CN118968801A - A snow-clearing vehicle operation path planning method based on key impact road section identification - Google Patents
A snow-clearing vehicle operation path planning method based on key impact road section identification Download PDFInfo
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Abstract
The invention is applicable to the technical field of emergency management, and provides a snow removing vehicle operation path planning method based on important influence road section identification, which comprises the following steps: firstly, the urban key influence road sections under the snowfall condition are identified by analyzing the influence of road section intensity, side betters, interest point influence and snow on the total traffic time of road section vehicles. And then, establishing a snowplow path planning model, inputting an initial key influence road section set into the model, and generating an initial working path of the snowplow. In the snow cleaning process, the traffic state information of the road network is updated timely, including the condition of snow accumulated on the road surface and the traffic flow change. And re-evaluating and updating the key influence road segment set according to the updated road network information. And inputting the newly determined key influence road segment set into a snowplow path planning model to obtain an updated snowplow operation path. The invention improves the emergency response capability of the urban snow disaster, ensures that the traffic is quickly recovered to be normal and ensures the public trip safety.
Description
Technical Field
The invention belongs to the technical field of emergency management, and particularly relates to a snow removing vehicle operation path planning method based on important influence road section identification.
Background
In snowfall, road snow can seriously influence the normal operation of urban road traffic systems. In order to ensure the safety and efficiency of traffic, it is important to formulate a scientific, reasonable and efficient snow removal strategy. These strategies aim to reduce the impact of snowfall on the urban road network and ensure that the traffic system can resume normal operation quickly.
Road snow removal work is a key to cope with winter snowfall weather. Many students have studied the winter road network snow removal problem. For example, the forest countryside defines the road section criticality by the increased value of the travel expense of the whole road network after the road section fails, divides the priority service class of the road section, and establishes a snow removing vehicle path planning model aiming at the shortest snow removing completion time. In consideration of constraint conditions such as service level, fleet size and the like in winter road maintenance in the ocean and the like, an integer planning model for optimizing and scheduling snow removing vehicles under real-time information is established. Quirion-Blais et al propose that in an emergency situation, the problem of optimizing the path of a large-scale snow removing vehicle should be solved with the aim of minimizing the weighted working time of each priority road segment. Wang Jing aims at improving the toughness of the road network, establishes a mathematical model of the road network recovery problem in ice and snow weather, and solves the problem of the layout of urban road network snow removal emergency materials and the problem of optimization of snow removal operation in uncertain information of extreme ice and snow weather.
However, existing snowplow path planning studies still have some limitations. First, prior studies often do not adequately account for the effects of adjacent facility points in selecting a snow removal road segment, thereby affecting snow removal efficiency and overall operation of the road network. Secondly, because snowfall is a dynamic loading process, the state of the road network can be changed dynamically. Therefore, the path planning of the snow removing vehicle needs to be able to adapt to the change, reduce invalid operation and accelerate road network recovery. Therefore, the snow removing vehicle operation path planning method based on important influence road section identification is provided.
Disclosure of Invention
The invention aims to provide a snow removing vehicle operation path planning method based on important influence road section identification, and aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a snow removing vehicle operation path planning method based on important influence road section identification comprises the following steps:
Step S1, identifying important influence road sections of cities under the snowfall condition: urban key influence road sections under snowfall conditions are identified based on road network structures, functions and adjacent facility point characteristics, and the method specifically comprises the following steps: identifying urban key influence road sections by calculating the road section strength U ij, the side bets D ij, the interest point influence degree P ij and the influence B ij of snow on the total traffic time of the road section vehicles, and forming a key influence road section set;
Step S2, planning a snow removal vehicle operation path according to dynamic update of the important influence road section of the city: establishing a snow removing vehicle operation path planning model, inputting an initial key influence road section set into the snow removing vehicle operation path planning model, and generating an initial operation path of the snow removing vehicle; in the snow removing process, dynamically updating a key influence road section set according to the snow reducing information and the traffic state of each road section, and bringing a snow removing vehicle operation path planning model to obtain a path planning scheme of the snow removing vehicle in a snow removing operation area; the input of the snow-removing vehicle working path planning model is a key influence road section set, and the output is the travel path of the whole snow-removing process of the snow-removing vehicle and the time sequence of working road sections.
