TW202111658A - Traffic incident detection system and method - Google Patents

Abstract

The invention discloses traffic incident detection system and method. First, selecting a road to be detected for traffic incident, and then detecting or extracting current and past travel time and traffic flow of the road from data source of traffic information. Second, calculating historical statistics of the past travel time and traffic flow of the road, and comparing the current travel time and traffic flow with the historical statistics of the past travel time and traffic flow to calculate a relative value of travel time and a relative value of traffic flow, and then inputting the relative values into a traffic incident analysis table. Third, analyzing or estimating a corresponding traffic event based on the relative values of the travel time and the relative values of the traffic flow from the traffic incident analysis table. Thereby, the invention can quickly analyze or estimate the traffic events of the selected road.

Description

Traffic incident detection system and method

The present invention relates to a traffic incident detection technology, in particular to a traffic incident detection system and method.

Most of the current traffic incidents are implemented manually, such as the use of monitors to determine the content of the screen or the use of passers-by to report, etc., while a small number of them are automatically determined by the processing unit based on the images obtained by the general camera lens. However, when manual methods are used to determine or report traffic incidents, there may be insufficient monitoring manpower or delays in reporting. In addition, when judging traffic incidents through images, misjudgment of traffic incidents may be caused due to disturbances such as image dislocation, shadows, and color of the road surface.

In the prior art, a road real-time traffic accident risk control method is proposed, which is a road real-time traffic accident risk prediction and control method based on multi-class Support Vector Machine (SVM), which can be used to predict the occurrence of traffic accidents on the detected road section. possibility. Establish an accident prediction model based on multi-class support vector machines for the detected road section, and bring the collected real-time traffic characteristic parameters into the accident prediction model to determine whether there is a risk of a traffic accident. However, this method needs to use multi-class support vector machines to distinguish the accident risk of the traffic accident learning classification model. And classification, so that it takes a lot of time and cost to build a multi-class support vector machine and a traffic accident learning classification model.

Therefore, how to provide a novel or innovative traffic incident detection technology has actually become a major research topic for those skilled in the art.

The present invention provides a novel or innovative traffic incident detection system and method, which can economically or quickly analyze or estimate traffic incidents on selected roads.

The traffic incident detection system of the present invention includes: a selection module, a detection module, a calculation module, and an analysis module, wherein the selection module selects the road to detect the traffic incident, and the detection The module detects or captures the current and past travel time and traffic flow of the road selected by the selection module through the data source of the traffic information. The calculation module calculates the past roads detected or captured by the detection module The historical statistical values of travel time and traffic volume, and then the current travel time and traffic volume of the road detected by the detection module are compared with the historical statistical values of the past travel time and traffic volume of the road calculated by the calculation module , To calculate the relative value of travel time and the relative value of traffic flow, so that the calculation module will input the relative value of travel time and the relative value of traffic flow into the traffic incident analysis table, and the analysis module is derived from the traffic incident analysis According to the relative value of travel time and the relative value of traffic flow in the table, the corresponding traffic incident can be analyzed or estimated.

The traffic incident detection method of the present invention includes: selecting a road to detect a traffic incident, and detecting or extracting the current and past travel time and traffic flow of the selected road from the data source of the traffic information; and calculating the detected road Or retrieve the historical statistics of the travel time and traffic volume of the road in the past, and then compare the current travel time and traffic volume of the detected road to the calculation The historical statistical values of the past travel time and traffic volume of the road to calculate the relative value of travel time and the relative value of traffic volume, so as to input the relative value of travel time and the relative value of traffic volume into the traffic incident analysis table respectively; and Analyze or estimate the corresponding traffic event from the traffic event analysis table based on the relative value of travel time and the relative value of traffic volume.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, embodiments are specifically described below in conjunction with the accompanying drawings. In the following description, the additional features and advantages of the present invention will be partially explained, and these features and advantages will be partly known from the description, or can be learned by practicing the present invention. The features and advantages of the present invention are realized and achieved by means of the elements and combinations specifically pointed out in the scope of the patent application. It should be understood that the foregoing general description and the following detailed description are both illustrative and explanatory, and are not intended to limit the scope of the present invention.

