CN115953901B - A driving safety assessment method and system for dynamic routes - Google Patents
A driving safety assessment method and system for dynamic routes Download PDFInfo
- Publication number
- CN115953901B CN115953901B CN202310122892.1A CN202310122892A CN115953901B CN 115953901 B CN115953901 B CN 115953901B CN 202310122892 A CN202310122892 A CN 202310122892A CN 115953901 B CN115953901 B CN 115953901B
- Authority
- CN
- China
- Prior art keywords
- value
- route
- sample
- evaluated
- time
- 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
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000011156 evaluation Methods 0.000 claims description 41
- 239000013598 vector Substances 0.000 claims description 34
- 238000012549 training Methods 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 18
- 238000012544 monitoring process Methods 0.000 claims description 14
- 238000010276 construction Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000009194 climbing Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
本发明提供了一种动态路线的行车安全评估方法及系统，将待评估路线上每条路段的限制信息与运输车辆的车辆参数进行比对以确定第一安全指标的数值；利用平均车速预测模型预测每条路段在未来多个时间片内的预测平均车速；根据预测平均车速和参考速度分别确定通过待评估路线的预测耗时和参考耗时，并确定第二安全指标的数值；采集当前时刻待评估路线上的负面事件及其持续时间；预测行驶至负面事件位置的预估到达时刻，基于预估到达时刻和负面事件的持续时间确定第三安全指标的数值；结合第一安全指标、第二安全指标和第三安全指标的数值确定总安全指标的数值。通过总安全指标的数值来对待评估路线进行安全评估，以确保运输的可靠性、及时性和高效性。
The present invention provides a driving safety assessment method and system for a dynamic route, which compares the restriction information of each road section on the route to be assessed with the vehicle parameters of the transport vehicle to determine the value of the first safety index; uses the average vehicle speed prediction model to predict the predicted average vehicle speed of each road section in multiple future time slices; determines the predicted time and reference time of passing the route to be assessed according to the predicted average vehicle speed and the reference speed, and determines the value of the second safety index; collects negative events and their duration on the route to be assessed at the current moment; predicts the estimated arrival time of driving to the location of the negative event, and determines the value of the third safety index based on the estimated arrival time and the duration of the negative event; and determines the value of the total safety index by combining the values of the first safety index, the second safety index and the third safety index. The route to be assessed is safety assessed by the value of the total safety index to ensure the reliability, timeliness and efficiency of transportation.
Description
Technical Field
The invention relates to the technical field of traffic route security situation assessment, in particular to a driving security assessment method and system of a dynamic route.
Background
The road transportation is the main transportation mode at present, and the adaptability of road transportation is stronger than other transportation modes, can adapt to the freight traffic of equidimension through the mode of constituteing the motorcade, and the road transportation has special meaning to the freight transportation when salvaging the emergency and disaster.
For the conditions of complex road network and oversized overrun cargo transportation limitation, adopting different driving routes has great influence on the reliability, timeliness and high efficiency of transportation, so that the driving routes are necessary to be safely evaluated before driving.
Disclosure of Invention
In view of this, the embodiment of the invention provides a driving safety evaluation method and system for a dynamic route, so as to perform safety evaluation on the driving route.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
The first aspect of the embodiment of the invention discloses a driving safety evaluation method of a dynamic route, which comprises the following steps:
Acquiring limit information of each road section on a route to be evaluated;
comparing the limiting information with vehicle parameters of the transport vehicle, and determining the value of a first safety index according to the comparison result;
Collecting average speed, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated so as to form an input vector;
Inputting the input vector into a pre-constructed average speed prediction model to predict the speed of the vehicle so as to obtain the predicted average speed of each road section on the route to be evaluated in a plurality of time slices in the future, wherein the average speed prediction model is obtained by training a preset model based on sample data, and the sample data at least comprises road network data, historical road network speed monitoring data for N continuous days and historical weather data;
Determining the predicted time consumption of the transport vehicle passing through the route to be evaluated according to the predicted average vehicle speed, and determining the reference time consumption of the transport vehicle passing through the route to be evaluated according to a preset reference speed;
determining a value of a second security indicator using the predicted time consumption and the reference time consumption;
Collecting negative events influencing driving safety on the route to be evaluated at the current moment and duration time of the negative events;
Predicting the estimated arrival time of the transport vehicle from the predicted average vehicle speed to the position of the negative event, and determining the value of a third safety index based on the estimated arrival time and the duration of the negative event;
and determining the value of the total safety index by combining the value of the first safety index, the value of the second safety index and the value of the third safety index.
Preferably, the process of training the preset model based on the sample data to obtain the average vehicle speed prediction model includes:
Based on the sample data, counting the sample average speed and sample weather conditions of each road section in each time slice, and counting the sample time period information and the sample date information corresponding to each time slice;
Constructing a sample input vector by using the sample average speed, sample weather conditions, the sample period information and the sample date information;
training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model, wherein the preset model is constructed based on a transducer structure.
Preferably, comparing the restriction information with vehicle parameters of the transport vehicle, and determining a value of the first safety index according to the comparison result includes:
Respectively comparing the limit information of each road section on the route to be evaluated with vehicle parameters of a transport vehicle;
if the vehicle parameters meet the limit information of each road section on the route to be evaluated, determining the numerical value of a first safety index as a first preset value;
And if the vehicle parameters do not meet the limit information of any road section on the route to be evaluated, determining that the numerical value of the first safety index is a second preset value.
Preferably, determining the value of the third safety index based on the estimated arrival time and the duration of the negative event includes:
For each negative event, if the estimated arrival time of the transport vehicle traveling to the position where the negative event is located is within the duration of the negative event, accumulating the value of a specified variable by 1, wherein the value of the specified variable is used for representing the number of times of encountering the negative event;
A value of a third security indicator is determined based on the value of the specified variable.
