CN114550482A - Low-carbon target-based navigation method and parking lot navigation method - Google Patents

Low-carbon target-based navigation method and parking lot navigation method Download PDF

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CN114550482A
CN114550482A CN202210111131.1A CN202210111131A CN114550482A CN 114550482 A CN114550482 A CN 114550482A CN 202210111131 A CN202210111131 A CN 202210111131A CN 114550482 A CN114550482 A CN 114550482A
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road
path
carbon emission
parking lot
parking
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CN114550482B (en
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沈童
赵娟
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Engineering University of Chinese Peoples Armed Police Force
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    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces

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Abstract

The invention provides a navigation method based on a low-carbon target and a parking lot navigation method, which are carried out according to the following steps: step 1: determining a starting point o and an end point d; step 2: obtain the set K of all feasible paths between od in the whole city range, K ═ K1，k2，k3，…，kmDefining a road segments in all feasible paths among the od, and obtaining a road segment set a ═ 1, 2, 3 … a }; according to the road network carbon emission optimal principle, solving MinZ (X), calculating the carbon emission of each path in the set K to obtain an optimal path K 'when the carbon emission is minimum, and navigating the vehicle to the destination according to the optimal path K'; the invention takes the dynamic parking lot planning of the destination of the real-time road condition as the guided travel path optimization scheme, and realizes the optimization of the carbon dioxide emission of the vehicles in the whole road traffic network, rather than the optimal distance or time cost efficiency discussed by the traditional method. Thus, is a city baseThe infrastructure planning technology provides a new method for realizing the aim of slow blockage and emission reduction.

Description

Navigation method based on low-carbon target and parking lot navigation method
Technical Field
The invention belongs to the technical field of urban building planning, and particularly relates to a low-carbon target-based navigation method and a parking lot navigation method.
Background
The 'difficulty in parking' and 'normalization of congestion' become important reasons which bother urban residents to go out and cause increase of carbon emission of traffic. However, recent research shows that the parking lot as the starting and ending point of private car traveling can realize the blockage relieving and emission reduction by influencing the selection of the driving path. However, this theory presents a series of problems in practical applications. Firstly, driving path planning with a parking lot as a destination lacks dynamic linkage with road conditions and parking lot use information, and dynamic regulation of traffic flow distribution of a road network through parking navigation cannot be realized. Second, current navigation systems recommend driving paths to users only at time or distance optimality. However, the carbon emission amount of the vehicle running is not in a linear relation with the running time or distance, and the lowest carbon emission amount of the whole vehicle in the road network cannot be guaranteed by planning the individual driving path with the shortest time or distance. More importantly, linkage loss of dynamic parking information and driving planning causes that when a driver arrives at a destination, the driver often finds that no vacant parking space exists, and parking waiting, illegal parking, parking cruising and the like caused by the linkage loss further aggravate road traffic burden and congestion carbon emission. According to the record of the prior art, in a busy traffic area, vehicles spend 3.5 to 14 minutes for finding parking spaces, so that the traffic flow is increased by 20 to 45 percent, and the problem of serious congestion and high carbon emission is caused. On a road with a traffic capacity of 1200veh/h, an illegal in-road stop of 18 minutes would increase the carbon emissions of the vehicle by 26.5%. Therefore, how to rapidly and effectively provide a parking lot navigation scheme beneficial to urban carbon emission reduction for a driver is an important problem in the technical field of low-carbon urban infrastructure planning.
With the arrival of the 5G communication era, parking decision and traffic guidance systems based on internet big data become development trends. When a driver navigates using a mobile phone or an in-vehicle device, a vehicle becomes a terminal for generating, transmitting, receiving data and executing navigation results. The dynamic data stream generated by the running of the automobile realizes the bidirectional communication and calculation of the cloud end through the internet of vehicles technology, and the low-carbon path planning innovation in the form of parking navigation is established.
Disclosure of Invention
Aiming at the defects and shortcomings of the prior art, the invention aims to provide a low-carbon-target-based navigation method and a parking lot navigation method, and solves the problem that a parking site selection and route guidance method considering real-time road conditions and the linkage of the carbon emission of the whole road network is lacked in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a navigation method based on a low-carbon target is carried out according to the following steps:
step 1: determining a starting point o and an end point d;
step 2: obtaining all feasible paths k among od in the whole city range1，k2，k3，…，kmK, K ═ K1，k2，k3，…，kmDefining a road segments in all feasible paths among the od, and obtaining a road segment set a ═ 1, 2, 3 … a };
according to a road network carbon emission optimal principle, solving MinZ (X), calculating the carbon emission of each path in the set K to obtain the carbon emission of each path in the set K, obtaining an optimal path K ' when the carbon emission is minimum, (K, K ' belongs to K), and navigating the vehicle to a destination according to the optimal path K ';
the optimal carbon emission principle of the road network is calculated according to the following formula:
Figure BDA0003487760300000021
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
k represents the kth path in the set K;
a is a road section number;
Lais the length (m) of the section a;
Figure BDA0003487760300000031
is the free flow time(s) of segment a, i.e. the zero flow impedance of segment a;
cathe traffic capacity of the road section a (pcu/h);
alpha and beta are retardation coefficients, and 0.15 and 4 are respectively selected;
Figure BDA0003487760300000032
the flow (pcu) on the kth path between od points with the starting point o and the end point d;
Figure BDA0003487760300000033
is a link-path variable, being a 0 or 1 variable; if the road section a belongs to the kth path between the od points with the starting point o and the end point d, the path is divided into a first path and a second path
Figure BDA0003487760300000034
Otherwise
Figure BDA0003487760300000035
Figure BDA0003487760300000036
A design vehicle speed (m/s) for road segment a;
f is a sixth order function that follows a speed-to-carbon emission rate conversion.
A parking lot navigation method based on a low-carbon target comprises the following steps:
the method comprises the following steps: determining a starting point o and an end point d;
step two: acquiring real-time road condition information and parking facility use information at the current moment based on an open source map platform, wherein the real-time road condition information and the parking facility use information comprise the number of remaining parking spots, the average parking time and the parking spot turnover rate; estimating the arrival time S according to the real-time road condition information, judging whether a vacant parking space is allocated in the parking lot when the destination d is reached, if the vacant parking space is allocated in the parking lot when the destination d is reached, executing the step three, if the vacant parking space is not allocated in the parking lot when the destination d is reached or the parking lot is not allocated in the destination d, executing the step four,
for example: the predicted arrival time was 10:07 minutes earlier at 10:00 hours according to the real-time traffic information provided by the Baidu map platform (Table 2). According to the use information of the configured parking facility, the hourly use rate of the parking spaces is 0.8, the configured parking lot has 1000 parking spaces, the number of the remaining parking spaces is 1000 (1-0.8) ═ 200, the hourly parking space turnover rate is 1, and after 7 minutes are estimated, the vacant parking spaces of the configured parking lot are: 200-1 (7/60) ═ 200. And d is free to build a parking lot when the vehicle arrives. And (4) calculating according to the formula (1) to obtain an optimal path, and navigating the vehicle to d to configure the parking lot according to a path planning scheme.
Step three: planning a path according to the optimal carbon emission principle of a road network, and navigating a vehicle to a terminal point d according to a path planning scheme to construct a parking lot;
the road network carbon emission optimal principle is calculated according to the following formula:
Figure BDA0003487760300000041
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
a is a road section number;
Lais the length (m) of the section a;
Figure BDA0003487760300000042
is the free flow time(s) of segment a, i.e. the zero flow impedance of segment a;
cathe traffic capacity of the road section a (pcu/h);
alpha and beta are retardation coefficients, and 0.15 and 4 are respectively selected;
Figure BDA0003487760300000043
traffic (pcu) on the kth path between pairs of od points starting at point o and ending at point d;
Figure BDA0003487760300000044
is a road section-path variable, is a 0-1 variable;
if the road section a belongs to the kth path between the od points with the starting point o and the end point d, the path is divided into a first path and a second path
Figure BDA0003487760300000045
Otherwise
Figure BDA0003487760300000046
Figure BDA0003487760300000047
A design vehicle speed (m/s) for road segment a;
f is a sixth order function that follows a speed-to-carbon emission rate conversion.
If no parking space exists, executing the step 4;
when the destination is to configure a parking lot, the navigation path calculation process does not need to divide the road network structure again, so that the solving process does not influence the traveling paths of other vehicles, and the path with the minimum carbon emission of the vehicle represents the calculation result with the minimum carbon emission of the whole road network. Since the carbon emission is a sixth-order function of the vehicle speed, the vehicle travel path is determined by the road flow. When the carbon emission model is constructed, a relational expression of vehicle speed and flow (Da) is established, and the vehicle speed is used for expressing the flow to establish the carbon emission model for solving.
The derivation process of formula one is as follows:
resolving a target formula (I) of the second principle of the warp (system equalization).
Figure BDA0003487760300000051
In the formula, DaIs road section a flow (pcu/h);
tais a road block, can be understood as the actual time(s) of driving through the road section a;
ta(Da) The BPR function takes the flow of the road section a as an independent variable;
according to the second principle of the warp, the solution of the formula (I) is actually an optimization problem of the road flow Da objective function. The carbon emission and the vehicle speed are in a six-order function relationship, and the carbon emission model is established and solved by expressing the flow through the vehicle speed. Mainly comprises the following steps:
(1) determining the relation between the running time and the actual flow rate, see formula (II):
Figure BDA0003487760300000052
in the formula, caThe traffic capacity of the road section a (pcu/h);
Figure BDA0003487760300000053
the zero flow impedance, namely the time(s) for the vehicle to freely run through the road section in the road empty state, can be regarded as the quotient of the length of the road section and the designed vehicle speed;
alpha and beta are retardation coefficients, and 0.15 and 4 are respectively taken in the distribution program of the Federal road administration in the United states.
Figure BDA0003487760300000054
(2) Determining the relation between the running time and the average vehicle speed:
Figure BDA0003487760300000061
in the formula, LaIndicating the length (m) of the link a.
(3) Determining the relationship between the vehicle speed and the carbon emission rate:
Qa＝w(Va) (V)
in the formula, QaRepresenting the carbon emissions per kilometer for one vehicle on the road segment a,
obeying the sixth order function of the speed-carbon emission rate conversion, see formula VI:
Figure BDA0003487760300000062
(3) target formula of carbon emission
The objective function is that the carbon emission of the system is minimum, and according to a second balance principle: establishing a target formula by using a system optimization principle:
Figure BDA0003487760300000063
wherein the content of the first and second substances,
Figure BDA0003487760300000064
a sixth order function of the speed-carbon emission rate conversion is followed.
(4) Constructing a fitness function as:
Figure BDA0003487760300000071
wherein the content of the first and second substances,
Figure BDA0003487760300000072
Figure BDA0003487760300000073
wherein X represents the carbon emission of the road network; z represents the overall carbon emission level of the traffic network; daIs the traffic flow for road segment a; l isaIs the length of the road segment a; t is taIs at DaThe time it takes for the vehicle to pass through section a under flow conditions; caThe road traffic capacity of the section a;
Figure BDA0003487760300000074
the road zero-flow impedance is obtained by taking the free flow time of a road section a; alpha and beta are retardation coefficients, and 0.15 and 4 are respectively selected;
Figure BDA0003487760300000075
(iii) traffic on the kth path between pairs of od points starting at o and ending at d (pcu);
Figure BDA0003487760300000076
taking a road section-path variable quantity, and taking 1 when the road section a is on a path k, and otherwise, taking 0;
Figure BDA0003487760300000077
a design vehicle speed (m/s) for road segment a; and sending a request to the map platform at intervals of T, wherein the updating frequency of the calculation result is T + nt.
