Information management method, system and equipment for electronic bus stop board and medium

Abstract

The invention relates to the technical field of intelligent traffic, and discloses an information management method, system and device for an electronic bus stop board and a medium. The method comprises the following steps: acquiring driving data, historical traffic data and urban traffic system data of a bus; preprocessing the driving data of the bus and the urban traffic system data to obtain a feature vector of the bus; inputting the feature vector of the bus into a preset prediction model, and calculating to obtain predicted waiting time and a carriage crowding degree; and sending the predicted waiting time and the carriage crowding degree to a corresponding electronic stop board and displaying the predicted waiting time and the carriage crowding degree. According to the method, the waiting time of the bus is accurately predicted by establishing the random forest model combining the traffic jam condition and the weather condition, and the carriage crowding degree is calculated according to the historical data, so that the riding experience of passengers is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to an information management method, system, equipment and medium for an electronic bus stop sign. Background Art

[0002] In recent years, with the development of public transportation, as well as the continuous growth of society, economy and urban population, people's demand for public transportation has become greater and greater, and the requirements have become higher and higher. Urban public transportation has become the main mode of transportation for the public. At present, most cities' buses are equipped with electronic bus stop systems, which provide real-time dynamic information such as the real-time location of buses, arrival time, and distance to the station by installing LCD display screens in the platform.

[0003] The existing bus electronic stop information management method mainly transmits the bus location information to the LCD screen through a wireless communication system, and sets a periodically changing static template for the displayed content and style according to a certain rule. In actual application, this method has problems such as inaccurate information, which can easily cause passengers to be unable to obtain accurate location information, mainly reflected in the following aspects: the bus company does not update the vehicle location information in a timely manner, resulting in the vehicle not arriving at the station within the waiting time displayed on the stop, delaying passengers' travel; due to weather, road conditions and other conditions, the vehicle runs slowly, but the passengers do not have updated waiting time information during the waiting time at the stop, delaying passengers' boarding; the path information displayed on the electronic stop can only be preset in advance. When the bus needs to choose other routes to temporarily change the running route according to road conditions, it cannot be displayed at the station in the first time, causing inconvenience to passengers.

[0004] In addition, in the existing real-time display method of public transportation electronic bus stop information, the calculation principle of the real-time arrival time of the bus is generally as follows: the electronic bus stop information shows that the estimated time from a certain station to this station is T minutes, then the time from this station to the next station = the time from the previous station to this station - the time used, that is, (the time to this station - the time to the previous station). When the vehicle travels from station A to the next station, station B, the travel time from station A to station B is obtained through the positioning system installed in the vehicle, and then the time taken from station A to station B is obtained on the electronic screen of station B. Since the arrival time is only an estimate of the running time, when the vehicle is affected by weather, road conditions, temporary stops, etc. when running between platforms, the time between stations increases, and the electronic bus stop often cannot accurately display the estimated arrival time at the station.

[0005] In summary, the electronic bus stop signs in the prior art cannot accurately display the estimated time of arrival at the station, causing inconvenience to passengers. Summary of the invention

[0006] The present invention provides an information management method, system, device and medium for a public transportation electronic bus stop board, so as to solve the technical problem that the electronic bus stop board in the prior art cannot accurately display the estimated arrival time at the station, causing inconvenience to passengers.

[0007] In the first aspect, in order to solve the above technical problems, the present invention provides an information management method for an electronic bus stop sign, comprising:

[0008] Obtain bus driving data, historical traffic data and urban transportation system data;

[0009] Preprocessing the driving data of the bus and the urban transportation system data to obtain a feature vector of the bus;

[0010] Inputting the characteristic vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage;

[0011] The estimated waiting time and the degree of congestion in the carriage are sent to the corresponding electronic station sign and displayed.

[0012] As an optional implementation, the bus driving data includes the date, driving route data, vehicle location data, the bus's maximum passenger capacity, the number of passengers in the car and driving speed data; the historical traffic data includes the historical stop time of each station, the average number of passengers getting on and off at each station on weekdays and the average number of passengers getting on and off at each station on holidays; the urban transportation system data includes traffic flow data of bus operating sections, electronic bus stop location data, traffic signal data, the maximum traffic capacity of each section and weather conditions data.

[0013] As an optional implementation manner, the preprocessing of the bus travel data and the urban transportation system data to obtain the feature vector of the bus includes:

[0014] Filtering and averaging the driving speed data contained in the driving data of the bus to obtain an average vehicle speed;

[0015] The weather condition data contained in the urban traffic system data is classified according to the degree of influence on the vehicle speed. The weather condition data of a certain day is assumed to be w, w={0,1,2,3,4,5,6}, where 0 represents sunny, cloudy and overcast, 1 represents light rain, 2 represents heavy rain, 3 represents light haze, 4 represents heavy haze, 5 represents light snow, and 6 represents heavy snow, and the weather influence coefficient is obtained;

[0016] The congestion coefficient is calculated based on the traffic flow data of the bus operation section contained in the urban transportation system data and the maximum traffic capacity of each section;

[0017] The distance between the bus and the electronic bus stop and the number of traffic lights passed by are calculated based on the driving route data and vehicle position data contained in the driving data of the bus and the electronic bus stop position data and traffic signal data contained in the urban transportation system data;

[0018] The average speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and date of each station, the number of passengers on the bus, the number of passengers in the car, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays are vectorized as the feature vector of the bus.

[0019] As an optional implementation, the preset prediction model includes a random forest model for predicting waiting time and a carriage congestion calculation model, wherein the training process of the random forest model includes:

[0020] Generate a waiting time training set based on the average vehicle speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and historical waiting time of each station in the historical database;

[0021] Using the bootstrap sampling method on the waiting time training set to generate a sub-training set, using the sub-training set to train the initial random forest model, and constructing a random forest model including k CART decision trees;

[0022] The random forest model composed of k CART decision trees is expressed as:

[0023] F={h1(T),h2(T),…,h k (T)}

[0024] Where T is the input data of the random forest model, h i In (T), i=1,2,…,k represents the CART decision tree.

[0025] As an optional implementation, the inputting the characteristic vector of the bus into a preset prediction model to calculate the estimated waiting time and the degree of congestion in the carriage includes:

[0026] The average speed, weather influence coefficient, congestion coefficient, distance between the bus and the electronic bus stop sign, and number of traffic lights passed in the feature vector of the bus are input into the random forest model included in the prediction model for prediction, and the k CART decision trees configured in the random forest model vote on the prediction results to obtain the estimated waiting time;

[0027] The date, the rated passenger capacity of the bus, the number of passengers in the carriage, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays in the feature vector of the bus are input into the carriage crowding calculation model contained in the prediction model for calculation to obtain the carriage crowding degree.

[0028] As an optional implementation, the date, vehicle location data, electronic stop sign location data, the number of passengers allowed to be carried by the bus, the number of passengers in the carriage, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays in the feature vector of the bus are input into the carriage crowding degree calculation model included in the prediction model for calculation to obtain the carriage crowding degree, specifically:

[0029] The date is determined. If the date is a weekday, the number of passengers in the carriage is added to the average number of passengers getting on and off on weekdays corresponding to all stations between the vehicle location data and the electronic station sign location data to obtain the expected number of passengers. 预计 / n 核载 As the degree of crowdedness in the carriage, where n 预计 is the expected number of passengers, and n 核载 is the number of passengers that the bus can carry; if the date is a holiday, the number of passengers in the car is added to the average number of passengers getting on and off on holidays at all stations between the vehicle location data and the electronic bus stop location data to obtain the expected number of passengers, and n is 预计 / n 核载 As the degree of crowdedness in the carriage.

[0030] As an optional implementation, the sending and displaying of the estimated waiting time and the carriage congestion level to a corresponding electronic station sign includes:

[0031] The estimated waiting time is divided by the historical average waiting time in the database to obtain a division result. If the division result is greater than or equal to a preset first multiple, the road congestion is taken as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board; if the division result is less than the preset first multiple and greater than a preset second multiple, the road is relatively smooth as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board; if the division result is less than the preset second multiple, the road is smooth as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board;

[0032] Sending the degree of congestion in the carriage to a corresponding electronic station sign;

[0033] After receiving the estimated waiting time and the degree of congestion in the carriage, the electronic station board displays the information in a scrolling manner on a display screen configured for the electronic station board.

[0034] In a second aspect, the present invention provides an information management system for electronic bus stop signs, comprising:

[0035] Data acquisition module, used to obtain bus driving data, historical traffic data and urban traffic system data;

[0036] A preprocessing module, used for preprocessing the driving data of the bus and the urban transportation system data to obtain a feature vector of the bus;

[0037] A prediction module, used for inputting the characteristic vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage;

[0038] The display module is used to send the estimated waiting time and the degree of congestion in the carriage to a corresponding electronic bus stop sign and display them.

[0039] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for managing the information of an electronic bus stop sign described in any one of the above is implemented.

[0040] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for managing the information of the electronic bus stop.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] Obtain the driving data, historical traffic data and urban traffic system data of the bus; pre-process the driving data of the bus and the urban traffic system data to obtain the characteristic vector of the bus; input the characteristic vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage; send the expected waiting time and the degree of congestion in the carriage to the corresponding electronic bus stop and display them. The present invention accurately predicts the waiting time of the bus by establishing a random forest model that combines traffic congestion and weather conditions, and calculates the degree of congestion in the carriage based on historical data, thereby improving the riding experience of passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for managing information of an electronic bus stop sign provided by the first embodiment of the present invention;

[0044] Figure 2It is a structural diagram of an information management system for an electronic bus stop sign provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Reference Figure 1 The first embodiment of the present invention provides a method for managing information of an electronic bus stop sign, comprising the following steps:

[0047] S11, obtaining bus driving data, historical traffic data and urban transportation system data;

[0048] S12, preprocessing the travel data of the bus and the urban transportation system data to obtain a feature vector of the bus;

[0049] S13, inputting the characteristic vector of the bus into a preset prediction model to calculate the estimated waiting time and the degree of congestion in the carriage;

[0050] S14, sending the estimated waiting time and the degree of congestion in the carriage to a corresponding electronic bus stop sign and displaying them.

[0051] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0052] In step S11, it should be noted that the driving data, historical traffic data and urban traffic system data of the bus are obtained, wherein the driving data of the bus includes date, driving route data, vehicle location data, the number of passengers on the bus, the number of passengers in the car and driving speed data. Furthermore, the date is used to distinguish between working days and holidays, the driving route data and the vehicle location data are used to calculate the distance between the location of the bus and each electronic bus stop and the number of traffic lights passed, the driving route data comes from the bus operation system, and the vehicle location data comes from the GPS positioning system configured on the bus. The number of passengers on the bus and the number of passengers in the car are used to calculate the degree of congestion in the car, wherein the number of passengers on the bus comes from the bus operation system and can be found by the model of the bus, and the number of passengers in the car comes from the thermal imaging collector configured on the bus, and the number of passengers in the car is calculated by processing the images in the bus collected by the thermal imaging collector. The driving speed data comes from the speed measurement system of the bus, which is the speed change curve of the bus during the measurement period, and is used to calculate the expected waiting time.

[0053] The historical traffic data includes the historical stop time of each station, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays. Furthermore, the historical stop time of each station is the average time of the historical stop of the bus on this route at each station along the route, which is used for the subsequent calculation of the expected waiting time. Since the number of passengers on weekdays and holidays at some stations is quite different, the calculation should be performed separately for weekdays and holidays when calculating the crowdedness of the carriage. The average number of passengers getting on and off at each station on weekdays is the average number of passengers getting on and off at each station along the route on weekdays, which is obtained by collecting the average number of passengers getting on and off at each station on weekdays during the operation time period of the bus on this route. If the number of people getting on the bus is greater than the number of people getting off the bus, the average number of passengers getting on and off at each station on weekdays is a positive number. If the number of people getting on the bus is equal to the number of people getting off the bus, the average number of passengers getting on and off at each station on weekdays is zero. If the number of people getting on the bus is less than the number of people getting off the bus, the average number of passengers getting on and off at each station on weekdays is a negative number. Similarly, the average number of passengers getting on and off at each station on holidays is the average number of passengers getting on and off at each station along the route on statutory holidays including weekends, and is obtained by collecting the average number of passengers getting on and off at each station on holidays during the operating period of the bus on this route. In another embodiment, the average number of passengers getting on and off at each station on weekdays can be further subdivided into the average number of passengers getting on and off at each station on Monday, the average number of passengers getting on and off at each station on Tuesday, the average number of passengers getting on and off at each station on Wednesday, the average number of passengers getting on and off at each station on Thursday, and the average number of passengers getting on and off at each station on Friday. The average number of passengers getting on and off at each station on holidays can be further subdivided into the average number of passengers getting on and off at each station on Saturday, the average number of passengers getting on and off at each station on Sunday, and the average number of passengers getting on and off at each station on holidays.

[0054] The urban transportation system data includes the traffic flow data of the bus operation section, the electronic bus stop location data, the traffic signal data, the maximum traffic capacity of each section and the weather condition data. Furthermore, the traffic flow data of the bus operation section and the maximum traffic capacity of each section are used to calculate the congestion coefficient of each section. The congestion coefficient is a characterization of the congestion degree on the bus operation route. The traffic flow data of the bus operation section and the maximum traffic capacity of each section are obtained through the urban transportation system, wherein the traffic flow data of the bus operation section is the actual traffic flow of each section where the bus is running during the measurement period, and the maximum traffic capacity of each section is the maximum traffic capacity of each section where the bus is running, which is related to the number of lanes of the road in the section. The electronic bus stop location data and the traffic signal data are used to calculate the distance between the location of the bus and each electronic bus stop and the number of traffic lights passed. The weather condition data is the weather condition data of the area where the bus route is located on that date. On the premise of fully considering the local climate characteristics, the possible weather conditions are defined, including sunny, cloudy, overcast, light rain, heavy rain, light haze, heavy haze, light snow and heavy snow.

[0055] In step S12, it should be noted that the driving data of the bus and the urban transportation system data are preprocessed to obtain the characteristic vector of the bus. Furthermore, the driving speed data contained in the driving data of the bus is filtered and averaged to obtain the average vehicle speed. Specifically, the driving speed data is first smoothed using a moving average filter, and then the driving speed data is filtered using a Kalman filter method to effectively filter out the noise contained in the driving speed data. The filtered driving speed data is then averaged to obtain the average vehicle speed.

[0056] Furthermore, the weather condition data contained in the urban traffic system data is classified according to the degree of influence on the speed of the vehicle. Assume that the weather condition data of a certain day is w, w = {0, 1, 2, 3, 4, 5, 6}, where 0 represents sunny, cloudy and overcast, 1 represents light rain, 2 represents heavy rain, 3 represents light haze, 4 represents heavy haze, 5 represents light snow, and 6 represents heavy snow. Then, according to the different influences of different weather conditions on the running speed of the bus, different weather conditions are converted into values ​​of different sizes as weather influence coefficients. The weather influence coefficient takes a value between 0 and 1. For example, the weather influence coefficients corresponding to sunny, cloudy and overcast are defined as 1, the weather influence coefficients corresponding to light rain are defined as 0.95, and the weather influence coefficients corresponding to heavy rain are defined as 0.85.

[0057] Furthermore, the congestion coefficient is calculated based on the traffic data of the bus operating sections contained in the urban traffic system data and the maximum traffic capacity of each section. Specifically, the traffic data of the bus operating section on a certain section is divided by the maximum traffic capacity of the section to obtain the congestion coefficient, and the value is between 0 and 1, for example 0.6.

[0058] Furthermore, the distance between the bus and the electronic bus stop and the number of traffic lights passed by are calculated based on the driving route data and vehicle position data contained in the driving data of the bus and the electronic bus stop position data and traffic signal data contained in the urban transportation system data. Specifically, the driving route data contained in the driving data of the bus is divided into multiple sections, and then the sections that the bus needs to pass through to reach each electronic bus stop are calculated based on the vehicle position data and the electronic bus stop position data contained in the urban transportation system data, and then the lengths of these sections are summed to obtain the distance between the bus and the electronic bus stop. Preferably, the number of traffic lights contained in the sections that the bus needs to pass through to reach each electronic bus stop is calculated in combination with the traffic signal data to obtain the number of traffic lights passed by.

[0059] Furthermore, the average vehicle speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and date of each station, the maximum number of passengers on the bus, number of passengers in the car, average number of passengers getting on and off at each station on weekdays, and average number of passengers getting on and off at each station on holidays are vectorized as feature vectors of the bus.

[0060] In step S13, it should be noted that the feature vector of the bus is input into a preset prediction model to calculate the expected waiting time and the degree of crowdedness in the carriage. Furthermore, the preset prediction model includes a random forest model for predicting the waiting time and a carriage crowdedness calculation model. Among them, the random forest model is a supervised machine learning algorithm for classification and regression, which has the advantages of high classification accuracy, fast training speed, strong model generalization ability and good interpretability, and is widely used in classification problems. Its basic idea is to randomly select and train multiple base classifiers with weak classification capabilities so that the integrated classifier composed of them has a strong classification ability, and each CART decision tree is trained by randomly selecting a subset from the original data set, and each CART decision tree is trained on a random subset. The prediction result of the random forest model is the majority vote or average of the prediction results of all CART decision trees. Each CART decision tree in the random forest is generated using the replacement sampling sample in the data set, and each CART decision tree is divided according to a certain feature of the sample. In this process, the generated random vector is used to control the generation of each CART decision tree. Among them, CART decision tree is a base classifier. According to the tree model of feature splitting, starting from the root node of the tree, a feature with the best splitting effect is selected for splitting each time. The split sub-nodes repeat this splitting process, and the branches of the tree grow layer by layer until the sub-nodes can no longer split. CART decision tree uses the Gini coefficient as the basis for splitting decision tree nodes. The smaller the Gini coefficient, the lower the impurity and the better the classification effect. The expression of the Gini coefficient is a relatively simple quadratic operation. Therefore, using CART decision tree as a base classifier can reduce the computational complexity of the random forest model to a certain extent. Multiple CART decision trees are organically connected to form a random forest. The original training sample is first randomly sampled multiple times to obtain multiple sub-training samples, and the original feature geometry is randomly sampled multiple times to obtain multiple sub-feature sets; then, CART decision tree models are constructed on each sub-training sample according to the random sub-feature set; finally, the classification results of multiple CART decision tree models are combined based on the voting method to obtain a random forest model and results.

[0061] Furthermore, the training process of the random forest model includes:

[0062] A waiting time training set is generated based on the average vehicle speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and historical waiting time of each station in the historical database. The bootstrap sampling method is used for the waiting time training set to generate a sub-training set. One sample is randomly and with replacement drawn from the waiting time training set containing n training samples each time, and repeated n times to obtain a sub-sample set containing n. There may be repeated samples in the sub-sample set, but since this repetition is random, it will not affect the model effect. Repeat the above sampling process k times to obtain k sub-sample sets, where k is the number of base classifiers in the random forest model, for example, k = 100.

[0063] The sub-training set is used to train the initial random forest model, and a random forest model containing k CART decision trees is constructed. The bootstra sampling method makes it possible to construct multiple sample sets under the condition that the data size of each sub-sample set is not less than the complete sample set. The CART algorithm is used to construct a decision tree model on each bootstrap sub-sample set. The decision tree based on the CART algorithm uses the Gini coefficient as a measure of feature selection and splitting. The Gini coefficient represents the classification impurity of the feature. The smaller the Gini coefficient, the higher the classification purity and the better the classification effect of the feature. Therefore, all features of the decision tree feature subset are traversed, and the feature with the smallest Gini coefficient is selected as the split feature of the current node. The k CART decision trees are combined into a random forest model.

[0064] The random forest model composed of k CART decision trees is expressed as:

[0065] F={h1(T),h2(T),…,h k (T)}

[0066] Where T is the input data of the random forest model, h i In (T), i=1,2,…,k represents the CART decision tree.

[0067] Furthermore, the feature vector of the bus is input into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage, including: the average speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop sign, and number of traffic lights passed in the feature vector of the bus are input into the random forest model contained in the prediction model for prediction, and the k CART decision trees configured in the random forest model vote on the prediction results, and the prediction result with the highest number of votes is used as the expected waiting time.

[0068] Furthermore, the date, the rated passenger capacity of the bus, the number of passengers in the car, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays in the feature vector of the bus are input into the car crowding degree calculation model contained in the prediction model for calculation to obtain the car crowding degree. Specifically, the date is first determined to determine whether the current date is a weekday or a holiday. Since the number of passengers at some stations differs greatly on weekdays and holidays, weekdays and holidays should be calculated separately when calculating the car crowding degree. If the date is a weekday, the number of passengers in the car and the average number of passengers getting on and off at all stations corresponding to the vehicle location data and the electronic bus stop location data on weekdays are added to obtain the expected number of passengers, and n is calculated. 预计 / n 核载 As the degree of crowdedness in the carriage, where n 预计 is the expected number of passengers, and n 核载 is the number of passengers that the bus can carry; if the date is a holiday, the number of passengers in the car is added to the average number of passengers getting on and off on holidays at all stations between the vehicle location data and the electronic bus stop location data to obtain the expected number of passengers, and n is 预计 / n 核载 As the degree of crowdedness in the carriage.

[0069] In step S14, it should be noted that the estimated waiting time and the degree of congestion in the carriage are sent to the corresponding electronic station board and displayed. Furthermore, the estimated waiting time and the historical average waiting time in the database are divided to obtain a division result. If the division result is greater than or equal to the preset first multiple, the road congestion is taken as the judgment result, and the estimated waiting time is sent to the corresponding electronic station board, wherein the preset first multiple is preset based on past experience and has a value greater than 1, such as 1.5. If the division result is less than the preset first multiple and greater than the preset second multiple, the road is relatively smooth as the judgment result, and the estimated waiting time is sent to the corresponding electronic station board, wherein the preset second multiple is preset based on past experience and has a value greater than 1 and less than the preset first multiple, such as 1.1. If the division result is less than the preset second multiple, the road is smooth as the judgment result, and the estimated waiting time is sent to the corresponding electronic station board.

[0070] Furthermore, the data terminal sends the degree of congestion in the carriage to the corresponding electronic bus stop sign.

[0071] Furthermore, after receiving the estimated waiting time and the degree of congestion in the carriage, the electronic station board scrolls and displays them on a display screen configured for the electronic station board.

[0072] The existing bus electronic stop information management method mainly transmits the bus location information to the LCD screen through a wireless communication system, and sets a periodically changing static template for the displayed content and style according to a certain rule. In actual application, this method has problems such as inaccurate information, which can easily cause passengers to be unable to obtain accurate location information, mainly reflected in the following aspects: the bus company does not update the vehicle location information in a timely manner, resulting in the vehicle not arriving at the station within the waiting time displayed on the stop, delaying passengers' travel; due to weather, road conditions and other conditions, the vehicle runs slowly, but the passengers do not have updated waiting time information during the waiting time at the stop, delaying passengers' boarding; the path information displayed on the electronic stop can only be preset in advance. When the bus needs to choose other routes to temporarily change the running route according to road conditions, it cannot be displayed at the station in the first time, causing inconvenience to passengers.

[0073] In addition, in the existing real-time display method of public transportation electronic bus stop information, the calculation principle of the real-time arrival time of the bus is generally as follows: the electronic bus stop information shows that the estimated time from a certain station to this station is T minutes, then the time from this station to the next station = the time from the previous station to this station - the time used, that is, (the time to this station - the time to the previous station). When the vehicle travels from station A to the next station, station B, the travel time from station A to station B is obtained through the positioning system installed in the vehicle, and then the time taken from station A to station B is obtained on the electronic screen of station B. Since the arrival time is only an estimate of the running time, when the vehicle is affected by weather, road conditions, temporary stops, etc. when running between platforms, the time between stations increases, and the electronic bus stop often cannot accurately display the estimated arrival time at the station.

[0074] In view of the above-mentioned problems, the present invention provides an information management method for electronic bus stops, which obtains the travel data, historical traffic data and urban traffic system data of the bus; pre-processes the travel data of the bus and the urban traffic system data to obtain a feature vector of the bus; inputs the feature vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage; and sends the expected waiting time and the degree of congestion in the carriage to the corresponding electronic bus stop and displays them.

[0075] The method provided by the present invention accurately predicts the waiting time of the bus by establishing a random forest model that combines traffic congestion and weather conditions, and calculates the degree of congestion in the carriage based on historical data, thereby improving the riding experience of passengers.

[0076] Reference Figure 2 , is a system structure diagram of an information management system for a bus electronic stop sign provided by the present invention.

[0077] The second embodiment of the present invention provides an information management system for an electronic bus stop sign, comprising:

[0078] The data acquisition module 210 is used to acquire the bus driving data, historical traffic data and urban traffic system data;

[0079] A preprocessing module 220, used for preprocessing the travel data of the bus and the urban transportation system data to obtain a feature vector of the bus;

[0080] Prediction module 230, used to input the feature vector of the bus into a preset prediction model to calculate the estimated waiting time and the degree of congestion in the carriage;

[0081] The display module 240 is used to send the estimated waiting time and the degree of congestion in the carriage to a corresponding electronic bus stop sign and display them.

[0082] Preferably, the data acquisition module 210 is used to acquire the driving data, historical traffic data and urban transportation system data of the bus, wherein the driving data of the bus includes the date, driving route data, vehicle location data, the number of passengers on the bus, the number of passengers in the car and the driving speed data. The historical traffic data includes the historical stop time of each station, the average number of passengers getting on and off at each station on weekdays and the average number of passengers getting on and off at each station on holidays. The urban transportation system data includes the traffic flow data of the bus operation section, the location data of the electronic bus stop, the traffic signal data, the maximum traffic capacity of each section and the weather condition data.

[0083] Preferably, the preprocessing module 220 is used to preprocess the bus driving data and the urban traffic system data to obtain the bus feature vector. The preprocessing module 220 includes a vehicle speed processing unit, a weather processing unit, a congestion coefficient calculation unit, a traffic route unit and a vectorization unit.

[0084] Furthermore, the vehicle speed processing unit is used to filter and average the travel speed data contained in the travel data of the bus to obtain an average vehicle speed.

[0085] Furthermore, the weather processing unit is used to classify the weather condition data contained in the urban traffic system data according to the degree of influence on the vehicle speed. Suppose the weather condition data of a certain day is w, w={0,1,2,3,4,5,6}, where 0 represents sunny, cloudy and overcast, 1 represents light rain, 2 represents heavy rain, 3 represents light haze, 4 represents heavy haze, 5 represents light snow, and 6 represents heavy snow, and the weather impact coefficient is obtained.

[0086] Furthermore, the congestion coefficient calculation unit is used to calculate the congestion coefficient based on the traffic data of the bus running sections contained in the urban transportation system data and the maximum traffic capacity of each section.

[0087] Furthermore, the traffic route unit is used to calculate the distance between the bus and the electronic bus stop and the number of traffic lights passed by based on the driving route data and vehicle position data contained in the driving data of the bus and the electronic bus stop position data and traffic signal data contained in the urban traffic system data.

[0088] Furthermore, the vectorization unit is used to vectorize the average vehicle speed, weather impact coefficient, congestion coefficient, the distance between the bus and the electronic bus stop, the number of traffic lights passed, the historical stop time of each station, the date, the bus's rated passenger capacity, the number of passengers in the car, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays as the feature vector of the bus.

[0089] Preferably, the prediction module 230 is used to input the feature vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage. The prediction module 230 includes a training unit, a waiting time prediction unit, and a degree of congestion in the carriage calculation unit.

[0090] Furthermore, the training unit is used to generate a waiting time training set based on the average vehicle speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and historical waiting time of each station in the historical database;

[0091] Using the bootstrap sampling method on the waiting time training set to generate a sub-training set, using the sub-training set to train the initial random forest model, and constructing a random forest model including k CART decision trees;

[0092] The random forest model composed of k CART decision trees is expressed as:

[0093] F={h1(T),h2(T),…,h k (T)}

[0094] Where T is the input data of the random forest model, h i In (T), i=1,2,…,k represents the CART decision tree.

[0095] Furthermore, the waiting time prediction unit is used to input the average speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, and number of traffic lights passed in the feature vector of the bus into the random forest model contained in the prediction model for prediction. The k CART decision trees configured in the random forest model vote on the prediction results to obtain the estimated waiting time.

[0096] Furthermore, the carriage crowding degree calculation unit is used to input the date in the feature vector of the bus, the number of passengers on the bus, the number of passengers in the carriage, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays into the carriage crowding degree calculation model included in the prediction model for calculation. The carriage crowding degree calculation model determines the date. If the date is a weekday, the number of passengers in the carriage is added to the average number of passengers getting on and off at all stations corresponding to the vehicle position data and the electronic bus stop position data on weekdays to obtain the expected number of passengers, and n is calculated. 预计 / n 核载 As the degree of crowdedness in the carriage, where n 预计 is the expected number of passengers, and n 核载 is the number of passengers that the bus can carry; if the date is a holiday, the number of passengers in the car is added to the average number of passengers getting on and off on holidays at all stations between the vehicle location data and the electronic bus stop location data to obtain the expected number of passengers, and n is 预计 / n 核载 As the degree of crowdedness in the carriage.

[0097] Preferably, the display module 240 is used to send the estimated waiting time and the degree of congestion in the carriage to the corresponding electronic station sign and display them. The display module 240 includes a determination unit, a sending unit, and a display unit.

[0098] Furthermore, the determination unit is used to determine the road congestion condition by dividing the expected waiting time by the historical average waiting time in the database to obtain a division result. If the division result is greater than or equal to a preset first multiple, road congestion is taken as the determination result; if the division result is less than the preset first multiple and greater than a preset second multiple, relatively smooth road is taken as the determination result; if the division result is less than the preset second multiple, smooth road is taken as the determination result.

[0099] Furthermore, the sending unit is used to send the determination result in combination with the estimated waiting time to the corresponding electronic bus stop sign, and send the carriage congestion level to the corresponding electronic bus stop sign.

[0100] Furthermore, the display unit is used to scroll and display the received estimated waiting time and the degree of congestion in the carriage on a display screen configured for the electronic bus stop.

[0101] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned bus electronic stop information management method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0102] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0103] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0104] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0105] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0106] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0107] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0108] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing information of an electronic bus stop sign, characterized in that: Executed by the data terminal include: Obtain bus driving data, historical traffic data and urban transportation system data; Preprocessing the driving data of the bus and the urban transportation system data to obtain a feature vector of the bus; Inputting the characteristic vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage; The estimated waiting time and the degree of congestion in the carriage are sent to the corresponding electronic station sign and displayed.

2. The method for managing the information of the electronic bus stop sign according to claim 1, characterized in that: The bus driving data includes the date, driving route data, vehicle location data, the bus's maximum passenger capacity, the number of passengers in the car and driving speed data; the historical traffic data includes the historical stop time of each station, the average number of passengers getting on and off at each station on weekdays and the average number of passengers getting on and off at each station on holidays; the urban transportation system data includes the traffic flow data of the bus operating section, electronic bus stop location data, traffic signal data, the maximum traffic capacity of each section and weather conditions data.

3. The method for managing the information of the electronic bus stop sign according to claim 1, characterized in that: The preprocessing of the bus driving data and the urban traffic system data to obtain the feature vector of the bus includes: Filtering and averaging the driving speed data contained in the driving data of the bus to obtain an average vehicle speed; The weather condition data contained in the urban traffic system data is classified according to the degree of influence on the vehicle speed. The weather condition data of a certain day is assumed to be w, w={0,1,2,3,4,5,6}, where 0 represents sunny, cloudy and overcast, 1 represents light rain, 2 represents heavy rain, 3 represents light haze, 4 represents heavy haze, 5 represents light snow, and 6 represents heavy snow, and the weather influence coefficient is obtained; The congestion coefficient is calculated based on the traffic flow data of the bus operation section contained in the urban transportation system data and the maximum traffic capacity of each section; The distance between the bus and the electronic bus stop and the number of traffic lights passed by are calculated based on the driving route data and vehicle position data contained in the driving data of the bus and the electronic bus stop position data and traffic signal data contained in the urban transportation system data; The average speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and date of each station, the number of passengers on the bus, the number of passengers in the car, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays are vectorized as the feature vector of the bus.

4. The method for managing information of electronic bus stop signs according to claim 1, characterized in that: The preset prediction model includes a random forest model for predicting waiting time and a carriage congestion calculation model, wherein the training process of the random forest model includes: Generate a waiting time training set based on the average vehicle speed, weather impact coefficient, congestion coefficient, distance between the bus and the electronic bus stop, number of traffic lights passed, historical stop time and historical waiting time of each station in the historical database; Using the bootstrap sampling method on the waiting time training set to generate a sub-training set, using the sub-training set to train the initial random forest model, and constructing a random forest model including k CART decision trees; The random forest model composed of k CART decision trees is expressed as: F={h1(T),h2(T),…,h k (T)} Where T is the input data of the random forest model, h i In (T), i=1,2,…,k represents the CART decision tree.

5. The method for managing information of electronic bus stop signs according to claim 1, characterized in that: The step of inputting the characteristic vector of the bus into a preset prediction model to calculate the estimated waiting time and the degree of congestion in the carriage includes: The average speed, weather influence coefficient, congestion coefficient, distance between the bus and the electronic bus stop sign, and number of traffic lights passed in the feature vector of the bus are input into the random forest model included in the prediction model for prediction, and the k CART decision trees configured in the random forest model vote on the prediction results to obtain the estimated waiting time; The date, the rated passenger capacity of the bus, the number of passengers in the carriage, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays in the feature vector of the bus are input into the carriage crowding calculation model contained in the prediction model for calculation to obtain the carriage crowding degree.

6. The method for managing information of electronic bus stop signs according to claim 5, characterized in that: The date, vehicle location data, electronic stop sign location data, the number of passengers on the bus, the number of passengers in the carriage, the average number of passengers getting on and off at each station on weekdays, and the average number of passengers getting on and off at each station on holidays in the feature vector of the bus are input into the carriage crowding degree calculation model included in the prediction model for calculation to obtain the carriage crowding degree, specifically: The date is determined. If the date is a weekday, the number of passengers in the carriage is added to the average number of passengers getting on and off on weekdays corresponding to all stations between the vehicle location data and the electronic station sign location data to obtain the expected number of passengers. 预计 / n 核载 As the degree of crowdedness in the carriage, where n 预计 is the expected number of passengers, and n 核载 is the number of passengers that the bus can carry; if the date is a holiday, the number of passengers in the car is added to the average number of passengers getting on and off on holidays at all stations between the vehicle location data and the electronic bus stop location data to obtain the expected number of passengers, and n is 预计 / n 核载 As the degree of crowdedness in the carriage.

7. The method for managing information of an electronic bus stop sign according to claim 1, characterized in that: The sending and displaying of the estimated waiting time and the carriage congestion level to a corresponding electronic station sign comprises: The estimated waiting time is divided by the historical average waiting time in the database to obtain a division result. If the division result is greater than or equal to a preset first multiple, the road congestion is taken as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board; if the division result is less than the preset first multiple and greater than a preset second multiple, the road is relatively smooth as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board; if the division result is less than the preset second multiple, the road is smooth as a judgment result, and the estimated waiting time is sent to the corresponding electronic station board; Sending the degree of congestion in the carriage to a corresponding electronic station sign; After receiving the estimated waiting time and the degree of congestion in the carriage, the electronic station board displays the information in a scrolling manner on a display screen configured for the electronic station board.

8. An information management system for electronic bus stops, characterized in that: include: Data acquisition module, used to obtain bus driving data, historical traffic data and urban traffic system data; A preprocessing module, used for preprocessing the driving data of the bus and the urban transportation system data to obtain a feature vector of the bus; A prediction module, used for inputting the characteristic vector of the bus into a preset prediction model to calculate the expected waiting time and the degree of congestion in the carriage; The display module is used to send the estimated waiting time and the degree of congestion in the carriage to a corresponding electronic bus stop sign and display them.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for managing the information of the electronic bus stop sign according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for managing the information of the electronic bus stop as claimed in any one of claims 1 to 7.

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