CN109145954B - Network taxi appointment travel safety evaluation method and system based on multi-source time-space data - Google Patents
Network taxi appointment travel safety evaluation method and system based on multi-source time-space data Download PDFInfo
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Abstract
The invention relates to a network appointment vehicle travel safety evaluation method and system based on multi-source time-space data, and the method comprises the following steps: data acquisition and preprocessing, road section division, map matching and multi-dimensional safety index calculation; the system comprises a multi-source time-space data analysis platform, a geographic server, a government terminal, an enterprise terminal, a driver terminal and a passenger terminal. Compared with the existing static supervision system based on identity attributes, the method is based on multi-source time-space data mining, multi-dimensionally analyzes the road safety of each road section at each time and the driver safety of each driver at each road section, further more accurately evaluates the net appointment vehicle travel safety level, quantifies the net appointment vehicle travel risk by means of safety indexes, innovates a supervision system, is an important embodiment of a management system and management capability modernization, can improve government management efficiency, realizes enterprise dynamic supervision, standardizes driver driving behaviors, and assists passenger travel decisions.
Description
Technical Field
The invention relates to the technical field of network taxi booking, in particular to a network taxi booking travel safety evaluation method and system based on multi-source time-space data.
Background
The evaluation and supervision of the travel safety of the online taxi appointment become a hot spot and difficult problem to be solved urgently at present. The existing related technologies and methods at home and abroad are mostly qualitative research based on static data, and quantitative research based on multi-source space-time data is lacked. Most of the existing monitoring systems at present consist of online declaration, qualification audit and state monitoring, mainly are static monitoring systems based on identity attributes, and lack dynamic monitoring systems based on driving behaviors.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a method and a system for evaluating the travel safety of a networked car appointment based on multi-source time-space data.
The purpose of the invention can be realized by the following technical scheme:
a network appointment vehicle travel safety evaluation method based on multi-source space-time data comprises the following steps:
s1: collecting multi-source time-space data;
collecting and fusing multi-source space-time data such as network car appointment track data, order data, road accident data, camera position data, crime and police data, urban road traffic indexes, road facility data, bridge facility data, mobile communication data, base station position data, meteorological data and the like.
S2: preprocessing data;
a Hadoop and Spark-based big data distributed parallel processing framework performs preprocessing such as data cleaning (denoising and irrelevant data), data integration (combining data in a plurality of data sources and storing the data in a consistent data storage), data transformation (converting original data into a form suitable for data mining), data reduction (dimensionality reduction, data compression, numerical value reduction and discretization) on massive multi-source space-time data.
S3: road section division;
the method comprises the steps of dividing road sections of ground roads by taking intersections as nodes, dividing road sections of urban expressways by taking ramp entrances and exits as nodes, discretizing urban road networks and giving unique numbers.
S4: matching a map;
and matching different types of multi-source space-time data (point data, line data and surface data) to the corresponding road sections based on the space analysis of the geographic information system to serve as a reference basis for safety index evaluation.
S5: calculating a safety index;
and extracting characteristic parameters related to safety evaluation from the multi-source space-time data matched to the corresponding road section in the step S4, and carrying out characteristic parameter standardization and safety index calculation based on machine learning.
Safety feature extraction: and calculating characteristic parameters related to safety evaluation based on the multisource space-time data matched to the road sections, wherein the characteristic parameters are specifically divided into road safety characteristic parameters (including road accident parameters, road congestion parameters, road facility parameters, camera parameters, crowd gathering parameters, crime parameters, weather parameters and the like of different road sections at different time intervals) and driver safety characteristic parameters (including average speed parameters, average acceleration parameters, average brake number parameters, average deflection angle parameters and the like of different drivers at different road sections).
And (3) characteristic parameter standardization: and respectively carrying out percentile conversion on all the parameters to obtain standardized parameters, thereby facilitating comparative analysis and model input.
Machine learning based safety index calculation:
and (I) classifying and predicting the road safety level and the driver safety level and calculating a safety index by using the standardized characteristic parameters and adopting a Naive Bayes model.
The safety index calculation method comprises the following specific steps:
(1) obtaining the frequency of occurrence P (y) of each class in the training samplei)；
(2) Obtaining each characteristic parameter a under each categoryjConditional probability P (a) ofj|yi)；
(3) Based on Bayes model, calculating each classification grade yiConditional probability of (2):
wherein:
(4) based on the cross validation model evaluation, if the validation requirements are met, outputting an optimal classifier model, if the validation requirements are not met, modifying model parameters, and returning to the step (1);
(5) inputting a sample x to be classified as a1，a2，...，amCalculating the probability P (y) that the items to be classified belong to each safety level based on the optimal classifier modeli| x), outputting the security level with the maximum conditional probability as a predicted security level:
Max{P(y1|x)，P(y2|x)，...，P(yn|x)}
(6) calculating a safety index K based on the conditional probability of each safety levelx：
Kx＝∑P(yi|x)×bi
Wherein: biTo a security level yiCorresponding standard security index weight (b)A＝100，bB＝75，bC＝50， bD＝25)。
And (II) according to the calculation method of the safety index, synthesizing the driver safety index and the road safety index, and calculating the safety index K of the network car-booking travel path according to the length of the road section through which the travel path passes by weighting:
wherein lnFor the length of the section of road n,
and
respectively the driver safety index and the road safety index of the road section.
A network appointment car trip safety evaluation system based on multi-source space-time data comprises:
multi-source spatio-temporal data analysis platform: the multi-source space data acquisition, storage and calculation device is used for acquiring, storing and calculating real-time and historical multi-source space data; the multi-source spatiotemporal data analysis platform integrates the online appointment vehicle travel safety evaluation method based on the multi-source spatiotemporal data, and analyzes and calculates the real-time and historical spatiotemporal data by using a Spark Streaming processing technology, and regularly updates the safety index result in the database.
A geographic server: the safety index visualization map is used for analyzing, generating and issuing safety indexes;
background database: the safety index data storage device is used for storing the output safety index data and updating the data periodically;
a Web server: the system is used for connecting a database and a geographic server and building a visual query terminal;
a government terminal: the system is used for a government user to check the road safety level visual map of any area and inquire the road safety index numerical value of any road section at any time period so as to assist the government to carry out safety management on urban roads and construct and perfect potential dangerous road sections; meanwhile, the government user can also perform real-time dynamic ranking on the comprehensive safety index (the weighted average of the safety index and the accident number of all the vehicle sharing drivers of the enterprise) of the vehicle sharing enterprises supervised by the government user at the terminal, so that the government can be assisted to evaluate and assess the vehicle sharing enterprises, and the benign competition among the enterprises can be promoted.
Enterprise terminal: the online car booking enterprise user is used for inquiring safety indexes and ranking conditions of all online car booking drivers of respective enterprises, viewing driver safety level visual maps of any driver on different road sections and assisting the enterprises to evaluate and assess the driving behavior safety of the online car booking driver; meanwhile, the users of the network car booking enterprise can also check the real-time running states (position, speed, acceleration and the like) of any driver at the terminal, so that the enterprise can conveniently carry out emergency treatment on emergency situations
Driver terminal: the method is used for the online taxi appointment driver users to check visual maps of driver safety levels of different road sections, search and query driver safety indexes of any road section, sequence the driver safety indexes of all road sections, facilitate drivers to be alert of dangerous driving road sections, improve driving behaviors and improve service level.
Passenger terminal: the online car booking app is embedded in the form of the API, safety index data are pushed to the online car booking passenger, the functions of travel path safety index prediction and detour monitoring are achieved, the online car booking driver and a travel path are selected by auxiliary passengers, and the passengers can master travel risks in real time.
Compared with the prior art, the invention has the following advantages:
the invention is different from the prior static supervision system based on identity attributes, and based on multi-source time-space data mining, the road safety of each road section at each time and the driver safety of each driver at each road section at each time are analyzed in a multi-dimensional manner, so that the network appointment vehicle travel safety level is evaluated more accurately, the network appointment vehicle travel risk is quantified by means of a safety index, a network appointment vehicle supervision system is innovated, the network appointment vehicle supervision system is an important embodiment of a management system and management capability modernization, the government management efficiency can be improved, the enterprise dynamic supervision is realized, the driver driving behavior is standardized, and the passenger travel decision is assisted;
the invention can meet the requirements of objects in different levels: the method is oriented to the government and provides a road safety index evaluation function and an enterprise safety index dynamic supervision function in different days at different time intervals; the method is characterized by comprising the following steps of providing a safety index evaluation function and a vehicle operation dynamic supervision function of each driver on each road section for enterprises; the method comprises the steps of providing a driver safety index ranking query function of each road section for a driver; the travel route safety index evaluation function is provided for passengers.
Drawings
FIG. 1 is a schematic flow chart of a network appointment vehicle travel safety evaluation method based on multi-source spatiotemporal data;
FIG. 2 is a schematic flow chart of the calculation of the safety index;
FIG. 3 is a flow chart of a Naive Bayes model algorithm employed for computing a security index;
FIG. 4 is a system architecture diagram of the networked car appointment travel safety evaluation system based on multi-source spatiotemporal data.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The invention relates to a network appointment vehicle travel safety evaluation method based on multi-source time-space data, which comprises the following steps as shown in figure 1:
step one, multi-source space-time data acquisition:
through a data open platform and project cooperation of related departments, the time-space data related to the network car booking trip safety evaluation are collected from a network car booking enterprise, a traffic department, a public security department, an urban construction department, a communication department and a meteorological department respectively, and the time-space data comprises but is not limited to the following data types and data fields:
(1) network appointment vehicle trajectory data (point data): driver ID, vehicle ID, time, longitude, latitude, speed, braking, direction, empty, overhead, … …
(2) Network appointment order data (point data): order ID, driver ID, vehicle ID, order starting point longitude and latitude, order ending point longitude and latitude, order starting time, order ending time, travel ID, … …
(3) Road traffic index (line data): link ID, status, area, current index, reference index, index difference, … …
(4) Road accident data (point data): accident ID, accident type, accident location, accident time … …
(5) Camera position data (dot data): number ID, name, installation location, longitude, latitude, … …
(6) Crime data (face data): region, killing number, injury number, robbery number, rape number, fraud number, theft number, … …
(7) Police data (face data): branch name, alarm receiving time, alarm reason … …
(8) Infrastructure data (line data): road ID, start and stop points, road width, total road area, reconstruction time, … …
(9) Bridge facility data (line data): bridge ID, bridge name, length, width, year of transformation, … …
(10) Mobile communication data (point data): handset ID, time, base station cell ID, event ID, … …
(11) Base station position data (point data): base station cell ID, longitude, latitude, … …
(12) Weather data (surface data): time, area, temperature, wind speed, wind direction, precipitation … …
Step two, preprocessing multi-source space-time data:
a Hadoop and Spark-based big data distributed parallel processing framework performs preprocessing such as data cleaning (denoising and irrelevant data), data integration (combining data in a plurality of data sources and storing the data in a consistent data storage), data transformation (converting original data into a form suitable for data mining), data reduction (dimensionality reduction, data compression, numerical value reduction and discretization) on multi-source space-time data.
Step three, road section division:
the method comprises the steps of obtaining urban road network vector data based on Open Street Map service, carrying out road segment division on ground roads by taking intersections as nodes, carrying out road segment division on urban expressways by taking ramp entrances and exits as nodes, discretizing the urban road network and endowing the urban road network with a unique number. For the bidirectional road section, different driving directions are considered, and the bidirectional road section can be subdivided into 2 different numbers.
Step four, map matching:
and matching the different types of space-time data (point data, line data and surface data) to corresponding road sections based on the space analysis and calculation of the geographic information system, and using the different types of space-time data as reference bases for safety index evaluation.
Step five, calculating a multi-dimensional safety index:
the method comprises the steps of calculating a driver safety index, calculating a road safety index and calculating a travel path safety index, wherein a specific process is shown in fig. 2.
Firstly, calculating characteristic parameters related to safety evaluation based on the multisource space-time data matched to the road sections, wherein the characteristic parameters are specifically divided into road safety characteristic parameters (including road accident parameters, road congestion parameters, road facility parameters, camera parameters, crowd gathering parameters, crime parameters and weather parameters of different road sections in different time periods) and driver safety characteristic parameters (including average speed parameters, average acceleration parameters, average brake number parameters, average deflection angle parameters and the like of different drivers in different road sections).
In order to facilitate comparative analysis and model input, the characteristic parameters are grouped according to road sections, and percentile conversion is performed to obtain standardized parameters: x ═ a1，a2，...，amIn which a isjIs a percentile conversion value of the security feature parameter.
Then, a Delphi method is adopted to determine the parameter weight, a part of characteristic parameter data is manually graded, and a security grade label (A, B, C, D four grades) is marked, namely label ═ y1，y2，...，ynGet the data of training samples: x is the number of0＝{a1，a2，...，am，yiIn which y isiFor classification, a security level is represented.
Secondly, training the marked characteristic parameters based on a Naive Bayes model, obtaining an optimal classifier model through cross validation, respectively performing classification prediction on road safety level and driver safety level, and calculating a safety index according to prediction probability of each level, as shown in FIG. 3, specifically comprising the following steps:
(1) calculating the frequency of occurrence P (y) of each class in the training samplei)；
(2) Calculating each characteristic parameter a under each categoryjConditional probability P (a) ofj|yi)；
(3) Based on Bayes model, calculating each classification grade yiConditional probability of (2):
wherein:
(4) based on the cross validation model evaluation, if the validation requirements are met, outputting an optimal classifier model, if the validation requirements are not met, modifying the model parameters, and returning to the step (1);
(5) inputting a sample x to be classified as a1，a2，...，amCalculating the probability P (y) that the items to be classified belong to each safety level based on the optimal classifier modeli| x), outputting the security level with the maximum conditional probability as a predicted security level:
Max{P(y1|x)，P(y2|x)，...，P(yn|x)}
(6) calculating a safety index K based on the conditional probability of each safety levelx：
Kx＝∑P(yi|x)×bi
Wherein: biTo a security level yiCorresponding standard security index weight (b)A＝100，bB＝75，bC＝50， bD＝25)。
Finally, the safety index of the driver of each road section n through which the comprehensive network car booking travel path passes
And road safety index
And carrying out weighting calculation on the safety index of the network car appointment travel path according to the length of the road section.
Wherein: lnIs the length of the section n.
The invention also relates to a network appointment vehicle travel safety evaluation system based on multi-source time-space data, which comprises the following steps: the system comprises a multi-source time-space data analysis platform, a geographic background server, a government terminal, an enterprise terminal, a driver terminal and a passenger terminal. The system architecture is shown in fig. 4, and the implementation steps are as follows:
step one, building a multi-source space-time data analysis platform:
a multi-source spatio-temporal data analysis platform is constructed on the basis of a Spark + Hadoop big data distributed parallel processing framework, the platform integrates the above network appointment trip safety evaluation method based on the multi-source spatio-temporal data, and a Spark Streaming processing technology is applied to analyze and calculate real-time and historical multi-source spatio-temporal data, and multi-dimensional driver and road safety indexes are output.
Step two, building a geographic server and a background database:
and storing the safety index calculated and output by the multi-source spatiotemporal data analysis platform in a MySQL database, and regularly updating the safety index result in the database. Meanwhile, map visualization is carried out on the calculated safety index based on a geographic information system ArcGIS Desktop, and a safety index map service is issued through a geographic Server ArcGIS Server.
Step three, developing a visual terminal:
safety index data stored based on a background database and a safety index map issued by a geographic server are developed towards different object requirements, including government terminals, enterprise terminals, driver terminals and passenger terminals, respectively: the webpage terminal serves the government and has the functions of road safety index visual query, dynamic ranking, enterprise safety index dynamic ranking and the like; the webpage terminal serves for the online taxi appointment enterprises, and the functions of the webpage terminal comprise visual inquiry of safety indexes of drivers, dynamic ranking, dynamic supervision of vehicles and the like; the mobile terminal serves a driver and has the functions of visual inquiry of self safety indexes, safe travel path navigation and the like; the functions of the mobile terminal serving passengers include travel path safety index prediction, detour monitoring and the like.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A network appointment vehicle travel safety evaluation method based on multi-source space-time data is characterized by comprising the following steps:
1) collecting multi-source space-time data;
2) carrying out data preprocessing on multi-source space-time data based on a Hadoop and Spark distributed parallel processing framework;
3) dividing road sections of the urban road, and numbering the divided road sections;
4) matching the multi-source space-time data processed in the step 2) to the divided corresponding road sections based on the space analysis of the geographic information system;
5) acquiring characteristic parameters related to safety evaluation based on the multi-source space-time data matched to the corresponding road section in the step 4), and performing safety level classification prediction and safety index calculation on road related data and driver related data by adopting a Naive Bayes model so as to predict a network appointment travel path safety index; the method specifically comprises the following steps:
501) dividing the multi-source time-space data matched to the corresponding road section into a road safety characteristic parameter and a driver safety characteristic parameter;
502) grouping the road safety characteristic parameters and the driver safety characteristic parameters according to road sections, and carrying out percentile conversion to obtain standardized characteristic parameters;
503) determining the weight of the characteristic parameters by a Delphi method, manually grading a part of characteristic parameter data, marking a security level label, and acquiring training sample data;
504) training the characteristic parameters of the labeled safety level labels based on a Naive Bayes model, respectively carrying out classification prediction on the road safety level and the driver safety level after an optimal classifier model is obtained through cross validation, and calculating a road safety index and a driver safety index according to classification probability;
505) and comprehensively evaluating the safety index of the net appointment vehicle travel path by combining the road safety index and the driver safety index.
2. The networked car appointment trip safety evaluation method based on the multi-source space-time data according to claim 1, wherein the multi-source space-time data in the step 1) includes, but is not limited to, networked car appointment trajectory data, order data, road accident data, camera position data, crime and police data, urban road traffic indexes, road facility data, bridge facility data, mobile communication data, base station position data and meteorological data.
3. The networked car appointment trip safety evaluation method based on the multi-source spatiotemporal data according to claim 1, wherein in the step 2), the data preprocessing comprises data cleaning, data transformation and data stipulation.
4. The online appointment vehicle travel safety evaluation method based on the multi-source space-time data according to claim 1, characterized in that the specific contents of the step 3) are as follows:
the method comprises the steps of dividing road sections of ground roads by taking intersections as nodes, dividing road sections of urban expressways by taking ramp entrances and exits as nodes, discretizing urban road networks and giving unique numbers.
5. An evaluation system for implementing the online taxi appointment travel safety evaluation method based on multi-source spatiotemporal data according to any one of claims 1 to 4, the system comprises:
multi-source spatio-temporal data analysis platform: the multi-source space data acquisition, storage and calculation device is used for acquiring, storing and calculating real-time and historical multi-source space data;
a geographic server: the safety index visualization map is used for analyzing, generating and issuing safety indexes;
background database: the safety index data storage device is used for storing the output safety index data and updating the data periodically;
a Web server: the system is used for connecting a database and a geographic server and building a visual query terminal;
a government terminal: the method is used for the government users to check the road safety level visual map of any area and inquire the road safety index numerical value of any road section at any time period;
enterprise terminal: the system is used for the users of the online car booking enterprises to check the safety indexes and the ranking conditions of all online car booking drivers of the enterprises, and check the safety level visual maps of any driver on different road sections;
driver terminal: the system is used for checking visual maps of driver safety levels of different road sections by a driver user of the online taxi appointment, searching and inquiring driver safety indexes of any road section, and sequencing the driver safety indexes of all road sections;
passenger terminal: the safety index data is used for being embedded into the online car booking service app in the form of an API (application programming interface) interface and pushing the safety index data to online car booking passengers.
6. The evaluation system of claim 5, wherein the multi-source spatiotemporal data analysis platform analyzes and calculates real-time and historical multi-source spatiotemporal data by using a Spark Streaming processing method.
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| CN114694052A (en) * | 2020-12-29 | 2022-07-01 | 中兴通讯股份有限公司 | Road safety evaluation method, video processing center and storage medium |
| CN113379233B (en) * | 2021-06-08 | 2023-02-28 | 重庆大学 | Travel time reliability estimation method and device based on high-order moment |
| CN114386737B (en) * | 2021-10-09 | 2023-10-13 | 浙江警察学院 | Method for monitoring safety of passengers on internet about car in mobile interconnection era |
| CN114756975B (en) * | 2022-06-15 | 2022-09-02 | 江苏新晖测控科技有限公司 | Dynamic liquid level wireless monitoring system and method based on double channels |
| CN114997527B (en) * | 2022-07-18 | 2022-11-11 | 苏州智能交通信息科技股份有限公司 | Enterprise assessment and evaluation method, system and terminal based on road transportation dynamic data |
| CN117014473B (en) * | 2023-09-28 | 2023-12-19 | 东莞中科智城软件有限公司 | Intelligent networking automobile monitoring system based on end cloud fusion technology |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855756A (en) * | 2012-09-28 | 2013-01-02 | 深圳华宏联创科技有限公司 | Intelligent method, server and system for calling taxi |
| CN103531025A (en) * | 2013-11-08 | 2014-01-22 | 宁波市康惠网络科技有限公司 | Carpooling system for matching information through route segmenting method |
| CN104978853A (en) * | 2014-04-01 | 2015-10-14 | 中国移动通信集团公司 | Road traffic safety evaluating method and system |
| CN105844384A (en) * | 2016-03-14 | 2016-08-10 | 北京工业大学 | Road safety evaluation method and apparatus |
| CN107679636A (en) * | 2017-08-29 | 2018-02-09 | 明光泰源安防科技有限公司 | A kind of driver safety appraisal procedure based on net about car |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9483741B2 (en) * | 2013-03-28 | 2016-11-01 | Wal-Mart Stores, Inc. | Rule-based item classification |
-
2018
- 2018-07-24 CN CN201810821097.0A patent/CN109145954B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855756A (en) * | 2012-09-28 | 2013-01-02 | 深圳华宏联创科技有限公司 | Intelligent method, server and system for calling taxi |
| CN103531025A (en) * | 2013-11-08 | 2014-01-22 | 宁波市康惠网络科技有限公司 | Carpooling system for matching information through route segmenting method |
| CN104978853A (en) * | 2014-04-01 | 2015-10-14 | 中国移动通信集团公司 | Road traffic safety evaluating method and system |
| CN105844384A (en) * | 2016-03-14 | 2016-08-10 | 北京工业大学 | Road safety evaluation method and apparatus |
| CN107679636A (en) * | 2017-08-29 | 2018-02-09 | 明光泰源安防科技有限公司 | A kind of driver safety appraisal procedure based on net about car |
Non-Patent Citations (1)
| Title |
|---|
| 基于动态朴素贝叶斯分类器的明渠水华风险评估模型;王中锋;《南水北调与水利科技》;20170430;第15卷(第2期);全文 * |
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