CN113723761B - Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data - Google Patents
Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data Download PDFInfo
- Publication number
- CN113723761B CN113723761B CN202110877673.5A CN202110877673A CN113723761B CN 113723761 B CN113723761 B CN 113723761B CN 202110877673 A CN202110877673 A CN 202110877673A CN 113723761 B CN113723761 B CN 113723761B
- Authority
- CN
- China
- Prior art keywords
- reliability
- bus
- interval
- data
- travel time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000003064 k means clustering Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 30
- 230000001502 supplementing effect Effects 0.000 claims 1
- 238000011158 quantitative evaluation Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 208000025174 PANDAS Diseases 0.000 description 1
- 208000021155 Paediatric autoimmune neuropsychiatric disorders associated with streptococcal infection Diseases 0.000 description 1
- 240000000220 Panda oleosa Species 0.000 description 1
- 235000016496 Panda oleosa Nutrition 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a multi-dimensional urban public transportation operation service reliability evaluation method based on operation data, which comprises the following steps of 1) selecting a sample line to be evaluated from a public transportation network; 2) Collecting bus arrival data of a sample line and preprocessing the bus arrival data; 3) Calculating urban bus running service reliability, including arrival interval reliability based on stations, travel time reliability based on station intervals, bus running service reliability based on travel phases and bus running service reliability based on lines; 4) And grading the reliability of urban public transportation operation service based on a K-Means clustering method. The invention can meet the requirements of multiple subjects on quantitative evaluation of the reliability of the bus running service, and is suitable for evaluating the reliability of the bus running service in urban bus networks in large scale, fine granularity and full period.
Description
Technical Field
The invention relates to a multi-dimensional urban public transport operation service reliability evaluation method based on operation data, and belongs to the field of public transport operation monitoring and service evaluation.
Background
With the rapid construction of urban rail transit, the rapid development of network traffic, shared single vehicles and other emerging travel modes, the ground bus passenger traffic and travel sharing rate of large cities in China are reduced year by year in recent years. Compared with other travel modes, the road traffic conditions and other factors in the ground bus running process are complex and changeable, the arrival interval and the travel time fluctuation are large, the running unreliability of the road traffic conditions and the road traffic conditions cause poor riding experience, insufficient attraction and competitiveness of passengers, and partial passenger flow turns to the travel mode with higher reliability. In the existing bus reliability evaluation method, most of the bus reliability evaluation methods rely on bus schedule scheduling plans or the results of vehicle following investigation, and the bus running service reliability evaluation of the urban bus network in a large scale, fine granularity and full period cannot be met; meanwhile, most bus reliability evaluation is from the perspective of a manager, and the reliability of public transportation operation service cannot be objectively and scientifically evaluated from the perspective of a traveler. Reliable bus running service can improve predictability of waiting time and travel time for passengers, reduce anxiety of waiting and in-transit and reduce time cost of taking bus; for an operation enterprise, the normal conveying speed and schedule can be better maintained, the configuration of bus resources is optimized, and the operation efficiency and passenger satisfaction are improved.
The invention method of China with the application number of CN202010847093.7 provides a city road classification method based on the reliability of the running state of the ground bus, and the method utilizes the GPS data of the ground bus, the bus timetable, the normal running input quantity of the bus and other data to calculate the traffic congestion index and the running time fluctuation of the road section in the research period, and classifies the reliability of the running state of the bus by a K-means clustering method. However, the method only considers the bus running time and the volatility thereof, and the determination of the classification threshold depends on operation data such as bus timetable and the like, so that the calculation is complex when facing a large-scale network.
According to the method, device and electronic equipment for evaluating the bus running reliability of the Chinese invention with the application number of CN201911409014.8, the running time deviation of each bus shift at each approach station and the previous shift is calculated, and then a reliability scoring formula is preset to evaluate the bus running reliability. The method does not rely on bus schedules, but cannot identify problems in bus operation, and does not consider how to face multi-subject applications.
Ground public transportation is an important component of public transportation, and has good accessibility and coverage. Along with the wide application of the information automatic acquisition equipment, large-scale bus running data are gradually accumulated, and rich bus running state information is contained. The increasingly high-quality data conditions provide powerful support for monitoring and quantitatively evaluating the bus running service state, and also lay an important foundation for further developing quantitative evaluation of the bus running service reliability in large scale, fine granularity and full period.
Disclosure of Invention
The invention aims to provide a multi-dimensional urban bus running service reliability evaluation method based on running data, which comprises a bus arrival data preprocessing method, a multi-dimensional bus running service reliability evaluation model and a bus running service reliability grading method, and provides support for realizing quantitative evaluation of multi-main-body-oriented bus running service reliability, wherein the specific flow is as shown in figure 1:
in order to achieve the above object, the present invention adopts the following technical scheme.
The urban bus running service reliability evaluation method based on the multi-source bus data is characterized by comprising the following steps of:
step 1: sample line selection to be evaluated
In recent years, the construction and development of public transport infrastructure in China are rapid, the number of public transport operation lines and operation mileage are continuously increased, and large cities often have large-scale public transport networks. Due to factors such as data acquisition, data quality, calculation capability and the like, bus running service reliability evaluation often cannot or does not need to cover all bus lines, and sample bus lines need to be selected for evaluation and analysis. In order to ensure the accuracy of the evaluation result, the selection rule of the sample line needs to be defined.
The sample bus route selection rule comprises the following points:
1. the selected bus route has good data quality. The bus arrival time-space data is used for calculating the reliability evaluation index, a lower loss rate needs to be ensured, otherwise, an excessive arrival interval is contained, and meanwhile, the data loss also causes insufficient sample size and influences the evaluation accuracy. The data loss rate of the repaired bus arrival time-space data is controlled within 15 percent.
2. The selected bus route can cover more road grades as much as possible. The design factors, traffic conditions and traffic states of roads of different grades influence the running service of buses, and the buses cover the road grades of urban express ways, arterial roads, secondary arterial roads, branches and the like, so that evaluation samples can be enriched, and more reliability states of the running service of buses are included in the evaluation.
3. The repetition rate of selecting the route of the bus route is reduced. The repeated rate of the selected transit roads of the bus line is too high, so that the bus line is concentrated in a certain area or a plurality of lines, the characteristic of the reliability of the bus running service is not easy to grasp on the whole, and the calculated amount of the evaluation work is increased.
4. Covering more line types. Different types of bus routes have different functions, such as a route with a main commute, a connection route of a large transportation hub, a travel route around a scenic spot, and the like. And by selecting various types of bus routes, the reliability evaluation of the bus running service can be more comprehensive.
Step 2: bus arrival data preprocessing method
The bus arrival data are derived from bus positioning data, and the time when each bus number arrives at each station is extracted. The bus arrival data can accurately record the running track of the bus, and is an important data base for evaluating the reliability of bus running service. However, some data is erroneous or missing due to equipment and the like, and data preprocessing is required.
Aiming at bus arrival data, a data preprocessing method is provided to improve the data quality and ensure the accuracy of bus running service reliability evaluation indexes. The bus arrival data preprocessing method comprises the following steps:
step 2.1, removing repeated data in the bus arrival data
And (3) the first station in the bus arrival data has a repetition phenomenon, the original data are sequenced according to the number of the vehicle and the arrival time, all the data are traversed, and if the serial numbers of two adjacent data stations are the same, only the first data are reserved.
Step 2.2, bus number identification
If the station serial number of the (i+1) th data is smaller than the station serial number of the (i) th data, the two data belong to different train numbers, and a column is added in the data table to be distinguished by different numbers.
Step 2.3, effective bus number identification
The missing data causes missing arrival time of a large number of stations of part of train number, and the accuracy is low after repair. The method and the device identify the effective train number when the station arrival time data loss rate of a certain train number is lower than 50%.
Step 2.4, calculating the travel time of the bus stop interval
Wherein:
-bus travel time for the ith stop section with number k;
-arrival time of the ith station with number k;
-arrival time of the (i+1) th station with number k.
Step 2.5 data Table reconstruction
The data were rearranged using the pivot function of the pandas library in Python as follows.
Wherein:
-arrival time of the nth station with number k;
and 2.6, repairing bus arrival data.
Suppose that a certain train number k lacks the arrival data of site xThen->Calculated according to the following rule。
Wherein:
-number of passes k missing station x arrival data;
-arrival time of station o, the first one not null, forward or backward from station x;
-the bus journey time mean of the ith stop interval.
