CN114724372B - Intelligent transportation system based on fog calculation - Google Patents
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Abstract
The invention discloses an intelligent traffic system based on fog calculation, which solves the problems of response performance and road condition analysis in the existing intelligent traffic system. The intelligent traffic system has three layers, including cloud layer, fog layer and equipment sensor layer for data storage and analysis, is a responsive and lightweight frame, uses fog nodes to process and match user riding requests, and completes information communication between different vehicles, thereby obtaining the geographic position and road condition information of the vehicles more accurately. Compared with a cloud-based system only, the intelligent traffic system based on fog calculation provides better response capability, and is safer, more reliable and more efficient.
Description
Technical Field
The invention relates to the technical field of intelligent transportation and computer security, in particular to an intelligent transportation system based on fog calculation.
Background
As the world population grows to 77.8 million people, the urban population continues to grow rapidly and urban traffic becomes more and more challenging. United nationality's foundation reports that more than half of the population now resides in towns in the world, and this number is expected to rise as more and more people migrate to urban areas. Rapid urbanization has had a great impact on public transportation systems, and traffic congestion, insufficient parking spaces, prolonged travel time, environmental pollution, etc. have become significant challenges for urban traffic development. This problem is a worldwide problem and it is reported that the 2017 uk drivers waste on average 31 hours in peak hours each year, whereas in 2011 traffic related problems have lost approximately 4% of GDP in the European Union (EU). Current traditional approaches to solving road traffic challenges, such as expanding roads and constructing new lanes, are expensive and less desirable because these approaches tend to be difficult to keep up with the urbanization process, and rapid urbanization requires more innovative techniques and methods to solve the traffic challenges.
On this basis, there have been increasing technological advances in which Intelligent Transportation Systems (ITS) have achieved good results in improving urban traffic. ITS systems combine information and communication systems with existing traffic infrastructure to provide sustainable and efficient traffic systems. Technologies used in ITS systems include mobile technology, internet of things (IoT), cloud computing, global Positioning System (GPS) technology, etc., which are addressed by planning the optimal route for travel. However, this approach still has a number of problems. First, the use of cloud servers associates user experience with network latency, and low latency can impact the performance of an application. In addition, using GPS location information and recent vehicle dispatch algorithms to determine location and match drivers and passengers remains a challenge, especially in urban areas, where vehicles may take longer than expected to reach the location of a passenger due to traffic or other conditions on the road.
With the development of technology, the development of fog calculation and other technologies, a new idea is provided for solving the core problems in the intelligent traffic system.
Disclosure of Invention
In order to overcome the defects of the prior art and solve the problems existing in the existing intelligent traffic system, the invention provides the intelligent traffic system based on fog calculation, which can solve the problems of response performance and road condition analysis existing in the existing intelligent traffic system and is a safer, more reliable and more efficient intelligent traffic system.
The technical scheme provided by the invention is as follows: an intelligent traffic system based on fog calculation is used for processing and matching a user riding request based on fog calculation and completing information communication among different vehicles, so that the geographic position and road condition information of the vehicles are acquired more accurately. The system is divided into three layers, namely a cloud layer, a fog layer and an equipment sensor layer, and has the following characteristics:
(1) The cloud layer consists of a plurality of cloud nodes distributed in an area (such as a city), and each cloud node is connected to a central server in the cloud layer; each foggy node is assigned a predefined area and is aware of or connected to at least one other foggy node, its neighbors;
(2) One area has only one main fog node, the main fog node is a base station which is specially set in the area and has higher computing performance, one area can also have smaller fog nodes connected to the main fog node of the area, the network and some computing resources are provided for the main fog node and devices connected to the main fog node, and the smaller fog nodes can be roadside units (RSUs) or vehicles with storage and computing capabilities;
(3) The foggy nodes also act as federated nodes, each of which trains the local model using data from vehicles within its area to forward the local model to aggregators that may be in the cloud layer. An aggregator aggregates local models from the federated nodes to create a global model for traffic management and public safety management.
