CN116343498A - Big data bus route congestion point-based reminding system - Google Patents
Big data bus route congestion point-based reminding system Download PDFInfo
- Publication number
- CN116343498A CN116343498A CN202310210000.3A CN202310210000A CN116343498A CN 116343498 A CN116343498 A CN 116343498A CN 202310210000 A CN202310210000 A CN 202310210000A CN 116343498 A CN116343498 A CN 116343498A
- Authority
- CN
- China
- Prior art keywords
- congestion
- analysis
- coefficient
- road section
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the field of traffic guiding, relates to a data analysis technology, and aims to solve the problem that an existing bus route congestion point reminding system cannot predict and analyze the congestion condition of the remaining complete route of a bus route, and particularly relates to a big data bus route congestion point reminding system, which comprises a congestion reminding platform, wherein the congestion reminding platform is in communication connection with a road condition analysis module, a congestion analysis module, a grade assessment module and a storage module; the road condition analysis module is used for detecting and analyzing the driving road condition of the bus route: marking a bus in a running state as a detection object, and marking an undriven road section in a running line of the detection object as an analysis line; the invention can detect and analyze the running road condition of the bus route, comprehensively analyze a plurality of parameters such as the bearing vehicles of the road and the bearing capacity of the vehicles to obtain the congestion coefficient, and further feed back the running road condition state of the bus route through the congestion coefficient.
Description
Technical Field
The invention belongs to the field of traffic guiding, relates to a data analysis technology, and particularly relates to a traffic route congestion point reminding system based on big data.
Background
Urban traffic jam refers to a phenomenon that vehicles are crowded and the speed of the vehicles is slow, and the phenomenon usually occurs in holidays or rush hours. This is often the case in metropolitan areas of the world, highways connecting two cities, and areas where the utilization of automobiles is high.
The existing bus route congestion point reminding system can only detect and remind the congestion condition of the current driving road section, but cannot predict and analyze the congestion condition of the remaining complete path of the bus route, and cannot evaluate the congestion level of the bus by combining the congestion condition of the complete path and the distribution condition of the congestion road section, so that a bus driver cannot acquire real and comprehensive road congestion data.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide a big data-based bus route congestion point reminding system, which is used for solving the problem that the existing bus route congestion point reminding system cannot predict and analyze the congestion condition of the remaining complete paths of a bus route;
the technical problems to be solved by the invention are as follows: how to provide a congestion point reminding system based on big data bus route, which can predict and analyze the congestion condition of the remaining complete path of the bus route.
The aim of the invention can be achieved by the following technical scheme:
the system comprises a congestion reminding platform which is in communication connection with a road condition analysis module, a congestion analysis module, a grade assessment module and a storage module;
the road condition analysis module is used for detecting and analyzing the driving road condition of the bus route: marking a bus in a running state as a detection object, acquiring a running line of the detection object, marking an undriven road section in the running line of the detection object as an analysis line, dividing the analysis line into a plurality of analysis road sections, and acquiring a congestion coefficient YD of the analysis road sections; the congestion coefficient YD of the analysis road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion coefficient YD to a congestion analysis module after receiving the congestion coefficient YD;
the congestion analysis module is used for detecting and analyzing the congestion condition of the bus line: the L1 analysis road sections which are ranked at the front are marked as direct road sections, and the rest analysis road sections are marked as comprehensive road sections; carrying out congestion prediction analysis on the comprehensive road sections, carrying out numerical replacement on the congestion coefficient YD of the comprehensive road sections according to the congestion prediction analysis result, obtaining a congestion threshold value YDmax through a storage module, comparing the congestion coefficient YD of the analysis road sections with the congestion threshold value YDmax, and marking the analysis road sections as normal road sections or congestion road sections through the comparison result; the method comprises the steps that a congestion road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion road section to a grade assessment module after receiving the congestion road section;
the grade evaluation module is used for evaluating and analyzing the congestion grade of the bus route.
As a preferred embodiment of the present invention, the process of obtaining the congestion factor YD of the analysis link includes: sequencing and numbering the analysis road sections according to the travelling direction of the travelling line; the method comprises the steps of obtaining lane data CD, traffic flow data CL and red and green data HL of an analysis road section, wherein the lane data CD is the number of lanes of the analysis road section in the driving direction of a detection object, the traffic flow data CL is the number of vehicles of the analysis road section in the driving direction of the detection object, the red and green data HL is the number of traffic lights in the analysis road section, and the congestion coefficient YD of the analysis road section is obtained by carrying out numerical calculation on the lane data CD, the traffic flow data CL and the red and green data HL of the analysis road section.
As a preferred embodiment of the present invention, the specific process of congestion prediction analysis for the integrated road segment includes: drawing a circle by taking the central point of the comprehensive road section as the circle center and r1 as the radius, marking the obtained circular area as an analysis area, obtaining the number of schools, the number of hospitals and the number of markets in the analysis area and marking the number of schools, the number of hospitals and the number of markets as XS, YS and SS respectively, and obtaining a prediction coefficient YC of the comprehensive road section by carrying out numerical calculation on the XS, YS and SS; and obtaining a prediction threshold YCmax through a storage module, comparing the prediction coefficient YC with the prediction threshold YCmax, and carrying out numerical replacement on the congestion coefficient YD of the comprehensive road section through a comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the prediction coefficient YC with the prediction threshold YCmax includes: if the prediction coefficient YC is smaller than the prediction threshold YCmax, judging that the prediction state of the comprehensive road section meets the requirement; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, judging that the prediction state of the comprehensive road section does not meet the requirement, and obtaining a new congestion coefficient YDn of the comprehensive road section through a formula YDn =t1×YD, wherein t1 is a proportional coefficient, and t1 is more than or equal to 1.15 and less than or equal to 1.25; and carrying out numerical replacement on the new congestion coefficient YDn on the congestion coefficient YD of the comprehensive road section.
As a preferred embodiment of the present invention, the specific process of comparing the congestion coefficient YD with the congestion threshold YDmax includes: if the congestion coefficient YD is smaller than the congestion threshold value YDmax, judging that the congestion state of the analysis road section meets the requirement, and marking the corresponding analysis road section as a normal road section; if the congestion coefficient YD is greater than or equal to the congestion threshold value YDmax, judging that the congestion state of the analysis road section does not meet the requirement, and marking the corresponding analysis road section as a congestion road section.
As a preferred embodiment of the present invention, the specific process of the rating module for performing the rating analysis on the congestion level of the bus route includes: obtaining the number of the congestion road sections in the running line of the detection object, marking the number as LD, obtaining the number of the congestion road sections in the running line of the detection object, summing and averaging to obtain BH, obtaining the grade coefficient DJ of the detection object through a formula DJ=γ1, obtaining grade thresholds DJmin and DJmax through a storage module, comparing the grade coefficient DJ of the detection object with the grade thresholds DJmin and DJmax, and marking the congestion grade of the detection object through a comparison result; and sending the congestion level of the detection object to a congestion reminding platform, and sending the congestion level to a mobile phone terminal of a driver of the detection object after the congestion reminding platform receives the congestion level.
As a preferred embodiment of the present invention, the specific process of comparing the rank coefficient DJ of the detection object with the rank threshold values DJmin, DJmax includes: if DJ is less than or equal to DJmin, marking the congestion level of the detection object as three levels; if DJmin is less than DJ and less than DJmax, marking the congestion level of the detection object as a second level; if DJ is larger than or equal to DJMax, marking the congestion level of the detection object as a level.
