CN106529778A - Bus ride comfort index construction method based on smart phone - Google Patents
Bus ride comfort index construction method based on smart phone Download PDFInfo
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- CN106529778A CN106529778A CN201610935591.0A CN201610935591A CN106529778A CN 106529778 A CN106529778 A CN 106529778A CN 201610935591 A CN201610935591 A CN 201610935591A CN 106529778 A CN106529778 A CN 106529778A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a bus ride comfort index construction method based on a smart phone. The method comprises the steps of (1) calling a mobile phone motion sensor and a GPS module to collect the 3D acceleration information, running direction information, three-dimensional angular velocity information, velocity information, longitude and latitude information and time information in real time in a vehicle operation process, (2) deeply analyzing the intrinsic link between the vehicle running state and ride comfort from passenger psychological and physical angles and time value angle of a passenger, extracting the original variable set which can reflect the influence of bus running smoothness on the ride comfort through investigation and analysis, and eliminating the influence of a bus station plan factor on the vehicle running sate in analyzing parking state related variables, and (3) carrying out dimensionality reduction on an original index set through a principal component method, and extracting a main component with a principal that a characteristic cumulative contribution rate is larger than 90%. According to the bus ride comfort index construction method, from the angle of traffic engineering, the running comfort of a bus is analyzed, the goal is clear, and the idea is feasible.
Description
Technical field
The invention belongs to intelligent transportation field, is related to smartphone data acquisition technique and based on smartphone data
Bus running comfort objective indicator construction method.
Background technology
Urban public transport is first developed, it is to attract passenger to take transit trip to improve bus service quality,
Alleviate the important channel of traffic congestion.However, China major part city bus trip share rate is only 10% or so, megalopolis
20% can be about reached, but ratio is still very low.What public transport share rate cannot be improved always main reason is that service level
It is low, it is difficult to meet passenger demand.Therefore, study public transport Comfort Evaluation index and there is directive significance for improving bus service.
Most of research both at home and abroad stresses to study bus Comfort Evaluation index system, less discussion Comfort Evaluation
The construction method of index.And index is based on qualitative description, lack objectivity.Impact bus is researched and analysed and has ridden in part
The factor of influence of comfortableness, constructs objective measurement index, but the objective measurement index inquired into is for environment inside car mostly
Factor, and it is excessively single to be directed to the measurement index constructed by public transport operation ride comfort, and lack process of going on a journey to bus
The ground data acquisition of long-time seriality, the index of acquisition cannot embody bus operation shadow of the ride comfort to ride comfort
Ring.Additionally, research extracts key factor to the mining analysis that the initial parameter for being gathered does not carry out system in the past, portion is caused
Divide index to lack reasonability, evaluation result is adversely affected.
The content of the invention
Present invention aim to overcome that the deficiencies in the prior art, a kind of disclosed bus based on smart mobile phone is ridden
The parameters such as comfort index construction method, speed, acceleration, longitude and latitude in collection bus running, analyse in depth
Operation ride comfort and comfortableness internal association, are excavated by the statistics to initial parameter and extract the original variable for characterizing comfortableness,
Bus running comfort is built by Data Modeling Method, and comprehensively objective measurement index makes up in research in the past and ignores vehicle
The deficiency that running status change is affected on comfortableness.
The present invention employs the following technical solutions to realize：
A kind of bus ride comfort index construction method based on smart mobile phone, it is characterised in that including as follows
Step：
The first step：In order to obtain the parameters in bus running, calling mobile phone motion sensor, GPS moulds
Three-dimensional acceleration information, traffic direction information, three-dimensional angular velocity information, speed letter in block Real-time Collection vehicle operation
Breath, latitude and longitude information and temporal information.Pretreatment is carried out to raw process parameter data simultaneously, subsequent data analysis demand is met.
Second step：Travel condition of vehicle is analysed in depth from the time value angle of passenger mentality, physiological angle and passenger
With the internal relation of ride comfort, and analysis is extracted and can embody bus operation ride comfort to relaxing by bus by inquiry
The original variable collection that adaptive affects.During analysis dead ship condition correlated variabless, bus station planning factor should be rejected and (advised bus station
Draw relevant with passenger flow and land character, there is stationarity to affect public transport operation state, there is for passenger acceptability) it is right
The impact of travel condition of vehicle.The analytical mathematics of this second step have novelty, are not directed in studying in the past.
3rd step：The dimension of original variable collection described in step 2 is larger and has information overlap, to public transport Comfort Evaluation mould
The stability of type has adverse effect.Therefore, the dependency between each original variable will be quantified.Retaining, original variable is basic
The effect of variable independence is reached by the method dimensionality reduction of data modeling under conditions of information, is easy to later stage evaluation.The present invention passes through
Principal component analysis extract main constituent as criterion more than 90% using eigenvalue accumulation contribution rate to original index collection dimensionality reduction.With reference to friendship
Logical engineering relevant knowledge is parsed to main constituent meaning, final to establish that bus running comfort is comprehensively objective to estimate finger
Mark.
The present invention is from Traffic Engineering angle analysis bus running comfort, with clearly defined objective, and thinking feasibility is high,
And there is novelty.
Description of the drawings
Fig. 1 general flow charts of the present invention
Fig. 2 software data capture programs interface
In Fig. 3 second steps of the present invention, data analysis algorithm flow chart
The schematic flow sheet of Fig. 4 the 3rd steps of the present invention
Specific embodiment
Below in conjunction with drawings and Examples, technical solution of the present invention is elaborated.
Embodiment 1
The present invention is using smart mobile phone as data collection station, altofrequency, seriality ground collection bus running
In speed, acceleration, the parameter such as longitude and latitude.Operation ride comfort and comfortableness internal association are analysed in depth, by original ginseng
Several statistics is excavated and extracts the original variable for characterizing comfortableness, ignores travel condition of vehicle and change to comfortable in making up research in the past
Property affect deficiency, these original variables from multiple angles characterize operation ride comfort objective influence degree, but dimension is larger,
And have information overlap to each other, upper strata Comfort Evaluation is adversely affected.For this purpose, the present invention is considered by Data Modeling Method
Build bus running comfort comprehensively objective measurement index.The objective measurement index of constructed synthesis can be fully demonstrated
The external factor such as driving behavior, road traffic management and control information, road Alignment Design and road traffic state are relaxed to public transport operation
The impact of adaptive, with novelty and operability, can provide scientific and effective research for bus running comfort evaluation
Basis.Particular flow sheet of the present invention is as shown in Figure 1.
The first step：Raw data acquisition and pretreatment
In order to obtain the parameters in bus running, handset program is write, calling mobile phone motion sensor,
Three-dimensional acceleration information, traffic direction information in GPS module Real-time Collection vehicle operation, three-dimensional angular velocity information, speed
Degree information, latitude and longitude information and temporal information.Handset program is adopted with the frequency of 100HZ with the frequency collection GPS information of 5HZ
Collection three-dimensional acceleration and angular velocity information.Record the corresponding temporal information of each data collection point simultaneously.As shown in Figure 2.Then,
Initial data to collecting carries out pretreatment, reduces the interference of drift data, while repairing to missing data, this belongs to
Routine techniquess.
Second step：
By data analysis and process algorithm (Fig. 3), the original variable based on vehicle operating parameters is obtained.Original variable is believed
Breath, including：
Speed average
Median median (m/s)；
Extreme difference range (m/s)；
Complete stop frequency Nc (secondary/min) in unit interval, except the stop frequency of bus station；
Incomplete stop frequency Nuc (secondary/min) in unit interval
Maximum parking waiting time mst (s)
Maximum three-dimensional resultant acceleration maxa (m/s2)
Three-dimensional resultant acceleration variance, stda (m/s2)
Down time ratio R as=tV=0/ T, is dimensionless, with data point that speed is zero sum and number during Practical Calculation
Determine according to the ratio of total amount.Wherein, stop completely vehicle deceleration is referred to until resting state, not exclusively stops and refer to that vehicle has
Moderating process but will not be totally stationary.In view of sensitivity of the passenger to velocity perturbation, only when reduction of speed amplitude is less than certain threshold
Just it is judged to incomplete docking process influential on ride comfort during value.
