CN111882858B - Multi-source data-based method for predicting queuing length of highway abnormal event - Google Patents
Multi-source data-based method for predicting queuing length of highway abnormal event Download PDFInfo
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- CN111882858B CN111882858B CN202010484814.2A CN202010484814A CN111882858B CN 111882858 B CN111882858 B CN 111882858B CN 202010484814 A CN202010484814 A CN 202010484814A CN 111882858 B CN111882858 B CN 111882858B
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- 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
- G08G1/0133—Traffic data processing for classifying traffic situation
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- 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
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Abstract
The invention discloses a method for predicting the queuing length of an abnormal event on a highway based on multi-source data, which solves the problem of difficult data acquisition required by queuing length prediction to a certain extent by adopting a modal division method based on vehicle inspection device data and charging data under the condition of considering sparse distribution of road detection equipment, and is suitable for predicting the queuing length of an abnormal event under a certain scene; the method of the invention obtains the traffic flow parameters of two parts by combining the historical vehicle detector data and the toll data, obtains the average travel speed between two toll stations by combining the historical toll data and the OD characteristic between the toll stations, and achieves the purpose of predicting the queuing length by combining the established queuing length prediction model under the target scene.
Description
Technical Field
The invention relates to the field of traffic data analysis and processing, in particular to a method for predicting the queuing length of an abnormal event on a highway based on multi-source data.
Background
The expressway has been developed rapidly in China from the 20 th century 90 s, and has extremely important status and function in modern transportation by the inherent characteristics and advantages of the expressway. With more and more vehicles running on the expressway, various problems follow, and the first place is the traffic jam problem. Due to the occurrence of abnormal events such as traffic accidents, road maintenance and the like on the expressway, the originally quite limited expressway resources are difficult to be fully utilized, and further serious traffic jam and vehicle queuing problems are caused. Different from urban roads, vehicles on expressways generally have higher driving speeds, so once traffic jam occurs, serious consequences are often caused, the influence time of the jam is generally longer, and the problem of serious economic loss can be caused.
The current method for predicting the queuing length is improved on the basis of a queuing theory or a traffic wave model, wherein the patent CN106887141A obtains the queuing length of a road section on the basis of assuming that the vehicle arrival rate obeys a certain distribution according to the queuing length between each node by setting continuous flow collection nodes based on the queuing theory. The patent CN106571030A proposes a traffic wave model-based queuing length prediction method for a specific scene of a road intersection based on multi-source data acquired by floating cars, and although the method has a low requirement for the layout of detection equipment, it requires that a certain proportion of floating cars are required to be present on the road, which is obviously difficult to satisfy in most cases for an expressway. Meanwhile, most of the existing methods for predicting the queuing length aim at simpler and closed road environments such as intersections, but non-closed road scenes including ramp toll stations and the like exist on expressways, and relevant researches are lacked.
Therefore, by means of multi-source data which can be obtained on the highway, the influence range of the abnormal event and the change process of the queuing length are effectively analyzed and grasped, and the method is helpful for guiding a traffic manager to make a reasonable traffic control strategy, so that the improvement of the control and service level of the highway is an urgent need for the development of the current intelligent traffic system and is also a key and difficult problem of research.
Disclosure of Invention
In view of this, the present invention provides a method for predicting the queuing length of an abnormal event on a highway based on multi-source data.
The purpose of the invention is realized by the following technical scheme:
a method for predicting the queuing length of an abnormal event on a highway based on multi-source data comprises the following steps:
the method comprises the following steps: dividing the road model into a plurality of types based on the relative positions of traffic parameter detection equipment arranged on the expressway and ramp toll stations;
step two: performing road mode matching based on spatial information of an abnormal event, wherein the spatial information of the abnormal event refers to an abnormal event occurrence place;
step three: extracting relevant historical data based on the modal type and the abnormal event time information, wherein the relevant historical data comprises vehicle information data detected by traffic parameter detection equipment and charging data of a ramp toll station;
step four: extracting historical traffic flow parameters and constructing a future traffic flow prediction model, wherein the specific process is as follows:
4.1: detecting device data based on the extracted traffic parameters according to the occurrence time t of the abnormal eventsExtracting corresponding historical traffic flow parameter q' (t) in a fixed period delta ts+Δt)；
4.2: historical traffic flow parameter q' (t) obtained based on 4.1s+ delta t), constructing a future traffic flow prediction model by combining an exponential smoothing method,
step five: extracting traffic flow and road section average travel speed parameters based on the charging data extracted in the third step and the fourth step;
5.1: obtaining historical traffic flow parameters in different travel directions based on the charging data and the OD relation of the ramp charging station, and then predicting the future traffic flow by a future traffic flow prediction model in the fourth step;
5.2: taking two ramp toll stations as a road section and a certain time interval deltaT screening corresponding charging data according to the distance S between two ramp charging stationsnFurther obtain the historical average travel speed of different road sections
Namely:
step six: establishing a queuing length prediction model under different modes by combining the number of lanes, the average road blocking density and the information obtained in the fourth step and the fifth step;
step seven: and predicting the vehicle queue length generated by the abnormal event based on the queue length prediction model established in the step six.
Further, the traffic parameter detection device in the first step is a vehicle detector.
Further, the modality of the step one is divided into the following four modalities:
firstly, the upstream of the incident place is a vehicle inspection device, and the downstream of the incident place is a vehicle inspection device; secondly, a vehicle inspection device is arranged upstream of the incident place, and a ramp toll station is arranged downstream of the incident place; thirdly, the accident site is an upstream ramp toll station, and the downstream of the accident site is a vehicle inspection device; fourthly, the ramp toll station is arranged at the upstream of the incident place, and the ramp toll station is arranged at the downstream of the incident place.
Further, the future traffic flow prediction model expression constructed in the fourth step is
In the formula ytRepresents a time series of the time series,
represents the first exponential smoothing value of the t period, alpha represents the exponential smoothing coefficient, and alpha is more than 0 and less than 1.
Further, the expression of the queue length prediction model in the step six is
In the formula: l (t) represents the vehicle queue length of the section where the abnormal event occurs at the time t; qi(t) represents the number of vehicle arrivals upstream of the event occurrence at time t; qu(t) represents the number of vehicle departures from the event occurrence at time t; m represents the number of lanes owned by the road; kjRepresents the average blocking density; n is a radical ofcThe vehicle accumulation number is in the initial stage.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
under the condition of considering that the distribution of road detection equipment is sparse, the problem that data required by queuing length prediction is difficult to obtain is solved to a certain extent by adopting a modal division method based on vehicle detector data and charging data, and the method is suitable for abnormal event queuing length prediction in a certain scene; the method of the invention obtains the traffic flow parameters of two parts by combining the historical vehicle detector data and the toll data, obtains the average travel speed between two toll stations by combining the historical toll data and the OD characteristic between the toll stations, and achieves the purpose of predicting the queuing length by combining the established queuing length prediction model under the target scene.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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The drawings of the present invention are described below.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic view of the partitioned road modes of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
Example 1
As shown in fig. 1-2, the method for predicting the queuing length of the abnormal events on the highway based on the multi-source data according to the embodiment includes the following steps:
the method comprises the following steps: dividing the road model into a plurality of types based on the relative positions of traffic parameter detection equipment arranged on the expressway and ramp toll stations;
step two: performing road mode matching based on spatial information of an abnormal event, wherein the spatial information of the abnormal event refers to an abnormal event occurrence place;
step three: extracting relevant historical data based on the modal type and the abnormal event time information, wherein the relevant historical data comprises vehicle information data detected by traffic parameter detection equipment and charging data of a ramp toll station;
step four: extracting historical traffic flow parameters and constructing a future traffic flow prediction model, wherein the specific process is as follows:
4.1: detecting device data based on the extracted traffic parameters according to the occurrence time t of the abnormal eventsExtracting corresponding historical traffic flow parameter q' (t) in a fixed period delta ts+Δt)；
4.2: historical traffic flow parameter q' (t) obtained based on 4.1s+ delta t), constructing a future traffic flow prediction model by combining an exponential smoothing method,
step five: extracting traffic flow and road section average travel speed parameters based on the charging data extracted in the third step and the fourth step;
5.1: obtaining historical traffic flow parameters in different travel directions based on the charging data and the OD relation of the ramp charging station, and then predicting the future traffic flow by a future traffic flow prediction model in the fourth step;
5.2: taking two ramp toll stations as a road section, screening corresponding toll data at a certain time interval delta T, and according to the corresponding toll data
Distance S between two ramp toll stationsnFurther obtain the historical average travel speed of different road sections
Namely:
step six: establishing a queuing length prediction model under different modes by combining the number of lanes, the average road blocking density and the information obtained in the fourth step and the fifth step;
step seven: and predicting the vehicle queue length generated by the abnormal event based on the queue length prediction model established in the step six.
In this embodiment, the traffic parameter detection device in the first step is a vehicle inspection device.
In this embodiment, the modalities of the step one are divided into the following four types:
firstly, the upstream of the incident place is a vehicle inspection device, and the downstream of the incident place is a vehicle inspection device; secondly, a vehicle inspection device is arranged upstream of the incident place, and a ramp toll station is arranged downstream of the incident place; thirdly, the accident site is an upstream ramp toll station, and the downstream of the accident site is a vehicle inspection device; fourthly, the ramp toll station is arranged at the upstream of the incident place, and the ramp toll station is arranged at the downstream of the incident place.
In this embodiment, the future traffic flow prediction model expression constructed in the fourth step is
In the formula ytRepresents a time series of the time series,
represents the first exponential smoothing value of the t period, alpha represents the exponential smoothing coefficient, and alpha is more than 0 and less than 1.
In this embodiment, the expression of the queue length prediction model in step six is
In the formula: l (t) represents the vehicle queue length of the section where the abnormal event occurs at the time t; qi(t) represents the number of vehicle arrivals upstream of the event occurrence at time t; qu(t) represents the number of vehicle departures from the event occurrence at time t; m represents the number of lanes owned by the road; kjRepresenting the average blocking density.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered in the protection scope of the present invention.
Claims (5)
1. A method for predicting the queuing length of an abnormal event on a highway based on multi-source data is characterized by comprising the following steps:
the method comprises the following steps: dividing the road modes into a plurality of types based on the relative positions of traffic parameter detection equipment arranged on the expressway and ramp toll stations;
step two: performing road mode matching based on spatial information of an abnormal event, wherein the spatial information of the abnormal event refers to an abnormal event occurrence place;
step three: extracting relevant historical data based on the modal type and the abnormal event time information, wherein the relevant historical data comprises vehicle information data detected by traffic parameter detection equipment and charging data of a ramp toll station;
step four: extracting historical traffic flow parameters and constructing a future traffic flow prediction model, wherein the specific process is as follows:
4.1: detecting device data based on the extracted traffic parameters according to the occurrence time t of the abnormal eventsExtracting the corresponding calendar at a fixed period Δ tHistorical traffic flow parameter q' (t)s+Δt)；
4.2: historical traffic flow parameter q' (t) obtained based on 4.1s+ delta t), constructing a future traffic flow prediction model by combining an exponential smoothing method,
step five: extracting traffic flow and road section average travel speed parameters based on the charging data extracted in the third step;
5.1: obtaining historical traffic flow parameters in different travel directions based on the charging data and the OD relation of the ramp charging station, and then predicting the future traffic flow by using a future traffic flow prediction model in the fourth step;
5.2: taking two ramp toll stations as a road section, screening corresponding toll data at a certain time interval delta T, and according to the distance S between the two ramp toll stationsnFurther obtain the historical average travel speed of different road sections
Namely:
step six: establishing a queuing length prediction model under different modes by combining the number of lanes, the average road blocking density and the information obtained in the fourth step and the fifth step;
step seven: and predicting the vehicle queue length generated by the abnormal event based on the queue length prediction model established in the step six.
2. The method for predicting the queuing length of the abnormal events on the expressway based on the multi-source data according to claim 1, wherein the traffic parameter detection device in the first step is a vehicle detector.
3. The method for predicting the queuing length of the abnormal events of the expressway based on the multi-source data according to claim 1 or 2, wherein the mode of the step one is divided into the following four modes:
firstly, the upstream of the incident place is a vehicle inspection device, and the downstream of the incident place is a vehicle inspection device; secondly, a vehicle inspection device is arranged upstream of the incident place, and a ramp toll station is arranged downstream of the incident place; thirdly, the accident site is an upstream ramp toll station, and the downstream of the accident site is a vehicle inspection device; fourthly, the ramp toll station is arranged at the upstream of the incident place, and the ramp toll station is arranged at the downstream of the incident place.
4. The method for predicting the queuing length of the abnormal events of the expressway based on the multi-source data according to claim 1, wherein the future traffic flow prediction model expression constructed in the fourth step is
In the formula ytRepresents a time series of the time series,
represents the first exponential smoothing value of the t period, alpha represents the exponential smoothing coefficient, and alpha is more than 0 and less than 1.
5. The method for predicting the queuing length of the abnormal events of the expressway based on the multi-source data according to claim 1, wherein the expression of the queuing length prediction model in the step six is
In the formula: l (t) represents the vehicle queue length of the section where the abnormal event occurs at the time t; qi(t) represents the number of vehicle arrivals upstream of the event occurrence at time t; qu(t) represents the number of vehicle departures from the event occurrence at time t; m represents the number of lanes owned by the road; kjRepresents the average blocking density; n is a radical ofcThe vehicle accumulation number is in the initial stage.
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| CN112991724B (en) * | 2021-02-09 | 2022-08-12 | 重庆大学 | Method and device for estimating occurrence position and occurrence time of highway abnormal event |
| CN113850991B (en) * | 2021-08-31 | 2022-11-01 | 北京北大千方科技有限公司 | Traffic condition identification method and device for toll station, storage medium and terminal |
| CN114627642B (en) * | 2022-02-25 | 2023-03-14 | 青岛海信网络科技股份有限公司 | Traffic jam identification method and device |
| CN115424432A (en) * | 2022-07-22 | 2022-12-02 | 重庆大学 | Upstream shunting method under highway abnormal event based on multi-source data |
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