Further, the road section importance I ij is obtained by performing the deviation normalization on the road section intensity U ij, the edge betweenness D ij and the interest point influence degree P ij:
formula 12: ；
wherein: Representing the importance of the road section; representing performing dispersion normalization processing on the data; Weights of D ij、Uij and P ij are represented, respectively; b ij represents the influence of snow on the total traffic time of the road section vehicle; h represents the snow depth on the road segment r ij;
carrying out dispersion normalization on the road section importance I ij:
Formula 13: ；
When the result is at When the road section is judged to be a key influence road section; when the result is atWhen the road section is determined to be a secondary road section; when the road area snow depth is 0, judging that the road is not affected seriously,
And updating the traffic state of each road section according to the snowfall information, calculating to obtain the importance I ij of each road section, and judging whether the road section is an important influence road section according to a formula 13 to obtain an important influence road section set A m={Em, wherein A m represents the important influence road section set obtained when the traffic state of the road section is updated for the mth time, and E m represents the set of directed road sections.
Further, the formula for calculating the road section intensity U ij is as follows:
Formula 1: ；
Formula 2: ；
formula 3: ；
Formula 4: ；
Formula 5: ；
Formula 6: ；
Formula 7: ；
Formula 8: ；
Wherein: c i represents the comprehensive strength of the node i; s i represents the strength of the target node i; representing the influence coefficient; representing a set of neighbor nodes of node i; q j represents the neighbor node strength of the target node j; q i represents the neighbor node strength of the target node i; λ represents a degree value influence coefficient; Representing an incoming intensity value of the target node i; Representing the outgoing intensity value of the target node i; θ ji represents the directional road segment r ji weight; θ ij represents the weight of the directed road segment r ij; q ij denotes the number of vehicles on the road segment r ij; g represents a set of directed road segments of the road network, and any arc (i, j) represents a directed road segment in a direction from node i to adjacent node j; j represents the neighbor node of the target node i.
Further, the formula for calculating the edge betweenness D ij is as follows:
Formula 9: ；
Wherein: n G represents the total number of shortest paths between any nodes in the road network G; n ij denotes the number of paths passing through the road segment r ij among the shortest paths.
Further, the formula for calculating the interest point influence degree P ij is as follows:
Formula 10: ；
Wherein: p ij represents the point of interest influence; n represents the number of categories of interest points; Representing the number of interest points of omega category in the buffer of the road segment r ij; IP ω represents the importance value of the point of interest for the ω class.
Further, the formula for calculating the influence B ij of the snow on the total traffic time of the road section vehicle is as follows:
Formula 11: ；
Wherein: l ij denotes the road segment r ij length; When the snow depth on the road section r ij is h, the average running speed of the vehicle on the road section r ij is represented; v ij represents the average running speed of the vehicle on the road segment r ij when the snow depth on the road segment r ij is 0.
