Expressway induction strategy evaluation method for emergencies

Abstract

The invention discloses a highway induction strategy evaluation method for emergencies. The method comprises the following steps: constructing a modular induction strategy evaluation framework, and carrying out analysis through a Vissim microscopic simulation platform based on different strategies; evaluating the effect of the active induction strategy and outputting an evaluation result; wherein the step of constructing the modular induction strategy evaluation framework comprises the following steps: establishing a model; calibrating the model; and evaluating the strategy. A business closed loop of demand customization, analogue simulation, quantitative evaluation and decision support can be realized by defining a traffic flow induction strategy model and an index system.

Description

Technical Field

[0001] The invention relates to the field of traffic signal control and traffic signal simulation, and in particular to a highway induction strategy evaluation method for emergencies. Background Art

[0002] Expressways have advantages such as high speed and large traffic capacity, but in actual operation, traffic accidents and other emergencies will inevitably occur, causing traffic congestion or even secondary accidents, reducing the safety of expressway operation. Therefore, the prevention of emergencies and emergency management after emergencies are crucial. Existing perception strategies include using video surveillance, radar detection, GPS positioning, mobile phone operator signal positioning, etc. to obtain vehicle location status for real-time photography, positioning and detection, and then using traffic signs, LED induction screens, traffic broadcasts, navigation path planning, road expansion, public transportation systems, expressway reservations and other strategies to intervene in decision-making, to a certain extent, alleviate the impact of emergencies and improve the efficiency of expressway traffic. However, the above perception and decision intervention strategies lack effective real-time feedback, it is difficult to form a closed loop, it is impossible to evaluate the advantages and disadvantages of decision strategies, and it is impossible to guarantee traffic efficiency in real time, resulting in insufficient real-time performance, obvious lag, and difficulty in evaluating and quantifying the effects of existing intervention strategies. Summary of the invention

[0003] The main purpose of the present invention is to provide a highway guidance strategy evaluation method for emergencies, aiming to solve the above technical problems.

[0004] To achieve the above object, the present invention provides a highway guidance strategy evaluation method for emergencies.

[0005] The highway guidance strategy evaluation method for emergencies comprises the following steps:

[0006] Construct a modular induction strategy evaluation framework and analyze different strategies using the Vissim micro-simulation platform;

[0007] Evaluate the effectiveness of active induction strategies and output evaluation results;

[0008] The steps of constructing a modular induction strategy evaluation framework include:

[0009] Establishing a model, wherein the steps of establishing the model include road network modeling, traffic demand and allocation input, simulation operation and result data collection;

[0010] Model calibration, wherein the steps of model calibration include result analysis, data comparison and simulation parameter calibration;

[0011] Strategy modeling, wherein the steps of strategy modeling include traffic control strategy modeling, simulation operation and MOEs collection;

[0012] Strategy evaluation, the steps of which include qualitative and quantitative MOEs analysis and providing evaluation results for the strategy.

[0013] In one embodiment, the step of road network modeling includes:

[0014] A road network model is built based on real-time map information to construct information on the actual road section’s section length, number of lanes, lane width, ramp location, and ramp weaving area.

[0015] In one embodiment, the step of inputting traffic demand and allocation includes:

[0016] Enter vehicle composition, expected speed, and traffic demand. The expected speed is set based on historical data of the current actual road section.

[0017] According to the historical traffic data of the road section and the road capacity, different traffic demands are input, and time intervals are input to simulate traffic fluctuations.

[0018] In one embodiment, the traffic demand flow in the input time interval all passes through the simulated road section.

[0019] In one embodiment, the steps of running the simulation include:

[0020] Setting accident points, screening accident-prone points based on historical accident data of the current road section and inputting them into the road network model;

[0021] Select a path, construct the starting and ending points of the vehicle trip, and simulate at least one path on the actual road section;

[0022] After an accident occurs on the path, the starting and ending points of the vehicle remain unchanged, and the path selection is re-performed to simulate the vehicle's detour and driving path.

[0023] In one embodiment, the step of running the simulation further includes:

[0024] The driving rules such as lane change behavior model, deceleration behavior model, vehicle following model, conflict point priority rule, deceleration area, etc. are set during the vehicle path driving process to simulate the driving behavior and traffic operation status before and after the accident.

[0025] In one embodiment, the steps of traffic control strategy modeling include:

[0026] By associating the dynamic traffic assignment model of speed and path and the dynamic feedback strategy model of induction strategy, the traffic operation status simulation under different induction strategy inputs is realized.

[0027] In one embodiment, the steps of the strategy evaluation are specifically as follows:

[0028] The feasibility of the induction strategy and the stability of the induction strategy on traffic flow control are evaluated by the average passing time and path passing time deviation.

[0029] In one embodiment, the strategy evaluation step further includes:

[0030] The feasibility of the induction strategy is evaluated through indicators such as road queue length, congestion dissipation time, road saturation, number of road accidents, number of road casualties, and number of secondary accidents.

[0031] In one embodiment, the strategy evaluation step further includes:

[0032] Based on the different impacts of different strategies on average travel time and path travel time deviation, the system inputs the average travel time and path travel time deviation of the current strategy and the all-or-nothing shortest path allocation, and outputs the ratio of the two as a quantitative indicator of the quality of the current strategy.

[0033] Beneficial effects that can be achieved by the present invention: A highway induction strategy evaluation method for emergencies proposed in the embodiment of the present invention constructs a modular induction strategy evaluation framework and analyzes it through the Vissim micro-simulation platform based on different strategies; evaluates the effect of the active induction strategy and outputs the evaluation results. In this application, by defining the traffic flow induction strategy model and indicator system, a business closed loop of demand customization, simulation, quantitative evaluation, and decision support can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of a highway guidance strategy evaluation method for emergencies according to an embodiment of the present invention;

[0035] Figure 2 The highway network model of the present invention;

[0036] Figure 3 A road network model for simulating accident points of the present invention;

[0037] Figure 4 is the path simulation model of the present invention;

[0038] Figure 5 It is a comparison diagram of the induction strategies of the average maximum queue length of the present invention;

[0039] Figure 6 It is a comparison chart of the induction strategies of the average formation time of the present invention.

[0040] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0042] Please refer to Figure 1 The present invention provides a highway induction strategy evaluation method for emergencies, and the highway induction strategy evaluation method for emergencies comprises:

[0043] Construct a modular induction strategy evaluation framework and analyze different strategies using the Vissim micro-simulation platform;

[0044] Evaluate the effectiveness of active induction strategies and output evaluation results;

[0045] The steps of constructing a modular induction strategy evaluation framework include:

[0046] Establishing a model, wherein the steps of establishing the model include road network modeling, traffic demand and allocation input, simulation operation and result data collection;

[0047] Model calibration, wherein the steps of model calibration include result analysis, data comparison and simulation parameter calibration;

[0048] Strategy modeling, wherein the steps of strategy modeling include traffic control strategy modeling, simulation operation and MOEs collection;

[0049] Strategy evaluation, the steps of which include qualitative and quantitative MOEs analysis and providing evaluation results for the strategy.

[0050] In this embodiment, based on the dynamic traffic flow distribution of the highway network and scenario-based applications for traffic emergencies, the traffic flow induction strategy model and indicator system are defined by software to achieve a business closed loop of demand customization, simulation, quantitative evaluation, and decision support.

[0051] Specifically, the steps of road network modeling include:

[0052] A road network model is built based on real-time map information to construct information about the length of the actual road section, the number of lanes, lane width, ramp location, and ramp weaving area. In the following embodiment, basic information such as the length of the actual road section of the Shanghai-Hangzhou-Ningbo Expressway, the number of lanes, lane width, ramp location, and ramp weaving area are constructed. Given that the research object is a highway, the road section behavior type should be a free lane change on the highway, and the ramp should be modeled according to the actual traffic organization, and the emergency lane should be considered.

[0053] Furthermore, the step of inputting traffic demand and allocation includes:

[0054] Enter vehicle composition, expected speed, and traffic demand. The expected speed is set based on historical data of the current actual road section.

[0055] According to the historical traffic data of the road section and the road capacity, different traffic demands are input, and the time interval is input to simulate traffic fluctuations. Among them, the time interval can be set to simulate traffic fluctuations, and the traffic input distribution function can also be set. Specifically, the input traffic according to the historical traffic data of the road section is the road section collection data. Whether setting the time interval or the traffic input distribution function, it should be ensured that the traffic within the simulation time passes through the simulated road section, that is, the cross-section traffic is consistent with the actual.

[0056] Construct a path selection model, set accident points, screen out accident-prone points based on historical accident data of the current road section and input them into the road network model; the accident lane setting should be based on actual conditions, simulating different accident situations such as occupying one lane, two lanes or all lanes.

[0057] Select a path, construct the starting and ending points of the vehicle trip, and simulate at least one path on the actual road section;

[0058] After an accident occurs on the path, the starting and ending points of the vehicle remain unchanged, and the path selection is re-performed to simulate the vehicle's detour and driving path.

[0059] In addition, the step of the simulation operation also includes:

[0060] The driving rules such as lane change behavior model, deceleration behavior model, vehicle following model, conflict point priority rule, deceleration area, etc. are set during the vehicle path driving process to simulate the driving behavior and traffic operation status before and after the accident.

[0061] In the above embodiment, the steps of traffic control strategy modeling include:

[0062] By associating the dynamic traffic assignment model of speed and path and the dynamic feedback strategy model of induction strategy, the traffic operation status simulation under different induction strategy inputs is realized.

[0063] The steps of the strategy evaluation are specifically as follows:

[0064] The feasibility of the induction strategy and the stability of the induction strategy on traffic flow control are evaluated by the average passing time and the path passing time deviation. Among them, the average passing time refers to the average time for vehicles to pass through the road section on the same path, comparing the presence or absence of the induction strategy or the setting of different induction strategies, to evaluate the feasibility of the induction strategy. The path passing time deviation specifically refers to the deviation value between the path passing time of each vehicle when there is an induction strategy. Of course, the stability of the induction strategy on traffic flow control can also be evaluated based on other indicators, such as the length of the road queue, congestion dissipation time, road saturation, number of road accidents, number of road casualties, number of secondary accidents, etc.

[0065] Furthermore, the strategy evaluation step also includes:

[0066] Based on the different impacts of different strategies on average travel time and path travel time deviation, the system inputs the average travel time and path travel time deviation of the current strategy and the all-or-nothing shortest path allocation, and outputs the ratio of the two as a quantitative indicator of the quality of the current strategy.

[0067] The present application is explained below through an implementation case. It can be understood that the case only shows one scenario of an emergency on a highway. For other scenarios, such as when the accident point is located near a ramp, other corresponding induction measures and corresponding evaluation indicators may be adopted.

[0068] Please refer to Figure 2 First, we build a highway network model, and build a 1.5-kilometer-long, closed, ramp-free, one-way, three-lane highway section with one emergency lane. The input lane width is 3.75 meters, and the emergency lane is a solid white line. The vehicle direction is from left to right, and the lanes are 1-4 from bottom to top.

[0069] Secondly, the simulation parameters were calibrated. The simulation time was 3900 seconds, the simulation step size was 10, the random seed was 42, and the following model parameters that had a significant impact on the accuracy of traffic simulation were calibrated, such as the expected headway (1.2s), the following random oscillation distance (3.6m), the threshold for entering the following state (-7.4s), and the maximum deceleration (-6m / s 2 ) and other lane-changing model parameters.

[0070] Third, set the vehicle composition. For cars, the expected speed distribution is 100km / h; for trucks, the expected speed distribution is 60km / h; for buses, the expected speed distribution is 80km / h. The relative flow rates are 80% for cars, 15% for trucks, and 5% for buses.

[0071] Fourth, define vehicle input and set different traffic demands of 2000pcu / h, 2500pcu / h, 3000pcu / h, 3500pcu / h, 4000pcu / h, and 4500pcu / h to simulate the operating status of vehicles under different traffic demands.

[0072] Fifth, please refer to Figure 3 , set up a simulated accident point, and simulate the accident point by setting up parking spaces at 1 km. This modeling sets up 2 parking spaces to simulate a traffic accident in which two vehicles collide. The accident point is located in the third lane, and the parking time is 3900s.

[0073] Sixth, please refer to Figure 4 , define the path selection, set the static path decision starting point at 800 meters, set two vehicle paths for the second lane and the fourth lane, and the end points are both at 1100 meters. That is, when the vehicle passes the local path decision point, it will bypass the second lane and bypass the fourth lane to travel two vehicle paths.

[0074] Seventh, induce strategy input.

[0075] When there is no induction strategy, the vehicle follows the Wiedemann99 highway driving behavior model and changes lanes freely.

[0076] Input strategy one is that after an accident occurs, lane changes are prohibited in non-accident lanes, small cars in the third lane of the accident lane can change lanes to the second and fourth lanes, and large vehicles (trucks and buses) are prohibited from changing lanes to the fourth lane.

[0077] Input strategy 2 is that after the accident occurs, lane changes are prohibited in the non-accident lane, and vehicles in the third lane of the accident lane can change lanes freely to the second lane and the fourth lane.

[0078] Eighth, the evaluation indicators are determined as the average maximum queue length and the average travel time. A queue counter is set up upstream of the accident point to detect the queue length, and a vehicle travel time measurement is set up at the beginning and end of the road section to detect the travel time of vehicles passing through the road section.

[0079] Average maximum queue length: Represents the average value of the maximum queue length of m simulation experiments.

[0080] in: It represents the maximum distance between vehicle i and the queue counter in the j-th simulation, that is, the maximum queue length in the j-th simulation.

[0081] Average travel time: It represents the average time required for all vehicles to travel from the start point to the end point of the travel time measurement.

[0082] Where: t iZrepresents the time when the i-th vehicle passes the end point of the travel time measurement;

[0083] t iQ It represents the time when the i-th vehicle passes the starting point of travel time measurement.

[0084] 9. Result Evaluation

[0085] Please refer to Figure 5 , average maximum queue length:

[0086] As the input flow increases, the input of both strategies can reduce the maximum queue length of the road section, and the induction effect of strategy 2 is slightly better than that of strategy 1. When the flow of the accident section is 3000, the average maximum queue length can be reduced by 500 meters by using strategy 2 for induction. It can be seen that when an accident occurs, prohibiting lane changes in non-accident lanes can greatly reduce the queue length.

[0087] Please refer to Figure 6 , average travel time:

[0088] As the input flow increases, the input of both strategies can reduce the average travel time of vehicles. The induction effect of strategy 2 is slightly better than that of strategy 1, indicating that vehicles in the accident lane should change lanes freely without restricting vehicle types. When the flow rate of the accident section is 3000, strategy 2 is used for induction, and the average travel time of vehicles is reduced by 38.7%. It can be seen that when an accident occurs, prohibiting lane changes in the non-accident lane can greatly reduce the travel time of vehicles.

[0089] In the above embodiment, the average passing time of the vehicle on the road with the strategy is 15% less than that without the strategy, and the deviation of the vehicle's path passing time does not exceed 70%.

[0090] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0091] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0092] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0093] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A highway guidance strategy evaluation method for emergencies, characterized in that: The highway guidance strategy evaluation method for emergencies comprises the following steps: Construct a modular induction strategy evaluation framework and analyze different strategies using the Vissim micro-simulation platform; Evaluate the effectiveness of active induction strategies and output evaluation results; The steps of constructing a modular induction strategy evaluation framework include: Establishing a model, wherein the steps of establishing the model include road network modeling, traffic demand and allocation input, simulation operation and result data collection; Model calibration, wherein the steps of model calibration include result analysis, data comparison and simulation parameter calibration; Strategy modeling, wherein the steps of strategy modeling include traffic control strategy modeling, simulation operation and MOEs collection; Strategy evaluation, the steps of which include qualitative and quantitative MOEs analysis and providing evaluation results for the strategy.

2. The highway guidance strategy evaluation method for emergencies according to claim 1 is characterized in that: The steps of road network modeling include: A road network model is built based on real-time map information to construct information on the actual road section’s section length, number of lanes, lane width, ramp location, and ramp weaving area.

3. The highway guidance strategy evaluation method for emergencies as claimed in claim 2 is characterized in that: The steps of inputting traffic demand and allocation include: Enter vehicle composition, expected speed, and traffic demand. The expected speed is set based on historical data of the current actual road section. According to the historical traffic data of the road section and the road capacity, different traffic demands are input, and time intervals are input to simulate traffic fluctuations.

4. The highway guidance strategy evaluation method for emergencies according to claim 3 is characterized in that: The traffic demand flow in the input time interval all passes through the simulated road section.

5. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The steps of the simulation operation include: Setting accident points, screening accident-prone points based on historical accident data of the current road section and inputting them into the road network model; Select a path, construct the starting and ending points of the vehicle trip, and simulate at least one path on the actual road section; After an accident occurs on the path, the starting and ending points of the vehicle remain unchanged, and the path selection is re-performed to simulate the vehicle's detour and driving path.

6. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The step of the simulation operation also includes: The driving rules such as lane change behavior model, deceleration behavior model, vehicle following model, conflict point priority rule, deceleration area, etc. are set during the vehicle path driving process to simulate the driving behavior and traffic operation status before and after the accident.

7. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The steps of traffic control strategy modeling include: By associating the dynamic traffic assignment model of speed and path and the dynamic feedback strategy model of induction strategy, the traffic operation status simulation under different induction strategy inputs is realized.

8. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The steps of the strategy evaluation are specifically as follows: The feasibility of the induction strategy and the stability of the induction strategy on traffic flow control are evaluated by the average passing time and path passing time deviation.

9. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The step of strategy evaluation also includes: The feasibility of the induction strategy is evaluated through indicators such as road queue length, congestion dissipation time, road saturation, number of road accidents, number of road casualties, and number of secondary accidents.

10. The highway guidance strategy evaluation method for emergencies according to claim 4 is characterized in that: The step of strategy evaluation also includes: Based on the different impacts of different strategies on average travel time and path travel time deviation, the system inputs the average travel time and path travel time deviation of the current strategy and the all-or-nothing shortest path allocation, and outputs the ratio of the two as a quantitative indicator of the quality of the current strategy.

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