CN114353816A - Multi-vehicle path planning method under 5G networking in Jobshop - Google Patents
Multi-vehicle path planning method under 5G networking in Jobshop Download PDFInfo
- Publication number
- CN114353816A CN114353816A CN202111606156.0A CN202111606156A CN114353816A CN 114353816 A CN114353816 A CN 114353816A CN 202111606156 A CN202111606156 A CN 202111606156A CN 114353816 A CN114353816 A CN 114353816A
- Authority
- CN
- China
- Prior art keywords
- node
- vehicle
- time
- information
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a multi-vehicle path planning method under 5G networking in Jobshop, and relates to the technical field of vehicle path planning. The invention comprises the following steps: inputting map node information and restriction information between nodes; updating the locking time windows of the nodes and the locking time windows of the edges according to the time of respectively occupying the nodes and the edges in the historical access lists of all vehicles which have finished the path planning; generating a distribution scheme of a plurality of vehicle driving path information by using a Jobshop scheduling algorithm; calculating the inter-node route cost in the inter-node route information corresponding to the driving path information of each vehicle; based on the route cost for each solution, the optimal delivery solution is selected. According to the invention, the node information is drawn on the map, the path information between adjacent nodes is calculated, the planning path schemes of the starting node and the target node of the user are determined, the route cost of each scheme is calculated, and the optimal scheme is implemented, so that the multi-vehicle distribution efficiency is improved, and the transportation cost is reduced.
Description
Technical Field
The invention belongs to the technical field of vehicle path planning, and particularly relates to a multi-vehicle path planning method under a 5G networking in Jobshop.
Background
The vehicle path planning is an important link of urban logistics distribution and is also a foundation and an important module established by a logistics distribution information system. The good and bad vehicle path planning has a great influence on the logistics distribution efficiency. The traditional Jobshop scheduling problem usually minimizes the maximum completion time of delivery of vehicles as an optimization target of the path planning problem, but in the actual path planning, the scheduling problem targets of different requirements are two or more.
In recent years, although the distribution efficiency has been improved, road congestion in cities and the like has been severe. In order to relieve the increasingly severe traffic congestion pressure, restriction measures are successively introduced in various places to restrict the passage of delivery vehicles. For example, there are regional, lane-dividing type restriction measures that restrict the passage of vehicles of certain specific vehicle types for certain specific regions. Conventional vehicle path planning techniques have been unable to meet the new requirements under the restriction measures.
Disclosure of Invention
The invention aims to provide a multi-vehicle path planning method under 5G networking in Jobshop.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a multi-vehicle path planning method under 5G networking in Jobshop, which comprises the following steps:
step S1: inputting map node information and restriction information between nodes;
step S2: scheduling and sequencing the vehicles to be scheduled, and inputting a starting node and a target node of each vehicle;
step S3: updating a locking time window of the node and a locking time window of the edge according to the time of respectively occupying the node and the edge in the historical access list of all vehicles which have finished the path planning, wherein the locking time window of the node and the locking time window of the edge are respectively one or more time intervals;
step S4: according to the starting node and the target node of the second vehicle needing path planning and the locking time window of the node obtained in the step S3, a current access list and a historical access list passing through each node from the starting node to the target node are obtained, the current access list is used for recording the time when the next vehicle needing path planning reaches the target node based on the earliest current node, the legal time window and the preposed node, and the preposed nodes in the historical access list are output in sequence to be used as path planning;
step S5: generating a distribution scheme of the running path information between each starting node and each target node of the vehicles by using a Jobshop scheduling algorithm;
step S6: calculating the inter-node route cost in the inter-node route information corresponding to the elapsed time of each inter-node route represented by the travel path information of each vehicle;
step S7: from the individual delivery plans, an optimal delivery plan is selected based on the route cost of each plan.
As a preferable technical solution, in step S1, when the map information is input, each edge between each adjacent nodes in the road network node map is traversed to obtain the passing time passing through each edge, the shortest estimated distance between any two points in the map, and the restriction information of each edge.
As a preferred technical solution, in step S2, the start node goes to the target node along the corresponding path of the shortest estimated distance by passing through the edge, and obtains a current access list and a historical access list passing through each node in sequence from the start node, where the current access list is used to record the time when the first vehicle reaches the target node earliest based on the current node, a legal time window and a front node, and the legal time window is a time interval; and sequentially outputting each front node in the historical access list as the path plan of the first vehicle.
As a preferred technical solution, in step S3, the time interval of the legal time window is [ g, h ], where g is the time when the vehicle arrives at the current node at the earliest time, h is the legal time when the vehicle leaves the current node at the latest time, the value of g in the legal time window of each current node is equal to the time when the node arrives from the previous node through the adjacent edge with the previous node, and the value of h is equal to positive infinity.
In a preferred embodiment, in step S4, the valid time window is a single time interval or a union of multiple time intervals, and if the time interval of the valid time window is included in the time interval of the locked time window of the node, the node is discarded.
As a preferred technical scheme, an iterative algorithm is adopted when an initial node goes to a target node, and the specific steps of each iteration are as follows:
step S41: judging whether the current node is a target node;
step S42: if the target node is not reached, taking the current node in the current access list as a front node and relevant access information, moving the current node out of the current access list and adding the current node into the historical access list of the current iteration;
step S43: obtaining access information related to adjacent nodes of the current node and adding the access information into the current access list;
step S44: selecting a node of the estimated time which reaches the target node earliest forever in the current access list as a current node;
step S45: sequentially outputting each front node in the historical access list as the path plan of the vehicle, and finishing the iteration;
step S46: and when the target node is visited, sequentially outputting each front node in the historical visit list as the path plan of the vehicle, and finishing the iteration.
As a preferable technical solution, in step S43, the access information includes an estimated time of the current node reaching the target node earliest, a legal time window and a previous node, where the legal time window is a time interval or a union of multiple time intervals, and if the time interval of the legal time window is included in the time interval of the locked time window of the node, the corresponding current node is discarded.
As a preferable technical solution, in the step S5, the distribution plan generating step includes: extracting an inter-node route between two nodes from the unrestricted inter-node route information as an initial route; judging whether the initial route violates the restriction condition based on the restriction information; and under the condition that the initial route does not violate the limiting condition, introducing the initial route and the elapsed time thereof into the distribution scheme, and extracting the inter-node route cost between the two nodes from the unlimited inter-node route information as the inter-node route cost of the vehicle between the two nodes in the distribution scheme.
As a preferable configuration, in step S6, the distribution plan cost is calculated by reflecting the inter-node route costs of the respective inter-node routes that are passed by all the vehicles included in the distribution plan, on the basis of the inter-node route costs in the inter-node route information that correspond to the vehicle types of the respective vehicles and the passing time of the respective inter-node routes that are indicated by the travel route information of the respective vehicles.
The invention has the following beneficial effects:
according to the invention, the node information is drawn on the map, the path information between adjacent nodes is calculated, the planning path schemes of the starting node and the target node of the user are determined, the route cost of each scheme is calculated, and the optimal scheme is implemented, so that the multi-vehicle distribution efficiency is improved, and the transportation cost is reduced.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a multi-vehicle path planning method under 5G networking in Jobshop according to the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, the present invention is a method for planning paths of multiple vehicles under 5G networking in Jobshop, including the following steps:
step S1: inputting map node information and restriction information between nodes;
step S2: scheduling and sequencing the vehicles to be scheduled, and inputting a starting node and a target node of each vehicle;
step S3: updating a locking time window of the node and a locking time window of the edge according to the time of respectively occupying the node and the edge in the historical access list of all vehicles which have finished the path planning, wherein the locking time window of the node and the locking time window of the edge are respectively one or more time intervals;
step S4: according to the starting node and the target node of the second vehicle needing path planning and the locking time window of the node obtained in the step S3, a current access list and a historical access list passing through each node from the starting node to the target node are obtained, the current access list is used for recording the time when the next vehicle needing path planning reaches the target node based on the earliest current node, the legal time window and the preposed node, and the preposed nodes in the historical access list are output in sequence to be used as path planning;
step S5: generating a distribution scheme of the running path information between each starting node and each target node of the vehicles by using a Jobshop scheduling algorithm;
step S6: calculating the inter-node route cost in the inter-node route information corresponding to the elapsed time of each inter-node route represented by the travel path information of each vehicle;
step S7: from the individual delivery plans, an optimal delivery plan is selected based on the route cost of each plan.
In step S1, when map information is input, traversing each edge between each adjacent nodes in the road network node map, and obtaining the passing time passing through each edge, the shortest estimated distance between any two points in the map, and the restriction information of each edge; the node of the map is represented as coordinate information of each intersection or inflection point information of roads on the map, the intersections are intersections between each road, including crossroads, T-shaped intersections and the like, the inflection point information is that when one road is too long and does not intersect with any other road, the inflection point with the turning angle more than 45 degrees on the road is also regarded as the node; the restriction information comprises speed limit information, lane information, red road lamp time length information and steering information of the road.
In step S2, the start node goes to the target node along the corresponding shortest estimated distance path passing edge, and obtains a current access list and a historical access list passing through each node in sequence from the start node, where the current access list is used to record the time when the first vehicle reaches the target node based on the earliest of the current node, a legal time window and a front node, and the legal time window is a time interval; and sequentially outputting each front node in the historical access list as the path plan of the first vehicle.
In step S3, the time interval of the legal time window is [ g, h ], where g is the earliest time that the vehicle arrives at the current node, h is the latest legal time that the vehicle leaves the current node, the value of g in the legal time window of each current node is equal to the time that the preceding node arrives at the node through the adjacent edge with the preceding node, and the value of h is equal to positive infinity.
In step S4, the valid time window is a single time interval or a union of multiple time intervals, and if the time interval of the valid time window is included in the time interval of the locked time window of the node, the node is discarded.
An iterative algorithm is adopted when the starting node goes to the target node, and the specific steps of each iteration are as follows:
step S41: judging whether the current node is a target node;
step S42: if the target node is not reached, taking the current node in the current access list as a front node and relevant access information, moving the current node out of the current access list and adding the current node into the historical access list of the current iteration;
step S43: obtaining access information related to adjacent nodes of the current node and adding the access information into the current access list;
step S44: selecting a node of the estimated time which reaches the target node earliest forever in the current access list as a current node;
step S45: sequentially outputting each front node in the historical access list as the path plan of the vehicle, and finishing the iteration;
step S46: and when the target node is visited, sequentially outputting each front node in the historical visit list as the path plan of the vehicle, and finishing the iteration.
In step S43, the access information includes an estimated time of the current node that reaches the target node earliest, a legal time window, and a previous node, where the legal time window is a time interval or a union of multiple time intervals, and if the time interval of the legal time window is included in the time interval of the locked time window of the node, the corresponding current node is discarded.
In step S5, the distribution plan generating step includes: extracting an inter-node route between two nodes from the unrestricted inter-node route information as an initial route; judging whether the initial route violates the restriction condition based on the restriction information; and under the condition that the initial route does not violate the limiting condition, introducing the initial route and the elapsed time thereof into the distribution scheme, and extracting the inter-node route cost between the two nodes from the unlimited inter-node route information as the inter-node route cost of the vehicle between the two nodes in the distribution scheme.
In step S6, a distribution plan cost is calculated by reflecting the inter-node route costs of the respective inter-node routes that all vehicles included in the distribution plan pass through, on the basis of the inter-node route costs in the inter-node route information that correspond to the vehicle types of the respective vehicles and the passing time of the respective inter-node routes indicated by the travel route information of the respective vehicles.
One specific application of this embodiment is: .
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A multi-vehicle path planning method under 5G networking in Jobshop is characterized by comprising the following steps:
step S1: inputting map node information and restriction information between nodes;
step S2: scheduling and sequencing the vehicles to be scheduled, and inputting a starting node and a target node of each vehicle;
step S3: updating a locking time window of the node and a locking time window of the edge according to the time of respectively occupying the node and the edge in the historical access list of all vehicles which have finished the path planning, wherein the locking time window of the node and the locking time window of the edge are respectively one or more time intervals;
step S4: according to the starting node and the target node of the second vehicle needing path planning and the locking time window of the node obtained in the step S3, a current access list and a historical access list passing through each node from the starting node to the target node are obtained, the current access list is used for recording the time when the next vehicle needing path planning reaches the target node based on the earliest current node, the legal time window and the preposed node, and the preposed nodes in the historical access list are output in sequence to be used as path planning;
step S5: generating a distribution scheme of the running path information between each starting node and each target node of the vehicles by using a Jobshop scheduling algorithm;
step S6: calculating the inter-node route cost in the inter-node route information corresponding to the elapsed time of each inter-node route represented by the travel path information of each vehicle;
step S7: from the individual delivery plans, an optimal delivery plan is selected based on the route cost of each plan.
2. The method according to claim 1, wherein in step S1, when inputting map information, each edge between each adjacent nodes in the road network node map is traversed to obtain a passing time passing through each edge, a shortest estimated distance between any two points in the map, and constraint information of each edge.
3. The method for planning paths of multiple vehicles under a 5G networking in Jobshop according to claim 1, wherein in step S2, the start node goes to the target node along the corresponding path with the shortest estimated distance by passing along the edge, and a current access list and a historical access list passing through each node from the start node are obtained, wherein the current access list is used for recording the time when the first vehicle reaches the target node at the earliest based on the current node, a legal time window and a front node, and the legal time window is a time interval; and sequentially outputting each front node in the historical access list as the path plan of the first vehicle.
4. The method according to claim 1, wherein in step S3, the time interval of the legal time window is [ G, h ], where G is the time when the vehicle arrives at the current node at the earliest time, h is the legal time when the vehicle leaves the current node at the latest time, the value of G in the legal time window of each current node is equal to the time when the node arrives from the previous node through the adjacent edge with the previous node, and the value of h is equal to positive infinity.
5. The method of claim 1, wherein in step S4, the legal time window is one time interval or a union of multiple time intervals, and if the time interval of the legal time window is included in the time interval of the locked time window of the node, the node is discarded.
6. The method for planning the paths of the multiple vehicles under the 5G networking in the Jobshop according to claim 1, wherein an iterative algorithm is adopted when the starting node goes to the target node, and the specific steps of each iteration are as follows:
step S41: judging whether the current node is a target node;
step S42: if the target node is not reached, taking the current node in the current access list as a front node and relevant access information, moving the current node out of the current access list and adding the current node into the historical access list of the current iteration;
step S43: obtaining access information related to adjacent nodes of the current node and adding the access information into the current access list;
step S44: selecting a node of the estimated time which reaches the target node earliest forever in the current access list as a current node;
step S45: sequentially outputting each front node in the historical access list as the path plan of the vehicle, and finishing the iteration;
step S46: and when the target node is visited, sequentially outputting each front node in the historical visit list as the path plan of the vehicle, and finishing the iteration.
7. The method according to claim 6, wherein in step S43, the access information includes an estimated time of the current node reaching the target node earliest, a legal time window and a previous node, the legal time window is a time interval or a union of multiple time intervals, and if the time interval of the legal time window is included in the time interval of the locked time window of the node, the corresponding current node is discarded.
8. The method for planning the paths of the multiple vehicles under the 5G networking in the Jobshop of claim 1, wherein in the step S5, the step of generating the distribution scheme comprises: extracting an inter-node route between two nodes from the unrestricted inter-node route information as an initial route; judging whether the initial route violates the restriction condition based on the restriction information; and under the condition that the initial route does not violate the limiting condition, introducing the initial route and the elapsed time thereof into the distribution scheme, and extracting the inter-node route cost between the two nodes from the unlimited inter-node route information as the inter-node route cost of the vehicle between the two nodes in the distribution scheme.
9. The method for planning a route for multiple vehicles under a 5G network in Jobshop according to claim 1, wherein in step S6, a distribution plan cost is calculated by reflecting an inter-node route cost for each inter-node route through which all vehicles included in the distribution plan pass, based on an inter-node route cost in the inter-node route information corresponding to a vehicle type of each vehicle and a time of passage of each inter-node route indicated by the travel route information of each vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111606156.0A CN114353816A (en) | 2021-12-25 | 2021-12-25 | Multi-vehicle path planning method under 5G networking in Jobshop |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111606156.0A CN114353816A (en) | 2021-12-25 | 2021-12-25 | Multi-vehicle path planning method under 5G networking in Jobshop |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114353816A true CN114353816A (en) | 2022-04-15 |
Family
ID=81101368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111606156.0A Pending CN114353816A (en) | 2021-12-25 | 2021-12-25 | Multi-vehicle path planning method under 5G networking in Jobshop |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114353816A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114970989A (en) * | 2022-05-16 | 2022-08-30 | 北京路凯智行科技有限公司 | Mining area vehicle scheduling method and system |
| CN115348178A (en) * | 2022-08-29 | 2022-11-15 | 安天科技集团股份有限公司 | Node control scheme generation method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108759851A (en) * | 2018-06-01 | 2018-11-06 | 上海西井信息科技有限公司 | More vehicle paths planning methods, system, equipment and storage medium based on time window |
| CN111044060A (en) * | 2018-10-12 | 2020-04-21 | 株式会社日立制作所 | Multi-vehicle path planning method and multi-vehicle path planning system |
| CN113821039A (en) * | 2021-09-27 | 2021-12-21 | 歌尔股份有限公司 | Time window-based path planning method, device, equipment and storage medium |
-
2021
- 2021-12-25 CN CN202111606156.0A patent/CN114353816A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108759851A (en) * | 2018-06-01 | 2018-11-06 | 上海西井信息科技有限公司 | More vehicle paths planning methods, system, equipment and storage medium based on time window |
| CN111044060A (en) * | 2018-10-12 | 2020-04-21 | 株式会社日立制作所 | Multi-vehicle path planning method and multi-vehicle path planning system |
| CN113821039A (en) * | 2021-09-27 | 2021-12-21 | 歌尔股份有限公司 | Time window-based path planning method, device, equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114970989A (en) * | 2022-05-16 | 2022-08-30 | 北京路凯智行科技有限公司 | Mining area vehicle scheduling method and system |
| CN115348178A (en) * | 2022-08-29 | 2022-11-15 | 安天科技集团股份有限公司 | Node control scheme generation method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111947678B (en) | Automatic driving navigation method and system for structured road | |
| WO2021196566A1 (en) | Full-day train operation diagram generating method based on relationship between time-sharing plan and activity events | |
| CN104318766B (en) | A kind of road network method of public transport GPS track data | |
| CN107919014B (en) | Taxi running route optimization method for multiple passenger mileage | |
| CN114353816A (en) | Multi-vehicle path planning method under 5G networking in Jobshop | |
| CN104101353B (en) | A kind of air navigation aid, device and real-time navigation system | |
| CN107490384B (en) | Optimal static path selection method based on urban road network | |
| CN109959388A (en) | A kind of intelligent transportation fining paths planning method based on grid extended model | |
| Kirchler | Efficient routing on multi-modal transportation networks | |
| CN109447340B (en) | Method for optimizing customized bus route with shortest reliability | |
| CN107944605A (en) | A kind of dynamic traffic paths planning method based on data prediction | |
| CN108335483B (en) | Method and system for inferring traffic jam diffusion path | |
| CN110222912B (en) | Railway travel route planning method and device based on time dependence model | |
| CN105489000A (en) | Night-shift bus stop and path selection method | |
| CN111898793B (en) | Path selection method considering user perception difference in combined travel mode | |
| CN102169512A (en) | Urban mass transit network dynamic accessibility computing method based on time window constraints | |
| CN114179873B (en) | Multi-road multi-time-interval all-day train operation diagram automatic compilation method and system | |
| CN103376116A (en) | Scenic route planning in vehicle navigation | |
| CN115100867A (en) | Urban intelligent traffic simulation method based on digital twins | |
| CN113987103A (en) | Combined travel path searching method and system for urban comprehensive transportation network | |
| CN115662124A (en) | GPS track data road section flow matching method based on network coding | |
| CN113739812B (en) | Distribution plan generating method, device, system and computer readable storage medium | |
| Li et al. | Bilevel programming for traffic signal coordinated control considering pedestrian crossing | |
| Kotoula et al. | Calculating Optimal School Bus Routing and Its Impact on Safety and the Environment | |
| Liang et al. | A personalized route guidance approach for urban travelling and parking to a shopping mall |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |