CN115099685A - Urban public transport service level evaluation method based on multi-source data - Google Patents
Urban public transport service level evaluation method based on multi-source data Download PDFInfo
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Abstract
The invention discloses a method for evaluating urban public transport service level based on multi-source data, which relates to the technical field of intelligent transportation, and is characterized in that urban public transport operation and planning data are obtained from institutions such as an urban planning management platform and a public transport general company, and indexes influencing the urban public transport service level are calculated according to the operation and planning data; according to the scores of the traffic experts and the citizens, the weight of each index is obtained by using an analytic hierarchy process; by calculating and scoring each index by time intervals, the problem of dynamic evaluation of the urban public transportation service level is solved.
Description
Technical Field
The invention belongs to the field of intelligent transportation, relates to an intelligent transportation technology, and particularly relates to a method for evaluating urban public transportation service level based on multi-source data.
Background
From the perspective of environment and energy, public transportation is a necessary choice and a preferred choice for the development of large-city traffic; buses are used as an important part of public transportation, and the important position is more and more prominent; however, the quality of the urban public transportation service greatly influences the willingness of citizens to adopt public transportation for travel, and the quality of the urban public transportation service also becomes a basic index for guiding traffic policies and financial policies; therefore, how to measure the urban public transportation service level becomes an important problem which needs to be solved urgently;
the existing scheme for measuring the urban public transport service level often has the following problems:
1. partial dynamic indexes such as bus sharing rate, full load rate and the like of urban buses cannot be considered; the dynamic index changes along with the change of time, so the measurement of the urban bus service level needs to be divided into time intervals;
2. when the index weight is calculated, the fact that the public transportation service is served to the residents cannot be considered, so that the scoring of the passengers needs to be calculated when the weight is calculated;
therefore, the urban public transport service level evaluation method based on the multi-source data is provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. The urban public transport service level evaluation method based on the multi-source data obtains urban public transport operation and planning data from an urban planning management platform, a public transport general company and other organs, and calculates indexes influencing the urban public transport service level according to the operation and planning data; according to the scores of the traffic experts and citizens, acquiring the weight of each index by using an analytic hierarchy process; by calculating and scoring each index by time intervals, the problem of dynamic evaluation of the urban public transportation service level is solved.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides a method for evaluating an urban public transportation service level based on multi-source data, including the following steps:
step S1: collecting relevant data of urban public transport service; the city public transport service related data comprises but is not limited to geographic information data of all regions of a city, city public transport station and route data, city public transport scheduling data, city public transport passenger carrying data, passenger riding data, city traffic data and city public transport operation data;
step S2: according to the collected relevant data of the urban public transport service, counting urban public transport service level evaluation indexes; the urban bus service level indexes include, but are not limited to, passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate, population coverage rate and the like of bus service;
step S3: the method comprises the steps of quantifying indexes such as passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate and population coverage rate of bus service, adopting an analytic hierarchy process to score experts and passengers to obtain weights of the indexes, calculating the weighted sum of the indexes, and obtaining the urban bus service level score.
Compared with the prior art, the invention has the beneficial effects that:
1. dynamic indexes such as the occupation rate, the full load rate and the like of the public transport city are provided, and the scores of the urban public transport service levels in different time periods are different, so that more accurate guide indexes are provided for the improvement of the urban public transport service levels;
2. and a scheme of integrating the marking of indexes by traffic experts and passengers and calculating the index weight by using an analytic hierarchy process is provided, so that the authority and the citizenship of the index weight balance is ensured.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a method for evaluating urban public transportation service level based on multi-source data includes the following steps:
step S1: collecting relevant data of urban public transport service; the city public transport service related data comprises but is not limited to geographic information data of all regions of a city, city public transport station and route data, city public transport scheduling data, city public transport passenger carrying data, passenger riding data, city traffic data and city public transport operation data;
step S2: according to the collected urban public transport service related data, counting urban public transport service level evaluation indexes; the urban bus service level indexes include, but are not limited to, passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate, population coverage rate and the like of bus service;
step S3: quantifying the indexes such as passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate and population coverage rate of bus service, scoring the experts and the passengers by adopting an analytic hierarchy process to obtain the weight of each index, calculating the weighted sum of each index, and obtaining the urban bus service level score.
The method for collecting data in step S1 includes:
geographic information data of each region of a city can be acquired from a city planning management platform;
the urban bus stop, route data and urban bus scheduling data can be obtained from a data interface provided by an urban bus head office;
the urban bus passenger carrying data comprises the number of passengers getting on and off the bus at each stop and the total number of passengers at each stop of each bus;
the passenger riding data comprises the walking distance of each passenger, the waiting time of each passenger at the platform and passenger transfer data;
it can be understood that all buses are currently provided with a camera device, face recognition software can be installed on the basis of the camera device, and the number of faces on the bus can be recognized through the face recognition software; thereby obtaining the total number of passengers per station;
further, the number of passengers getting on or off the bus at each station can be obtained by calculating the number difference between every two stations;
it can be understood that the walk data of each passenger cannot be obtained based on the protection of the privacy of the passenger; therefore, statistics is carried out in a fuzzy calculation mode; calculating the average distance from a residential area and an office building near each station to each station through geographic information data of each area, and taking the average distance as the walking distance to each station;
at present, cameras are arranged near all stations, and the time length from appearance of each face to disappearance of each face from the station can be obtained by installing face recognition software in the cameras; the length of the waiting time is the waiting time of each passenger at the platform;
furthermore, all buses support card swiping for getting on, after passengers swipe cards for getting on the bus, a background can record the bus getting-on station and the bus getting-on time of users, and for the same passenger, the background checks whether the bus station taken by each passenger and the bus getting-on time are crossed; the presence of a cross-over, i.e. the presence of a passenger transfer;
the urban traffic data can be acquired from mechanisms such as an urban traffic administration; the urban traffic data is the condition that citizens use the transportation means on roads in each time period of the city;
the urban bus operation data comprises departure time and arrival time of each bus in each driving and the length of each bus operation line; the urban bus operation data can be obtained from a bus head office data interface;
in step S2, the statistical evaluation index of the urban public transportation service level includes:
it can be understood that the walking distance, waiting time and transfer times of the passengers are a data distribution; what is more important to the evaluation of the urban public transport service level is the conditions of longer walking distance, longer waiting time and more transfer times; therefore, the walking distance, waiting time and transfer times of the passengers are divided into an average number and a high figure; wherein, the average is the average value of all the passenger walking distances, waiting time and transfer times; the high digit is the times that the walking distance, the waiting time and the transfer times exceed the threshold value of the walking distance, the threshold value of the waiting time and the threshold value of the transfer times; wherein, the walking distance threshold, the waiting time threshold and the transfer times threshold are set according to the actual conditions of each city;
the travel time of the buses is the average time length of the driving routes required by all the buses after the buses finish driving; and the average time length of one bus station running;
it is understood that the full rate is dynamically changing data; dividing the daily time into four time periods of 8:00-14:00, 14:00-20:00, 20:00-2:00 and 2:00-8: 00; respectively counting the full load rate of the bus in each time period every day; the full load rate is the proportion of buses with the passenger number larger than the rated passenger capacity of the buses to the total number of the buses in each time period; considering that the number of passengers in the bus is different at different stations, the bus with the number of passengers exceeding the rated passenger capacity can be regarded as the bus with the number of passengers larger than the rated passenger capacity of the bus at any station;
the bus operation speed is the average speed of all buses, and the average speed is the average speed of all the buses on the required driving route after the buses finish driving;
the bus sharing rate is the ratio of the running amount of the selected bus to the total running amount in the urban resident trip mode and can be obtained from urban traffic data; it can be understood that the bus sharing rate is time-sharing, so that the daily time is divided into four time periods of 8:00-14:00, 14:00-20:00, 20:00-2:00 and 2:00-8: 00; respectively counting the average value of the bus sharing rate in each time period;
the time coverage rate of the bus service is the time range of the statistical urban bus coverage, and it can be understood that certain intersection exists in the operation time of the bus; therefore, the bus operation time is divided into 24 hours by hour every day, and the buses operated in each hour are countedThe number of buses is marked as Mi, wherein i is hour, and the total number of buses is marked as N; using the formula
Calculating the time coverage rate;
the space coverage rate of the public transportation service is the proportion of urban space covered by the urban public transportation in urban area is counted; specifically, city space in a certain range near each bus stop in a map is used as a coverage space; counting the proportion of the total coverage area in the urban area;
the population coverage rate of the public transportation service is the proportion of urban population covered by the urban public transportation in the urban general population; specifically, residential population, commercial buildings and office area regular population in a certain range near each bus stop in the map are used as covered urban population; counting the total number of coverage population accounting for the total population of the city;
in step S3, the scoring of experts and passengers by an analytic hierarchy process to obtain the weight of each index includes:
the scoring of the experts and the passengers is specifically to invite experts in a plurality of urban traffic fields and visit residents in a plurality of cities in advance and invite the experts and the passengers to score decision indexes; the score of each index represents the importance of the index in the heart of the person being visited; it will be appreciated that at different time periods, the importance of each decision index in the heart of the interviewee is different, and therefore, the interviewee is invited to score each index over different time periods;
the decision indexes comprise passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate and population coverage rate of bus service;
the analytic hierarchy process is AHP (multi-scheme decision method); calculating the weight of each index in each time period by using an analytic hierarchy process;
in each time period, calculating the weighted sum of all indexes according to the calculated weight; the weighted sum is the grade of the urban public transport service level; in each index, the values of the bus sharing rate and the full load rate are the values of the corresponding time period;
the service levels of the urban public transport service in different time periods can be obtained according to the grade of the urban public transport service level; further providing better quality of service.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A multi-source data-based urban public transport service level evaluation method is characterized by comprising the following steps:
step S1: collecting relevant data of urban public transport service; the city public transport service related data comprises geographic information data of all regions of a city, city public transport station and route data, city public transport scheduling data, city public transport passenger carrying data, passenger riding data, city traffic data and city public transport operation data;
step S2: according to the collected relevant data of the urban public transport service, counting urban public transport service level evaluation indexes; the urban public transport service level indexes comprise passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, public transport operation speed, public transport sharing rate, time coverage rate, space coverage rate and population coverage rate of public transport service;
step S3: quantifying the passenger waiting time distribution, the passenger walking distance, the passenger transfer times, the travel time, the full load rate, the bus operation speed, the bus sharing rate, the time coverage rate, the space coverage rate and the population coverage rate of the bus service, adopting an analytic hierarchy process to score the experts and the passengers to obtain the weight of each index, calculating the weighted sum of each index and obtaining the urban bus service level score.
2. The urban public transportation service level evaluation method based on multi-source data according to claim 1, wherein the method for urban public transportation service related data comprises the following steps:
geographic information data of each region of a city are acquired from a city planning management platform;
the urban bus stop and route data and the urban bus scheduling data are obtained from a data interface provided by an urban bus main company;
the urban bus passenger carrying data comprises the number of passengers getting on and off each bus at each station and the total number of passengers at each station;
the passenger riding data comprises the walking distance of each passenger, the waiting time of each passenger at the platform and passenger transfer data;
the number of the human faces on the bus is identified through a camera with face identification software on the bus, and the total number of passengers per station is obtained;
calculating the number difference of passengers between every two stations to obtain the number of passengers getting on and off the bus at each station;
calculating the average distance from residential areas and office buildings near each station to each station from the geographic information data of each area, and taking the average distance as the walking distance from each station;
the method comprises the steps that the camera of face recognition software is installed on a bus station, and the time length from appearance of each face to disappearance of each face from the station is obtained; the time length is the waiting time length of each passenger at the platform;
after passengers swipe cards to get on the bus, a background records the bus getting-on station and the bus getting-on time of the users, and for the same passenger, the background checks whether the bus station taken by each passenger and the bus getting-on time are crossed; the presence of a cross-over, i.e. the presence of a passenger transfer;
the urban traffic data is acquired from an urban traffic management bureau; the urban traffic data is the condition that citizens use the transportation means on roads of each time period of the city;
the urban bus operation data comprises departure time and arrival time of each bus in each running and the length of each bus operation line; the urban public transport operation data can be obtained from a public transport bus data interface.
3. The urban public transportation service level evaluation method based on multi-source data according to claim 1, wherein the statistics of urban public transportation service level evaluation indexes comprises:
dividing the walking distance, waiting time and transfer times of passengers into an average number and a high digit number; wherein, the average is the average of the walking distance, waiting time and transfer times of all passengers; the high digit is the times that the walking distance, the waiting time and the transfer times exceed the walking distance threshold value, the waiting time threshold value and the transfer time threshold value;
the travel time of the buses is the average time length of all the buses needing to run the running routes; and the average time length of all buses running for one stop;
dividing the daily time into four time periods of 8:00-14:00, 14:00-20:00, 20:00-2:00 and 2:00-8: 00; respectively counting the full load rate of the bus in each time period every day; the full load rate is the proportion of buses with the passenger number larger than the rated passenger capacity of the buses to the total number of the buses in each time period; considering that the number of passengers of the bus is different at different stations, the bus with the number of passengers exceeding the rated passenger capacity can be considered as the bus with the number of passengers larger than the rated passenger capacity of the bus at any station;
the bus operation speed is the average speed of all buses, and the average speed is the average speed of all buses on the required driving route after the buses finish driving;
the bus sharing rate is the ratio of the travel volume of the selected bus to the total travel volume in the urban resident travel mode and is obtained from urban traffic data; dividing the daily time into four time periods of 8:00-14:00, 14:00-20:00, 20:00-2:00 and 2:00-8: 00; respectively counting the average value of the bus sharing rate in each time period;
counting the time range covered by urban buses according to the time coverage rate of the bus service;
counting the proportion of urban space covered by urban buses to urban area by the space coverage rate of the bus service;
the population coverage rate of the public transportation service is the proportion of urban population covered by the urban public transportation service in the urban general population.
4. The method of claim 1, wherein the scoring of experts and passengers by an analytic hierarchy process to obtain the weight of each index comprises:
the scoring of the experts and the passengers is specifically to invite experts in a plurality of urban traffic fields and visit residents in a plurality of cities in advance and invite the experts and the passengers to score decision indexes; inviting the interviewee to score each index in different time periods;
the decision indexes comprise passenger waiting time distribution, passenger walking distance, passenger transfer times, travel time, full load rate, bus operation speed, bus sharing rate, time coverage rate, space coverage rate and population coverage rate of bus service;
calculating the weight of each index in each time period by using an analytic hierarchy process;
in each time period, calculating the weighted sum of all indexes according to the calculated weight; the weighted sum is the grade of the urban public transport service level; in each index, the values of the bus sharing rate and the full load rate are the values of the corresponding time period;
and obtaining the service levels of the urban public transport service in different time periods according to the grade of the urban public transport service level.
5. The multi-source data-based urban public transportation service level evaluation method according to claim 3, wherein the analytic hierarchy process is a multi-scheme decision method.
6. The urban public transportation service level evaluation method based on multi-source data according to claim 3, characterized in that the time coverage statistical method is as follows:
dividing the bus into 24 hours every day according to hours, counting the number of buses operated in each hour, marking the number of buses operated in each hour as Mi, wherein i is hour, and marking the total number of buses as N; using the formula
The temporal coverage is calculated.
7. The urban public transportation service level evaluation method based on multi-source data according to claim 3, characterized in that the spatial coverage statistical method is as follows:
taking city space in a certain range near each bus stop in a map as a coverage space; and counting the proportion of the total coverage area in the urban area.
8. The urban public transportation service level evaluation method based on multi-source data according to claim 3, wherein the population coverage statistical method is as follows:
taking residential population, commercial buildings and office area regular population in a certain range near each bus stop in the map as covered urban population; the total number of coverage population of the statistics accounts for the total population of the city.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117495204A (en) * | 2023-12-29 | 2024-02-02 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117495204A (en) * | 2023-12-29 | 2024-02-02 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
| CN117495204B (en) * | 2023-12-29 | 2024-04-16 | 济南市城市交通研究中心有限公司 | Urban bus running efficiency evaluation method and system based on data analysis |
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Application publication date: 20220923 |