CN117238131B - Traffic flow characteristic analysis method in Internet of vehicles environment - Google Patents
Traffic flow characteristic analysis method in Internet of vehicles environment Download PDFInfo
- Publication number
- CN117238131B CN117238131B CN202311184320.2A CN202311184320A CN117238131B CN 117238131 B CN117238131 B CN 117238131B CN 202311184320 A CN202311184320 A CN 202311184320A CN 117238131 B CN117238131 B CN 117238131B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- traffic flow
- driving behavior
- driving
- environment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 49
- 230000006399 behavior Effects 0.000 claims abstract description 100
- 230000006855 networking Effects 0.000 claims abstract description 54
- 238000004088 simulation Methods 0.000 claims abstract description 44
- 238000005516 engineering process Methods 0.000 claims abstract description 24
- 230000008447 perception Effects 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims description 31
- 238000010586 diagram Methods 0.000 claims description 21
- 230000035699 permeability Effects 0.000 claims description 19
- 230000001133 acceleration Effects 0.000 claims description 15
- 238000011160 research Methods 0.000 claims description 13
- 230000003993 interaction Effects 0.000 claims description 8
- 238000012546 transfer Methods 0.000 claims description 8
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 3
- 230000035484 reaction time Effects 0.000 claims description 3
- 230000035945 sensitivity Effects 0.000 claims description 3
- 101100340331 Arabidopsis thaliana IDM1 gene Proteins 0.000 claims description 2
- 101100340333 Arabidopsis thaliana IDM2 gene Proteins 0.000 claims description 2
- 101100018407 Arabidopsis thaliana IDM3 gene Proteins 0.000 claims description 2
- 230000008878 coupling Effects 0.000 claims description 2
- 238000010168 coupling process Methods 0.000 claims description 2
- 238000005859 coupling reaction Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 abstract description 5
- 241000854291 Dianthus carthusianorum Species 0.000 description 9
- 238000010276 construction Methods 0.000 description 5
- 230000008520 organization Effects 0.000 description 5
- 230000001149 cognitive effect Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- SFKQVVDKFKYTNA-OGNLOKFRSA-N (2S)-N-[(2R)-6-amino-1-[(2-amino-2-oxoethyl)amino]-1-oxohexan-2-yl]-1-[(4R,7S,10S,13S,16S,19R)-19-amino-7-(2-amino-2-oxoethyl)-10-(3-amino-3-oxopropyl)-13,16-dibenzyl-6,9,12,15,18-pentaoxo-1,2-dithia-5,8,11,14,17-pentazacycloicosane-4-carbonyl]pyrrolidine-2-carboxamide Chemical compound NCCCC[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@@H]1CSSC[C@H](N)C(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(N)=O)C(=O)N1)C(=O)NCC(N)=O SFKQVVDKFKYTNA-OGNLOKFRSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000005065 mining Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000007429 general method Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000010206 sensitivity analysis Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004141 dimensional analysis Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 230000006461 physiological response Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a traffic flow characteristic analysis method in an Internet of vehicles environment, which comprises the following steps: constructing a comprehensive perception and simulation analysis platform for application influence study of the Internet of vehicles technology; based on the comprehensive perception and simulation analysis platform, acquiring multi-dimensional holographic space-time microscopic data of a driver in the presence/absence of the vehicle networking environment, and analyzing driving behavior characteristics in the vehicle networking environment based on the multi-dimensional holographic space-time microscopic data; acquiring the time-space relevance of driving behaviors in the Internet of vehicles environment based on the driving behavior characteristics; and analyzing traffic flow characteristics of the intelligent network-mixed motorcade based on the space-time correlation of the driving behaviors. The invention carries out system analysis on the traffic capacity and the stability of the mixed traffic flow, thereby providing technical support for formulating a traffic control optimization scheme under the technical background of the Internet of vehicles.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and particularly relates to a traffic flow characteristic analysis method in an Internet of vehicles environment.
Background
The running level of the road traffic system is not only an important index for measuring the development degree of a country, but also an important factor for reflecting the life quality and prosperity degree of modern urban residents. Since the innovation was opened, china has paid great attention to the construction of traffic infrastructure, and the construction of road traffic systems is continuously enhanced. The road traffic network infrastructure of China is greatly developed while the society of China is continuously deeply transformed and the economy is rapidly developed. The extension of the road network and the perfection of the road infrastructure bring unprecedented convenience to the travel of people. However, as the number of motor vehicles on the road is increased, people travel more frequently, and the road traffic problem is also more serious. The ever-increasing motor vehicle traffic volume and the limited road resource mismatch make bad driving phenomenon on the road endless, and road traffic problems are gradually highlighted, and are mainly manifested by aggravation of traffic jam, high traffic accident rate, increasingly serious traffic pollution problem, rising traffic travel cost and the like. The traditional intelligent traffic system realizes the integrated integration of road traffic elements through the perception-transmission-processing-application of traffic information, thereby guaranteeing the orderly organization and operation of road traffic flow. Traffic management, however, lacks inter-coordination between vehicles and road side facilities based on existing facility equipment. With the continuous progress of information communication technology, man-machine interaction technology and vehicle automatic driving technology, the development of traditional intelligent traffic infrastructure and vehicles to an intelligent networking direction has become a trend. Intelligent networked traffic systems, which integrate the internet of vehicles technology and the vehicle automated driving technology, are gradually developing and moving towards application, and intelligent networked vehicle (INTELLIGENT CONNECTED VEHICLE, ICV) technology is increasingly receiving attention from researchers and government traffic management departments at home and abroad as an important technical means for solving the traffic problems.
The current intelligent networking traffic development still faces a plurality of key technical problems to be solved, and the application effect of the Internet of vehicles technology in road traffic needs to be evaluated. With the development of driving assistance systems and internet of vehicles, drivers can acquire more traffic information in time, and the operation of the drivers is converted from visual-based stimulus response behaviors to psychological-expected initiative coping behaviors based on information induction. How the holographic traffic environment of the Internet of vehicles affects the individual driving behaviors and how the traffic behavior collection and counting effect is achieved; in the process of increasing the market share of intelligent network vehicles, different market share and fleet organization modes have influence on traffic flow capacity and stability, and answers to the problems are not clear at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a traffic flow characteristic analysis method in an Internet of vehicles environment, which is used for carrying out driving behavior research based on driving simulation, considering the cognitive characteristics of a driver to accurately describe traffic phenomena related to human factor, analyzing individual driving behavior characteristics in the Internet of vehicles environment, mining space-time track meter collection characteristics of vehicles, and providing a refined description method from microscopic driving behavior characteristics to mesoscopic traffic flow performance in the Internet of vehicles environment driven by human factors. On the basis of grasping the cognitive mechanism of a driver, the spatial distribution characteristics of traffic flow configuration and the characteristics of a vehicle control system in the vehicle networking environment, developing systematic research on the characteristics of mixed traffic flow in the vehicle networking environment; the specific implementation path is based on the establishment of a basic map model and stability discriminant of the traffic flow of the intelligent network-mixed vehicle, and the system analysis is carried out on the traffic capacity and stability of the mixed traffic flow, so that technical support is provided for the establishment of a traffic control optimization scheme under the technical background of the vehicle network.
In order to achieve the above purpose, the invention provides a traffic flow characteristic analysis method in an internet of vehicles environment, comprising the following steps:
Constructing a comprehensive perception and simulation analysis platform for application influence study of the Internet of vehicles technology;
Based on the comprehensive perception and simulation analysis platform, driving behavior data of a driver in the presence/absence of the vehicle networking environment is collected, and based on the driving behavior data, driving behavior characteristics in the vehicle networking environment are analyzed;
Acquiring the time-space relevance of driving behaviors in the Internet of vehicles environment based on the driving behavior characteristics;
and analyzing traffic flow characteristics of the intelligent network-mixed motorcade based on the space-time correlation of the driving behaviors.
Optionally, constructing the comprehensive perception and simulation analysis platform for application influence study of the internet of vehicles technology comprises:
Based on driving simulation and man-machine interaction technology, building an application scene of a man-machine double-ring in the car networking environment, and building a driving behavior characteristic research simulation experiment platform in the car networking environment facing to human factors;
Constructing a traffic flow theoretical simulation analysis platform of the intelligent network-mixed vehicle, and constructing a mixed traffic flow characteristic analysis model; the hybrid traffic characteristic analysis model includes: the system is mixed with an intelligent network-connected vehicle traffic flow basic diagram model, an intelligent network-connected vehicle traffic flow stability discriminant model and a cell-based transmission model.
Optionally, analyzing driving behavior characteristics in the internet of vehicles environment includes:
acquiring vehicle parameters based on the driving behavior data; the vehicle parameters include: vehicle position, speed, acceleration, inter-vehicle distance, and relative speed;
constructing a driving behavior database based on the driving behavior data and the vehicle parameters;
And based on the driving behavior database, carrying out driving behavior risk classification, and evaluating an individual driving behavior risk transfer mode of a driver from a traditional driving environment to a car networking environment.
Optionally, performing driving behavior risk classification, evaluating an individual driving behavior risk transfer mode of the driver from the traditional driving environment to the internet of vehicles environment, including:
Carrying out standardized processing on driving behavior data;
Calculating the distance between the space-time sequences of the standardized driving behavior data by using the DTW;
calculating DBI index to obtain optimal cluster number k under the condition of on/off of the vehicle-mounted system;
The DBI index is:
Wherein, For the average distance between observation i and all other observations in the cluster, A i is the centroid of cluster i, the number of data points in cluster i, T i, a m,i is the m-th centroid of cluster i, k is the cluster number, j is the data point,/>For the average distance between observation j and all other observations in the cluster, w i is the cluster i center position, w j is the cluster j center position;
Dividing drivers into k groups based on the optimal clustering number k results; calculating a speed standard deviation, an acceleration standard deviation and an acceleration change rate standard deviation as safety indexes, and sequencing risk levels of the clustering group, wherein the risk levels comprise a high risk level, a medium risk level and a low risk level; the highest set of DBI indices is at a high risk level; the lowest exponential set is at a low risk level; the other group is at a medium risk level; and comparing the driving behavior difference of the driver in the process of passing through the tunnel section in the on/off state of the vehicle-mounted system, classifying the individual driving behavior risks based on the driving behavior time-space sequence data, and evaluating the individual driving behavior risk transfer mode of the driver from the traditional driving environment to the Internet of vehicles environment.
Optionally, acquiring the driving behavior time-space correlation in the internet of vehicles environment includes:
describing the space-time characteristics of the vehicle track in a typical scene by taking a vehicle-mounted driving auxiliary system which is not started as a comparison group, and drawing a space-time characteristic diagram;
Under the condition of considering all microscopic traffic individual experimental characteristics, the composition modes thereof and the minimum safety headway time constraint, a method for converging driving behavior individual characteristics to mesoscopic traffic flow characteristics in a vehicle networking environment is provided based on the space-time characteristic diagram, the traffic efficiency space distribution characteristics of road scene sections are obtained, and the driving behavior space-time correlation in the vehicle networking environment is further obtained.
Optionally, the spatiotemporal feature map comprises a vehicle spatiotemporal trajectory; the vehicle space-time track is used for recording vehicle running behavior characteristics, and comprises the following steps: recording the time point, the corresponding driving position, the vehicle running speed and the vehicle acceleration.
Optionally, acquiring the traffic efficiency spatial distribution characteristic of the road scene road segment includes:
Acquiring driving behavior data under the condition of whether the vehicle networking information is included or not in a driving simulation environment, and further extracting space-time track data of each driver;
Respectively determining the preset reaction time of drivers in the traditional driving environment and the Internet of vehicles environment as the minimum following time interval requirement, and based on the minimum following time interval, taking the following time interval and the minimum following time interval of all sections of a road as targets to obtain the optimal sequencing mode of converging the space-time tracks of all drivers;
and calculating the average headway of the road section, and obtaining the space distribution characteristics of the road section passing efficiency according to the passing efficiency calculation method.
Optionally, analyzing traffic flow characteristics of the intelligent network-mixed fleet comprises:
Aiming at traffic flows mixed with intelligent network linkage vehicle teams, traffic flow configuration and spatial random distribution characteristics are analyzed, and logic analysis and mathematical expression capable of describing spatial random distribution of different types of vehicles in the traffic flows are established;
Based on the running state condition of the balanced state traffic flow, on the basis of analyzing traffic flow characteristics by considering the permeability and the control parameter sensitivity of the intelligent network vehicle, increasing the limit of the maximum vehicle queue size when the intelligent network vehicle is in queue running, and establishing a discriminant mixed with the basic graph model and the stability of the traffic flow of the intelligent network vehicle queue;
and based on the discriminant model mixed with the traffic flow basic diagram model and the stability of the intelligent network connected vehicle team, completing traffic flow characteristic analysis in the environment of the vehicle networking.
Optionally, the logic parsing and mathematical expression is:
Wherein p rv represents the permeability of the HDV vehicle, p lv1 represents the permeability of the PL1 vehicle, p lv2 represents the permeability of the PL2 vehicle, p ca represents the permeability of the PF vehicle, p represents the permeability of the intelligent network vehicle, and S represents the fleet size.
Optionally, the discriminant of the traffic flow basic graph model and the stability of the intelligent network coupled vehicle team is:
wherein, F represents the traffic flow stability of the network-connected vehicle team, F IDM1 represents the traffic flow stability of the same mass of the HDV vehicle, F IDM2 represents the traffic flow stability of the same mass of the PL1 vehicle, F IDM3 represents the traffic flow stability of the same mass of the PL2 vehicle, F CACC represents the traffic flow stability of the same mass of the PF vehicle, and F represents the partial differential form of the F function.
Compared with the prior art, the invention has the following advantages and technical effects:
According to the invention, driving behavior research is carried out based on driving simulation, the cognitive characteristics of a driver are considered to accurately describe traffic phenomena related to human factor, individual driving behavior characteristics in the environment of the internet of vehicles are analyzed, the space-time track set meter characteristics of the vehicles are excavated, and a refined description method is provided for microscopic driving behavior characteristics to mesoscopic traffic flow performance in the environment of the internet of vehicles driven by human factors. On the basis of grasping the cognitive mechanism of a driver, the spatial distribution characteristics of traffic flow configuration and the characteristics of a vehicle control system in the vehicle networking environment, developing systematic research on the characteristics of mixed traffic flow in the vehicle networking environment; the specific implementation path is based on the establishment of a basic map model and stability discriminant of the traffic flow of the intelligent network-mixed vehicle, and the system analysis is carried out on the traffic capacity and stability of the mixed traffic flow, so that technical support is provided for the establishment of a traffic control optimization scheme under the technical background of the vehicle network.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic diagram of a novel mixed traffic flow composition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a numerical simulation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a workflow of a numerical simulation platform according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a data processing flow according to an embodiment of the present invention;
FIG. 5 is a schematic illustration of a vehicle space-time trajectory in accordance with an embodiment of the present invention; wherein, (a) is a single vehicle space-time trajectory curve and (b) is all vehicle space-time trajectory curves;
Fig. 6 is a schematic flow chart of a feature mining method of a vehicle space-time trajectory set meter according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Under the novel mixed traffic flow condition of the Internet of vehicles environment, the conditions of strong human-caused vehicle types (HDV, CV) and weak human-caused vehicle types (AV, CAV) exist in traffic flow, and different traffic flow composition structures are caused by the conditions of different intelligent networking degree vehicle permeability, different vehicle team organization modes and the like, so that different traffic flow characteristics are presented. In addition, CV vehicle driver operation is converted from a physiological response behavior based on visual stimuli to an active response behavior based on psychological expectations under information inducement. Furthermore, due to the difference of individual driving behaviors and the timeliness of the internet of vehicles information service, the driving behaviors show a certain time-space correlation. Therefore, the embodiment is based on the driving simulation platform, firstly, the individual driving behavior characteristics under the environment of the internet of vehicles are depicted by facing to the human factors, and the traffic behavior time-space meter characteristics are mined. Meanwhile, the influence of the traffic flow configuration on the traffic capacity and stability of the novel mixed traffic flow is further analyzed, and based on the influence, an application scheme of the intelligent network-mixed vehicle traffic flow refined traffic control is explored.
The embodiment is based on traffic engineering, applies methods such as statistical analysis, traffic simulation and the like, and develops research work according to the technical framework of platform construction, data acquisition, feature depiction, microscopic modeling, macroscopic analysis and application case. And constructing a test scene under the high-simulation car networking environment by using a driving simulation and man-machine interaction technology, carrying out a driving simulation experiment, collecting multi-dimensional holographic space-time microscopic data under the car networking environment or not, and further establishing a driving behavior characteristic database taking an individual driver as a unit. Based on the driving simulation experiment data base, the method is oriented to human factor to identify and characterize the driving behavior of the network-connected manual driving vehicle. Under the condition of considering all microscopic traffic individual experimental characteristics, the composition modes thereof and the minimum safety headway time constraint, a method for converging driving behavior individual characteristics to mesoscopic traffic flow characteristics and a method for solving traffic capacity in a vehicle networking environment are provided. In addition, a mathematical expression for accurately describing the spatial distribution characteristics of the road traffic flow configuration is given for the traffic flow of the intelligent network vehicle team mixed in the environment of the internet of vehicles; on the basis of considering the permeability of the intelligent network vehicles and the sensitivity analysis traffic flow characteristics of control parameters, the maximum vehicle team scale limit is increased when the intelligent network vehicles are in train driving, further, an analysis type of network mixed traffic flow basic diagram model and stability judgment under a vehicle team mode is established, and sensitivity analysis of traffic capacity and stability is carried out on relevant model parameters such as the intelligent network vehicles permeability and the maximum vehicle team scale limit.
With the progressive maturity of the internet of vehicles technology, new mixed traffic flows consisting of manually driven vehicles (Human-DRIVEN VEHICLE, HDV) in traditional visual perception-reaction-operation modes, networked manually driven vehicles (Connected Vehicle, CV) assisted by information perceived by means of the internet of vehicles technology, single intelligent vehicles (Autonomous Vehicle, AV) realizing automatic driving through vehicle self-detection equipment, and networked automatic vehicles (Connected and Autonomous Vehicle, CAV) capable of assisting intelligent vehicle decisions by using technologies such as vehicle-to-vehicle, vehicle-to-road communication and the like will appear in road traffic flows. As shown in fig. 1.
Constructing a research thought of platform construction, data acquisition, feature characterization, microscopic modeling and macroscopic analysis. Traffic flow characteristic analysis content based on the simulated internet of vehicles environment comprises the following steps:
(1) Comprehensive perception and simulation analysis platform for establishing application influence research oriented to Internet of vehicles technology
① Based on driving simulation and man-machine interaction technology, key problems such as scene design, event design, information interaction realization and experimental control are solved, an application scene of a man-machine double-ring in a car networking environment is built, a driving behavior characteristic research simulation experiment platform in the car networking environment facing to human factors is built, and driving behavior data sensing and acquisition in the car networking environment with or without the car networking environment are realized.
② The method comprises the steps of constructing a traffic flow theory simulation analysis platform of the intelligent network-mixed vehicle, and constructing a traffic flow characteristic analysis model of the intelligent network-mixed vehicle, wherein the traffic flow basic diagram model of the intelligent network-mixed vehicle, the traffic flow stability discriminant of the intelligent network-mixed vehicle and the multi-lane traffic flow simulation analysis method based on a cell transmission model. A schematic diagram based on a numerical simulation method is shown in fig. 2.
The intelligent network-connected vehicles are mixed, vehicles with different intelligent network connection degrees and vehicle team organization forms exist in the traffic flow, so that the characteristics of numerical simulation technology and vehicle networking technology under different traffic scenes are fully combined, the virtual environment construction of traffic flow configuration and space probability distribution characteristics is realized, and finally, the traffic flow state sensing and simulation analysis platform under the vehicle networking environment is integrated. And (3) researching a traffic flow theory based on the mixed traffic flow in the environment of the Internet of vehicles, and analyzing traffic flow characteristics of the intelligent Internet of vehicles by utilizing a numerical simulation platform. The numerical simulation platform workflow is shown in fig. 3.
Before the large-scale application of the Internet of vehicles technology, a computer numerical simulation experiment is a necessary means for researching the running characteristics of the mixed traffic flow in the Internet of vehicles environment. The simulation platform which accords with the road intelligent network traffic running environment and accurately describes the microcosmic simulation model (microcosmic following/lane changing model) of various intelligent vehicles and manual driving vehicles in a typical road scene is the basis for carrying out road intelligent network traffic control and resource optimization configuration research. And establishing a road scene driving rule and a vehicle-to-vehicle and vehicle-to-road communication virtual rule, and testing different vehicle networking road application scenes, including test scenes such as initiative management and control strategies, special road section early warning prompts and the like under different vehicle networking environments. And constructing a hybrid traffic flow theoretical simulation platform under the Internet of vehicles environment based on Matlab by carrying out parameter discussion selection on a traffic flow model mixed with the intelligent Internet of vehicles. The general method for establishing the mixed traffic flow analysis comprises a general method for analyzing a basic map model of the traffic flow of the intelligent network-connected vehicle, a method for judging the stability of the traffic flow of the intelligent network-connected vehicle and a multi-lane traffic flow simulation method based on a cell transmission model.
(2) Multi-dimensional analysis of driving behavior characteristics in Internet of vehicles environment
① The multi-dimensional holographic space-time microscopic data of a driver in the presence/absence of the vehicle networking environment is obtained through a driving simulation experiment, and parameters such as the vehicle position, the speed, the acceleration, the following distance, the relative speed and the like are calculated and obtained.
② And (3) taking the individual driver as a unit, and establishing a driving behavior database of the driver in the presence/absence of the internet of vehicles.
③ Taking a special tunnel section as an example, the influence of the car networking environment on the speed control behavior, the driving comfort and the driving ecology of a driver is analyzed. And using DTW and K-means algorithm to provide a driving behavior risk classification method based on time-space sequence data, so as to evaluate the individual driving behavior risk transfer mode of the driver from the traditional driving environment to the Internet of vehicles environment.
Firstly, carrying out standardized processing on experimental analysis road section driving behavior data; then, calculating the distance between the space-time sequences by using the DTW; secondly, in order to determine the optimal cluster number, DBI measurement is calculated, and the optimal cluster number k under the condition that the vehicle-mounted system is turned on/off is obtained. Finally, based on the DTW distance, the drivers are classified into K groups by using a K-means algorithm. Calculating a speed standard deviation, an acceleration standard deviation and an acceleration change rate standard deviation as safety indexes, and sequencing the risk levels of the clustering groups: high risk level, medium risk level, and low risk level. The highest exponential set is at a high risk level; the lowest exponential set is at a low risk level; the other group is at a medium risk level. And comparing the driving behavior difference of the driver in the process of passing through the tunnel section in the on/off state of the vehicle-mounted system, classifying the individual driving behavior risks based on the driving behavior time-space sequence data, and further evaluating the individual driving behavior risk transfer mode of the driver from the traditional driving environment to the Internet of vehicles environment. The data processing flow is shown in fig. 4.
(3) Space-time correlation of driving behaviors in Internet of vehicles
① Taking a typical scene of a highway as an example, carrying out space-time correlation research on driving behaviors in an internet of vehicles environment for human factors, taking a vehicle-mounted driving auxiliary system which is not started as a comparison group, finely describing the space-time characteristics of vehicle tracks in the typical scene, and drawing a space-time characteristic diagram.
To describe the state of spatiotemporal movement of a vehicle, the simplest approach is to use a time-distance diagram of the vehicle trajectory. The vehicle space-time trajectory curves are shown in fig. 5, where fig. 5 (a) is a single vehicle space-time trajectory curve and fig. 5 (b) is all vehicle space-time trajectory curves.
② Under the condition of considering all microscopic traffic individual experimental characteristics, the composition modes thereof and the minimum safety headway time constraint, a method for converging driving behavior individual characteristics to mesoscopic traffic flow characteristics and a method for solving traffic capacity under the environment of the Internet of vehicles are provided.
The vehicle space-time track records the running behavior characteristics of the vehicle, including the attributes of recording time points, corresponding running positions, running speeds of the vehicle, acceleration of the vehicle and the like. In the driving environment of the Internet of vehicles, a driver obtains early warning or service information fed back by the vehicle-mounted system according to the change of the traffic environment in real time, so that the driving behavior presents a certain time-space relevance. Considering the characteristics of traffic environment complexity, driving behavior uncertainty and vehicle networking information induction timeliness, it is necessary to describe driving behavior time-space characteristics aiming at specific road scenes, mine traffic behavior set counting characteristics, further explore driving behavior time and air evolution rules and influence thereof on traffic efficiency in specific application scenes of vehicle networking technology, and provide data basis and theoretical support for road traffic efficiency improvement in the vehicle networking environment. And respectively obtaining the optimal space-time track convergence mode in the presence/absence of the vehicle networking environment based on the minimum headway principle, calculating the road section traffic efficiency under the experimental condition, and further evaluating the influence of the vehicle networking environment on the road traffic efficiency space-time distribution characteristics. And extracting driving behavior space-time characteristic data of a driver in the presence/absence of the vehicle networking environment of the specific scene road section, wherein the space-time track curves of the driver in the two driving environments are randomly ordered according to a minimum following time interval principle (namely, the time difference of the space-continuous space-time track curves of the front and rear vehicles passing through any section of the road is larger than the minimum following time interval). And (3) researching and utilizing a Matlab simulation experiment method, repeating a test N (recommended N > 1000) according to the method, and sequencing when the sum of the vehicle space-time track line convergence and the vehicle heel distance of all sections is minimum to obtain a space-time track convergence optimal mode. The vehicle space-time trajectory set feature mining method is shown in fig. 6.
To search for traffic efficiency spatial distribution characteristics for a particular road scene segment, the following three steps are required:
1) Acquiring driving behavior data under the condition of whether the vehicle networking information is included or not in a driving simulation environment, and further extracting space-time track data of each driver;
2) Respectively determining the general reaction time of drivers in two environments as a minimum following moment requirement, and based on the minimum following moment, taking the following moment and the minimum of all sections of a road as targets to obtain an optimal sequencing mode of converging space-time tracks of all drivers;
3) And calculating the average headway of the road section, and obtaining the space distribution characteristics of the road section passing efficiency according to the passing efficiency calculation method.
(4) System analysis mixed intelligent network vehicle team traffic flow characteristic
① Aiming at traffic flows mixed with intelligent network connected vehicle teams, traffic flow configuration and spatial random distribution characteristics are analyzed, and logic analysis and mathematical expression capable of accurately describing spatial random distribution of different types of vehicles in the traffic flows are established.
The motorcade considered is composed of intelligent network vehicles which are in the same direction and are spatially continuous on the same lane, the motorcade cluster heads (Platoon Leader, PL) are borne by the head vehicles of a motorcade, and other vehicles in the motorcade are following vehicles (Platoon Follower, PF). For driving safety reasons, fleet cluster heads typically select drivers with a rich driving experience to drive, and the following vehicles within the fleet are typically considered CACC control mode vehicles. In addition, intelligent network fleets typically have a certain maximum size limit (assuming a maximum size limit value of S) considering the effective range and stability of communication between vehicles, i.e., when a fleet exceeds the maximum size of S, a new fleet needs to be built. 4 vehicle types existing in the traffic flow of the intelligent network connected vehicle team are mixed: the system comprises a traditional manual driving vehicle HDV, a cluster head PL1 of a fleet following the traditional vehicle, a cluster head PL2 of a fleet following an intelligent network and a vehicle PF following the intelligent network. The mixed traffic flow will correspondingly have 4 head intervals, which are in turn: HDV head pitch h rv, PL1 vehicle head pitch h lv1, PL2 vehicle head pitch h lv2, PF vehicle head pitch h ca. Because the fleet cluster head PL1 cannot perform information interaction with the front vehicle HDV, compared with the fleet cluster head PL2 which is connected with the intelligent network and can perform information interaction with the fleet vehicles, the head space of the fleet cluster head PL1 under the balanced state speed condition is larger, namely h lv2<hlv1. When the vehicle is driven in a vehicle team organization mode, vehicles in a train can keep smaller following distance, and the following relation exists between different types of vehicle head distances under the condition of mixed traffic flow equilibrium state: h ca<hlv2<hlv1<hrv.
Under the steady-state condition of traffic flow of the intelligent network vehicle team mixed with the expressway basic road section, assuming that the permeability of the intelligent network vehicle is p, p rv,plv1,plv2,pca is used for representing the permeability of HDV, PL1, PL2 and PF vehicles respectively, and then p=p lv1+plv2+pca; the probability of a vehicle type HDV is: p rv = 1-p.
The 4 vehicle types HDV, PL1, PL2 and PF probability p rv、plv1、plv2、pca in the traffic flow of the intelligent network coupled vehicle are respectively:
When the permeability of the intelligent network-connected vehicle is p, the probability of 4 different following headtimes (HDV, PL1, PL2, PF) in the mixed traffic flow can be obtained by the theoretical formula.
② Based on the running state condition of the balanced state traffic flow, the maximum vehicle queue scale limit when the intelligent network vehicle queues are driven is increased and considered on the basis of analyzing the traffic flow characteristics by considering the permeability of the intelligent network vehicle and the sensitivity of the control parameters, and a traffic flow basic diagram model and a stability discriminant of the intelligent network vehicle queue are built.
When the traffic flow is the equilibrium speed v e, the speed difference between the following vehicle and the leading vehicle is 0, and all the vehicle accelerations are 0, that is, the vehicle running state in the equilibrium traffic flow satisfies the following conditions:
Wherein v is the vehicle speed, v e is the traffic flow equilibrium speed, deltav is the speed difference between the following vehicle and the leading vehicle in the equilibrium state, Is the vehicle acceleration.
Various heel models have been proposed by students in the past to describe the behavior of a manually driven vehicle, and each model has advantages and disadvantages. The IDM model is selected as a following model of the manual driving vehicle, and the model is as follows:
In the method, in the process of the invention, The acceleration of the manually driven vehicle, a m is the maximum acceleration, v 0 is the free flow velocity, v is the equilibrium velocity, Δv is the difference between the vehicle and the leading vehicle, δ is the acceleration index, s 0 is the minimum safety distance during vehicle braking, T i is the safety headway of different types of manually driven vehicles (i=1, 2,3; wherein i=1 represents HDV vehicles, i=2 represents fleet cluster head PL1 vehicles, i=3 represents fleet cluster head PL2 vehicles), b is the desired deceleration, h is the equilibrium headway, and l is the vehicle body length.
The IDM model parameters have different values, and can better reflect the following characteristics of different networked intelligent level manual driving vehicles. The IDM parameter values are shown in table 1:
TABLE 1
In this embodiment, a CACC heel model, which is proposed by the PATH laboratory at university of california to be a nonlinear dynamic head space strategy, is selected as the application case analysis, and the heel model is as follows:
According to the condition of the traffic flow balance state, in the traffic flow of the intelligent network-mixed motorcade mixed with the expressway, assuming that all vehicles run at the balance state speed v e, according to the traffic flow density definition, the density of the traffic flow of the intelligent network-mixed motorcade can be calculated as follows:
Starting from the function relation of the speed-head distance of the equilibrium state, a basic diagram model of the traffic flow of the intelligent network coupling vehicle team mixed in the environment of the vehicle networking is established, and the basic diagram model is as follows:
The traffic flow stability analysis is beneficial to revealing the inherent mechanism of the improvement of the road traffic running efficiency. Based on the above-mentioned mixed traffic flow vehicle type (HDV, PL1, PL2, PF) composition analysis and following model selection, the partial differential form in the traffic flow stability calculation formula of the intelligent network vehicle team mixed in the internet of vehicles environment is as follows:
Where IDM i is a following model of HDV, PL1, and PL2 vehicles, i=1 denotes an HDV vehicle, i=2 denotes a PL1 vehicle, and i=3 denotes a PL2 vehicle.
In addition, the research starts from the general structure of the traditional following model, and proposes that the traffic flow stability discriminant of the network-mixed motorcade is as follows:
The simplified form is as follows:
The intelligent network vehicle team traffic flow basic diagram model derivation and stability discrimination analytic method is mixed in the vehicle networking environment, scientific method support is provided for traffic flow capacity analysis, stability analysis and related traffic flow optimization design in the vehicle networking environment, and theoretical basis is provided for future large-scale intelligent network traffic system infrastructure construction.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (3)
1. The traffic flow characteristic analysis method in the environment of the Internet of vehicles is characterized by comprising the following steps:
Constructing a comprehensive perception and simulation analysis platform for application influence study of the Internet of vehicles technology;
based on the comprehensive perception and simulation analysis platform, driving behavior data of a driver in the presence or absence of the vehicle networking environment is collected, and based on the driving behavior data, driving behavior characteristics in the vehicle networking environment are analyzed;
Acquiring the time-space relevance of driving behaviors in the Internet of vehicles environment based on the driving behavior characteristics;
Based on the space-time correlation of the driving behaviors, analyzing traffic flow characteristics of the intelligent network-mixed motorcade;
Analyzing driving behavior characteristics in the internet of vehicles environment comprises the following steps:
acquiring vehicle parameters based on the driving behavior data; the vehicle parameters include: vehicle position, speed, acceleration, inter-vehicle distance, and relative speed;
constructing a driving behavior database based on the driving behavior data and the vehicle parameters;
Based on the driving behavior database, performing driving behavior risk classification, and evaluating an individual driving behavior risk transfer mode of a driver from a traditional driving environment to a car networking environment;
Performing driving behavior risk classification, evaluating individual driving behavior risk transfer modes of a driver from a traditional driving environment to a car networking environment, and comprising:
Carrying out standardized processing on driving behavior data;
Calculating the distance between the space-time sequences of the standardized driving behavior data by using the DTW;
calculating DBI indexes to obtain an optimal cluster number k under the condition that a vehicle-mounted driving auxiliary system is turned on and turned off;
The DBI index is:
Wherein, For the average distance between observation i and all other observations in the cluster, k is the number of clusters, j is the observation,/>For the average distance between observation j and all other observations in the cluster, w i is the observation i center position, w j is the observation j center position;
Dividing drivers into k groups based on the optimal clustering number k results; ranking risk levels of a cluster group based on the DBI index, wherein the risk levels include a high risk level, a medium risk level, and a low risk level; the highest set of DBI indices is at a high risk level; the lowest exponential set is at a low risk level; the other group is at a medium risk level; comparing the driving behavior difference of a driver in the process of passing through a tunnel section under the on-vehicle driving assistance system and off state, classifying individual driving behavior risks based on driving behavior time-space sequence data, and evaluating an individual driving behavior risk transfer mode of the driver from a traditional driving environment to a vehicle networking environment;
The method for acquiring the time-space relevance of the driving behavior in the car networking environment comprises the following steps:
describing the space-time characteristics of the vehicle track in a typical scene by taking a vehicle-mounted driving auxiliary system which is not started as a comparison group, and drawing a space-time characteristic diagram;
under the condition of considering all microscopic traffic individual experimental characteristics, the composition modes thereof and the minimum safety headway time constraint, based on the space-time characteristic diagram, providing a method for converging individual driving behavior characteristics to mesoscopic traffic flow characteristics in a vehicle networking environment, acquiring traffic efficiency space distribution characteristics of road scene sections, and further acquiring the space-time correlation of driving behavior in the vehicle networking environment;
The method for acquiring the traffic efficiency spatial distribution characteristics of the road scene road sections comprises the following steps:
Acquiring driving behavior data under the condition of whether the vehicle networking information is included or not in a driving simulation environment, and extracting space-time track data of each driver;
Respectively determining the preset reaction time of drivers in the traditional driving environment and the Internet of vehicles environment as the minimum following time interval requirement, and based on the minimum following time interval, taking the following time interval and the minimum following time interval of all sections of a road as targets to obtain the optimal sequencing mode of converging the space-time tracks of all drivers;
Calculating the average headway of the road section, and obtaining the space distribution characteristics of the road section passing efficiency according to a passing efficiency calculation method;
the analysis of traffic flow characteristics of the intelligent network-mixed motorcade comprises the following steps:
Aiming at traffic flows mixed with intelligent network linkage vehicle teams, traffic flow configuration and spatial random distribution characteristics are analyzed, and logic analysis and mathematical expression capable of describing spatial random distribution of different types of vehicles in the traffic flows are established;
Based on the running state condition of the balanced state traffic flow, on the basis of analyzing traffic flow characteristics by considering the permeability and the control parameter sensitivity of the intelligent network vehicle, increasing the limit of the maximum vehicle queue size when the intelligent network vehicle is in queue running, and establishing a discriminant mixed with the basic graph model and the stability of the traffic flow of the intelligent network vehicle queue;
Based on the basic diagram model of the traffic flow mixed with the intelligent network coupling vehicle team and the discriminant of the stability, completing traffic flow characteristic analysis in the environment of the vehicle networking;
the logic analysis and mathematical expression is as follows:
wherein, p rv represents the permeability of the HDV vehicle, p lv1 represents the permeability of the PL1 vehicle, p lv2 represents the permeability of the PL2 vehicle, p ca represents the permeability of the PF vehicle, p represents the permeability of the intelligent network vehicle, and S represents the fleet size;
The discriminant mixed with the basic map model and the stability of the traffic flow of the intelligent network connected vehicle team is as follows:
Wherein, F represents the traffic flow stability of the network-connected vehicle team, F IDM1 represents the traffic flow stability of the same mass of the HDV vehicle, F IDM2 represents the traffic flow stability of the same mass of the PL1 vehicle, F IDM3 represents the traffic flow stability of the same mass of the PL2 vehicle, F CACC represents the traffic flow stability of the same mass of the PF vehicle, and F represents the partial differential form of the F function.
2. The method for analyzing traffic flow characteristics in an internet of vehicles environment according to claim 1, wherein constructing a comprehensive perception and simulation analysis platform for application influence study of internet of vehicles technology comprises:
Based on driving simulation and man-machine interaction technology, building an application scene of a man-machine double-ring in the car networking environment, and building a driving behavior characteristic research simulation experiment platform in the car networking environment facing to human factors;
Constructing a traffic flow theoretical simulation analysis platform of the intelligent network-mixed vehicle, and constructing a mixed traffic flow characteristic analysis model; the hybrid traffic characteristic analysis model includes: the system is mixed with an intelligent network-connected vehicle traffic flow basic diagram model, an intelligent network-connected vehicle traffic flow stability discriminant model and a cell-based transmission model.
3. The method for traffic flow characteristic analysis in a vehicle networking environment according to claim 1, wherein the spatiotemporal feature map comprises a vehicle spatiotemporal trajectory; the vehicle space-time track is used for recording vehicle running behavior characteristics, and comprises the following steps: recording the time point, the corresponding driving position, the vehicle running speed and the vehicle acceleration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311184320.2A CN117238131B (en) | 2023-09-14 | 2023-09-14 | Traffic flow characteristic analysis method in Internet of vehicles environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311184320.2A CN117238131B (en) | 2023-09-14 | 2023-09-14 | Traffic flow characteristic analysis method in Internet of vehicles environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117238131A CN117238131A (en) | 2023-12-15 |
| CN117238131B true CN117238131B (en) | 2024-05-07 |
Family
ID=89083727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311184320.2A Active CN117238131B (en) | 2023-09-14 | 2023-09-14 | Traffic flow characteristic analysis method in Internet of vehicles environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117238131B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110053631A (en) * | 2019-04-17 | 2019-07-26 | 北京工业大学 | A kind of driving behavior optimization method and device |
| CN110633729A (en) * | 2019-08-06 | 2019-12-31 | 清华大学苏州汽车研究院（相城） | Driving risk hierarchical clustering method for intelligent networking vehicle group test |
| CN111552926A (en) * | 2020-04-28 | 2020-08-18 | 重庆长安新能源汽车科技有限公司 | Driving behavior evaluation method and system based on Internet of vehicles and storage medium |
| CN112700642A (en) * | 2020-12-19 | 2021-04-23 | 北京工业大学 | Method for improving traffic passing efficiency by using intelligent internet vehicle |
| CN113066282A (en) * | 2021-02-26 | 2021-07-02 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Vehicle-following coupling relation modeling method and system in mixed-line environment |
| CN113657432A (en) * | 2021-06-30 | 2021-11-16 | 桂林电子科技大学 | Commercial vehicle driving behavior risk level identification method based on Internet of vehicles data |
| CN114611292A (en) * | 2022-03-12 | 2022-06-10 | 北京工业大学 | Traffic flow characteristic simulation method for ACC and CACC vehicle mixing based on cellular automaton |
| CN114707573A (en) * | 2022-02-25 | 2022-07-05 | 吉林大学 | Unsupervised driving style analysis method based on basic driving operation event |
| CN116592903A (en) * | 2023-05-06 | 2023-08-15 | 四川警察学院 | Ecological driving path real-time planning method for group preference under vehicle-road cooperative environment |
| CN116704775A (en) * | 2023-06-27 | 2023-09-05 | 大连海事大学 | Mixed traffic flow traffic capacity calculation method considering intelligent network bus |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11548515B2 (en) * | 2020-12-22 | 2023-01-10 | Toyota Motor Engineering & Manufacturing North America, Inc. | Systems and methods for managing driver habits |
| FI129297B (en) * | 2021-02-19 | 2021-11-15 | Taipale Telematics Oy | Device, method and computer program for determining the driving manner of a vehicle driver |
| US20230039738A1 (en) * | 2021-07-28 | 2023-02-09 | Here Global B.V. | Method and apparatus for assessing traffic impact caused by individual driving behaviors |
-
2023
- 2023-09-14 CN CN202311184320.2A patent/CN117238131B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110053631A (en) * | 2019-04-17 | 2019-07-26 | 北京工业大学 | A kind of driving behavior optimization method and device |
| CN110633729A (en) * | 2019-08-06 | 2019-12-31 | 清华大学苏州汽车研究院（相城） | Driving risk hierarchical clustering method for intelligent networking vehicle group test |
| CN111552926A (en) * | 2020-04-28 | 2020-08-18 | 重庆长安新能源汽车科技有限公司 | Driving behavior evaluation method and system based on Internet of vehicles and storage medium |
| CN112700642A (en) * | 2020-12-19 | 2021-04-23 | 北京工业大学 | Method for improving traffic passing efficiency by using intelligent internet vehicle |
| CN113066282A (en) * | 2021-02-26 | 2021-07-02 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Vehicle-following coupling relation modeling method and system in mixed-line environment |
| CN113657432A (en) * | 2021-06-30 | 2021-11-16 | 桂林电子科技大学 | Commercial vehicle driving behavior risk level identification method based on Internet of vehicles data |
| CN114707573A (en) * | 2022-02-25 | 2022-07-05 | 吉林大学 | Unsupervised driving style analysis method based on basic driving operation event |
| CN114611292A (en) * | 2022-03-12 | 2022-06-10 | 北京工业大学 | Traffic flow characteristic simulation method for ACC and CACC vehicle mixing based on cellular automaton |
| CN116592903A (en) * | 2023-05-06 | 2023-08-15 | 四川警察学院 | Ecological driving path real-time planning method for group preference under vehicle-road cooperative environment |
| CN116704775A (en) * | 2023-06-27 | 2023-09-05 | 大连海事大学 | Mixed traffic flow traffic capacity calculation method considering intelligent network bus |
Non-Patent Citations (12)
| Title |
|---|
| Analysis on the characteristics of traffic flow in expressway weaving area under mixed connected and autonomous environment;Zhang Weihua等;Journal of Southeast University (Natural Science Edition);20230120;156-164 * |
| Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles;Chang, X等;PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS;20201101;1-14 * |
| Chang, X等.Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS.2020,1-14. * |
| Spatiotemporal Characteristics of Vehicle Trajectories in a Connected Vehicle Environment—A Case of an Extra-Long Tunnel Scenario;Xin Chang等;IEEE Systems Journal;20200518;2293-2304 * |
| 动态时空数据驱动的认知车联网频谱感知与共享技术研究;郭彩丽;陈九九;宣一荻;张荷;;物联网学报;20200818(第03期);96-105 * |
| 基于驾驶行为模式转移的驾驶行为风险评估方法;孙宫昊等;汽车技术;20211130;22-29 * |
| 孙宫昊等.基于驾驶行为模式转移的驾驶行为风险评估方法.汽车技术.2021,22-29. * |
| 常鑫等.隧道路段预警作用下的时空集计通行能力分析.华南理工大学学报(自然科学版).2020,107-115+123. * |
| 混有智能网联车队的交通流基本图模型分析;常鑫等;东南大学学报(自然科学版);20200720;782-788 * |
| 考虑交通运行条件影响的驾驶员特征聚类;张建波;交通运输系统工程与信息;20220228;330-336 * |
| 车联网环境下跟驰行为建模及交通流稳定性分析;李腾龙;中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑);20180215;C034-1051 * |
| 隧道路段预警作用下的时空集计通行能力分析;常鑫等;华南理工大学学报(自然科学版);20200915;107-115+123 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117238131A (en) | 2023-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Development of a representative urban driving cycle construction methodology for electric vehicles: A case study in Xi’an | |
| CN103247091B (en) | A kind of driving evaluation system and method | |
| CN108198425A (en) | A kind of construction method of Electric Vehicles Driving Cycle | |
| CN106127586A (en) | Vehicle insurance rate aid decision-making system under big data age | |
| CN104200267A (en) | Vehicle driving economy evaluation system and vehicle driving economy evaluation method | |
| CN110836675B (en) | Decision tree-based automatic driving search decision method | |
| CN102136190A (en) | Dispatching management system and method for event emergency response of urban bus passenger transport | |
| Pi et al. | Automotive platoon energy-saving: A review | |
| CN117079459A (en) | Method and system for constructing traffic flow velocity dense energy spectrum of hybrid automatic driving | |
| CN113450564B (en) | Intersection passing method based on NARX neural network and C-V2X technology | |
| CN109979198B (en) | Urban expressway vehicle speed discrete identification method based on large-scale floating vehicle data | |
| Wang et al. | ARIMA model and few-shot learning for vehicle speed time series analysis and prediction | |
| He et al. | Visualization and analysis of mapping knowledge domain of heterogeneous traffic flow | |
| CN110867075A (en) | Method for evaluating influence of road speed meter on reaction behavior of driver under rainy condition | |
| Zhai et al. | Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities: Toronto and Beijing | |
| CN115221234A (en) | Method and system for portraying user based on power assembly data | |
| CN113112137A (en) | Method for evaluating linear safety of interchange ramps | |
| CN117238131B (en) | Traffic flow characteristic analysis method in Internet of vehicles environment | |
| Ma | Effects of vehicles with different degrees of automation on traffic flow in urban areas | |
| Chen et al. | Platoon separation strategy optimization method based on deep cognition of a driver’s behavior at signalized intersections | |
| Ma et al. | A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods | |
| Naidu et al. | Application of Data Analytics to Decouple Historical Real-World Trip Trajectories into Representative Maneuvers for Driving Characterization | |
| Saint Pierre et al. | Impact of intelligent speed adaptation systems on fuel consumption and driver behaviour | |
| Wen et al. | Analysis of vehicle driving styles at freeway merging areas using trajectory data | |
| Liu | Driving volatility in instantaneous driving behaviors: Studies using Large-Scale trajectory data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |