CN104794301A - Method for exploring influence factors for running time reliability of bus - Google Patents
Method for exploring influence factors for running time reliability of bus Download PDFInfo
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- CN104794301A CN104794301A CN201510218699.3A CN201510218699A CN104794301A CN 104794301 A CN104794301 A CN 104794301A CN 201510218699 A CN201510218699 A CN 201510218699A CN 104794301 A CN104794301 A CN 104794301A
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Abstract
The invention relates to a method for exploring influence factors for the running time reliability of a bus. Multiple typical bus routes are selected, statistics is performed on position data of the bus in a selected period of time, the significance degree of each independent variable is checked through software, and then the influence degrees of the influence factors on the running time reliability of the bus are compared. The logic is rigorous and easy to understand, and the method is suitable for studying bus routes of all cities and wide in application range.
Description
Technical field
The present invention relates to a kind of method of probing into bus running time reliability influence factor, belong to intelligent transportation system technical field.
Technical background
In order to improve public transit system service reliability, public transport company takes a large amount of material resources and financial resources to implement some state-of-the-art transport and communication technologys.Progress in public transit system service reliability brings income not only can to the network operator of public transit system, and greatly can facilitate the trip of passenger.The raising of bus arrival on time rate not only can reduce the fluctuation of bus arrival time, thus saves the stand-by period of passenger; And bus network operator can be allowed to reduce extra surge time when formulating timetable, so just can reduce the unnecessary garage that goes out is utilize bus resource more effectively.The raising of public transit system service reliability can reduce front and back bus and arrive a certain website or two, front and back bus simultaneously and arrive the generation that larger time interval phenomenon appears in a certain website, also can reduce the stand-by period of passenger at platform simultaneously, ensure that bus resource can be fully utilized.For supplier and the passenger of Bus Service, the unreliable matter of utmost importance brought of bus service is exactly expenditure unnecessary in a large number.
But the city road network system of China is very complicated, and the operation of bus is subject to the impact of other private cars, bicycle and taxi.In order to the trip be better convenient for people to, there is a lot of APP software on the market, such as the softwares such as micro-stepping, wireless city palm public transport, rainbow public transport, but the developer of these softwares does not analyse in depth the service data of bus, do not analyze bus reliability, so the data that these softwares provide exist deviation, result in the decline of service quality.Investigation display, bus running time reliability has become the important means strengthening city bus attractive force, it not only left and right the choice for traveling of people, also affect the decision-making that public transport company formulates dispatch list aspect, to Bus Service plan, formulation timetable, operation controls and passenger's trip has a lot of enlightenment.But owing to being subject to the impact of the factors such as weather, condition of road surface, time, signal lamp split, current bus running time reliability is also not fully up to expectations, have impact on the trip arrangement of people.
Summary of the invention
For the deficiencies in the prior art, the invention discloses a kind of method of probing into bus running time reliability influence factor.
Technical scheme of the present invention is:
Probe into a method for bus running time reliability influence factor, concrete steps comprise:
(1) selected public bus network, marks off three representative research sections, chooses search time section, gather and store the position of bus data on research section described in search time section;
(2) pre-service is carried out to the position of bus data in the research section stored, calculate all working times in each research section;
(3) average of the working time of the bus calculating each research section between two adjacent websites and standard deviation, calculate the standard rate of working time, the average of the standard deviation/working time of standard rate=working time of described working time, the standard rate of definition working time is dependent variable;
(4) select five class influence factors, defining five class influence factors is independent variable, gathers the data of this five classes influence factor in search time section;
(5) spss software is used to analyze independent variable and dependent variable;
(6) according to the analysis result of spss software, the size of each influence factor to the influence degree of bus running time reliability is probed into.
Preferred according to the present invention, in step (1), described representative research section be in business district section, be located in remote area section and between the section of business district with remote area centre.
Preferred according to the present invention, in step (1), selected search time section is 5:45AM-9:00PM every day of at least 27 days.
Preferred according to the present invention, in step (1), the described position of bus data i.e. bus GPS data of selected public bus network, described bus GPS data comprises line number, vehicle ID, arrival time, time leaving from station, longitude, latitude value, speed and through website number, is stored in excel tables of data by above-mentioned bus GPS data.
Preferred according to the present invention, the concrete steps of described step (2) are as follows:
(21) repeating data, misdata is rejected;
(22) working time of bus between two websites is obtained with the time leaving from station that the arrival time of a rear website deducts last website.
Preferred according to the present invention, the computation process in step (3) is, uses matlab software to calculate average and the standard deviation of bus on each research section working time between two adjacent websites.
Preferred according to the present invention, in step (4), five described class influence factors are weather, time period, date, split and with or without bus special lane, and described split is the ratio that in signal lamp, long green light time accounts for the total duration of signal lamp.
Beneficial effect of the present invention
1, technical scheme of the present invention is studied by choosing several representational public bus networks, analysis software is utilized to check the significance degree of each independent variable, thus comparing the size that each influence factor affects bus running time reliability, logic is rigorous understandable.
2, method step of the present invention is succinct, practical, realizes fast by software program.
3, technical scheme of the present invention is applicable to the public bus network research in each city, and applicable surface is extensive, has higher dirigibility and operability.
Accompanying drawing explanation
Fig. 1 is analysis process schematic diagram of the present invention.
Embodiment
Below in conjunction with Figure of description and embodiment, the present invention will be further described, but be not limited thereto.
Embodiment
Probe into a method for bus running time reliability influence factor, as shown in Figure 1, concrete steps comprise:
(1) selected public bus network, marks off three representative research sections, chooses search time section, gather and store the position of bus data on research section described in search time section:
Using between two adjacent websites as minimum research unit, for Jinan City's public bus network, three selected research section situations are as follows: the research section of 1 bus: the large north of a road in Hongjialou-Hongjialou West Road and Hongjialou West Road-mountain section, is in business district; The research section of 35 bus: six Li Shan South Road-seven Li Shannan villages and seven Li Shannan village-four seasons garden, be located in remote area; The research section of 101 bus: eastern suburb restaurant-mountain main road and mountain main road-central hospital, in the middle of business district and remote area.Gather and store above-mentioned research section in the position of bus data of January 28 2 days to 2014 January in 2014 within the 5:45AM-9:00PM time period, the described position of bus data i.e. bus GPS data of selected public bus network, described bus GPS data comprises line number, vehicle ID, arrival time, time leaving from station, longitude, latitude value, speed and through website number, is stored in excel tables of data by above-mentioned bus GPS data.
(2) pre-service is carried out to the position of bus data in the research section stored, calculate all working times in each research section, specifically comprise the steps:
(21) reject repeating data, misdata, described misdata is the data do not meeted the requirements of apparent error, such as overlong time or too shortly all do not meet actual conditions;
(22) working time of bus between two websites is obtained with the time leaving from station that the arrival time of a rear website deducts last website.
(3) matlab software is used to calculate average and the standard deviation of bus on each research section working time between two adjacent websites.In search time section, there is the bus of a lot of selected public bus networks through research section, so need to obtain all average and the standard deviation of passing through the working time of the bus in research section in search time section.The average of the standard deviation/working time of standard rate=working time of described working time, the standard rate of definition working time is dependent variable.
Concrete steps are as follows:
First excel tables of data is imported in matlab, calculates the average of working time of bus between two adjacent websites and the statement of standard deviation is:
[x]=xlsread (' tables of data ')
Mean (x) average
Std (x) ask standard deviation.
(4) select five class influence factors, defining five class influence factors is independent variable, gathers the data of this five classes influence factor in search time section:
Concrete, as shown in table 1, select weather, time period, date, split and be five class influence factors with or without bus special lane, described split is the ratio that in signal lamp, long green light time accounts for the total duration of signal lamp.Gather this five classes influence factor in the data of January 28 2 days to 2014 January in 2014 within the 5:45AM-21:00PM time period.Time period influence factor is divided into early ebb (5:45AM-7:00AM), morning peak (7:00AM-9:00AM), flat peak (9:00AM-17:00PM), evening peak (17:00PM-19:00PM), ebb in evening (19:00PM-21:00PM), if early ebb is x1, morning peak is x2, flat peak is x3, evening peak is x4, evening, ebb was x5, in each data gathered, bus only may run within a kind of time period, that is: if bus travelled in slack hours section in morning in the data gathered, then x1 value is 1, accordingly, in same data, other times section is as x2, x3, x4, the value of x5 is 0, if date influence factor is x6, date factor is divided into working day and weekend, if the duration on lower working day of date factor is 0, duration at weekend is 1, being provided with without bus special lane influence factor is x7, is divided into and has bus special lane and without bus special lane two kinds of situations, if be 0 without the duration of bus special lane, have the duration of bus special lane to be 1, weather effect factor is divided into sunny, haze and sleet, in each data gathered, under bus only may be in a kind of weather condition, be x8 if sunny, haze is x9, sleet is x10, if in the data gathered, bus is in fair weather, then x8 value is 1, accordingly, x9, x10 value is 0, split influence factor is actual measured value, if split is x11.
(5) spss software is used to analyze independent variable and dependent variable:
The value of independent variable and dependent variable is imported in spss, carries out multiple linear regression analysis.Concrete, as shown in table 2, early ebb x1 and sunny x8 is got rid of by spss software, and namely spss software has been used as ebb x1 and sunny x8 morning with reference to amount, and early ebb x1 and sunny x8 does not participate in the sequence of the influence value to dependent variable.
(6) according to the analysis result of spss software, the size of each influence factor to the influence degree of bus running time reliability is probed into.
Concrete, as shown in table 3, the absolute value of Beta value represents the size of the influence degree of each independent variable, so the size sequence of influence degree is as follows: evening peak x4> morning peak x2> sleet x10> split x11> is with or without bus special lane x7> date x6> flat peak x3> ebb x5> haze in evening x9.
Can draw the following conclusions thus:
A. at weather, split, with or without in public transportation lane, time period and date five kinds of influence factors, the morning peak in the time period and the impact of evening peak on bus running time reliability are maximum.Due to the restriction of data, do not find accurately data to quantize the jam situation of road, so by the congestion of time period approximate road.Morning peak and the general vehicle flowrate of evening peak are large, and road compares and blocks up, and can find out that the impact of congestion in road on bus running time reliability is larger.
B. be secondly the impact of sleety weather, compared with haze weather, the impact of sleety weather on bus running time reliability is larger
C. within the specific limits, the split of signal lamp is larger, more can improve bus running time reliability.Because when the total duration of signal lamp is constant, long green light time is longer, the traveling of bus is more not easy to be hindered, and reliability is higher.The split of signal lamp suitably can improve in public transport planning department, also can carry out public transport look ahead strategy at major trunk roads.
D. be also influential with or without bus special lane to raising bus running time reliability, so operation department can arrange public transportation lane on road with good conditionsi, narrow road can arrange public transportation lane in peak period.
E. workaday reliability is lower than the reliability at weekend generally.
Passenger according to the size of the influence degree of reliability effect factor, can carry out selection to trip and judges; Public transport company, operation department also can carry out analysis and summary according to the size of the influence degree of reliability effect factor, and the operation plan in conjunction with self is carried out formulation dispatch list, Bus Service plan, run controlling decision.
