CN111063208A - Lane-level traffic guidance method and system based on Internet of vehicles - Google Patents
Lane-level traffic guidance method and system based on Internet of vehicles Download PDFInfo
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Abstract
The invention discloses a lane-level traffic guidance method and a lane-level traffic guidance system based on Internet of vehicles, wherein the method comprises the following steps: collecting vehicle running data and extracting effective field information in the vehicle running data; constructing a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information; link division is carried out on the road sections in the basic road network to form a divided road network; calculating lane-level travel time according to the driving data and the divided road network; calculating the predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time; and calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path. The invention makes up the defects of the traditional guidance mode and provides more accurate, safe and reliable traffic guidance service for travelers.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control and management, in particular to a lane-level traffic guidance method and system based on Internet of vehicles.
Background
The traffic guidance system is used as an important component of an intelligent traffic system, and is increasingly applied in an increasingly severe traffic environment. On one hand, the driving path of the vehicle is dynamically induced based on the real-time traffic state of the road, and an optimal route is selected for travelers, so that the traveling efficiency is improved, and the traveling cost is saved; more importantly, the traffic guidance system solves the traffic problem of the road network by solving the individual trip problem, prevents traffic jam, reduces the stay time of vehicles on the road, and finally realizes the reasonable distribution of the traffic flow on each road section in the road network.
The traffic guidance system is mainly divided into static traffic guidance and dynamic traffic guidance. The static traffic guidance utilizes the historical data to calculate the travel route before the traveler goes out, so that the travel time is reduced to a certain extent, the traffic flow of a road network is reasonably distributed, and the traffic jam is avoided; however, the complexity and uncertainty of traffic conditions determine the limitations of static traffic inducement capabilities. The dynamic traffic guidance combines real-time road network traffic state with historical data, and updates the driving route in real time by continuously predicting the travel time of the OD (ORIGIN, DESTINATION) of a traveler, thereby improving the accuracy of traffic guidance information. The existing dynamic traffic guidance can provide different guidance strategies, such as distance shortest path guidance, travel time shortest path guidance, carbon emission minimum path guidance and the like. In these strategies, the planning of the guidance route is studied by taking a natural road segment as a basic object, that is, a road segment between adjacent intersections in an urban road as a whole, and traffic characteristics of different lanes on the road segment are not distinguished, so that microscopic guidance such as lane-level navigation and lane change information assistance cannot be realized.
In summary, how to provide a traffic characteristic capable of distinguishing different lanes on a road section and provide a more accurate, safe and reliable traffic guidance scheme for travelers is a problem to be solved in the field.
Disclosure of Invention
The invention aims to provide a lane-level traffic guidance method and system based on the Internet of vehicles, which estimate the road section travel time taking a lane as a unit by using lane-level positioning data and implement traffic guidance control, make up the defects of the traditional guidance mode and provide more accurate, safe and reliable traffic guidance service for travelers.
In order to achieve the above object, the present invention provides a lane-level traffic guidance method based on internet of vehicles, the method comprising:
collecting vehicle running data and extracting effective field information in the vehicle running data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle;
constructing a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information;
link division is carried out on the road sections in the basic road network to form a divided road network; the road sections in the divided road network consist of a plurality of links;
calculating lane-level travel time according to the driving data and the divided road network;
calculating the predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time;
and calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
Optionally, Link division is performed on the road segments in the basic road network to form a divided road network, which specifically includes:
determining the length of each road section in the basic road network as
Link division is carried out on each road section by utilizing three-level length threshold values which are respectively
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The length of the last Link is the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and numbering the links in each road section in sequence from the downstream to the upstream of the road section.