Preferably, determining the value of the total security indicator by combining the value of the first security indicator, the value of the second security indicator and the value of the third security indicator includes:
and calculating the product among the numerical value of the first safety index, the numerical value of the second safety index and the numerical value of the third safety index to obtain the numerical value of the total safety index.
Preferably, the method further comprises:
determining a preset value interval in which the value of the total safety index is located from a plurality of preset value intervals;
outputting a driving safety evaluation grade corresponding to a preset value interval in which the value of the total safety index is located, wherein each preset value interval is provided with a corresponding driving safety evaluation grade.
The second aspect of the embodiment of the invention discloses a driving safety evaluation system of a dynamic route, which comprises:
The acquisition unit is used for acquiring the limit information of each road section on the route to be evaluated;
the first processing unit is used for comparing the limit information with vehicle parameters of the transport vehicle and determining the numerical value of a first safety index according to the comparison result;
the first acquisition unit is used for acquiring average vehicle speed, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated so as to form an input vector;
The prediction unit is used for inputting the input vector into a pre-constructed average speed prediction model to predict the speed of the vehicle so as to obtain the predicted average speed of each road section on the route to be evaluated in a plurality of time slices in the future, wherein the average speed prediction model is obtained by training a preset model based on sample data, and the sample data at least comprises road network data, historical road network speed monitoring data for N continuous days and historical weather data;
The second processing unit is used for determining the predicted time consumption of the transport vehicle passing through the route to be evaluated according to the predicted average vehicle speed and determining the reference time consumption of the transport vehicle passing through the route to be evaluated according to a preset reference speed;
a first determining unit configured to determine a value of a second security index using the predicted time consumption and the reference time consumption;
The second acquisition unit is used for acquiring negative events influencing driving safety on the route to be evaluated at the current moment and duration time of the negative events;
The third processing unit is used for predicting the estimated arrival time of the transport vehicle from the predicted average vehicle speed to the position where the negative event is located, and determining the value of a third safety index based on the estimated arrival time and the duration of the negative event;
And a second determining unit, configured to determine a value of a total security index by combining the value of the first security index, the value of the second security index, and the value of the third security index.
Preferably, the prediction unit includes:
the statistics module is used for counting the average sample speed and the sample weather condition of each road section in each time slice based on the sample data, and counting the sample time period information and the sample date information corresponding to each time slice;
A construction module for constructing a sample input vector using the sample average vehicle speed, sample weather conditions, the sample period information, and the sample date information;
And the training module is used for training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model, wherein the preset model is constructed based on a transducer structure.
Preferably, the first processing unit is specifically configured to compare the restriction information of each road section on the route to be evaluated with the vehicle parameter of the transport vehicle, determine the value of the first security index as a first preset value if the vehicle parameter meets the restriction information of each road section on the route to be evaluated, and determine the value of the first security index as a second preset value if the vehicle parameter does not meet the restriction information of any road section on the route to be evaluated.
Preferably, the third processing unit is configured to determine a value of a third safety indicator based on the estimated arrival time and the duration of the negative event, specifically configured to:
For each negative event, if the estimated arrival time of the transport vehicle traveling to the position where the negative event is located is within the duration of the negative event, accumulating the value of a specified variable by 1, wherein the value of the specified variable is used for representing the number of times of encountering the negative event;
A value of a third security indicator is determined based on the value of the specified variable.
The driving safety evaluation method and system for the dynamic route provided by the embodiment of the invention are based on the steps of obtaining limit information of each road section on the route to be evaluated, comparing the limit information with vehicle parameters of the transportation vehicle, determining a numerical value of a first safety index according to comparison results, collecting average vehicle speeds, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment on each road section on the route to be evaluated, inputting the input vectors into a pre-constructed average vehicle speed prediction model to conduct vehicle speed prediction so as to obtain a predicted average vehicle speed of each road section on the route to be evaluated in a plurality of future time slices, determining predicted time consumption of the transportation vehicle passing through the route to be evaluated according to the predicted average vehicle speed, determining reference time consumption of the transportation vehicle passing through the route to be evaluated according to preset reference speed, determining a numerical value of a second safety index according to the predicted time consumption and the reference time consumption, collecting a negative event affecting driving safety on the route to be evaluated at the current moment and duration of the negative event, predicting arrival time of the transportation vehicle to the position at the negative event according to the predicted average vehicle speed, and determining a third numerical value of the total safety index according to the predicted arrival time and the third numerical value of the second safety index. In the scheme, the numerical value of the first safety index is determined by comparing the vehicle parameters of the transport vehicle with the limit information of each road section on the route to be evaluated. And determining the predicted time consumption passing through the route to be evaluated by using the average vehicle speed prediction model, and determining the value of the second safety index based on the predicted time consumption and the reference time consumption. And determining the value of the third safety index by using the duration of the negative event and the predicted arrival time of the transport vehicle at the position of the negative event. The numerical value of the total safety index is calculated by combining the numerical values of the first safety index, the second safety index and the third safety index, and the safety evaluation is carried out on the route to be evaluated through the numerical value of the total safety index so as to ensure the reliability, timeliness and high efficiency of transportation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a driving safety evaluation method for a dynamic route according to an embodiment of the present invention;
FIG. 2 is a flowchart of a training average vehicle speed prediction model provided by an embodiment of the present invention;
fig. 3 is a block diagram of a driving safety evaluation system for a dynamic route according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
In the present disclosure, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
According to the background technology, under the conditions of complex road network and oversized overrun cargo transportation limitation, different driving routes are adopted to have great influence on the reliability, timeliness and high efficiency of transportation, so that the driving routes are required to be safely evaluated before driving.
Therefore, the embodiment of the invention provides a driving safety evaluation method and a driving safety evaluation system for a dynamic route, wherein the numerical value of a first safety index is determined by comparing vehicle parameters of a transport vehicle with limit information of each road section on the route to be evaluated. And determining the predicted time consumption passing through the route to be evaluated by using the average vehicle speed prediction model, and determining the value of the second safety index based on the predicted time consumption and the reference time consumption. And determining the value of the third safety index by using the duration of the negative event and the predicted arrival time of the transport vehicle at the position of the negative event. The numerical value of the total safety index is calculated by combining the numerical values of the first safety index, the second safety index and the third safety index, and the safety evaluation is carried out on the route to be evaluated through the numerical value of the total safety index so as to ensure the reliability, timeliness and high efficiency of transportation.
Referring to fig. 1, a flow chart of a driving safety evaluation method of a dynamic route according to an embodiment of the present invention is shown, where the driving safety evaluation method includes:
step S101, obtaining limit information of each road section on the route to be evaluated.
It should be noted that the route to be evaluated is a dynamic route requiring driving safety evaluation, and the route to be evaluated is one or more routes, and each route to be evaluated can be processed by adopting the content of each step in fig. 1 according to the embodiment of the present invention.
In the specific implementation process of step S101, the limit information of each road segment on the route to be evaluated is obtained from road network data (such as national road network data), and the limit information of the road segment includes, but is not limited to, height limit, weight limit, turning radius, gradient, and the like.
And S102, comparing the limit information with vehicle parameters of the transport vehicle, and determining the value of the first safety index according to the comparison result.
It should be noted that, the transportation vehicle is a vehicle to be driven on the route to be evaluated, and in order to ensure the reliability, timeliness and high efficiency of transportation, the transportation vehicle needs to adopt the scheme to perform driving safety evaluation on each route to be evaluated before transporting through the route to be evaluated, so as to determine whether each route to be evaluated is suitable for transportation.
In the specific implementation process of step S102, vehicle parameters of the transport vehicle are respectively compared with the limit information of each road section on the route to be evaluated, and the value of the first safety index (denoted as K 1) is determined according to the comparison result.
The vehicle parameters of the transport vehicle include, but are not limited to, the vehicle height of the transport vehicle, the load of the transport vehicle, the turning radius of the transport vehicle, and the maximum climbing data of the transport vehicle.
When comparing the vehicle parameters of the transport vehicle with the limit information of each road section on the route to be evaluated, comparing the vehicle height, load, turning radius and maximum climbing data of the transport vehicle with the limit information of each road section on the route to be evaluated to obtain corresponding comparison results, wherein the comparison results corresponding to each road section on the route to be evaluated can be used for indicating whether the transport vehicle meets the limit information of the road section or not, if so, the road section is suitable for the transport vehicle to pass, and if not, the road section is not suitable for the transport vehicle to pass. By comparing the vehicle parameters with the limit information, whether a road section where the transport vehicle cannot pass exists on the route to be evaluated can be determined, and whether the route to be evaluated meets rigid requirements such as vehicle height limit and vehicle weight limit or not is further determined.
In some embodiments, the restriction information of each road section on the route to be evaluated is compared with the vehicle parameter of the transport vehicle, if the vehicle parameter satisfies the restriction information of each road section on the route to be evaluated, the value of the first security index is determined to be a first preset value (e.g. 1), and if the vehicle parameter does not satisfy the restriction information of any road section on the route to be evaluated, the value of the first security index is determined to be a second preset value (e.g. 0).
That is, if the route to be evaluated includes a section where the transport vehicle cannot pass, K 1 =0, and if all sections where the transport vehicle can pass, K 1 =1.
Step S103, collecting average vehicle speed, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated to form an input vector.
In the specific implementation step S103, average speed, weather condition, time interval information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated are acquired, for example, average speed, weather condition, time interval information and date information of T continuous time slices corresponding to the current moment of each road section on the route to be evaluated are acquired.
And respectively utilizing the acquired average speed, weather condition, time period information and date information of continuous T time slices of each road section corresponding to the current moment to construct an input vector.
The input vector is composed of an average vehicle speed of a certain road section corresponding to a certain time slice of a certain day, a weather condition of a certain road section, time zone information, and date information. For example, taking the ith day and jth time slice of a road section g on a route to be evaluated as an example, the constructed input vector is recorded as For the average vehicle speed corresponding to the ith and jth time slices of the road section g on the road to be evaluated,For the weather condition corresponding to the ith and jth time slices of the road section g on the route to be evaluated, the time i,j is the time period information corresponding to the ith and jth time slices, and the day i,j is the date information corresponding to the ith and jth time slices.
And step S104, inputting the input vector into a pre-constructed average vehicle speed prediction model to predict the vehicle speed so as to obtain the predicted average vehicle speed of each road section on the route to be evaluated in a plurality of time slices in the future.
It should be noted that, the average vehicle speed prediction model is obtained by training a preset model based on sample data, and the specific process of training to obtain the average vehicle speed prediction model can be seen from the following content in fig. 2 according to the embodiment of the present invention. The sample data at least comprises road network data, historical road network speed monitoring data for continuous N days and historical weather data of corresponding dates.
The road network data comprises the following fields of road start longitude and latitude, road end longitude and latitude, road grade, road height limit, road weight limit, turning radius and gradient. The historical road network speed monitoring data comprises the following fields of a time stamp, a license plate number and a vehicle running speed. Historical weather data includes fields for time stamp, temperature, weather conditions.
In the specific implementation process of step S104, the input vector constructed in step S103 is input into an average vehicle speed prediction model to perform vehicle speed prediction, so as to predict and obtain a predicted average vehicle speed of each road section on the route to be evaluated in a plurality of time slices in the future. For example, the predicted average speed of each road segment on the route to be evaluated in the future de_max_step time slices is predicted.
The "predicted average vehicle speed of the road section in the future for a plurality of time slices" specifically refers to a predicted average vehicle speed of the road section in each time slice in the future.
The predicted average vehicle speed may be recorded as And (3) representing the predicted average speed of the road section G on the route to be evaluated in the m-th time slice in the future, wherein m epsilon [1, de_max_step ], G epsilon [1, G ] and G are the total number of the road sections on the route to be evaluated.
Step S105, the predicted time consumption of the transport vehicle passing through the route to be evaluated is determined according to the predicted average vehicle speed, and the reference time consumption of the transport vehicle passing through the route to be evaluated is determined according to the preset reference speed.
In the process of embodying step S105, the predicted time consumption of the transport vehicle passing through the route to be evaluated (corresponding to the time spent by the transport vehicle passing through the route to be evaluated) is determined according to the predicted average vehicle speed, and is calculated by assuming that the transport vehicle starts from the starting point of the route to be evaluated at time t 0, and updating the vehicle speed of the transport vehicle to the predicted average vehicle speed in the time slice corresponding to the corresponding road segment (the road segment on which the transport vehicle is traveling) every time the transport vehicle travels to a new road segment (or the time is in a new time slice) on the route to be evaluatedDividing the distance travelled by the transport vehicle in each state of the predicted average speed by the corresponding predicted average speed to obtain the time consumption of each section of the transport vehicle in the driving process, namely the time consumption of a certain section = the distance travelled by the transport vehicle in a state of a certain predicted average speed/the predicted average speed, and accumulating the time consumption of each section from the starting point to the end point of the route to be evaluated to obtain the predicted time consumption (marked as T predict) of the transport vehicle passing through the route to be evaluated.
The method comprises the steps of determining the reference time consumption of the transport vehicle passing through the route to be evaluated according to the preset reference speed, wherein the reference time consumption is calculated in a mode of calculating the linear distance (marked as S) between the starting point and the end point of the route to be evaluated according to the longitude and the latitude of the starting point and the longitude and the latitude of the end point of the route to be evaluated, setting the reference speed V ref, and calculating the reference time consumption of the transport vehicle passing through the route to be evaluated by using the linear distance and the reference speed obtained through calculation (marked as T ref).
It should be noted that, the manner of calculating the linear distance between the start point and the end point of the route to be evaluated by using the latitude and longitude can be seen from the following description of the embodiment of the present invention in step S201 of fig. 2.
And S106, determining the value of the second safety index by using the predicted time consumption and the reference time consumption.
In the process of implementing step S106, the value of the second security index (denoted as K 2) is determined using the predicted time and the reference time. Specifically, the ratio between the reference time and the predicted time is calculated, resulting in the value of the second safety index, i.e., K 2＝Tref/Tpredict.
And S107, collecting negative events influencing driving safety and duration time of the negative events on the route to be evaluated at the current moment.
In the specific implementation step S107, negative events affecting driving safety on the route to be evaluated at the current time are collected, and the duration of each negative event is collected.
For example, the method comprises the steps of collecting negative events which seriously affect driving safety, such as traffic accidents, landslide, road surface collapse, severe weather (heavy to heavy rain, heavy to heavy snow and heavy fog) and the like on a road to be evaluated at the current moment, recording the duration of the traffic accidents as TS 1, recording the duration of the landslide as TS 2, recording the duration of the road surface collapse as TS 3 and recording the duration of the severe weather as TS 4.
Step S108, predicting the estimated arrival time of the transport vehicle at the position of the negative event according to the predicted average vehicle speed, and determining the value of the third safety index based on the estimated arrival time and the duration of the negative event.
In the process of implementing step S108, the time when the transport vehicle travels to the position where the negative event is located (this time is called estimated arrival time) is predicted according to the predicted average vehicle speed, where "position where the negative event is located" specifically refers to the occurrence point of the negative event.
For each negative event, if the estimated arrival time of the transportation vehicle traveling to the location of the negative event is within the duration of the negative event, the value of the specified variable (denoted as C) is incremented by 1, the value of the specified variable being indicative of the number of times the negative event was encountered.
Specifically, the estimated arrival time of the transport vehicle at the occurrence point of the negative event is predicted, if the transport vehicle is still within the duration of the negative event when the transport vehicle is traveling to the occurrence point of the negative event (i.e., the estimated arrival time of the transport vehicle at the location of the negative event is within the duration of the negative event), the value of C is added to 1, and it should be noted that the initial value of C is set to 0 (the maximum value may be set to 5, and the maximum value of C is merely exemplary).
After updating the specified variable C in the above manner, the value of the third safety index (denoted as K 3) is determined based on the value of the specified variable, specifically, the value of the third safety index is calculated using formula (1).
K3=1-0.2C (1)
Step S109, determining the value of the total safety index by combining the value of the first safety index, the value of the second safety index and the value of the third safety index.
In the specific implementation process of step S109, the product of the value of the first security index, the value of the second security index and the value of the third security index is calculated, so as to obtain the value of the total security index (denoted as K) corresponding to the route to be evaluated.
Specifically, the value of the total safety index is calculated by the formula (2).
K=K1×K2×K3 (2)
It should be noted that, a plurality of preset numerical value intervals are preset, each preset numerical value interval is provided with a corresponding driving safety evaluation grade, and the driving safety evaluation grades can be divided into excellent, good, medium, poor and poor.
In some embodiments, after calculating the value of the total safety index, determining a preset value interval in which the value of the total safety index is located from a plurality of preset value intervals, and outputting a driving safety evaluation grade corresponding to the preset value interval in which the value of the total safety index is located. The driving safety evaluation grade corresponding to the preset value interval where the value of the total safety index is located is the driving safety evaluation grade of the route to be evaluated.
For example, when K is more than or equal to 0.8, the vehicle safety evaluation level corresponding to the value of the total safety index is excellent, when K is more than or equal to 0.6 and less than or equal to 0.8, the vehicle safety evaluation level corresponding to the value of the total safety index is good, when K is more than or equal to 0.4 and less than or equal to 0.6, the vehicle safety evaluation level corresponding to the value of the total safety index is medium, when K is more than or equal to 0.2 and less than or equal to 0.4, the vehicle safety evaluation level corresponding to the value of the total safety index is poor, and when K is more than or equal to 0 and less than or equal to 0.2, the vehicle safety evaluation level corresponding to the value of the total safety index is poor.
It should be noted that, each route to be evaluated can calculate the value of the corresponding total safety index in the above manner, and the driving safety evaluation level of the corresponding route to be evaluated can be determined according to the value of the total safety index.
In the embodiment of the invention, the numerical value of the first safety index is determined by comparing the vehicle parameters of the transport vehicle with the limit information of each road section on the route to be evaluated. And determining the predicted time consumption passing through the route to be evaluated by using the average vehicle speed prediction model, and determining the value of the second safety index based on the predicted time consumption and the reference time consumption. And determining the value of the third safety index by using the duration of the negative event and the predicted arrival time of the transport vehicle at the position of the negative event. The numerical value of the total safety index is calculated by combining the numerical values of the first safety index, the second safety index and the third safety index, and the safety evaluation is carried out on the route to be evaluated through the numerical value of the total safety index so as to ensure the reliability, timeliness and high efficiency of transportation.
The content of the training average vehicle speed prediction model referred to in step S104 of fig. 1 in the above embodiment of the present invention is referred to fig. 2, which is a flowchart illustrating the training average vehicle speed prediction model provided in the embodiment of the present invention, and fig. 2 includes the following steps:
step S201, based on sample data, counting sample average speed and sample weather conditions of each road section in each time slice, and counting sample time period information and sample date information corresponding to each time slice.
In the specific implementation process of step S201, sample data is obtained, where the sample data at least includes road network data, historical road network speed monitoring data for N consecutive days, and historical weather data for corresponding dates.
The sample data is preprocessed, and the specific preprocessing mode at least comprises the following steps of carrying out data cleaning on the sample data, deleting invalid data in the sample data, supplementing missing fields in the sample data, correcting historical road network speed monitoring data, and if the historical road network speed monitoring data monitored on a road section is greater than the speed limit of the road section, the historical road network speed monitoring data which is greater than the speed limit is required to be adjusted down to the highest speed limit.
In some embodiments, after preprocessing the sample data, statistical analysis is performed on the preprocessed sample data to calculate the road segment length of each road segment, specifically, the road segment length is calculated according to the starting longitude and latitude and the ending longitude and latitude of the road segment, and the formula (3) and the formula (4) are combined.
θ=arccos(sinSlat×sinElat+cosSlat×cosElat×cos(Elon-Slon)) (3)
lSE=R×θ (4)
In the formulas (3) and (4), S lon is the longitude of the start point of the link, S lat is the latitude of the start point of the link, E lon is the longitude of the end point of the link, E lat is the latitude of the end point of the link, R is the earth radius, and l SE is the link length between the start point (S point) and the end point (E point) of the link.
After preprocessing the sample data, equally dividing the historical road network speed monitoring data of continuous N days into cnt time slices from 0:00 time to 23:50 time according to tau 0 (30 minutes for example) as intervals, counting the average speed of the samples of each road section in each time slice, and specifically calculating the average speed of the samples of each road section in each time slice through a formula (5).
In the formula (5) of the present invention,For the total number of vehicles passing on road segment g monitored in the j-th time slice on day i,For the speed of the kth vehicle passing over the road section g monitored in the jth time slice on the ith day,The average vehicle speed of the sample corresponding to the road section g in the j-th time slice on the i-th day.
And then counting the sample weather conditions of each road section in each time slice, wherein the sample weather conditions can be divided into five types, namely, up to heavy rain, medium snow, up to heavy snow, large fog and the like, and the five types of sample weather conditions, i.e. up to heavy rain, medium snow, up to heavy snow, large fog and the like, are respectively represented by 1, 2, 3, 4 and 5. Here the sample weather conditions are noted as And (5) representing the sample weather condition corresponding to the road section g in the j-th time slice on the i-th day.
And counting the sample period information corresponding to each time slice, specifically dividing one day from 0 time, every 3 hours into one sample period, and dividing the sample period into 8 sample periods in total, wherein the 8 sample periods are respectively represented by 1, 2, 3, 4, 5, 6,7 and 8, and the sample period information is recorded as time i,j,timei,j to represent the sample period information corresponding to the j-th time slice on the i-th day.
Next, the sample date information corresponding to each time slice is counted, specifically, the date information of each day in the above-mentioned "continuous N days" is counted, each day is divided into a rest day (legal holiday, weekend, etc.) and a working day, the rest day 0 is represented by 1, and the sample date information is denoted by day i,j,dayi,j and represents the sample date information corresponding to the j-th time slice of the i-th day.
Step S202, constructing a sample input vector by using the sample average speed, the sample weather condition, the sample time period information and the sample date information.
In the specific implementation process of step S202, after the average sample vehicle speed, the sample weather condition, the sample period information and the sample date information are obtained through statistics in step S201, a preset model (the average vehicle speed prediction model after training convergence) is constructed, and the preset model is constructed based on a transducer structure, that is, the preset model is constructed based on the transducer structure (which may be referred to as a transducer model).
The main parameters of the built pre-set model, for example, the main parameters of the pre-set model are set such that embedding _size is set to 128, en_layers_num is set to 2, en_seq_len is set to 16, en_hidden_size is set to 128, and en_head_num is set to 4 for the encoder section.
For the decoder part, the decoder part adopts an autoregressive structure to realize multi-step prediction, wherein de_lay_num is set to 4, de_max_step is set to 128, de_hidden_size is set to 128, and de_head_num is set to 4. The input of each step of the autoregressive structure is the predicted average speed predicted by the last time slice.
After setting the preset model, the average speed of the vehicle is obtained by using the sampleSample weather conditionsSample period information time i,j and sample date information day i,j, a sample input vector is constructed that inputs the preset model. Taking the ith day and the jth time slice of the road section g as an example, the corresponding sample input vector is
And step 203, training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model.
In the specific implementation process of step S203, after the sample input vector is constructed, training the preset model based on the sample input vector until the preset model converges, so as to obtain the average vehicle speed prediction model.
Specifically, each time data composed of sample input vectors of T consecutive time slices is input into a preset model, the preset model outputs a prediction result of the average speed of the road segments in the future de_max_step time slices (i.e. outputs a predicted average speed), the prediction result is compared with actual monitoring data (i.e. actual average speed) and the error is propagated in the opposite direction, so that a final average speed prediction model is trained.
The above is related to training to obtain an average vehicle speed prediction model.
As can be seen from the contents of fig. 1 and 2 in the above embodiment of the present invention, the present solution has the following advantages:
(1) According to the scheme, factors which possibly influence the safety of the driving route are comprehensively analyzed, an average speed prediction model is built by combining road network data, speed monitoring data, weather, time period and date, and the predicted time consumption of the transportation vehicle passing through the route to be evaluated can be calculated based on the average speed prediction model, so that the passing efficiency of the route to be evaluated is judged.
(2) Aiming at the condition that time sequence coupling exists in the average speed of the road section, the scheme adopts a transducer network in a targeted manner and captures the time sequence association of the average speed of the road section by utilizing the self-attention structure of the transducer network, so that the accuracy of predicting the average speed is improved.
(3) According to the scheme, the reliability, the high efficiency and the safety of the route to be evaluated are comprehensively analyzed from three aspects of passing ability, passing efficiency and passing safety, and finally, the driving safety evaluation grade of the route to be evaluated can be obtained more accurately and comprehensively. Therefore, effective guidance is provided for a decision maker to select a route to be evaluated, and safety evaluation can be performed on a plurality of routes to be evaluated so as to assist the decision maker to select an optimal driving route.
Corresponding to the above-mentioned driving safety evaluation method for a dynamic route provided by the embodiment of the present invention, referring to fig. 3, the embodiment of the present invention further provides a structural block diagram of a driving safety evaluation system for a dynamic route, where the driving safety evaluation system includes an acquisition unit 301, a first processing unit 302, a first acquisition unit 303, a prediction unit 304, a second processing unit 305, a first determination unit 306, a second acquisition unit 307, a third processing unit 308, and a second determination unit 309;
An obtaining unit 301, configured to obtain constraint information of each road segment on the route to be evaluated.
The first processing unit 302 is configured to compare the restriction information with vehicle parameters of the transport vehicle, and determine a value of the first safety index according to the comparison result.
In a specific implementation, the first processing unit 302 is specifically configured to compare the restriction information of each road segment on the route to be evaluated with the vehicle parameter of the transport vehicle, determine the value of the first security index as a first preset value if the vehicle parameter meets the restriction information of each road segment on the route to be evaluated, and determine the value of the first security index as a second preset value if the vehicle parameter does not meet the restriction information of any road segment on the route to be evaluated.
The first collection unit 303 is configured to collect average vehicle speed, weather condition, time period information and date information of a plurality of continuous time slices corresponding to a current time on each road section on the route to be evaluated, so as to form an input vector.
The prediction unit 304 is configured to input an input vector into a pre-constructed average speed prediction model to perform speed prediction, so as to obtain a predicted average speed of each road section on the route to be evaluated in a plurality of time slices in the future, where the average speed prediction model is obtained by training a preset model based on sample data, and the sample data at least includes road network data, historical road network speed monitoring data for N consecutive days, and historical weather data.
The second processing unit 305 is configured to determine a predicted time consumption of the transport vehicle passing through the route to be evaluated according to the predicted average vehicle speed, and determine a reference time consumption of the transport vehicle passing through the route to be evaluated according to a preset reference speed.
The first determining unit 306 is configured to determine the value of the second security indicator by using the predicted time consumption and the reference time consumption.
The second collection unit 307 is configured to collect negative events affecting driving safety on the route to be evaluated at the current time and duration of the negative events.
The third processing unit 308 is configured to predict an estimated arrival time when the transport vehicle travels to a location where the negative event is located according to the predicted average vehicle speed, and determine a value of the third safety index based on the estimated arrival time and the duration of the negative event.
In a specific implementation, the third processing unit 308 is configured to determine the value of the third safety index based on the estimated arrival time and the duration of the negative event, specifically configured to, for each negative event, if the estimated arrival time of the transport vehicle traveling to the location where the negative event is located is within the duration of the negative event, accumulate 1 the values of the specified variables, where the values of the specified variables are used to represent the number of times the negative event is encountered, and determine the value of the third safety index based on the values of the specified variables.
The second determining unit 309 is configured to determine a value of the total security index by combining the value of the first security index, the value of the second security index, and the value of the third security index.
In a specific implementation, the second determining unit 309 is specifically configured to calculate a product between the value of the first security indicator, the value of the second security indicator, and the value of the third security indicator, so as to obtain the value of the total security indicator.
In the embodiment of the invention, the numerical value of the first safety index is determined by comparing the vehicle parameters of the transport vehicle with the limit information of each road section on the route to be evaluated. And determining the predicted time consumption passing through the route to be evaluated by using the average vehicle speed prediction model, and determining the value of the second safety index based on the predicted time consumption and the reference time consumption. And determining the value of the third safety index by using the duration of the negative event and the predicted arrival time of the transport vehicle at the position of the negative event. The numerical value of the total safety index is calculated by combining the numerical values of the first safety index, the second safety index and the third safety index, and the safety evaluation is carried out on the route to be evaluated through the numerical value of the total safety index so as to ensure the reliability, timeliness and high efficiency of transportation.
Preferably, in conjunction with the content shown in fig. 3, the prediction unit 304 includes a statistics module, a construction module and a training module, where the execution principle of each module is as follows:
And the statistics module is used for counting the average sample speed and the sample weather condition of each road section in each time slice based on the sample data, and counting the sample time period information and the sample date information corresponding to each time slice.
And the construction module is used for constructing a sample input vector by using the sample average speed, the sample weather condition, the sample time period information and the sample date information.
The training module is used for training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model, wherein the preset model is constructed based on a transducer structure.
Preferably, in combination with the content shown in fig. 3, the driving safety assessment system further includes:
And the third determining unit is used for determining a preset value interval in which the value of the total safety index is located from a plurality of preset value intervals.
The output unit is used for outputting the driving safety evaluation grades corresponding to the preset value intervals in which the values of the total safety indexes are located, wherein each preset value interval is provided with a corresponding driving safety evaluation grade.
In summary, the embodiment of the invention provides a driving safety evaluation method and system for a dynamic route, which determine a value of a first safety index by comparing vehicle parameters of a transport vehicle with limit information of each road section on the route to be evaluated. And determining the predicted time consumption passing through the route to be evaluated by using the average vehicle speed prediction model, and determining the value of the second safety index based on the predicted time consumption and the reference time consumption. And determining the value of the third safety index by using the duration of the negative event and the predicted arrival time of the transport vehicle at the position of the negative event. The numerical value of the total safety index is calculated by combining the numerical values of the first safety index, the second safety index and the third safety index, and the safety evaluation is carried out on the route to be evaluated through the numerical value of the total safety index so as to ensure the reliability, timeliness and high efficiency of transportation.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part. The systems and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A traffic safety assessment method for a dynamic route, the method comprising:
Acquiring limit information of each road section on a route to be evaluated;
comparing the limiting information with vehicle parameters of the transport vehicle, and determining the value of a first safety index according to the comparison result;
Collecting average speed, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated so as to form an input vector;
Inputting the input vector into a pre-constructed average speed prediction model to predict the speed of the vehicle so as to obtain the predicted average speed of each road section on the route to be evaluated in a plurality of time slices in the future, wherein the average speed prediction model is obtained by training a preset model based on sample data, and the sample data at least comprises road network data, historical road network speed monitoring data for N continuous days and historical weather data;
Determining the predicted time consumption of the transport vehicle passing through the route to be evaluated according to the predicted average vehicle speed, and determining the reference time consumption of the transport vehicle passing through the route to be evaluated according to a preset reference speed;
determining a value of a second security indicator using the predicted time consumption and the reference time consumption;
Collecting negative events influencing driving safety on the route to be evaluated at the current moment and duration time of the negative events;
Predicting the estimated arrival time of the transport vehicle from the predicted average vehicle speed to the position of the negative event, and determining the value of a third safety index based on the estimated arrival time and the duration of the negative event;
and determining the value of the total safety index by combining the value of the first safety index, the value of the second safety index and the value of the third safety index.
2. The method of claim 1, wherein training the predetermined model based on the sample data to obtain the average vehicle speed prediction model comprises:
Based on the sample data, counting the sample average speed and sample weather conditions of each road section in each time slice, and counting the sample time period information and the sample date information corresponding to each time slice;
Constructing a sample input vector by using the sample average speed, sample weather conditions, the sample period information and the sample date information;
training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model, wherein the preset model is constructed based on a transducer structure.
3. The method of claim 1, wherein comparing the constraint information to vehicle parameters of the transportation vehicle and determining a value of the first safety indicator based on the comparison comprises:
Respectively comparing the limit information of each road section on the route to be evaluated with vehicle parameters of a transport vehicle;
if the vehicle parameters meet the limit information of each road section on the route to be evaluated, determining the numerical value of a first safety index as a first preset value;
And if the vehicle parameters do not meet the limit information of any road section on the route to be evaluated, determining that the numerical value of the first safety index is a second preset value.
4. The method of claim 1, wherein determining a value of a third security indicator based on the estimated time of arrival and the duration of the negative event comprises:
For each negative event, if the estimated arrival time of the transport vehicle traveling to the position where the negative event is located is within the duration of the negative event, accumulating the value of a specified variable by 1, wherein the value of the specified variable is used for representing the number of times of encountering the negative event;
A value of a third security indicator is determined based on the value of the specified variable.
5. The method of claim 1, wherein determining the value of the total security indicator in combination with the value of the first security indicator, the value of the second security indicator, and the value of the third security indicator comprises:
and calculating the product among the numerical value of the first safety index, the numerical value of the second safety index and the numerical value of the third safety index to obtain the numerical value of the total safety index.
6. The method of any one of claims 1-5, further comprising:
determining a preset value interval in which the value of the total safety index is located from a plurality of preset value intervals;
outputting a driving safety evaluation grade corresponding to a preset value interval in which the value of the total safety index is located, wherein each preset value interval is provided with a corresponding driving safety evaluation grade.
7. A traffic safety assessment system for a dynamic route, the system comprising:
The acquisition unit is used for acquiring the limit information of each road section on the route to be evaluated;
the first processing unit is used for comparing the limit information with vehicle parameters of the transport vehicle and determining the numerical value of a first safety index according to the comparison result;
the first acquisition unit is used for acquiring average vehicle speed, weather conditions, time period information and date information of a plurality of continuous time slices corresponding to the current moment of each road section on the route to be evaluated so as to form an input vector;
The prediction unit is used for inputting the input vector into a pre-constructed average speed prediction model to predict the speed of the vehicle so as to obtain the predicted average speed of each road section on the route to be evaluated in a plurality of time slices in the future, wherein the average speed prediction model is obtained by training a preset model based on sample data, and the sample data at least comprises road network data, historical road network speed monitoring data for N continuous days and historical weather data;
The second processing unit is used for determining the predicted time consumption of the transport vehicle passing through the route to be evaluated according to the predicted average vehicle speed and determining the reference time consumption of the transport vehicle passing through the route to be evaluated according to a preset reference speed;
a first determining unit configured to determine a value of a second security index using the predicted time consumption and the reference time consumption;
The second acquisition unit is used for acquiring negative events influencing driving safety on the route to be evaluated at the current moment and duration time of the negative events;
The third processing unit is used for predicting the estimated arrival time of the transport vehicle from the predicted average vehicle speed to the position where the negative event is located, and determining the value of a third safety index based on the estimated arrival time and the duration of the negative event;
And a second determining unit, configured to determine a value of a total security index by combining the value of the first security index, the value of the second security index, and the value of the third security index.
8. The system of claim 7, wherein the prediction unit comprises:
the statistics module is used for counting the average sample speed and the sample weather condition of each road section in each time slice based on the sample data, and counting the sample time period information and the sample date information corresponding to each time slice;
A construction module for constructing a sample input vector using the sample average vehicle speed, sample weather conditions, the sample period information, and the sample date information;
And the training module is used for training a preset model based on the sample input vector until the preset model converges to obtain an average vehicle speed prediction model, wherein the preset model is constructed based on a transducer structure.
9. The system of claim 7, wherein the first processing unit is specifically configured to compare the restriction information of each road segment on the route to be evaluated with a vehicle parameter of a transport vehicle, determine a value of a first security indicator as a first preset value if the vehicle parameter satisfies the restriction information of each road segment on the route to be evaluated, and determine the value of the first security indicator as a second preset value if the vehicle parameter does not satisfy the restriction information of any road segment on the route to be evaluated.
10. The system according to claim 7, wherein the third processing unit for determining a value of a third safety index based on the estimated arrival time and the duration of the negative event is specifically configured to:
For each negative event, if the estimated arrival time of the transport vehicle traveling to the position where the negative event is located is within the duration of the negative event, accumulating the value of a specified variable by 1, wherein the value of the specified variable is used for representing the number of times of encountering the negative event;
A value of a third security indicator is determined based on the value of the specified variable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310122892.1A CN115953901B (en) | 2023-02-16 | 2023-02-16 | A driving safety assessment method and system for dynamic routes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310122892.1A CN115953901B (en) | 2023-02-16 | 2023-02-16 | A driving safety assessment method and system for dynamic routes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115953901A CN115953901A (en) | 2023-04-11 |
| CN115953901B true CN115953901B (en) | 2025-01-28 |
Family
ID=87289410
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310122892.1A Active CN115953901B (en) | 2023-02-16 | 2023-02-16 | A driving safety assessment method and system for dynamic routes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115953901B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117236779B (en) * | 2023-10-09 | 2024-06-28 | 速度科技股份有限公司 | Data transportation evaluation method for large database |
| CN117851788B (en) * | 2024-03-04 | 2024-05-07 | 泓浒(苏州)半导体科技有限公司 | Automatic wafer transmission scheduling method and system based on crown block |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105788335A (en) * | 2016-04-05 | 2016-07-20 | 广东欧珀移动通信有限公司 | Navigation method and terminal |
| CN110033615A (en) * | 2019-03-22 | 2019-07-19 | 山西省交通科学研究院有限公司 | A kind of road hazard cargo transport dynamic risk appraisal procedure based on Internet of Things |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9792575B2 (en) * | 2016-03-11 | 2017-10-17 | Route4Me, Inc. | Complex dynamic route sequencing for multi-vehicle fleets using traffic and real-world constraints |
| TWI623920B (en) * | 2016-04-28 | 2018-05-11 | 財團法人資訊工業策進會 | Speed prediction method |
| EP3971780A1 (en) * | 2020-07-24 | 2022-03-23 | Tata Consultancy Services Limited | Method and system for dynamically predicting vehicle arrival time using a temporal difference learning technique |
-
2023
- 2023-02-16 CN CN202310122892.1A patent/CN115953901B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105788335A (en) * | 2016-04-05 | 2016-07-20 | 广东欧珀移动通信有限公司 | Navigation method and terminal |
| CN110033615A (en) * | 2019-03-22 | 2019-07-19 | 山西省交通科学研究院有限公司 | A kind of road hazard cargo transport dynamic risk appraisal procedure based on Internet of Things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115953901A (en) | 2023-04-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115953901B (en) | A driving safety assessment method and system for dynamic routes | |
| Nanthawichit et al. | Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway | |
| JP4175312B2 (en) | Traffic information prediction device | |
| EP2255347B1 (en) | Travel pattern information obtaining device, travel pattern information obtaining method, travel pattern information obtaining program and computer readable medium | |
| Myung et al. | Travel time prediction using k nearest neighbor method with combined data from vehicle detector system and automatic toll collection system | |
| Thomas et al. | Predictions of urban volumes in single time series | |
| US7755509B2 (en) | Use of pattern matching to predict actual traffic conditions of a roadway segment | |
| Li et al. | Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks | |
| JP5424754B2 (en) | Link travel time calculation device and program | |
| CN105825310A (en) | Taxi passenger-searching path recommendation method based on information entropy | |
| Padiath et al. | Prediction of traffic density for congestion analysis under Indian traffic conditions | |
| Kumar et al. | A model based approach to predict stream travel time using public transit as probes | |
| CN105405294A (en) | Early warning method of traffic congestion roads | |
| Zhi-Peng et al. | An improved adaptive exponential smoothing model for short-term travel time forecasting of urban arterial street | |
| CN111583628B (en) | Prediction method of heavy truck traffic flow on road network based on data quality control | |
| CN117251722A (en) | Intelligent traffic management system based on big data | |
| CN119784120A (en) | A safety management system for highway maintenance | |
| CN109493449B (en) | Freight train cargo state estimation method based on freight train GPS track data and high-speed transaction data | |
| US20120265428A1 (en) | Vehicle motion estimating device, vehicle motion estimating method, and vehicle motion estimating program | |
| CN119090306A (en) | Intelligent decision analysis system and method based on multi-source data | |
| Olayode et al. | Application of adaptive neuro-fuzzy inference system model on traffic flow of vehicles at a signalized road intersections | |
| Faouzi et al. | Motorway travel time prediction based on toll data and weather effect integration | |
| Shen | Freeway travel time estimation and prediction using dynamic neural networks | |
| CN116052435A (en) | Urban road congestion influence range definition and road influence calculation method | |
| CN118761513B (en) | Short-time prediction method for service area incoming passenger flow based on operator 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 |