Step 3: urban bus running service reliability calculation
The reliability of the bus running service is defined as the capability of keeping the arrival interval consistent with the departure interval and the travel time stable in the running process of the bus system, and reliable riding service is provided for passengers. The bus running service reliability comprises two basic indexes of arrival interval reliability based on stations and travel time reliability based on station intervals, and the bus running service reliability based on travel stages and the bus running service reliability based on lines are obtained through weighted calculation and used for providing travel information and line-level bus running service reliability evaluation for passengers.
Step 3.1: site-based arrival interval reliability
The arrival interval reliability is reflected in the degree to which the arrival interval and departure interval remain consistent, and is characterized by the coincidence area of probability density function curves of arrival interval and departure interval in a statistical period (30 minutes). For the convenience of calculation, a frequency distribution histogram approximation solution is adopted. Assuming that the maximum of the arrival interval and departure interval is M seconds, the group distance is D seconds, and n=m// D groups are sharedThe frequency/group spacing of the arrival and departure intervals of each group is DI i I=1, 2, … N and AI i I=1, 2, … N. The calculation formula of the arrival interval reliability is that
The range of the arrival interval reliability is [0,10], the larger the value is, the higher the consistency of the arrival interval and the departure interval is, and the higher the probability that passengers wait to get to buses in expected time is.
Step 3.2: travel time reliability calculation based on site intervals
The interval travel time reliability reflects the ability of a bus to remain stable in running time over the stop interval. For a bus system, along with the interference of factors such as road traffic conditions, passengers getting on and off, the uncertainty of bus travel time is increased, and more complex travel time information is generated. The information entropy is a measure of uncertainty, is large, represents that the interval travel time is in various states, and the bus running reliability is reduced. Thus, the travel time reliability is characterized by means of an information entropy method.
The value of the interval travel time is continuous, and the information entropy calculation of the continuous random variable is complex, so the interval travel time is discretized. Assuming that the maximum value of the interval travel time is M seconds, taking D seconds as the group distance, N=M// D groups are shared, and the probability of the interval travel time of each group is p respectively i I=1, 2, … N. The information entropy calculation formula of the interval travel time is as follows
In particular, when p i When=0, p is i log 2 p i And setting 0 to prevent the calculation from not conforming to the mathematical definition.
The value range of H (T) is 0, ++ infinity a) of the above-mentioned components, to normalize the index value, the base of the logarithm takes N, sinceThe value range is [0,1 ]]。
Finally, the calculation formula of the interval travel time reliability is as follows
The range of travel time reliability is [0,10], the larger the value, the more stable the representative interval travel time, and the more easily the in-vehicle time of the passenger can be estimated.
The arrival interval reliability and the trip time reliability evaluate the bus running service reliability from a microscopic layer, but passengers and managers often pay attention to the bus running service reliability at the trip origin or line level. Therefore, the invention provides a bus running service reliability calculation method based on a trip stage and a line.
Step 3.3: bus operation service reliability calculation based on trip stage
The trip stage refers to the process that passengers arrive at the boarding station and arrive at the alighting station through waiting and riding. When transfer occurs, it is considered a new trip phase. The bus operation service reliability calculation method based on the trip stage comprises the following steps:
RI i,j ＝RI i station to station interval ×W h +RI ij travel time ×W t
Wherein:
RI i,j -trip phase operation service reliability for i-j in OD pair;
RI i station to station interval -arrival interval reliability of departure station i;
W h -a weight value for inter-arrival reliability;
RI ij travel time The travel time reliability of the OD pair i-j;
W t -weight value of travel time reliability.
The arrival interval reliability of the departure station i and the weight of the running service reliability of the OD to the i-j are calculated according to the ratio of the arrival interval mean value of the departure station to the travel time mean value of the OD to the i-j, and the calculation formula is as follows:
wherein:
-counting the average of station i to station intervals over a period of time;
-counting the average value of OD over the travel time of i-j over the period.