Further, the snow removing vehicle operation path planning model is shown in the figures 14-22;
formula 14: ；
formula 15: ；
Formula 16: ；
Formula 17: ；
formula 18: ；
formula 19: ；
formula 20: ；
formula 21: ；
Formula 22: ；
equation 14 is an objective function, which indicates that the snow-removing vehicle is shortest when cleaning all important influence road sections; equation 15 shows that the snow-removing vehicle can perform snow-removing operation only on a road section in its travel path; equation 16 represents the continuity of the vehicle p path; equation 17 represents the flow balance constraints of the snowplow at each node; equation 18 represents the flow balance constraint of a snowplow vehicle entering and exiting the entire road network; equation 19 represents the time when the snow removing vehicle starts and completes snow removal in each period; equation 20 shows whether the snow cleaning vehicle p runs on the road r ij when the m-th important influence road is focused on the snow cleaning task; the road section r ij is cleaned by the snow cleaning vehicle p when the m-th important influence road section is concentrated to perform a snow cleaning task; equation 22 represents the number of times the snow removal truck p accesses a road segment when performing a snow removal task in the m-th important influence road segment set;
Wherein: t represents the time when all snow removing vehicles complete snow removing operation of all road sections; Indicating the total non-working running time of the snow removing vehicle p; Indicating the total running time of the snowplow vehicle p; m represents the number of times of updating the key influence road segment set; m represents the m-th updating key influence road segment set; a m represents the m-th important influence road segment set; p represents a collection of snow removing vehicles; p represents the p-th snowplow; l ij denotes the road segment r ij length; Representing the average running speed of the vehicle on the road segment r ij when the m-th updating of the key influence road segment set; v 0 denotes the rated operating speed of the snow-removing vehicle; when the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ji or not is indicated; When the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ij or not is indicated; indicating whether the snow removal vehicle p works on the road sections in the important influence road section set A m; o represents a virtual node of all snow-removing vehicles entering the road network; d represents the virtual nodes of all the snow-removing vehicles exiting the road network; A service start time indicating the snowplow vehicle p; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m-1; The number of times that the snow removing vehicle p accesses over the road r ij is indicated; i represents a node; j represents a neighboring node; g represents a road network; x ijp represents whether the snowplow p is traveling on the road segment r ij; x jip represents whether the snowplow p is traveling on the road segment r ji; y ijp represents whether the snow cleaning vehicle p works on the road segment r ij; y jip represents whether the snow cleaning vehicle p works on the road segment r ji; x ojp represents whether the snow remover vehicle p is traveling on the virtual starting point o to the node j; x jdp indicates whether the snowplow vehicle p is traveling on the node j to the virtual destination d.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a snow removing vehicle operation path planning method based on important influence road section identification, aiming at enhancing emergency response capability of urban traffic when a snow disaster occurs. By the method, the operation path of the snow removing vehicle can be reasonably planned, the accumulated snow on the road surface is ensured to be effectively cleaned in time, and repeated operation or omission of key areas is avoided. The method is beneficial to quickly recovering the normal operation of urban traffic, and ensures that the social and economic activities can be smoothly carried out, thereby improving the emergency response efficiency of the city and ensuring the travel safety of the public under the condition of snowy disaster.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a road traffic network topology structure of a part of a vinca city area in the present invention.
Fig. 3 is a schematic diagram of a first snow-removing vehicle according to the present invention.
Fig. 4 is a schematic diagram of a second snow-removing vehicle according to the present invention.
Fig. 5 is a layout diagram of a snow cleaning operation path of a third snow cleaning vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
As shown in fig. 1, one embodiment of the present invention provides a method for planning a working path of a snow removing vehicle based on identification of important influence road segments, comprising the following steps:
Step S1, identifying important influence road sections of cities under the snowfall condition: urban key influence road sections under snowfall conditions are identified based on road network structures, functions and adjacent facility point characteristics, and the method specifically comprises the following steps: identifying urban key influence road sections by calculating the road section strength U ij, the side bets D ij, the interest point influence degree P ij and the influence B ij of snow on the total traffic time of the road section vehicles, and forming a key influence road section set; the content is as follows:
(1) Calculating road segment intensity ；
Road section intensityIs the comprehensive strength of the nodes at the two ends of the edgeProduct of (2), node integrated strengthIs made up of target node strengthAnd adjacent node strengthComposition, target node strengthFrom the incident strengthAnd out strengthAdd up to intensityIs the directed road segment weight pointing to this nodeThe sum, the outgoing strength is the directed edge weight from this nodeSum, directed road segment weightsFor the number of vehicles on the directed road sectionRatio to total number of vehicles on road network, adjacent node affects intensityCalculating the node intensity value of the adjacent node of the target node;
Formula 1: ；
Formula 2: ；
formula 3: ；
Formula 4: ；
Formula 5: ；
Formula 6: ；
Formula 7: ；
Formula 8: ；
Wherein: u ij represents the road segment strength; c i represents the comprehensive strength of the node i; s i represents the strength of the target node i; representing the influence coefficient; representing a set of neighbor nodes of node i; q j represents the neighbor node strength of the target node j; q i represents the neighbor node strength of the target node i; λ represents a degree value influence coefficient; Representing an incoming intensity value of the target node i; Representing the outgoing intensity value of the target node i; θ ji represents the directional road segment r ji weight; θ ij represents the weight of the directed road segment r ij; q ij denotes the number of vehicles on the road segment r ij; units: a vehicle; g represents a set of directed road segments of the road network, and any arc (i, j) represents a directed road segment in a direction from node i to adjacent node j; j represents the neighbor node of the target node i.
(2) Calculating an edge medium number D ij;
Edge betweenness D ij is the ratio of the number of paths through road segment r ij to the shortest path number in all the shortest paths in the network:
Formula 9: ；
Wherein: d ij represents edge betweenness; n G represents the total number of shortest paths between any nodes in the road network G; n ij denotes the number of paths passing through the road segment r ij among the shortest paths.
(3) Calculating interest point influence degree P ij;
The point of interest influence P ij is equal to the number of facility points multiplied by the importance of the type of facility point:
Formula 10: ；
Wherein: p ij represents the point of interest influence; n represents the number of categories of interest points; Representing the number of interest points of omega category in the buffer of the road segment r ij; IP ω represents the importance value of the point of interest for the ω class.
(4) Calculating the influence B ij of the snow on the total traffic time of the road section vehicle;
The influence B ij of the snow on the total passing time of the road section vehicles is equal to the increment of the travel time of all vehicles on the road section:
Formula 11: ；
Wherein: b ij denotes the effect of snow on the total transit time of the road segment vehicle, unit: hours; l ij denotes the length of the road segment r ij, unit: kilometers; When the snow depth on the road segment r ij is h, the average running speed of the vehicle on the road segment r ij is expressed as the unit: kilometers per hour; v ij represents the average running speed of the vehicle on the road segment r ij in units when the snow depth on the road segment r ij is 0: kilometers per hour.
(5) Calculating the importance I ij of the road section;
The link importance I ij is obtained by performing dispersion normalization on the link strength U ij, the edge bets D ij, and the interest point influence P ij, as shown in formula 12:
formula 12: ；
wherein: Representing the importance of the road section; representing performing dispersion normalization processing on the data; Weights of D ij、Uij and P ij are represented, respectively; b ij represents the influence of snow on the total traffic time of the road section vehicle; h represents the snow depth on the road segment r ij;
(6) Judging important influence road sections;
The link importance I ij is subjected to dispersion normalization as shown in formula 13:
Formula 13: ；
When the result is at When the road section is judged to be a key influence road section; when the result is atWhen the road section is determined to be a secondary road section; when the road area snow depth is 0, it is determined that the road is not affected.
And updating the traffic state of each road section according to the snowfall information, calculating to obtain the importance I ij of each road section, and judging whether the road section is an important influence road section according to a formula 13 to obtain an important influence road section set A m={Em, wherein A m represents the important influence road section set obtained when the traffic state of the road section is updated for the mth time, and E m represents the set of directed road sections.
Step S2, planning a snow removal vehicle operation path according to dynamic update of the important influence road section of the city: establishing a snow removing vehicle operation path planning model, inputting an initial key influence road section set into the snow removing vehicle operation path planning model, and generating an initial operation path of the snow removing vehicle; in the snow removing process, dynamically updating a key influence road section set according to the snow reducing information and the traffic state of each road section, and bringing a snow removing vehicle operation path planning model to obtain a path planning scheme of the snow removing vehicle in a snow removing operation area; the input of the snow-removing vehicle working path planning model is a key influence road section set, and the output is the travel path of the whole snow-removing process of the snow-removing vehicle and the time sequence of working road sections.
The snow removing vehicle operation path planning model is shown in the specification of 14-22;
formula 14: ；
formula 15: ；
Formula 16: ；
Formula 17: ；
formula 18: ；
formula 19: ；
formula 20: ；
formula 21: ；
Formula 22: ；
equation 14 is an objective function, which indicates that the snow-removing vehicle is shortest when cleaning all important influence road sections; equation 15 shows that the snow-removing vehicle can perform snow-removing operation only on a road section in its travel path; equation 16 represents the continuity of the vehicle p path; equation 17 represents the flow balance constraints of the snowplow at each node; equation 18 represents the flow balance constraint of a snowplow vehicle entering and exiting the entire road network; equation 19 represents the time when the snow removing vehicle starts and completes snow removal in each period; equation 20 shows whether the snow cleaning vehicle p runs on the road r ij when the m-th important influence road is focused on the snow cleaning task; the road section r ij is cleaned by the snow cleaning vehicle p when the m-th important influence road section is concentrated to perform a snow cleaning task; equation 22 represents the number of times the snow removal truck p accesses a road segment when performing a snow removal task in the m-th important influence road segment set;
Wherein: t represents the time in units of all snow-removing vehicles completing snow-removing operations on all road sections: hours; The total time of the non-working running of the snow-removing vehicle p is expressed in units of: hours; The total running time of the snowplow p is expressed in units of: hours; m represents the number of times of updating the key influence road segment set; m represents the m-th updating key influence road segment set; a m represents the m-th important influence road segment set; p represents a collection of snow removing vehicles; p represents the p-th snowplow; l ij denotes the length of the road segment r ij, unit: kilometers; The average running speed of the vehicle on the road segment r ij in the m-th update emphasis influence road segment set is represented by the unit: kilometers per hour; v 0 denotes the rated operating speed of the snow-removing vehicle in units of: kilometers per hour; when the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ji or not is indicated; When the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ij or not is indicated; indicating whether the snow removal vehicle p works on the road sections in the important influence road section set A m; o represents a virtual node of all snow-removing vehicles entering the road network; d represents the virtual nodes of all the snow-removing vehicles exiting the road network; A service start time indicating the snowplow vehicle p; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m-1; The number of times that the snow removing vehicle p accesses over the road r ij is indicated; i represents a node; j represents a neighboring node; g represents a road network; x ijp represents whether the snowplow p is traveling on the road segment r ij; x jip represents whether the snowplow p is traveling on the road segment r ji; y ijp represents whether the snow cleaning vehicle p works on the road segment r ij; y jip represents whether the snow cleaning vehicle p works on the road segment r ji; x ojp represents whether the snow remover vehicle p is traveling on the virtual starting point o to the node j; x jdp indicates whether the snowplow vehicle p is traveling on the node j to the virtual destination d.
Example 1 the above method was applied to the Jilin province Changchun market segment. Considering the characteristics of snow removing vehicles, an intersection is abstracted into nodes, a road section is abstracted into edges, and the total number of the nodes is 13, and 36 are directed edges. The selected area is converted into a directed graph g= (I, E), where i= { i|i=1, 2,..13 } represents a set of nodes corresponding to intersections or endpoints of the urban road, e= { e|e=1, 2,..36 } represents a set of directed road segments connecting two adjacent nodes, as shown in fig. 2, the road segments, nodes are numbered.
Collecting relevant parameters of a research area, wherein the basic conditions of the research area are shown in table 1;
table 1 basic conditions of investigation region
| Road section | Road length (km) | Traffic volume (pcu/5 min) | Initial average speed (km/h) |
| 1 | 0.54 | 211 | 20.7 |
| 2 | 0.54 | 263 | 20.7 |
| 3 | 0.84 | 148 | 24.4 |
| 4 | 0.84 | 217 | 36.6 |
| 5 | 0.12 | 183 | 30 |
| 6 | 0.12 | 115 | 10 |
| ... | ... | ... |
Planning a snow removing vehicle working path according to the important influence road sections of the city: the snowfall was set for 1 hour, the snowfall was 0.3mm, the running speed of the specified snowplow at the time of the snowplow operation was 10km/h, and 3 snowplow vehicles were dispatched in total to perform the operation.
First, an initial emphasis directed road segment set A 1 is calculated according to equations 1-13. Wherein, when calculating the road section intensity, the degree value influences the coefficientTaking 0.5; when the influence degree of the interest points is calculated, 10 kinds of interest points are selected, the importance values of the interest points are shown in table 2, and the interest points in a road 30m buffer area are regarded as effective facilities for influencing the importance of the road; when the importance of the road section is calculated, three index weight values of the road section strength U ij, the edge medium number D ij and the interest point influence degree P ij are respectively 0.297, 0.164 and 0.539. And then bringing the initial key directed road segment set A 1 into a snowplow operation path planning model, wherein the model is shown as 14-22, and solving by using a heuristic algorithm to obtain an initial snowplow operation path. And updating the traffic state of the road network every 30 minutes during the snowing period, updating the key directed road segment set according to the formulas 1-13, and solving the next-stage working path of the snowing vehicle by using the models such as the formulas 14-22 based on the current position of the snowing vehicle until the snowing stops to finish the updating process.
TABLE 2 Point of interest importance values
| First class classification | Two-stage classification | Weighting of |
| Residence land | Residential building land | 0.171 |
| Public management | Government authorities, administrative offices | 0.063 |
| Educational and scientific research land | College land, middle and professional school land | 0.051 |
| Land for middle and primary school | 0.120 | |
| Medical and sanitary land | Comprehensive hospital | 0.183 |
| Community health service center | 0.049 | |
| Commercial establishment land | Retail business land, hotel land | 0.039 |
| Farm trade market land | 0.039 | |
| Hotel land | 0.032 | |
| Commercial establishment land | Comprehensive office land for finance, insurance, securities, news publishing and the like | 0.040 |
| Public facility business site land | Land for fueling and gas filling station | 0.092 |
| Traffic facility land | Bus ground station, head and tail station | 0.121 |
Table 3 is path information for the snow removing vehicle to complete the entire road network snow removing operation, including: the service path of the snow-removing vehicle, the time for each vehicle to complete the snow-removing operation and the path free time. The resulting snowplow path schemes are shown in fig. 3, 4 and 5, wherein the solid line represents the working segment and the dashed line represents the non-working travel segment. The result of the service route in table 3 shows only the order of the working links, that is, the order in which the solid lines occur, and the non-actual travel route.
Table 3 path planning results
| Snow removing vehicle number | Service path (road section number) | Service completion time (h) | Time of empty (h) |
| 1 | 18-1-3-4-18-31-33-34-32-19-16-15-20-17 | 1.78 | 0.25 |
| 2 | 10-8-7-10-8-25-27-36-35-28-26-29 | 1.93 | 0.16 |
| 3 | 29-12-30-12-6-1-3-13-23-24-22-21-14-5 | 1.59 | 0.25 |
From table 3 it can be seen that it takes 1.93 hours from the start of the work of the first snow-removing vehicle to the completion of its task by the last snow-removing vehicle. Part of the road segments undergo multiple cleaning due to the continuous loading of snow on the road surface and the continuous updating of traffic conditions of the road network, so that the road segments are regarded as important influence road segments needing preferential cleaning again. In addition, all road sections in the research area finally finish cleaning work, and the method benefits from the design of a judging method of the important influence road sections, and the method ensures that all road sections can be identified and judged to be the important influence road sections after the snowfall is finished, so that corresponding cleaning service is obtained. Most of current path planning researches adopt a static model for optimal design, namely a snow removal route is preset before snow removal begins, and the dynamic property and uncertainty of actual snowfall are ignored. The method integrates real-time data, such as snowfall condition change, road state and the like, and improves the flexibility and timeliness of snow removal scheduling.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and improvements can be made without departing from the spirit of the invention, and it should also be considered that the scope of the invention is not affected by the practice of the invention or the utility of the patent.
Claims (7)
1. The snow removing vehicle operation path planning method based on the important influence road section identification is characterized by comprising the following steps of:
Step S1, identifying important influence road sections of cities under the snowfall condition: urban key influence road sections under snowfall conditions are identified based on road network structures, functions and adjacent facility point characteristics, and the method specifically comprises the following steps: identifying urban key influence road sections by calculating the road section strength U ij, the side bets D ij, the interest point influence degree P ij and the influence B ij of snow on the total traffic time of the road section vehicles, and forming a key influence road section set;
Step S2, planning a snow removal vehicle operation path according to dynamic update of the important influence road section of the city: establishing a snow removing vehicle operation path planning model, inputting an initial key influence road section set into the snow removing vehicle operation path planning model, and generating an initial operation path of the snow removing vehicle; in the snow removing process, dynamically updating a key influence road section set according to the snow reducing information and the traffic state of each road section, and bringing a snow removing vehicle operation path planning model to obtain a path planning scheme of the snow removing vehicle in a snow removing operation area; the input of the snow-removing vehicle working path planning model is a key influence road section set, and the output is the travel path of the whole snow-removing process of the snow-removing vehicle and the time sequence of working road sections.
2. The method for planning a working path of a snow removing vehicle based on the important influence road section identification according to claim 1, wherein the road section importance I ij is obtained by performing the deviation normalization on the road section intensity U ij, the edge bets D ij and the interest point influence degree P ij:
formula 12: ；
wherein: Representing the importance of the road section; representing performing dispersion normalization processing on the data; Weights of D ij、Uij and P ij are represented, respectively; b ij represents the influence of snow on the total traffic time of the road section vehicle; h represents the snow depth on the road segment r ij;
carrying out dispersion normalization on the road section importance I ij:
Formula 13: ；
When the result is at When the road section is judged to be a key influence road section; when the result is atWhen the road section is determined to be a secondary road section; when the road area snow depth is 0, judging that the road section is not affected;
And updating the traffic state of each road section according to the snowfall information, calculating to obtain the importance I ij of each road section, and judging whether the road section is an important influence road section according to a formula 13 to obtain an important influence road section set A m={Em, wherein A m represents the important influence road section set obtained when the traffic state of the road section is updated for the mth time, and E m represents the set of directed road sections.
3. The method for planning a working path of a snowplow vehicle based on the identification of important-influence road segments according to claim 1, wherein the formula for calculating the road segment strength U ij is as follows:
Formula 1: ；
Formula 2: ；
formula 3: ；
Formula 4: ；
Formula 5: ；
Formula 6: ；
Formula 7: ；
Formula 8: ；
Wherein: c i represents the comprehensive strength of the node i; s i represents the strength of the target node i; representing the influence coefficient; representing a set of neighbor nodes of node i; q j represents the neighbor node strength of the target node j; q i represents the neighbor node strength of the target node i; λ represents a degree value influence coefficient; Representing an incoming intensity value of the target node i; Representing the outgoing intensity value of the target node i; θ ji represents the directional road segment r ji weight; θ ij represents the weight of the directed road segment r ij; q ij denotes the number of vehicles on the road segment r ij; g represents a set of directed road segments of the road network, and any arc (i, j) represents a directed road segment in a direction from node i to adjacent node j; j represents the neighbor node of the target node i.
4. The method for planning a working path of a snowplow vehicle based on identification of important influencing road segments according to claim 1, wherein the formula for calculating the edge bets D ij is as follows:
Formula 9: ；
Wherein: n G represents the total number of shortest paths between any nodes in the road network G; n ij denotes the number of paths passing through the road segment r ij among the shortest paths.
5. The method for planning a working path of a snowplow vehicle based on the identification of important influence segments according to claim 1, wherein the formula for calculating the interest point influence degree P ij is as follows:
Formula 10: ；
Wherein: p ij represents the point of interest influence; n represents the number of categories of interest points; Representing the number of interest points of omega category in the buffer of the road segment r ij; IP ω represents the importance value of the point of interest for the ω class.
6. The method for planning a working path of a snowplow vehicle based on the identification of important influencing road segments according to claim 1, wherein the formula for calculating the influence B ij of the snow on the total transit time of the road segment vehicle is as follows:
Formula 11: ；
Wherein: l ij denotes the road segment r ij length; When the snow depth on the road section r ij is h, the average running speed of the vehicle on the road section r ij is represented; v ij represents the average running speed of the vehicle on the road segment r ij when the snow depth on the road segment r ij is 0.
7. The method for planning a working path of a snow removing vehicle based on the identification of important influence road segments according to claim 1, wherein the model for planning the working path of the snow removing vehicle is shown in formulas 14-22;
formula 14: ；
formula 15: ；
Formula 16: ；
Formula 17: ；
formula 18: ；
formula 19: ；
formula 20: ；
formula 21: ；
Formula 22: ；
equation 14 is an objective function, which indicates that the snow-removing vehicle is shortest when cleaning all important influence road sections; equation 15 shows that the snow-removing vehicle can perform snow-removing operation only on a road section in its travel path; equation 16 represents the continuity of the vehicle p path; equation 17 represents the flow balance constraints of the snowplow at each node; equation 18 represents the flow balance constraint of a snowplow vehicle entering and exiting the entire road network; equation 19 represents the time when the snow removing vehicle starts and completes snow removal in each period; equation 20 shows whether the snow cleaning vehicle p runs on the road r ij when the m-th important influence road is focused on the snow cleaning task; the road section r ij is cleaned by the snow cleaning vehicle p when the m-th important influence road section is concentrated to perform a snow cleaning task; equation 22 represents the number of times the snow removal truck p accesses a road segment when performing a snow removal task in the m-th important influence road segment set;
Wherein: t represents the time when all snow removing vehicles complete snow removing operation of all road sections; Indicating the total non-working running time of the snow removing vehicle p; Indicating the total running time of the snowplow vehicle p; m represents the number of times of updating the key influence road segment set; m represents the m-th updating key influence road segment set; a m represents the m-th important influence road segment set; p represents a collection of snow removing vehicles; p represents the p-th snowplow; l ij denotes the road segment r ij length; Representing the average running speed of the vehicle on the road segment r ij when the m-th updating of the key influence road segment set; v 0 denotes the rated operating speed of the snow-removing vehicle; when the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ji or not is indicated; When the m-th important influence road section is concentrated to perform a snow removing task, whether the snow removing vehicle p runs on the road section r ij or not is indicated; indicating whether the snow removal vehicle p works on the road sections in the important influence road section set A m; o represents a virtual node of all snow-removing vehicles entering the road network; d represents the virtual nodes of all the snow-removing vehicles exiting the road network; A service start time indicating the snowplow vehicle p; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m; Indicating the time when the snow-removing vehicle p completes the snow-removing work in a m-1; The number of times that the snow removing vehicle p accesses over the road r ij is indicated; i represents a node; j represents a neighboring node; g represents a road network; x ijp represents whether the snowplow p is traveling on the road segment r ij; x jip represents whether the snowplow p is traveling on the road segment r ji; y ijp represents whether the snow cleaning vehicle p works on the road segment r ij; y jip represents whether the snow cleaning vehicle p works on the road segment r ji; x ojp represents whether the snow remover vehicle p is traveling on the virtual starting point o to the node j; x jdp indicates whether the snowplow vehicle p is traveling on the node j to the virtual destination d.
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