CN115273457A - Global optimal path planning method considering dynamic change of travel time of urban road network - Google Patents
Global optimal path planning method considering dynamic change of travel time of urban road network Download PDFInfo
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Abstract
The invention discloses a global optimal path planning method considering dynamic changes of urban road network travel time, which comprises the following steps of 1) obtaining traffic flow data of each road section of an urban road network; 2) Obtaining dynamic travel time of the road section in any time period through the traffic flow data of the road section and based on the mapping relation, and constructing a change database; 3) On the premise of knowing a dynamic change rule of the travel time of a future road section of the road network in advance, the traditional Dijkstra algorithm is improved, the accumulated travel time of each road section of each path which is possibly driven to the destination is dynamically calculated, and then the shortest path of the overall travel time is planned. According to the method, the traffic flow data are obtained, and then the traffic flow data are mapped into the travel time according to the road section; and (3) dynamically calculating the travel time of each road section of each route which is possibly traveled to the destination from the starting place of the vehicle by utilizing the improved Dijkstra algorithm to obtain the dynamic shortest travel time, so that the whole travel route from the starting place to the destination is optimized.
Description
Technical Field
The invention relates to the technical field of driving path planning of an intelligent driving automobile, in particular to a global optimal path planning method considering dynamic changes of urban road network travel time.
Background
Intelligence is one of the representatives of the new four types of automobiles, and the technology development is more rapid nowadays, wherein the automatic driving technology of automobiles is the main position of intelligent development. The automatic driving technology can be divided into six parts of perception, positioning, prediction, decision, planning and control from the technical flow direction, wherein the planning layer plays a key role in starting and stopping. The planning layer can be further divided into global planning and local planning according to the size of the spatial scale. The global planning generally refers to global path planning, and mainly develops research from the aspects of shortest travel time or shortest driving mileage and the like, a path from a starting point to a terminal point is output, and the space-time span of the generated path is large. The global path planning determines the movement route of the vehicle in future space and time from a macroscopic level, plays a key role in the local path planning of the rear end, and the mutual support of the two technologies can greatly improve the intelligent level of the vehicle and accelerate the commercial landing process.
The current global Path Planning can be divided into Static Path Planning (SPP) and Rolling Path Planning (RPP): (1) The SPP regards the road network weight as a fixed value, the common method is shortest path planning, various path optimization algorithms including Dijkstra algorithm, A-x algorithm, ant colony algorithm and the like are mainly researched, but the SPP ignores updating of traffic information, so that the planned path cannot adapt to change of dynamic traffic. (2) The RPP regards the road network weight as dynamically changing, and plans the path continuously by rolling when the weight of the road network changes, which is usually the shortest path planning of travel time. The road planning mainly researches the prediction of future traffic information and the fusion processing of the information, and continuously calls a road planning algorithm of the SPP to obtain an actual driving road; generally, the result of RPP is that a multi-stage local optimal path is spliced, and the path obtained by such splicing cannot guarantee global optimization.
In the prior art, as disclosed in CN111982142A, an intelligent vehicle global path planning method based on an improved a-star algorithm, application number 202010763228.1, the planning method includes the following steps: the parking lot in an outdoor specific area is divided into grids, each grid center is regarded as a control point, all the control points are placed into an L set and numbered, an initial weight matrix OM of each control point is initialized according to a space state, a starting point is automatically determined, a target terminal point is manually selected, and obstacles are dynamically identified; then planning a globally optimal path according to an intelligent vehicle global path planning method for improving the A star algorithm; and displaying the travelable path on a user interface, and carrying out safety prompts such as intersection information, deceleration, steering and the like. The invention solves the problem that the intelligent vehicle path planning can not plan the shortest path efficiently, completes the comprehensive driving target of the shortest path planning and the global path obstacle avoidance of the outdoor parking lot, and reminds the safety of intersection information, deceleration, steering and the like, and the invention is visual and clear, has perfect functions and can be suitable for the path planning of most scenes; the technical scheme disclosed by the application mainly improves the algorithm, but changes of environment information are ignored.
A network appointment scheduling method based on the improved Dijkstra algorithm as disclosed in CN112562309A, application number 202110222684.x; the anti-latency share firstly acquires road network data, POI data, taxi journey GPS data and real-time road condition data in a designated area; secondly, selecting a classic Dijkstra algorithm, adjusting the weight on the basis of the Dijkstra algorithm, and comprehensively considering road network distance influence factors and time influence factors based on real-time road conditions to form a new passenger demand and driver matching strategy; and finally, generating random passenger and driver distribution according to the POI data and the comprehensive data of the taxi journey GPS, setting a dispatching range for each randomly generated passenger, and searching for the driver and the route in the dispatching range by improving the Dijkstra algorithm to obtain an optimized route taking the benefits of the passenger, the driver and the network platform into consideration. The invention effectively improves the utilization rate of drivers, the order receiving rate of platforms and reduces the waiting time of passengers.