Step 3.4: route-based bus operation service reliability calculation
1. Line-to-station interval reliability
Calculating the line-to-station interval reliability every 30 minutes, wherein the line-to-station interval reliability is based on the station interval reliability, and corresponding weight values are given according to the contribution rate of the station to the line, and the calculation formula is as follows:
wherein:
RI line-to-station spacing -bus route to station interval reliability;
W i -weight value of site i;
the data base of the station weight value is the landing amount of different stations of the bus route. The landing amount can be obtained by the card swiping data of the public transport smart card. The time of no special holiday or special management and control measures is selected from at least 5 continuous working days, 6 days, weekends or holidays. Passenger flow data of working days and non-working days are counted respectively, and a bus route station weight value is calculated based on the passenger flow data.
The weight calculating method comprises the following steps:
the number of passengers on the station is taken as the weight value of the station, namely, the larger the number of passengers on the station is, the larger the contribution rate of the reliability of the line-to-station interval is, and the larger the occupied proportion is. The weight value calculation formula is as follows:
wherein:
W i -a site i weight value;
PBV i -the number of boarding persons at station i;
pbv—number of boarding passes;
n is the number of stations contained in the line;
2. route travel time reliability
Calculating the reliability of the line travel time every 30 minutes, wherein the reliability of the line travel time is based on the reliability of the interval travel time, and corresponding weight values are given to the line contribution rate according to the site interval, and the calculation formula is as follows:
wherein:
RI route travel -bus route travel time reliability;
W i,i+1 -weight value of the site interval (i, i+1).
The number of kilometers (PKT) is used as a weight value of a station section, namely, the station section with larger passenger capacity and longer journey has larger contribution rate to the reliability of the line, and the occupied proportion is larger in the calculation of the reliability of the line. The weight value calculation formula is as follows:
wherein:
W ij -a site interval ij weight value;
PKT ij -number of people mileage of the site interval ij;
pkt—number of people mileage of line;
PLV ij -average daily passenger capacity of the site interval ij;
L ij mileage of site interval ij;
n is the number of stations contained in the line;
3. bus running service reliability of line
RI＝RI Line-to-station spacing ×W 1 +RI Line travel time ×W 2
In which W is 1 、W 2 The method is determined by an entropy method and is based on the line-to-station interval reliability and the travel time reliability of each sample line.
Step 4: the bus running service reliability is classified, so that the bus running service reliability can be conveniently applied to the aspects of monitoring, providing trip information and the like.
The bus running service reliability grading method adopts a K-Means clustering method and comprises the following specific steps:
step 4.1: determining the reliability clustering center number N of public transport operation service
K objects are selected from the data as initial cluster centers. Considering the practicability of the reliability evaluation of the bus running service, the clustering center number N is not too large or too small, so N is 3-5.
Step 4.2: calculating contour coefficients
And respectively calculating the profile coefficient under the clustering center number N of the reliability of each bus running service, wherein the larger the profile coefficient is, the more compact the examples in the clusters are, the larger the distance between the clusters is, and the better the clustering effect is. And selecting a clustering result under the N value with the maximum profile coefficient as a bus running service reliability grading standard.
Firstly, providing a bus arrival data preprocessing method based on bus arrival data, and improving the effectiveness of the data; then constructing a quantitative evaluation model of the bus running service reliability from four layers of stations, intervals, travel stages and lines; and finally, determining a bus running service reliability grading standard based on a K-Means clustering algorithm, and providing support for monitoring and improving the bus running service reliability.
Compared with the prior art, the urban public transportation service reliability evaluation method based on public transportation operation data at least comprises the following beneficial effects:
1. the invention constructs the quantitative evaluation model of the bus running service reliability based on four layers of stations, intervals, trip stages and lines, and can meet the requirement of multiple subjects on quantitative evaluation of the bus running service reliability. The reliability of the bus running service is characterized by the reliability of the arrival interval and the reliability of the travel time, the bus running service state is reflected, the waiting and in-transit stages of bus passengers are corresponded, the unified evaluation of bus running and service is realized, and meanwhile, the monitoring evaluation of the bus industry and the provision of the passenger trip information are supported.
2. The bus arrival data preprocessing method based on the bus arrival data evaluates the reliability of the bus operation service, has the advantages of low data cost and high evaluation accuracy, and is suitable for evaluating the reliability of the bus operation service in urban bus networks in large scale, fine granularity and full period.
3. The bus running service reliability grading method provided by the invention can be used for monitoring the bus running service reliability and traveling information service, and has the advantage of wide application.
Drawings
FIG. 1 is a schematic diagram of a multi-subject oriented urban bus operation service reliability evaluation flow;
FIG. 2 is a sample bus route location diagram of an embodiment;
FIG. 3 is a graph of reliability of a 1-way bus running service according to an embodiment;
FIG. 4 is a distribution diagram of the reliability of a 20-way bus running service according to an embodiment;
FIG. 5 is a diagram showing a reliability distribution diagram of a 430-way bus running service according to an embodiment;
Detailed Description
In this example, the reliability of the bus running service of 9 bus lines in beijing city is evaluated as an example, and the reliability of the bus running service is evaluated quantitatively and classified.
The evaluation time range is the working day of 1 month in 2019.
The present example comprises the following steps:
step 1: selecting a sample line;
according to the principle of selecting sample lines to be evaluated, in this example, the sample lines are 9 bus lines in Beijing city, and the route roads comprise high-grade roads such as Beijing harbor Australian expressways, beijing Tibetan expressways, two-loop circuits, three-loop circuits, beijing Tong expressways, changan street and the like, and also comprise branches. The lines comprise suburban commute lines such as 430 lines, urban trunk lines such as 1 line, travel trunk lines such as 20 lines, and the like, and the types of the lines are rich. The location distribution of the sample bus route is shown in fig. 2.
Step 2: and preprocessing bus arrival data.
TABLE 1 bus arrival time-space data sample
If the station 1 and the station 2 arrival data are missing in a certain train number, the original data only comprise the arrival data of the rest stations (as shown in the part below the dotted line of the table 1).
The bus arrival data preprocessing flow can realize the repair of missing arrival data.
Step 3: and calculating the reliability of the bus running service.
And (3) calculating the arrival interval reliability and the travel time reliability of each line according to the bus arrival data of the sample line obtained through the processing in the step (2). And selecting a typical route for display, wherein specific calculation results are shown in fig. 3, 4 and 5, and the section travel time reliability thermodynamic diagrams respectively correspond to the uplink directions of three typical routes of 1 route, 20 routes and 430 routes of Beijing bus.
The least reliable section of the 1-path section travel time is section 22 (Beijing station east-Japanese road), the station section is a traffic jam point through the signal intersection of the large street outside the building country and the east-second ring, and the duration is long, and the section covers the period from the early peak to the late peak. The travel time unreliable periods of other intervals are concentrated on the early-late peak. The unreliable interval of the travel time of 20 routes is mainly from the overpass to the front door east, and the unreliable interval is from the early peak to the late peak. The unreliable travel time interval of the 430-road city entering direction is mainly composed of a subway Tiantong Yuan North station, an east three flag south station, tiantong Yuan Taibanzhuang, a subway standing water bridge station, a standing water bridge North station, a Huiximai street south port, a Pingxiqiao North and a Pingxiqiao bridge south, and is distributed on a site interval passing through a subway station and a large-scale intersection. The unreliable time period of the travel time of the 430-road-city direction is mainly 07:30-10:00 and 16:30-18:00, and the time period is large in landing amount and basically coincides with the early and late peak time of road traffic. The analysis shows that the interval-based travel time reliability model can reflect unreliable intervals and time periods of bus interval travel time, and is consistent with actual conditions.
Step 4: and grading the reliability of the bus running service.
And grading the arrival interval reliability and the travel time reliability by adopting a K-Means clustering method, namely respectively calculating the profile coefficient under the clustering center number N of each bus running service reliability, and selecting the clustering result under the value N with the maximum profile coefficient as a bus running service reliability grading standard. When n=5, the contour coefficients are all larger than 0.6, and the clustering effect is good. The specific classification results are shown in tables 1 and 2, and the arrival interval reliability and the travel time reliability are equally divided into five stages.
