Bridge low-visibility operation risk multi-level prevention and control and utility verification method

Abstract

The invention discloses a bridge low-visibility operation risk multi-level prevention and control and utility verification method. The method comprises the steps of determining a low-visibility operation risk level of a to-be-detected bridge based on weather and traffic conditions; when the operation risk grade is grade A, the prevention and control facility is a traditional traffic safety facility; when the operation risk level is B level, the prevention and control facilities are traditional traffic safety facilities, risk early warning system I level and sight guidance system I level; when the operation risk level is C level, the prevention and control facilities are traditional traffic safety facilities, risk early warning system II level, dynamic speed limiting system II level and sight guidance system II level; when the operation risk level is D level, the prevention and control facilities are the traditional traffic safety facilities, the risk early warning system III level, the dynamic speed limiting system and the sight guidance system III level; a bridge low-visibility operation risk multi-level prevention and control effectiveness test platform is built based on a driving simulation technology, and fine-grained micro driving behaviors and vehicle operation data are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation Internet of Things, and in particular to a multi-level prevention and control and utility verification method for low-visibility operation risks of bridges based on different risk levels. Background Art

[0002] With the rapid development of my country's economy, the total mileage of highways open to traffic and the number of cars per capita are increasing day by day. As a technical support for ensuring the safety and efficient operation of road traffic, traffic safety risk prevention and control plays an increasingly important role in the field of intelligent transportation Internet of Things. A traffic operation safety risk prevention and control technology with risk identification, risk prevention and control, and effect evaluation as the link has been formed. Among them, the intelligent transportation system with roadside traffic safety facilities and vehicle-mounted terminals as carriers and wireless communication technology as the medium has brought travelers a safer, more convenient and comfortable travel service experience. However, for the typical scenes and high-risk sections of special structures such as bridges, the degree of intelligence of their operation safety prevention and control technology is relatively low. At present, the problem of organic integration of traditional facilities and intelligent equipment has not been solved. It is impossible to carry out targeted bridge operation risk level classification according to the coupling status of real-time weather and traffic conditions in a low-visibility risk environment, propose targeted and differentiated risk prevention and control methods and verify them based on human factors.

[0003] In the prior art, some traffic operation safety prevention and control methods and related supporting facilities are disclosed, which are mainly summarized as follows:

[0004] 1. A transient warning method and device for road dangerous events with application number 201910309887.5. This invention studies the categories and timeliness of all dangerous events on the road, proposes a dangerous event identification, classification and grading method based on timeliness evaluation, and designs a transient warning device for road dangerous events. It can define the corresponding vehicle terminal functions and vehicle device design according to the driver's acceptance and adaptability characteristics, and give a comprehensive analysis of the possible warning strategies for road dangerous events under the influence of road environment and meteorological environment. This invention has low investment cost and is easy to deploy on a large scale, providing travelers with real-time transient information services during travel, but it only involves vehicle-side warning in the field of V2V network technology, and fails to reflect the configuration of roadside facilities and vehicle-road collaborative warning capabilities, and does not mention systematic utility verification methods.

[0005] 2. The invention with application number 201910665948.1 is a method for formulating recommended vehicle speed based on safety risk and distance. The invention integrates and matches multi-source data that may affect vehicle operation, calculates and clusters traffic risk values ​​through traffic risk assessment models, implements traffic risk warning classification, and calculates safe road speed based on traffic risk distribution and safe parking sight distance. However, the invention cannot be fully applied to special road sections with high risks, such as bridges.

[0006] 3. A method for assessing the operational safety risk of long and large bridges with application number 201911073386.8. This invention provides a method for assessing the operational safety risk of long and large bridges. It systematically conducts the operational safety risk assessment of long and large bridges in service for the first time. Based on the risk matrix theory, it combines the possibility of risk events and the severity of the consequences of events, divides the risk levels of long and large bridges, and proposes targeted risk control measures, forming a set of systematic semi-quantitative assessment methods for the operational safety risks of long and large bridges. However, the invention involves relatively little content on the operational risk control measures for bridges, and does not propose multi-level prevention and control technologies and new ITS equipment configuration solutions based on the different low-visibility operational risk levels of bridges.

[0007] 4. A multi-level differentiated prevention and control method for bridge operation safety with application number 202110302536.9. This invention provides a multi-level differentiated prevention and control method for bridge operation safety. It systematically divides different operation risk levels according to the severity of the impact of various adverse weather conditions on traffic operation safety on bridges, and proposes a multi-level differentiated prevention and control method for bridges based on this. However, this invention only considers weather conditions when dividing the operation risk levels, and does not incorporate traffic conditions. Moreover, the invention does not mention the verification of the effectiveness of the prevention and control method, and the effectiveness and scientificity of the implementation oriented to engineering applications are still unknown.

[0008] In summary, the existing traffic risk prevention and control method system is relatively mature in highways and urban roads. However, the current application of intelligent devices is relatively single. With the rapid development of intelligent transportation Internet of Things technology, there is still a lack of multi-level prevention and control methods for operation risks based on the integration and step-by-step transition of traditional traffic engineering facilities and new ITS equipment based on human factors. Especially for bridges, the existing results are mostly considered from the perspective of structural risks, and lack analysis and research on the safety of low-visibility traffic operation. In addition, the quantitative improvement effect of the implementation of prevention and control methods under multi-dimensional goals is not clear, and it is difficult to meet the differentiated needs of human factors. Therefore, it is urgent to propose a multi-level prevention and control and utility verification method for low-visibility operation risks of bridges based on different risk levels. Summary of the invention

[0009] In view of the above problems existing in the prior art, the present invention provides a multi-level prevention and control method for low-visibility operation risks of bridges and a method for effectiveness verification.

[0010] The present invention discloses a multi-level prevention and control method for bridge low-visibility operation risk and effectiveness verification method, comprising:

[0011] Determining a low visibility operation risk level of the bridge to be tested based on weather and traffic conditions; wherein the operation risk level is level A, level B, level C or level D;

[0012] When the operation risk level is A, it corresponds to mandatory prevention and control facilities, and its prevention and control facilities are traditional traffic safety facilities;

[0013] When the operation risk level is B, the corresponding improved prevention and control facilities are traditional traffic safety facilities, risk warning system level I, and sight guidance system level I;

[0014] When the operation risk level is C, the corresponding enhanced prevention and control facilities are traditional traffic safety facilities, risk warning system level II, dynamic speed limit system, and sight guidance system level II;

[0015] When the operation risk level is D, it corresponds to complete prevention and control facilities, which include traditional traffic safety facilities, risk warning system level III, dynamic speed limit system, and sight guidance system level III;

[0016] Based on driving simulation technology, a multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges is established to obtain fine-grained micro-driving behavior and vehicle operation data. The prevention and control methods to be implemented are determined according to the low-visibility operation risk level of the bridge to be tested. Starting from the physical and psychological needs of drivers and considering the goals of safe and efficient traffic operation status, a multi-dimensional utility verification indicator system is constructed.

[0017] As a further improvement of the present invention, the step of determining the low visibility operation risk level of the bridge to be tested includes:

[0018] Comprehensively analyze the historical weather and traffic condition data of the bridge, select visibility and traffic service level as the judgment indicators, and reflect the possibility and severity of low visibility operation risks;

[0019] According to the influence of the discrimination index on the road traffic operation, each discrimination index is divided into four levels: A, B, C, and D;

[0020] The low visibility operation risk level of the bridge to be tested is determined based on the level of the discrimination index of the current visibility and traffic service level data of the bridge to be tested.

[0021] As a further improvement of the present invention,

[0022] Visibility reflects the possibility of risk, and the four levels of identification indicators are:

[0023] Grade A is visibility ≥ 500m, Grade B is visibility 400-500m, Grade C is visibility 200-400m, and Grade D is visibility ≤ 200m;

[0024] The service level reflects the severity of the risk, and the four levels of the judgment indicators are:

[0025] Grade A means traffic flow density ≤11 (pc / mi / ln), Grade B means traffic flow density 11-18 (pc / mi / ln), Grade C means traffic flow density 18-26 (pc / mi / ln), and Grade D means traffic flow density 26-35 (pc / mi / ln).

[0026] As a further improvement of the present invention, the method of determining the low visibility operation risk level of the bridge to be tested according to the current visibility of the bridge to be tested and the level of the discrimination index to which the traffic service level data belongs comprises:

[0027] If the visibility of the bridge to be tested is consistent with the traffic service level level, then this level is taken as the low visibility operation risk level of the bridge to be tested;

[0028] If the visibility of the bridge to be tested is inconsistent with the traffic service level, the worst level of the two is taken as the low visibility operation risk level of the bridge to be tested; as a further improvement of the present invention, the risk warning system is used to change the corresponding prevention and control level in real time based on the detected meteorological conditions, traffic flow, and vehicle operation data. As the low visibility operation risk level of the bridge increases, considering the differences in the form and degree of refinement of information provision, the road-side VMS risk information warning is gradually transitioned to the vehicle-road collaborative warning, and finally the warning content is delivered to the driver in the form of a combination of "voice + image" vehicle-side warning and "text + graphic" road-side warning, so as to realize accompanying information service.

[0029] As a further improvement of the present invention,

[0030] Level I of the risk warning system is to publish VMS risk warning information on roadside facilities;

[0031] Risk warning system level II is to publish VMS risk warning information and driving advice information on roadside facilities;

[0032] The Level III risk warning system publishes VMS risk warning information and driving advice information on roadside facilities, and transmits weather and specific behavioral guidance to the driver in the form of "voice + image" vehicle-side warnings. The content includes current time, weather information, driving route, vehicle speed, speed limit, vehicle lateral offset position, vehicle longitudinal driving distance, risk conditions ahead and behavioral guidance suggestions.

[0033] As a further improvement of the present invention, the dynamic speed limit system is used to consider the smooth transition of vehicles in different speed limit sections according to weather conditions and traffic flow conditions, and implement lane-by-lane dynamic speed limit control on the basis of meeting the minimum braking distance, so as to obtain the maximum safe vehicle speed under the influence of low visibility weather.

[0034] As a further improvement of the present invention, the sight line induction system can realize different degrees of induction warning functions according to different low visibility operation risk levels. It includes warning lights set on both sides of the road;

[0035] Level I of the sight guidance system means that the yellow warning light starts flashing at intervals;

[0036] Level II of the sight guidance system means that all yellow warning lights flash;

[0037] The Level III sight guidance system activates the rear-end collision prevention warning mode when a vehicle passes by, which can trigger the red warning light of the upstream preset group to light up.

[0038] As a further improvement of the present invention, a method for implementing the sight line induction system includes:

[0039] When the operating risk level is A, the sight guidance system does not need to work and is in standby mode;

[0040] When the operating risk level is B, the road contour enhancement mode (level I sight guidance) is activated, and the yellow warning light starts flashing with a brightness of 1500cd / m 2 , the frequency is 60 times / min, and the lighting interval is 40m;

[0041] When the operating risk level is C, the active driving guidance mode (level II sight guidance) is activated, the yellow warning light increases the flashing brightness and frequency, and reduces the lighting interval. The brightness is 2500cd / m 2 , the frequency is 120 times / min, and the lighting interval is 20m;

[0042] When the operating risk level is D, the radar-vision integrated device can detect whether there is a vehicle passing through the current section. If no vehicle passes, the active driving induction mode will continue to be maintained; if a vehicle passes, the anti-rear-end collision warning mode (Level III fog induction) will be activated, which can trigger the red lights of the upstream preset group of warning lights to light up, forming a red tail trail to remind the rear vehicle that there is a vehicle in front and to maintain a safe distance; at this time, the yellow light of other warning lights can flash synchronously, and when the vehicle drives forward and passes the next group of warning lights, the red tail trail will move forward synchronously with the vehicle's dynamics.

[0043] As a further improvement of the present invention,

[0044] A multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges is built based on driving simulation technology. The required hardware includes driving simulation equipment and 6 high-performance computers. The driving simulation equipment is a 1 / 4 real car compact driving simulator, and the main exterior components are real car parts such as steering wheel, combination switch, shift handle, dashboard, seat belt, seat, etc. Combined with 3dMax scene development software, the design and development of driving simulation scenes are realized, including complex road construction, built-in parameter settings, and scenario control functions. The developed virtual scenes are imported into the driving simulation equipment. Through the application program interface (API), scene elements such as weather, traffic flow, and sight induction system lighting effects can be added to provide a real bridge low-visibility driving experience.

[0045] As a further improvement of the present invention,

[0046] Equipped with a data acquisition device in the driving simulation equipment, it can obtain vehicle operation and driving behavior information, and realize data interaction between the driving simulator central control platform and the data collaborative processing center based on the user data protocol interface (UDP). During the test, the driving simulation equipment can dynamically collect data such as time, mileage, speed, acceleration, accelerator pedal status, brake pedal status, steering wheel angle, etc.

[0047] As a further improvement of the present invention,

[0048] Starting from the physical and psychological needs of drivers, with safe and efficient traffic operation as the goal, based on fine-grained micro-driving behavior and vehicle operation data, three dimensions of psychological feelings, safety utility and traffic efficiency are selected to construct an indicator system for the effectiveness of prevention and control methods.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention takes bridges in highways as the implementation object, and proposes a low-visibility traffic operation risk level classification method based on visibility and traffic service level indicators through the influence of weather conditions and traffic conditions on the possibility and severity of vehicle driving risks; on this basis, different prevention and control facilities are selected for different operation risk levels; and the configuration parameters of the prevention and control system can be adjusted according to real-time weather conditions to achieve targeted and differentiated prevention and control parameter triggering; the present invention comprehensively considers weather conditions and traffic conditions, solves the problem of multi-level organic integration of traditional facilities and intelligent devices, can effectively assist drivers from the perspective of human needs, enable vehicles to pass through bridges safely and efficiently, improve traffic operation quality, improve traffic service levels, and provide certain technical guarantees for the operation safety of bridges in low-visibility environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1A flowchart of a multi-level prevention and control method for low-visibility operation risks of bridges and a method for verifying their effectiveness, disclosed in an embodiment of the present invention;

[0052] Figure 2 A flowchart of a method for determining a low visibility operation risk level of a bridge to be tested based on weather and traffic conditions disclosed in an embodiment of the present invention;

[0053] Figure 3 A configuration chart of a multi-level prevention and control method for low-visibility operation risk of bridges based on different risk levels according to the present invention;

[0054] Figure 4 This is the work flow chart of the sight line guidance system;

[0055] Figure 5 This is a schematic diagram of the physical framework of level I sight induction;

[0056] Figure 6 This is a schematic diagram of the physical framework for level II sight induction;

[0057] Figure 7 This is a schematic diagram of the physical framework of level III sight induction;

[0058] Figure 8 This is a schematic diagram of the framework of a multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges based on driving simulation technology;

[0059] Fig. 9 Schematic diagram of a multi-dimensional utility verification indicator system built with people as the orientation. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0062] The present invention takes bridges in highways as the implementation object, and proposes a low-visibility traffic operation risk level classification method based on the impact of weather conditions and traffic conditions on the possibility and severity of vehicle driving risks and visibility and traffic service level indicators. Taking weather and traffic conditions into comprehensive consideration, the risk levels are quantified and differentiated through indicator thresholds, which facilitates the reasonable determination of risk prevention and control levels and equipment configurations. On this basis, according to different low-visibility operation risk levels, with the layout of layers (mandatory, improved, enhanced, and complete) and subsystems (risk warning system, dynamic speed limit system, and sight guidance system) as the principles, the idea is to transition from static notification (transmitting traffic information with traditional traffic engineering signs and markings as the core) to dynamic guidance (real-time dynamic prevention and control based on traditional facilities combined with vehicle-road collaborative warning, dynamic speed limit control, and sight guidance). New bridge risk prevention and control equipment is gradually integrated into the system, and different equipment configuration schemes are innovatively divided into four levels: mandatory, improved, enhanced, and complete. The prevention and control level is gradually enhanced. A multi-level prevention and control equipment configuration method for low-visibility operation risks of bridges that integrates traditional traffic engineering facilities with new ITS equipment is proposed. In addition, considering the impact of human factors on traffic safety, the configuration parameters of the prevention and control system can be adjusted according to real-time weather and traffic conditions to achieve differentiated prevention and control parameter triggering. On this basis, a multi-level prevention and control method utility test platform for low-visibility operation risk of bridges is built with the help of driving simulation technology, which can obtain fine-grained driving behavior and vehicle operation data. Starting from the physical and psychological needs of drivers, while considering safe and efficient traffic operation status, a multi-dimensional utility verification index system oriented towards human factors is constructed, which can conduct a comprehensive and detailed comprehensive utility evaluation for prevention and control methods. While making full use of facility resources and assisting drivers in manipulating vehicles, prevention and control methods can reduce traffic operation risks and accident rates, provide travelers with a safer and more comfortable travel service experience, and open up a new research perspective for the design and verification of operation risk prevention and control methods in low-visibility bridge environments in addition to structural monitoring, providing certain reference value for management departments and scholars in related fields.

[0063] Specific:

[0064] like Figure 1 As shown, the present invention provides a multi-level prevention and control method for low-visibility operation risk of bridges and a method for effectiveness verification, comprising:

[0065] Step 1: Determine the low visibility operation risk level of the bridge to be tested based on weather and traffic conditions; wherein the low visibility operation risk level is A level, B level, C level or D level;

[0066] like Figure 2 As shown in the figure, determining the low visibility operation risk level of the bridge to be tested specifically includes:

[0067] Step 11: Comprehensively analyze the historical weather and traffic condition data of the bridge to study the impact of visibility on the possibility of vehicle driving risk and the impact of traffic service level on the severity of vehicle driving risk; select visibility and traffic service level as discrimination indicators. The lower the visibility, the higher the possibility of risk; the worse the traffic service level and the greater the traffic flow density, the higher the severity of risk;

[0068] Step 12: According to the influence of the discrimination index on the road traffic operation, each discrimination index is divided into four levels: A, B, C, and D. The specific classification thresholds are shown in Table 1 below;

[0069] Table 1

[0070]

[0071] Step 13: Determine the low visibility operation risk level of the bridge to be tested according to the current visibility of the bridge to be tested and the level of the discrimination index to which the traffic service level data belongs; wherein,

[0072] If the visibility of the bridge to be tested is consistent with the traffic service level grade, then this grade is taken as the low visibility operation risk grade of the bridge to be tested; for example, if the average annual visibility grade of the area where a bridge is located is B, and the average annual traffic service level grade of the road section is also B, it can be determined that the low visibility operation risk grade of the bridge is B;

[0073] If the visibility of the bridge to be tested is inconsistent with the traffic service level, the worst level of the two is taken as the low visibility operation risk level of the bridge to be tested; for example, if the average annual visibility level of the area where a bridge is located is A, and the average annual traffic service level level of the road section is B, it can be determined that the low visibility operation risk level of the bridge is B. Step 2: Based on the determined low visibility operation risk level, determine its prevention and control facilities;

[0074] That is, after the risk level of low-visibility operation of bridges is determined, based on the different levels of risk, taking into account the impact of human factors on traffic safety, referring to the EU infrastructure digitalization level classification, and according to the amount of information that road participants can receive and the degree of impact of the facilities on them, the idea is to transition from static notification to dynamic guidance, and gradually integrate new bridge risk prevention and control equipment. A multi-level prevention and control equipment configuration method for low-visibility operation risks of bridges is proposed, which is guided by different equipment configuration levels such as mandatory, improved, enhanced, and complete, and integrates traditional traffic engineering facilities with new ITS equipment.

[0075] Specifically:

[0076] When the operation risk level is A, it corresponds to mandatory prevention and control facilities, and its prevention and control facilities are traditional traffic safety facilities;

[0077] When the operational risk level is B, the corresponding improved prevention and control facilities are traditional traffic safety facilities, risk warning system level I, and risk disposal system level I;

[0078] When the operation risk level is C, the corresponding enhanced prevention and control facilities are traditional traffic safety facilities, risk warning system level II, dynamic speed limit system, and sight guidance system level II;

[0079] When the operating risk level is D, the corresponding complete prevention and control facilities are traditional traffic safety facilities, risk warning system level III, dynamic speed limit system, and sight guidance system level III.

[0080] At the same time, in actual applications, differentiated prevention and control parameter triggering can be achieved according to real-time weather and traffic conditions; for example, when the risk level changes from A to B, the corresponding prevention and control system configuration parameters are adopted.

[0081] Among the above-mentioned prevention and control facilities:

[0082] The risk warning system is used to change the corresponding prevention and control level in real time based on the detected meteorological conditions, traffic flow, and vehicle operation data. With the increase of the risk level of low visibility operation of bridges, considering the differences in the form and degree of refinement of information, the road-side VMS risk information warning is gradually transitioned to the vehicle-road collaborative warning. Finally, the warning content is delivered to the driver in the form of a combination of "voice + image" vehicle-side warning and "text + graphic" road-side warning, realizing accompanying information services, providing drivers with more personalized notifications and warnings, and bringing travelers a safer, more convenient and comfortable travel service experience. Further, the risk warning system level I is to publish VMS risk warning information on roadside facilities; the risk warning system level II is to publish VMS risk warning information and driving advice information on roadside facilities; the risk warning system level III is to publish VMS risk warning information and driving advice information on roadside facilities, and to deliver weather and specific behavior guidance to the driver in the form of "voice + image" vehicle-side warning, including the current time, weather information, driving route, vehicle speed, speed limit, vehicle lateral offset position, vehicle longitudinal driving distance, forward risk conditions and behavior guidance suggestions.

[0083] The dynamic speed limit system is used to consider the smooth transition of vehicles in different speed limit sections according to weather conditions and traffic flow operation, and implement lane-by-lane dynamic speed limit control on the basis of meeting the minimum braking distance, so as to obtain the maximum safe speed under the influence of low visibility weather; especially on bridges under the influence of low visibility weather, the speed limited by the static speed limit may still be unsafe for the driver, and the dynamic speed limit system can limit the speed to its safe value. Furthermore, if extremely bad weather or traffic conditions occur, such as visibility less than 50m or traffic flow density greater than 35 (pc / mi / ln), it still belongs to the D level of low visibility operation risk level of the bridge, and is equipped with complete prevention and control facilities. However, in order to avoid traffic accidents at this time, emergency traffic management measures should be taken, and ramp control and vehicle diversion can be used to close the bridge section.

[0084] Furthermore, the sight guidance system can adjust the parameter configuration based on human factors according to different low visibility operation risk levels to achieve different degrees of guidance warning functions. Including warning lights set on both sides of the road;

[0085] Level I of the sight guidance system means that the yellow warning light starts flashing at intervals;

[0086] Level II of the sight guidance system means that all yellow warning lights flash;

[0087] The Level III sight guidance system activates the rear-end collision prevention warning mode when a vehicle passes by, which can trigger the red warning light of the upstream preset group to light up.

[0088] Example:

[0089] like Figure 3 As shown, the present invention provides a multi-level prevention and control method for low-visibility operation risk of bridges and a method for effectiveness verification, comprising:

[0090] By analyzing the weather and traffic data of previous years, considering the influence of visibility and traffic service level on a bridge all year round, and then based on two identification indicators, the low visibility operation risk level of the bridge is determined. According to the monitoring of meteorological stations or weather sensors and video surveillance or radar-vision integrated devices, the area where a bridge is located is foggy all year round, with an average visibility of ≤200m all year round, and the visibility level is D; the traffic flow density of this section is between 26-35 (pc / mi / ln) all year round, and the traffic service level is D. Therefore, it can be determined that the low visibility operation risk level of the bridge is D, and complete prevention and control facilities should be configured. The configuration plan is traditional traffic safety facilities, risk warning system level III, dynamic speed limit system, and sight guidance system level III.

[0091] This plan is based on the appropriate prevention and control level under the low visibility operation risk level. However, in the actual bridge traffic operation management process, it is necessary to start a differentiated real-time dynamic prevention and control plan according to the risk level faced at that time. If the visibility is greater than 200m and the traffic flow density is less than 26 (pc / mi / ln), the prevention and control strategy should be changed in time according to the corresponding level. Figure 3 As shown, traditional traffic safety facilities can play a role of static notification, but without any digital information. With the improvement of real-time risk level, new ITS equipment is gradually integrated, including risk warning system, dynamic speed limit system and sight induction system, which are all composed of information collection module, information transmission module, information processing module and information release module. The information collection module needs to collect meteorological conditions, traffic flow and vehicle operation data, such as visibility, vehicle speed, headway and lane offset distance. The information transmission module usually adopts 4G or 5G wireless transmission. The information processing module can gather and process data and generate prevention and control strategies according to the pre-set algorithm. The information release module is located at the road end or vehicle end, and can timely transmit risk prevention and control information to the driver.

[0092] In the risk warning and dynamic speed limit system, the VMS risk warning is a roadside information release that can clearly and explicitly inform the driver of the danger ahead, such as "foggy bridge ahead, visibility ≤200m"; driving advice notification is to display driving advice information in the roadside VMS, such as "slow down and keep a safe distance"; the vehicle-side warning takes into account the impact of human factors on driving behavior, and delivers weather and specific behavioral guidance to the driver in the form of "voice + image" vehicle-side warning. The content includes current time, visibility, driving route, speed, speed limit, vehicle lateral offset position, vehicle longitudinal driving distance, risk conditions ahead and behavioral guidance suggestions. Level I line of sight induction-Level III line of sight induction belongs to the line of sight induction system, and its specific work flow is as follows Figure 4 As shown, the main functions of the system include road contour enhancement (level I sight guidance), active driving guidance (level II sight guidance), and anti-rear-end collision warning (level III sight guidance). Based on the real-time visibility and traffic service level on site, the system automatically switches the working mode according to the pre-set thresholds to guide vehicles to drive safely in different risk environments.

[0093] Specific:

[0094] like Figure 4 As shown in the figure, the spacing between the roadside sight guidance terminals (warning lights) is 20m. When the risk level is A, the sight guidance system does not need to work and is in standby mode. When the risk level is B, the road contour enhancement mode is activated, and the yellow light starts to flash with a brightness of 1500cd / m 2 , the frequency is 60 times / min, and the lighting interval is 40m. Figure 5When the risk level is C, the active driving induction mode is activated, focusing on the impact of human vision on driving behavior. Generally, obvious flashing lights are more eye-catching and have a stronger warning effect. Therefore, in this mode, the flashing brightness and frequency are increased, and the lighting interval is reduced. Preferably, the brightness is 2500cd / m 2 , the frequency is 120 times / min, and the lighting interval is 20m. Figure 6 As shown; when the risk level is D, the integrated radar vision device can detect whether there is a vehicle passing through the current section. If no vehicle passes, the active driving induction mode will continue to be maintained. If a vehicle passes, the anti-rear-end collision warning mode will be activated, which can trigger a certain group of upstream induction terminals to light up the red warning lights, forming a red tail to remind the rear vehicle that there is a vehicle in front and to maintain a safe distance. At this time, the yellow induction lights of other induction terminals can flash synchronously. When the vehicle drives forward and passes the next group of induction terminals, the red tail will move forward synchronously with the vehicle dynamics, such as Figure 7 As shown; when the risk level is more severe, the emergency management strategy is activated, and the ramp control + vehicle diversion method is adopted in a timely manner to close the bridge section and ensure the safety of traffic operation as much as possible.

[0095] After determining the configuration plan of the prevention and control equipment, a multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges is built based on driving simulation technology, such as Figure 8 As shown. The hardware required by the platform includes driving simulation equipment and 6 high-performance computers. The driving simulation equipment is a 1 / 4 real car compact driving simulator, and the main exterior components are real car parts such as steering wheel, combination switch, shift handle, dashboard, seat belt, seat, etc. The pedals, steering, lights, buttons and various fuel gauge signals can reproduce the driving experience like a real car. Combined with 3d Max scene development software, the design and development of driving simulation scenes are realized, including complex road construction, built-in parameter settings, and scenario control functions. The developed virtual scenes are imported into the driving simulation equipment. Through the application program interface (API), scene elements such as weather, traffic flow and sight induction system lighting effects can be added to form a driving simulation scene and provide a real low-visibility bridge driving environment.

[0096] During the test, the data acquisition device installed in the driving simulation equipment can obtain vehicle operation and driving behavior information at a fixed frequency, and realize data interaction between the driving simulator central control platform and the data collaborative processing center based on the user data protocol interface (UDP). After obtaining the driver's operation information, the central control platform performs vehicle status analysis and calculation, and the calculation results (speed, speed, etc.) are transmitted to the data acquisition device, and then the corresponding information is displayed on the instrument. The driving simulator can dynamically collect data such as time, mileage, speed, acceleration, accelerator pedal status, brake pedal status, steering wheel angle, etc., and gather them together with the video and subjective data obtained by the human factors data acquisition system to the data collaborative processing center.

[0097] After obtaining fine-grained vehicle operation and driving behavior data, starting from the driver's life and psychological needs, while considering the safe and efficient traffic operation status, a total of 7 indicators in three dimensions of psychological feelings, safety utility and traffic efficiency were selected to construct a human-oriented utility verification indicator system, such as Fig. 9 As shown. Psychological feelings are divided into confidence and tension, which are represented by the curvature of the space-time graph and the absolute acceleration respectively; safety utility is divided into operation level and control level. The operation level reflects the vehicle status, and the control level reflects the driving behavior. The indicators of each level are further divided into lateral (direction perpendicular to the vehicle's driving direction) and longitudinal (vehicle driving direction) by the direction of movement. The lateral operation level is represented by lateral offset, the longitudinal operation level is represented by acceleration, the lateral control level is represented by the steering wheel angle, and the longitudinal control level is represented by the throttle effect. Traffic efficiency reflects the efficiency of bridge traffic operation in low visibility environment, which is represented by traffic capacity.

[0098] In one embodiment, subjects are selected, and each subject randomly completes three groups of scenes under the experimental scheme. The driving time of each scene is about 15 minutes. In order to prevent driving fatigue, there is at least a 5-minute rest between each scene. During the rest period, the experimenter will save the test data of each scene; the subjects fill in the subjective questionnaire after the test and complete the evaluation of the bridge scene simulation degree and the subjective utility of the fog induction lamp. After eliminating the invalid data that did not perform the experiment normally according to the instructions, 29 subjects were screened out, and there were 87 valid data segments under the three schemes. Among them, according to the above experimental design scheme, the comparison of the control group and the experimental group data can show the overall utility generated by the superposition of the prevention and control strategy; the comparison of the experimental group-A and the experimental group-B data can further highlight the impact of the different brightness of the fog induction lamp. Since this study does not consider the impact of vehicle-to-vehicle interaction, the area (about 825m) where the main vehicle does not interact with the surrounding vehicles in the cross-river bridge section is selected as the key section analyzed in this paper. In order to be able to analyze the significant differences in the spatial dimension in a fine-grained manner (significance = 1, significant difference; significance = 0, no significant difference), a point was intercepted every 5 meters to further reflect the changes in driving behavior. The specific data processing steps are as follows: (1) Data interception. Using Python, according to the preset variables marked in the driving simulation system before the experiment, all valid data were intercepted to extract the road sections that this paper focused on; (2) Overall significance test. Using Python, MATLAB and SPSS, each evaluation index was extracted, and appropriate significance test methods were used according to the data situation to verify whether each index had significant differences between different schemes; (3) Distribution feature characterization. Using MATLAB, the kernel density and cumulative distribution curves of each index of all schemes were plotted to reveal the overall distribution of the data and the differences between different schemes; (4) Point-by-point significance test. Using MATLAB, the time series data was converted into spatial series data with one point every 5 meters (average within 5 meters) to fully reflect whether the changes in driving behavior between different schemes had significant differences in spatial position; (5) Improvement utility quantification. Taking into account the percentage of change, the degree of improvement of indicators in each dimension was calculated, and the effectiveness of the prevention and control strategy and its improvement in sight-induced brightness was quantitatively characterized.

[0099] Starting from the physical and psychological needs of drivers, and considering the safe and efficient traffic operation status, a total of 5 indicators in three dimensions, namely psychological feelings, safety utility and traffic efficiency, were selected to construct an evaluation index system. Among them, psychological feelings are divided into two levels: confidence and tension. Safety utility is divided into two levels, vertical and horizontal, which fully reflect the safety characteristics of the prevention and control strategy. Finally, the improvement rate of each indicator between different schemes is used to quantify the improvement degree of the prevention and control strategy.

[0100] The curvature of the space-time graph is used to characterize the driving confidence. The average time-displacement relationship of all vehicles is expressed in the form of a two-dimensional image to obtain the average space-time graph of the vehicle driving process. The slope of the curve in the average space-time graph represents the running speed of the vehicle, and the curvature represents the adjustment rate of the driver's speed direction to the spatial position at a certain moment. The smaller the curvature of the space-time graph, the weaker the adjustment intensity of the driving state, the lower the driver's panic level, and the higher his driving confidence, which can be expressed as:

[0101]

[0102] Where: K is the curvature value of the driver's average space-time graph; y″ is the second-order derivative of the driver's average space-time graph; y′ is the first-order derivative of the driver's average space-time graph.

[0103] Acceleration is the first-order derivative of acceleration with respect to time, which reflects the speed of acceleration change and can indicate the driver's adaptability to acceleration changes. This indicator is correlated with the driver's internal state during driving and can characterize the driver's psychological stress. Therefore, this study uses this indicator to explore the driver's behavior and tension. In order to better observe the change trend of acceleration, its absolute value, namely absolute jerk, is selected. The larger the value, the worse the driver's adaptability and the higher the tension, which can be expressed as:

[0104]

[0105] Where: J is the absolute acceleration; a is the acceleration.

[0106] Acceleration is used to characterize longitudinal safety effectiveness. Acceleration is used to describe the speed of change of vehicle running speed, which can reflect the stability of vehicle longitudinal running. The smaller the acceleration change, the smoother the vehicle runs, the higher the safety, and the more comfortable the driver will feel.

[0107] Traffic capacity is used to characterize traffic efficiency. Traffic capacity refers to the maximum number of vehicles passing through a certain section or road section per unit time under certain road and traffic conditions, reflecting the operation characteristics of traffic flow. The higher the traffic capacity, the smoother the traffic flow, that is, the traffic efficiency has been improved. Traffic capacity can be expressed as:

[0108]

[0109] Where: C is the road section capacity; is the average headway time of the road section.

[0110] The improvement rate is used to characterize the degree of improvement. The improvement rate refers to a certain indicator between two groups (experimental group-A compared to the control group, experimental group-B compared to experimental group-A, or experimental group-B compared to the control group), which is the change ratio due to the adoption of prevention and control strategies or the improvement of the brightness of fog induction lights. The value is positive and the larger it is, the stronger the improvement ability and the higher the degree, and the prevention and control effect has been improved. The improvement rate can be expressed as:

[0111]

[0112] Where: I is the improvement rate; △v is the change of a certain indicator between the two groups; v c It is the benchmark value of the indicator, that is, the value of the indicator in the comparison plan.

[0113] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-level prevention and control method for low-visibility operation risks of bridges, characterized in that: include: Determining a low visibility operation risk level of the bridge to be tested based on weather and traffic conditions; wherein the operation risk level is level A, level B, level C or level D; When the operation risk level is A, it corresponds to mandatory prevention and control facilities, and its prevention and control facilities are traditional traffic safety facilities; When the operation risk level is B, the corresponding improved prevention and control facilities are traditional traffic safety facilities, risk warning system level I, and sight guidance system level I; When the operation risk level is C, the corresponding enhanced prevention and control facilities are traditional traffic safety facilities, risk warning system level II, dynamic speed limit system, and sight guidance system level II; When the operation risk level is D, it corresponds to complete prevention and control facilities, which include traditional traffic safety facilities, risk warning system level III, dynamic speed limit system, and sight guidance system level III; Based on driving simulation technology, a multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges is established to obtain fine-grained micro-driving behavior and vehicle operation data. The prevention and control methods to be implemented are determined according to the low-visibility operation risk level of the bridge to be tested. Starting from the physical and psychological needs of drivers and considering the goals of safe and efficient traffic operation status, a multi-dimensional utility verification indicator system is constructed.

2. The method according to claim 1, characterized in that The low visibility operation risk level of the bridge to be tested is determined based on weather and traffic conditions, including: Comprehensively analyze the historical weather and traffic condition data of the bridge, select visibility and traffic service level as the judgment indicators, and reflect the possibility and severity of low visibility operation risks; According to the influence of the discrimination index on the road traffic operation, each discrimination index is divided into four levels of A, B, C, and D through historical data to determine the low visibility operation risk level of the bridge to be tested; According to the level of the judgment index of the current real-time visibility and traffic service level data of the bridge to be tested, the multi-level prevention and control strategy is dynamically triggered.

3. The method according to claim 2, characterized in that Visibility reflects the possibility of risk, and the four levels of identification indicators are: Grade A is visibility ≥ 500m, Grade B is visibility 400-500m, Grade C is visibility 200-400m, and Grade D is visibility ≤ 200m; The service level reflects the severity of the risk, and the four levels of the judgment indicators are: Grade A means traffic flow density ≤11pc / mi / ln, Grade B means traffic flow density 11-18pc / mi / ln, Grade C means traffic flow density 18-26pc / mi / ln, and Grade D means traffic flow density 26-35pc / mi / ln.

4. The method according to claim 2 or 3, characterized in that The method of determining the low visibility operation risk level of the bridge to be tested according to the current visibility of the bridge to be tested and the level of the discrimination index to which the traffic service level data belongs includes: If the visibility of the bridge to be tested is consistent with the traffic service level level, then this level is taken as the low visibility operation risk level of the bridge to be tested; If the visibility of the bridge to be tested is inconsistent with the traffic service level level, the worst level of the two shall be taken as the low visibility operation risk level of the bridge to be tested.

5. The method according to claim 1, characterized in that The risk warning system is used to change the corresponding prevention and control level in real time based on the detected meteorological conditions, traffic flow, and vehicle operation data. As the risk level of low visibility operation of the bridge increases, considering the differences in the form and degree of refinement of information given, the road-side VMS risk information warning is gradually transitioned to the vehicle-road collaborative warning. Finally, the warning content is delivered to the driver in the form of a combination of "voice + image" vehicle-side warning and "text + graphic" road-side warning, thereby realizing accompanying information service.

6. The method according to claim 5, characterized in that Level I of the risk warning system is to publish VMS risk warning information on roadside facilities; Risk warning system level II is to publish VMS risk warning information and driving advice information on roadside facilities; The Level III risk warning system publishes VMS risk warning information and driving advice information on roadside facilities, and transmits weather and specific behavioral guidance to the driver in the form of "voice + image" vehicle-side warnings. The content includes current time, weather information, driving route, vehicle speed, speed limit, vehicle lateral offset position, vehicle longitudinal driving distance, risk conditions ahead and behavioral guidance suggestions.

7. The method according to claim 1, characterized in that The dynamic speed limit system is used to consider the smooth transition of vehicles on different speed limit sections based on weather conditions and traffic flow conditions, and implement lane-by-lane dynamic speed limit control on the basis of meeting the minimum braking distance, so as to obtain the maximum safe vehicle speed under the influence of low visibility weather.

8. The method according to claim 1, characterized in that The sight line induction system realizes different degrees of induction warning functions according to different low visibility operation risk levels; including warning lights set on both sides of the road; Level I of the sight guidance system means that the yellow warning light starts flashing at intervals; Level II of the sight guidance system means that all yellow warning lights flash; Level III of the sight guidance system activates the anti-rear-end collision warning mode when a vehicle passes by, triggering the red warning light of the upstream preset group to light up.

9. The method according to claim 8, characterized in that The method for implementing the sight line induction system comprises: When the operation risk level is A, the sight guidance system does not need to work and is in standby mode; When the operating risk level is B, the road contour enhancement mode is activated and the yellow warning light starts flashing with a brightness of 1500cd / m 2 , the frequency is 60 times / min, and the lighting interval is 40m; When the operating risk level is C, the active driving induction mode is activated, the yellow warning light increases the flashing brightness and frequency, and reduces the lighting interval; When the operating risk level is D, the radar vision integrated device detects whether there is a vehicle passing through the current section. If no vehicle passes, it continues to maintain the active driving induction mode; if a vehicle passes, the anti-rear-end collision warning mode is activated, which can trigger the red lights of the upstream preset group of warning lights to light up, forming a red tail to remind the rear vehicle that there is a vehicle in front and to maintain a safe distance; at this time, the yellow light of other warning lights flashes synchronously, and when the vehicle drives forward and passes the next group of warning lights, the red tail will move forward synchronously with the vehicle's dynamics.

10. A method for verifying the effectiveness of multi-level prevention and control of low-visibility operation risks of bridges, characterized in that: A multi-level prevention and control effectiveness test platform for low-visibility operation risks of bridges is built based on driving simulation technology. The required hardware includes driving simulation equipment and high-performance computers; the driving simulation equipment is a 1 / 4 real car compact driving simulator, and the exterior components include a steering wheel, combination switch, shift handle, dashboard, seat belts, and seats; combined with 3d Max scene development software, the driving simulation scene design and development is realized, including complex road construction, built-in parameter settings, and scenario control functions. The developed virtual scene is imported into the driving simulation device, and weather, traffic flow, and sight induction system lighting effects are added through the application program interface to verify the effectiveness of the method described in claim 1.

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