A tram carbon footprint monitoring method and system

Abstract

This invention provides a method and system for monitoring the carbon footprint of trams. The method includes: acquiring the hourly traction and non-traction energy consumption of trams daily for a week, and calculating the carbon emissions of each component; acquiring the number of passengers boarding and alighting at each station and their travel time hourly for a week, and calculating the number of passengers on each tram hourly; adjusting the tram departure time and frequency based on the hourly passenger count for each tram daily for a week; calculating the passenger carbon footprint between stations hourly for a week based on the hourly passenger count and carbon emissions of each tram daily for a week; predicting the passenger carbon footprint between stations hourly for a future week based on the passenger carbon footprint at each station, and adjusting the operation strategy in a timely manner. This achieves real-time adjustment of the operation strategy based on passenger carbon footprints, thereby reducing carbon emissions.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint, and more particularly to a method and system for monitoring the carbon footprint of trams. Background Technology

[0002] In the existing technology, research on trams mainly focuses on energy consumption data collection, energy consumption analysis methods, operation energy consumption optimization methods and energy-saving operation of trams. There is still a gap in research on tram carbon emissions per unit transport turnover and passenger carbon footprint. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a method and system for monitoring the carbon footprint of trams that overcomes or at least partially solves the above problems.

[0004] According to one aspect of the present invention, a method for monitoring the carbon footprint of a tram is provided, comprising:

[0005] Obtain the hourly traction and non-traction energy consumption of trams every day for a week, and calculate the carbon emissions of each part of the electricity.

[0006] Get the number of passengers getting on and off the tram at each station every day of the week, and the travel time, and calculate the number of passengers on each tram every hour.

[0007] Based on the number of passengers on each tram every hour during the week and the carbon emissions from electricity, predict the number of passengers on each tram every hour during the next week and the carbon emissions from electricity.

[0008] Based on the predicted number of passengers on each tram line every hour for the next week, the departure times and frequencies of trams will be adjusted to optimize tram operation efficiency and station utilization efficiency.

[0009] Based on the number of passengers on each tram line every hour of the week and the amount of electricity carbon emissions, calculate the passenger carbon footprint between stations every hour of the week.

[0010] Based on the passenger carbon footprint of each station, predict the hourly passenger carbon footprint of each station for the next week, and adjust the operation strategy in a timely manner.

[0011] Optionally, the carbon emissions also include: station carbon emissions, specifically including station lighting carbon emissions and display screen carbon emissions.

[0012] The present invention also provides a tram carbon footprint monitoring system comprising:

[0013] The system includes a passenger statistics module, a carbon emission module, a data storage module, a prediction module, and a carbon footprint display module.

[0014] The passenger statistics module is connected to the tram card reader, video surveillance system and data storage module to obtain the number of passengers in the tram, passenger boarding and alighting times and station information in real time, and transmit the passenger information to the data storage module.

[0015] The carbon emission module is connected to the tram energy consumption monitoring system, the station energy consumption monitoring system and the data storage module respectively, to obtain the operating energy consumption of the tram and the station in real time, and to send the energy consumption data to the data storage module to calculate carbon emissions respectively;

[0016] The prediction module, connected to the data storage module, is used to predict the expected number of passengers and passenger carbon footprint for each time period on future weekdays and non-weekdays by using historical passenger data and historical carbon emission data stored in the storage module, and by using mathematical algorithms such as neural networks and support vector machines.

[0017] The carbon footprint display module is connected to the data storage module and the carbon emission prediction module. It is used to display in real time the number of passengers getting on and off at a certain station in the past time period, the number of passengers between stations, and the carbon footprint of each passenger, as well as the predicted number of passengers and carbon footprint of each station in the future time period.

[0018] Optionally, the monitoring system further includes a carbon footprint monitoring platform, which is connected to the passenger statistics module, the carbon emission module, the data storage module, the prediction module, and the carbon footprint display module.

[0019] This invention provides a method and system for monitoring the carbon footprint of trams. The method includes: acquiring the hourly traction and non-traction energy consumption of trams daily for a week, and calculating the carbon emissions of each component; acquiring the number of passengers boarding and alighting at each station and their travel time hourly for a week, and calculating the number of passengers on each tram hourly; predicting the number of passengers and the carbon emissions of each type of electricity for each tram hourly for the next week based on the hourly passenger count and the carbon emissions; adjusting tram departure times and frequencies based on the predicted hourly passenger count for the next week to optimize tram operation efficiency and station utilization efficiency; calculating the passenger carbon footprint between stations hourly for a week based on the hourly passenger count and the carbon emissions; and predicting the passenger carbon footprint between stations hourly for the next week based on the passenger carbon footprint at each station, and adjusting the operation strategy in a timely manner. This achieves real-time adjustment of the operation strategy based on passenger carbon footprints, thereby reducing carbon emissions.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a structural block diagram of a tram carbon footprint monitoring system disclosed in this invention. Detailed Implementation

[0023] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0024] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0025] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0026] like Figure 1 As shown, this embodiment provides a method for monitoring the carbon footprint of trams, including:

[0027] Step 100: Obtain the hourly traction and non-traction energy consumption of the tram for each day of the week, as well as the electricity consumption of station lighting, displays and other components, and calculate the carbon emissions of each component.

[0028] Step 200: Obtain the number of passengers getting on and off the tram at each station every day of the week, the travel time, and calculate the number of passengers on each tram every hour.

[0029] Step 300: Based on the number of passengers on each tram every hour of the day during the week and the carbon emissions from electricity, predict the number of passengers on each tram every hour of the day for the next week and the carbon emissions from electricity.

[0030] Step 400: Based on the predicted number of passengers on each tram every day for the next week, adjust the departure time and frequency of trams to optimize tram operation efficiency and station utilization efficiency.

[0031] Step 500: Based on the number of passengers on each tram every hour of the week and the carbon emissions of electricity, calculate the passenger carbon footprint between stations every hour of the week, predict the passenger carbon footprint between stations every hour of the next week, and adjust the operation strategy in a timely manner according to the predicted carbon footprint to reduce the overall carbon emissions of the tram.

[0032] Step S1: Connect to the tram passenger system and obtain the number of passengers (P1 = 180, P1 = 187, P1 = 213) of a tram A passing through stations 1, 2, and 3 between 8:00 and 9:00 AM via card reader and video surveillance, as well as the time information of the stations it passes through, and transmit this information to the passenger flow database.

[0033] Step S2: Connect to the tram GPS system and obtain the distances of station sections 1, 2, and 3 as 2.5km, 1.8km, and 1km, respectively. Transmit these distances to the passenger flow database and calculate the passenger transport turnover as T1, T2, and T3, respectively. The transport turnover is the number of passengers multiplied by the section distance. Therefore, T1 = 180 × 2.5 = 450 people·km, T2 = 187 × 1.8 = 336.6 people·km, and T3 = 213 × 1 = 213 people·km.

[0034] Step S3: Connect to the tram energy consumption monitoring system to obtain the electricity consumption QA of a tram A between 8:00 AM and 9:00 AM, which is 514 kWh (including traction and non-traction energy consumption). Based on the station time information, calculate the electricity consumption through station sections 1, 2, and 3 as 218 kWh, 182 kWh, and 114 kWh, respectively. The electricity consumption of tram A at the stations it passes through during this period is 85 kWh, 67 kWh, 103 kWh, and 91 kWh, respectively. Transmit this data to the database (the average CO2 emission factor of the East China power grid is 0.7035 kgCO2 / kWh) and calculate the carbon emissions of electricity from tram A through station sections 1, 2, and 3 during this period as E1, E2, and E3, respectively.

[0035] E1=(218+(85+67) / 2)×0.7035=206.829kgCO2;

[0036] E2=(182+(67+103) / 2)×0.7035=187.8345kgCO2;

[0037] E3=(114+(103+91) / 2)×0.7035=148.4385kgCO2.

[0038] Step S4: Process the passenger flow and various operational carbon emission data in the data storage module.

[0039] Calculate the carbon footprint of a single tram A during the period from 8:00 to 9:00 AM, based on the passenger transport turnover per unit.

[0040] E 碳足迹1 =E1 / T1=206.829 / 450=0.4596kgCO2 / (person·km)

[0041] E 碳足迹2 =E2 / T2 = 187.8345 / 336.6 = 0.5581 kg CO2 / (person·km)

[0042] E 碳足迹3 =E3 / T3=148.4385 / 213=0.6969kgCO2 / (person·km)

[0043] Step S5: After continuous monitoring for a week, the number of passengers and passenger carbon footprint of each tram running every day of the week can be obtained and transmitted to the data storage module.

[0044] Support Vector Machine (SVM) is used to classify historical passenger flow data into two categories: dense passenger flow data and sparse passenger flow data. The time period characteristics of the two types of data are identified, and the data is updated on a rolling basis according to the clustering results.

[0045] For peak passenger flow periods, the following characteristic data were selected:

[0046] 1) Predict passenger flow data for the corresponding time period of the previous week's peak passenger flow data;

[0047] 2) Predict the electricity carbon emission data for the time period corresponding to the passenger flow density data in the previous week.

[0048] For time periods with sparse passenger flow, the following characteristic data were selected:

[0049] 1) Predict passenger flow data for the time period corresponding to the sparse passenger flow data of the previous week;

[0050] 2) Predict the electricity carbon emission data for the time period corresponding to the sparse passenger flow data in the previous week;

[0051] All data were normalized, and a Support Vector Regression (SVR) model was used to train the data on both dense and sparse passenger flow. In the hyperparameter part, the penalty parameter was set to 1.14, the insensitive loss function to 0.01, and the radial basis function kernel parameter to 29.1.

[0052] A BP neural network model was used to train data on both dense and sparse passenger flow, with a learning rate of 0.1 and 3 hidden layers.

[0053] The trained (SVR) model and BP neural network model were used to predict the number of passengers and passenger carbon footprint of each tram operating hourly for each day of the coming week. The prediction result was the average of the results calculated by the two models.

[0054] Step S6: The carbon footprint display module connects to the data storage module to display the carbon footprint of the tram's operating stations, passenger numbers, and unit passenger transport turnover for each day of the past week and the coming week.

[0055] Step S7: Based on the predicted hourly passenger numbers and carbon footprint information for the next week, optimize the tram departure times and frequencies, and optimize the operation of station lighting, displays and other equipment in real time.

[0056] The present invention provides a tram carbon footprint monitoring system comprising: a carbon footprint monitoring platform, a passenger statistics module, a carbon emission module, a data storage module, a prediction module, and a carbon footprint display module. The carbon footprint monitoring platform integrates passenger flow monitoring, tram and station carbon emission monitoring, passenger flow and carbon emission prediction, and carbon footprint display. The passenger statistics module records the boarding stops and travel distances of card-swiping passengers based on card reader information, and the boarding stops and travel distances of cash-using passengers based on the video system, while also recording the total number of passengers inside the vehicle. The electricity carbon emission module tracks carbon emissions from the electricity consumed by tram and station operations, including traction and non-traction carbon emissions, as well as carbon emissions from station lighting, displays, and other equipment. The data storage module stores data on electricity consumption by various tram equipment, passenger numbers, travel distances, stops, times, and electronic carbon emission factors for different regions in my country, and updates the database based on the latest grid carbon emission factors published by the National Development and Reform Commission and local governments. The prediction module uses mathematical algorithms and data mining techniques to predict passenger boarding stops, times, and numbers at various stations on a future day and time period, as well as predicting tram and station electricity consumption and passenger carbon footprints. The carbon footprint display module displays past and future passenger carbon footprints and passenger flow.

[0057] A method for monitoring the carbon footprint of a tram, the method comprising: a carbon footprint monitoring platform, a passenger statistics module, a carbon emission module, a data storage module, a prediction module, and a carbon footprint display module.

[0058] The carbon footprint monitoring platform enables monitoring of carbon emissions from trams, carbon emissions from stations, passenger traffic flow, storage of carbon emissions and passenger flow data, prediction of carbon emissions and passenger flow, and display of passenger carbon footprint.

[0059] The passenger statistics module is connected to the tram card reader, video surveillance system and data storage module respectively, to obtain the number of passengers on the tram, the time of passengers getting on and off the tram and the station information of passengers in real time, and transmit the passenger information to the data storage module.

[0060] The carbon emission module is connected to the tram energy consumption monitoring system, the station energy consumption monitoring system, and the data storage module, respectively. It acquires the operating energy consumption of the tram and the station in real time, and sends the energy consumption data to the data storage module to calculate carbon emissions.

[0061] Electric vehicle carbon emissions also include traction carbon emissions and non-traction carbon emissions.

[0062] Station carbon emissions also include carbon emissions from station lighting, displays, and other carbon emissions.

[0063] The prediction module is connected to the data storage module. Using historical passenger data and historical carbon emission data from the storage module, and through mathematical algorithms such as neural networks and support vector machines, it predicts the expected number of passengers and passenger carbon footprints for each time period on future weekdays and non-weekdays.

[0064] The specific prediction method is as follows:

[0065] The first step is to use Support Vector Machine (SVM) to classify the historical passenger flow data into two categories. The time period characteristics of the two categories are identified, and the data is updated on a rolling basis according to the clustering results.

[0066] The second step is to select feature data for each of the two types of data features.

[0067] The third step is to normalize all the data.

[0068] The fourth step is to train the support vector regression (SVR) model on both types of data respectively.

[0069] The fifth step is to train a BP neural network model on both types of data separately.

[0070] The sixth step involves using the trained (SVR) model and BP neural network model to predict the number of future passengers. The prediction result is the average of the results calculated by the two models, and the carbon footprint is calculated.

[0071] Passenger carbon footprint refers to the carbon emissions of trams and stations per unit of transport turnover, which is the product of the number of passengers and the distance traveled between stations.

[0072] The carbon footprint display module is connected to the data storage module and the carbon emission prediction module to display in real time the number of passengers getting on and off at a certain station during a certain period of time, the number of passengers in a certain station interval, and the carbon footprint of each passenger, as well as the prediction of the number of passengers and carbon footprint of each station during a certain period of time in the future.

[0073] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A tram carbon footprint monitoring method, characterized by, The monitoring method comprises: acquiring the trams' traction energy and non-traction energy per hour per day in a week, and calculating the carbon emissions of each part of the power; acquiring the number of passengers getting on and off and the time of each tram per hour per day in a week, and calculating the number of passengers per hour per tram; predicting the number of passengers per hour per tram and the carbon emissions of each type of power in the future week according to the number of passengers per hour per tram and the carbon emissions of each tram in the week; adjusting the tram departure time and frequency according to the predicted number of passengers per hour per tram in the future week, and optimizing the tram operation efficiency and station use efficiency; calculating the passenger carbon footprint between stations per hour per day in a week according to the number of passengers per hour per tram and the carbon emissions of each tram in the week; predicting the passenger carbon footprint between stations per hour per day in the future week according to the passenger carbon footprint between stations, and adjusting the operation strategy in time; The carbon emissions also include: station carbon emissions, specifically including station lighting carbon emissions and display screen carbon emissions.

2. A tram carbon footprint monitoring system characterized in that, The monitoring system comprises: a passenger statistics module, a carbon emission module, a data storage module, a prediction module and a carbon footprint display module; The passenger statistics module is connected with the tram card reader, the video monitoring system and the data storage module, and real-time acquires the number of passengers, the time of passengers getting on and off and the station information of passengers, and transmits the passenger information to the data storage module; The carbon emission module is connected with the tram energy consumption monitoring system, the station energy consumption monitoring system and the data storage module, and real-time acquires the operation energy consumption of the tram and the station, and sends the energy consumption data to the data storage module to calculate the carbon emissions respectively; The prediction module is connected with the data storage module, and is used for predicting the predicted number of passengers and the passenger carbon footprint in each time period of the future working day and non-working day through the historical passenger data and operation carbon emission historical data of the storage module, and through the mathematical algorithm of neural network and support vector machine; The carbon footprint display module is connected with the data storage module and the carbon emission prediction module, and is used for real-time displaying the number of passengers getting on and off at a certain station in a time period, the number of passengers between stations, and the carbon footprint of each passenger, and predicting the number of passengers and the carbon footprint of each station in a future time period.

3. A tramcar carbon footprint monitoring system according to claim 2, wherein, The monitoring system further comprises: a carbon footprint monitoring platform connected with the passenger statistics module, the carbon emission module, the data storage module, the prediction module and the carbon footprint display module.

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