CN114170831A - City K time-varying shortest path acquisition method considering safety and efficiency - Google Patents

City K time-varying shortest path acquisition method considering safety and efficiency Download PDF

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CN114170831A
CN114170831A CN202111495756.4A CN202111495756A CN114170831A CN 114170831 A CN114170831 A CN 114170831A CN 202111495756 A CN202111495756 A CN 202111495756A CN 114170831 A CN114170831 A CN 114170831A
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node
time
starting
label
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CN114170831B (en
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丁建勋
刘海生
周润东
江宇鹏
樊银超
丁卫东
满忠运
冯战雨
徐小明
龙建成
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Hefei University of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route

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Abstract

The invention discloses a city K time-varying shortest path obtaining method considering safety and efficiency, comprising the following steps: 1. acquiring a real-time urban road network map; 2. initializing a variable list tag for each intersection node; 3. obtaining pareto optimal label set through pareto ordering
Figure DDA0003400689770000011
4. By passing
Figure DDA0003400689770000012
The intersection node in the system completes the forward update of the kth short path; 5. finding the node v from the starting point intersectionstartNode v of intersection to terminalendAnd the distance values and the shortest path are traced back and output. The invention can obtain K optimal travel paths with efficiency and safety taken into account, thereby ensuring personal travel safetyAnd the stability and the high efficiency of social traffic.

Description

City K time-varying shortest path acquisition method considering safety and efficiency
Technical Field
The invention belongs to the field of vehicle navigation path optimization, and particularly relates to a method for acquiring a time-varying shortest path of an urban K time, which considers safety and efficiency.
Background
With the rapid increase of the automobile holding capacity of residents, a large number of users and a wide development space are obtained by navigation software, and the development of functions of reducing travel time, avoiding congestion, reducing charge and the like in a corresponding navigation algorithm provides great convenience for daily travel of people.
However, in the prior art, the following disadvantages exist, firstly, the existing navigation algorithm rarely establishes more definite relation with the future real-time road conditions, so that the navigation result is unlikely to be close to the actual situation; secondly, comprehensive consideration on risk cost and the like except time cost is lacked, and the existing algorithm cannot induce the vehicle to find an optimal path with lower travel cost and higher safety; thirdly, with the continuous enlargement of the urban road network scale, the journey selection between the origin and destination can have a plurality of different paths.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a method for acquiring the shortest time-varying urban K time path with safety and efficiency considered, so that K optimal travel paths with efficiency and safety considered can be obtained, and the safety of personal travel and the stability and high efficiency of social traffic are guaranteed.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a method for acquiring a time-varying shortest path of an urban K time with safety and efficiency considered, which is characterized by comprising the following steps:
step 1: defining parameters and initializing;
acquiring real-time road network data and obtaining an urban road network graph G ═ (V, A), wherein V represents an intersection node set, and V ═ V1,v2,v3,...,vn,...,vN}，vnN represents the total number of the nodes of the N-th intersection, wherein N is 1,2,3.. N; let vstartIndicating the starting intersection node, vendRepresents an endpoint intersection node, and vstart、vend∈V；
Defining n intersection node vnThe kth short circuit of (a) indicates a node v from the starting intersectionstartReach the n intersection node vnThe kth route after the driving time and the driving risk are arranged in all the routes;
node v of any n-th intersectionnSet of tags D (v)n) Wherein the kth tag Dk(vn) Node v representing starting point intersectionstartReach the n-th intersection node v in the city road network graph GnIs the relevant property of the current kth short, i.e. Dk(vn)＝(vn,dk(vn),ek(vn),pk(vn),qk(vn) Wherein d) isk(vn) Indicating a node v from the starting intersectionstartReach the n-th intersection node v in the city road network graph GnCurrent kth short-circuited driving time, ek(vn) Indicating a node v from the starting intersectionstartReach the n-th intersection node v in the city road network graph GnIs currently being usedRisk of kth short circuit, pk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnRequired time dk(vn) And driving risk ek(vn) A corresponding predecessor intersection node; q. q.sk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnThe kth short-circuit of (c) is at the tag set D (p) of the intersection node of the preceding drivek(vn) Tag number in); a denotes a set of links between intersection nodes, and a ═ aij＝(vi,vj)|i,j＝1,2,...N}，(vi,vj) Represents the ith intersection node viTo the jth intersection node vjThe distance between, let omegaij(t) is the time t of the road section (v)i,vj) The running time weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let omegaij(t) ± infinity; let epsilonij(t) is the time t of the road section (v)i,vj) The driving risk weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let epsilonij(t)＝+∞；
Defining the number of shortest paths as K;
defining a total label set as D; the h-th label in the total label set D is marked as Dh；
Defining P as the total number of labels in the total label set D;
definition of
Figure BDA0003400689750000021
Searching a pareto optimal label set in a total label set D under the mth iteration during the kth short circuit;
definition of
Figure BDA0003400689750000022
Is composed of
Figure BDA0003400689750000023
The u-th label；
Defining a set R for storing each precursor intersection node of the path where the node is located in the process of backtracking the path of any intersection node;
definition of t0Is the departure time;
initializing intersection nodes v of starting point start1 st tag of
Figure BDA0003400689750000024
Label set D (v) of any other n-th intersection noden) 1 st tag D in (1)1(vn) Are all initialized to
Figure BDA0003400689750000025
vn≠vstart(ii) a Let D be { D ═ D1(vstart)}；
Initializing k to 1, m to 1;
step 2: calculating the kth short path;
step 2.1: updating pareto optimal label sets
Figure BDA0003400689750000031
Step 2.1.1: initializing a pareto optimal tag set: the initialization sequence number p is 1,
Figure BDA0003400689750000032
step 2.1.2: sequentially connecting the labels in the total label set D with
Figure BDA0003400689750000033
The tags in (1) are subjected to pareto comparison;
step 2.1.2.1: traversing the labels in the total label set D;
assigning P +1 to P, if P > P holds, then we will assign P +1 to P
Figure BDA0003400689750000034
Assigning a value to D, and turning to the step 2.2; otherwise, get
Figure BDA0003400689750000035
The total number of the tags in (1) is W; making the current comparison frequency w equal to 0; wherein the \ representation removes the latter symbol from the former;
step 2.1.2.2: will DpAnd
Figure BDA0003400689750000036
the labels in (1) are compared in sequence;
if W is true, then it will
Figure BDA0003400689750000037
Is assigned to
Figure BDA0003400689750000038
And updates W, turning to step 2.1.2.1; otherwise, after assigning w +1 to w, DpAnd
Figure BDA0003400689750000039
w th label of
Figure BDA00034006897500000310
Comparing; wherein the content of the first and second substances,
Figure BDA00034006897500000311
indicating label DpThe described nodes of the intersection are described as,
Figure BDA00034006897500000312
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure BDA00034006897500000313
The current drive time of the kth short circuit,
Figure BDA00034006897500000314
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure BDA00034006897500000315
The current driving risk of the kth short circuit,
Figure BDA00034006897500000316
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure BDA00034006897500000317
Time required
Figure BDA00034006897500000318
And driving risk
Figure BDA00034006897500000319
A corresponding predecessor intersection node;
Figure BDA00034006897500000320
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure BDA00034006897500000321
The kth short-circuit of (2) a label set of a preceding drive intersection node
Figure BDA00034006897500000322
The label number in (1);
Figure BDA00034006897500000323
presentation label
Figure BDA00034006897500000324
The described nodes of the intersection are described as,
Figure BDA00034006897500000325
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure BDA00034006897500000326
The current drive time of the kth short circuit,
Figure BDA00034006897500000327
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure BDA00034006897500000328
The current driving risk of the kth short circuit,
Figure BDA00034006897500000329
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure BDA00034006897500000330
Time required
Figure BDA00034006897500000331
And driving risk
Figure BDA00034006897500000332
A corresponding predecessor intersection node;
Figure BDA00034006897500000333
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure BDA00034006897500000334
The kth short-circuit of (2) a label set of a preceding drive intersection node
Figure BDA00034006897500000335
The label number in (1); if it is
Figure BDA00034006897500000336
And is
Figure BDA00034006897500000337
If true, go to step 2.1.2.1;
if it is
Figure BDA0003400689750000041
And is
Figure BDA0003400689750000042
Then will be
Figure BDA0003400689750000043
Is assigned to
Figure BDA0003400689750000044
Assigning W-1 to W, assigning W-1 to W, and turning to step 2.1.2.2;
if it is
Figure BDA0003400689750000045
And is
Figure BDA0003400689750000046
If true, go to step 2.1.2.2;
if it is
Figure BDA0003400689750000047
And is
Figure BDA0003400689750000048
If true, go to step 2.1.2.2;
step 2.2: detecting a pareto optimal label set;
step 2.2.1: judging whether a terminal intersection v is foundendThe kth short circuit of (1);
if it is
Figure BDA0003400689750000049
If the value is empty, turning to the step 3; if not, then,
Figure BDA00034006897500000410
judgment of
Figure BDA00034006897500000411
In (1)
Figure BDA00034006897500000412
Whether it is the terminal intersection vendIf yes, turning to step 2.2.2; otherwise, assigning m +1 to m, and turningStep 2.3;
step 2.2.2: judging whether a terminal intersection v is foundendK short circuits of (2):
if K is larger than K, turning to step 3; otherwise, after k +1 is assigned to k, making m equal to 1, and turning to step 2.3;
step 2.3: forward updating;
judgment of
Figure BDA00034006897500000413
If the value is empty, assigning m +1 to m, and turning to the step 2.1; otherwise, from
Figure BDA00034006897500000414
1 st tag of
Figure BDA00034006897500000415
Get its intersection node
Figure BDA00034006897500000416
Will be provided with
Figure BDA00034006897500000417
Is assigned to
Figure BDA00034006897500000418
Then, the nodes of the current intersection are connected
Figure BDA00034006897500000419
All adjacent intersection nodes are added into the temporary adjacent intersection node set
Figure BDA00034006897500000420
Then turning to step 2.3.1;
step 2.3.1:
Figure BDA00034006897500000421
forward search of (2);
judgment of
Figure BDA00034006897500000422
If the current state is empty, turning to the step 2.3; otherwise, take out
Figure BDA00034006897500000423
The node of the first adjacent intersection is recorded as
Figure BDA00034006897500000424
Turning to step 2.3.2;
step 2.3.2: loop detection;
order to
Figure BDA00034006897500000425
Node of slave intersection
Figure BDA00034006897500000426
The kth tag of (1)
Figure BDA00034006897500000427
Get out the front-drive intersection node
Figure BDA00034006897500000428
And label number
Figure BDA00034006897500000429
Judgment of
Figure BDA00034006897500000430
I.e. whether a ring is present, if yes, go to step 2.3.1; otherwise it will be
Figure BDA00034006897500000431
Assigning a value to R, and continuously according to the nodes of the predecessor intersection
Figure BDA00034006897500000432
First in the set of labels
Figure BDA00034006897500000433
Information of the front-driving intersection of each label is sent to the starting intersection vstartRepeating the above operations to backtrack if the whole process is completedIf no ring exists in the process, turning to the step 2.3.3;
step 2.3.3: updating the driving time and the driving risk of the feasible path;
from
Figure BDA0003400689750000051
Take out the running time
Figure BDA0003400689750000052
And driving risk
Figure BDA0003400689750000053
And calculating the node v of the intersection from the starting pointstartThrough
Figure BDA0003400689750000054
Arrive at
Figure BDA0003400689750000055
Temporary distance of
Figure BDA0003400689750000056
And intersection node v from the starting pointstartThrough
Figure BDA0003400689750000057
Arrive at
Figure BDA0003400689750000058
Temporary driving risk of
Figure BDA0003400689750000059
Figure BDA00034006897500000510
To represent
Figure BDA00034006897500000511
Time road section
Figure BDA00034006897500000512
The running time weight value;
Figure BDA00034006897500000513
to represent
Figure BDA00034006897500000514
Time road section
Figure BDA00034006897500000515
The driving risk weight value; order to
Figure BDA00034006897500000516
Will be provided with
Figure BDA00034006897500000517
Assigning to D; turning to step 2.3.1;
and step 3: output vstartAnd vendThe shortest driving time, driving risk and path among K pieces of the bridge are determined;
according to the node v of the terminal intersectionendSet of tags D (v)end) Middle | D (v)end) L tags, from end point vendThe kth tag (v)end,dk(vend),ek(vend),pk(vend),qk(vend) Start by passing p continuouslyk(vend) Backtracking the front-driving node to the intersection node v of the starting pointstartThereby obtaining a slave vstartTo vendTo obtain | D (v) from the k-th short pathend) I shortest path.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention organically unifies the two important indexes of the driving time and the driving risk in the path selection by comprehensively analyzing the two important indexes, and comprehensively considers the two aspects of the driving time and the driving risk by pareto sorting. The method not only realizes the shortest time of selecting the route, but also greatly reduces the accident rate on each route, ensures the travel safety of residents to the maximum extent, and plays a positive role in maintaining the safety, stability and high efficiency of the social traffic network.
2. According to the invention, through analysis of a large amount of historical data counted by navigation software, the driving time of a road section and the relation between the driving risk and the time when a driver arrives at the road section are obtained, and only when the K shortest path is solved, the path planning can be dynamically carried out for the driver, the line condition can be more truly provided for the driver, and the traffic jam and the occurrence of traffic accidents can be reduced to a certain extent.
3. The method is particularly suitable for the conditions of complex urban road network structure and large road network node number, the optimal path search range is limited by adopting the K shortest path algorithm, and the planned path not only can meet the requirement of minimizing the driving risk of a single vehicle, but also fully relieves the urban traffic pressure, reduces the environmental pollution and realizes the reasonable allocation of road network resources under the condition of updating the road network traffic state information in real time.
4. The city K time-varying shortest-path problem considering safety and efficiency is degraded into a standard shortest-path problem if the driving risks of all road sections are 0, namely the city K time-varying shortest-path problem considering efficiency. If the driving time of all the road sections is 0, the problem is degraded to the standard safest road problem, namely a time-varying shortest-circuit problem in the safety-considered city K.
Drawings
FIG. 1 is a flowchart of a city K time-varying shortest path acquisition method considering security and efficiency dual objectives;
FIG. 2 is a flowchart illustrating the detailed steps of finding a pareto optimal tag set in FIG. 1;
FIG. 3 is a diagram of an exemplary network utilized in the present invention;
FIG. 4 is a diagram illustrating a pareto optimal label set and a total label set at the 1 st iteration when the 1 st short is found according to an exemplary embodiment of the present invention;
FIG. 5 is a diagram illustrating pareto optimal label sets and total label sets at the 7 th iteration when finding the 1 st short in the exemplary embodiment of the present invention;
FIG. 6 is a schematic diagram of the present invention showing the pareto optimal labels found when the 3 rd short circuit is found;
Detailed Description
In this embodiment, a method for acquiring a time-varying shortest path in an urban K time considering security and efficiency is described, where a specific flowchart is shown in fig. 1 and is performed according to the following processes:
step 1: defining parameters and initializing;
the method for obtaining real-time road network data and obtaining the city road network graph G ═ (V, a), the algorithm adopted by the invention is shown in fig. 3, for example, V represents the intersection node set, and V ═ V1,v2,v3,...,vn,...,vN}，vnN represents the total number of the nodes of the N-th intersection, wherein N is 1,2,3.. N; let vstartIndicating the starting intersection node, vendRepresents an endpoint intersection node, and vstart、vend∈V；
Defining n intersection node vnThe kth short circuit is a path with the kth in the ascending order of the comprehensive evaluation of the driving time and the driving risk value in all found paths reaching the node;
node v of any n-th intersectionnSet of tags D (v)n) Wherein the kth tag Dk(vn) Node v representing starting point intersectionstartReach the n-th intersection node v in the city road network graph GnThe current kth short; let Dk(vn)＝(vn,dk(vn),ek(vn),pk(vn),qk(vn) Wherein d) isk(vn) Indicating a node v from the starting intersectionstartReach the n-th intersection node v in the city road network graph GnCurrent kth short-circuited driving time value ek(vn) Indicating a node v from the starting intersectionstartReach the n-th intersection node v in the city road network graph GnCurrent kth short-circuit driving risk value, pk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnRequired time value dk(vn) And driving risk value ek(vn) A corresponding predecessor intersection node; q. q.sk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnThe kth short-circuit of (c) is at the tag set D (p) of the intersection node of the preceding drivek(vn) Tag number in); a denotes a set of links between intersection nodes, and a ═ aij＝(vi,vj)|i,j＝1,2,...N}，(vi,vj) Represents the ith intersection node viTo the jth intersection node vjThe distance between, let omegaij(t) is a road section (v)i,vj) The running time weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let omegaij(t) ± infinity; let epsilonij(t) is a road section (v)i,vj) The driving risk weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let epsilonij(t) ± infinity; the time variable t here is of a continuous type; the time variable t is a discrete variable and is also applicable; in the present example, the time variable gap value is 10, and the specific driving time variable weight and driving risk time variable weight are shown in table 1; the driving risk is obtained by converting the accident occurrence rate;
table 1 is a sample road segment time varying weight table used in the present invention
Figure BDA0003400689750000071
Defining the number of shortest paths as K;
defining a total label set as D; the h-th label in the total label set D is marked as Dh；
Defining P as the total number of labels in the total label set D;
definition of
Figure BDA0003400689750000081
Finding a pareto optimal label set in a total label set D under the mth iteration when the kth shortest path is found;
definition of
Figure BDA0003400689750000082
Is composed of
Figure BDA0003400689750000083
The u-th tag;
defining a set R for storing each precursor intersection node of the path where the node is located in the process of backtracking the path of any intersection node;
definition of t0Is the departure time;
initializing intersection nodes v of starting point start1 st tag of
Figure BDA0003400689750000084
Label set D (v) of any other n-th intersection noden) 1 st tag D in (1)1(vn) Are all initialized to
Figure BDA0003400689750000085
vn≠vstart(ii) a Let D be { D ═ D1(vstart)}；
Initializing k to 1, m to 1;
step 2: calculating the kth short path;
step 2.1: updating pareto optimal label sets
Figure BDA0003400689750000086
As shown in fig. 2;
step 2.1.1: initializing a pareto optimal label set;
the initialization sequence number p is 1,
Figure BDA0003400689750000087
when k is 1 and m is 1, v is updated at this timestartAdjacent intersection node v1The first label in the label set is D1(v1)＝(v1,20,40,vstart1), update vstartAdjacent intersection node v2Set of tags ofWherein the first label is D1(v2)＝(v2,10,50,vstart1), mixing D with1(v1) And D1(v2) Placing in D; as shown in fig. 4;
step 2.1.2: the labels in the total label set D are sequentially connected with
Figure BDA0003400689750000088
The tags in (1) are subjected to pareto comparison;
step 2.1.2.1: traversing the labels in the total label set D;
assigning P +1 to P, if P > P holds, then we will assign P +1 to P
Figure BDA0003400689750000089
Assigning a value to D, and turning to the step 2.2; otherwise, get
Figure BDA00034006897500000810
The total number of the tags in (1) is W; making the current comparison frequency w equal to 0; wherein the \ representation removes the latter symbol from the former;
step 2.1.2.2: will DpAnd
Figure BDA00034006897500000811
the labels in (1) are compared in sequence;
if W is true, then it will
Figure BDA00034006897500000812
Is assigned to
Figure BDA00034006897500000813
And updates W, turning to step 2.1.2.1; otherwise, after assigning w +1 to w, DpAnd
Figure BDA0003400689750000091
w th label of
Figure BDA0003400689750000092
Comparing; wherein the content of the first and second substances,
Figure BDA0003400689750000093
Figure BDA0003400689750000094
if it is
Figure BDA0003400689750000095
And is
Figure BDA0003400689750000096
If true, go to step 2.1.2.1;
if it is
Figure BDA0003400689750000097
And is
Figure BDA0003400689750000098
Then will be
Figure BDA0003400689750000099
Is assigned to
Figure BDA00034006897500000910
Assigning W-1 to W, assigning W-1 to W, and turning to step 2.1.2.2;
if it is
Figure BDA00034006897500000911
And is
Figure BDA00034006897500000912
If true, go to step 2.1.2.2;
if it is
Figure BDA00034006897500000913
And is
Figure BDA00034006897500000914
If true, go to step 2.1.2.2;
step 2.2: detecting a pareto optimal label set;
step 2.2.1: judging whether a terminal intersection v is foundendThe kth short circuit of (1);
if it is
Figure BDA00034006897500000915
If the value is empty, turning to the step 3; if not, then,
Figure BDA00034006897500000916
judgment of
Figure BDA00034006897500000917
In (1)
Figure BDA00034006897500000918
Whether it is the terminal intersection vendIf yes, turning to step 2.2.2; otherwise, assigning m +1 to m, and turning to the step 2.3;
when k is 1 and m is 7, the process is repeated
Figure BDA00034006897500000919
Update v4Adjacent intersection node v3The third label in the label set is D4(v3)＝(v3,90,110,v4,3), update v4Adjacent intersection node vendThe fourth label in the label set is D4(vend)＝(vend,80,90,v43), mixing D4(v3) And D4(vend) Placing in D; as shown in fig. 5;
step 2.2.2: judging whether a terminal intersection v is foundendK short circuits of (2):
if K is larger than K, turning to step 3; otherwise, assigning k +1 to k, enabling m to be 1, and turning to step 2.3;
step 2.3: forward updating;
judgment of
Figure BDA00034006897500000920
If the current state is empty, turning to the step 2.1;
otherwise, from
Figure BDA00034006897500000921
1 st tag of
Figure BDA00034006897500000922
Get its intersection node
Figure BDA00034006897500000923
Will be provided with
Figure BDA00034006897500000924
Is assigned to
Figure BDA00034006897500000925
Creating collections
Figure BDA00034006897500000926
And join the node of the current intersection
Figure BDA00034006897500000927
All adjacent intersection nodes of (1); then turning to step 2.3.1;
step 2.3.1:
Figure BDA00034006897500000928
forward search of (2);
judgment of
Figure BDA00034006897500000929
If the current state is empty, turning to the step 2.3; otherwise, take out
Figure BDA00034006897500000930
The node of the first adjacent intersection is recorded as
Figure BDA0003400689750000101
Turning to step 2.3.2;
step 2.3.2: loop detection;
order to
Figure BDA0003400689750000102
Node of slave intersection
Figure BDA0003400689750000103
The kth tag of (1)
Figure BDA0003400689750000104
Get out the front-drive intersection node
Figure BDA0003400689750000105
And label number
Figure BDA0003400689750000106
Will be provided with
Figure BDA0003400689750000107
Assigning a value to R, and continuously according to the first label in the precursor intersection node label set
Figure BDA0003400689750000108
Information of the front-driving intersection of each label is sent to the starting intersection vstartBacktracking is carried out, and each backtracking precursor intersection node is added into the set R; judging in the whole backtracking process
Figure BDA0003400689750000109
Namely whether the adjacent intersection node which is updated currently through the forward direction is contained in the R
Figure BDA00034006897500001010
If so, it indicates that
Figure BDA00034006897500001011
So that a loop exists, and the process is shifted to step 2.3.1; otherwise, turning to step 2.3.3;
step 2.3.3: updating the time value and the driving risk value of the feasible path;
from
Figure BDA00034006897500001012
Taking out
Figure BDA00034006897500001013
Calculating the node v of the intersection from the starting pointstartThrough
Figure BDA00034006897500001014
Arrive at
Figure BDA00034006897500001015
Temporary distance value of
Figure BDA00034006897500001016
And intersection node v from the starting pointstartThrough
Figure BDA00034006897500001017
Arrive at
Figure BDA00034006897500001018
Temporary driving risk value
Figure BDA00034006897500001019
Order to
Figure BDA00034006897500001020
Handle
Figure BDA00034006897500001021
Assigning to D; turning to step 2.3.1;
and step 3: output vstartAnd vendThe shortest driving time, driving risk and path among K pieces of the bridge are determined;
according to the node v of the terminal intersectionendSet of tags D (v)end) Middle | D (v)end) I tags, sequentially will | D (v)end) The intersection nodes where the | paths pass, the driving time and the driving risk are output; wherein, the passed intersection nodes are output in a backtracking mode, and one of the labels is recorded as (v)end,dk(vend),ek(vend),pk(vend),qk(vend) Constantly passing through pk(vend) Backtracking the front-driving node to the intersection node v of the starting pointstartObtaining a piece of slave vstartTo vendThe kth short full path of (1); when the intersection is ended, a schematic diagram obtained by sequencing all found pareto optimal labels in each intersection node is shown in fig. 6; it can be seen that v is reachedendThe driving time of the first short circuit is 50, and the driving risk is 80; reaches vendThe driving time of the second short circuit is 70, and the driving risk is 90; reaches vendThe driving time of the third short circuit is 70 and the driving risk is 95.

Claims (1)

1. A city K time-varying shortest path obtaining method considering safety and efficiency is characterized by comprising the following steps:
step 1: defining parameters and initializing;
acquiring real-time road network data and obtaining an urban road network graph G ═ (V, A), wherein V represents an intersection node set, and V ═ V1,v2,v3,...,vn,...,vN}，vnN represents the total number of the nodes of the N-th intersection, wherein N is 1,2,3.. N; let vstartIndicating the starting intersection node, vendRepresents an endpoint intersection node, and vstart、vend∈V；
Defining n intersection node vnThe kth short circuit of (a) indicates a node v from the starting intersectionstartReach the n intersection node vnThe kth route after the driving time and the driving risk are arranged in all the routes;
node v of any n-th intersectionnSet of tags D (v)n) Wherein the kth tag Dk(vn) Node v representing starting point intersectionstartReach the n-th intersection node v in the city road network graph GnIs the relevant property of the current kth short, i.e. Dk(vn)＝(vn,dk(vn),ek(vn),pk(vn),qk(vn) Wherein d) isk(vn) Indicating a node from the starting pointPoint vstartReach the n-th intersection node v in the city road network graph GnCurrent kth short-circuited driving time, ek(vn) Indicating a node v from the starting intersectionstartReach the n-th intersection node v in the city road network graph GnCurrent kth short-circuit risk, pk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnRequired time dk(vn) And driving risk ek(vn) A corresponding predecessor intersection node; q. q.sk(vn) Indicating a node v from the starting intersectionstartReach the n intersection node vnThe kth short-circuit of (c) is at the tag set D (p) of the intersection node of the preceding drivek(vn) Tag number in); a denotes a set of links between intersection nodes, and a ═ aij＝(vi,vj)|i,j＝1,2,...N}，(vi,vj) Represents the ith intersection node viTo the jth intersection node vjThe distance between, let omegaij(t) is the time t of the road section (v)i,vj) The running time weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let omegaij(t) ± infinity; let epsilonij(t) is the time t of the road section (v)i,vj) The driving risk weight value; if the ith intersection node viAnd j intersection node vjThere is no link between them, let epsilonij(t)＝+∞；
Defining the number of shortest paths as K;
defining a total label set as D; the h-th label in the total label set D is marked as Dh；
Defining P as the total number of labels in the total label set D;
definition of
Figure FDA0003400689740000011
Searching a pareto optimal label set in a total label set D under the mth iteration during the kth short circuit;
definition of
Figure FDA0003400689740000021
Is composed of
Figure FDA0003400689740000022
The u-th tag;
defining a set R for storing each precursor intersection node of the path where the node is located in the process of backtracking the path of any intersection node;
definition of t0Is the departure time;
initializing intersection nodes v of starting pointstart1 st tag of
Figure FDA0003400689740000023
Label set D (v) of any other n-th intersection noden) 1 st tag D in (1)1(vn) Are all initialized to
Figure FDA0003400689740000024
vn≠vstart(ii) a Let D be { D ═ D1(vstart)}；
Initializing k to 1, m to 1;
step 2: calculating the kth short path;
step 2.1: updating pareto optimal label sets
Figure FDA0003400689740000025
Step 2.1.1: initializing a pareto optimal tag set: the initialization sequence number p is 1,
Figure FDA0003400689740000026
step 2.1.2: sequentially connecting the labels in the total label set D with
Figure FDA0003400689740000027
The tags in (1) are subjected to pareto comparison;
step 2.1.2.1: traversing the labels in the total label set D;
assigning P +1 to P, if P > P holds, then we will assign P +1 to P
Figure FDA0003400689740000028
Assigning a value to D, and turning to the step 2.2; otherwise, get
Figure FDA0003400689740000029
The total number of the tags in (1) is W; making the current comparison frequency w equal to 0; wherein the \ representation removes the latter symbol from the former;
step 2.1.2.2: will DpAnd
Figure FDA00034006897400000210
the labels in (1) are compared in sequence;
if W is true, then it will
Figure FDA00034006897400000211
Is assigned to
Figure FDA00034006897400000212
And updates W, turning to step 2.1.2.1; otherwise, after assigning w +1 to w, DpAnd
Figure FDA00034006897400000213
w th label of
Figure FDA00034006897400000214
Comparing; wherein the content of the first and second substances,
Figure FDA00034006897400000215
Figure FDA00034006897400000216
indicating label DpThe described nodes of the intersection are described as,
Figure FDA00034006897400000217
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure FDA00034006897400000218
The current drive time of the kth short circuit,
Figure FDA00034006897400000219
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure FDA00034006897400000220
The current driving risk of the kth short circuit,
Figure FDA00034006897400000221
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure FDA00034006897400000222
Time required
Figure FDA00034006897400000223
And driving risk
Figure FDA00034006897400000224
A corresponding predecessor intersection node;
Figure FDA00034006897400000225
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure FDA0003400689740000031
The kth short-circuit of (2) a label set of a preceding drive intersection node
Figure FDA0003400689740000032
The label number in (1);
Figure FDA0003400689740000033
Figure FDA0003400689740000034
Figure FDA0003400689740000035
presentation label
Figure FDA0003400689740000036
The described nodes of the intersection are described as,
Figure FDA0003400689740000037
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure FDA0003400689740000038
The current drive time of the kth short circuit,
Figure FDA0003400689740000039
indicating a node v from the starting intersectionstartNode reaching intersection in urban road network graph G
Figure FDA00034006897400000310
The current driving risk of the kth short circuit,
Figure FDA00034006897400000311
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure FDA00034006897400000312
Time required
Figure FDA00034006897400000313
And driving risk
Figure FDA00034006897400000314
A corresponding predecessor intersection node;
Figure FDA00034006897400000315
indicating a node v from the starting intersectionstartNode for reaching intersection
Figure FDA00034006897400000316
The kth short-circuit of (2) a label set of a preceding drive intersection node
Figure FDA00034006897400000317
The label number in (1); if it is
Figure FDA00034006897400000318
And is
Figure FDA00034006897400000319
If true, go to step 2.1.2.1;
if it is
Figure FDA00034006897400000320
And is
Figure FDA00034006897400000321
Then will be
Figure FDA00034006897400000322
Is assigned to
Figure FDA00034006897400000323
Assigning W-1 to W, assigning W-1 to W, and turning to step 2.1.2.2;
if it is
Figure FDA00034006897400000324
And is
Figure FDA00034006897400000325
If true, go to step 2.1.2.2；
If it is
Figure FDA00034006897400000326
And is
Figure FDA00034006897400000327
If true, go to step 2.1.2.2;
step 2.2: detecting a pareto optimal label set;
step 2.2.1: judging whether a terminal intersection v is foundendThe kth short circuit of (1);
if it is
Figure FDA00034006897400000328
If the value is empty, turning to the step 3; if not, then,
Figure FDA00034006897400000329
judgment of
Figure FDA00034006897400000330
In (1)
Figure FDA00034006897400000331
Whether it is the terminal intersection vendIf yes, turning to step 2.2.2; otherwise, assigning m +1 to m, and turning to the step 2.3;
step 2.2.2: judging whether a terminal intersection v is foundendK short circuits of (2):
if K is larger than K, turning to step 3; otherwise, after k +1 is assigned to k, making m equal to 1, and turning to step 2.3;
step 2.3: forward updating;
judgment of
Figure FDA00034006897400000332
If the value is empty, assigning m +1 to m, and turning to the step 2.1; otherwise, from
Figure FDA00034006897400000333
1 st tag of
Figure FDA00034006897400000334
Get its intersection node
Figure FDA00034006897400000335
Will be provided with
Figure FDA00034006897400000336
Is assigned to
Figure FDA00034006897400000337
Then, the nodes of the current intersection are connected
Figure FDA00034006897400000338
All adjacent intersection nodes are added into the temporary adjacent intersection node set
Figure FDA00034006897400000339
Then turning to step 2.3.1;
step 2.3.1:
Figure FDA00034006897400000340
forward search of (2);
judgment of
Figure FDA0003400689740000041
If the current state is empty, turning to the step 2.3; otherwise, take out
Figure FDA0003400689740000042
The node of the first adjacent intersection is recorded as
Figure FDA0003400689740000043
Turning to step 2.3.2;
step 2.3.2: loop detection;
order to
Figure FDA0003400689740000044
Node of slave intersection
Figure FDA0003400689740000045
The kth tag of (1)
Figure FDA0003400689740000046
Get out the front-drive intersection node
Figure FDA0003400689740000047
And label number
Figure FDA0003400689740000048
Judgment of
Figure FDA0003400689740000049
I.e. whether a ring is present, if yes, go to step 2.3.1; otherwise it will be
Figure FDA00034006897400000410
Assigning a value to R, and continuously according to the nodes of the predecessor intersection
Figure FDA00034006897400000411
First in the set of labels
Figure FDA00034006897400000412
Information of the front-driving intersection of each label is sent to the starting intersection vstartRepeating the above operations for backtracking, and if no loop exists in the whole process, turning to the step 2.3.3;
step 2.3.3: updating the driving time and the driving risk of the feasible path;
from
Figure FDA00034006897400000413
Take out the running time
Figure FDA00034006897400000414
And driving risk
Figure FDA00034006897400000415
And calculating the node v of the intersection from the starting pointstartThrough
Figure FDA00034006897400000416
Arrive at
Figure FDA00034006897400000417
Temporary distance of
Figure FDA00034006897400000418
And intersection node v from the starting pointstartThrough
Figure FDA00034006897400000419
Arrive at
Figure FDA00034006897400000420
Temporary driving risk of
Figure FDA00034006897400000421
Figure FDA00034006897400000422
To represent
Figure FDA00034006897400000423
Time road section
Figure FDA00034006897400000424
The running time weight value;
Figure FDA00034006897400000425
to represent
Figure FDA00034006897400000426
Time road section
Figure FDA00034006897400000427
The driving risk weight value; order to
Figure FDA00034006897400000428
Will be provided with
Figure FDA00034006897400000429
Assigning to D; turning to step 2.3.1;
and step 3: output vstartAnd vendThe shortest driving time, driving risk and path among K pieces of the bridge are determined;
according to the node v of the terminal intersectionendSet of tags D (v)end) Middle | D (v)end) L tags, from end point vendThe kth tag (v)end,dk(vend),ek(vend),pk(vend),qk(vend) Start by passing p continuouslyk(vend) Backtracking the front-driving node to the intersection node v of the starting pointstartThereby obtaining a slave vstartTo vendTo obtain | D (v) from the k-th short pathend) I shortest path.
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