CN114627654A - Bus-mounted passenger capacity quantification method based on space-time characteristics - Google Patents
Bus-mounted passenger capacity quantification method based on space-time characteristics Download PDFInfo
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Abstract
The invention discloses a bus-mounted passenger quantity quantification method based on space-time characteristics, which comprises the following steps: firstly, acquiring original data of a bus running state according to a sampling period; wherein, bus driving state includes: the system comprises a vehicle state, a vehicle speed, a braking state, a driving state, longitude and latitude coordinates of the vehicle, a rotating speed of a driving motor and a torque of the driving motor; secondly, performing data cleaning on the original data, and removing abnormal values to obtain available data; dividing the available data into a long-stroke data segment and a micro-stroke data segment; wherein the long-stroke data section comprises data of all sampling points in a single pass from the starting station to the terminal station; the micro-travel data section comprises data of all sampling points between two adjacent stations; thirdly, determining the mass of the bus with each micro-travel, and obtaining the average mass of the long-travel bus according to the mass of the bus with the micro-travel; fourthly, determining the one-way passenger capacity grade of the bus according to the average quality of the long-distance bus.
Description
Technical Field
The invention belongs to the technical field of driving economy evaluation, and particularly relates to a method for quantifying the passenger capacity of a bus based on space-time characteristics.
Background
In recent years, the problem of carbon emission becomes the focus of global attention, and with the continuous development of economy, the automobile industry advances at a high speed, which brings convenience to people for traveling and brings social problems such as dependence on petroleum energy and environmental pollution to the nation. In order to deal with the increasingly severe contradiction between supply and demand of petroleum energy, environmental pollution and the pressure of carbon dioxide emission space, the energy-saving and emission-reduction technology of automobiles becomes an important support means for realizing the aim of carbon neutralization. According to the research on the energy-saving strategy of automobiles in China, the technologies for reducing the oil consumption of vehicles in driving can be divided into 4 categories: 1) the design of an energy-saving engine and a whole vehicle; 2) driver economy driving;
3) intelligent traffic management; 4) alternative energy utilization and electric drive trains. The fuel-saving potential of economical driving reaches 15%, so that the optimization of the driving behavior of a driver has great potential on the aspect of realizing the aim of energy conservation and emission reduction to a greater extent, and two key points of the method are as follows: 1) personnel training is enhanced, and driving operation is performed strictly according to energy-saving driving operation specifications; 2) a scientific oil consumption assessment system is set, and a driver is prompted to actively apply energy-saving driving operation specifications. A scientific oil consumption assessment system needs to be established to establish an accurate, objective, fair and fair economic driving evaluation model.
The energy consumption of the vehicle intuitively reflects the economic driving capability of a driver, but the influence factors of the energy consumption are various (including the driver, the vehicle, the road, the weather and the like), and the economic evaluation directly by taking the energy consumption as an index is limited. Under the condition that influence factors except the driver are controlled in a unified mode, the energy consumption is meaningful to be used for evaluating the economic driving level of the driver. For buses, the bus is characterized in that lines are fixed, passengers get on or off the bus at a station, and the passenger capacity is changed. The passenger capacity is directly related to the weight of the whole bus, the bus preparation mass is about 11000kg, the full load mass is 18000kg, the mass fluctuation range is larger, and a driver with higher ecological driving level can consume more energy consumption when driving the full load bus than a driver with lower ecological driving level. Therefore, in order to evaluate the ecological driving ability of the driver under the condition of similar passenger capacity, so as to eliminate the energy consumption difference caused by the vehicle weight, how to use the low-frequency data to calculate the quality and how to quantify the passenger capacity of the bus is a key problem to be solved.
Disclosure of Invention
The invention aims to provide a method for quantifying the bus-mounted passenger quantity based on the space-time characteristic, which can quantify the bus-mounted passenger quantity so that the bus-mounted passenger quantity is described in a grade form.
The technical scheme provided by the invention is as follows:
a bus-mounted passenger quantity quantification method based on space-time characteristics comprises the following steps:
step one, acquiring original data of a bus running state according to a sampling period;
wherein, the bus driving state includes: the system comprises a vehicle state, a vehicle speed, a braking state, a driving state, longitude and latitude coordinates of the vehicle, a rotating speed of a driving motor and a torque of the driving motor;
step two, performing data cleaning on the original data, and removing abnormal values to obtain available data; dividing the available data into a long-stroke data segment and a micro-stroke data segment;
wherein the long-stroke data section comprises data of all sampling points in a single pass from the starting station to the terminal station; the micro-travel data section comprises data of all sampling points between two adjacent stations;
determining the mass of the bus with each micro-travel, and obtaining the average mass of the long-travel bus according to the mass of the bus with the micro-travel;
and step four, determining the one-way passenger capacity grade of the bus according to the average quality of the long-distance bus.
Preferably, in the third step, the method for determining the bus mass of each micro-trip comprises:
screening out the determined uniform acceleration points, and obtaining the mass of the micro-travel bus according to the mass of the determined uniform acceleration points;
wherein, if the number n of the uniform acceleration points determined under the same micro-travel is more than 1, the mass of the bus in the micro-travel is as follows:
if the number n of the uniform acceleration points determined under one micro-travel is 1, the mass of the bus in the micro-travel is as follows:
mwv＝mk；
if the number n of the uniform acceleration points determined under one micro-travel is 0, determining the bus quality of the micro-travel by adopting a quality filling method based on space-time characteristics;
wherein w represents the w-th long stroke; v represents the v-th micro-stroke under a long stroke; k represents the kth determined uniform acceleration point in a micro-stroke.
Preferably, screening out the determined uniform acceleration point comprises the following steps:
the sampling point when the opening degree of an accelerator pedal is greater than 0 and the opening degree of a brake pedal is equal to 0 is a driving point;
where i is the ith sample point, viRepresenting the velocity, v, corresponding to the ith sample pointi-1The speed of the ith-1 sampling point;
and 3, calculating the mass of the bus at the possible uniform acceleration points, eliminating points with the mass not within the theoretical mass range of the bus, and taking the rest points as the determined uniform acceleration points.
Preferably, the possible uniform acceleration points are calculated for bus mass by the following formula:
wherein eta isTRepresenting the mechanical efficiency of the drive train, m representing the mass of the vehicle, f representing the rolling resistance coefficient, uaIndicates vehicle speed, CDThe coefficient is an air resistance coefficient, A is a windward area, delta is a rotating mass conversion coefficient, and a is an acceleration.
Preferably, the calculation formula of the acceleration is:
wherein, aiRepresents the acceleration corresponding to the ith sampling point, and Δ t represents the time interval between the ith sampling point and the (i-1) th sampling point.
Preferably, the quality filling method based on the spatiotemporal characteristics comprises:
taking a micro-stroke with the determined number n of uniform acceleration points being 0 as a first micro-stroke;
searching a micro-stroke which is identical to the first micro-stroke in time and space and has a determined uniform acceleration point larger than 0 as a second micro-stroke;
and taking the mass of the bus with the second micro-travel as the mass of the bus with the first micro-travel.
Preferably, the theoretical mass of the bus is as follows: mass value between bus servicing quality and bus full load quality.
Preferably, in the third step, the method for calculating the average mass of the long-distance bus comprises the following steps: and (4) calculating the average value of all the micro-travel masses in a single-pass, and taking the average value as the average mass of the long-travel bus.
Preferably, in the fourth step, the passenger capacity per pass of the bus is classified into 4 grades, including:
if m isAre all made of≤m1Meanwhile, the one-way passenger capacity grade of the bus is 1 grade;
if m is1＜mAre all made of≤m2The one-way passenger capacity grade of the bus is 2 grade;
if m is2＜mAre all made of≤m3The one-way passenger capacity grade of the bus is 3 grade;
if m isAre all made of＞m3The one-way passenger capacity grade of the bus is 4 grade;
wherein m1 ═ mServicing+500kg；m2＝m1+1000kg；m3＝m2+1000kg；mServicingFor bus reorganization quality, unit: kg; m is a unit ofAre all made ofThe average mass of the long-distance bus is obtained.
Preferably, in the second step, the method for dividing the available data into the long-stroke data segment and the micro-stroke data segment includes:
calculating the space distance between the sampling point and the station:
taking the point with the minimum distance as a demarcation point, wherein the target function is as follows:
Distancej＝min(Distanceij),i＝1,2,...,M；
in the formula, Xi(Ingi,Lait) (i ═ 1, 2., M is the longitude and latitude coordinates of the sampling points, M is the number of sampling points; xj(Ingj,Latj) (j ═ 1, 2.·, N) are station longitude and latitude coordinates, and N is the number of stations;
wherein, the data segment between two adjacent demarcation points is the micro-stroke data segment; the data section between the first demarcation point and the last demarcation point is the long-stroke data section.