Unit interval, incomplete stop frequency Nuc was calculated by formula (1)
In formula (1),
ΔviRapid change amount is represented,
vthresholdThreshold value is represented, the selection of threshold value needs to perceive with reference to passenger and is analyzed, and the present invention passes through statistical inquiry
Method threshold value is 2m/s.
T represents the sampling period.
u(Δvi-vthreshold) it is the property shown letter.That is, as Δ vi-vthreshold>U (Δ v when 0i-vthreshold)=1, otherwise for
0.
Complete stop frequency Nc (beat/min) and incomplete stop frequency Nuc in the unit interval in unit interval (beat/min
Clock) index needs specific data processing method to obtain with skill, and concrete data processing algorithm is referring to flow process Fig. 3.
Additionally, the parking in bus station is not included in complete stop frequency index.Therefore, public affairs are obtained by data analysiss
XGeocoding softwares are also passed through after all stop GPS coordinate informations during automobile trip altogether and is obtained gps coordinate
Geographical Gray code information, rejects the impact that bus station is stopped to index calculating.
3rd step：
As the original index dimension that obtains in step 2 is too many, and existence information overlaps, the stability to evaluation model
Adversely affect.Therefore dimension-reduction treatment is carried out with principal component analysis to original index, main constituent is extracted, and is retained original change as far as possible
The quantity of information of amount.In order that the coefficient matrix of principal component analysis meets nonsingular condition, need collection a plurality of (more than original index
Number) public bus network trip data is used as training set.Shown in principal component analysis model tormulation such as formula (2) and (3).Concrete model algorithm is such as
Shown in Fig. 4
OrderBy the optimization equation solution optimum coefficient matrix A of (2)-(3)
Wherein,
Represent coefficient vector,
xiRepresent original index,
P represents dimension, and in the present invention, original index has 9, i.e.,
cov(yi,yj) represent the two covariance.
Σ represents (y1,y2,...y9) between covariance matrix.
By matrix analyses, with random vector covariance matrix Σ eigenvalue (λ1≥λ2…≥λp>=0) it is corresponding just
Friendship characteristic vector is (ζ1,ζ2…ζp).It is then arbitrarily vectorialThe linear combination of orthogonal eigenvectors is represented by, i.e.,：
According to
According to constraints, (2-3) Goal Programming Problem can be converted into following Multivariate Extreme Value problem：
Maxv (y can be obtainedi)=λi, therefore, belong to y in population varianceiThe contribution rate of composition is λi/∑λi, accumulated with eigenvalue
Contribution rate extracts main constituent as criterion more than 90%, extracts original index main constituent objective as bus running comfort
Measurement index, provides reference to improve bus comfortableness.
The specifically used method of the present invention is illustrated below：
A plurality of public bus network operational parameter data is gathered, the initial data to collecting carries out pretreatment, completes abnormal number
According to rejecting, the interference of drift data is reduced, while repairing to missing data.
As the parking of bus station is not included in complete stop frequency original variable.Therefore, when calculating the index, first lead to
Cross data analysis algorithm (Fig. 3) and obtain all stop GPS informations, the geographical Gray code of gps coordinate is then obtained by software
Information, rejects after bus station is stopped and recalculates the desired value.
Waypoint location information table
| Latitude | Longitude | Waiting time | Positional information | Position description |
| 31.29366891 | 121.1592612 | 9.953 | Jiading District in Shanghai City Zepu road Lane 212 3 | Bus station |
| 31.29695258 | 121.1590261 | 1.96 | Jiading District in Shanghai City black jade road 83 3 | Section (non-website) |
| 31.2970143 | 121.16124 | 11.915 | Jiading District in Shanghai City Changji road 163 | Crossing |
| 31.29709659 | 121.1641684 | 7.945 | Jiading District in Shanghai City Changji road 153 | Section (non-website) |
| 31.29709562 | 121.1641783 | 8.927 | Jiading District in Shanghai City Changji road 153 | Section (non-website) |
| 31.29708709 | 121.1641854 | 3.973 | Jiading District in Shanghai City Changji road 153 | Section (non-website) |
| 31.29019961 | 121.1712088 | 1.913 | Jiading District in Shanghai City Miquan road 1 | Crossing |
| 31.29006566 | 121.1712347 | 8.886 | Jiading District in Shanghai City Miquan road 1 | Crossing |
| 31.28960048 | 121.1720909 | 6.943 | CaoAn Highway, Jiading District, ShangHai City 5388 | Section (non-website) |
| 31.2886218 | 121.1768039 | 9.93 | CaoAn Highway, Jiading District, ShangHai City 5277 | Bus station |
For other original variables are calculated with reference to Fig. 3 algorithm flows.
Original variable is calculated for every circuit trip data.Different trip data original index values are recorded with matrix.By
It is inconsistent in dimension, standardization is first done to matrix, covariance matrix is then sought, covariance matrix eigenvalue and right is calculated
The characteristic vector answered, using contribution rate of accumulative total more than 90% as selected characteristic value and the standard of characteristic vector.Main constituent is original
The linear combination of index, constitutes the aggregative indicator of new sign running comfort.Wherein coefficient vector be exactly corresponding feature to
Amount.And combination is experienced by bus and aggregative indicator is verified, it is ensured that its reasonability.
Using the method applied in the present invention, the visitor related to bus running comfort can be built from many levels
See measurement index.With operability and science.
Claims (1)
1. a kind of bus ride comfort index construction method based on smart mobile phone, comprises the steps：
The first step：Three-dimensional acceleration information in calling mobile phone motion sensor, GPS module Real-time Collection vehicle operation,
Traffic direction information, three-dimensional angular velocity information, velocity information, latitude and longitude information and temporal information；
Second step：Analyse in depth travel condition of vehicle and take advantage of from the time value angle of passenger mentality, physiological angle and passenger
The internal relation of car comfortableness, and by inquiry analysis extract can embody bus run ride comfort to ride comfort
The original variable collection of impact；During analysis dead ship condition correlated variabless, reject bus station and plan factor to travel condition of vehicle
Affect；
3rd step：Under conditions of original variable essential information is retained, by principal component analysis to original index collection dimensionality reduction, with feature
Value accumulation contribution rate extracts main constituent as criterion more than 90%.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109448364A (en) * | 2018-10-15 | 2019-03-08 | 同济大学 | A kind of public transport dynamic trajectory optimization method considering comfort level and energy-saving and emission-reduction |
| CN109829663A (en) * | 2019-04-11 | 2019-05-31 | 郑州大学 | A kind of light rail train comfort level evaluating system based on cloud platform |
-
2016
- 2016-11-01 CN CN201610935591.0A patent/CN106529778A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109448364A (en) * | 2018-10-15 | 2019-03-08 | 同济大学 | A kind of public transport dynamic trajectory optimization method considering comfort level and energy-saving and emission-reduction |
| CN109448364B (en) * | 2018-10-15 | 2020-08-14 | 同济大学 | Bus dynamic trajectory optimization method considering comfort level, energy conservation and emission reduction |
| CN109829663A (en) * | 2019-04-11 | 2019-05-31 | 郑州大学 | A kind of light rail train comfort level evaluating system based on cloud platform |
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Application publication date: 20170322 |