CN104573849A - Bus dispatch optimization method for predicting passenger flow based on ARIMA model - Google Patents
Bus dispatch optimization method for predicting passenger flow based on ARIMA model Download PDFInfo
- Publication number
- CN104573849A CN104573849A CN201410764609.6A CN201410764609A CN104573849A CN 104573849 A CN104573849 A CN 104573849A CN 201410764609 A CN201410764609 A CN 201410764609A CN 104573849 A CN104573849 A CN 104573849A
- Authority
- CN
- China
- Prior art keywords
- period
- bus
- model
- flow
- passenger flow
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 title claims abstract description 21
- 238000005457 optimization Methods 0.000 title claims abstract description 21
- 238000005311 autocorrelation function Methods 0.000 claims description 12
- 238000007689 inspection Methods 0.000 claims description 12
- 230000014509 gene expression Effects 0.000 claims description 3
- 238000000926 separation method Methods 0.000 abstract 1
- 230000007812 deficiency Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000002699 waste material Substances 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a bus dispatch optimization method for predicting passenger flow based on an ARIMA model. The bus dispatch optimization method for predicting the passenger flow based on the ARIMA model comprises the following steps: mainly establishing an optimal ARIMA model to predict passenger flow volume of a period of time; calculating number of passengers getting on the bus and the number of passengers getting off the bus in each period and each station according to the predicted passenger flow; performing a curve-fitting by using an ten-order polynomial to obtain a function of the number of passengers getting on the bus and the function of the number of passengers getting off the bus in related time of the each station, thereby representing data relative to the bus dispatch through two functions, for example, a straight line parallel to a y-axis can be used to divide the function curve of the number of passengers getting on the bus and a separation distance of adjacent two straight lines is departure interval of two regular buses; establishing two satisfaction objective combination optimization model in different intervals; obtaining departure times and departure interval of each period; and besides, formulating a whole day bus dispatch time table considering a problem of over-period of the bus during driving; improving and adjusting the conventional departure time table in real time and obtaining a best dispatch plan.
Description
Technical field
The present invention relates to bus scheduling method field, specifically a kind of bus dispatching optimization method based on ARIMA model prediction passenger flow.
Background technology
Bus passenger flow on the line, on direction, all have DYNAMIC DISTRIBUTION on the time, on place, on section, has certain periodic law, simultaneously by the impact of random fluctuation.If analyzed the periodic regularity of passenger flow, important certainty information can only be extracted, too much waste is existed to randomness information, and the interactively that effective method cannot be provided to judge between each certainty factor, then cause the fitting precision of model not high.
Bus passenger flow prediction is the basis of bus dynamic dispatching, when predicting in advance, grasping passenger flow Changing Pattern, scientifically could formulate the plan of public transit vehicle running scheduling, and arranged rational is dispatched, and improves operation plan in time.But in the dispatching operation of reality, be mostly the change of micro-judgment passenger flow relying on yardman, driver, thus formulate the departure interval, carry out management and running.Although have certain practicality, there is obvious deficiency, often there is comparatively big error with actual conditions, cause passenger to wait less than car for a long time, or the situation of stayer.
The data accuracy that traditional bus passenger flow data acquisition obtains is not high, now a lot of public transport uses passenger flow counting equipment, every bar circuit can very real-time getting at the passenger flow data of each website, and the accuracy of passenger flow data also reaches certain level.But it is not enough to the utilization of these passenger flow datas.
summary of the inventionthe object of this invention is to provide a kind of bus dispatching optimization method based on ARIMA model prediction passenger flow, to solve prior art Problems existing.