The invention has the beneficial effects that:
the bus-mounted passenger volume quantification method based on the space-time characteristics can quantify the bus-mounted passenger volume, so that the bus-mounted passenger volume is described in a grade form; therefore, the driving economy can be evaluated by comparison under the same passenger capacity level, and the energy consumption difference caused by passenger capacity change is eliminated.
Drawings
Fig. 1 is a flow chart of a method for quantifying the passenger capacity of a bus based on space-time characteristics according to the present invention.
Fig. 2 is a schematic diagram of a bus quality calculation method according to the invention.
FIG. 3 is a flow chart of screening identified smooth acceleration points in accordance with the present invention.
Fig. 4 is a schematic diagram of a result of the bus one-way passenger capacity grading in the embodiment of the invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1 to 3, the invention provides a method for quantifying the passenger capacity of a bus based on a space-time characteristic, which comprises the following steps:
first, preprocessing the original data
The original data come from data collected by a big data platform of the new energy automobile, and the used data comprise vehicle states, vehicle speeds, braking and driving states, longitudes, latitudes, motor rotating speeds, motor torques and the like.
The original data conforms to GB/T32960.3-2016 technical Specification part 3 of electric vehicle remote service and management System: communication protocol and data format, which need to be calculated according to the national standard when using data to obtain real operation data. Some important data descriptions and requirements are shown in table 1.
TABLE 1 description and requirements of the data
The raw data needs to be preprocessed, including abnormal data cleaning and driving segment division, to support subsequent analysis and research.
1. Data cleansing
Because the original data has abnormal values, subsequent interpolation, acceleration calculation and the like are influenced, and the data needs to be processed in a targeted manner.
(1) Data inconsistency problem
There may be problems in the raw data as shown in table 2:
TABLE 2 data inconsistent segmentation
It can be seen that the problem of the same time occurs in adjacent data, that is, data is collected twice at the same time, which may cause trouble in operations such as data interpolation in subsequent operations, and therefore the problem needs to be identified and processed. The processing method is to only reserve the first row of data collected at the moment.
(2) Problem of time jump
There may be problems in the raw data as shown in table 3:
TABLE 3 time hopping segment
Column time 09: 10: 51 represents 9 hours, 10 minutes and 51 seconds, and it can be seen that the time interval of adjacent sampling points is 10 seconds, but the time interval of the last two rows is about 21 minutes, the data in the middle is not available, it can be noted that the problems usually occur before the bus leaves the starting station or after the bus enters the terminal station, and the vehicle states of the first few data are flameout, the bus is not restarted until 32 minutes at 9 hours, which indicates that the data stops sampling after the bus is flameout, and the data is not restarted until the bus is started, so that the time jump of the data recording is caused, and the data flameout data points should be found and deleted.
2. Data segment partitioning
Considering that the operation characteristic of the bus is that one way is from the starting station to the terminal station, the one way is named as a long journey; the bus stops according to the stop within a long journey, passengers get on or off the bus at the stop, the bus-mounted passenger capacity between the stops, namely the quality of the bus is unchanged, and the journey between adjacent stops is named as a micro-journey. For subsequent quality calculation, data needs to be subjected to long-stroke division and micro-stroke division.
And the data required for dividing the data segments are the actual longitude and latitude of each sampling point and the longitude and latitude coordinates of each station. Setting longitude and latitude coordinates of sampling point as Xi(Ingi,Lati) ( i 1, 2.. said., M), where M is the number of sampling points; station latitude and longitude coordinates Xj(Ingj,Latj) ( j 1, 2.. multidot.n), where N is the number of stations, and the distance between any sampling point and any station is (note that longitude and latitude coordinates are converted from radian system to angle system)
Calculating the space distance between the sampling point and the station, taking the point with the minimum distance as a boundary point, and taking the objective function as
Distancej＝min(Distanceij),i＝1,2,...,M (2)
Thus, the boundary position of the starting station and the terminal station is obtained, and the long-stroke division of data is carried out; and similarly, obtaining the boundary position of each station, and carrying out micro-stroke division in each long stroke.