TABLE 1 to station gap reliability ranking criteria
| Grade | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Value range | (8,10] | (7,8] | (6,7] | (4,6] | [0,4] |
| Description of the invention | Is very reliable | Is more reliable | Is generally reliable | Unreliable (unreliable) | Very unreliable |
TABLE 2 Stroke time reliability grading Standard
Claims (4)
1. The method for evaluating the reliability of the multidimensional urban public transportation operation service based on the operation data is characterized by comprising the following steps of:
step 1) selecting a sample line to be evaluated from a public transportation network, wherein the evaluation of the sample line comprehensively considers data quality, road grade, repetition rate of the public transportation line and public transportation line type data;
step 2) collecting the evaluation data of the sample line in the step 1), and preprocessing the collected bus arrival data of the sample line, wherein the preprocessing method comprises the steps of de-duplication, identification of bus times and effective bus times, calculation of bus interval travel time, reconstruction of a data table and data repair;
step 3) calculating urban bus running service reliability, including arrival interval reliability based on stations, travel time reliability based on station intervals, bus running service reliability based on travel phases and bus running service reliability based on lines;
step 4) classifying the reliability of urban public transportation operation service based on a K-Means clustering method;
in the step 3), the station-based arrival interval reliability calculation method is as follows;
assuming that the maximum value of the arrival interval and the departure interval is M seconds, taking D seconds as the group distance, n=m// D groups are shared, and the frequency/group distance of the arrival interval and the departure interval of each group is DI, respectively i I=1, 2, … N and AI i I=1, 2, … N; the calculation formula of the arrival interval reliability is that
In the step 3), the travel time reliability calculation method based on the site interval is as follows;
discretizing the interval travel time, assuming the maximum interval travel timeThe value is M seconds, the group distance is D seconds, N=M// D groups are shared, and the probability of interval travel time of each group is p respectively i I=1, 2, … N; finally, the calculation formula of the interval travel time reliability is as follows
When p is i When=0, p is i log 2 p i Setting 0;
in the step 3), the bus running service reliability calculation method based on the trip stage is as follows;
based on the arrival interval reliability and the travel time reliability obtained in the step 3), the calculation formula of the bus running service reliability in the trip stage is as follows
RI i,j ＝RI i station to station interval ×W h +RI ij travel time ×W t
Wherein:
RI i,j -OD operational service reliability for i-j;
RI i station to station interval -arrival interval reliability of departure station i;
W h -a weight value for inter-arrival reliability;
RI ij travel time -OD versus travel time reliability of i-j;
W t -a weight value of travel time reliability;
the arrival interval reliability of the departure station i and the weight of the running service reliability of the OD to the i-j are calculated according to the ratio of the arrival interval average value of the departure station to the travel time average value of the OD to the i-j;
in the step 3), the bus running service reliability calculation method based on the line is as follows;
RI＝RI line-to-station spacing ×W 1 +RI Line travel time ×W 2
In which W is 1 、W 2 The entropy method determines that the line-to-station interval reliability and the travel time reliability of each sample line areA foundation; the number of the passengers on the site is used as the weight value of the site, and the kilometers are used as the weight value of the interval.
2. The method for evaluating the reliability of the operation data-based multidimensional urban public transportation operation service according to claim 1, wherein the method comprises the following steps: in the step 1), the sample bus route selection rule includes the following points:
a) The selected bus route has data quality, and the data loss rate of the repaired bus arrival time-space data is controlled within 15 percent; b) Enabling the selected bus route to cover road grades; c) Reducing the repetition rate of selecting a bus route road; d) Covering the type of line.
3. The method for evaluating the reliability of the operation data-based multidimensional urban public transportation operation service according to claim 1, wherein the method comprises the following steps: in the step 2), the data preprocessing process is as follows:
a) Removing repeated data in bus arrival data; b) Identifying the number of buses; c) Identifying effective bus number, and identifying the effective bus number when the stop-to-stop time data loss rate of a certain bus number is lower than 50%; d) Calculating the travel time of the bus section; e) The arrival data of each train number are arranged according to the time sequence; f) And supplementing missing arrival time according to the bus travel time average value.