Optionally, the calculating the lane-level travel time according to the driving data and the divided road network specifically includes:
acquiring a lane-level map corresponding to the basic road network, projecting positioning information of the vehicle in the lane-level map, and performing lane-level map matching by using a shortest distance principle;
entering the vehicle driving data, the lane and the divided Link according to the positioning informationThe line positions correspond to each other, and the vehicle running data is clustered according to the vehicle ID, so that the vehicle information with the same vehicle ID is classified into the same set
Wherein,
the vehicle driving data of the ith Link representing the road section ab and the vehicle identification on the K lane are ID, and the vehicle driving data of the ith vehicle collecting area where the vehicle identification on the ab Link representing the road section ab and the identification on the K lane is ID are e;
screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and supplementing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
formula for utilizing vehicle driving data according to complement
Calculating the vehicle travel time of each lane in each Link; wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents an acquisition time interval of the vehicle travel data;
according to the formula
Calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as a time step, and saving the result to
And is
Represents the lane level travel time of the ith Δ T.
Optionally, the calculating the predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time specifically includes:
using the vehicle running data of the surrounding vehicle except the current day as historical running data, using the vehicle running data of the current day as real-time running data, and using a formula according to the lane-level travel time calculated by using delta T as a time step
Sequentially rolling and predicting the vehicle travel time of the next delta T, and updating the prediction information in real time; wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
α and β are weighted values of the historical driving data and the real-time driving data respectively, and α + β is equal to 1;
according to the formula
Calculating the average travel time of the vehicle in each lane in the i +1 th delta T section by taking the delta T as the time step, namely the predicted lane-level travel time, and storing the result to
Optionally, the calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path specifically includes: primary guidance path planning: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; the optimal path selection is carried out by adopting a Dijkstra shortest path search algorithm and taking the road section as a basic unit to obtain a primary optimal path set Po1＝{Eab}; wherein, Po1Representing the optimal road taking the road section as the basic unit after the induction decisionA path set; eabRepresenting each road section in the optimal path;
optionally, the calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path further includes:
and (3) secondary guidance path planning: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
Optionally, the method further includes: and executing guidance decision control according to the guidance planning path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
The invention also provides a lane-level traffic guidance system based on the internet of vehicles, which comprises:
the vehicle driving data acquisition module is used for acquiring vehicle driving data and extracting effective field information in the vehicle driving data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle;
the basic road network building module is used for building a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information;
the Link dividing module is used for performing Link division on the road sections in the basic road network to form a divided road network; the road sections in the divided road network consist of a plurality of links;
the lane-level travel time calculation module is used for calculating lane-level travel time according to the driving data and the divided road networks;
the lane-level travel time prediction module is used for calculating predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time;
and the traffic guidance module is used for calculating the shortest vehicle travel time between OD points according to the predicted lane travel time and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
Optionally, the Link dividing module specifically includes:
a road segment length determining submodule for determining the length of each road segment in the basic road network as
Three-level division submoduleThe method is used for performing Link division on each road section by utilizing three levels of length threshold values which are respectively
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The last Link is an area of the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and the numbering submodule is used for sequentially numbering the links in each road section from the downstream to the upstream of the road section.
Optionally, the lane-level travel time calculation module specifically includes:
the map matching submodule is used for acquiring a lane-level map corresponding to the basic road network, projecting the positioning information of the vehicle in the lane-level map and matching the lane-level map by using the shortest distance principle;
the clustering submodule is used for carrying out position correspondence on the vehicle running data, the lanes and the divided links according to the positioning information and clustering the vehicle running data according to the vehicle ID so as to enable the vehicle information with the same vehicle ID to be grouped into the same set
Wherein,
the vehicle driving data of the ith Link representing the road section ab and the vehicle identification on the K lane are ID, and the vehicle driving data of the ith vehicle collecting area where the vehicle identification on the ab Link representing the road section ab and the identification on the K lane is ID are e;
the vehicle running data completion sub-module is used for screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and completing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
a vehicle travel time calculation submodule for utilizing a formula based on the supplemented vehicle travel data
Calculating the vehicle travel time of each lane in each Link; wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents an acquisition time interval of the vehicle travel data;
a lane-level travel time calculation submodule for calculating travel time according to a formula
Calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as a time step, and saving the result to
And is
Represents the lane level travel time of the ith Δ T.
Optionally, the lane-level travel time prediction module specifically includes:
a rolling prediction calculation submodule for using the vehicle running data of the previous and surrounding area excluding the current day as the history running data, using the vehicle running data of the current day as the real-time running data, and using the formula according to the lane-level travel time calculated by using the time step Δ T
Sequentially rolling and predicting the vehicle travel time of the next delta T, and updating the prediction information in real time; wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
α and β are weighted values of the historical driving data and the real-time driving data respectively, and α + β is equal to 1;
a predicted lane-level travel time calculation submodule for calculating a predicted lane-level travel time based on a formula
Calculating the average travel time of the vehicle in each lane in the i +1 th delta T section by taking the delta T as the time step, namely the predicted lane-level travel time, and storing the result to
Optionally, the traffic inducing module specifically includes:
the first-level guidance planning submodule is used for planning a first-level guidance path: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; miningUsing Dijkstra shortest path search algorithm to select optimal path by using road section as basic unit to obtain primary optimal path set Po1＝{Eab}; wherein, Po1Representing an optimal path set which takes the road section as a basic unit after the induction decision; eabRepresenting each road section in the optimal path; and/or
And the secondary guidance planning submodule is used for planning a secondary guidance path: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
Optionally, the system further includes a decision control module, where the decision control module is configured to execute an induced decision control according to the induced planning path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the lane-level traffic guidance method and system based on the internet of vehicles predict the vehicle driving time of each lane in each Link by dividing links of road sections in a basic road network and combining the historical driving data of vehicles and the real-time driving data collected under the environment of the internet of vehicles, judge the OD shortest travel time according to the predicted lane-level driving time, and determine the shortest path corresponding to the shortest travel time of the vehicles as a guidance planning path. The traditional traffic guidance system takes the road section as a research object, and cannot realize microscopic guidance such as lane-level navigation and lane change information assistance.
In addition, the invention can also issue guidance information to the driver, predict the secondary travel time in the guidance process and ensure the optimization of the driving path. The invention adopts the lane-level positioning information of the road vehicle, not only can acquire the travel time of the vehicle through accurate positioning information, but also can assist a driver to perform operations such as steering, lane changing and the like during traffic guidance, so that the driving behavior is safer and more reliable. The invention can realize macroscopic traffic path planning and induction, and can also carry out microscopic lane change decision in the road section according to the actual traffic state, thereby realizing auxiliary driving, improving driving efficiency, increasing the driving safety factor of a driver and improving driving experience.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described 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 to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a lane-level traffic guidance method based on internet of vehicles according to an embodiment of the present invention;
fig. 2 is a system block diagram of a lane-level traffic guidance system based on the internet of vehicles according to an embodiment of 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.
The invention aims to provide a lane-level traffic guidance method and system based on the Internet of vehicles, which estimate the road section travel time taking a lane as a unit by using lane-level positioning data and implement traffic guidance control, make up the defects of the traditional guidance mode and provide more accurate, safe and reliable traffic guidance service for travelers.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the lane-level traffic guidance method based on internet of vehicles provided by this embodiment includes:
step 101: collecting vehicle running data and extracting effective field information in the vehicle running data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle.
In practical application, the embodiment CAN utilize the sub-meter positioning equipment installed on the vehicle to collect the position of the vehicle, read the running condition data of the vehicle through the CAN bus, analyze the position of the vehicle and the running condition data, and filter and extract the effective field information. In the valid field information, the vehicle ID is the unique ID identification of the vehicle provided with the acquisition equipment; the acquisition time is the sampling time of the vehicle information; the longitude and the latitude are vehicle running position information collected by the positioning equipment; the steering angle of the steering wheel is the steering angle of the vehicle, when the wheel is positive, the angle is 0, the left turning of the steering wheel is negative, and the right turning of the steering wheel is positive.
Step 102: constructing a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information.
In this embodiment, the basic road network can be expressed as formula (1):
G＝(V,E,D) (1)
where V denotes an intersection set, E denotes a link set, D denotes a lane set, and K denotes a lane number and starts from the left side of the vehicle traveling direction.
V＝{v1,…,vm,…,vM} (2)
Eab＝{＜va,vb＞}a,b∈M (3)
Wherein v isa,vbRespectively represent a section of road EabThe starting intersection and the ending intersection; a. b respectively represents the corresponding intersection numbers; m represents the number set of all intersections.
Step 103: link division is carried out on the road sections in the basic road network to form a divided road network; the road sections in the road network are divided into a plurality of links.
In order to make the prediction result of the lane-level travel time prediction module more accurate, Link division is performed on the road sections on the basis of a basic road network, and the divided Link is shown as a formula (5):
wherein,
represents the length of this Link;
represents the average travel time of the vehicle at the ith Δ T time interval in the Link range;
represents the predicted average travel time of the (i + 1) th Δ T vehicle; the Δ T represents a time step for calculating the lane-level travel time, and usually takes 5min, and may be adjusted according to actual conditions, such as 15min and 30 min.
The Link dividing method of this embodiment may be:
determining the length of each road section in the basic road network as
Link division is carried out on each road section by utilizing three-level length threshold values which are respectively
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The last Link is an area of the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and numbering the links in each road section in sequence from the downstream to the upstream of the road section.
Step 104: and calculating the lane-level travel time according to the driving data and the divided road network.
The driving data of the vehicles in the road network is acquired by a vehicle driving data acquisition module and is expressed as a formula (7),
Hr＝{IDr,Tr,Lgr,Ltr,Sr} (7)
wherein the IDrRepresents a vehicle identification; t isrRepresenting a data acquisition time; lgrRepresenting vehicle longitude information; lt (total reflection)rRepresenting vehicle latitude information; srIndicating vehicle steering wheel steering angle information; and r represents the collected driving data of the r-th vehicle.
This step 104 specifically includes:
step 1: acquiring a lane-level map corresponding to the basic road network, projecting positioning information of the vehicle in the lane-level map, and performing lane-level map matching by using a shortest distance principle;
among them, the lane-level map is a map containing lane information with high accuracy.
Step 2: and carrying out position correspondence on the vehicle running data, lanes and divided links according to the positioning information, and clustering the vehicle running data according to the vehicle ID to enable the vehicle information with the same vehicle ID to be grouped into the same set: the expression is shown in equation (8).
Wherein,
vehicle travel data indicating that the vehicle identification on the nth Link, K lane of the road section ab is ID; and e represents the vehicle driving data of the e-th vehicle collected by the vehicle marked as the ID on the ab-th Link and the K lane of the road section ab.
Step 3: screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and supplementing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
step 4: calculating the vehicle travel time of each lane in each Link by using a formula (9) according to the supplemented vehicle driving data;
wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; j denotes on the road section EabThe jth vehicle on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents the collection time interval of the vehicle travel data.
Step 5: and calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as the time step according to the formula (9), namely the lane-level travel time, and storing the calculation result as shown in the following formula.
Step 105: and calculating the predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time.
In the embodiment, the vehicle running data of the four weeks before the current day is taken as the historical running data, the vehicle running data of the current day is taken as the real-time running data, the lane-level travel time calculated by the time step Δ T is calculated, the vehicle travel time of the next Δ T is calculated by sequentially rolling prediction by using the formula (11), and the prediction information is updated in real time, wherein the prediction formula (11) is as follows:
wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
indicating the lane-level travel time within the ith Δ T in the real-time travel data, α, β are weight values of the history travel data and the real-time travel data, respectively, and α + β is 1.