Thus, equation (VII) is derived as (1):
Figure BDA0003487760300000078
step IV: acquiring real-time road condition information and public parking lot use information at the current moment, and prejudging whether available public parking lots exist within 300 meters around the destination d;
if not, recommending the user to change the destination;
if so, acquiring m public parking lots with spare parking spaces in the whole city range at the current moment, and calculating the mass center (m)x，my) Dividing the Thiessen polygons for the feature points to obtain a set M of parking partitions {1, 2, … i … j … M } (i is more than or equal to 1 and less than or equal to M, j is more than or equal to 1 and less than or equal to M, and i is not equal to j), wherein i is (ix，iy) A Thiessen polygon partition with j as the starting point of (j)x，jy) A Thiessen polygon partition that is an endpoint; m '(m' is more than or equal to 2 and less than or equal to m) parking lots are selected from m public parking lots to form a public parking lot combination alternative scheme {1, 2 }; {1, 2}, {1, 3}, {2, 3}, {1, 2, 3 }; …, respectively; {1, 2}, {1, 3} … {1, m '}, {1, 2, 3} … {1, 2, m' } … {1, 2, 3 … m '-1, m' } in total
Figure BDA0003487760300000081
Dividing an urban road network by using a boundary of an alternative public parking lot combination scheme to obtain a road section set R ═ {1, 2, 3 … R };
according to the optimal principle of the carbon emission of the road network, solving MinZ (X), and calculating the carbon emission of the urban road network divided by the combined alternative schemes of all the public parking lots to obtain the optimal running path S' of the vehicle;
and (3) programming according to a formula (2) on a Matlab platform according to the step (4), realizing dynamic solution of MinZ (X) through a single parent genetic algorithm, calculating to obtain a combination scheme M ' of the public parking lot under the condition that the carbon emission of the whole road network is minimum, obtaining a Thiessen polygon i ' of a starting point, obtaining a Thiessen polygon j ' of a terminal point, and navigating the vehicle to d according to the optimal driving path S ' calculated according to the combination scheme M '.
The specific formula is as follows:
Figure BDA0003487760300000091
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
r is a road section number;
ξ is the coefficient of the gravity model;
Lris the length (m) of the section r;
f is the proportion of travel in a self-driving mode in the Thiessen polygonal subarea;
Drkcalculating the flow of the traffic volume between the cells distributed to the r road section according to the gravity model;
Dprnumber of public parking lots (capacity) on the r section road for the exit;
Derbuilding parking lot parking numbers (saturation) for buildings at the exit on the r section road;
Eerthe method comprises the steps of (1) configuring a parking lot peak hour parking space turnover rate for a building;
Uprthe peak hour parking space utilization rate of the public parking lot is achieved;
Uerbuilding a parking lot with a peak hour parking space utilization rate for a building;
Eprthe peak hour parking space turnover rate of the public parking lot is obtained;
Pirepresenting the traffic volume of traffic produced by the Thiessen polygon partition with i as a starting point;
Bjrepresenting the traffic attraction generated by the Thiessen polygon partition with j as the terminal point;
tr(Dr) Is a Thiessen polygon with i as the starting pointTraffic impedance of the Thiessen polygon partition with j as a starting point; taking the shortest time from i to j estimated according to the road condition at the current moment;
Dris the traffic flow on the road section r (pcu/h)
Figure BDA0003487760300000101
Is the free flow time(s) of the segment r, i.e. the zero flow impedance of the segment r;
crthe traffic capacity (pcu/h) of the road section r;
g is the number of ij pairs, and G is the number of ij pairs;
s is the number of feasible paths, and H is the number of paths passing through the r section in the feasible paths;
e is a natural logarithm base number;
μ is an expected value, being the shortest time most vehicles spend from i to j;
theta is a traffic conversion parameter, and theta is 3-3.5;
TSis the total travel time of any one of the feasible paths S;
alpha and beta are retardation coefficients, and alpha is 0.15 and beta is 4 respectively according to empirical values;
f is a sixth order function that follows a speed-to-carbon emission rate conversion.
Since the navigation of public parking lots is discussed at this time, the default built parking lot is saturated
The derivation process of equation 2 is as follows:
under the condition of systematic equilibrium according to the second warp principle, road network traffic flow should be distributed according to the average or total travel cost minimum, so that when navigating to a public parking lot and parking, a fitness function is constructed as follows:
Figure BDA0003487760300000111
wherein r is a road segment number subdivided according to the Thiessen polygon boundary; drIs the traffic flow on road segment r (pcu/h); l isrIs the length of the section r; qrIs a function of the average speed of carbon emissions per kilometer of a vehicle in a road section r:
Qr＝f(Vr) (XI)
average velocity VrCan be expressed as:
Figure BDA0003487760300000112
wherein L isrIs the length of the road r; t is trIs at DrTime taken for a vehicle to pass through a road under flow conditions, tr(Dr) Flow rate (D) for road section rr) Impedance function, also called travel time function, as an independent variable:
Figure BDA0003487760300000113
formula (X) can be further expressed as:
Figure BDA0003487760300000114
Figure BDA0003487760300000115
wherein: crThe unit is the road section traffic capacity of the r road section, and is (pcu/h), t0Is road zero-flow impedance, i.e. the time of the vehicle freely running through the road section in the road empty and static state, the unit is(s), and can be regarded as t0Is the quotient of the road length and the road design speed, in the above formula, alpha and beta are retardation coefficients,
Figure BDA0003487760300000116
representing the traffic flow on the road section r from i to j; drkIs the total flow from i to j; drIs the actual flow rate of the r road section, CrIs the road capacity.
Since the Thiessen polygonal partitions divide the road network into r sections, the actual flow of the r sections is equal to the flow distributed to the r sections minus the flow entering the public parking lots built on the r sections. Considering that the parking attraction generated by the residential area is solved preferentially at the building construction parking lot and the excess is solved by the public parking lot according to the principle of proximity, the formula (XIV) default road section construction parking lot is saturated,
thus:
Dr＝FDrk-DprUprEpr-DerUerEer (XIV)
in the formula (I), the compound is shown in the specification,
f is the proportion of travel in a self-driving mode in the Thiessen polygonal subarea;
Drthe actual traffic flow of the road section r;
Drkcalculating the flow of the traffic volume between the cells distributed to the r road section according to the gravity model;
Dprnumber of public parking lots (capacity) on the r section road for the exit;
Derbuilding parking lot parking numbers (saturation) for buildings at the exit on the r section road;
Eerthe method comprises the steps of (1) configuring a parking lot peak hour parking space turnover rate for a building;
Uprthe peak hour parking space utilization rate of a public parking lot is achieved;
Uerbuilding a parking lot with a peak hour parking space utilization rate for a building;
Eprthe peak hour parking space turnover rate of the public parking lot is obtained;
therefore, the problem of optimizing the carbon emission is converted into the problem of allocating the traffic flow between the traffic cells.
According to the gravity model, the method comprises the following steps:
Figure BDA0003487760300000121
in the formula, DijTraffic volume from cell i to cell j (i ≠ j), and is characterized by the mass center of the Thiessen polygon in which the cell i, j is locatedPoint;
xi is a gravity model parameter;
Pitraffic volume for cell i;
Bjthe amount of traffic attraction generated for cell j;
tr(Dr) Traffic impedance from the Thiessen polygon zoning taking i as a starting point to the Thiessen polygon zoning taking j as a starting point; take the shortest time from i to j.
DrIs the traffic flow on the road section r (pcu/h)
Pi＝∑ζhMh (XVI)
In the formula:
h is the land type of the land parcel;
ζhgenerating the trip rate of the h-type land;
Mhthe building area of the h-type land.
BjAnd PiThe algorithms of (a) are similar to each other,
Bj＝∑bhMh (XVII)
in the formula (I), the compound is shown in the specification,
bhthe traffic attraction coefficient of the h-type land is used;
Figure BDA0003487760300000131
g is the number of ij pairs;
g is an ij pair number;
wgris the g-th ij pair (D)ij)gThe proportion allocated to the r road section;
taking an od pair, and selecting the second from the i cell to the j cell on the assumption that S feasible paths exist from the i cell to the j cell and H paths pass through the Kth road section
Figure BDA0003487760300000133
The probability of a bar path is:
Figure BDA0003487760300000132
u＝tr(Dr) And taking the shortest time from i to j for the minimum impedance of the traffic from the i cell to the j cell.
In the formula (I), the compound is shown in the specification,
Figure BDA0003487760300000142
to select the first
Figure BDA0003487760300000143
Probability of a bar path;
Figure BDA0003487760300000144
total travel time for each path;
and theta is a traffic conversion parameter and is taken as 3.0-3.5.
The probability that a road with lower impedance is selected as the optimal path is higher in the feasible paths from i to j, and the distribution number is the largest. Thus, the probability of selecting the optimal path follows the positive tai distribution curve, where there are H paths through r segments.
The total proportion of the g-th ij to the r-th segment assignment is then:
Figure BDA0003487760300000141
the steps of determining the feasible paths S and H are as follows:
step1 determines the valid road segment: if the serial numbers of the road section nodes are aa and bb, calculating the shortest travel time T (aa) and T (bb) from the road section nodes to the starting point, wherein T (aa) > T (bb) aa to bb are effective road sections;
step2 is composed of valid road segments to form a valid path;
the number of times that the aa-bb road segment in the step3 distribution is searched to have the traffic flow distributed to the road segment is H.
Will Drk，wgr，PsSubstituting into formula (XIV) to obtain Min Z (X),
Figure BDA0003487760300000151
in the fourth step, if an emergency occurs:
if the traffic accidents occur along the optimal path k', the step2 is returned to for recalculation and judgment.
And if the destination d is provided with the spare parking spaces in the parking lot, performing path planning by using a formula (1) to obtain an optimal path until the navigation is finished to the destination.
If no available parking space exists in the configured parking lot, step 4 is executed according to the road condition of T + nt, and the vehicle running path s under the condition that the carbon emission of the whole road network is minimum is obtainedT+ntAnd d, navigating.
If the public parking lot where the terminal point d is located is saturated (the utilization rate is 100%), judging whether the current driving position of the vehicle is within the range of d300 meters of the destination;
if not, the Thiessen polygonal meshes are re-divided according to the mass center of the public parking lot with vacant parking spaces at T + nt as a characteristic point. Recalculating the position of the public parking lot and the optimal path s 'according to formula (2)'T+nt；
If yes, navigating to a public parking lot where the Thiessen polygonal subarea of the road section where the vehicle is located at T + nt;
and ending until the navigation is carried out to the destination.
Because the formula (2) relates to dynamic optimization of road traffic guidance and has a plurality of variable parameters, a genetic algorithm is adopted for solving to obtain the mass center coordinates (xd ', yd ') of the public parking lot position d ' and the navigation path within the range of d300 meters of the end point under the condition that the whole carbon emission of the road network is optimal.
During navigation, a request is sent to the map platform at intervals of T, and the updating frequency of the formula calculation result is T + nt. Can send the instruction to the map platform at interval time T, reacquire the real-time road conditions information, the parking facility service information at T + nt moment, then equation 1 and equation 2 can be:
Figure BDA0003487760300000161
Figure BDA0003487760300000171
compared with the prior art, the invention has the following technical effects:
(I) the invention relates to a travel path optimization scheme which takes dynamic parking lot (position) planning of destinations of real-time road conditions as guidance, and realizes the optimization of the carbon dioxide emission of vehicles in the whole road traffic network, rather than the optimization of distance or time cost efficiency discussed by the traditional method. Therefore, a new method is provided for realizing the aim of slow blockage and emission reduction of the urban infrastructure planning technology.
(II) the dynamic public parking lot partitioning method based on the Thiessen polygon is developed and practically realized in an open source map navigation system. By utilizing the geometric characteristics of the Thiessen polygonal partition and the feedback of the open source data platform and the dynamic traffic network system, the dynamic optimization is realized, the handling of urban road traffic emergencies is realized, the parking lot is quickly selected, the calculation steps are simplified, the road traffic induction efficiency is improved, and the beneficial method of carbon emission reduction is realized. This zoning approach provides an accurate approach to traffic allocation and parking demand estimation that is not quadrilateral or radial zoning but rather focuses more on the time-varying characteristics of the parking spaces and their impact on congested carbon emissions resulting from dynamic traffic routing. More importantly, the optimization method for parking site selection and partition by applying the Thiessen polygon nearest neighbor principle can realize the common target of the shortest distance by combining the functions. Aiming at the application of complex path optimization research, real-time path planning and navigation systems, the method is of great importance for improving the calculation and feedback efficiency of the real-time navigation and path planning systems.
(III) the invention uses a Genetic Algorithm (GA) to find the optimal set of public parking lots. The GA has great application value in the aspect of accelerating the global optimization of random search. It is particularly suitable for simulating complex and large volume problems involving practical solutions. The most important point is that the method provides a practical and operable method for regulating and controlling the static traffic and the dynamic traffic, and proves that the static traffic has strong influence capability on the dynamic traffic. Especially in terms of congestion control, such dynamic partitioning methods can be explored in real time for a wider range. With real-time traffic information provided by roadside sensors of the ITS, dynamic zone-based parking guidance will likely improve the performance of the traffic system with the support of the smart city infrastructure. By applying the dynamic partitioning and modeling method to the intelligent transportation system, the parking guidance efficiency and the dynamic optimization of traffic distribution of future intelligent travel can be ensured.