4. The method for evaluating the reliability of the operation data-based multidimensional urban public transportation operation service according to claim 1, wherein the method comprises the following steps:
in the step 4), the bus running service reliability grading method is as follows;
and grading the arrival interval reliability and the travel time reliability by adopting a K-Means clustering method, namely respectively calculating the profile coefficient under the clustering center number N of each bus running service reliability, and selecting the clustering result under the value N with the maximum profile coefficient as a bus running service reliability grading standard.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110877673.5A CN113723761B (en) | 2021-08-01 | 2021-08-01 | Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110877673.5A CN113723761B (en) | 2021-08-01 | 2021-08-01 | Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113723761A CN113723761A (en) | 2021-11-30 |
| CN113723761B true CN113723761B (en) | 2024-03-22 |
Family
ID=78674646
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110877673.5A Active CN113723761B (en) | 2021-08-01 | 2021-08-01 | Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113723761B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745089A (en) * | 2013-12-20 | 2014-04-23 | 北京工业大学 | Multi-dimensional public transport operation index evaluation method |
| WO2018032808A1 (en) * | 2016-08-19 | 2018-02-22 | 大连理工大学 | Big data based bus line schedule collaborative optimization method |
| CN111489551A (en) * | 2020-04-23 | 2020-08-04 | 河海大学 | Urban bus line multi-station linkage scheduling optimization method in Internet of vehicles environment |
| CN112150802A (en) * | 2020-08-21 | 2020-12-29 | 同济大学 | Urban road grade division method based on ground bus running state reliability |
-
2021
- 2021-08-01 CN CN202110877673.5A patent/CN113723761B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745089A (en) * | 2013-12-20 | 2014-04-23 | 北京工业大学 | Multi-dimensional public transport operation index evaluation method |
| WO2018032808A1 (en) * | 2016-08-19 | 2018-02-22 | 大连理工大学 | Big data based bus line schedule collaborative optimization method |
| CN111489551A (en) * | 2020-04-23 | 2020-08-04 | 河海大学 | Urban bus line multi-station linkage scheduling optimization method in Internet of vehicles environment |
| CN112150802A (en) * | 2020-08-21 | 2020-12-29 | 同济大学 | Urban road grade division method based on ground bus running state reliability |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113723761A (en) | 2021-11-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112598182B (en) | Intelligent scheduling method and system for rail transit | |
| CN108564226B (en) | Bus route optimization method based on taxi GPS and mobile phone signaling data | |
| CN111091720B (en) | Congestion road section identification method and device based on signaling data and floating car data | |
| Li et al. | Public bus arrival time prediction based on traffic information management system | |
| CN111653099B (en) | Bus passenger flow OD obtaining method based on mobile phone signaling data | |
| CN102521965B (en) | Effect evaluation method of traffic demand management measures based on identification data of license plates | |
| Gong et al. | Hybrid dynamic prediction model of bus arrival time based on weighted of historical and real-time GPS data | |
| CN110555544B (en) | Traffic demand estimation method based on GPS navigation data | |
| CN112036757B (en) | Mobile phone signaling and floating car data-based parking transfer parking lot site selection method | |
| CN112017429B (en) | Overload control monitoring stationing method based on truck GPS data | |
| CN110363985A (en) | A kind of traffic data analyzing method, apparatus, storage medium and equipment | |
| CN110836675A (en) | Decision tree-based automatic driving search decision method | |
| CN111768619A (en) | Express way vehicle OD point determining method based on checkpoint data | |
| Zhou et al. | Research on traffic situation analysis for urban road network through spatiotemporal data mining: a case study of Xi’an, China | |
| CN114139251B (en) | Integral layout method for land ports of border regions | |
| CN104851287A (en) | Method for urban road link travel time detection based on video detector | |
| CN112150802B (en) | Urban road grade division method based on ground bus running state reliability | |
| CN106327867B (en) | Bus punctuation prediction method based on GPS data | |
| Ghasedi et al. | Robust optimization of bus stop placement based on dynamic demand using meta heuristic approaches: a case study in a developing country | |
| CN116090785B (en) | Custom bus planning method for two stages of large-scale movable loose scene | |
| CN113723761B (en) | Multi-dimensional urban public transportation operation service reliability evaluation method based on operation data | |
| Kutlimuratov et al. | Impact of stops for bus delays on routes | |
| CN109633716A (en) | City distribution vehicle driving chain and its characteristic recognition method and equipment based on GPS | |
| CN114742131A (en) | Method for identifying urban excessive tourism area based on pattern mining | |
| CN114118766A (en) | Passenger flow OD algorithm based on bus passenger travel multiple matching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |