A data analysis method and device for urban road network

Abstract

The embodiment of the present invention provides a data analysis method and device for an urban road network, including: determining the spatiotemporal impact range of an emergency event based on the acquired speed data; determining the spatiotemporal propagation law of the emergency event in the urban road network based on the spatiotemporal impact range; expressing the spatiotemporal propagation law as a linear constraint, and estimating the spatiotemporal impact of the emergency event on the urban road network through an optimization model. Compared with the existing methods, the estimation method proposed by the present invention takes into account the spatiotemporal propagation law of the impact of the emergency event on different road topological structures, and the estimation result of the model meets the propagation law of traffic waves, ensuring the rationality of the estimated spatiotemporal impact range.

Description

Technical Field

[0001] The present invention relates to the field of urban traffic congestion analysis, and in particular to a data analysis method and device for an urban road network. Background Art

[0002] An emergency is an accident, disaster or social security incident that occurs suddenly and causes or may cause damage to transportation facilities, interruption or blockage of transportation, and requires emergency response measures to evacuate or rescue personnel and provide emergency transportation guarantees, such as sudden traffic accidents and vehicle breakdowns.

[0003] Sudden incidents in urban road networks will hinder the normal travel of vehicles and may cause regional congestion and traffic paralysis. This will not only affect people's normal travel, but also lead to increased vehicle emissions and damage to the natural environment.

[0004] At present, the methods for estimating the spatiotemporal impact range of emergencies mainly focus on emergencies occurring on highways or expressways, and there is no research method that can be used to estimate the spatiotemporal impact range of emergencies in urban road networks. Considering the complex topological structure of urban road networks, the spatiotemporal propagation of the impact of emergencies is even more difficult to characterize.

[0005] With regard to the above-mentioned problem, since there is no research method in the relevant technology that can be used to estimate the spatiotemporal impact range of emergencies in urban road networks, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present invention provide a data analysis method and device for an urban road network, so as to at least solve the problem that there is no research method in the related art that can be used for estimating the spatiotemporal impact range of emergencies in urban road networks.

[0007] The present invention provides a data analysis method for urban road networks, which adopts the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a data analysis method for an urban road network, comprising: determining the spatiotemporal impact range of an emergency event based on acquired speed data; determining the spatiotemporal propagation law of the emergency event in the urban road network based on the spatiotemporal impact range; expressing the spatiotemporal propagation law as a linear constraint, and estimating the spatiotemporal impact of the emergency event on the urban road network through an optimization model.

[0009] Preferably, determining the spatiotemporal impact range of the emergency event based on the acquired speed data includes:

[0010] Determine the spatiotemporal unit of the emergency by setting the location, spatial boundary and time boundary of the emergency;

[0011] Obtain historical driving data, and calculate the standard deviation and average value based on the historical driving data;

[0012] Calculate the spatiotemporal impact range based on standard deviation, mean, and spatiotemporal units.

[0013] Preferably, calculating the standard deviation and the average value based on historical driving data includes:

[0014] Get the first driving speed of the vehicle at the specified section and time after the emergency occurred in the historical driving data, recorded as k is the designated road section, m is the designated time; the second driving speed of the vehicle in the designated road section and designated time without any emergency in at least two days in the historical driving data is obtained, recorded as s k,m , the standard deviation and mean value are obtained according to the second driving speed, and the standard deviation is denoted as σ k,m , the average value is recorded as

[0015]

[0016] Preferably, calculating the spatiotemporal influence range based on the standard deviation, the mean value and the spatiotemporal unit includes:

[0017]

[0018] Among them, P k,m Indicates whether the space-time unit is affected by the sudden event; is the first driving speed; is the average value; α is the parameter; σ k,m is the standard deviation.

[0019] Preferably, determining the spatiotemporal propagation law of an emergency event in an urban road network based on the spatiotemporal impact range includes:

[0020] The temporal and spatial propagation patterns of emergencies in the urban road network are determined through preset rules and temporal and spatial impact ranges.

[0021] Preferably, the preset rules include:

[0022] First rule: when a spatiotemporal unit is affected by an emergency, according to the propagation law of traffic waves, it is determined that the impact is propagated from the downstream sections adjacent to the designated section, or the impact of the emergency on the designated section in the adjacent time interval is retained;

[0023] The second rule: when the space-time unit of the adjacent time interval is affected by the emergency and the downstream section of the designated section is also affected by the emergency in the time interval, where the propagation of the traffic wave is continuous, the space-time unit is affected by the emergency;

[0024] The third rule: when the space-time unit is affected by an emergency and the upstream section of the designated section is also affected by the emergency, if the cumulative number of vehicles on the upstream section of the designated section is greater than a preset value, the upstream section is affected by the emergency.

[0025] Preferably, a data analysis method for an urban road network provided by an embodiment of the present invention further includes:

[0026] Set decision variables and constraints;

[0027] Construct an optimization model based on decision variables and constraints.

[0028] Preferably, the decision variables include:

[0029]

[0030] Among them, k is the specified road section and m is the specified time.

[0031] In a second aspect, an embodiment of the present invention provides a data analysis device for an urban road network, comprising:

[0032] A first data processing module is used to determine the spatiotemporal impact range of the emergency event based on the acquired speed data;

[0033] The second data processing module is used to determine the spatiotemporal propagation law of the emergency in the urban road network according to the spatiotemporal impact range;

[0034] The data analysis module is used to express the spatiotemporal propagation laws as linear constraints and estimate the spatiotemporal impact of emergencies on urban road networks through optimization models.

[0035] Preferably, the first data processing module includes:

[0036] A data determination unit, used to determine the spatiotemporal unit of the emergency event by setting the occurrence location, spatial boundary and time boundary of the emergency event;

[0037] A data acquisition unit, used to acquire historical driving data and calculate the standard deviation and average value based on the historical driving data;

[0038] The data processing unit is used to calculate the spatiotemporal influence range based on the standard deviation, the mean value and the spatiotemporal unit.

[0039] Compared with related technologies, an urban road network data analysis method and device provided in an embodiment of the present invention more precisely models the congestion propagation of adjacent roads by introducing the cumulative number of vehicles, thereby improving the accuracy of estimation; at the same time, since there may be loops in the urban road network that cause deadlock, this phenomenon is modeled by analyzing the congestion propagation of the loops, making the results more interpretable; the spatiotemporal propagation laws of the impact of emergencies on different road topological structures are taken into account, and the estimation results of the model meet the propagation laws of traffic waves, ensuring the rationality of the estimated spatiotemporal impact range.

[0040] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0042] Figure 1 A schematic diagram of a process flow of a method for analyzing urban road network data provided in Embodiment 1 of the present invention;

[0043] Figure 2 A schematic diagram of the spatiotemporal impact range of an emergency event obtained by using an integer programming model in a data analysis method for an urban road network provided in the first embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a ring structure in an urban road network of a method for analyzing urban road network data provided in Embodiment 1 of the present invention;

[0045] Figure 4 A schematic diagram of the overall process of a method for analyzing urban road network data provided in Embodiment 1 of the present invention;

[0046] Figure 5 A schematic diagram of a data analysis device for an urban road network provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0048] Obviously, the drawings described below are only some examples or embodiments of the present invention. For ordinary technicians in this field, the present invention can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed by the present invention, some changes in design, manufacturing or production based on the technical content disclosed by the present invention are just conventional technical means, and should not be understood as insufficient content disclosed by the present invention.

[0049] Embodiment 1

[0050] In a first aspect, an embodiment of the present invention provides a method for analyzing data of an urban road network. Figure 1 The following is a flow chart of a method for analyzing urban road network data provided by the first embodiment of the present invention. Figure 1 As shown, a data analysis method for an urban road network provided in an embodiment of the present application includes:

[0051] Step S102, determining the spatiotemporal impact range of the emergency event based on the acquired speed data;

[0052] Optionally, in step S102, determining the spatiotemporal impact range of the emergency event based on the acquired speed data includes:

[0053] Determine the spatiotemporal unit of the emergency by setting the location, spatial boundary and time boundary of the emergency; obtain historical driving data, calculate the standard deviation and average value based on the historical driving data; calculate the spatiotemporal impact range based on the standard deviation, average value and spatiotemporal unit;

[0054] As an example, in this embodiment, the emergency incident that occurred on the West Second Ring Road on August 3, 2015 is used as an example of the present invention, wherein the emergency incident in the embodiment of the present application may include: sudden traffic accidents, changes in road conditions caused by vehicles breaking down on the road during driving.

[0055] In the embodiment of the present application, it is known that the time of occurrence of the emergency is 19:28 according to the emergency record, and the longitude and latitude of the section where the emergency occurs is (116.35592507999999, 39.92919236999999). The speed data of the whole August is used as historical speed data, and there are 22 upstream sections no more than 1 km away from the location of the emergency, which are marked as 1-22 in turn. The observation time of the emergency is 0.5 hours. Next, the time interval (19:28PM-19:58PM) studied is divided into 6 intervals of equal length, each interval is 5 minutes, and is marked as 1-6 in chronological order.

[0056] Further, optionally, calculating the standard deviation and the average value based on the historical driving data includes: obtaining the first driving speed of the vehicle in the specified section and at the specified time after the emergency occurred in the historical driving data, recorded as k is the designated road section, m is the designated time; the second driving speed of the vehicle in the designated road section and designated time without any emergency in at least two days in the historical driving data is obtained, recorded as s k,m , the standard deviation and mean value are obtained according to the second driving speed, and the standard deviation is denoted as σ k,m , the average value is recorded as

[0057] In this embodiment of the present application, the speed data provided by the floating vehicle can be used to obtain the driving speed of the vehicle at the road section k and the time interval m, which is recorded as s k,m When an emergency occurs, the impact of the emergency will affect the normal operation of the vehicle and will be affected by the s after the emergency. k,m Value, denoted as Using the historical speed data of multiple days when no emergencies occur, we can get s k,m The corresponding standard deviation and mean are denoted as σ k,m and

[0058] Further, optionally, calculating the spatiotemporal influence range based on the standard deviation, the average value, and the spatiotemporal unit includes:

[0059]

[0060] Among them, P k,m Indicates whether the space-time unit is affected by the sudden event; is the first driving speed; is the average value; α is the parameter; σ k,m is the standard deviation.

[0061] By analyzing the historical speed data, the vehicle speed in the absence of emergencies can be obtained. and vehicle speed after the incident when When significantly affected by the sudden event, that is, significantly less than When (where α is a parameter with a value greater than 0, usually 4.2)<k,m> Affected by the sudden event. Otherwise, the cell is considered<k,m> May not be affected by the sudden event. Definition P k,m To indicate whether the cell is affected by an emergency, the calculation method is as follows:

[0062]

[0063] Among them, P k,m Indicates whether the space-time unit is affected by the sudden event; is the first driving speed; is the average value; α is the parameter; σ k,m is the standard deviation;

[0064] At this point, speed data can be used to preliminarily determine whether a section of the urban road network is affected by an emergency.

[0065] Step S104, determining the spatiotemporal propagation law of the emergency in the urban road network according to the spatiotemporal impact range;

[0066] Optionally, determining the spatiotemporal propagation law of the emergency event in the urban road network according to the spatiotemporal influence range in step S104 includes: determining the spatiotemporal propagation law of the emergency event in the urban road network according to preset rules and the spatiotemporal influence range;

[0067] Due to the influence of noise and other exogenous factors in speed data, the spatiotemporal impact range obtained in step S102 may have large errors. Existing literature believes that the spatiotemporal impact range of an emergency should meet the propagation law of traffic waves, and improve the accuracy of the estimation based on the propagation law of traffic waves, and ensure the rationality of the spatiotemporal impact range. Compared with highways, the road topology of urban road networks is more complex, so there are more sophisticated requirements for the spatiotemporal propagation law of the impact of emergencies.

[0068] Preferably, the preset rules include: the first rule: when the space-time unit is affected by an emergency, according to the propagation law of traffic waves, it is determined that the impact is obtained by propagating to the downstream section adjacent to the designated section, or the impact of the emergency on the designated section in adjacent time intervals is retained; the second rule: when the space-time unit in adjacent time intervals is affected by the emergency and the downstream section of the designated section is also affected by the emergency in the time interval, where the propagation of traffic waves is continuous, it is obtained that the space-time unit is affected by the emergency; the third rule: when the space-time unit is affected by the emergency and the upstream section of the designated section is also affected by the emergency, if the cumulative number of vehicles in the upstream section of the designated section is greater than the preset value, then the upstream section is affected by the emergency.

[0069] Specifically, the preset rules in the embodiments of the present application include:

[0070] Rule 1 (i.e., the first rule in the embodiment of the present application): When the space-time unit<k,m> When a sudden event occurs to a section k (i.e., a space-time unit in the embodiment of the present application), according to the propagation law of traffic waves, it can be known that the impact must be propagated from the downstream section adjacent to the section k or the retention of the sudden event impact on the section k in the time interval m-1 (i.e., the adjacent time interval in the embodiment of the present application). It is worth noting that, unlike the simple topological structure on the highway, the sections in the urban road network may form a closed loop (see Figure 3 , Figure 3 A schematic diagram of the ring structure in an urban road network of a data analysis method for an urban road network provided in Example 1 of the present invention) shows that the impact of an emergency in a closed loop must be transmitted from the road sections outside the ring.

[0071] Rule 2 (i.e., the second rule in the embodiment of the present application): When the space-time unit<k,m-1> (i.e., the space-time units of adjacent time intervals in the embodiment of the present application) are affected by the emergency and the downstream section of section k is also affected by the emergency at time interval m. Since the propagation of traffic waves is continuous, the space-time unit can be obtained.<k,m> It must also be affected by unexpected events.

[0072] Rule 3 (i.e., the third rule in the embodiment of the present application): When the space-time unit<k,m> If the cumulative number of vehicles on the upstream section l′ of section k is q l′m >q l,m , where q l′m is the cumulative number of vehicles on the upstream section of the specified section, q l,m is the preset value in the embodiment of the present application, it can be known that the road section l′ must also be affected by the emergency.

[0073] Step S106, expressing the spatiotemporal propagation law as a linear constraint, and estimating the spatiotemporal impact of the emergency event on the urban road network through the optimization model;

[0074] Optionally, the urban road network data analysis method provided by the embodiment of the present invention further includes: setting decision variables and constraints; and constructing an optimization model based on the decision variables and constraints.

[0075] The decision variables include:

[0076]

[0077] Among them, k is the specified road section and m is the specified time.

[0078] Specifically, since the speed data provided by the floating car has errors, and the congestion in the spatiotemporal congestion level map may also be caused by other accidental factors other than emergencies, the spatiotemporal impact range of emergencies determined based on speed data is not accurate, and the result will also violate the propagation law of traffic waves. In order to determine whether the road section k is actually affected by the emergency in the time interval m, the following integer decision variables are used to represent it:

[0079]

[0080] Among them, k is the specified road section and m is the specified time.

[0081] Further, optional, constraints include:

[0082] (a) Based on the records of sudden events, we can determine the starting time and space unit of the sudden event in advance<k0,m0> Since the starting time and space unit of an emergency event is inevitably affected by the emergency event, the following constraints are used to express it:

[0083]

[0084] (b) According to the propagation rule of traffic waves 1, the space-time unit<k,m> When affected by an emergency, it can be known that the impact must be propagated from the downstream sections adjacent to section k or the retention of the impact of the emergency on section k in time interval m-1. The set of downstream sections adjacent to section k is denoted as D k , we know that when δ k,m = 1, there must be δ k,m-1 =1 or Space-time unit<k,m-1> or<l,m> (l∈D k ) At least one of them is affected by the sudden event. The above requirements can be expressed using the following constraints:

[0085]

[0086] In addition, if Figure 3 As shown, Figure 3 A schematic diagram of a ring structure in an urban road network of a data analysis method for an urban road network provided in the first embodiment of the present invention; the road segments in the urban road network may form a closed loop, and the impact of emergencies in the closed loop must be propagated from the road segments outside the loop. Assume that the road segment k is a road segment in the closed loop, and let R k is the downstream section of section k in the closed loop, that is, When D k =R k When , constraint (2) can guarantee the rule 1 of the traffic wave. Otherwise, it is necessary to ensure that the influence of road section k is propagated from outside the ring. This requirement can be expressed by the following constraint:

[0087]

[0088] This step requires that all closed loops in the road network of interest be detected in advance, and the detection can be performed using algorithms such as depth-first search provided in existing literature.

[0089] (c) According to the propagation rule 2 of traffic waves, when the space-time unit<k,m-1> If the downstream section of section k is affected by the emergency and is also affected by the emergency at time interval m, the spatiotemporal unit can be determined.<k,m> It must also be affected by unexpected events. k,m-1 =1 and When k,m = 1. This requirement can be expressed using the following constraints:

[0090]

[0091] λ1+λ2+λ3≤2; Constraint (7)

[0092] λ1,λ2,λ3∈{0,1}. Constraint (8)

[0093] (d) According to the propagation rule 3 of traffic waves, when the space-time unit<k,m> The upstream segment l of segment k is also affected by the emergency. The set of upstream segments adjacent to segment k is denoted as U k , the cumulative number of vehicles on section l in time interval m is q l,m , section l' is another upstream section of section k. If q l′,m ≥q l,m , we can see that the road section l' must also be affected by the emergency in the time interval m. This requirement can be expressed using the following constraints:

[0094]

[0095] λ4+λ5+λ6≤2; Constraint (12)

[0096] λ4,λ5,λ6∈{0,1}. Constraint (13)

[0097] (e) Other constraints on the decision variables are expressed as follows:

[0098]

[0099] The difference between the congestion level output by the model and the congestion level obtained from the actual data should be as small as possible.<k,m> , when P k,m = 1 or 0, the decision variable δ of the model k,m It is also best to be 1 or 0. Therefore, the objective function corresponding to the model is expressed as follows:

[0100]

[0101] To convert it into a linear model, the above objective function can be rewritten as:

[0102]

[0103] As an example, in this embodiment, the solver is used to obtain the optimal solution of the optimization model, and then the spatiotemporal impact area of ​​the emergency event in the urban road network can be obtained;

[0104] Optionally, the solver is used to obtain the optimal solution of the optimization model, and then the spatiotemporal impact area of ​​the emergency event in the urban road network can be obtained, including:

[0105] The integer programming optimization model is solved using the standard branch-and-bound method. In this example, Python is used to call the Gurobi solver for solving the problem. The model estimates the spatiotemporal impact range of the emergency event. Figure 2 As shown; after optimization using the above integer programming model, the spatiotemporal impact range of emergencies that meets the law of traffic wave propagation can be obtained.

[0106] like Figure 4 As shown, Figure 4 The present invention is a schematic diagram of the overall process of a data analysis method for an urban road network provided in the first embodiment of the present invention. The data analysis method for an urban road network provided in the embodiment of the present invention uses speed data provided by a floating vehicle to preliminarily determine whether each road section is affected by an emergency at different time intervals; based on traffic wave theory, the spatiotemporal propagation law of the impact of emergencies is characterized under different road topological structures; the above propagation law is expressed using linear constraints, an optimization model is established to estimate the spatiotemporal impact range of the emergency, and the estimation result is made as consistent as possible with the existing speed data; a solver is used to obtain the optimal solution of the optimization model, and then the spatiotemporal impact area of ​​the emergency in the urban road network can be obtained.

[0107] In summary, a data analysis method for urban road networks provided in an embodiment of the present invention is combined with an analysis of the basic demands that need to be solved urgently. There is no research method in the relevant technology that can be used to estimate the spatiotemporal impact range of emergencies in urban road networks. The spatiotemporal impact range of emergencies is preliminarily determined by using speed data, and the spatiotemporal propagation law of the impact of emergencies on urban road networks is analyzed. The constraints are analyzed according to integer decision variables to achieve the solution and construction of the integer programming model. The spatiotemporal propagation law of the impact of emergencies on different road topological structures is taken into account, and the estimation result of the model meets the propagation law of traffic waves, ensuring the rationality of the estimated spatiotemporal impact range.

[0108] Embodiment 2

[0109] In a second aspect, an embodiment of the present invention provides a data analysis device for an urban road network. Figure 5 A schematic diagram of a data analysis device for a city road network provided in Embodiment 2 of the present invention; Figure 5 As shown, the data analysis device for the urban road network provided by the embodiment of the present invention includes: a first data processing module 52, used to determine the spatiotemporal impact range of the emergency event based on the acquired speed data; a second data processing module 54, used to determine the spatiotemporal propagation law of the emergency event in the urban road network based on the spatiotemporal impact range; a data analysis module 56, used to express the spatiotemporal propagation law as a linear constraint, and estimate the spatiotemporal impact of the emergency event on the urban road network through an optimization model.

[0110] Preferably, the first data processing module provided by the embodiment of the present invention includes: a data determination unit, used to determine the space-time unit of the emergency event by setting the occurrence location, spatial boundary and time boundary of the emergency event; a data acquisition unit, used to acquire historical driving data and calculate the standard deviation and average value based on the historical driving data; a data processing unit, used to calculate the space-time impact range based on the standard deviation, average value and space-time unit.

[0111] The embodiment of the present invention provides a data analysis device for an urban road network. Combined with the analysis of the basic demands that need to be solved urgently, there is no research method in the relevant technology that can be used to estimate the spatiotemporal impact range of emergencies in urban road networks. The spatiotemporal impact range of emergencies is preliminarily determined by using speed data, the spatiotemporal propagation law of the impact of emergencies on urban road networks is analyzed, and the constraint conditions are analyzed according to integer decision variables to achieve the solution and construction of integer programming models. The spatiotemporal propagation law of the impact of emergencies on different road topological structures is considered, and the estimation results of the model meet the propagation law of traffic waves, ensuring the rationality of the estimated spatiotemporal impact range.

[0112] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A data analysis method for urban road network, characterized in that: include: Determine the spatial and temporal impact range of the emergency event based on the acquired speed data; Determining the spatiotemporal propagation law of the emergency event in the urban road network according to the spatiotemporal impact range; The spatiotemporal propagation law is expressed as a linear constraint, and the spatiotemporal impact of the emergency event on the urban road network is estimated through an optimization model; Wherein, the method of determining the spatiotemporal propagation law of the emergency in the urban road network based on the spatiotemporal influence range includes: determining the spatiotemporal propagation law of the emergency in the urban road network through preset rules and the spatiotemporal influence range; wherein, the preset rules include: the first rule: when the spatiotemporal unit is affected by the emergency, according to the propagation law of traffic waves, determine whether the impact is propagated from the downstream section adjacent to the designated section, or the impact of the emergency on the designated section in adjacent time intervals is retained; the second rule: when the spatiotemporal unit in adjacent time intervals is affected by the emergency and the downstream section of the designated section is also affected by the emergency in the time interval, wherein the propagation of traffic waves is continuous, it is obtained that the spatiotemporal unit is affected by the emergency; the third rule: when the spatiotemporal unit is affected by the emergency and the upstream section of the designated section is also affected by the emergency, if the cumulative number of vehicles on the upstream section of the designated section is greater than the preset value, then the upstream section is affected by the emergency; Setting decision variables and constraints; constructing the optimization model according to the decision variables and the constraints, wherein the decision variables are: Among them, k is the specified road section, and m is the specified time; The constraints include: (a) According to the emergency event record, determine the starting time and space unit of the emergency event in advance<k0,m0> , since the starting time-space unit of the emergency event is affected by the emergency event, the following constraints are used to express it: (b) According to the first law of traffic wave propagation, the space-time unit<k,m> When affected by an emergency, the downstream sections adjacent to section k are propagated or the section k is retained by the emergency in the time interval m-1. The set of downstream sections adjacent to section k is denoted as D k , when δ k,m =1, there is δ k,m-1 =1 or Space-time unit<k,m-1> or<l,m> (l∈D k ) is affected by at least one of the incidents, expressed using the following constraints: When the road segments in the urban road network form a closed loop, and the impact of the emergency in the closed loop is transmitted from the road segments outside the loop, assume that the road segment k is the road segment in the closed loop, and let R k is the downstream section of section k in the closed loop, that is, When D k =R k When , constraint (2) guarantees the first rule of traffic waves; otherwise, it is necessary to ensure that the influence of road section k propagates from outside the ring, which is expressed by the following constraint: (c) According to the second law of traffic wave propagation, when the space-time unit<k,m-1> The downstream section of section k is also affected by the emergency event at time interval m, and the time-space unit is determined.<k,m> It must also be affected by unexpected events, that is, δ k,m-1 =1 and When k,m =1, expressed using the following constraints: λ1+λ2+λ3≤2; Constraint (7) λ1,λ2,λ3∈{0,1}; Constraint (8) (d) According to the third law of traffic wave propagation, when the space-time unit<k,m> The upstream section l of section k is also affected by the emergency, and the set of upstream sections adjacent to section k is denoted as U k , the cumulative number of vehicles on section l in time interval m is q l,m , section l' is another upstream section of section k, if q l′,m ≥q l,m , the road section l' must also be affected by the emergency in the time interval m, which can be expressed using the following constraints: λ4+λ5+λ6≤2; Constraint (12) λ4,λ5,λ6∈{0,1}; Constraint (13) (e) Other constraints on the decision variables are expressed as follows:

2. The urban road network data analysis method according to claim 1, characterized in that: Determining the spatiotemporal impact range of the emergency event based on the acquired speed data includes: Determine the spatiotemporal unit of the emergency event by setting the occurrence location, spatial boundary and time boundary of the emergency event; Obtaining historical driving data, and calculating a standard deviation and an average value based on the historical driving data; The spatiotemporal influence range is calculated based on the standard deviation, the average value and the spatiotemporal unit.

3. The urban road network data analysis method according to claim 2, characterized in that: The calculating of the standard deviation and the average value based on the historical driving data comprises: Obtain the first driving speed of the vehicle at the specified road section and the specified time after the emergency occurs in the historical driving data, recorded as k is the designated road section, m is the designated time; the second driving speed of the vehicle in the designated road section and the designated time without any emergency in at least two days in the historical driving data is obtained, recorded as s k,m , the standard deviation and the average value are obtained according to the second driving speed, and the standard deviation is denoted as σ k,m , the average value is recorded as 4. The urban road network data analysis method according to claim 3, characterized in that: Calculating the spatiotemporal influence range according to the standard deviation, the average value and the spatiotemporal unit includes: Among them, the P k,m Indicates whether the space-time unit is affected by the emergency event; is the first driving speed; is the average value; α is the parameter; σ k,m is the standard deviation.

5. A data analysis device for an urban road network, characterized in that: include: A first data processing module is used to determine the spatiotemporal impact range of the emergency event based on the acquired speed data; A second data processing module is used to determine the spatiotemporal propagation law of the emergency event in the urban road network according to the spatiotemporal impact range; A data analysis module, used to express the spatiotemporal propagation law as a linear constraint, and estimate the spatiotemporal impact of the emergency event on the urban road network through an optimization model; Wherein, the method of determining the spatiotemporal propagation law of the emergency in the urban road network based on the spatiotemporal influence range includes: determining the spatiotemporal propagation law of the emergency in the urban road network through preset rules and the spatiotemporal influence range; wherein, the preset rules include: the first rule: when the spatiotemporal unit is affected by the emergency, according to the propagation law of traffic waves, determine whether the impact is propagated from the downstream section adjacent to the designated section, or the impact of the emergency on the designated section in adjacent time intervals is retained; the second rule: when the spatiotemporal unit in adjacent time intervals is affected by the emergency and the downstream section of the designated section is also affected by the emergency in the time interval, wherein the propagation of traffic waves is continuous, it is obtained that the spatiotemporal unit is affected by the emergency; the third rule: when the spatiotemporal unit is affected by the emergency and the upstream section of the designated section is also affected by the emergency, if the cumulative number of vehicles on the upstream section of the designated section is greater than the preset value, then the upstream section is affected by the emergency; Setting decision variables and constraints; constructing the optimization model according to the decision variables and the constraints, wherein the decision variables are: Among them, k is the specified road section, and m is the specified time; The constraints include: (a) According to the emergency event record, determine the starting time and space unit of the emergency event in advance<k0,m0> , since the starting time-space unit of the emergency event is affected by the emergency event, the following constraints are used to express it: (b) According to the first law of traffic wave propagation, the space-time unit<k,m> When affected by an emergency, the downstream sections adjacent to section k are propagated or the section k is retained by the emergency in the time interval m-1. The set of downstream sections adjacent to section k is denoted as D k , when δ k,m =1, there is δ k,m-1 =1 or Space-time unit<k,m-1> or<l,m> (l∈D k ) is affected by at least one of the incidents, expressed using the following constraints: When the road segments in the urban road network form a closed loop, and the impact of the emergency in the closed loop is transmitted from the road segments outside the loop, assume that the road segment k is the road segment in the closed loop, and let R k is the downstream section of section k in the closed loop, that is, When D k =R k When , constraint (2) guarantees the first rule of traffic waves; otherwise, it is necessary to ensure that the influence of road section k propagates from outside the ring, which is expressed by the following constraint: (c) According to the second law of traffic wave propagation, when the space-time unit<k,m-1> The downstream section of section k is also affected by the emergency event at time interval m, and the time-space unit is determined.<k,m> It must also be affected by unexpected events, that is, δ k,m-1 =1 and When k,m =1, expressed using the following constraints: λ1+λ2+λ3≤2; Constraint (7) λ1,λ2,λ3∈{0,1}; Constraint (8) (d) According to the third law of traffic wave propagation, when the space-time unit<k,m> The upstream section l of section k is also affected by the emergency, and the set of upstream sections adjacent to section k is denoted as U k , the cumulative number of vehicles on section l in time interval m is q l,m , section l' is another upstream section of section k, if q l′,m ≥q l,m , the road section l' must also be affected by the emergency in the time interval m, which can be expressed using the following constraints: λ4+λ5+λ6≤2; Constraint (12) λ4,λ5,λ6∈{0,1}; Constraint (13) (e) Other constraints on the decision variables are expressed as follows:

6. The data analysis device for urban road network according to claim 5, characterized in that: The first data processing module comprises: A data determination unit, used to determine the spatiotemporal unit of the emergency event by setting the occurrence location, spatial boundary and time boundary of the emergency event; A data acquisition unit, used to acquire historical driving data, and calculate a standard deviation and an average value based on the historical driving data; A data processing unit is used to calculate the spatiotemporal influence range according to the standard deviation, the average value and the spatiotemporal unit.

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