The invention has the following beneficial effects:
1. the road condition analysis module can detect and analyze the running road condition of the bus route, comprehensively analyze a plurality of parameters such as the bearing vehicle of the road and the bearing capacity of the vehicle to obtain a congestion coefficient, and further feed back the running road condition state of the bus route through the congestion coefficient;
2. the congestion analysis module can detect and analyze the congestion condition of the bus route, the congestion prediction of the whole route is realized by adopting different detection and analysis modes for different analysis road sections, and the congestion coefficient value of the comprehensive road section is updated and replaced by the congestion prediction analysis result, so that the accuracy of the congestion prediction result of the whole route is improved;
3. the method comprises the steps of carrying out evaluation analysis on the congestion level of the bus route through a level evaluation module, carrying out data calculation on the number and the distribution state of the congestion road sections to obtain a level coefficient, and carrying out visual feedback on the whole congestion condition of the bus route through the numerical value of the level coefficient, so that a bus driver can directly obtain the real congestion state of the whole route.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the system for reminding the congestion point of the bus route based on big data comprises a congestion reminding platform, wherein the congestion reminding platform is in communication connection with a road condition analysis module, a congestion analysis module, a grade assessment module and a storage module.
The road condition analysis module is used for detecting and analyzing the driving road condition of the bus route: marking a bus in a running state as a detection object, acquiring a running line of the detection object, marking a non-running road section in the running line of the detection object as an analysis line, dividing the analysis line into a plurality of analysis road sections, and sequencing and numbering the analysis road sections according to the running direction of the running line; obtaining lane data CD, traffic flow data CL and red-green data HL of an analysis road section, wherein the lane data CD is the number of lanes of the analysis road section in the running direction of a detection object, the traffic flow data CL is the number of vehicles of the analysis road section in the running direction of the detection object, the red-green data HL is the number of traffic lights in the analysis road section, and the congestion coefficient YD of the analysis road section is obtained through a formula YD= (alpha 1 x HL+alpha 2 x CL)/(alpha 3 x CD), wherein alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 is more than alpha 2 is more than alpha 3 is more than 1; the congestion coefficient YD of the analysis road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion coefficient YD to a congestion analysis module after receiving the congestion coefficient YD; the method comprises the steps of detecting and analyzing the running road condition of a bus route, comprehensively analyzing a plurality of parameters such as a bearing vehicle of a road and bearing capacity of the vehicle to obtain a congestion coefficient, and feeding back the running road condition state of the bus route through the congestion coefficient.
The congestion analysis module is used for detecting and analyzing the congestion condition of the bus line: the L1 analysis road sections which are ranked at the front are marked as direct road sections, and the rest analysis road sections are marked as comprehensive road sections; and carrying out congestion prediction analysis on the comprehensive road section: drawing a circle by taking the central point of the comprehensive road section as the circle center and r1 as the radius, wherein r1 is a constant value, and the value of r1 is set by a manager; marking the obtained circular area as an analysis area, obtaining the number of schools, the number of hospitals and the number of markets in the analysis area, and marking the number of schools, the number of hospitals and the number of markets as XS, YS and SS respectively, and obtaining a prediction coefficient YC of a comprehensive road section through a formula YC=β1XS+β2YS+β3SS, wherein β1, β2 and β3 are all proportional coefficients, and β1 > β2 > β3 > 1; the prediction threshold YCmax is obtained through the storage module, and the prediction coefficient YC is compared with the prediction threshold YCmax: if the prediction coefficient YC is smaller than the prediction threshold YCmax, judging that the prediction state of the comprehensive road section meets the requirement; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, judging that the prediction state of the comprehensive road section does not meet the requirement, and obtaining a new congestion coefficient YDn of the comprehensive road section through a formula YDn =t1×YD, wherein t1 is a proportional coefficient, and t1 is more than or equal to 1.15 and less than or equal to 1.25; numerical replacement is carried out on the new congestion coefficient YDn on the congestion coefficient YD of the comprehensive road section, a congestion threshold value YDmax is obtained through the storage module, and the congestion coefficient YD of the analysis road section is compared with the congestion threshold value YDmax: if the congestion coefficient YD is smaller than the congestion threshold value YDmax, judging that the congestion state of the analysis road section meets the requirement, and marking the corresponding analysis road section as a normal road section; if the congestion coefficient YD is greater than or equal to the congestion threshold value YDmax, judging that the congestion state of the analysis road section does not meet the requirement, and marking the corresponding analysis road section as a congestion road section; the method comprises the steps that a congestion road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion road section to a grade assessment module after receiving the congestion road section; and detecting and analyzing the congestion condition of the bus line, realizing the congestion prediction of the whole path by adopting different detection and analysis modes for different analysis road sections, and updating and replacing the congestion coefficient value of the comprehensive road section by using the congestion prediction analysis result, thereby improving the accuracy of the congestion prediction result of the whole path.
The grade evaluation module is used for evaluating and analyzing the congestion grade of the bus route: obtaining the number of the congestion road sections in the running line of the detection object, marking the number as LD, obtaining the number of the congestion road sections in the running line of the detection object, summing and averaging to obtain BH, obtaining the grade coefficient DJ of the detection object through the formula DJ=γ1, obtaining the grade threshold DJmin and DJmax through a storage module, and comparing the grade coefficient DJ of the detection object with the grade threshold DJmin and DJmax: if DJ is less than or equal to DJmin, marking the congestion level of the detection object as three levels; if DJmin is less than DJ and less than DJmax, marking the congestion level of the detection object as a second level; if DJ is more than or equal to DJMax, marking the congestion level of the detection object as a level; the congestion level of the detection object is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion level to a mobile phone terminal of a driver of the detection object after receiving the congestion level; the method comprises the steps of evaluating and analyzing the congestion level of the bus route, calculating data of the number and the distribution state of the congested road sections to obtain a level coefficient, and intuitively feeding back the overall congestion condition of the bus route through the numerical value of the level coefficient, so that a bus driver can directly obtain the real congestion state of the whole route.
When the system for reminding the congestion point of the bus route based on big data works, a bus in a running state is marked as a detection object, a running route of the detection object is obtained, a non-running road section in the running route of the detection object is marked as an analysis route, the analysis route is divided into a plurality of analysis road sections, and the congestion coefficient YD of the analysis road sections is obtained; marking the L1 analysis road sections which are ranked at the front as direct road sections, wherein L1 is a constant value, and the specific value of L1 is set by a manager; marking the rest analysis road sections as comprehensive road sections; and carrying out congestion prediction analysis on the comprehensive road sections, carrying out numerical replacement on the congestion coefficient YD of the comprehensive road sections according to the congestion prediction analysis result, obtaining a congestion threshold value YDmax through a storage module, comparing the congestion coefficient YD of the analysis road sections with the congestion threshold value YDmax, and marking the analysis road sections as normal road sections or congestion road sections through the comparison result.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula yd= (α1×hl+α2×cl)/(α3×cd); collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding congestion coefficient for each group of sample data; substituting the set congestion coefficient and the collected sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are 3.74, 2.97 and 2.65 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding congestion coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the congestion coefficient is directly proportional to the value of the traffic flow data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The system is characterized by comprising a congestion reminding platform which is in communication connection with a road condition analysis module, a congestion analysis module, a grade assessment module and a storage module;
the road condition analysis module is used for detecting and analyzing the driving road condition of the bus route: marking a bus in a running state as a detection object, acquiring a running line of the detection object, marking an undriven road section in the running line of the detection object as an analysis line, dividing the analysis line into a plurality of analysis road sections, and acquiring a congestion coefficient YD of the analysis road sections; the congestion coefficient YD of the analysis road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion coefficient YD to a congestion analysis module after receiving the congestion coefficient YD;
the congestion analysis module is used for detecting and analyzing the congestion condition of the bus line: the L1 analysis road sections which are ranked at the front are marked as direct road sections, and the rest analysis road sections are marked as comprehensive road sections; carrying out congestion prediction analysis on the comprehensive road sections, carrying out numerical replacement on the congestion coefficient YD of the comprehensive road sections according to the congestion prediction analysis result, obtaining a congestion threshold value YDmax through a storage module, comparing the congestion coefficient YD of the analysis road sections with the congestion threshold value YDmax, and marking the analysis road sections as normal road sections or congestion road sections through the comparison result; the method comprises the steps that a congestion road section is sent to a congestion reminding platform, and the congestion reminding platform sends the congestion road section to a grade assessment module after receiving the congestion road section;
the grade evaluation module is used for evaluating and analyzing the congestion grade of the bus route.
2. The system for reminding a congestion point of a bus route based on big data according to claim 1, wherein the process of obtaining the congestion coefficient YD of the analysis section comprises: sequencing and numbering the analysis road sections according to the travelling direction of the travelling line; the method comprises the steps of obtaining lane data CD, traffic flow data CL and red and green data HL of an analysis road section, wherein the lane data CD is the number of lanes of the analysis road section in the driving direction of a detection object, the traffic flow data CL is the number of vehicles of the analysis road section in the driving direction of the detection object, the red and green data HL is the number of traffic lights in the analysis road section, and the congestion coefficient YD of the analysis road section is obtained by carrying out numerical calculation on the lane data CD, the traffic flow data CL and the red and green data HL of the analysis road section.
3. The big data bus route congestion point-based reminding system according to claim 2, wherein the specific process of congestion prediction analysis of the comprehensive road section comprises the following steps: drawing a circle by taking the central point of the comprehensive road section as the circle center and r1 as the radius, marking the obtained circular area as an analysis area, obtaining the number of schools, the number of hospitals and the number of markets in the analysis area and marking the number of schools, the number of hospitals and the number of markets as XS, YS and SS respectively, and obtaining a prediction coefficient YC of the comprehensive road section by carrying out numerical calculation on the XS, YS and SS; and obtaining a prediction threshold YCmax through a storage module, comparing the prediction coefficient YC with the prediction threshold YCmax, and carrying out numerical replacement on the congestion coefficient YD of the comprehensive road section through a comparison result.
4. A system for reminding a congestion point on a mass transit route based on big data according to claim 3, wherein the specific process of comparing the prediction coefficient YC with the prediction threshold YCmax comprises: if the prediction coefficient YC is smaller than the prediction threshold YCmax, judging that the prediction state of the comprehensive road section meets the requirement; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, judging that the prediction state of the comprehensive road section does not meet the requirement, and obtaining a new congestion coefficient YDn of the comprehensive road section through a formula YDn =t1×YD, wherein t1 is a proportional coefficient, and t1 is more than or equal to 1.15 and less than or equal to 1.25; and carrying out numerical replacement on the new congestion coefficient YDn on the congestion coefficient YD of the comprehensive road section.
5. The big data bus route congestion point reminding system according to claim 4, wherein the specific process of comparing the congestion coefficient YD with the congestion threshold YDmax comprises: if the congestion coefficient YD is smaller than the congestion threshold value YDmax, judging that the congestion state of the analysis road section meets the requirement, and marking the corresponding analysis road section as a normal road section; if the congestion coefficient YD is greater than or equal to the congestion threshold value YDmax, judging that the congestion state of the analysis road section does not meet the requirement, and marking the corresponding analysis road section as a congestion road section.
6. The big data bus route congestion point reminding system according to claim 5, wherein the specific process of the rating module for rating and analyzing the congestion level of the bus route comprises the following steps: obtaining the number of the congestion road sections in the running line of the detection object, marking the number as LD, obtaining the number of the congestion road sections in the running line of the detection object, summing and averaging to obtain BH, obtaining the grade coefficient DJ of the detection object through a formula DJ=γ1, obtaining grade thresholds DJmin and DJmax through a storage module, comparing the grade coefficient DJ of the detection object with the grade thresholds DJmin and DJmax, and marking the congestion grade of the detection object through a comparison result; and sending the congestion level of the detection object to a congestion reminding platform, and sending the congestion level to a mobile phone terminal of a driver of the detection object after the congestion reminding platform receives the congestion level.
7. The big data bus route congestion point-based reminding system according to claim 6, wherein the specific process of comparing the grade coefficient DJ of the detected object with the grade thresholds DJmin and DJmax comprises: if DJ is less than or equal to DJmin, marking the congestion level of the detection object as three levels; if DJmin is less than DJ and less than DJmax, marking the congestion level of the detection object as a second level; if DJ is larger than or equal to DJMax, marking the congestion level of the detection object as a level.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310210000.3A CN116343498A (en) | 2023-03-03 | 2023-03-03 | Big data bus route congestion point-based reminding system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310210000.3A CN116343498A (en) | 2023-03-03 | 2023-03-03 | Big data bus route congestion point-based reminding system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116343498A true CN116343498A (en) | 2023-06-27 |
Family
ID=86886970
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310210000.3A Pending CN116343498A (en) | 2023-03-03 | 2023-03-03 | Big data bus route congestion point-based reminding system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116343498A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118212782A (en) * | 2024-05-20 | 2024-06-18 | 厦门搜谷信息科技有限公司 | Bus driving route congestion prediction system suitable for intelligent bus stop |
| CN118212782B (en) * | 2024-05-20 | 2024-08-27 | 厦门搜谷信息科技有限公司 | Bus driving route congestion prediction system suitable for intelligent bus stop |
-
2023
- 2023-03-03 CN CN202310210000.3A patent/CN116343498A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118212782A (en) * | 2024-05-20 | 2024-06-18 | 厦门搜谷信息科技有限公司 | Bus driving route congestion prediction system suitable for intelligent bus stop |
| CN118212782B (en) * | 2024-05-20 | 2024-08-27 | 厦门搜谷信息科技有限公司 | Bus driving route congestion prediction system suitable for intelligent bus stop |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109697854A (en) | The urban road traffic state assessment method of various dimensions | |
| CN113870570B (en) | ETC-based road network operation state method, system and storage medium | |
| CN114783183A (en) | Monitoring method and system based on traffic situation algorithm | |
| CN107240264B (en) | A kind of non-effective driving trace recognition methods of vehicle and urban road facility planing method | |
| CN103065469A (en) | Travel time determination method and travel time determination device | |
| CN112734242A (en) | Method and device for analyzing availability of vehicle running track data, storage medium and terminal | |
| CN115662113A (en) | Signalized intersection people-vehicle game conflict risk assessment and early warning method | |
| CN114005275B (en) | Highway vehicle congestion judging method based on multi-data source fusion | |
| CN115798212A (en) | Traffic jam detection method based on taxi track | |
| CN113920727B (en) | Prediction method and system for road congestion caused by construction | |
| CN111767644B (en) | Method for estimating actual traffic capacity of expressway road section by considering speed limit influence of single tunnel | |
| CN109166336B (en) | Real-time road condition information acquisition and pushing method based on block chain technology | |
| CN114078322B (en) | Bus running state evaluation method, device, equipment and storage medium | |
| CN109754606A (en) | A method of based on taxi location prediction congestion in road situation | |
| CN117194923A (en) | Road condition analysis system, method, equipment and medium | |
| CN116343498A (en) | Big data bus route congestion point-based reminding system | |
| CN112927497B (en) | Floating car identification method, related method and device | |
| CN113268709A (en) | Urban electric vehicle charging demand prediction method and system based on intelligent agent simulation | |
| CN113688687B (en) | Traffic jam state rapid identification and prediction method based on electric warning data | |
| CN115171379B (en) | Emergency response control system based on intelligent traffic | |
| CN118212782B (en) | Bus driving route congestion prediction system suitable for intelligent bus stop | |
| CN117885745B (en) | Driving behavior monitoring system based on Internet of vehicles | |
| Uswaththa et al. | Study On The Applicability Of Capacity Estimation Methods To Evaluate Multilane Highway Capacity Under Heterogeneous Conditions | |
| CN117894192B (en) | Road section vehicle average passenger carrying number estimation method based on mobile phone signaling data | |
| CN116343474B (en) | Expressway bottleneck dynamic prediction method and system based on microscopic traffic simulation |
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 |