Calculating the vehicle running data acquisition time T according to the formula (11) by taking Delta T as a time stepi+1The average travel time of the inner road section is the predicted lane-level travel time, which is shown in the following formula.
Step 106: and calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
In the embodiment, a traveler is taken as an object, the shortest travel time path of the vehicle between OD points is calculated, and guidance decision information is provided for a driver. The method mainly comprises two stages of induced path planning and induced decision, and specifically comprises the following steps:
two-stage induced path planning:
primary guidance path planning: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; the optimal path selection is carried out by adopting a Dijkstra shortest path search algorithm and taking the road section as a basic unit to obtain a primary optimal path set Po1＝{Eab}; wherein, Po1Representing an optimal path set which takes the road section as a basic unit after the induction decision; eabRepresenting each road section in the optimal path;
and (3) secondary guidance path planning: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
And (3) inducing decision:
and executing guidance decision control according to the guidance planning path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
The induction decision can be issued to an intelligent vehicle-mounted terminal and a mobile phone terminal with map display and voice broadcast functions.
As shown in fig. 2, the present embodiment further provides a system corresponding to the lane-level traffic guidance method based on internet of vehicles provided in the foregoing embodiment, where the system includes:
the vehicle driving data acquisition module 201 is used for acquiring vehicle driving data and extracting effective field information in the vehicle driving data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle;
a basic road network construction module 202, configured to construct a basic road network by using intersections as nodes and road segments between the intersections as connecting lines; the road section contains lane information;
a Link dividing module 203, configured to perform Link division on road segments in the basic road network to form a divided road network; the road sections in the divided road network consist of a plurality of links;
a lane-level travel time calculation module 204, configured to calculate lane-level travel time according to the driving data and the divided road networks;
a lane-level travel time prediction module 205, configured to calculate predicted lane-level travel times for each time interval according to the historical travel data and the lane-level travel times;
and the traffic guidance module 206 is configured to calculate the shortest vehicle travel time between the OD points according to the predicted lane-level travel time, and determine a shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
The Link dividing module 203 specifically includes:
a road segment length determining submodule for determining the length of each road segment in the basic road network as
A third-level division submodule for performing Link division on each road section by using a third-level length threshold value
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The last Link is an area of the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and the numbering submodule is used for sequentially numbering the links in each road section from the downstream to the upstream of the road section.
The lane-level travel time calculation module 204 specifically includes:
the map matching submodule is used for acquiring a lane-level map corresponding to the basic road network, projecting the positioning information of the vehicle in the lane-level map and matching the lane-level map by using the shortest distance principle;
the clustering submodule is used for carrying out position correspondence on the vehicle running data, the lanes and the divided links according to the positioning information and clustering the vehicle running data according to the vehicle ID so as to enable the vehicle information with the same vehicle ID to be grouped into the same set
Wherein,
the vehicle driving data of the ith Link representing the road section ab and the vehicle identification on the K lane are ID, and the vehicle driving data of the ith vehicle collecting area where the vehicle identification on the ab Link representing the road section ab and the identification on the K lane is ID are e;
the vehicle running data completion sub-module is used for screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and completing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
a vehicle travel time calculation submodule for utilizing a formula based on the supplemented vehicle travel data
Calculating each lane in each LinkThe vehicle travel time of (d); wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents the collection time interval of the vehicle travel data.
A lane-level travel time calculation submodule for calculating travel time according to a formula
Calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as a time step, and saving the result to
And is
Represents the lane level travel time of the ith Δ T.
The lane-level travel time prediction module 205 specifically includes:
a rolling prediction calculation submodule for using the vehicle running data of the previous and surrounding area excluding the current day as the history running data, using the vehicle running data of the current day as the real-time running data, and using the formula according to the lane-level travel time calculated by using the time step Δ T
Sequentially rolling and predicting the vehicle travel time of the next delta T, and updating the prediction information in real time; wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
indicating the lane-level travel time within the ith Δ T in the real-time travel data, α, β are weight values of the history travel data and the real-time travel data, respectively, and α + β is 1.
A predicted lane-level travel time calculation submodule for calculating a predicted lane-level travel time based on a formula
Calculating the average travel time of the vehicle in each lane in the i +1 th delta T section by taking the delta T as the time step, namely the predicted lane-level travel time, and storing the result to
The traffic guidance module 206 specifically includes:
the first-level guidance planning submodule is used for planning a first-level guidance path: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; the optimal path selection is carried out by adopting a Dijkstra shortest path search algorithm and taking the road section as a basic unit to obtain a primary optimal path set Po1＝{Eab}; wherein, Po1Representing an optimal path set which takes the road section as a basic unit after the induction decision; eabRepresenting each road section in the optimal path; and/or
And the secondary guidance planning submodule is used for planning a secondary guidance path: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
The system also comprises a decision control module, wherein the decision control module is used for executing the induction decision control according to the induction planning path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (13)
1. A lane-level traffic guidance method based on Internet of vehicles is characterized by comprising the following steps:
collecting vehicle running data and extracting effective field information in the vehicle running data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle;
constructing a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information;
link division is carried out on the road sections in the basic road network to form a divided road network; the road sections in the divided road network consist of a plurality of links;
calculating lane-level travel time according to the driving data and the divided road network;
calculating the predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time;
and calculating the shortest vehicle travel time between OD points according to the predicted lane-level travel time, and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
2. The vehicle networking based lane-level traffic guidance method according to claim 1, wherein the Link division is performed on the road segments in the basic road network to form a divided road network, and specifically comprises the following steps:
determining the length of each road section in the basic road network as
Link division is carried out on each road section by utilizing three-level length threshold values which are respectively
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The length of the last Link is the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and numbering the links in each road section in sequence from the downstream to the upstream of the road section.
3. The method for inducing traffic at lane level based on Internet of vehicles according to claim 1, wherein the calculating of the travel time at lane level according to the driving data and the divided road network comprises:
acquiring a lane-level map corresponding to the basic road network, projecting positioning information of the vehicle in the lane-level map, and performing lane-level map matching by using a shortest distance principle;
the vehicle running data are corresponding to the positions of the lanes and the divided links according to the positioning information, and the vehicle running data are clustered according to the vehicle IDs, so that the vehicle information with the same vehicle ID is classified into the same set
Wherein,
the vehicle driving data of the ith Link representing the road section ab and the vehicle identification on the K lane are ID, and the vehicle driving data of the ith vehicle collecting area where the vehicle identification on the ab Link representing the road section ab and the identification on the K lane is ID are e;
screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and supplementing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
formula for utilizing vehicle driving data according to complement
Calculating the vehicle travel time of each lane in each Link; wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents an acquisition time interval of the vehicle travel data;
according to the formula
Calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as a time step, and saving the result to
And is
Represents the lane level travel time of the ith Δ T.
4. The internet-of-vehicles-based lane-level traffic guidance method according to claim 1, wherein the calculating the predicted lane-level travel time for each time interval according to the historical travel data and the lane-level travel time specifically comprises:
taking the vehicle running data of the four previous weeks excluding the current day as historyDriving data, using current day vehicle driving data as real-time driving data, and using formula according to lane-level travel time calculated by using delta T as time step
Sequentially rolling and predicting the vehicle travel time of the next delta T, and updating the prediction information in real time; wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
α and β are weighted values of the historical driving data and the real-time driving data respectively, and α + β is equal to 1;
according to the formula
Calculating the average travel time of the vehicle in each lane in the i +1 th delta T section by taking the delta T as the time step, namely the predicted lane-level travel time, and storing the result to
5. The internet-of-vehicles-based lane-level traffic guidance method according to claim 1, wherein the calculating of the shortest vehicle travel time between OD points according to the predicted lane-level travel time and the determining of the shortest path corresponding to the shortest vehicle travel time as a guidance planning path specifically comprises:
primary guidance path planning: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; the optimal path selection is carried out by adopting a Dijkstra shortest path search algorithm and taking the road section as a basic unit to obtain a primary optimal path set Po1＝{Eab}; wherein, Po1Representing an optimal path set which takes the road section as a basic unit after the induction decision; eabRepresenting the individual segments in the optimal path.
6. The Internet of vehicles-based lane-level traffic guidance method according to claim 5, wherein the method for calculating the shortest travel time of the vehicle between OD points according to the predicted lane-level travel time and determining the shortest path corresponding to the shortest travel time of the vehicle as a guidance planning path further comprises:
and (3) secondary guidance path planning: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
7. The internet of vehicles based lane-level traffic inducing method according to claim 6, wherein the method further comprises: and executing guidance decision control according to the guidance planning path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
8. A vehicle networking based lane-level traffic inducing system, the system comprising:
the vehicle driving data acquisition module is used for acquiring vehicle driving data and extracting effective field information in the vehicle driving data; the vehicle driving data comprises real-time driving data and historical driving data of the vehicle; the valid field information includes: vehicle ID, acquisition time, longitude, latitude, and steering wheel steering angle;
the basic road network building module is used for building a basic road network by taking intersections as nodes and road sections among the intersections as connecting lines; the road section contains lane information;
the Link dividing module is used for performing Link division on the road sections in the basic road network to form a divided road network; the road sections in the divided road network consist of a plurality of links;
the lane-level travel time calculation module is used for calculating lane-level travel time according to the driving data and the divided road networks;
the lane-level travel time prediction module is used for calculating predicted lane-level travel time of each time interval according to the historical travel data and the lane-level travel time;
and the traffic guidance module is used for calculating the shortest vehicle travel time between OD points according to the predicted lane travel time and determining the shortest path corresponding to the shortest vehicle travel time as a guidance planning path.
9. The Internet of vehicles based lane-level traffic guidance system of claim 8, wherein the Link partitioning module specifically comprises:
a road segment length determining submodule for determining the length of each road segment in the basic road network as
A third-level division submodule for performing Link division on each road section by using a third-level length threshold value
And is
When in use
When the Link is used, the whole road section is taken as a Link;
when in use
When the Link is divided from the downstream to the upstream of the road section, each Link has the length of
The last Link is an area of the remaining division length of the road section;
when in use
When the Link is divided from the downstream to the upstream of the road section, the first Link is the length
Other links have lengths of
The length of the last Link is the remaining division length of the road section;
and the numbering submodule is used for sequentially numbering the links in each road section from the downstream to the upstream of the road section.
10. The internet-of-vehicles based lane-level traffic guidance system of claim 8, wherein the lane-level travel time calculation module specifically comprises:
the map matching submodule is used for acquiring a lane-level map corresponding to the basic road network, projecting the positioning information of the vehicle in the lane-level map and matching the lane-level map by using the shortest distance principle;
the clustering submodule is used for carrying out position correspondence on the vehicle running data, the lanes and the divided links according to the positioning information and clustering the vehicle running data according to the vehicle ID so as to enable the vehicle information with the same vehicle ID to be grouped into the same set
Wherein,
the vehicle driving data of the ith Link representing the road section ab and the vehicle identification on the K lane are ID, and the vehicle driving data of the ith vehicle collecting area where the vehicle identification on the ab Link representing the road section ab and the identification on the K lane is ID are e;
the vehicle running data completion sub-module is used for screening vehicles with incomplete vehicle running data in each Link according to the vehicle running data acquisition time interval and the length of each Link, and completing the incomplete vehicle running data according to the incomplete vehicle running data and the length of the corresponding Link;
a vehicle travel time calculation submodule for utilizing a formula based on the supplemented vehicle travel data
Calculating the vehicle travel time of each lane in each Link; wherein Q is indicated on the road section EabThe total number of vehicles on the Nth Link and the Kth lane; q represents the number of travel data acquisitions for each vehicle; t represents an acquisition time interval of the vehicle travel data;
a lane-level travel time calculation submodule for calculating travel time according to a formula
Calculating the average travel time of the vehicle in each lane in the road section by taking the delta T as a time step, and saving the result to
And is
Represents the lane level travel time of the ith Δ T.
11. The internet-of-vehicles based lane-level traffic guidance system of claim 8, wherein the lane-level travel time prediction module specifically comprises:
a rolling prediction calculation submodule for using the vehicle running data of the previous and surrounding area excluding the current day as the history running data, using the vehicle running data of the current day as the real-time running data, and using the formula according to the lane-level travel time calculated by using the time step Δ T
Sequentially rolling and predicting the vehicle travel time of the next delta T, and updating the prediction information in real time; wherein,
a lane-level travel time within the i +1 st Δ T representing the same time in the history traveling data;
α and β are weighted values of the historical driving data and the real-time driving data respectively, and α + β is equal to 1;
a predicted lane-level travel time calculation submodule for calculating a predicted lane-level travel time based on a formula
Calculating the average travel time of the vehicle in each lane in the i +1 th delta T section by taking the delta T as the time step, namely the predicted lane-level travel time, and storing the result to
12. The internet of vehicles based lane-level traffic induction system of claim 8, wherein the traffic induction module specifically comprises:
the first-level guidance planning submodule is used for planning a first-level guidance path: taking the predicted lane-level travel time of each lane as the road resistance attribute of each Link in the road section; the optimal path selection is carried out by adopting a Dijkstra shortest path search algorithm and taking the road section as a basic unit to obtain a primary optimal path set Po1＝{Eab}; wherein, Po1Representing an optimal path set which takes the road section as a basic unit after the induction decision; eabRepresenting each road section in the optimal path; and/or
And the secondary guidance planning submodule is used for planning a secondary guidance path: on the basis of primary guidance path planning, the travel time of each Link lane level of each Link lane of the downstream road section of the current road section where the vehicle is located is predicted, the prediction is carried out according to the shortest time, a Link lane set with the shortest travel time is established in the road section, the shortest lane path for the vehicle to travel in the road section is formed, and a secondary optimal path set is obtained
Wherein, Po2Representing an optimal path set which takes the lane as a basic unit after the induction decision;
representing the lanes in each road segment in the optimal path.
13. The internet of vehicles based lane-level traffic guidance system of claim 8, wherein the system further comprises a decision control module for performing an inducement decision control according to the inducement planned path:
the driver selects the road section according to the primary optimal path set;
or in the driving process, reminding the driver to drive in a lane change mode according to the secondary optimal path set: when the vehicle is in the (i-1) th Link, comparing the number n of times that the lane needs to be changed between the (i) th Link and the (i + 1) th Link; if n is less than or equal to 1, a lane change prompt is issued on the ith Link, and if n is less than or equal to 1, a lane change prompt is issued on the ith Link; if n is larger than or equal to 2, issuing a lane change prompt at the i-1 Link, and taking the i Link as a transition stage to safely change lanes;
if the vehicle needs to change the driving direction at the downstream intersection, according to the Link dividing basis, if the number of the links of the current road section is more than or equal to 3, the vehicle drives the Link with the number of 2, and then the driver is prompted to change the lane to prepare for turning to the next intersection; if the number of the links of the current road section is less than 2, the vehicle prompts a driver after entering the current road section;
prompting a driver whether to change lanes or not according to steering wheel steering angle data in the vehicle driving data; meanwhile, the information of the steering lamp is monitored, and the driver is prompted to correctly use the steering lamp when changing lanes.
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