Disclosure of Invention
The invention aims to provide a global optimal path planning method considering dynamic change of urban road network travel time, aiming at solving the problems of poor path and long time of the intelligent vehicle in the whole travel process from an origin to a destination of the intelligent vehicle.
The technical scheme of the invention is realized as follows:
the global optimal path planning method considering the dynamic change of the travel time of the urban road network is characterized by comprising the following steps of:
1) Acquiring traffic flow data of each road section of the urban road network on different dates and different time periods;
2) The method comprises the steps that the travel time of a specific road section in any time period on a certain day is mapped into the travel time through the traffic flow data of the road section, and the dynamic travel time of the road section in any time period is obtained;
3) And (3) planning a global optimal path from the starting place to the destination of the vehicle, calculating all paths from the starting place of the vehicle to the destination by using an improved Dijkstra algorithm on the premise of acquiring the specific travel time of each specific road section of the urban road network in the predicted passing time period of the current travel in the step 2), and adding the travel time of each road section of each path to obtain the path with the shortest accumulated travel time.
Therefore, the dynamic travel time of any time segment of the road section is obtained by acquiring the traffic flow data of each road section of the urban road network and mapping the traffic flow data of the road section into the travel time; and calculating all paths to the destination by using a modified Dijkstra algorithm from the starting place of the vehicle, and adding the travel time of each road section of each path to obtain the path with the shortest accumulated travel time.
Further: the step 1) of obtaining the traffic flow data of each road section of the urban road network is to collect the traffic flow data of each road section in real time through a road vehicle information collecting device of the urban road network and send the traffic flow data back to a data center, and the data center can obtain the traffic flow data of the road section on different dates and different time periods through statistics.
Further: the traffic data of a specific road section is mapped to travel time based on the following formula,
wherein q represents the traffic flow of a certain road section, subscript i represents the number of the period w, and fqRepresents the period,
The function mapping relation of date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range; r istFor the time of travel of the road section, ftAs a function of the travel time of the road section, t0The time of the free road journey of the road section, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section.
Further: after the traffic flow data of each road section is collected in real time, on the premise that holidays and temporary sudden major traffic accidents are not considered, the traffic flow of the urban road network has obvious periodicity, which is called as periodic similarity for short, and the formula (1) shows that:
qi＝fq(wi,d,t)≈fq(wi+nw,d,t) (1)
wherein q represents the traffic flow of a certain road section, the subscript i represents the number of the period w, and fqRepresents the period,
The function mapping relation of date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range;
according to the traffic time road resistance function model, as shown in equation (2):
in the formula, rtIs the road section travel time, ftAs a function of the travel time of the road section, t0The free journey time of the road section, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section;
the travel time of a certain road section of the urban road network is only related to the actual traffic flow of the road section, so the formula (2) can be rewritten as follows:
the travel time of a specific road section of the urban road network in any time period on a certain day can be counted by the early-stage road vehicle information acquisition equipment, and the traffic flow is mapped into the travel time of the road section based on the formula (3).
Further: if the time-varying period of the traffic flow is set to be T, the travel time of a certain road section of the urban road network is updated according to the period T, and a dynamic change database of the travel time of the road section of the urban road network is established, wherein the dynamic change database reflects the dynamic change condition of the travel time of the urban road network.
Further: and the vehicle carries out global optimal path planning by utilizing an improved Dijkstra algorithm based on the dynamically changed travel time according to the dynamic change of the travel time of each road section of the urban road network along with the travel time process of the current driving from the starting point.
Further: the travel time of each road section is distinguished according to the time periods, the travel time of adjacent time periods is generally different, and if the vehicle crosses two or more time periods when passing through the road section, the actual travel time is weighted by the travel time of each different time period according to the proportion.
Further: according to the global optimal path planning, dijkstra is used as an algorithm carrier, an actual weight calculation link of a road section crossing time intervals is newly added, and an actual weight of a road section where the farthest position of a vehicle arrives at each time interval is calculated.
Further: the Dijkstra algorithm comprises the following steps:
1) Initialization
Setting a source node at an origin as S, a target node at a destination as t, di represents a minimum accumulated weight from the source node S to a node i, pi is a set of path nodes passing through to the node i, S represents a set of nodes with known minimum accumulated weights, and U is a set of points with unknown minimum accumulated weights; initial time, neighborhood of node sWeight d of child nodeiAnd path piRoad network data can be read and directly obtained, and the minimum cumulative weight of non-adjacent nodes is set to be infinity;
2) Traversing unknown node sets
Entering a loop, searching a node k with the minimum weight value from a source node S in the U, moving the point from the U to the S, and simultaneously respectively storing the weight value of the point k and a corresponding path to dkAnd pkPerforming the following steps;
3) Comparing and updating the weight of the neighboring node
Setting the sum of the road section weight from the node k to the adjacent child node j and the steering weight as w (k, j), and judging dk+ w (k, j) and djUpdate the minimum weight d of s to jjAnd corresponding path pj；
Judging whether the road network weight is updated or not;
4) If the minimum weight d to node kk>T·TnumThe road network weight is updated on the road section reaching the node k;
5) Calculating the actual accumulated weight of the critical node pair
All critical father nodes in the set S form a set ScAll critical child nodes in the set U form the set UcFurther forming a critical node pair set C, and calculating the actual weight of all the time-span sections of the critical node pair set; thereafter updating the period number TnumAnd then circulating the steps from the step 2) to the step 5) until
Further: let time interval 1 and time interval 2 be adjacent, and the travel time of time interval 1 and time interval 2 is T1And T2The driving distance of the vehicle in the time interval 1 and the time interval 2 of the road section is S respectively1And S2Then the total time for the vehicle to pass the road segment is:
in summary, the global optimal path planning method considering the dynamic change of the urban road network travel time has the following beneficial effects:
1. the invention provides a global optimal path planning method based on dynamic travel time, firstly provides a periodic similarity rule of urban traffic flow, then analyzes the defects of SPP and RPP, provides a global optimal path planning method of dynamic travel time of an urban road network by combining the periodic similarity rule, has the shortest travel time from a starting place to a destination, and further improves the intelligence of automobile global path planning.
2. According to the method, the simulation verification module is used for conducting path planning by respectively utilizing the SPP, the RPP and the GOPP on the basis of the established local road network in the Chongqing XX area, and the superiority of the GOPP in the aspect of shortening the travel time is verified.
3. The method comprises the steps of obtaining traffic flow data of each road section of the urban road network, and mapping the traffic flow data of the road section into travel time to obtain dynamic travel time of the road section in any time period; and calculating all paths to the destination by using the improved Dijkstra algorithm from the vehicle starting place to the vehicle starting place, and adding the travel time of each road section of each path to obtain the path with the shortest accumulated travel time.
Drawings
FIG. 1 is a schematic diagram of a dynamic road network global optimal path planning process according to the present invention;
FIG. 2 is a schematic diagram of traffic flow acquisition for a road segment;
FIG. 3 is a schematic diagram illustrating a road network and three path planning ideas according to an embodiment;
FIG. 4 is a schematic diagram illustrating the variation of the linear distance between the starting point and the planning concept of different routes;
FIG. 5-1 is a block diagram of an original Dijkstra algorithm, and FIG. 5-2 is a block diagram of an improved Dijkstra algorithm according to the present invention;
FIG. 6 is a comparison of simulation results for three path planning concepts;
fig. 7 is a D-T diagram of three path planning concepts.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance. Furthermore, the terms "horizontal", "vertical" and the like do not imply that the components are absolutely horizontal or hanging, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined. In the description of the present invention, it should also be noted that, unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1 to 7, the global optimal path planning method considering dynamic changes of travel time of an urban road network of the present invention includes the following steps:
1. acquiring traffic flow data of each road section of the urban road network on different dates and different time periods; the method comprises the steps that vehicle flow data of each road section are collected in real time through road vehicle information collection equipment such as a high-definition camera and a radar of an urban road network and are transmitted back to a data center, and the data center can obtain the vehicle flow data of the road section on different dates and different time periods through statistics;
2. the method comprises the steps that the travel time of a specific road section in any time period on a certain day is mapped into the travel time through the traffic flow data of the road section, and the dynamic travel time of the road section in any time period is obtained; the mapping relation between the traffic flow data and the travel time specifically adopts the following formula:
wherein q represents the traffic flow of a certain road section, subscript i represents the number of the period w, and fqRepresenting the function mapping relation of period, date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range; r istFor the time of travel of the road section, ftAs a function of the road section travel time mapping, t0The road section free flow time, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section;
3. the global optimal path planning from the starting place to the destination of the vehicle is to calculate all paths from the starting place of the vehicle to the destination by utilizing an improved Dijkstra algorithm on the premise of obtaining the specific travel time of each specific road section of the urban road network in the predicted passing time period of the current travel in the step 2), and add the travel time of each road section of each path to obtain the path with the shortest accumulated travel time.
According to the invention, after road side equipment of an urban road network, such as a high-definition camera, a radar and the like, as shown in fig. 2, vehicle flow data of each road section is collected in real time, on the premise of not considering holidays and temporary sudden major traffic accidents, urban road network traffic flow (vehicle flow) has obvious periodicity, which is referred to as periodic similarity, and the formula (1) shows that:
qi＝fq(wi,d,t)≈fq(wi+nw,d,t) (1)
wherein q represents the traffic flow of a certain road section, the subscript i represents the number of the period w, and fqRepresents the period,
And the date, the time and the traffic flow are in a functional mapping relation, t represents any time of each day, and n represents a range of integers.
A simple example of equation (1) is understood to be: the traffic flow of a certain road section from 8 to 9 morning of the second week is approximately equal to the traffic flow of the same road section from 8 to 9 morning of the last second week or the next second week. The urban traffic self-regulation is obtained through statistics and research of a large amount of data.
Secondly, the federal bureau of america has proposed a representative road resistance function model for transit time in 1964, as shown in equation (2):
in the formula, rtFor the time of travel of the road section, ftAs a function of the road section travel time mapping, t0The free road journey time of the road section, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section.
The equation (2) shows that when a road section is fixed, the actual traffic capacity and the free stream journey time of the road section are constants, and the journey time of a certain road section of the city road network is only related to the actual traffic flow of the road section. Thus, equation (2) can be rewritten as:
wherein q represents the traffic flow of a certain road section, subscript i represents the number of the period w, and fqRepresents the period,
The function mapping relation of date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range; r is a radical of hydrogentIs the road section travel time, ftAs a function of the road section travel time mapping, t0The time of the free road journey of the road section, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section. In summary, from a statistical perspective, it can be considered that the travel time of a specific road section of the urban road network in any time period of a certain day can be known in advance by counting the traffic flow and mapping the traffic flow to the travel time of the road section based on the formula (3) through the road vehicle information collecting device of the urban road network in the previous stage. If the time-varying period of the traffic flow is set to be T, the travel time of a certain road section of the urban road network is updated according to the period T, so that a dynamic change database of the travel time of the road section of the urban road network can be established, and the method has important significance in the global path planning of vehicles.
If the time-varying period of the traffic flow is set to be T, the travel time of a specific road section of the urban road network is updated according to the period T, and a dynamic change database of the travel time of the road section of the urban road network is established, wherein the dynamic change condition of the travel time of the urban road network is reflected by the database.
And the vehicle carries out global optimal path planning by utilizing an improved Dijkstra algorithm based on the dynamically changed travel time according to the dynamic change of the travel time of each road section of the urban road network along with the travel time process of the current driving from the starting point.
The invention establishes a dynamic change database of the travel time of the road section of the urban road network, and the dynamic change database is used for a certain range of the urban road network
In the enclosure, the future dynamic change rule of the road section travel time of the road network is known, and the vehicle fully processes the dynamic road section travel time information and plans a path which theoretically meets the global optimum when reaching a target point.
The globally optimal path is not a shortest path calculated based on the road network section travel time distribution of the vehicle at the starting point moment, is not a spliced path obtained by dynamically planning the vehicle in the driving process, fully considers the dynamic change of the road network section travel time in the future at the starting point, and utilizes an improved Dijkstra algorithm to plan the globally optimal path based on the dynamically changed travel time.
The travel time of each road section is distinguished according to the time periods, the travel time of the adjacent time periods is generally different, and if the vehicle crosses two or more time periods when passing through the road section, the actual travel time is weighted by the travel time of each different time period according to the proportion. According to the global optimal path planning, dijkstra is used as an algorithm carrier, an actual weight calculation link of a road section spanning time periods is newly added, and an actual weight of the road section where the farthest position reached by a vehicle in each time period is located is calculated.
Let time interval 1 and time interval 2 be adjacent, and the travel time of time interval 1 and time interval 2 is T1And T2The travel distance of the vehicle in time interval 1 and time interval 2 of the road section is S1And S2Then the total time for the vehicle to pass the road segment is:
in fig. 1, the modules related to the present invention include an urban road network dynamic travel time database module, a traditional global path planning defect introduction module, a dynamic travel time global optimal path planning module and a simulation verification module, wherein the urban road network dynamic travel time database module obtains traffic flow data of each road section of an urban road network at different dates and different time periods according to step 1; step 2, obtaining the dynamic travel time of the road section according to the travel time of the specific road section in any time period of a certain day, and mapping the travel time into the travel time according to the traffic flow data of the road section and based on the formula (3); the dynamic travel time database module of the urban road network can be obtained.
As shown in fig. 3, the road section travel time of the road network is dynamically updated in a period T, each update period is named as a time period, and table 1 shows the road section travel times of two adjacent time periods 1 and 2. Suppose that the current vehicle is located at the intersection 2, at the current time t0In time period 1, the target intersection is 11, and the conventional path planning idea with the origin-destination points (2, 11) is discussed.
The SPP only considers the weight of the road network at the current time, and the optimal path obtained by calculation according to the weight distribution of the time interval 1 is 2 → 6 → 7 → 11, as shown by the solid line path in FIG. 1. Now, it is assumed that the time when the vehicle arrives at the intersection 6 is just the critical time of the time period 1 and the time period 2, and the road network weight is immediately updated to the weight of the time period 2. The travel time consumed by the vehicle when passing 6 → 7 → 11 should be referenced to the weight distribution of slot 2, and the so-called "optimal path" planned in slot 1 cannot prove whether to continue to remain optimal in slot 2. Therefore, the path of SPP is the optimal path in a specific time domain (period 1), and it cannot be realized to remain optimal in the entire time domain.
Referring to the solid path planned by the SPP at the intersection 2, the road network weight value is updated when the vehicle reaches the intersection 6. At this time, the RPP plans the rest road network paths immediately according to the weight value of the time period 2, and the calculation finds that the accumulated weight value of the broken line path is smaller than that of the solid line path, so that the driving path is changed into the broken line path. Starting points of the two times of route planning of the RPP are respectively an intersection 1 and an intersection 6, corresponding routes are respectively 2 → 6 and 6 → 10 → 11, and airspace of route planning is obviously different. Therefore, the actual driving path of the RPP is formed by splicing local optimal paths in a specific airspace, and the local optimal superposition is not global optimal, so that the RPP cannot keep optimal in a full airspace. In fact, the dotted-line path of fig. 3 can be proved to be the optimal path by calculation according to the weight variation of table 1. The actual travel times for the three path planning concepts are shown in table 2.
TABLE 1 time-varying road travel time (unit: s) of road network in the embodiment
TABLE 2 actual travel time (units: s) for three paths of the road network of the embodiment
In order to more intuitively show the difference of the three path planning ideas, schematic diagrams of the path intersection and the starting point of the three path planning ideas, which are referred to as D-T diagrams for short, are drawn, the change relationship of the linear distance between the intersection passed by each path and the starting point along with the time can be intuitively observed by using the D-T diagrams, and the travel time change trends of different paths can be reflected, as shown in FIG. 4. The vehicle of fig. 4 departs from the intersection 2 and arrives at the intersection 11 over time. The SPP _1 represents an ideal straight-line distance change curve calculated by planning a path by using an SPP thought at the intersection 2, but an actual straight-line distance change curve of the vehicle continuously driving according to the SPP path is shown as SPP _2 due to updating of a road network weight when the vehicle is at the intersection 6. It should be noted that the dotted optimal path does not show its advantage in the time interval 1, but reaches the target point first when entering the time interval 2.
The dynamic travel time global optimal path planning module, as shown in fig. 4, analyzes fig. 4, and finds that the reason that the SPP path planning idea misses the dotted line path is that when the vehicle is located at the start node 2, the travel time of the path 2 → 3 → 7 → 11 in the time interval 1 is considered to be longer, but the travel time of the road segment 3 → 7 in the time interval 2 is ignored and will change to a smaller value; the reason why the RPP route planning idea misses the dotted line route is that when the vehicle is located at the node 6 to perform route planning again, although the vehicle knows that the travel time of the link 3 → 7 in the present period 2 is short, the vehicle is already located at the node 6 at this time, and the link 3 → 7 is no longer located in the link under investigation.
To this endIf the accumulated travel time of the dotted line path is calculated to be the shortest at the node 2, the travel time weights of the time interval 1 and the time interval 2 of the whole road network need to be comprehensively analyzed, and if three vehicles simultaneously travel from the node 2 along the solid line path, the dotted line path and the dotted line path respectively, the positions of the three vehicles in the road network are determined at the time of updating the weights. These three positions represent the vehicle at t of time period 10In the process of spreading the time from the node 2 to the periphery of the road network, the farthest positions which can be reached by the vehicle on three different paths are reached when the weight updating time is reached, and the farthest positions are usually located on the road sections rather than just on the intersections. Therefore, for any dynamic road network, if the vehicle is located in the period 1 at the starting point, firstly, the Dijkstra algorithm is used for calculating the farthest position which can be reached by the vehicle when the vehicle travels outwards from the starting point based on the weight value in the period 1, and the path of the node traversed in the period 1 is determined and optimal; and then, updating the road network weight, further continuously traversing the nodes which are not visited at the farthest positions based on the weight in the time period 2, and if the whole road network is not traversed in the time period 2, continuously traversing the remaining nodes based on the weight in the time period 3 until the whole road network is traversed.
In summary, on the premise that the topological structure of the road network is fixed and the rule of the weight update cycle is known, the global optimal path of the road network can be planned, and the key is to calculate the actual weight (driving time) of the road segment where the farthest position reached by the vehicle is located in each time period. Therefore, the Dijkstra is used as an algorithm carrier, the global optimal path planning idea is fused, an actual weight (driving time) calculation link of a section of a cross-period road is newly added, the Dijkstra algorithm is properly improved, and the global optimal path planning idea based on the Dijkstra algorithm is provided, as shown in FIG. 5.
By taking the example road network of fig. 3 as an example, the idea of global optimal path planning is introduced, and the Dijkstra algorithm includes 5 steps:
(1) initialization
Let the source node be s (origin), the target node be t (destination), di represent the minimum cumulative weight from the source node s to the node i, and pi is the node of the path passed by to the node iAnd (5) aggregating. S represents a set of nodes with known minimum accumulated weight, and U is a set of points with unknown minimum accumulated weight. At the initial moment, the weight d of the neighboring child node of the node siAnd path piRoad network data can be read and directly obtained, and the minimum cumulative weight of non-adjacent nodes is set to be infinity. In fig. 3, a source node is 1, a target node is 11, a time-varying period of a road network weight is T, and a time interval number is Tnum=1, time t of vehicle at source node 10Then t is0+TnumT is the weight update time. Initial time S = {1}, U = {2,3,4,5,6,7,8,9,10,11,12}. Node 2 is adjacent to node 1, so d2It can be known that the cumulative weights of the other nodes are ∞.
(2) Traversing unknown node sets
Entering into circulation, searching the node k with the minimum weight value from the source node S in the U, moving the point from the U to the S, and simultaneously respectively storing the weight value of the point k and the corresponding path to dkAnd pkIn (1). On the basis of the step (1), the node 2 in the set U is set to have the minimum accumulated weight d2Therefore, S = {1,2}, U = {3,4,5,6,7,8,9,10,11,12}.
(3) And comparing and updating the adjacent node weight.
Setting the sum of the road section weight from the node k to the adjacent child node j and the steering weight as w (k, j), and judging dk+ w (k, j) and djUpdate the minimum weight d of s to jjAnd corresponding path pj. On the basis of step (1), the neighboring nodes of node 2 include nodes 3 and 6, and since the cumulative weight of nodes 3 and 6 is infinite, d should be updated at this time3And d6. The traditional Dijkstra algorithm continuously loops from the step (1) to the step (3) until
The algorithm ends. The global optimal path planning algorithm proposed herein adds a new actual weight calculation link for the time-span segment, which determines whether the road network weight is updated and calculates the actual weight of the time-span segment, as shown by the dashed line box in fig. 5, and the specific steps are as follows (4) and (5).
(4) And judging whether the road network weight value is updated or not.
If the minimum weight d to node kk>T·TnumIndicating that the road segment arriving at node k will undergo road network weight update. On the basis of the previous three steps, assuming that the current S = {1,2,3,6}, and U = {4,5,7,8,9,10,11,12}, the node 10 in the set U has the smallest accumulated weight d10And d is10>T·TnumIndicating that the weight update was performed according to the route 1 → 2 → 6 → 10 and occurred at the road segment 6 → 10. We define node 6 as the critical parent node, node 10 as the critical child node, and nodes 6 and 10 together form the critical node pair.
(5) And calculating the actual accumulated weight of the critical node pair.
On the basis of step (4), the weight of the critical road segment 6 → 10 in time interval 1 and time interval 2 is set as ω6_10_1And omega6_10_2The running route proportion r of the vehicle in the section 6 → 10 in the time interval 1 is as follows:
the actual cumulative weight of path 1 → 2 → 6 → 10 is:
d10＝d6+r·ω6_10_1+(1-r)·ω6_10_2 (5)
all critical parent nodes in the set S are formed into the set S according to the abovecAll critical child nodes in the set U form the set UcAnd further constitute a critical node pair set C. Referring to equations (4) and (5), the actual weights of all the time-span segments in the critical node pair set can be calculated. Thereafter updating the period number TnumAnd then circulating the step (2) to the step (5) until
The simulation verification module of the invention is used for verifying the method of the invention:
and (3) establishing a local road network in the district XX of Chongqing in advance by using Vissim traffic simulation software, inputting different traffic flow into the road network to simulate traffic jam conditions in different periods, and recording data such as average travel time of each road section obtained by simulation. Taking (12, 209) of the road network as the beginning-end point of the simulation test, and setting the time when the vehicle is located at the source node 12 as 8 am (i.e. the starting time of period 1), the weight of the road network is updated at 8. The above simulation experiment takes (12,209) as an origin-destination, and verifies the superiority of the GOPP in realizing global optimal path planning compared with the SPP and the RPP. In fact, a set of origin-destination points is arbitrarily selected, and the magnitude relation of the accumulated travel time of the planned path of the SPP, RPP and GOPP can be expressed as:
TGOPP≤TRPP≤TSPP (6)
the magnitude relationship of equation (6) is further illustrated:
(1) when the selected target point is located in the farthest node range that the vehicle can traverse in the dynamic road network (e.g. the node 49 in fig. 6 is traversed in the period 1), there is TGOPP＝TRPP＝TSPP；
(2) If the selected target point in the dynamic road network is located outside the farthest node range that the vehicle can traverse in this period, i.e. the time-varying period is crossed (for example, the node 209 in fig. 6 is traversed in period 2), the relationship between the RPP and the global optimal path plan needs to be further analyzed, but the cumulative time of the RPP and the global optimal path plan is necessarily less than the SPP, i.e. T is TGOPP＝TRPP＜TSPP；
(3) If in the dynamic road network, the selected target point is located outside the farthest node range that the vehicle can traverse in the current time period, and the weights of the links of the nodes that are not traversed are decreased in the next time period, there may be a path with the minimum weight to the target point (as shown in fig. 6, the weight of the left dotted-line path of the node 184 is decreased in the time period 2, and the accumulated travel time of the dotted-line path from 184 to 209 is less than the travel time from 107 to 209), there is TGOPP＜TRPP＜TSPP。
TABLE 3 cumulative travel time of three planned paths of Chongqing XX local road network
In conclusion, the GOPP path planning idea based on the Dijkstra algorithm can plan a global optimal path in a road network with dynamically changed travel time, can shorten the travel time for a driver, and has certain practical value. Meanwhile, the property can be popularized to road networks with dynamically changed weights, such as average speed of road sections, energy consumption and the like.
Finally, it should be noted that the above-mentioned examples of the present invention are only examples for illustrating the present invention, and are not intended to limit the embodiments of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, it will be apparent to those skilled in the art that other variations and modifications can be made based on the above description. Not all embodiments are exhaustive. Obvious changes and modifications of the present invention are also within the scope of the present invention.
Claims (10)
1. The global optimal path planning method considering the dynamic change of the travel time of the urban road network is characterized by comprising the following steps of:
1) Acquiring traffic flow data of each road section of the urban road network on different dates and different time periods;
2) The method comprises the steps that the travel time of a specific road section in the urban road network in any time section on a certain day is mapped into the travel time of a vehicle in the time section through the traffic flow data of the road section, and the dynamic travel time of the road section in any time section is obtained;
3) The global optimal path planning of the vehicle from the starting place to the destination is to calculate all paths from the starting place of the vehicle to the destination by using an improved Dijkstra algorithm on the premise of acquiring the specific travel time of each specific road section of the urban road network in the predicted passing time period of the current travel in the step 2), and add the travel time of each road section of each path to obtain the path with the shortest accumulated travel time.
2. The global optimal path planning method considering dynamic variation of urban road network travel time according to claim 1, characterized in that: the step 1) of obtaining the traffic flow data of each road section of the urban road network is to collect the traffic flow data of each road section in real time through a road vehicle information collecting device of the urban road network and send the traffic flow data back to the data center, and the data center can obtain the traffic flow data of the road section on different dates and different time periods through statistics.
3. The global optimal path planning method considering dynamic variation of urban road network travel time according to claim 1, characterized in that: the traffic data of a specific road section is mapped to travel time based on the following formula,
wherein q represents the traffic flow of a certain road section, subscript i represents the number of the period w, and fqRepresenting the function mapping relation of period, date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range; r istFor the time of travel of the road section, ftAs a function of the travel time of the road section, t0The time of the free road journey of the road section, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section.
4. The global optimal path planning method considering dynamic variation of urban road network travel time according to claim 3, characterized in that: after the traffic flow data of each road section is collected in real time, on the premise that holidays and temporary sudden major traffic accidents are not considered, the traffic flow of the urban road network has obvious periodicity, which is called as periodic similarity for short, and the formula (1) shows that:
qi＝fq(wi,d,t)≈fq(wi+nw,d,t) (1)
wherein q represents the traffic flow of a certain road section, the subscript i represents the number of the period w, and fqRepresenting the function mapping relation of period, date, time and traffic flow, wherein t represents any time of each day, and n represents an integer in a certain range;
according to the transit time road resistance function model, as shown in equation (2):
in the formula, rtIs the road section travel time, ftAs a function of the road section travel time mapping, t0The road section free flow time, alpha and beta are model constant parameters, and C is the actual traffic capacity of the road section;
the travel time of a specific road section of the urban road network is only related to the actual traffic flow of the road section, so the formula (2) can be rewritten as follows:
the travel time of a specific road section of the urban road network in any time period of a certain day can be counted by the road surface vehicle information acquisition equipment in the previous stage, and the traffic flow is mapped into the travel time of the road section based on the formula (3).
5. The global optimal path planning method considering dynamic variation of urban road network travel time according to claim 4, characterized in that: if the time-varying period of the traffic flow is set to be T, the travel time of a specific road section of the urban road network is updated according to the period T, and a dynamic change database of the travel time of the road section of the urban road network is established, wherein the dynamic change condition of the travel time of the urban road network is reflected by the database.
6. The global optimal path planning method considering dynamic changes of urban road network travel time according to any one of claims 1 to 5, characterized in that: and the vehicle carries out global optimal path planning by utilizing an improved Dijkstra algorithm based on the dynamically changed travel time according to the dynamic change of the travel time of each road section of the urban road network along with the travel time process of the current driving from the starting point.
7. The global optimal path planning method considering dynamic changes of urban road network travel time according to any one of claims 1 to 5, characterized in that: the travel time of each road section is distinguished according to the time periods, the travel time of adjacent time periods is generally different, and if the vehicle crosses two or more time periods when passing through the road section, the actual travel time is weighted by the travel time of each different time period according to the proportion.
8. The global optimal path planning method considering dynamic changes of urban road network travel time according to any of claims 1 to 5, characterized in that: according to the global optimal path planning, dijkstra is used as an algorithm carrier, an actual weight calculation link of a road section crossing time intervals is newly added, and an actual weight of a road section where the farthest position of a vehicle arrives at each time interval is calculated.
9. The global optimal path planning method considering dynamic changes of urban road network travel time according to any one of claims 1 to 4, characterized in that: the Dijkstra algorithm comprises the following steps:
1) Initialization
Setting a source node of an origin as S, a target node of a destination as t, di represents a minimum accumulated weight from the source node S to a node i, pi is a set of path nodes passing through the node i, S represents a set of nodes with known minimum accumulated weights, and U is a set of points with unknown minimum accumulated weights; at the initial moment, the weight d of the adjacent child node of the node siAnd path piRoad network data can be read and directly obtained, and the minimum cumulative weight of non-adjacent nodes is set to be infinity;
2) Traversing unknown node sets
Entering a loop, searching the weight s from the source node in the UThe node k with the minimum value is moved from U to S, and the weight value and the corresponding path of the node k are respectively stored to dkAnd pkThe preparation method comprises the following steps of (1) performing;
3) Comparing and updating the weight of the neighboring node
Setting the sum of the road section weight from the node k to the adjacent child node j and the steering weight as w (k, j), and judging dk+ w (k, j) and djUpdate the minimum weight d of s to jjAnd a corresponding path pj；
4) Judging whether the road network weight is updated or not;
if the minimum weight d to node kk>T·TnumThe road network weight is updated on the road section reaching the node k;
5) Calculating the actual accumulated weight of the critical node pair
All critical father nodes in the set S form a set ScAll critical child nodes in the set U form the set UcFurther forming a critical node pair set C, and calculating the actual weight of all the time-span sections of the critical node pair set; thereafter updating the period number TnumAnd then circulating the steps from the step 2) to the step 5) until
10. The global optimal path planning method considering dynamic variation of urban road network travel time according to claim 7, characterized in that: let time interval 1 and time interval 2 be adjacent, and the travel time of time interval 1 and time interval 2 is T1And T2The driving distance of the vehicle in the time interval 1 and the time interval 2 of the road section is S respectively1And S2Then the total time for the vehicle to pass the road segment is:
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