store real-time congestion index. Real-time traffic refers to real-time data related to road traffic conditions, including traffic flow, vehicle speed, etc. Real-time traffic data can be obtained from a variety of sources, including ground induction coil sensors, infrared sensors, microwave radar sensors, and surveillance cameras.

[0097] The congestion index is a conceptual value used to indicate whether a road is unobstructed or congested. A larger number indicates a higher degree of congestion. Generally speaking, the congestion index can be calculated using traffic flow, travel time, and vehicle speed. In the embodiment of the present invention, the ratio of actual flow to maximum flow is used as the congestion index.

[0098] Traffic flow refers to the number of vehicles passing through a certain section of a road section per unit time, and its calculation formula is Q=N / T, where Q represents traffic flow, T represents observation time, and N represents the number of vehicles during observation time. In order to update the congestion index in a timely manner, the observation time in the embodiment of the present invention is set to 1 minute.

[0099] Since real-time traffic conditions change rapidly, the embodiment of the present invention uses Kafka message queue and Flink streaming computing framework to process traffic information. At the same time, considering the massive scale of traffic flow data, the embodiment of the present invention uses Elasticsearch as a distributed storage and search engine to save and query traffic information.

[0100] Based on the Kafka platform, according to the real-time traffic data of various sensors and cameras installed on each road section, the real-time traffic flow of the road section is calculated using the grouping operator and time window function of the Flink framework. First, all real-time road condition information (including road section number, device number, vehicle type, vehicle speed, vehicle passing time and other fields) detected by various sensors and cameras installed on each road section are connected to the streaming computing framework through the message queue. Then the road condition information is grouped and processed by device number, and the number of vehicles detected by each device within 1 minute is counted, which is the current traffic flow of the road section where the device is located.

[0101] The ratio of the current traffic flow to the maximum traffic flow of the road section is the real-time congestion index. The real-time traffic flow and the real-time congestion index are written into the memory for subsequent further calculations. The current time, current road section, current traffic flow, congestion index, whether it is a holiday, whether it is peak hours in the morning and evening, current weather and other information are stored in the Elasticsearch spatial database for querying historical congestion indexes.

[0102] The embodiment of the present invention adopts a curve fitting algorithm to query the congestion index of road section i in the past period of time, taking time as the independent variable and the congestion index as the dependent variable to predict the congestion index C of road section i at time t. xit .

[0103] (II) Predict the congestion index for a period of time in the future based on the upstream traffic. The upstream road network traffic will have a significant impact on the traffic congestion of the downstream section. When the upstream road network traffic increases, more vehicles will approach the downstream section, making it saturated, thus causing congestion. The steps for calculating and predicting the congestion index based on the upstream road network traffic are as follows: Figure 3 As shown:

[0104] (1) First, traverse all road sections and query the traffic flow Q of road section i at the current time. i ;

[0105] (2) Secondly, according to the current traffic flow and length of each road section, the time required for the traffic flow of this road section to converge into the intersection is calculated, recorded as time t, and calculated once every minute. Then, a traffic flow curve of the intersection based on time distribution will be obtained, and the flow Q flowing out of the road section i to the intersection at time t can be obtained. ict ;

[0106] (3) Secondly, based on the road network data, query all downstream road section data of the road section, including road grade, road width, section length, number of lanes, maximum flow and other fields;

[0107] (4) Since traffic flow is a dynamic process with strong randomness and contingency, how to plan the flow of the upstream section to the downstream section will be a complex system engineering. The embodiment of the present invention adopts a proportional planning strategy, that is, the flow of the upstream section entering the intersection at time t is planned according to the width ratio of all downstream sections;

[0108] Assuming that section i has three downstream sections a, b, and c, the flow calculation formula for section i flowing out to these three sections at time t is as follows:

[0109] Q at =Q ict W a / (W a +W b +W c )

[0110] Q bt =Q ict W b / (W a +W b +W c )

[0111] Q ct =Q ict W c / (W a +W b +W c )

[0112] Among them, W a is the width of section a, W b is the width of section b, W c is the width of section c, Q at is the flow from section i to section a at time t, Q bt is the flow from section i to section b at time t, Q ct is the flow rate from section i to section c at time t.

[0113] Furthermore, if the downstream road segment has road grade attributes, the strategy of weighted average of road grades (with configurable weights) can be used to plan the upstream flow;

[0114] (5) Sum up the flow of all upstream sections entering the intersection at time t for section i, and then subtract the flow outflow from section i at time t to get the flow of section i at time t;

[0115] Assuming that section i has three upstream sections a, b, and c, the flow calculation formula for section i at time t is as follows:

[0116] Q it =(Q ait +Q bit +Q cit )-Q ict

[0117] Among them, Q it is the flow rate of road section i at time t, Q ait is the flow from section a to section i at time t, Q bit is the flow from section b to section i at time t, Q cit is the flow from section c to section i at time t, Q ict is the flow rate from link i to the downstream link at time t.

[0118] (6) The flow Q from upstream of section i at time t it Compared with the maximum flow of the road section, the congestion index C of road section i at time t based on flow prediction can be obtained. yit , the calculation formula is as follows:

[0119] C yit =Q it / Q imax

[0120] Among them, C yit is the congestion index based on upstream traffic prediction, Q it is the flow from upstream of section i at time t, Q imax is the maximum flow rate of section i.

[0121] (III) Refer to the historical congestion index to predict the congestion situation in the future. The historical congestion index can reflect the traffic congestion situation of a specific road in a certain period of time in the past. Although the traffic situation will be affected by many accidental factors, historical data can often help us summarize some rules and patterns, so the historical congestion index will provide an important reference for us to predict the future congestion situation.

[0122] The historical congestion information of the road section is stored in the spatial database. The congestion situation of each road section yesterday, the same period last week, the same period last month and the same period last year at time t is traversed and queried, including historical congestion index, whether it is a holiday, weather and other information. Holidays and weather conditions with the same weight are given a larger weight. Then, the weighted average algorithm is used to calculate the congestion index C of road section i at time t based on historical data. zit .

[0123] (iv) Add in the impact of other traffic interference factors and further assess the congestion index for the next period of time.

[0124] First, the above real-time congestion index, the congestion index based on traffic prediction and the congestion index based on historical data prediction are added together through a weighted average algorithm (the weight is configurable) to obtain a comprehensive congestion index.

[0125] Secondly, it is necessary to consider the continuous impact of factors such as traffic incidents reported by the traffic command center (such as traffic accidents, vehicle breakdowns, road obstacles, road construction, etc.), traffic adjustments (such as natural disasters, important events, etc.), and severe weather (such as hail, heavy rain, heavy snow, and heavy fog) on ​​the congestion situation, and assign an impact coefficient greater than 1 to each influencing factor. Multiplying by this coefficient will increase the congestion index.

[0126] The calculation formula for the influence coefficient of other traffic interference factors is as follows:

[0127]

[0128] That is W o =F1xF2x…xF n , where W o is the superposition influence coefficient of all other traffic interference factors, F i is the influence coefficient of a certain type of interference factor.

[0129] Example 1: Traffic events and traffic adjustment factors

[0130] Traffic events and traffic adjustment factors will cause some lanes to be unavailable. An impact coefficient can be calculated based on the total number of lanes. Multiple factors will have a cumulative impact. The calculation formula for this type of impact coefficient is as follows:

[0131] F1=T / U

[0132] Among them, F1 is the influence coefficient of traffic events and traffic adjustment factors, T is the total number of lanes, and U is the number of unavailable lanes.

[0133] Furthermore, if all lanes are unavailable, the traffic control center needs to promptly release information to the public through traffic broadcasting, network platforms, or on-site traffic guidance screens. This road section will no longer participate in subsequent calculations.

[0134] Example 2: Bad weather

[0135] Bad weather will cover all lanes, and different impact coefficients can be assigned according to the type and level. This type of impact coefficient can be determined based on historical data. By comparing traffic flow, vehicle speed and other data under different types and levels of weather conditions, the additional impact coefficient of bad weather on traffic congestion can be estimated. The calculation formula is as follows:

[0136]

[0137] Among them, F2 is the influence coefficient of bad weather, m is the number of historical data samples of sunny weather, S ic is the speed of historical data sample i in sunny weather, n is the number of historical data samples in bad weather, S iw is the speed of severe weather historical data sample i.

[0138] Based on the above steps, by traversing all road sections, we will get the congestion index every minute in the future. For sections where the congestion index remains high, it is necessary to prioritize the planning of sufficient congestion relief capacity.

[0139] The second step is to calculate the traffic controllers needed for the congested road sections.

[0140] First, the congestion level is divided according to the congestion index. If the congestion index is less than 1.5, it is considered smooth. If it is greater than or equal to 1.5 and less than 1.8, it is considered slow. If it is greater than or equal to 1.8 and less than 2.0, it is considered congested. 2.0 and above is considered severe congestion. For congested and severely congested sections, we need to deploy a certain number of traffic controllers. The calculation formula is as follows:

[0141] P it =C it x L i x W i

[0142] Among them, P it is the diversion force required for section i at time t (rounded up), C it is the congestion index of road section i at time t, L i is the length of road section i (unit: kilometer), Wi is the road section grade coefficient (the coefficient of expressway is set to 3, the coefficient of main road is set to 2, and the coefficient of other roads is set to 1).

[0143] The third step is to check the traffic controllers currently planned for congested or severely congested sections of road.

[0144] Since the road section is a linear feature, it is necessary to generate a buffer surface with a certain distance as the radius, and then query the number of mobile positioning terminals that have uploaded real-time locations within 5 minutes within the buffer surface based on the API of the spatial database, which is the total number of traffic counselors currently planned for the road section.

[0145] The fourth step is to calculate the traffic flow tension index and render the road section in red, yellow and green. The calculation formula of the traffic flow tension index is as follows:

[0146] T it =(P it -P i ) / P it

[0147] Among them, T it is the traffic flow tension index of section i at time t, with a value range of [0,1]; P it is the diversion force required to be configured for section i at time t; Pi is the diversion force currently planned for section i.

[0148] If the traffic control force tension index of the road section is less than 0.3, it is considered that the traffic control force is sufficiently configured and can meet the needs of dealing with congestion risks, and it is rendered in green; if the traffic control force tension index is greater than or equal to 0.3 and less than 0.6, it is considered that the traffic control force is insufficient, and there may be loopholes in traffic management, and the congestion situation may not be adjusted in a timely and effective manner, and it is rendered in yellow; if the traffic control force tension index is greater than 0.6, it is considered that the traffic control force is seriously insufficient, which may lead to some traffic violations not being corrected, causing further congestion, and it is rendered in red.

[0149] Furthermore, green rendering is also used for road sections without congestion risk.

[0150] The last step is to automatically calculate the best diversion force scheduling plan for the road sections with insufficient diversion force configuration. The conventional diversion force scheduling plan follows the principle of proximity, and generally schedules the diversion force nearby based on the straight-line distance query. The embodiment of the present invention comprehensively considers the "gravitational force" of the distance and the congestion index of the road section on the diversion force, and automatically dispatches the diversion force to the road section with the largest gravitational force. The calculation of the gravitational force of the road section on the diversion force is shown as follows:

[0151] G ikt =C it / L ik

[0152] Among them, G ikt is the gravitational force of road section i on diversion force k at time t, C it is the congestion index of road section i at time t, L ik is the shortest path distance from diversion force k to road section i.

[0153] Query the road sections within a certain distance around the green section duty diversion force, calculate the gravity of each road section on the diversion force in turn, and the relevant road section with the largest gravity value is the recommended planning destination of the diversion force, so as to generate the best planning scheme for the traffic command center to dispatch and complete the diversion force planning work in advance.

[0154] This system and method comprehensively considers factors such as real-time road conditions, historical congestion index, traffic incidents and upstream flow, and predicts the congestion situation of road sections in the future. Combined with the planned traffic controllers in the current road sections, it can accurately grasp which sections have weaker traffic control capabilities, so as to dispatch resources in time and plan in advance to deal with possible traffic congestion, which greatly improves the operating efficiency of the existing traffic regulation system and greatly improves the travel efficiency of urban residents.

[0155] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A planning system based on traffic spatiotemporal regulation requirements, characterized in that: Included are: Spatial database, used to store basic information of road sections and real-time location information of traffic controllers; Stream processing framework, used to calculate the real-time traffic conditions of road sections; A distributed search engine to store and quickly retrieve massive amounts of historical congestion data; The data processing module calculates the congestion index C of each road section in the future through the data obtained and processed by the system it ; Based on the mobile positioning terminal equipped by the traffic controller, query the traffic control force within a certain buffer zone of the road section; superimpose the congestion situation of the road section and the traffic controller to generate a trend chart of the traffic control force tension index; The drainage force scheduling module schedules the drainage force based on the data obtained by the data processing module.

2. A planning system based on traffic spatiotemporal regulation requirements according to claim 1, characterized in that: The basic information of the road section stored in the spatial database includes road grade, length, width, speed limit, and maximum flow; the real-time location information of the traffic counselor includes the terminal number of the service equipment, location upload time, longitude, and latitude.

3. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 1 is characterized in that: Kafka message queue and Flink streaming computing framework are used to process traffic information; Elasticsearch is used as distributed storage and search engine to save and query traffic information.

4. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 1 is characterized in that: The following steps are involved: Step S1: Calculate the congestion index C of each road section in the future it ; Specifically include: a) All real-time traffic information detected by various sensors and cameras installed on each road section is connected to the streaming computing framework through the message queue; b) grouping the traffic information by device number; c) Count the number of vehicles N detected by each device per unit time T, which is the current traffic flow Q = N / T of the road section where the device is located; d) The ratio of the current flow rate to the maximum flow rate of the road section is the real-time congestion index. The real-time flow rate and the real-time congestion index are saved, and the current time, current road section, current flow rate, congestion index, whether it is a holiday, whether it is a morning or evening peak, and current weather information are stored in the spatial database for querying historical congestion indexes; e) Use the curve fitting algorithm to query the congestion index of road section i in the past period of time, take time as the independent variable and congestion index as the dependent variable, and predict the congestion index C of road section i at time t xit ; f) Based on the real-time flow of the upstream section, the flow into the downstream section is distributed according to the width of the section to calculate the congestion index C of section i at time t based on flow prediction yit ; g) Based on the historical congestion information of the road sections stored in the spatial database, the congestion situation of each road section at time t yesterday, the same period last week, the same period last month and the same period last year is traversed and queried, including historical congestion index, whether it is a holiday, weather and other information. Holidays and weather conditions with the same weight are given a larger weight, and then the weighted average algorithm is used to calculate the congestion index C of road section i at time t based on historical data. zit ; Step S2: Calculate the traffic controllers required for the congested road section; Step S3: Query the traffic controllers currently assigned to the congested or severely congested road section; Step S4: Calculate the drainage force tension index, render the road section with multiple colors, and generate a drainage force tension index trend graph; Step S5: Automatically calculate the optimal traffic control force scheduling plan for the road sections with insufficient traffic control force.

5. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 4 is characterized in that: Calculating the congestion index based on traffic prediction in step f) includes the following steps: 1) Traverse all road sections and query the traffic flow Q of road section i at the current time i ; 2) According to the current traffic flow and length of each road section, calculate the time required for the traffic flow of this road section to converge into the intersection, record it as time t, calculate it once every minute, and you will get a traffic flow curve of the intersection based on time distribution, and you can also get the flow Q of road section i flowing out to the intersection at time t ict ; 3) Based on the road network data, query all downstream road segment data of the road segment, including road grade, road width, section length, number of lanes, and maximum flow fields; 4) Adopting a proportional allocation strategy, that is, allocating the flow out of the intersection of the upstream section at time t according to the width ratio of all downstream sections; Assuming that section i has three downstream sections a, b, and c, the flow calculation formula for section i flowing out to these three sections at time t is as follows: Q at =Q ict W a / (W a +W b +W c ) Q bt =Q ict W b / (W a +W b +W c ) Q ct =Q ict W c / (W a +W b +W c ) Among them, W a is the width of section a, W b is the width of section b, W c is the width of section c, Q at is the flow from section i to section a at time t, Q bt is the flow from section i to section b at time t, Q ct is the flow from section i to section c at time t; and the upstream flow is allocated using the weighted average strategy of road grade; 5) Sum up the flow of all upstream sections entering the intersection at time t for section i, and then subtract the flow outflow of section i at time t to get the flow of section i at time t; Assuming that section i has three upstream sections a, b, and c, the flow calculation formula for section i at time t is as follows: Q it =(Q ait +Q bit +Q cit )-Q ict Among them, Q it is the flow rate of road section i at time t, Q ait is the flow from section a to section i at time t, Q bit is the flow from section b to section i at time t, Q cit is the flow from section c to section i at time t, Q ict is the flow rate from section i to the downstream section at time t; 6) The flow Q from upstream of section i at time t it Compared with the maximum flow of the road section, the congestion index C of road section i at time t based on flow prediction can be obtained. yit , the calculation formula is as follows: C yit =Q it / Q imax Among them, C yit is the congestion index based on upstream traffic prediction, Q it is the flow from upstream of section i at time t, Q imax is the maximum flow rate of section i.

6. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 4 is characterized in that: In step S1, the influence of other traffic interference factors needs to be added to further evaluate the congestion index in the future: Firstly, the above real-time congestion index, the congestion index based on traffic prediction and the congestion index based on historical data prediction are combined through a weighted average algorithm to obtain a comprehensive congestion index; Secondly, it is necessary to consider the continuous impact of traffic incidents, traffic corrections, and bad weather factors reported by the traffic control command center on the congestion situation, and assign an impact coefficient greater than 1 to each influencing factor. Multiplying this coefficient will increase the congestion index; the calculation formula for the impact coefficient of other traffic interference factors is as follows: That is W o =F1xF2x…xF n , Among them, W o is the superposition influence coefficient of all other traffic interference factors, F i is the influence coefficient of a certain type of interference factor.

7. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 6 is characterized in that: The calculation formula for the impact coefficient of traffic incidents and traffic correction factors is as follows: F1=T / U Among them, F1 is the influence coefficient of traffic incidents and traffic correction factors, T is the total number of lanes, and U is the number of unavailable lanes. If all lanes are unavailable, the traffic control command center needs to promptly release information to the public through traffic broadcasting, online platforms or on-site traffic guidance screens, and the road section will no longer participate in subsequent calculations.

8. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 6 is characterized in that: The additional impact coefficient of bad weather on traffic congestion is calculated as follows: Among them, F2 is the influence coefficient of bad weather, m is the number of historical data samples of sunny weather, S ic is the speed of historical data sample i in sunny weather, n is the number of historical data samples in bad weather, S iw is the speed of severe weather historical data sample i.

9. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 4 is characterized in that: In step S3, the congestion level is first divided according to the congestion situation index. A congestion situation index less than 1.5 is considered to be unblocked, greater than or equal to 1.5 and less than 1.8 is considered to be slow, greater than or equal to 1.8 and less than 2.0 is considered to be congested, and 2.0 and above is considered to be severely congested. For congested and severely congested sections, we need to deploy a certain number of traffic counselors, and the calculation formula is as follows: P it =C it x L i x W i Among them, P it is the diversion force required for section i at time t, C it is the congestion index of road section i at time t, L i is the length of road section i, W i It is the road grade coefficient, where the coefficient of expressway is set to 3, the coefficient of main road is set to 2, and the coefficient of other roads is set to 1.

10. The method for allocating diversion force based on traffic spatiotemporal regulation demand according to claim 4 is characterized in that: In step S4, the calculation formula of the guidance force tension index is as follows: T it =(P it -P i ) / P it Among them, T it is the traffic flow tension index of section i at time t, with a value range of [0,1]; P it is the diversion force required for section i at time t; P i is the diversion force currently allocated to road section i; If the traffic control force tension index of the road section is less than 0.3, it is considered that the traffic control force is sufficiently configured and can meet the needs of dealing with congestion risks, and green rendering is used; if the traffic control force tension index is greater than or equal to 0.3 and less than 0.6, it is considered that the traffic control force is insufficient, and there may be loopholes in traffic management, and the congestion situation may not be controlled in a timely and effective manner, and yellow rendering is used; if the traffic control force tension index is greater than 0.6, it is considered that the traffic control force is seriously insufficient, which may lead to some traffic violations not being corrected, causing further congestion, and red rendering is used; for sections without congestion risks, green rendering is also used.

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