Cloud layers possess a large amount of computing, storage, and network resources. It is the central point of the system, maintaining the resources required for the efficient operation of the whole system. The cloud layer will consist of the following components:
(1) Cloud database: the system comprises a database of all users and account information thereof, passengers, drivers, vehicles and payment information, and fog nodes and control areas thereof, which are stored in a cloud database, the cloud database is accessed when the users create accounts on the system, and the fog nodes interact with the cloud database to authenticate the users and make copies of user records in a limited time;
(2) Blockchain consensus node: is responsible for receiving new data from the cloud database, updating the blockchain, generating new blocks and distributing the updated blocks to other nodes in the blockchain;
(3) Fog node monitor: actively maintaining connections with the foggy nodes to ensure that they are active, the foggy node monitor will also ensure that the foggy nodes are not overloaded, such as by reassigning areas of the foggy nodes to handle the overload;
(4) And the data analysis intelligent module: as shown in fig. 1, the cloud layer maintains a global model of various aspects of the traffic system, consisting of distributed local models from fog nodes, which may be useful for multiple departments including traffic management, law enforcement, and public safety. The data analysis intelligent module realizes the integrated training of the global model.
The fog layer is composed of a plurality of nodes with processing, storage and network functions. Each zone has a primary fog node for receiving and processing requests from passengers. In addition to the primary foggy node, other foggy nodes consist of Radio Access Networks (RANs), roadside units (RSUs) or vehicles with computing capabilities. Functionally, the entire mist layer is made up of the following components:
(1) Caching a database: each master fog node maintains records from a cloud database, namely each master fog node is provided with a cache database, and the cache database is updated along with new riding requests and vehicles entering an area controlled by the fog node;
(2) Regional map: the main fog node maintains an area map under the control of the main fog node, the map is used for monitoring traffic load on roads in the area, and each fog node also maintains at least one other adjacent fog node map so as to indirectly acquire the area map maintained by the adjacent fog node;
(3) Route analysis module: the route analysis module aggregates data from vehicles and sensors within the area to estimate the cost of driving along the road in the area, which may be part of the primary fog node or other fog node within the area, which uses the data to provide advice to the driver to the destination;
(4) The request processor: the passenger is matched with the vehicle nearest to the passenger according to records in a cache database when the passenger takes charge of receiving and processing the riding request of the passenger, and if the vehicle in the area cannot meet the request, the request processor can forward the request to the neighbor fog node;
(5) Neighbor list: each foggy node maintains a record of its immediate neighbors, the stored information possibly including the location of the neighbor foggy node, the area it controls, and the route to that area, for forwarding ride requests and handing over vehicles that are completing the request, vehicles that cross regional service requests being handed over to the next foggy node when moving from one area to another;
(6) Mist node controller: the fog node controller monitors other fog node devices within the primary fog node control region and assigns tasks to the other fog node devices, and roadside units and vehicles having computing, storage and network resources within the region are assigned tasks to assist the primary fog node in processing data, the tasks including computing travel costs on roads or checking traffic congestion conditions in the region.
The device sensor layer is composed of terminal devices and sensors, and is divided into three types:
(1) Mobile device: the passenger and driver will interact with the system using the mobile device, and the client application in the mobile device should send a request to the fog node closest to it;
(2) Intelligent vehicle: the intelligent vehicle sends the sensing data to the fog node to help determine traffic load on the vehicle driving road, and the intelligent vehicle is identified and recorded in the database;
(3) Roadside sensor: sensors along the road send data to the foggy nodes, such as the number of vehicles on the road, the average speed of the vehicles, weather conditions, etc., and the sensors send data to the foggy nodes periodically.