Quality calculation based on data screening
Firstly, a mass calculation method is introduced, the mass of the bus is obtained by utilizing power balance, and the equation of the automobile power is shown as a formula (3)
Wherein eta isTDenotes the mechanical efficiency of the drive train, m denotes the vehicle mass, f denotes the rolling resistance coefficient, uaRepresenting vehicle speed, i representing gradient, CDThe coefficient is an air resistance coefficient, A is a windward area, delta is a rotating mass conversion coefficient, and a is an acceleration.
The bus is a pure electric vehicle, and the power is provided by the motor, so that
Wherein T is motor torque, and n is motor rotation speed.
The power provided by the motor is equal to the power consumed in the running process of the bus, in addition, because the bus runs in an urban area and the road is flat, the slope resistance is not considered, and the bus quality calculation formula is shown as a formula (5)
The input and output of the mass calculation method are shown in fig. 2.
The set vehicle parameters are shown in Table 4
TABLE 4 vehicle parameters
The data specified by the national standard GB/T32960-2016 has no acceleration term, so that the acceleration at each moment can be obtained through calculation, and the acceleration calculation formula is shown as a formula (6)
Wherein i is the ith collected point, viRepresenting the speed corresponding to the ith point, aiRepresents the acceleration corresponding to the ith point, and delta t represents the time interval between the ith point and the (i-1) th point, namely, delta t is ti-ti-1Usually 10 to 15 seconds. Because the sampling time interval is long, the average acceleration is not necessarily the true acceleration of the point, and many points do not accord with the automobile running equation, therefore, the raw data needs to be screened before the quality estimation is carried out. The overall flow of data screening is shown in fig. 3.
1. All drive points are screened.
According to the formula 5, the driving resistance considered in calculating the mass is rolling resistance, air resistance and acceleration resistance, respectively, and the motor of the pure electric vehicle recovers braking energy during braking, but the vehicle power during regenerative braking cannot be accurately calculated due to the absence of a recovery strategy of the vehicle type, so that the braking point is not considered. The coast point is also a deceleration condition and is likewise excluded. The data used to determine the driving state are accelerator pedal opening and brake pedal opening, and table 5 shows typical data characteristics for the three states.
TABLE 5 State data characteristics
That is, the drive point is set when the accelerator pedal opening is greater than 0 and the brake pedal opening is equal to 0, the coasting point is set when both the accelerator pedal openings are 0, and the braking point is set when the accelerator pedal opening is equal to 0 and the brake pedal opening is greater than 0, whereby all the drive points are sorted out.
2. Possible uniform acceleration points are screened.
As can be seen from the equation (6), the calculated acceleration at each point is actually the average acceleration of the 10-15 s driving segment, and there may be the case as shown in Table 6
TABLE 6 acceleration vs. State mismatch data
Since the speed at the time immediately before the point is higher than the speed at the point, the average speed change is negative and does not match the driving state, and therefore, a condition v needs to be added in the screeningi＞vi-1So that the acceleration and the state can be matched.
3. And screening the determined uniform acceleration points.
The method comprises the steps that when the automobile is turned, the clutch is separated, the brake is braked, and the actual gear is in a neutral gear state, the automobile running equation is not met, the elimination method is to calculate the mass point by point, the points of which the mass is not in the theoretical mass range of the bus (bus full mass-load mass) are all eliminated, the acceleration of the screened possible uniform acceleration points is not necessarily accurate due to the particularity of the data interval of 10-15 s, if the acceleration is not accurate, the calculated mass cannot be in the theoretical mass range of the bus, after all the points are deleted by mass screening, the rest points are considered to be determined uniform acceleration points, and the calculated mass of the points is accurate.
Third, micro-travel quality calculation based on space-time characteristics
After the quality of the available points after strict screening is obtained, micro-travel quality calculation is carried out by using the available points. Because the bus-mounted passenger capacity between the stops, namely the quality of the bus is not changed, the quality of available points under the same micro-travel is similar, and the calculation methods are divided into three types according to the number of the available points after the screening of the same micro-travel:
(1) the number of micro-stroke available points after screening is more than 1
The mass of the micro-travel bus is the average value of the mass of the available points
Wherein w represents the w-th long stroke; v represents the v-th micro-stroke under a long stroke; k represents the kth available point in a micro-trip.
(2) The number of micro-stroke available points after screening is 1
mwv＝mk (8)
Wherein k is 1.
(3) The number of micro-stroke available points after screening is 0
If the micro-stroke has no available point after screening, the corresponding quality of the micro-stroke cannot be obtained, and a quality filling method based on the space-time characteristic is adopted to solve the problem. The time characteristic refers to that the passenger capacity in the bus operation time has a certain relation with the time, and the space characteristic refers to that the passenger capacity has a certain relation with the station position. By analyzing the average quality results for the case where the number of available points for the micro-process after screening is greater than or equal to 1, the following spatio-temporal characteristics can be obtained, including but not limited to:
the number of people getting on the bus is large in early peak period because of the neighborhood of the station;
secondly, a residential area is arranged near the station, and the number of people getting off the vehicle is large in the late peak period;
the number of people getting on the bus is large in early peak period because of the business area near the station;
fourthly, a commercial area is arranged near the station, and the number of people getting off the bus is large in late peak period;
fifthly, a subway entrance is arranged near the station, and the number of people getting on and off the train is increased;
sixthly, the number of people getting on or off the bus in the noon break period is reduced.
When the number of available micro-stroke points is 0, micro-strokes which are identical in time and space (the micro-strokes with the number of the available points being 0) and have the available points being larger than 0 are searched in other long strokes, and the quality of the micro-strokes is filled, so that the quality of the micro-strokes when the number of the available micro-strokes is zero after filling and screening is realized.
Fourth, quantification of bus-mounted passenger capacity
In order to evaluate the driving economy of the driver, the energy consumption needs to be compared in a long journey unit, and the passenger capacity of the bus needs to be quantified in a long journey unit. After the micro-stroke masses are calculated, the single-pass micro-stroke masses are averaged to calculate the average mass for each long stroke, and the long-stroke masses are ranked according to the long-stroke average mass results, as shown in table 7
TABLE 7 passenger load ratings
Therefore, the one-way passenger capacity grade division of the bus is completed.
Examples
In the embodiment, the passenger capacities of the buses on two working days and two rest days are taken as research objects, and the method provided by the invention is adopted to quantify (grade) the passenger capacities of the buses, and the result is shown in fig. 4.
Fig. 4 shows the results of one-way passenger rating of a bus for two working days and two weekdays, and it can be seen that on friday and monday, level 2 appears only once, accounting for approximately 20% of the number of working days on the whole day, while on saturday and sunday, level 2 appears five and three times, accounting for approximately 71% and 50% of the total. Therefore, the whole bus-mounted passenger volume on weekends is less than that of a working day; the 12:17-13:01 time period of friday is the noon break time period and is a single pass with the least passenger capacity on the day; the monday passenger level was 4 during the time periods 07:10-08:18 and 08:18-09:22, indicating that these two time periods were in early morning rush hours, and the number of people required to ride the bus was the greatest during these four days.
While embodiments of the invention have been described above, it is not intended to be limited to the details shown, described and illustrated herein, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed, and to such extent that such modifications are readily available to those skilled in the art, and it is not intended to be limited to the details shown and described herein without departing from the general concept as defined by the appended claims and their equivalents.
Claims (10)
1. A method for quantifying the passenger capacity of a bus based on space-time characteristics is characterized by comprising the following steps:
step one, acquiring original data of a bus running state according to a sampling period;
wherein, bus driving state includes: the system comprises a vehicle state, a vehicle speed, a braking state, a driving state, longitude and latitude coordinates of the vehicle, a rotating speed of a driving motor and a torque of the driving motor;
step two, performing data cleaning on the original data, and removing abnormal values to obtain available data; dividing the available data into a long-stroke data segment and a micro-stroke data segment;
wherein the long-stroke data section comprises data of all sampling points in a single pass from the starting station to the terminal station; the micro-travel data section comprises data of all sampling points between two adjacent stations;
determining the mass of the bus with each micro-travel, and obtaining the average mass of the long-travel bus according to the mass of the bus with the micro-travel;
and step four, determining the one-way passenger capacity grade of the bus according to the average quality of the long-distance bus.
2. The method for quantifying the passenger quantity on the bus based on the space-time characteristic as claimed in claim 1, wherein in the third step, the method for determining the bus quality of each micro-trip comprises the following steps:
screening out the determined uniform acceleration points, and obtaining the mass of the micro-travel bus according to the mass of the determined uniform acceleration points;
wherein, if the number n of the uniform acceleration points determined under the same micro-travel is more than 1, the mass of the bus in the micro-travel is as follows:
if the number n of the uniform acceleration points determined under one micro-travel is equal to 1, the mass of the bus in the micro-travel is as follows:
mwv＝mk；
if the number n of the uniform acceleration points determined under one micro-travel is 0, determining the bus quality of the micro-travel by adopting a quality filling method based on space-time characteristics;
wherein w represents the w-th long stroke; v represents the v-th micro-stroke under a long stroke; k represents the kth defined smooth acceleration point in a micro-trip.
3. The method for quantifying the passenger quantity on the bus based on the space-time characteristic as claimed in claim 2, wherein the step of screening out the determined uniform acceleration points comprises the following steps:
step 1, screening all driving points from the available data;
the sampling point is a driving point when the opening of the accelerator pedal is larger than 0 and the opening of the brake pedal is equal to 0;
step 2, screening out the data set of the driving points to satisfy vi＞vi-1As possible uniform acceleration points;
where i is the ith sample point, viRepresenting the velocity, v, corresponding to the ith sample pointi-1The speed of the ith-1 sampling point;
and 3, calculating the mass of the bus at the possible uniform acceleration points, eliminating points with the mass not within the theoretical mass range of the bus, and taking the rest points as the determined uniform acceleration points.
4. The method for quantifying the passenger quantity on the bus based on the space-time characteristic as claimed in claim 3, wherein the bus quality at the possible uniform acceleration point is calculated by the following formula:
wherein eta isTRepresenting the mechanical efficiency of the drive train, m representing the mass of the vehicle, f representing the rolling resistance coefficient, uaIndicating vehicle speed，CDThe coefficient is an air resistance coefficient, A is a windward area, delta is a rotating mass conversion coefficient, and a is an acceleration.
5. The method for quantifying the passenger quantity on the bus based on the spatiotemporal characteristics according to claim 4, wherein the calculation formula of the acceleration is as follows:
wherein, aiRepresents the acceleration corresponding to the ith sampling point, and deltat represents the time interval between the ith sampling point and the (i-1) th sampling point.
6. The space-time characteristic-based bus passenger volume quantification method according to claim 5, wherein the space-time characteristic-based quality filling method comprises the following steps:
taking a micro-stroke with the determined number n of uniform acceleration points being 0 as a first micro-stroke;
searching a micro-stroke which is identical to the first micro-stroke in time and space and has a determined uniform acceleration point larger than 0 as a second micro-stroke;
and taking the mass of the bus with the second micro-travel as the mass of the bus with the first micro-travel.
7. The method for quantifying the passenger quantity on the bus based on the space-time characteristic as claimed in claim 5 or 6, wherein the theoretical mass of the bus is as follows: mass value between bus servicing quality and bus full load quality.
8. The method for quantifying the passenger quantity on the bus based on the space-time characteristic as claimed in claim 7, wherein in the third step, the method for calculating the average mass of the long-distance bus comprises the following steps: and calculating the average value of all the micro-travel masses in a single travel, and taking the average value as the average mass of the long-travel bus.
9. The method for quantifying passenger capacity on a bus based on spatiotemporal characteristics as claimed in claim 8, wherein in the fourth step, the step of classifying the passenger capacity per pass of the bus into 4 grades comprises:
if m isAre all made of≤m1Meanwhile, the one-way passenger capacity grade of the bus is 1 grade;
if m is1＜mAre all made of≤m2The one-way passenger capacity grade of the bus is 2 grade;
if m is2＜mAre all made of≤m3The one-way passenger capacity grade of the bus is 3 grade;
if m isAre all made of＞m3The one-way passenger capacity grade of the bus is 4 grade;
wherein m is1＝mServicing+500kg；m2＝m1+1000kg；m3＝m2+1000kg；mServicingFor bus reorganization quality, unit: kg; m is a unit ofAre all made ofThe average mass of the long-distance bus is obtained.
10. The method for quantifying the passenger quantity on the bus based on the spatiotemporal characteristics as claimed in claim 9, wherein in the second step, the method for dividing the available data into the long-trip data segment and the micro-trip data segment comprises the following steps:
calculating the space distance between the sampling point and the station:
taking the point with the minimum distance as a demarcation point, wherein the target function is as follows:
Distancej＝min(Distanceij),i＝1,2,...,M；
in the formula, Xi(Ingi,Lait) (i ═ 1, 2., M is the longitude and latitude coordinates of the sampling points, M is the number of sampling points; xj(Ingj,Latj) (j ═ 1, 2.., N) is the station longitude and latitude coordinates, and N is the stationThe number of points;
wherein, the data segment between two adjacent demarcation points is the micro-stroke data segment; the data section between the first demarcation point and the last demarcation point is the long-stroke data section.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004360657A (en) * | 2003-06-09 | 2004-12-24 | Miyama Kk | Economical operation evaluation device of automobile |
| JP2012006485A (en) * | 2010-06-24 | 2012-01-12 | Toyota Motor Corp | Driving evaluation device |
| KR20120075856A (en) * | 2010-12-29 | 2012-07-09 | 한국과학기술원 | Intelligent eco-driving apparatus through driving situation awareness and method therefor |
| US20140067247A1 (en) * | 2012-08-31 | 2014-03-06 | IFP Energies Nouvelles | Method of determining an eco-driving indicator for the travel of a vehicle |
| CN103764473A (en) * | 2011-07-14 | 2014-04-30 | 斯堪尼亚商用车有限公司 | Method and device for determination of energy consumption of vehicles |
| CN105730450A (en) * | 2016-01-29 | 2016-07-06 | 北京荣之联科技股份有限公司 | Driving behavior analyzing method and evaluation system based on vehicle-mounted data |
| US20160236691A1 (en) * | 2014-02-25 | 2016-08-18 | Cartasite Inc. | Enhanced Driver and Vehicle Performance and Analysis |
| CN108369681A (en) * | 2015-12-15 | 2018-08-03 | 格瑞特坦有限责任公司 | Method and system for the stroke performance for evaluating driver |
| US20180354524A1 (en) * | 2015-12-15 | 2018-12-13 | Greater Than S.A. | Method and system for assessing the trip performance of a driver |
-
2022
- 2022-03-21 CN CN202210275734.5A patent/CN114627654B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004360657A (en) * | 2003-06-09 | 2004-12-24 | Miyama Kk | Economical operation evaluation device of automobile |
| JP2012006485A (en) * | 2010-06-24 | 2012-01-12 | Toyota Motor Corp | Driving evaluation device |
| KR20120075856A (en) * | 2010-12-29 | 2012-07-09 | 한국과학기술원 | Intelligent eco-driving apparatus through driving situation awareness and method therefor |
| CN103764473A (en) * | 2011-07-14 | 2014-04-30 | 斯堪尼亚商用车有限公司 | Method and device for determination of energy consumption of vehicles |
| US20140067247A1 (en) * | 2012-08-31 | 2014-03-06 | IFP Energies Nouvelles | Method of determining an eco-driving indicator for the travel of a vehicle |
| US20160236691A1 (en) * | 2014-02-25 | 2016-08-18 | Cartasite Inc. | Enhanced Driver and Vehicle Performance and Analysis |
| CN108369681A (en) * | 2015-12-15 | 2018-08-03 | 格瑞特坦有限责任公司 | Method and system for the stroke performance for evaluating driver |
| US20180354524A1 (en) * | 2015-12-15 | 2018-12-13 | Greater Than S.A. | Method and system for assessing the trip performance of a driver |
| CN105730450A (en) * | 2016-01-29 | 2016-07-06 | 北京荣之联科技股份有限公司 | Driving behavior analyzing method and evaluation system based on vehicle-mounted data |
Non-Patent Citations (4)
| Title |
|---|
| JUNSHI XU, RAN TU, USMAN AHMED: "An eco-score system incorporating driving behavior, vehicle characteristics, and traffic conditions", TRANSPORTATION RESEARCH PART D: TRANSPORT AND ENVIRONMENT, vol. 95 * |
| MJOZI R JELE;LESEDI MASISI: "An Eco-driving Strategy for an Electric Bus: IPMSM Drive-train", IECON 2021 – 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY * |
| 叶倩文: "联合驻站策略的公交车生态驾驶优化模型研究", 中国优秀硕士学位论文全文库(工程科技Ⅱ辑), no. 01 * |
| 张红妮: "基于驾驶行为的纯电动公交车能耗规律与节能对策", 中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑), no. 06 * |
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