The data layout table of table 1 five class influence factor
| Early ebb | Morning peak | Flat peak | Evening peak | Late ebb | Date | Public transportation lane | Sunny | Haze | Sleet | Split | Reliability |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.405 | 0.14 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0.405 | 0.50 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.405 | 0.36 |
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0.405 | 0.66 |
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0.405 | 0.25 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.405 | 0.15 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.405 | 0.52 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.405 | 0.37 |
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0.405 | 0.68 |
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0.405 | 0.26 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.405 | 0.24 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0.405 | 0.67 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.405 | 0.44 |
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0.405 | 0.84 |
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0.405 | 0.36 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0.405 | 0.19 |
| 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0.405 | 0.25 |
| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0.405 | 0.32 |
| 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0.405 | 0.56 |
| 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.405 | 0.29 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0.405 | 0.13 |
| 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0.405 | 0.19 |
| 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0.405 | 0.18 |
| 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0.405 | 0.30 |
| 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0.405 | 0.16 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.394 | 0.16 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0.394 | 0.56 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.394 | 0.34 |
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0.394 | 0.71 |
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0.394 | 0.28 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.394 | 0.18 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0.394 | 0.62 |
Table 2 system gets rid of independent variable form shfft
The analysis result of table 3 spss software of the present invention
| Model | Beta |
| X2 | 0.430 |
| X3 | 0.129 |
| X4 | 0.520 |
| X5 | 0.092 |
| X6 | -0.139 |
| X7 | -0.183 |
| X9 | 0.012 |
| X10 | 0.310 |
| X11 | -0.270 |
Claims (7)
1. probe into a method for bus running time reliability influence factor, concrete steps comprise:
(1) selected public bus network, marks off three representative research sections, chooses search time section, gather and store the position of bus data on research section described in search time section;
(2) pre-service is carried out to the position of bus data in the research section stored, calculate all working times in each research section;
(3) average of the working time of the bus calculating each research section between two adjacent websites and standard deviation, calculate the standard rate of working time, the average of the standard deviation/working time of standard rate=working time of described working time, the standard rate of definition working time is dependent variable;
(4) select five class influence factors, defining five class influence factors is independent variable, gathers the data of this five classes influence factor in search time section;
(5) spss software is used to analyze independent variable and dependent variable;
(6) according to the analysis result of spss software, the size of each influence factor to the influence degree of bus running time reliability is probed into.
2. method of probing into bus running time reliability influence factor according to claim 1, it is characterized in that, in step (1), described representative research section be in business district section, be located in the section of remote area and the section in the middle of business district and remote area.
3. method of probing into bus running time reliability influence factor according to claim 1, is characterized in that, in step (1), selected search time section is 5:45AM-9:00PM every day of at least 27 days.
4. method of probing into bus running time reliability influence factor according to claim 1, it is characterized in that, in step (1), the described position of bus data i.e. bus GPS data of selected public bus network, described bus GPS data comprises line number, vehicle ID, arrival time, time leaving from station, longitude, latitude value, speed and through website number, is stored in excel tables of data by above-mentioned bus GPS data.
5. method of probing into bus running time reliability influence factor according to claim 4, is characterized in that, the concrete steps of described step (2) are as follows:
(21) repeating data, misdata is rejected;
(22) working time of bus between two websites is obtained with the time leaving from station that the arrival time of a rear website deducts last website.
6. method of probing into bus running time reliability influence factor according to claim 1, it is characterized in that, computation process in step (3) is, uses matlab software to calculate average and the standard deviation of bus on each research section working time between two adjacent websites.
7. method of probing into bus running time reliability influence factor according to claim 1, it is characterized in that, in step (4), five described class influence factors are weather, time period, date, split and with or without bus special lane, and described split is the ratio that in signal lamp, long green light time accounts for the total duration of signal lamp.
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| CN107798865A (en) * | 2016-09-07 | 2018-03-13 | 高德信息技术有限公司 | A kind of public bus network running time predictor method and device |
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| CN112990658A (en) * | 2021-02-05 | 2021-06-18 | 福建工程学院 | Bus network timetable reliability calculation method for line simplification optimization |
| CN113298285A (en) * | 2021-01-28 | 2021-08-24 | 同济大学 | Intelligent passenger ahead-of-line planning method based on high-speed rail network |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107798865A (en) * | 2016-09-07 | 2018-03-13 | 高德信息技术有限公司 | A kind of public bus network running time predictor method and device |
| CN108831147A (en) * | 2018-05-24 | 2018-11-16 | 温州大学苍南研究院 | A kind of observation method of the city bus macroscopic view traveling fluctuation based on data-driven |
| CN108831147B (en) * | 2018-05-24 | 2020-11-10 | 温州大学苍南研究院 | Data-driven method for observing macro driving fluctuation of urban bus |
| CN113298285A (en) * | 2021-01-28 | 2021-08-24 | 同济大学 | Intelligent passenger ahead-of-line planning method based on high-speed rail network |
| CN112990658A (en) * | 2021-02-05 | 2021-06-18 | 福建工程学院 | Bus network timetable reliability calculation method for line simplification optimization |
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Application publication date: 20150722 |