1‧‧‧Traffic incident detection system

10‧‧‧Select Module

11‧‧‧Traffic incident

20‧‧‧Classification Module

21‧‧‧Road classification

30‧‧‧Detection Module

30'‧‧‧Collection Module

31‧‧‧Travel time

32‧‧‧Traffic

40‧‧‧Storage Module

41‧‧‧Traffic Information

50‧‧‧Calculation Module

51‧‧‧Historical statistics

52‧‧‧The relative value of travel time

53‧‧‧Relative value of traffic volume

60‧‧‧Analysis Module

61‧‧‧Traffic incident analysis table

70‧‧‧Link Module

80‧‧‧Sequencing Module

90‧‧‧Send module

Steps S11 to S16, S21 to S26‧‧‧

Figure 1 is a schematic diagram of the architecture of the traffic incident detection system of the present invention; Figure 2 is a traffic incident analysis table of the present invention; Figure 3 is a schematic flow diagram of the traffic incident detection method of the present invention; and Figure 4 is a flow chart of the traffic incident detection method of the present invention Schematic diagram of the flow of the invented traffic incident analysis table creation method.

The following describes the implementation of the present invention with specific specific embodiments. Those familiar with this technology can understand the other advantages and effects of the present invention from the content disclosed in this specification. It can also be implemented or applied by other different specific equivalent embodiments.

Figure 1 is a schematic diagram of the architecture of the traffic incident detection system 1 of the present invention, and Figure 2 is the traffic incident analysis table 61 of the present invention. At the same time, the main technical content of the traffic incident detection system 1 in Figure 1 is as follows, and the rest of the technical content is the same as the description in Figures 3 to 4, and will not be repeated here.

As shown in Figure 1, the traffic incident detection system 1 may include a selection module 10, a classification module 20, a detection module 30, a collection module 30', a storage module 40, and a calculation module. Group 50, an analysis module 60, a connection module 70, a sorting module 80, and a sending module 90. In some embodiments, the selection module 10 can be a selector or selection software, etc., the classification module 20 can be a classifier or classification software, etc., and the detection module 30 can be a detector or detection software, etc., to collect models. Group 30' can be a collector or collection software, etc. The storage module 40 can be a database, memory (such as memory card), hard disk (such as cloud disk, network disk), optical disk or flash drive, etc. The module 50 can be an arithmetic logic unit (ALU), calculation software or statistical software, etc., the analysis module 60 can be an analyzer or analysis software, etc., the connection module 70 can be a connection software, etc., and the sorting module 80 can be a sorting software Etc., the sending module 90 is a transmitter, a transceiver, sending software or receiving and dispatching software, etc. However, the present invention is not limited to this.

The selection module 10 can select the road to detect the traffic incident 11, the classification module 20 can classify the road according to the characteristics of the road, and the detection module 30 can detect or capture through the data source of the traffic information 41. The selection module 10 The current and past travel time 31 and traffic volume 32 of the selected road, and the storage module 40 can store traffic events 11 and traffic information 41 from the data source. In some embodiments, the traffic information 41 may include travel time 31, traffic volume 32, vehicle speed, occupancy rate, stop length and/or stop time for the detection module 30, storage module 40, and calculation The module 50 and the analysis module 60 detect, store, calculate and analyze the traffic information 41 respectively.

The calculation module 50 can calculate the historical statistics 51 of the past travel time 31 and the traffic volume 32 of the road detected or captured by the detection module 30, and then calculate the current travel time of the road detected by the detection module 30 31 and the traffic volume 32 are respectively compared with the past travel time 31 of the road calculated by the calculation module 50 and the historical statistical value 51 of the traffic volume 32 to calculate the relative value 52 of the travel time and the relative value 53 of the traffic volume. The calculation module 50 inputs the relative value 52 of the travel time and the relative value 53 of the traffic volume into the traffic incident analysis table 61 respectively. The analysis module 60 can analyze or estimate the corresponding traffic event 11 from the traffic event analysis table 61 according to the relative value 52 of travel time and the relative value 53 of the traffic volume.

The link module 70 can establish the corresponding link between the relative value of travel time 52, the relative value of traffic volume 53 and the traffic event 11, so as to establish the correlation between the relative value of travel time 52 and the relative value of traffic volume 53 in each road classification 21. Linked traffic incident 11. The sorting module 80 can sort the relative value 52 of travel time and the relative value 53 of traffic volume in each road classification 21 in order to establish or generate a traffic incident analysis table 61. The sending module 90 can send the traffic event 11 analyzed or estimated by the analysis module 60 to the vehicle on the road, so as to notify or warn the driver of the vehicle of the traffic event 11.

Therefore, the present invention can use the relationship between road travel time 31, traffic volume 32 and historical statistics 51 and other traffic information 41 to quickly detect traffic conditions and analyze or estimate traffic events 11 on the selected road. That is, the present invention can observe traffic conditions based on changes in road travel time 31 and traffic flow 32 and other traffic information 41, so as to quickly determine or estimate the traffic situation as a traffic event 11, such as serious car accidents, falling objects, congestion, etc. . In addition, when a traffic incident 11 occurs, it often causes a jam of vehicles behind. Therefore, the present invention can automatically detect the duration of the traffic incident 11 to provide necessary traffic information 41 for drivers behind.

In addition, the present invention can collect traffic incidents 11 on the road through the traffic database of the Highway Bureau of the Ministry of Communications, the police broadcast real-time traffic information on the government data open platform, etc., and through the use of mobile phone base stations based vehicle detection (Cellular -Based Vehicle Probe; CVP), Fixed Vehicle Detector (Vehicle Detector; VD), Electronic Toll Collection (ETC)-based vehicle detection (ETC-Based Vehicle Probe; EVP), global positioning The GPS-Based Vehicle Probe (GVP) of the system obtains relevant travel time 31 and traffic flow 32 and other traffic information 41, and then stores the traffic incident 11 and traffic information 41 in the storage module 40 (such as a database) , And then use big data analysis technology to automatically analyze and judge traffic incidents based on traffic information 41.

Figure 3 is a schematic flow diagram of the traffic incident detection method of the present invention, and please refer to Figures 1 to 2. At the same time, the traffic incident detection method in Fig. 3 mainly includes the following technical content from steps S11 to S16, and the rest of the technical content is the same as the description in Fig. 1 and Fig. 4, and will not be repeated here.

In step S11 in Fig. 3, the selection module 10 shown in Fig. 1 selects the road for detecting the traffic incident 11, and the classification module 20 classifies the road according to the characteristics of the road. For example, the selection module 10 selects the road in the area where the traffic incident 11 is to be detected, and the classification module 20 analyzes or determines which road classification 21 the road belongs to according to the road classification 21 of the administrative system, for example, the road belongs to a national road, Classification of roads such as provincial roads, city roads, county roads, district roads or township roads 21.

For example, on April 2, 2019, the selection module 10 selects the road on which the traffic incident 11 is to be detected as a national highway, such as National Highway No. 1 (Zhongshan Expressway).

In step S12 in Fig. 3, the detection module 30 detects or retrieves the current and past travel time 31 of the road selected by the selection module 10 through at least one (such as plural) data source Traffic information 41 such as traffic volume 32. For example, at least one data source of traffic information 41 may include vehicle detection (CVP) based on mobile phone base stations, fixed vehicle detector (VD), and vehicle detection (EVP) based on electronic road pricing system (ETC). ), the Detective Vehicle of Global Positioning System (GVP), the traffic database of the Highway Bureau of the Ministry of Communications, the police broadcast real-time traffic information of the government data open platform, etc., and the traffic information 41 can be stored in the storage module 40 (such as the database) In accordance with the road classification 21, the administrative system is used for classification.

For example, the detection module 30 obtained the real-time traffic information through the police broadcast on the government information open platform at 10:00 on April 02, 2019, and it was 71 kilometers (km) to 83 kilometers south of National Highway No. 1 (Zhongshan Expressway) Within the range, the average travel time 31 of the road is 43 minutes, and the traffic volume 32 per five minutes is 146 vehicles (for example, the total traffic volume of a certain detection station of National Highway No. 1 in this time range ).

In step S13 in FIG. 3, the calculation module 50 calculates the historical statistics 51 of the traffic information 41 such as the past travel time 31 and the traffic volume 32 of the road detected or captured by the detection module 30. For example, the calculation module 50 calculates the historical statistical value 51 of the traffic information 41 such as the travel time 31 and the traffic volume 32 of each unit time of the road in the past based on the traffic information 41 in the storage module 40 (such as a database). The historical statistical value 51 may be an arithmetic mean, median, mode, or standard deviation.

For example, the calculation module 50 calculates the historical statistical value 51 of the traffic information 41 such as the past travel time 31 and traffic volume 32 of the road in the traffic information 41 of the storage module 40 (such as a database), such as March 2019 From 10:00 to 10:05, within the range of 71 kilometers to 83 kilometers south of National Highway No. 1 (Zhongshan Expressway), the average travel time 31 of the road in the past is 8 minutes, and the five-minute traffic volume 32 is 520 Vehicles.

In step S14 in Figure 3, the calculation module 50 compares the current travel time 31 of the road detected by the detection module 50 in step S12 with the traffic volume 32 calculated by the calculation module 50 in step S13. The historical statistical value 51 of the past travel time 31 and the traffic volume 32 of the road is used to calculate the relative value 52 (including the proportional relationship) of the travel time and the relative value 53 (including the proportional relationship) of the traffic volume.

For example, the relative value of travel time 52 is the current travel time 31 (for example, 43 minutes) of the road in step S12 divided by the past travel time 31 (for example, 8 minutes) of the road in step S13, so travel in this embodiment The relative value of time 52 is 5.375 (that is, 43/8=5.375). At the same time, the relative value 53 of the traffic volume is the current traffic volume 32 (such as 146 vehicles) on the road in step S12 divided by the past traffic volume 32 (such as 520 vehicles) on the road in step S13, so the traffic volume in this embodiment is The relative value of 53 is 0.28 (that is, 146/520=0.28).

In step S15 in Fig. 3, the calculation module 50 inputs the relative value 52 of travel time and the relative value 53 of the traffic volume into the traffic incident analysis table 61 shown in Fig. 2, and then the analysis module 60 uses the traffic The event analysis table 61 analyzes or estimates the corresponding traffic event 11 based on the relative value 52 of travel time and the relative value 53 of the traffic volume. For example, the analysis module 60 first analyzes from the traffic incident analysis table 61 which road classification the road belongs to (such as national road, provincial road, city road, county road, district road or township road), and then from the traffic incident analysis table In 61, the corresponding traffic event 11 is extracted based on the location of the closest travel time 52 and the traffic volume 53 relative value.

For example, the calculation module 50 inputs the relative value 52 of travel time (such as the value 5.375) and the relative value 53 (such as the value 0.28) of the traffic flow in step S14 into the traffic incident analysis table 61 shown in Figure 2. Then the analysis module 60 compares the relative value of the closest travel time according to the relative value of travel time 52 (for example, 5.375) and the relative value of traffic volume 53 (for example, 0.28). The relative value of 52 (such as value 5) and traffic volume 53 (such as value 0.3) is based on the relative value 52 (such as value 5) of the closest travel time, the relative value of traffic volume 53 (such as value 0.3) and the road Road classification 21 (such as national highway) analyzes or estimates that the corresponding traffic incident 11 is a "crash accident."

In step S16 in Fig. 3, the sending module 90 sends the traffic event 11 analyzed or estimated by the analysis module 60 to the vehicles on the road, so as to notify or warn the driver of the vehicle of the traffic event 11.

For example, the sending module 90 sends to the vehicle at 10:00 on April 02, 2019, within the range of 71 kilometers to 83 kilometers south of National Highway No. 1 (Zhongshan Expressway), the traffic incident 11 on this road is analyzed or It is estimated to be the impact of a "chasing collision", causing serious back-blocking to notify or warn the driver of the vehicle.

Fig. 4 is a flow chart of the method of establishing the traffic incident analysis table 61 shown in Fig. 2 of the present invention, and please refer to Fig. 1. At the same time, the establishment method of the traffic incident analysis table 61 may include the following technical content from step S21 to step S26.

In step S21 in Fig. 4, the classification module 20 shown in Fig. 1 classifies roads according to the characteristics of the roads.

For example, the classification module 20 stores the road classification 21 of the road in the storage module 40 (such as a database) according to the characteristics of the road, and classifies the roads in the area into national roads, provincial roads, and city roads according to the administrative system. , County road, district road or township road, etc.

In step S22 in Figure 4, the collection module 30' collects traffic information such as road-related travel time 31 and traffic volume 32 of the traffic incident 11 through at least one (such as plural) data source of the traffic information 41 according to the road classification 21 41. For example, the collection module 30' broadcasts real-time traffic information through the traffic database of the Expressway Bureau of the Ministry of Communications and the police on the government information open platform The data source collects various traffic incidents on the road11, and uses mobile phone base station-based vehicle detection (CVP), fixed vehicle detector (VD), and electronic road pricing system (ETC)-based vehicle detection (EVP) ), Global Positioning System's Detective Vehicle (GVP), etc., obtain relevant travel time 31 and traffic information 41 such as traffic flow 32 to store traffic events 11 and traffic information 41 in a storage module 40 (such as a database).

For example, the collection module 30' collects traffic information 41 such as travel time 31 and traffic volume 32 related to the road (such as a national highway) of the traffic incident 11. For example, at 08:00 on April 1, 2019, the traffic incident 11 is a drop from the outside, and the average travel time 31 associated with the road is within the range of 71 km to 83 km south of National Highway No. 1 (Zhongshan Expressway) It is 30 minutes, and the five-minute traffic volume 32 is 208 vehicles.

In step S23 in FIG. 4, the calculation module 50 calculates the historical statistical value 51 of the traffic information 41 such as the travel time 31 and the traffic volume 32 of the road of the traffic incident 11 in the past. For example, the calculation module 50 calculates the historical statistical value 51 of the traffic information 41 such as the travel time 31 and the traffic volume 32 of each unit time of the road in the past based on the traffic information 41 in the storage module 40 (such as a database). The historical statistical value 51 may be an arithmetic mean, median, mode, or standard deviation.

For example, the calculation module 50 obtains the real-time road condition information from the police broadcast on the government information open platform or the traffic database of the Expressway Bureau of the Ministry of Communications at 08:00 in March 2019, and heads south on National Highway No. 1 (Zhongshan Expressway) 71 Within the range of km to 83 km, the average of the past travel time 31 of the road is 7.2 minutes, and the five-minute traffic volume 32 is 520 vehicles (for example, a detection station on National Highway No. 1 passed by at this time Therefore, the historical statistical value of travel time 31 is 7.2 minutes, and the historical statistical value of traffic volume 32 is 520 vehicles.

In step S24 in Figure 4, the calculation module 50 compares the road-related travel time 31 and the traffic volume 32 (see step S22) of the traffic incident 11 collected by the collection module 30' with the calculation module 50 respectively. The historical statistical value 51 of the past travel time 31 and the traffic volume 32 of the calculated traffic incident 11 (see step S23) to calculate the relative value 52 (including the proportional relationship) of the travel time and the relative value 53 (including the traffic volume) ratio).

For example, the relative value of travel time 52 is the road-related travel time 31 (for example, 30 minutes) in step S22 divided by the past travel time 31 (for example, 7.2 minutes) of the road in step S23, so travel in this embodiment The relative value of time 52 is 4.16 (that is, 30/7.2=4.16). At the same time, the relative value 53 of the traffic volume is the traffic volume 32 (such as 208 vehicles) related to the road in step S22 divided by the past traffic volume 32 (such as 520 vehicles) on the road in step S23. Therefore, the traffic volume in this embodiment is The relative value of 53 is 0.4 (that is, 208/520=0.4).

In step S25 in Fig. 4, the link module 70 establishes the corresponding link between the relative value 52 of travel time, the relative value 53 of traffic volume, and the traffic event 11, so that each road classification 21 is based on the relative value 52 of travel time. The traffic incident 11 is connected with the relative value 53 of the traffic volume.

In step S26 in Fig. 4, the sorting module 80 arranges the relative value 52 of travel time and the relative value 53 of traffic flow in each road classification 21 in order to establish or generate the traffic incident analysis shown in Fig. 2. Table 61.

In summary, the traffic incident detection system and method of the present invention can at least have the following features, advantages or technical effects.

1. The present invention eliminates the need for additional drive test equipment to greatly reduce the construction cost of traffic infrastructure, thereby economically or quickly analyzing or estimating traffic incidents on selected roads.

2. The present invention can use the relationship between road travel time, traffic volume, and historical statistics to quickly detect traffic conditions and analyze or estimate traffic incidents on selected roads. That is, the present invention can observe traffic conditions based on changes in road travel time and traffic information such as traffic flow, so as to quickly determine or estimate the traffic conditions as traffic incidents, such as serious car accidents, falling objects, and congestion.

3. When a traffic incident occurs, it often causes the traffic behind the vehicle to be congested. Therefore, the present invention can automatically detect the duration of the traffic incident so as to provide necessary traffic information for the drivers behind.

Fourth, the present invention can use big data analysis technology to automatically analyze and judge traffic incidents. That is, the present invention can collect traffic incidents on the road through the traffic database of the Highway Bureau of the Ministry of Communications, the police broadcast real-time traffic information on the government data open platform, etc., and through the use of mobile phone base station-based vehicle detection (CVP) , Fixed Vehicle Detector (VD), Electronic Road Pricing System (ETC) based Vehicle Detective (EVP), Global Positioning System Detective Vehicle (GVP), etc. to obtain relevant travel time and traffic information such as traffic flow, And store the traffic information in a storage module (such as a database) to automatically analyze and judge traffic incidents based on the traffic information using big data analysis technology.

5. The present invention can be applied to, for example, smart transportation, traffic control centers, or vehicle navigation systems.

The above-mentioned embodiments only illustrate the principles, features and effects of the present invention, and are not intended to limit the scope of implementation of the present invention. Anyone who is familiar with the art can comment on the above without departing from the spirit and scope of the present invention. Modifications and changes to the implementation form. Any equivalent changes and modifications made using the content disclosed in the present invention should still be covered by the scope of the patent application. Therefore, the protection scope of the present invention should be as listed in the scope of the patent application.

1‧‧‧Traffic incident detection system

10‧‧‧Select Module

11‧‧‧Traffic incident

20‧‧‧Classification Module

21‧‧‧Road classification

30‧‧‧Detection Module

30'‧‧‧Collection Module

31‧‧‧Travel time

32‧‧‧Traffic

40‧‧‧Storage Module

41‧‧‧Traffic Information

50‧‧‧Calculation Module

51‧‧‧Historical statistics

52‧‧‧The relative value of travel time

53‧‧‧Relative value of traffic volume

60‧‧‧Analysis Module

61‧‧‧Traffic incident analysis table

70‧‧‧Link Module

80‧‧‧Sequencing Module

90‧‧‧Send module

Claims (18)

A traffic incident detection system includes: a selection module, which selects the road to detect a traffic incident; a detection module, which detects or captures the selected road by the selection module through a data source of traffic information The current and past travel time and traffic volume of the road; a calculation module calculates the historical statistics of the past travel time and traffic volume of the road detected or captured by the detection module, and then the detection model The current travel time and traffic volume of the road detected by the group are compared with the historical statistics of the past travel time and traffic volume of the road calculated by the calculation module to calculate the relative value of the travel time and the vehicle volume. The relative value of the traffic flow, so that the calculation module inputs the relative value of the travel time and the relative value of the traffic volume into the traffic event analysis table respectively; and an analysis module is based on the travel time from the traffic event analysis table Analyze or estimate the relative value of the corresponding traffic incident with the relative value of the traffic volume. For example, the traffic incident detection system described in item 1 of the scope of patent application, wherein the traffic information includes travel time, traffic volume, vehicle speed, occupancy rate, length of stop or stop time, and the historical statistical value is an arithmetic average , Median, mode or standard deviation. For example, the traffic incident detection system described in item 1 of the scope of patent application, wherein the data sources of the traffic information include vehicle detection (CVP) based on mobile phone base stations, fixed vehicle detectors (VD), and electronic Vehicle Detective (EVP) based on the Road Toll System (ETC), Detective Vehicle (GVP) of the Global Positioning System, the traffic database of the Highway Bureau of the Ministry of Communications, or the police broadcast real-time traffic information on the government data open platform. For example, the traffic incident detection system described in item 1 of the scope of patent application further includes a classification module and a storage module, wherein the classification module classifies roads according to the characteristics of the road, and the storage module is Store traffic incidents and traffic information from this data source. For example, the traffic incident detection system described in item 1 of the scope of patent application further includes a collection module, which collects the road-related travel time and traffic flow of the traffic incident through the data source of the traffic information according to the road classification. For example, the traffic incident detection system described in item 5 of the scope of patent application, wherein the calculation module further calculates the historical statistics of the travel time and traffic volume of the road in the traffic incident, and then collects the data collected by the collection module The travel time and traffic volume related to the road of the traffic incident are respectively compared with the historical statistical values of the past travel time and traffic volume of the road of the traffic incident calculated by the calculation module to calculate the relative value of the travel time and the traffic volume. The relative value of traffic volume. For example, the traffic incident detection system described in item 1 of the scope of patent application further includes a link module that establishes the relative value of the travel time, the relative value of the traffic volume, and the corresponding link of the traffic event, so as to connect to each road The classification is based on the relative value of the travel time and the relative value of the traffic volume to establish a link between the traffic incidents. For example, the traffic incident detection system described in item 1 of the scope of patent application further includes a sorting module, which arranges the relative value of the travel time and the relative value of the traffic volume in each road classification in order to create or generate The traffic incident analysis table. For example, the traffic incident detection system described in item 1 of the scope of patent application further includes a sending module, which sends the traffic incident analyzed or estimated by the analysis module to the vehicles on the road, so that the traffic incident Notify or alert the driver of the vehicle. A method for detecting traffic incidents, including: Select the road to detect traffic incidents to detect or retrieve the current and past travel time and traffic volume of the selected road from the data source of the traffic information; calculate the past travel of the detected or retrieved road The historical statistics of time and traffic volume, and then compare the current travel time and traffic volume of the detected road to the calculated historical statistics of the road’s past travel time and traffic volume to calculate the travel time The relative value of the relative value and the relative value of the traffic volume, so as to input the relative value of the travel time and the relative value of the traffic volume into the traffic event analysis table respectively; and from the traffic event analysis table according to the relative value of the travel time and the Analyze or estimate the relative value of traffic volume to estimate the corresponding traffic incident. For example, the traffic incident detection method described in item 10 of the scope of patent application, wherein the traffic information includes travel time, traffic volume, vehicle speed, occupancy rate, stop length or stop time, and the historical statistical value is an arithmetic average , Median, mode or standard deviation. For example, the traffic incident detection method described in item 10 of the scope of patent application, wherein the data sources of the traffic information include vehicle detection (CVP) based on mobile phone base stations, fixed vehicle detectors (VD), and electronic Vehicle Detective (EVP) based on the Road Toll System (ETC), Detective Vehicle (GVP) of the Global Positioning System, the traffic database of the Highway Bureau of the Ministry of Communications, or the police broadcast real-time traffic information on the government data open platform. The traffic incident detection method described in item 10 of the scope of patent application further includes road classification based on the characteristics of the road, and storage of traffic incidents and traffic information from the data source. For example, the traffic incident detection method described in item 10 of the scope of the patent application further includes collecting the travel time and traffic flow related to the road of the traffic incident through the data source of the traffic information according to the road classification. For example, the traffic incident detection method described in item 14 of the scope of patent application further includes calculating the past travel time of the road of the traffic incident and historical statistics of traffic flow, and then collecting the travel time related to the road of the traffic incident Compare the calculated historical statistics of the road travel time and traffic volume of the traffic incident with the traffic volume to calculate the relative value of the travel time and the traffic volume. For example, the traffic incident detection method described in item 10 of the scope of patent application further includes establishing the relative value of the travel time, the relative value of the traffic flow and the corresponding link of the traffic incident, so as to classify each road according to the travel time The relative value of the traffic incident is connected with the relative value of the traffic volume. For example, the traffic incident detection method described in item 10 of the scope of patent application further includes arranging the relative value of the travel time and the relative value of the traffic volume in each road classification in order to establish or generate the traffic incident analysis table. The traffic incident detection method described in item 10 of the scope of patent application further includes sending the analyzed or estimated traffic incident to a vehicle on the road to notify or alert the driver of the vehicle of the traffic incident.

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