In order to achieve the above object, the technical solution adopted in the present invention is:
Based on the bus dispatching optimization method of ARIMA model prediction passenger flow, it is characterized in that: comprise the following steps:
(1), in conjunction with public bus network station data, gps data, carry out standardization to the real-time passenger flow data got and passenger flow historical data to arrange, arrange out the volume of the flow of passengers, each section volume of the flow of passengers of each website in the volume of the flow of passengers of circuit whole day in one period, each period;
(2), to passenger flow data sequence carry out pre-service, judge whether this sequence is stationary random sequence, if non-stationary series, then carries out tranquilization process to this sequence, make the sequence after processing be steady nonwhite noise sequence;
(3), calculate autocorrelation function and the partial autocorrelation function of passenger flow sequence, according to autocorrelation function and partial autocorrelation function, choose corresponding ARIMA model and carry out matching;
(4), to the ARIMA model set up carry out parameter estimation and applicability inspection, if the model of matching is not by inspection, returns step (3), reselect model and carry out matching;
(5), model optimization: if the model of matching is by inspection, still return step (3), take into full account various possibility, set up multiple model of fit, from all by choosing optimization model the model of inspection;
(6), utilize the optimum ARIMA model simulated, the volume of the flow of passengers in one period is predicted; Utilize the public bus network volume of the flow of passengers at times of prediction, according to passenger flow Changing Pattern, set up two satisfaction objective cross Optimized models at times of Prescribed Properties, obtain optimum solution, obtain dispatching a car number of times and departure interval of day part, consider the problem across the period in vehicle travel process simultaneously, make whole day bus scheduling timetable;
(7), according to the whole day volume of the flow of passengers division period completely, in the same time period, the departure interval is identical, and the departure interval namely in each time period is equispaced;
(8), according to the volume of the flow of passengers predicted, calculate the total number of getting on the bus of each website in day part and number of getting off, carry out curve fitting with ten rank polynomial expressions, obtain get on the bus ridership function and the get off ridership function of each website about the time;
(9) objective optimization function, is set up: the extent function of occupant comfort extent function, public transport company;
(10), constraint condition is considered: for public transport company's interests, the average load factor of the public transit vehicle that each period slave site is outputed can not lower than a value; For the interests of passenger, day part passenger can not exceed restriction in the stand-by period of platform, and then satisfaction can corresponding reduction to exceed the upper limit;
(11), according to the objective function set up and constraint condition, utilize Matlab mathematical tool, obtain the optimum solution of Nonlinear Parameter Optimized model, obtain dispatching a car number of times and departure interval of each period;
(12) dispatching a car number of times and departure interval, according to the day part solved, consider the problem across the period in vehicle travel process, draw dispatching a car number of times and departure interval of day part optimum, the volume of the flow of passengers generally in adjacent two periods is widely different; If the last period comparatively rear period is ebb, the vehicle sent in the previous time period, also in traveling after this time period terminates, then the passenger flow of this car after part website can run in the period, can cause the delay of passenger at these websites; Follow the tracks of according to one way is always consuming time each car sent in each period, find out in the period and send vehicle across the first car of reaching terminal after the period; Then the departure interval after this regular bus is adjusted to departure interval of next period, can solve the crowded scene do not expected that may occur across period vehicle;
And if the last period comparatively rear period is peak, vehicle number namely needed for the last period is more than a rear period, so the last period send across period car after still dispatch a car by the departure interval in the last period, until a rear period just changes the departure interval;
(13) the optimum departure interval taking into account passenger and company interest, according to step (12) obtained, number of times of dispatching a car, make suitable departure time-table.
The present invention is in passenger flow Short-term Forecasting Model, on the basis of the higher passenger flow data of accuracy obtained, utilize past and present passenger flow data, taken into full account passenger flow correlativity in time, periodically simultaneously, consider again the impact of random fluctuation, by the randomness to passenger flow data, stationarity and periodically analyze, set up optimum ARIMA time series models, applicability and the precision of prediction of model are better, in bus dispatching is optimized, bus scheduling process is regarded as Bi-objective nonlinear programming process, utilize get on the bus ridership function and the ridership function of getting off of curve, on the basis considering passenger and public transport company's interests, set up two satisfaction target (public transport company's satisfactions at times of bus dispatching, passenger satisfaction) Combinatorial Optimization Model, solve optimum solution, obtain dispatching a car number of times and departure interval of day part, consider the problem across the period in vehicle travel process simultaneously, make whole day bus scheduling timetable, simultaneously can be perfect in time to existing departure time-table, adjustment, obtain optimal scheduling plan.
Accompanying drawing explanation
Fig. 1 is the inventive method process flow diagram.
Embodiment
Shown in Figure 1, based on the bus dispatching optimization method of ARIMA model prediction passenger flow, comprise the following steps:
(1), in conjunction with public bus network station data, gps data, carry out standardization to the real-time passenger flow data got and passenger flow historical data to arrange, arrange out the volume of the flow of passengers, each section volume of the flow of passengers of each website in the volume of the flow of passengers of circuit whole day in one period, each period;
(2), to passenger flow data sequence carry out pre-service, judge whether this sequence is stationary random sequence, if non-stationary series, then carries out tranquilization process to this sequence, make the sequence after processing be steady nonwhite noise sequence;
(3), calculate autocorrelation function and the partial autocorrelation function of passenger flow sequence, according to autocorrelation function and partial autocorrelation function, choose corresponding ARIMA model and carry out matching;
(4), to the ARIMA model set up carry out parameter estimation and applicability inspection, if the model of matching is not by inspection, returns step (3), reselect model and carry out matching;
(5), model optimization: if the model of matching is by inspection, still return step (3), take into full account various possibility, set up multiple model of fit, from all by choosing optimization model the model of inspection;
(6), utilize the optimum ARIMA model simulated, the volume of the flow of passengers in one period is predicted; Utilize the public bus network volume of the flow of passengers at times of prediction, according to passenger flow Changing Pattern, set up two satisfaction objective cross Optimized models at times of Prescribed Properties, obtain optimum solution, obtain dispatching a car number of times and departure interval of day part, consider the problem across the period in vehicle travel process simultaneously, make whole day bus scheduling timetable;
(7), according to the whole day volume of the flow of passengers division period completely, in the same time period, the departure interval is identical, and the departure interval namely in each time period is equispaced;
(8), according to the volume of the flow of passengers predicted, calculate the total number of getting on the bus of each website in day part and number of getting off, carry out curve fitting with ten rank polynomial expressions, obtain get on the bus ridership function and the get off ridership function of each website about the time;
(9) objective optimization function, is set up: the extent function of occupant comfort extent function, public transport company;
(10), constraint condition is considered: for public transport company's interests, the average load factor of the public transit vehicle that each period slave site is outputed can not lower than a value; For the interests of passenger, day part passenger can not exceed restriction in the stand-by period of platform, and then satisfaction can corresponding reduction to exceed the upper limit;
(11), according to the objective function set up and constraint condition, utilize Matlab mathematical tool, obtain the optimum solution of Nonlinear Parameter Optimized model, obtain dispatching a car number of times and departure interval of each period;
(12) dispatching a car number of times and departure interval, according to the day part solved, consider the problem across the period in vehicle travel process, draw dispatching a car number of times and departure interval of day part optimum, the volume of the flow of passengers generally in adjacent two periods is widely different; If the last period comparatively rear period is ebb, the vehicle sent in the previous time period, also in traveling after this time period terminates, then the passenger flow of this car after part website can run in the period, can cause the delay of passenger at these websites; Follow the tracks of according to one way is always consuming time each car sent in each period, find out in the period and send vehicle across the first car of reaching terminal after the period; Then the departure interval after this regular bus is adjusted to departure interval of next period, can solve the crowded scene do not expected that may occur across period vehicle;
And if the last period comparatively rear period is peak, vehicle number namely needed for the last period is more than a rear period, so the last period send across period car after still dispatch a car by the departure interval in the last period, until a rear period just changes the departure interval;
(13) the optimum departure interval taking into account passenger and company interest, according to step (12) obtained, number of times of dispatching a car, make suitable departure time-table.
The present invention can make up the deficiency to acquired bus passenger flow data separate.By utilizing the passenger flow data information obtained from bus passenger flow counting assembly, suitable parameter is selected to set up the volume of the flow of passengers in optimum ARIMA model prediction following a period of time, and utilize the volume of the flow of passengers of prediction, set up two satisfaction target (public transport company's satisfaction, passenger satisfaction) Combinatorial Optimization Model at times, obtain taking into account the optimum departure interval of passenger and company interest, number of times of dispatching a car, make suitable departure time-table.
Claims (1)
1., based on the bus dispatching optimization method of ARIMA model prediction passenger flow, it is characterized in that: comprise the following steps:
(1), in conjunction with public bus network station data, gps data, carry out standardization to the real-time passenger flow data got and passenger flow historical data to arrange, arrange out the volume of the flow of passengers, each section volume of the flow of passengers of each website in the volume of the flow of passengers of circuit whole day in one period, each period;
(2), to passenger flow data sequence carry out pre-service, judge whether this sequence is stationary random sequence, if non-stationary series, then carries out tranquilization process to this sequence, make the sequence after processing be steady nonwhite noise sequence;
(3), calculate autocorrelation function and the partial autocorrelation function of passenger flow sequence, according to autocorrelation function and partial autocorrelation function, choose corresponding ARIMA model and carry out matching;
(4), to the ARIMA model set up carry out parameter estimation and applicability inspection, if the model of matching is not by inspection, returns step (3), reselect model and carry out matching;
(5), model optimization: if the model of matching is by inspection, still return step (3), take into full account various possibility, set up multiple model of fit, from all by choosing optimization model the model of inspection;
(6), utilize the optimum ARIMA model simulated, the volume of the flow of passengers in one period is predicted; Utilize the public bus network volume of the flow of passengers at times of prediction, according to passenger flow Changing Pattern, set up two satisfaction objective cross Optimized models at times of Prescribed Properties, obtain optimum solution, obtain dispatching a car number of times and departure interval of day part, consider the problem across the period in vehicle travel process simultaneously, make whole day bus scheduling timetable;
(7), according to the whole day volume of the flow of passengers division period completely, in the same time period, the departure interval is identical, and the departure interval namely in each time period is equispaced;
(8), according to the volume of the flow of passengers predicted, calculate the total number of getting on the bus of each website in day part and number of getting off, carry out curve fitting with ten rank polynomial expressions, obtain get on the bus ridership function and the get off ridership function of each website about the time;
(9) objective optimization function, is set up: the extent function of occupant comfort extent function, public transport company;
(10), constraint condition is considered: for public transport company's interests, the average load factor of the public transit vehicle that each period slave site is outputed can not lower than a value; For the interests of passenger, day part passenger can not exceed restriction in the stand-by period of platform, and then satisfaction can corresponding reduction to exceed the upper limit;
(11), according to the objective function set up and constraint condition, utilize Matlab mathematical tool, obtain the optimum solution of Nonlinear Parameter Optimized model, obtain dispatching a car number of times and departure interval of each period;
(12) dispatching a car number of times and departure interval, according to the day part solved, consider the problem across the period in vehicle travel process, draw dispatching a car number of times and departure interval of day part optimum, the volume of the flow of passengers generally in adjacent two periods is widely different; If the last period comparatively rear period is ebb, the vehicle sent in the previous time period, also in traveling after this time period terminates, then the passenger flow of this car after part website can run in the period, can cause the delay of passenger at these websites; Follow the tracks of according to one way is always consuming time each car sent in each period, find out in the period and send vehicle across the first car of reaching terminal after the period; Then the departure interval after this regular bus is adjusted to departure interval of next period, can solve the crowded scene do not expected that may occur across period vehicle;
And if the last period comparatively rear period is peak, vehicle number namely needed for the last period is more than a rear period, so the last period send across period car after still dispatch a car by the departure interval in the last period, until a rear period just changes the departure interval;
(13) the optimum departure interval taking into account passenger and company interest, according to step (12) obtained, number of times of dispatching a car, make suitable departure time-table.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410764609.6A CN104573849A (en) | 2014-12-12 | 2014-12-12 | Bus dispatch optimization method for predicting passenger flow based on ARIMA model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410764609.6A CN104573849A (en) | 2014-12-12 | 2014-12-12 | Bus dispatch optimization method for predicting passenger flow based on ARIMA model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104573849A true CN104573849A (en) | 2015-04-29 |
Family
ID=53089864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410764609.6A Pending CN104573849A (en) | 2014-12-12 | 2014-12-12 | Bus dispatch optimization method for predicting passenger flow based on ARIMA model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104573849A (en) |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321341A (en) * | 2015-12-03 | 2016-02-10 | 北京航空航天大学 | Resource supply method based on city moving mode |
| CN105741538A (en) * | 2016-03-31 | 2016-07-06 | 郑州天迈科技股份有限公司 | Bus fast dispatching system and dispatching method thereof |
| CN105810002A (en) * | 2016-04-20 | 2016-07-27 | 浙江大学 | Self-optimizable efficient public transit system based on Internet of Things and neural network |
| CN106297273A (en) * | 2016-09-29 | 2017-01-04 | 百度在线网络技术（北京）有限公司 | The processing method and processing device of regular bus route |
| CN106650976A (en) * | 2015-10-29 | 2017-05-10 | 深圳市综合交通运行指挥中心 | Travel analysis and forecasting method and system, and travel analysis and forecasting method and system based on IC card |
| CN106781432A (en) * | 2016-11-16 | 2017-05-31 | 青岛海信网络科技股份有限公司 | A kind of method and device of public traffic vehicles schedule |
| CN106845714A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | A kind of monthly passenger flow method of ARIMA model prediction urban track traffics based on seasonal index number |
| CN106991492A (en) * | 2017-03-13 | 2017-07-28 | 北京交通大学 | A kind of boreal climate fills pure electric bus transit operation optimization method soon |
| CN107194491A (en) * | 2017-04-06 | 2017-09-22 | 广东工业大学 | A kind of dynamic dispatching method based on Forecasting of Travel Time between bus passenger flow and station |
| CN107248280A (en) * | 2017-08-07 | 2017-10-13 | 江苏南大苏富特智能交通科技有限公司 | A kind of bus based on real-time passenger flow estimation is dispatched a car dispatching method |
| CN107316090A (en) * | 2017-06-09 | 2017-11-03 | 安徽富煌科技股份有限公司 | A kind of intelligent bus booking method |
| CN107330547A (en) * | 2017-06-15 | 2017-11-07 | 重庆交通大学 | A kind of city bus dynamic dispatching optimization method and system |
| CN107633680A (en) * | 2016-07-12 | 2018-01-26 | 阿里巴巴集团控股有限公司 | Acquisition methods, device, equipment and the system of trip data |
| CN107766981A (en) * | 2017-10-24 | 2018-03-06 | 东南大学 | Bus passenger flow OD estimations and Forecasting Approach for Short-term based on website WiFi |
| CN108197724A (en) * | 2017-12-02 | 2018-06-22 | 山东大学 | Efficiency weight calculation and public transport scheme performance estimating method in public transport complex network |
| CN108491958A (en) * | 2018-02-11 | 2018-09-04 | 浙江工业大学 | A kind of bus passenger flow string invariant prediction technique in short-term |
| CN109255972A (en) * | 2018-09-11 | 2019-01-22 | 北京市交通信息中心 | A kind of optimization method of the fixed route timetable of the routine bus system based on big data |
| CN110443657A (en) * | 2019-08-19 | 2019-11-12 | 泰康保险集团股份有限公司 | Customer traffic data processing method, device, electronic equipment and readable medium |
| 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 |
| CN111612226A (en) * | 2020-05-12 | 2020-09-01 | 中国电子科技集团公司电子科学研究院 | Group daily average arrival number prediction method and device based on hybrid model |
| CN111612327A (en) * | 2020-05-18 | 2020-09-01 | 广州云从人工智能技术有限公司 | Scheduling method, system, machine readable medium and equipment |
| CN111815166A (en) * | 2020-07-09 | 2020-10-23 | 北京交通大学 | Cooperative optimization method and device of urban public transport system based on passenger flow distribution |
| CN112347864A (en) * | 2020-10-21 | 2021-02-09 | 北京市地铁运营有限公司地铁运营技术研发中心 | Method, device, equipment and system for sensing and inducing rail transit passenger flow |
| CN112365130A (en) * | 2020-10-21 | 2021-02-12 | 北京市地铁运营有限公司地铁运营技术研发中心 | Rail transit integrated intelligent control dispatching system |
| CN112396271A (en) * | 2019-08-16 | 2021-02-23 | 顺丰科技有限公司 | Vehicle shift scheduling time reduction method and system |
| CN112465396A (en) * | 2020-12-15 | 2021-03-09 | 中北大学 | Bus scheduling method and system based on station events along line |
| CN112580962A (en) * | 2020-12-14 | 2021-03-30 | 俞涛 | Traffic optimization scheduling system and method based on smart city |
| CN112749862A (en) * | 2019-10-31 | 2021-05-04 | 株洲中车时代电气股份有限公司 | Subway self-adaptive scheduling method based on passenger flow classification and electronic equipment |
| CN114066032A (en) * | 2021-11-08 | 2022-02-18 | 郑州天迈科技股份有限公司 | Automatic compiling method for bus timetable |
| CN114241770A (en) * | 2021-12-21 | 2022-03-25 | 杭州图软科技有限公司 | Bus scheduling method and system based on accurate real-time information |
| CN116151598A (en) * | 2023-04-20 | 2023-05-23 | 武汉嘉联瑞通应用科技有限公司 | Intelligent bus balanced scheduling method and system based on passenger flow synchronous optimization |
| CN116523267A (en) * | 2023-06-28 | 2023-08-01 | 深圳市海成智联科技有限公司 | Vehicle dispatching optimization method, system and storage medium suitable for rail transit |
| CN117132083A (en) * | 2023-10-20 | 2023-11-28 | 惠州市金雄城建筑科技有限公司 | Urban rail transit hub operation and maintenance management system based on BIM |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090327206A1 (en) * | 2008-06-27 | 2009-12-31 | Microsoft Corporation | Forecasting by blending algorithms to optimize near term and long term predictions |
| CN102355381A (en) * | 2011-08-18 | 2012-02-15 | 网宿科技股份有限公司 | Method and system for predicting flow of self-adaptive differential auto-regression moving average model |
| CN103886743A (en) * | 2014-04-16 | 2014-06-25 | 湖州朗讯信息科技有限公司 | Urban public transport operation scheme optimization method |
| CN104200286A (en) * | 2014-09-10 | 2014-12-10 | 东南大学 | Urban railway transit timetable optimizing technology application framework |
-
2014
- 2014-12-12 CN CN201410764609.6A patent/CN104573849A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090327206A1 (en) * | 2008-06-27 | 2009-12-31 | Microsoft Corporation | Forecasting by blending algorithms to optimize near term and long term predictions |
| CN102355381A (en) * | 2011-08-18 | 2012-02-15 | 网宿科技股份有限公司 | Method and system for predicting flow of self-adaptive differential auto-regression moving average model |
| CN103886743A (en) * | 2014-04-16 | 2014-06-25 | 湖州朗讯信息科技有限公司 | Urban public transport operation scheme optimization method |
| CN104200286A (en) * | 2014-09-10 | 2014-12-10 | 东南大学 | Urban railway transit timetable optimizing technology application framework |
Cited By (44)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650976A (en) * | 2015-10-29 | 2017-05-10 | 深圳市综合交通运行指挥中心 | Travel analysis and forecasting method and system, and travel analysis and forecasting method and system based on IC card |
| CN105321341A (en) * | 2015-12-03 | 2016-02-10 | 北京航空航天大学 | Resource supply method based on city moving mode |
| CN105741538A (en) * | 2016-03-31 | 2016-07-06 | 郑州天迈科技股份有限公司 | Bus fast dispatching system and dispatching method thereof |
| CN105810002A (en) * | 2016-04-20 | 2016-07-27 | 浙江大学 | Self-optimizable efficient public transit system based on Internet of Things and neural network |
| CN107633680A (en) * | 2016-07-12 | 2018-01-26 | 阿里巴巴集团控股有限公司 | Acquisition methods, device, equipment and the system of trip data |
| CN106297273A (en) * | 2016-09-29 | 2017-01-04 | 百度在线网络技术（北京）有限公司 | The processing method and processing device of regular bus route |
| CN106781432A (en) * | 2016-11-16 | 2017-05-31 | 青岛海信网络科技股份有限公司 | A kind of method and device of public traffic vehicles schedule |
| CN106845714A (en) * | 2017-01-24 | 2017-06-13 | 东南大学 | A kind of monthly passenger flow method of ARIMA model prediction urban track traffics based on seasonal index number |
| CN106991492A (en) * | 2017-03-13 | 2017-07-28 | 北京交通大学 | A kind of boreal climate fills pure electric bus transit operation optimization method soon |
| CN106991492B (en) * | 2017-03-13 | 2020-09-08 | 北京交通大学 | Northern climate quick-charging pure electric bus operation scheduling optimization method |
| CN107194491A (en) * | 2017-04-06 | 2017-09-22 | 广东工业大学 | A kind of dynamic dispatching method based on Forecasting of Travel Time between bus passenger flow and station |
| CN107316090A (en) * | 2017-06-09 | 2017-11-03 | 安徽富煌科技股份有限公司 | A kind of intelligent bus booking method |
| CN107330547A (en) * | 2017-06-15 | 2017-11-07 | 重庆交通大学 | A kind of city bus dynamic dispatching optimization method and system |
| CN107330547B (en) * | 2017-06-15 | 2020-07-28 | 重庆交通大学 | Urban public transport dynamic scheduling optimization method and system |
| CN107248280A (en) * | 2017-08-07 | 2017-10-13 | 江苏南大苏富特智能交通科技有限公司 | A kind of bus based on real-time passenger flow estimation is dispatched a car dispatching method |
| CN107766981A (en) * | 2017-10-24 | 2018-03-06 | 东南大学 | Bus passenger flow OD estimations and Forecasting Approach for Short-term based on website WiFi |
| CN107766981B (en) * | 2017-10-24 | 2021-05-11 | 东南大学 | Bus passenger flow OD estimation and short-time prediction method based on station WiFi |
| CN108197724A (en) * | 2017-12-02 | 2018-06-22 | 山东大学 | Efficiency weight calculation and public transport scheme performance estimating method in public transport complex network |
| CN108491958A (en) * | 2018-02-11 | 2018-09-04 | 浙江工业大学 | A kind of bus passenger flow string invariant prediction technique in short-term |
| CN108491958B (en) * | 2018-02-11 | 2021-05-07 | 浙江工业大学 | Short-time bus passenger flow chord invariant prediction method |
| CN109255972B (en) * | 2018-09-11 | 2019-12-10 | 北京市交通信息中心 | Optimization method of ground public transport fixed line timetable based on big data |
| CN109255972A (en) * | 2018-09-11 | 2019-01-22 | 北京市交通信息中心 | A kind of optimization method of the fixed route timetable of the routine bus system based on big data |
| CN112396271A (en) * | 2019-08-16 | 2021-02-23 | 顺丰科技有限公司 | Vehicle shift scheduling time reduction method and system |
| CN110443657A (en) * | 2019-08-19 | 2019-11-12 | 泰康保险集团股份有限公司 | Customer traffic data processing method, device, electronic equipment and readable medium |
| CN110443657B (en) * | 2019-08-19 | 2022-03-18 | 泰康保险集团股份有限公司 | Client flow data processing method and device, electronic equipment and readable medium |
| CN112749862A (en) * | 2019-10-31 | 2021-05-04 | 株洲中车时代电气股份有限公司 | Subway self-adaptive scheduling method based on passenger flow classification and electronic equipment |
| 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 |
| CN111612226A (en) * | 2020-05-12 | 2020-09-01 | 中国电子科技集团公司电子科学研究院 | Group daily average arrival number prediction method and device based on hybrid model |
| CN111612327A (en) * | 2020-05-18 | 2020-09-01 | 广州云从人工智能技术有限公司 | Scheduling method, system, machine readable medium and equipment |
| CN111612327B (en) * | 2020-05-18 | 2021-05-18 | 广州云从人工智能技术有限公司 | Scheduling method, system, machine readable medium and equipment |
| CN111815166B (en) * | 2020-07-09 | 2023-10-17 | 北京交通大学 | Collaborative optimization method and device for urban public transport system based on passenger flow distribution |
| CN111815166A (en) * | 2020-07-09 | 2020-10-23 | 北京交通大学 | Cooperative optimization method and device of urban public transport system based on passenger flow distribution |
| CN112347864A (en) * | 2020-10-21 | 2021-02-09 | 北京市地铁运营有限公司地铁运营技术研发中心 | Method, device, equipment and system for sensing and inducing rail transit passenger flow |
| CN112365130A (en) * | 2020-10-21 | 2021-02-12 | 北京市地铁运营有限公司地铁运营技术研发中心 | Rail transit integrated intelligent control dispatching system |
| CN112365130B (en) * | 2020-10-21 | 2024-04-16 | 北京市地铁运营有限公司地铁运营技术研发中心 | Rail transit integrated intelligent control scheduling system |
| CN112580962A (en) * | 2020-12-14 | 2021-03-30 | 俞涛 | Traffic optimization scheduling system and method based on smart city |
| CN112465396A (en) * | 2020-12-15 | 2021-03-09 | 中北大学 | Bus scheduling method and system based on station events along line |
| CN114066032A (en) * | 2021-11-08 | 2022-02-18 | 郑州天迈科技股份有限公司 | Automatic compiling method for bus timetable |
| CN114241770A (en) * | 2021-12-21 | 2022-03-25 | 杭州图软科技有限公司 | Bus scheduling method and system based on accurate real-time information |
| CN116151598A (en) * | 2023-04-20 | 2023-05-23 | 武汉嘉联瑞通应用科技有限公司 | Intelligent bus balanced scheduling method and system based on passenger flow synchronous optimization |
| CN116523267A (en) * | 2023-06-28 | 2023-08-01 | 深圳市海成智联科技有限公司 | Vehicle dispatching optimization method, system and storage medium suitable for rail transit |
| CN116523267B (en) * | 2023-06-28 | 2024-01-12 | 深圳市海成智联科技有限公司 | Vehicle dispatching optimization method, system and storage medium suitable for rail transit |
| CN117132083A (en) * | 2023-10-20 | 2023-11-28 | 惠州市金雄城建筑科技有限公司 | Urban rail transit hub operation and maintenance management system based on BIM |
| CN117132083B (en) * | 2023-10-20 | 2024-01-30 | 惠州市金雄城建筑科技有限公司 | Urban rail transit hub operation and maintenance management system based on BIM |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104573849A (en) | Bus dispatch optimization method for predicting passenger flow based on ARIMA model | |
| CN104064028B (en) | Based on public transport arrival time Forecasting Methodology and the system of multiple information data | |
| CN108830543B (en) | Travel reminding method and terminal equipment | |
| Cortés et al. | Commercial bus speed diagnosis based on GPS-monitored data | |
| CN102157075B (en) | Method for predicting bus arrivals | |
| Liu et al. | Willingness to board: a novel concept for modeling queuing up passengers | |
| CN108151756A (en) | The travel time computation method and device of a kind of urban public tranlport system | |
| CN104884900A (en) | Travel time information providing apparatus and travel time information providing method | |
| CN103208034B (en) | A kind of track traffic for passenger flow forecast of distribution model is set up and Forecasting Methodology | |
| CN104157132A (en) | Self-adaptive dynamic optimization method for bus dispatching timetable | |
| CN105005959A (en) | Bus passenger flow volume calculation and statistical analysis method with characteristic attributes | |
| CN105512741A (en) | Bus passenger traffic combined prediction method | |
| CN105243868B (en) | A kind of public transit vehicle arrival time Forecasting Methodology and device | |
| CN102708701A (en) | System and method for predicting arrival time of buses in real time | |
| CN103903430A (en) | Dynamic fusion type travel time predicting method with multi-source and isomorphic data adopted | |
| CN102737500B (en) | Method for acquiring arrival interval reliability of urban bus | |
| CN104424812A (en) | Bus arrival time prediction system and method | |
| CN109886477B (en) | Water pollution prediction method and device and electronic equipment | |
| CN109272168A (en) | A kind of urban track traffic for passenger flow trend method | |
| CN108615391A (en) | A kind of intelligent bus dispatching method, system and medium | |
| CN108564228A (en) | A method of based on the temporal aspect predicted orbit traffic OD volumes of the flow of passengers | |
| CN103632546A (en) | Floating car data-based urban road traffic accident influence prediction method | |
| CN103253189A (en) | Vehicle maintenance reminding method based on Cloud service and vehicle-mounted terminal device thereof | |
| CN106650976A (en) | Travel analysis and forecasting method and system, and travel analysis and forecasting method and system based on IC card | |
| CN108831181A (en) | A kind of method for establishing model and system for Forecasting of Travel Time for Public Transport Vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150429 |
|
| RJ01 | Rejection of invention patent application after publication |