Drawings
FIG. 1 is a schematic diagram of an exemplary base location;
FIG. 2 is a current land situation of a road of a construction area arithmetic base;
FIG. 3 is a schematic view of a base building at a current state;
FIG. 4 is a schematic view of a base parking lot;
FIG. 5 is a diagram of a Thiessen polygon based public parking lot plan partition;
FIG. 6 is an optimization process of the genetic algorithm to dynamically solve the formula (2) to obtain the total carbon emission;
FIG. 6.1 is a Matlab model calculation work interface;
FIG. 6.2 is an iteratively generated 5-set road segment traffic matrix;
FIG. 6.3 is the distribution flow of 48 road sections in each group of matrixes;
FIG. 7 is a best scheme of a public parking lot under the condition of minimum carbon emission of the whole road network;
FIG. 8 is a flow chart of a genetic algorithm;
fig. 9 shows real-time road condition information obtained by mining the open source map data.
FIG. 10 is a schematic diagram of a path of a parking lot configured to navigate to a destination
FIG. 11 is a schematic diagram of a route of a public parking lot where navigation to a destination is located
FIG. 12 shows a parking path planning process for obtaining optimal carbon emission of road network
FIG. 13 parking site selection and path planning method based on dynamic open source map information
The present invention will be explained in further detail with reference to examples.
Detailed Description
The following embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention are within the protection scope of the present invention.
The general idea of the invention is as follows:
navigating to a configured parking lot:
and (4) whether a configured parking lot and an available parking space exist at the destination d of the estimated arrival time S provided by the real-time road condition information at the time T and the use information of the parking facilities from the open source map platform.
If the parking lot and the available parking space are configured, the vehicle advances from the starting point o to the end point d along the road section between the road network and the crossroads, when passing through each crossroad, the feasible road section in the advancing direction is judged, the feasible road section forms a feasible path k from the head to the tail in the advancing direction of the road section o-d, and the driving path k 'with the minimum carbon emission of all the feasible paths is calculated according to the formula 1, and the vehicle navigates to the destination along the optimal path k' to configure the parking lot.
Navigating to a public parking lot:
and if no parking lot is configured, judging whether a public parking lot exists within the range of the terminal d300 meters.
If there are public parking lots and available parking lots, all m public parking lots with spare parking lots in the whole city range at the time T are obtained, and m '(m is more than or equal to 2 and less than or equal to m') public parking lots are selected from m public parking lots to form
Figure BDA0003487760300000201
The alternative scheme of the combination of the public parking lots is characterized in that a Thiessen polygon partition is constructed by taking the mass center of each public parking lot in the alternative scheme as a characteristic point, the traffic volume and the traffic attraction volume generated by covering buildings in the partition are calculated, a mass center connecting rod is constructed, the mass center of the Thiessen polygon is connected to the nearest road intersection, and the traffic volume generated by building in the partition is distributed to a road network through the mass center connecting rod. According to the formula 2, the formula is dynamically optimized and solved by using a genetic algorithm, and the whole carbon emission of the road network in the alternative scheme is calculatedAnd obtaining a Thiessen polygon i ' to which the starting point o belongs, a Thiessen polygon j ' to which the end point d belongs and a distribution result of the vehicle on each road section under the optimal parking lot combination scheme by the minimum public parking lot combination scheme, obtaining a driving path from i ' to j ' according to the distribution result of the vehicle on each road section, and walking to the destination d after navigating to the public parking lot j ' to which the d belongs by the driving path.
Generally, there are several specific methods for solving the genetic algorithm of the multi-objective optimization problem, such as a weight coefficient modification method, a parallel selection method, a layout selection method, a shared function method, and a hybrid method. To reduce programming complexity and to meet the simultaneous reorganization of traffic and parking requirements at the same time, the present study employs a parallel selection method of single-factor inheritance. The specific programming process is as follows:
1) encoding operations
In this study, genes (alternative public parking lots) were initially encoded using a real number encoding method, with the encoding sequences 1, 2, 3, 4, and m.
2) Generating an initial population
Each chromosome (a possible combination scheme of public parking lots) is composed of genes. Randomly selecting a set of alternative public parking lots constitutes an initial gene. For example, if there are 10 alternative public parking lots numbered 1, 2, 3 … 10 within 300 meters of the terminal D, the chromosome may be defined as {1, 3, 7} or {1, 2, 9, 10} etc.
3) Defining a fitness function
The fitness function (equation 3) is used to calculate the solution result of the genetic algorithm.
4) Population selection
The purpose of population (collection of possible combination schemes for public parks) selection is to select a better individual in the initial group of parks as the male parent for the next generation of genetic crosses. The criteria for determining whether an individual is superior is the result of the fitness function of the set of individual solutions. The better the fitness value of an individual, the greater the likelihood of being selected by the next generation.
5) Recombination
Solutions in the mating pool will constantly produce a combination that reduces the overall carbon footprint of the road network due to the effects of replication between generations. The adaptability of the best individual in the group will not be reduced, since the replication, inheritance process does not produce new alternative parking lots. The population used in the gene recombination process was randomly selected from the mating pool. By selecting the parking lot group with the better personality, the last generation will contain the best genetic genes in the paternal line.
6) Variation of
For example, a current value of 0 for a gene indicates that the parking lot will not be selected in the genetic iteration. If a mutation occurs, the value will change to 1, which means that an opportunity can be selected for the next iteration. For each common parking lot in the chromosome, the mutation will be performed with the same probability.
In summary, as shown in fig. 8, the urban overall road traffic network CO is realized by using a genetic algorithm2The method comprises the following specific steps of:
step1, initialization. The population size was set to 8, the chromosome length to 7, the number of iterations to 5, the gene recombination mode (transposition, filling, translocation, inversion) and the mutation probability to 1/7.
And 2, applying the binary integers to the codes of the coordinates of each public parking lot, and randomly generating an initial population.
And 3, calculating the fitness function of each generation by using an iterative method. The method is divided by three parallel parts, namely partitions based on Thiessen polygons, traffic distribution and emission calculations. All individuals in the parents are classified, better individuals are selected, inferior individuals are eliminated, and a new combination of public parking lot site selection schemes is generated.
And 4, performing crossing between individuals with random connection according to the crossing probability. And (4) carrying out variation on the combination mode of the single parking lot according to the probability of mutation.
And 5, confirming whether the maximum iteration number 5 is reached and the calculation result does not generate a result with lower carbon emission. If so, outputting the best solution of the public parking lot location. Otherwise, please return to step3 to perform the next round of iterative computation.
Example 1:
a parking lot navigation method of a low-carbon target is carried out according to the following steps:
taking the New City plaza of Xian city as an example, the parking navigation planning scheme with the optimal carbon emission of the whole road network of the New City plaza is calculated. And the efficiency of the dynamic traffic system is optimized.
Inputting the origin east street (o) and the destination Xincheng square to remember seafood (d). According to the current situation map of the city of xi' an, drawing a map 2 according to the environment information of the region. Based on an open source map platform, acquiring early 8: and (5) estimating the time of arriving at the destination according to the real-time road condition information at the time 00 and the use information of the parking facility.
During early peak hours, most cars drive into public parking spaces, while relatively few, negligible, drive-out vehicles. Thus, without loss of generality, we use the incoming traffic flow directly to derive the net road segment flow. According to the using characteristics of the parking spaces in the areas where the parking lots are located, as shown in table 1, the remaining parking number, the average parking time and the parking space turnover rate of the destination parking lot are calculated, and the following results are obtained: the usage rate of the parking spaces is 0.5, the turnover rate is 3, the usage rate of the configured parking spaces is 1, and the turnover rate is 1.
TABLE 1 parking space usage
Figure BDA0003487760300000231
The estimated arrival time is 10:07 minutes according to the real-time road condition information (table 3) provided by the Baidu map platform and the use information of the parking facility at the time of 10:00 early. And d, when the vehicle arrives, spare parking lots are allocated. Planning the path according to the formula (1) to obtain 2 feasible paths (k) among od in the whole city range1＝11，12，6，7； k 211, 13, 14, 15, 7), a total of 7 links (link number: 11. 13, 14, 12, 6, 16, 7), link information is shown in table 2. Wherein, the information from Table 1 can be obtained
Figure BDA0003487760300000232
The same can be obtained
Figure BDA0003487760300000233
According to the optimal principle of road network carbon emission, calculating the carbon emission of the paths K1 and K2 in the set K according to the formula 1 to obtain the carbon emission of each path in the set K, and solving MinZ (X) to obtain Z (K)1)＝3.0388*107，Z(k2)＝7.2437*107Accordingly, MinZ (X) Z (k)2) Obtaining the optimal path k when the carbon emission is minimum1The vehicle is arranged according to the optimal path k1The (11, 12, 6, 7) scheme navigates to d to build a parking lot, as in fig. 10.
TABLE 2 road segment information
Figure BDA0003487760300000234
TABLE 3 road speed data samples from open source map analysis
Figure BDA0003487760300000241
And a night 18:00 terminal d is provided with no available parking space, and the parking needs to be solved by a public parking lot within the range of 300 meters of the destination. And (3) calculating a public parking lot combination scheme under the optimal road network carbon emission condition according to a formula (2), realizing the optimal distribution of flow of each road section in the research range partition, and obtaining the optimal path planning among the ods under the optimal condition.
Fig. 3 shows the building information in this range, with the lower left corner end point of the road network defined as the origin of coordinates. And obtaining the building information by taking the building material center point as a characteristic point. Based on the origin of coordinates, each building centroid coordinate is labeled, with the parameters shown in table 4.
TABLE 4 information of buildings and their affiliated parking spaces
Figure BDA0003487760300000242
Figure BDA0003487760300000251
TABLE 5 road information
Figure BDA0003487760300000252
Note that: in the "road type" column of table 3, a, L, and C represent the main road, the sub-road, and the city branch, respectively; here, all the roads listed here are two-way traffic roads. )
Fig. 4 shows the area configuration and public parking lot, and fig. 5 shows 7 candidate public parking lots in the area. By selecting the optimal combination mode of 7 parking lots, the traffic flow distribution mode of the road network is influenced, so that the optimal traffic performance, namely the optimization target of minimum carbon emission in the road network is realized. And acquiring the optimal path between the od in the optimal combination mode. The specific path planning calculation process is as follows:
in this case study, 7 independent Thiessen polygons were created as parking analysis zones based on the available common parking lot centroid positions. The information of each polygon is represented by its centroid. Table 6 lists the road information shown in fig. 5.
TABLE 6 road endpoint information
Figure BDA0003487760300000261
Table 7 provides basic information of the 7 public parking lots and their thiessen polygon analysis areas.
TABLE 7 public parking lot and road parameters
Figure BDA0003487760300000262
As shown in fig. 4, links are created from the thieson polygon analysis area centroid to the nearest road node, and all traffic flows generated by the building within the thieson polygon analysis area are distributed to the road network through the links. According to the generated connecting rod, the length, the centroid coordinate of the Thiessen polygon and the coordinates of only the road nodes are measured, and the parameters are shown in Table 7.
TABLE 8 barycentric link parameters
Figure BDA0003487760300000271
In this example, 7 polygonal centroids are considered as characteristic points of traffic distribution and are connected to the road network by different links.
And programming the scheme based on the MATLAB platform, realizing dynamic division and flow distribution of the Thiessen polygonal analysis area which can be established by the public parking lot in the research range, and realizing optimal public parking lot site selection through a parthenogenetic algorithm (PGA).
Matlab simulation results
Fig. 6.1Matlab model calculation work interface, source of the graph: programming screen shot
Matlab calculates the scheme to obtain a result, the result shows that 8 populations are inherited for 5 generations to obtain the optimal solution (minimum) for the whole carbon emission of the road network, and the optimal chromosome public parking lot combination number finally determined by GA is as follows: 4,7,1,5,6. In the calculation process, the solving process of the road flow is as follows:
fig. 6.2 is the matrix result of Matlab solving the model modeling. In this example, since the optimal solution appears in the calculation result after 5 generations of single inheritance, the calculation result of the total 5 groups of road section flows is shown in fig. 6.2 (a). Wherein, the optimal flow distribution of each generation of the inherited road sections is as shown in figure 6.2 (b):
in the present embodiment, there are 24 roads in total, which are bidirectional, so that 48 flow data are generated in each generation of inheritance, and the matrix structure is shown in fig. 6.3. And calculating the result according to the road section flow of the third generation genetic first population:
fig. 6.2 iteratively generated 5 sets of road segment traffic matrices, picture source: matlab Programming Screen shot
Fig. 6.3 shows that the flow rate is distributed to 48 road segments in each group of matrix, and the picture sources are as follows: matlab Programming Screen shot
Calculating the carbon emission of the whole road network according to the flow of each road section, and outputting emission historical data as shown in a table 9 according to the carbon emission calculation result after the five generations of inheritance;
table 9 optimal solution collects the flow carbon emission values of each section in units: 1.0e +0.4 x (g)
Figure BDA0003487760300000281
Fig. 7 shows the optimization effect of the overall carbon emission of the road network during the PGA iteration protocol. In this simulation, the carbon emissions decreased slightly after the first few rounds of optimization and reached the optimum after the fourth iteration, after which there was no further improvement. As shown in fig. 6.1, 4, 5, 6, 7 are the best parking lot combination solutions among the total 1-7 available parking spaces. The traffic flow of the road network is divided by the Thiessen polygonal parking sections divided by the 5 parking lots, and the driving path of the vehicle is calculated according to the formula (2), as shown in FIG. 11.
Carbon emission accounting rationality interpretation
1. Checking reasonability of lowest carbon emission of road network
The total length of a road network in the research area is 4950 meters, the road is bidirectional, and the length of the road is 9900 meters. According to the matlab simulation result, the road network traffic flow is counted: 414742 vehicles. Measuring and calculating CO according to different vehicle types2Emissions, Table 10, estimated as the vehicle carbon emission rate of about 200g/km, the road network emissions were about: 8.29X 107(g)。
TABLE 10 carbon emissions for different vehicle operating conditions
Figure BDA0003487760300000282
Figure BDA0003487760300000291
The calculation result is 3.5-3.7 multiplied by 107(g) In that respect Although the simulation results are different from the theoretical estimates,but the orders of magnitude are consistent, and the errors are in a reasonable range. Through analysis, the possible causes of the error of the calculated result are as follows: firstly, the congestion conditions of the same road section at different moments are different in industry, but numerical values are in a reasonable range, the carbon emission calculation index in a reference document is an empirical value of the working condition of the automobile in China, and the relation between the speed and the carbon emission is fitted according to the carbon emission of the U.S. traffic bureau and experimental results. Secondly, since the carbon emission is an accumulated value, the number of vehicles is large, and small differences of each vehicle are accumulated to generate large differences of the integral value.
2. Calculation of carbon emissions for genetic algorithm generation process schemes
The model generates a plurality of groups of parking lot combination schemes in the process of selecting the optimal solution by using a genetic algorithm, and the carbon emission of the road network is calculated according to the process scheme selected by the GA. And the scheme obtained by judging GA solution has the lowest carbon emission.
In conclusion, the calculation result verifies the accuracy of the proposed carbon emission optimization model; meanwhile, the feasibility of the parking site selection optimization model for the control of the carbon emission jam and the rationality of the model solution based on the genetic algorithm are verified.
The method is used as an important component of a low-carbon urban Intelligent Traffic System (ITS), and the beneficial effect of reducing the carbon emission of congestion is realized by a route selection method based on parking decision. The dynamic parking guidance is realized by combining the parking navigation with the road condition optimization through the dynamic parking subareas, the dynamic parking guidance is facilitated, the dynamic and static traffic efficiency of individual drivers in a complex urban environment is ensured, and the optimal and efficient overall path planning scheme with the lowest carbon and optimal travelers is provided for an urban road network.
All the public parking lots are the public parking lots which can be parked in real time, the availability of the parking lots at the moment is judged according to the driving conditions of vehicles, then the genetic algorithm is carried out for solving, the combination of reasonable organization schemes is solved, the parking partitions are divided, and the parking guidance is carried out. And according to a genetic algorithm, deleting the corresponding Thiessen polygonal centroids of unreasonable parking lots and parking lots without available parking spaces from the candidate s parking lots, and dividing and calculating the road traffic flow according to Thiessen polygonal meshes formed by all the available parking lots finally to realize the planning navigation of the optimized carbon emission scheme path.
The invention can fully plan the regulation and control function of public parking lot resources on road traffic operation conditions according to static traffic facilities, and reduce the carbon emission effect caused by urban road traffic jam. Except real-time linkage through parking stall information and road traffic condition planning, go into the dynamic guidance of urgent road conditions change more, there is the recommendation that the module got into the parking area to the vehicle with the method to road network c emission optimization in cooperation route simultaneously, not only makes the driver go out more convenient, high-efficient, more produces beneficial effect to the reduction of city carbon emission. The method is a revolution of realizing the dynamic traffic control of static traffic, coping with road emergency and a linkage technology.

Claims (3)

1. A navigation method based on a low-carbon target is carried out according to the following steps:
step 1: determining a starting point o and an end point d;
it is characterized in that the preparation method is characterized in that,
step 2: obtaining all feasible paths k among od in the whole city range1，k2，k3，…，kmK, K ═ K1，k2，k3，…，kmDefining a road segments in all feasible paths among the od, and obtaining a road segment set a ═ 1, 2, 3 … a };
according to the optimal principle of carbon emission of a road network, solving MinZ (X), calculating the carbon emission of each path in the set K to obtain the carbon emission of each path in the set K, obtaining the optimal path K 'when the carbon emission is minimum, and navigating the vehicle to a destination according to the optimal path K';
the optimal carbon emission principle of the road network is calculated according to the following formula:
Figure FDA0003487760290000011
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
k represents the kth path in the set K;
a is a road section number;
Lais the length (m) of the section a;
Figure FDA0003487760290000013
free flow time(s) for segment a;
cathe traffic capacity of the road section a (pcu/h);
alpha and beta are retardation coefficients, and 0.15 and 4 are respectively selected;
Figure FDA0003487760290000012
the flow (pcu) on the kth path between od points with the starting point o and the end point d;
Figure FDA0003487760290000021
is a link-path variable, being a 0 or 1 variable; if the road section a belongs to the kth path between the od points with the starting point o and the end point d, the path is divided into a first path and a second path
Figure FDA0003487760290000022
Otherwise
Figure FDA0003487760290000023
f is a sixth order function subject to a speed-carbon emission rate conversion;
Figure FDA0003487760290000024
2. a parking lot navigation method based on a low-carbon target is carried out according to the following steps:
the method comprises the following steps: determining a starting point o and an end point d;
step two: acquiring real-time road condition information and parking facility use information at the current time T based on an open source map platform, estimating arrival time S according to the real-time road condition information, judging whether a vacant parking space is allocated in a parking lot when a destination d is reached, if so, allocating the vacant parking space in the parking lot, executing the step three, and if the destination d is reached, allocating the vacant parking space in the parking lot or no parking lot in the destination d, and executing the step four;
it is characterized in that the preparation method is characterized in that,
step three: obtain all feasible paths b among od in the whole city1，b2，b3，…，bmSet of (B), B ═ B1，b2，b3，…，bmDefining P road segments in all feasible paths among the od, and obtaining a road segment set P ═ 1, 2, 3 … P };
according to the optimal principle of carbon emission of a road network, solving MinZ (X), calculating the carbon emission of each path in the set K to obtain the carbon emission of each path in the set K, obtaining the optimal path P 'when the carbon emission is minimum, and navigating the vehicle to a destination according to the optimal path P';
the optimal carbon emission principle of the road network is calculated according to the following formula:
Figure FDA0003487760290000031
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
b represents the B-th path in the set B;
p is a road section number;
Lpis the length (m) of the section p;
Figure FDA0003487760290000037
free flow time(s) for segment p;
cpthe traffic capacity (pcu/h) of the road section p;
alpha and beta are retardation coefficients, and 0.15 and 4 are respectively selected;
Figure FDA0003487760290000032
flow (pcu) on the b-th path between od points with start point o and end point d;
Figure FDA0003487760290000033
is a link-path variable, being a 0 or 1 variable; if the road segment p belongs to the b-th path between the od points with the starting point o and the end point d, the method comprises the steps of
Figure FDA0003487760290000034
Otherwise
Figure FDA0003487760290000035
f is a sixth order function subject to a speed-carbon emission rate conversion;
Figure FDA0003487760290000036
step IV: acquiring real-time road condition information and public parking lot use information at the current time T, and prejudging whether available public parking lots exist within 300 meters around the destination d;
if not, recommending the user to change the destination;
if so, acquiring m public parking lots with spare parking spaces in the whole city range at the current moment, and calculating the mass center (m)x，my) Dividing the Thiessen polygons for the feature points to obtain a set M of parking partitions {1, 2, … i … j … M } (i is more than or equal to 1 and less than or equal to M, j is more than or equal to 1 and less than or equal to M, and i is not equal to j), wherein i is (ix，iy) Thiessen polygon partition as starting point, j is represented by (j)x，jy) A Thiessen polygon partition that is an endpoint;
m '(m' is more than or equal to 2 and less than or equal to m) parking lots are selected from m public parking lots to form
Figure FDA0003487760290000041
Figure FDA0003487760290000042
Dividing the urban road network by the boundary of the alternative of the public parking lot combination to obtain a road section set R ═ {1, 2, 3 … R } and all feasible paths s between the od1，s2，s3，…，smS, S ═ S1，s2，s3，…，sm}；
According to the optimal principle of the carbon emission of the road network, solving MinZ (X), and calculating the carbon emission of the urban road network divided by the combined alternative schemes of all the public parking lots to obtain the optimal running path S' of the vehicle;
the vehicle navigates to the destination according to the optimal path S';
the specific formula is as follows:
Figure FDA0003487760290000043
x is the carbon emission of the road network;
z is the whole carbon emission level of the traffic network;
r is a road section number;
ξ is the coefficient of the gravity model;
Lris the length (m) of the section r;
f is the proportion of travel in a self-driving mode in the Thiessen polygonal subarea; (ii) a
PiRepresenting the traffic volume of traffic produced by the Thiessen polygon partition with i as a starting point;
Bjrepresenting the traffic attraction generated by the Thiessen polygon partition with j as the terminal point;
tr(Dr) Traffic impedance from the Thiessen polygon zoning taking i as a starting point to the Thiessen polygon zoning taking j as a starting point;
Dris the traffic flow on the road section r (pcu/h)
Figure FDA0003487760290000051
Free flow time(s) for a segment r;
crthe traffic capacity (pcu/h) of the road section r;
g is the number of ij pairs, and G is the number of ij pairs;
s is the number of feasible paths; h is the number of the paths passing through the r section in the feasible paths;
μ is an expected value;
e is a natural logarithm base number;
theta is a traffic conversion parameter, and theta is 3-3.5;
TSis the total travel time of any one of the feasible paths S;
alpha and beta are retardation coefficients, and respectively take alpha as 0.15 and beta as 4;
Dprthe number of public parking lot parking spaces with the exit on the r section road;
Derbuilding parking lot parking space numbers for buildings at the exit on the r section road;
Eprthe peak hour parking space turnover rate of the public parking lot is obtained;
Eerthe method comprises the steps of (1) configuring a high-peak hour parking space turnover rate of a parking lot for a building;
Uprthe peak hour parking space utilization rate of the public parking lot is achieved;
Uerthe method comprises the steps of (1) configuring a high peak hour parking space utilization rate of a parking lot for a building;
f is a sixth order function subject to a speed-carbon emission rate conversion;
Figure FDA0003487760290000061
3. the method as claimed in claim 2, wherein if an emergency occurs during navigation, the method comprises:
if the traffic accident occurs along the optimal path k', returning to the step II to recalculate and judge;
if the destination d is provided with vacant parking spaces in the parking lot, planning a path according to the formula (1), and updating to obtain an optimal path until the navigation is finished to the destination d to configure the parking lot;
if no available parking space exists in the configured parking lot, step 4(n is 1, 2, 3, 4 …) is executed according to the road condition at the time of T + nt, and the vehicle driving path s 'under the condition that the carbon emission of the whole road network is minimum is obtained'T+ntNavigating to d; t is the time interval for obtaining the update of the open source map platform information;
t is the current time, and T is the time interval for acquiring the real-time road condition information and the parking facility use information of the current time T;
if the public parking lot where the terminal point d is located is saturated, judging whether the current driving position of the vehicle is within the range of d300 meters of the destination;
if not, re-dividing the Thiessen polygonal mesh according to the characteristic point of the mass center of the public parking lot with vacant parking spaces at the time of T + nt, and re-calculating the position of the public parking lot and the optimal path s 'according to a formula (2)'T+nt；
If yes, navigating to a public parking lot where the Thiessen polygonal subarea of the road section where the vehicle is located at T + nt;
and ending until the navigation is carried out to the destination.
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