The beneficial effects of the invention are as follows:
the invention provides an integrated intelligent traffic system based on fog calculation. The system has three layers including a cloud layer, a fog layer, and a device sensor layer for data storage and analysis. The intelligent transportation system designed by the invention is a responsive and lightweight framework, and uses fog nodes to process and match user riding requests, fog devices directly receive the user requests and locally match the requests with drivers in the same area, and the intelligent transportation system provides better response capability compared with a cloud-based system only.
Drawings
FIG. 1 is a schematic diagram of the model training composition of the present invention.
Fig. 2 is a general architecture diagram of the intelligent transportation system of the present invention.
Detailed Description
The invention is further described by way of examples in the following with reference to the accompanying drawings, but in no way limit the scope of the invention.
Fig. 2 shows the general architecture of the intelligent transportation system according to the present invention, and the following description will be given for each part of the above architecture diagram:
(1) User attribute definition
The user is used for marking passengers, basic information of the passengers is stored in the cloud database, and when the passengers participate in traffic events, each fog node maintains an active user list, and the users are deleted from the list after leaving a fog area (an area controlled by the fog node) or being in an inactive state within a predefined time period; when a user sends a ride request to a foggy node, it is added to the active user list of the foggy node in the area. At the moment, the fog node requests the record of the user from the cloud database, and acquires the information such as the user position, the user receiving and sending time, the destination and the like.
(2) Vehicle attribute definition
The vehicle in the fog area needs to send a state update request to the fog node of the area where the vehicle is located at regular time, and identification is carried out by using (si, id, l, t, sp, dr and a), wherein si is the position information of each vehicle, id is a vehicle identifier, l is the current position of the vehicle, t is the time for sending update, sp is the running speed of the vehicle, dr is the running direction, and a represents whether the vehicle can accept the riding request or not.
(3) Map representation and speed calculation
Each fog node will model a map of its area as a directed graph F, each directed graph being represented using G (V, E), E representing a road, V being the road vertex, for some two fog nodes Fi and Fj (Fi and Fj representing two different fog nodes, i being different from j, with the corresponding roads being Ei and Ej), if they share at least one road, they are indicated as adjacent fog nodes, i.e.:
further, in each directed graph in adjacent fog nodes, each neighbor fog node is represented as a vertex in the graph. For the area for which each fog node is responsible, the running blocking degree Cost of the vehicle in the current area is estimated mainly using the following formula, wherein SPv represents the estimated speed of the vehicle and MAXv represents the allowable maximum speed on the road:
Cost＝1-(SPv/MAXv)
i.e. the cost of travelling in a given direction, is calculated by finding the average speed of the vehicle travelling in the given direction divided by the speed limit of the road, and subtracting 1. Note that a Cost of zero means that the vehicle on a given road is moving at a speed limit, i.e., means that traffic is limited. The above calculation may be performed by the master foggy node or other foggy nodes in the area under control thereof (e.g., a vehicle on which the foggy node is onboard). The adjacent fog nodes need to be connected and the map is exchanged, so that a route can be conveniently found in the fog region.
(4) Request processing matching with vehicles
The fog layer receives a ride request from a user for vehicle matching. The two parties involved in the matching are a pair of driver and passenger. For a request to be processed, the user's record must be in the active user record of the foggy node. If the requesting user is not an active user, the cloud node may send a request to the cloud to obtain user data before processing their ride request.
The process by which the fog node takes as input and processes the ride request to produce a vehicle-to-passenger match includes:
step one, a user initiates a request;
step two, after the fog node receives the request, judging whether the current user is in an active user list of the current fog node, if so, storing the riding request, and carrying out the next analysis, and if not, requesting the user record from the cloud database;
step three, if the user record does not exist in the cloud database, response information is returned to the user, the user needs to be created, the user record is added into the cloud database after the user is created successfully, and the user is added into an active user list of the fog node;
step four, according to the position information carried in the user request, the fog node inquires vehicles in the allocated area, and according to the distance, the vehicles which are close to the user and idle and available are found;
step five, traversing the available vehicle list found in the step four, analyzing the distance and the running cost of each vehicle, and sorting according to the distance and the running cost to find the optimal vehicle;
step six, the matched optimal vehicle information is sent to the user;
and step seven, if no matched vehicle is found, the user record is sent to the adjacent fog node, and the adjacent fog node processes the request of the user.
The ride request may be taken as input and processed to produce a match, in particular by the following algorithm. The ride request is stored as r, array D [ ] is the driver near the pick-up location, σ is the minimum allowed distance between the driver and the passenger, and meeting the minimum allowed distance can be considered a potential match.
It can be seen that once the user's record is verified, the foggy node will search for the driver within a defined radius, the request will be sent to all vehicles within a given range that can meet the request, and once the nearby driver accepts the request, the user will be notified. Where there is no driver nearby, the request is forwarded to the nearest neighbor foggy node to the user.
(5) Route recommendation function
After the driver takes the passenger, the fog node will provide advice on the best route to the trip destination. Each master fog node calculates the cost on each road using an equation. The fog node calculates the shortest/best route to the destination according to the weight, and the shortest route between two points is searched by using a variant of Djikstra shortest route algorithm in the calculation flow, so as to calculate the shortest distance between the starting point and the destination, which is specifically as follows:
step one, using an array dis to represent the shortest distance from the starting point of the vehicle and the vertexes at the two ends of the road to the destination, for the convenience of calculation, defining each distance as infinity initially, namely, when calculation is not started from the beginning, the shortest distance from each point to the destination is the same, and updating along with calculation later;
step two, counting the length of all roads in the area required to be passed by the vehicle, and storing by using a preV set;
step three, traversing from the road vertex directly approaching the destination (namely, only one road is separated from the destination between a certain road vertex), traversing each element in the array dis, calculating the distance from each element to the destination, and updating the value in the corresponding array dis to the distance if the distance can be directly reached;
step four, if elements in the array dis cannot be directly reached when traversing, updating the array dis by adding the distance from the vertex of the road to the adjacent fog node and the distance from the adjacent fog node to the destination;
step five, traversing from the road vertex next approaching the destination (namely, only two roads are separated from the destination between a certain road vertex), wherein the analysis flow is the same as that of the step three and the step four, and updating the array dis;
and step six, continuing to perform iterative analysis, wherein the vertex of each round of analysis is farther than the destination of the previous round of analysis until all fog nodes are subjected to iterative analysis, and the distance from the vehicle position to the destination in the array dis is the shortest distance from the vehicle to the destination.
The specific algorithm is as follows:
(6) Neighboring area vehicle handoff
To ensure that a continuous connection with the driver and vehicle is maintained during travel, the foggy node hands over the journey and user to the adjacent foggy node as the vehicle approaches the adjacent foggy node. When the current position of the vehicle is located on an edge shared with an adjacent foggy node and the direction of the vehicle is toward the adjacent foggy node, the foggy node in the vehicle exit area may send trip data to the adjacent foggy node that the vehicle is entering.
The handover procedure is as follows:
step one, obtaining the geographic position and the traveling direction of a vehicle;
judging whether the vehicle is positioned on a road where the current fog node and the adjacent fog node coincide, if so, performing next round of judgment;
and thirdly, judging whether the travelling position of the current vehicle faces the area controlled by the adjacent fog node, if so, handing over the information of the vehicle and the passenger allocation weight to the adjacent fog node, otherwise, updating the position of the vehicle.
This process is shown in the following algorithm:
the position s of the vehicle and its direction of travel are obtained in an algorithm. If s is located at an edge shared with another foggy node and the direction of travel is toward the other foggy node, the vehicle is handed over to the adjacent foggy node; otherwise, the location is updated.
Claims (9)
1. An intelligent traffic system processes and matches user riding requests based on fog calculation, and completes information communication among different vehicles, and accurately acquires geographic position and road condition information of the vehicles; the intelligent traffic system is divided into three layers, namely a cloud layer, a fog layer and an equipment sensor layer; the cloud layer consists of a plurality of cloud nodes distributed in one area, and each cloud node is connected to a central server in the cloud layer; each foggy node is assigned a predefined area and is aware of or connected to at least one other foggy node, its neighbors; only one main fog node in one area is provided, and the main fog node is a base station specially set in the area; an area may have smaller fog nodes connected to the area's primary fog nodes, providing network and computing resources for the primary fog nodes and devices connected thereto, the smaller fog nodes including roadside units, vehicles with storage and computing capabilities; the foggy nodes also act as federated nodes, each foggy node training a local model using data from vehicles within its area to forward the local model to the aggregator of cloud layers; an aggregator aggregates local models from the federated nodes to create a global model for traffic management and public safety management; wherein each fog node models a map of its area as a directed graph F, each directed graph represented using G (V, E), where E represents a road and V is a road vertex; for some two foggy nodes Fi and Fj, if they share at least one road, they are indicated as neighboring foggy nodes, i.e. Ei n j not equal to ∅; in each directed graph of adjacent fog nodes, each neighbor fog node is represented as a vertex in the graph; for the area that each fog node is responsible for, estimating the degree of obstruction of the vehicle in the current area, cost, i.e. the Cost of travelling in a given direction, by finding the average speed of the vehicle travelling in the given direction, divided by the speed limit of the road, and subtracting from 1, using the formula Cost = 1- (SPv/MAXv), cost zero meaning that the vehicle on the given road is moving at a speed limit, i.e. meaning that traffic is limited; the above calculation may be done by the master foggy node or by other foggy nodes in the area controlled by it; the adjacent fog nodes need to be kept in contact and the map is exchanged, so that a route is conveniently searched in a fog area; where SPv denotes the estimated speed of the vehicle and MAXv denotes the maximum allowable speed on the road.
2. The intelligent transportation system of claim 1, wherein the cloud layer comprises the following components:
1) Cloud database: the system comprises a database of all users and account information thereof, passengers, drivers, vehicles and payment information, and fog nodes and control areas thereof, which are stored in a cloud database, the cloud database is accessed when the users create accounts on the system, and the fog nodes interact with the cloud database to authenticate the users and make copies of user records in a limited time;
2) Blockchain consensus node: is responsible for receiving new data from the cloud database, updating the blockchain, generating new blocks and distributing the updated blocks to other nodes in the blockchain;
3) Fog node monitor: actively maintaining connections with the foggy nodes to ensure that they are active and to ensure that the foggy nodes are not overloaded;
4) And the data analysis intelligent module: integrated training of a global model of maintaining aspects of the traffic system is achieved, the global model consisting of distributed local models from fog nodes.
3. The intelligent transportation system of claim 1, wherein the fog layer comprises the following components:
1) Caching a database: each master fog node has a cache database for maintaining records from the cloud database, and the cache database is updated along with new riding requests and vehicles entering an area controlled by the fog node;
2) Regional map: the main fog node maintains an area map under the control of the main fog node, the area map is used for monitoring traffic load on a road of the area, and each fog node maintains a mapping of at least one other adjacent fog node;
3) Route analysis module: the route analysis module aggregates data from vehicles and sensors within the area to estimate the cost of driving along the road in the area, which is used by the primary fog node to provide advice to the driver to the destination;
4) The request processor: the passenger is matched with the vehicle nearest to the passenger according to records in a cache database when the passenger takes charge of receiving and processing the riding request of the passenger, and if the vehicle in the area cannot meet the request, the request processor can forward the request to the neighbor fog node;
5) Neighbor list: each foggy node maintains a record of its immediate neighbors, the stored information including the location of the neighbor foggy node, the controlled area, and the route to that area, for forwarding ride requests and handing over vehicles that are completing the request, the vehicles that are servicing requests across the area being handed over to the next foggy node when moving from one area to another;
6) Mist node controller: the fog node controller monitors other fog node devices within a primary fog node control region and assigns tasks to the other fog node devices, and roadside units and vehicles having computing, storage and network resources within the region are assigned tasks to assist the primary fog node in processing data.
4. The intelligent transportation system of claim 1, wherein the device sensor layer is comprised of terminal devices and sensors, including mobile devices, intelligent vehicles, and roadside sensors; the passenger and driver send a request to the nearest foggy node using a client application in the mobile device; the intelligent vehicle sends the sensing data to the fog node to help determine traffic load on the vehicle driving road, and the intelligent vehicle is identified and recorded in the database; roadside sensors periodically send data to fog nodes, including the number of vehicles on the road, the average speed of the vehicles, weather conditions.
5. The intelligent transportation system of claim 1, wherein passengers are identified by users in the intelligent transportation system, basic information of the users is stored in a cloud database, each fog node maintains a list of active users, and the users are deleted from the list after leaving the fog area or being inactive for a predefined period of time; when a user sends a riding request to a fog node, the riding request is added into an active user list of the fog node in the area; at the moment, the fog node requests the record of the user from the cloud database, and obtains the user position, the user receiving and sending time and the destination information.
6. The intelligent transportation system of claim 5, wherein the fog node takes as input a ride request from a user and processes it to produce a vehicle-to-passenger match, comprising the steps of:
1) A user initiates a request;
2) After the fog node receives the request, judging whether the current user is in an active user list of the current fog node, if so, storing the riding request, and carrying out next analysis, and if not, requesting a user record from a cloud database;
3) If the cloud database does not have the user record, response information is returned to the user, the user needs to be created, the user record is added into the cloud database after the user is created successfully, and the user is added into an active user list of the fog node;
4) According to the position information carried in the user request, the fog node inquires vehicles in the distributed area, and according to the distance, the vehicles which are close to the user and idle and available are found;
5) Traversing all the available vehicle lists found in the step 4), analyzing the distance and the running cost of each vehicle, and sorting according to the distance and the running cost to find the optimal vehicle;
6) Transmitting the matched optimal vehicle information to a user;
7) If no matching vehicle is found, the user record is sent to the neighboring foggy node, which processes the user's request.
7. The intelligent transportation system of claim 1, wherein the vehicle sends a status update request to the fog node of the area at regular time, and the status update request is identified by (si, id, l, t, sp, dr, a), where si is the location information of each vehicle, id is a vehicle identifier, l is the current location of the vehicle, t is the time of sending the update, sp is the vehicle running speed, dr is the running direction, and a indicates whether the vehicle can accept the riding request.
8. The intelligent transportation system of claim 1, wherein the step of the fog node calculating the shortest distance between the vehicle from the origin to the destination is as follows:
1) Using an array dis to represent the shortest distance from the vehicle starting point and the vertices at the two ends of the road to the destination, wherein each distance is initially defined as infinity;
2) Counting the length of all roads in the area required to be passed by the vehicle, and storing by using a preV set;
3) Traversing from the road vertex directly approaching the destination, traversing each element in the array dis, calculating the distance from each element to the destination, and updating the value in the corresponding array dis to the distance if the distance can be directly reached;
4) If the elements in the array dis cannot be directly reached when traversing, updating the array dis by adding the distance from the vertex of the road to the adjacent fog node and the distance from the adjacent fog node to the destination;
5) Traversing from the road vertex next to the destination, and updating the array dis by adopting the methods of the step 3) and the step 4);
6) And continuing to perform iterative analysis, wherein the vertex of each round of analysis is farther than the destination of the previous round of analysis until all fog nodes are subjected to iterative analysis, and the distance from the vehicle position to the destination in the array dis is the shortest distance from the vehicle to the destination.
9. The intelligent transportation system of claim 1, wherein the vehicle remains continuously connected to the fog node during travel, and the fog node in the vehicle exit area transmits trip data to an adjacent fog node that the vehicle is entering when the current location of the vehicle is on an edge shared with the adjacent fog node and the direction of the vehicle is toward the adjacent fog node.
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