CN115457793B - Method and system for non-uniform departure of origin stations in bus dispatching - Google Patents
Method and system for non-uniform departure of origin stations in bus dispatching Download PDFInfo
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Abstract
The invention relates to a method and a system for non-uniform departure of an origin station in bus dispatching. The system comprises an input module, a control module and a control module, wherein the input module is used for inputting data parameters; the data processing module is used for analyzing and processing data; the storage module is used for storing historical data; the output module is used for outputting data; the display module is used for displaying different types of data; the data processing module is electrically connected with the input module, the storage module, the output module and the display module. The invention not only reduces the time of passengers waiting for the vehicle and optimizes the traveling experience of the passengers, but also reduces the operation cost and improves the capability of dispatching strain while ensuring the transport capacity from the aspect of operation management.
Description
Technical Field
The invention relates to the technical field of bus dispatching, in particular to a method and a system for non-uniform departure of an initiating station in bus dispatching.
Background
Because the bus passenger flow volume in one day has great change, the bus scheduling with equal departure interval can not well meet the demands of passengers going out, and the problem of unmatched transport capacity and demands is easily caused. The non-uniform departure algorithm can adjust the departure interval according to the change rule of the passenger flow demand, so that bus scheduling is more suitable for the change of passenger flow, and the transportation capacity distribution is more reasonable. On the basis, when the real-time passenger flow and the historical passenger flow are not regularly matched, the previously determined departure interval needs to be adjusted in time so as to be in line with the current passenger flow condition, and therefore the travel experience of passengers is optimized.
At present, the arrangement method of the vehicle shifts in the existing bus enterprise driving plan is single, and most of the existing bus enterprise driving plans are regular average vehicle interval arrangement modes, namely vehicles are arranged to be sent from a starting station according to a fixed time interval. The shift time is fixed, manual calculation and adjustment are needed for each shift, and the shift scheduling mode does not accord with the change of the increasingly variable passenger flow rule, and wastes or lacks the public transportation capacity. Therefore, solving the above-mentioned shortcomings is a problem that we need to solve.
Disclosure of Invention
In view of the defects in the prior art, the invention provides the method and the system for starting the bus to uniformly start the bus in the bus dispatching, which not only reduce the time of passengers and the like and optimize the traveling experience of the passengers, but also reduce the operation cost and improve the dispatching strain capacity while ensuring the transportation capacity in terms of operation management.
In order to achieve the above object and other related objects, the present invention provides the following technical solutions:
a method for non-uniform departure of an origin station in bus dispatching comprises the following steps:
t1: analyzing the arrival rule of passengers with a day period according to the historical passenger flow data, and forming a group of ordered samples { p } in time sequence by the maximum end face passenger flow on a unit hour line in a day 1 ,p 2 ,...,p n N is obtained by setting bus operation time, p i The passenger flow is the maximum section passenger flow of the ith hour;
t2: based on ordered samples { p } 1 ,p 2 ,...,p n Establishing Fisher model partition characteristic time periods of ordered sample clustering, wherein the steps are as follows:
t21: calculating class diameter, calculating average value of ordered samples
Thereby obtaining the diameter of the sample class
T22: the loss function of the classification, b (n, k) represents a division of n ordered samples into k classes, denoted b (n, k) as
Wherein the dividing points are as follows: 1=i 1 <i 2 <…<i k <n, the loss function of the taxonomy is: />
Let p (n, k) be L [ b (n, k)]A classification method with extremely small values;
t23: establishing a minimum classification loss function table, calculating a minimum classification loss function { L [ p (L, k), wherein L is more than or equal to 3 and less than or equal to n, and k is more than or equal to 2 and less than or equal to n-1] }, and respectively calculating all results of the optimally segmented loss function when classifying L samples;
t24: obtaining optimal classification, obtaining a graph of the change of minimum loss function value along the up-down direction along with the classification number by using a fisher algorithm, analyzing and calculating to obtain a position with smaller descending gradient of the loss function, reasonably selecting the classification number F=k, and reversely pushing the time division condition according to the classification number to obtain the total number F of characteristic time periods, wherein F is E [1, F]Number indicating characteristic period, T f The time span representing the f characteristic time period is:
wherein ti represents the duration of each time period, the f feature time period comprises a small time period { n, n+1,.. M-1, m }, wherein n is less than or equal to m, wherein n and m represent a starting time period sequence number and a terminating time period sequence number of the f time period respectively;
t25: in the f period, the arrival rate lambda of the passenger at the k station k,f The calculation is as follows:
wherein p is k,f For the number of passengers arriving at station k in time period f, T f The size of the f period;
t3: respectively calculating the departure shift times n in each period f And calculating and obtaining the total departure times:
wherein F represents the number of the characteristic period, F represents the total number of the characteristic period, F is [1, F ]]，n f For departure shift in f period, p mf The passenger flow quantity is the high section peak hour passenger flow quantity of the bus line in the f time period, alpha is the full load rate of the bus in the f time period, the peak and the flat peak are divided, N is the rated passenger capacity of the bus, T f The time span of the characteristic period is f, and n is the number of line cars;
t4: based on the minimum waiting time of passengers and the maximum operating income, setting minimum departure interval constraint, taking all train departure time sequences as decision variables, and solving a basic departure schedule by using a genetic algorithm to enable the basic departure schedule to accord with the historical passenger flow law, so as to obtain the maximum operating income objective function:
at the same time, the objective function with minimum waiting time of passengers is obtained:
wherein lambda is n,k To the number of passengers getting on the nth shift at the k station, w n,k For maximum waiting time of passengers getting on the nth shift at the k station,
and respectively carrying out unified treatment on the two objective functions, wherein the treatment formula is as follows:
where f is the objective function value, f max Takes on the maximum possible value of the objective function, f min Taking the minimum possible value of the objective function, wherein f' is the normalized objective function value;
t5: based on normalized target processing formula f', for f 1 And f 2 Obtaining a target optimization function: minf=w 2 f′ 2 -w 1 f′ 1 ,
Wherein w is 1 ，w 2 The ratio of the two is indicative of the specific gravity of the two optimization targets as a weighting coefficient.
Further, constraint conditions of the objective optimization function are respectively as follows:
constraint 1: maximum minimum departure time interval constraint: the departure interval between any two adjacent vehicles is required to meet the maximum and minimum departure time interval constraint:
Δt min ≤t i -t i-1 ≤Δt max wherein Δt is max Representing the maximum departure interval between two adjacent vehicles, deltat min Representing the minimum departure interval between two adjacent vehicles,
constraint 2: constraint of the difference between two adjacent departure intervals: in order to ensure the continuity of departure time, the difference between any two adjacent departure intervals is small:
|(t i+1 -t i )-(t i -t i-1 )|≤ε，
constraint 3: constraint of average full load rate:
wherein Q represents the capacity of the vehicle when the vehicle is fully loaded, θ represents the average expected full load rate per vehicle, T f Time span representing f characteristic period, r k,f Indicating the passenger arrival rate at the kth station for the f-th characteristic period.
Further, in step T5, the w 1 ，w 2 Optimizing functions for the objectiveThe weighting coefficients of the numbers satisfy: w (w) 1 +w 2 ＝1。
To achieve the above and other related objects, the present invention also provides a system for non-uniform departure of an origin in a bus schedule, the system comprising:
the input module is used for inputting data parameters;
the data processing module is used for analyzing and processing data;
the storage module is used for storing historical data;
the output module is used for outputting data;
the display module is used for displaying different types of data;
the data processing module is electrically connected with the input module, the storage module, the output module and the display module.
Further, the data parameters include historical passenger flow data, rated passenger capacity of a bus, bus operation time, travel time of the vehicle between stations, departure interval constraint, maximum value of difference between adjacent departure intervals, minimum average full load rate, fare, cost of a vehicle operation unit and total length of an operation line.
Further, the data output comprises a basic driving schedule and arrival rates of passenger flows of all stations.
The invention has the following positive effects:
1) According to the invention, from the aspect of passenger satisfaction, the time of waiting for passengers is reduced, and the traveling experience of the passengers is greatly optimized.
2) In terms of operation management, the invention reduces the operation cost and improves the capacity of dispatching strain while ensuring the capacity.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a schematic flow chart of the fisher algorithm of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Examples: as shown in fig. 1 or 2, a method for non-uniform departure of an origin station in a bus schedule includes the following steps:
t1: analyzing the arrival rule of passengers with a day period according to the historical passenger flow data, and forming a group of ordered samples { p } in time sequence by the maximum end face passenger flow on a unit hour line in a day 1 ,p 2 ,...,p n N is obtained by setting bus operation time, p i The passenger flow is the maximum section passenger flow of the ith hour;
t2: based on ordered samples { p } 1 ,p 2 ,...,p n Establishing Fisher model partition characteristic time periods of ordered sample clustering, wherein the steps are as follows:
t21: calculating class diameter, calculating average value of ordered samples
Thereby obtaining the diameter of the sample class
T22: the loss function of the classification, b (n, k) represents a division of n ordered samples into k classes, denoted b (n, k) as
Wherein the dividing points are as follows: 1=i 1 <i 2 <…<i k <n, the loss function of the taxonomy is: />
Let p (n, k) be L [ b (n, k)]A classification method with extremely small values;
t23: establishing a minimum classification loss function table, calculating a minimum classification loss function { L [ p (L, k), wherein L is more than or equal to 3 and less than or equal to n, and k is more than or equal to 2 and less than or equal to n-1] }, and respectively calculating all results of the optimally segmented loss function when classifying L samples;
t24: solving the optimal classification, and obtaining the upper and lower parts by using a fisher algorithmThe curve graph of the minimum loss function value in the row direction along with the change of the classification number is analyzed and calculated to obtain the position with smaller descending gradient of the loss function, the classification number F=k is reasonably selected, and the time division situation is reversely pushed according to the classification number, so that the total number F of characteristic time periods F, F epsilon [1, F is obtained]Number indicating characteristic period, T f The time span representing the f characteristic time period is:
wherein ti represents the duration of each time period, the f feature time period comprises a small time period { n, n+1,.. M-1, m }, wherein n is less than or equal to m, wherein n and m represent a starting time period sequence number and a terminating time period sequence number of the f time period respectively;
t25: in the f period, the arrival rate lambda of the passenger at the k station kf The calculation is as follows:
wherein p is k,f For the number of passengers arriving at station k in time period f, T f The size of the f period;
t3: respectively calculating the departure shift times n in each period f And calculating and obtaining the total departure times:
wherein F represents the number of the characteristic period, F represents the total number of the characteristic period, F is [1, F ]]，n f For departure shift in f period, p mf The passenger flow quantity is the high section peak hour passenger flow quantity of the bus line in the f time period, alpha is the full load rate of the bus in the f time period, the peak and the flat peak are divided, N is the rated passenger capacity of the bus, T f The time span of the characteristic period is f, and n is the number of line cars;
t4: based on the minimum waiting time of passengers and the maximum operating income, setting minimum departure interval constraint, taking all train departure time sequences as decision variables, and solving a basic departure schedule by using a genetic algorithm to enable the basic departure schedule to accord with the historical passenger flow law, so as to obtain the maximum operating income objective function:
wherein r is k,f Representing the arrival rate, T, of the passenger at the kth station at the characteristic period f f The time span of the characteristic period f is represented, P represents uniform fare, C represents unit cost of vehicle operation, L represents total length of the line, n represents departure times,
at the same time, the objective function with minimum waiting time of passengers is obtained:
wherein lambda is n,k To the number of passengers getting on the nth shift at the k station, w n,k For maximum waiting time of passengers getting on the nth shift at the k station,
and respectively carrying out unified treatment on the two objective functions, wherein the treatment formula is as follows:
where f is the objective function value, f max Takes on the maximum possible value of the objective function, f min Taking the minimum possible value of the objective function, wherein f' is the normalized objective function value;
t5: based on normalized target processing formula f', for f 1 And f 2 Obtaining a target optimization function: minf=w 2 f′ 2 -w 1 f′ 1 Wherein w is 1 ，w 2 The ratio of the two is indicative of the specific gravity of the two optimization targets as a weighting coefficient.
Further, constraint conditions of the objective optimization function are respectively as follows:
constraint 1: maximum minimum departure time interval constraint: the departure interval between any two adjacent vehicles is required to meet the maximum and minimum departure time interval constraint:
Δt min ≤t i -t i-1 ≤Δt max wherein Δt is max Representing the maximum departure interval between two adjacent vehicles, deltat min Representing the minimum departure interval between two adjacent vehicles,
constraint 2: constraint of the difference between two adjacent departure intervals: in order to ensure the continuity of departure time, the difference between any two adjacent departure intervals is small:
|(t i+1 -t i )-(t i -t i-1 ) +.epsilon.and constraint 3: constraint of average full load rate:
wherein Q represents the capacity of the vehicle when the vehicle is fully loaded, θ represents the average expected full load rate per vehicle, T f Time span representing f characteristic period, r k,f Indicating the passenger arrival rate at the kth station for the f-th characteristic period.
Further, in step T5, the w 1 ，w 2 The weighting coefficients for the objective optimization function satisfy: w (w) 1 +w 2 ＝1。
To achieve the above and other related objects, the present invention also provides a system for non-uniform departure of an origin in a bus schedule, the system comprising:
the input module is used for inputting data parameters;
the data processing module is used for analyzing and processing data;
the storage module is used for storing historical data;
the output module is used for outputting data;
the display module is used for displaying different types of data;
the data processing module is electrically connected with the input module, the storage module, the output module and the display module.
Further, the data parameters include historical passenger flow data, rated passenger capacity of a bus, bus operation time, travel time of the vehicle between stations, departure interval constraint, maximum value of difference between adjacent departure intervals, minimum average full load rate, fare, cost of a vehicle operation unit and total length of an operation line.
Further, the data output comprises a basic driving schedule and arrival rates of passenger flows of all stations.
In summary, the invention not only reduces the time of passengers waiting for the vehicle and optimizes the traveling experience of the passengers, but also reduces the operation cost and improves the capability of dispatching strain while ensuring the transportation capability from the aspect of operation management.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and substitutions can be made by those skilled in the art without departing from the technical principles of the present application, and these modifications and substitutions should also be considered as being within the scope of the present application.
Claims (9)
1. The method for non-uniform departure of the origin station in the bus dispatching is characterized by comprising the following steps:
t1: analyzing the arrival rule of passengers with a day period according to the historical passenger flow data, and forming a group of ordered samples { p } in time sequence by the maximum end face passenger flow on a unit hour line in a day 1 ,p 2 ,...,p n N is obtained by setting bus operation time, p i The passenger flow is the maximum section passenger flow of the ith hour;
t2: based on ordered samples { p } 1 ,p 2 ,...,p n Establishing Fisher model partition characteristic time periods of ordered sample clustering, and respectively calculating shift times n in each time period f ；
T3: the total departure times are calculated according to the following functions:
wherein F represents the number of the characteristic period, F represents the total number of the characteristic period, F is [1, F ]]，n f For departure shift in f period, p mf The passenger flow quantity is the high section peak hour passenger flow quantity of the bus line in the f time period, alpha is the full load rate of the bus in the f time period, the peak and the flat peak are divided, N is the rated passenger capacity of the bus, T f The time span of the characteristic period is f, and n is the number of line cars;
t4: based on the minimum waiting time of passengers and the maximum operating income, setting minimum departure interval constraint, taking all train departure time sequences as decision variables, and solving a basic departure schedule by using a genetic algorithm to enable the basic departure schedule to accord with the historical passenger flow law, so as to obtain the maximum operating income objective function:
wherein r is k,f Representing the arrival rate, T, of the passenger at the kth station at the characteristic period f f The time span of the characteristic period f is represented, P represents uniform fare, C represents unit cost of vehicle operation, L represents total length of the line, n represents departure times,
at the same time, the objective function with minimum waiting time of passengers is obtained:
wherein lambda is n,k To the number of passengers getting on the nth shift at the k station, w n,k For maximum waiting time of passengers getting on the nth shift at the k station,
and respectively carrying out unified treatment on the two objective functions, wherein the treatment formula is as follows:
where f is the objective function value, f max Takes on the maximum possible value of the objective function, f min Taking the minimum possible value of the objective function, wherein f' is the normalized objective function value;
t5: based on normalized target processing formula f', for f 1 And f 2 Obtaining a target optimization function: minf=w 2 f′ 2 -w 1 f 1 ′,
Wherein w is 1 ，w 2 The ratio of the two is indicative of the specific gravity of the two optimization targets as a weighting coefficient.
2. The method for non-uniform departure of an origin station in a bus schedule according to claim 1, wherein: in step T2, the method further comprises the steps of:
t21: calculating class diameter, calculating average value of ordered samples
Thereby obtaining the diameter of the sample class
T22: the loss function of the classification, b (n, k) represents a division of n ordered samples into k classes, denoted b (n, k) as
Wherein the dividing points are as follows: 1=i 1 <i 2 <…<i k <n, the loss function of the taxonomy is: />
Let p (n, k) be L [ b (n, k)]A classification method with extremely small values;
t23: establishing a minimum classification loss function table, calculating a minimum classification loss function { L [ p (L, k), wherein L is more than or equal to 3 and less than or equal to n, and k is more than or equal to 2 and less than or equal to n-1] }, and respectively calculating all results of the optimally segmented loss function when classifying L samples;
t24: find the optimal classification, use fishThe er algorithm obtains a graph of the minimum loss function value in the up-down direction along with the change of the classification number, analyzes and calculates the position with smaller descending gradient of the loss function, reasonably selects the classification number F=k, and deduces the time division situation according to the classification number, thereby obtaining the total number F of characteristic time periods, F E [1, F]Number indicating characteristic period, T f The time span representing the f characteristic time period is:
wherein ti represents the duration of each time period, the f feature time period comprises a small time period { n, n+1,.. M-1, m }, wherein n is less than or equal to m, wherein n and m represent a starting time period sequence number and a terminating time period sequence number of the f time period respectively;
t25: in the f period, the arrival rate lambda of the passenger at the k station k,f The calculation is as follows:
wherein p is k,f For the number of passengers arriving at station k in time period f, T f Is the size of the f period.
3. The method for non-uniform departure of an origin station in a bus schedule according to claim 1, wherein: in step T4, the conditional function of the interval constraint is:
Δt min ≤t i -t i-1 ≤Δt max ，
wherein Δt is max Representing the maximum departure interval between two adjacent vehicles, deltat min Representing the minimum departure interval between two adjacent vehicles.
4. The method for non-uniform departure of an origin station in a bus schedule according to claim 1, wherein: in step T4, to ensure continuity of departure time, the conditional function of the difference between any two adjacent departure intervals is:
|(t i+1 -t i )-(t i -t i-1 )|≤ε。
5. the method for non-uniform departure of an origin station in a bus schedule according to claim 1, wherein: in step T3, the condition function of the full load rate is:
wherein Q represents the capacity of the vehicle when the vehicle is fully loaded, θ represents the average expected full load rate per vehicle, T f Time span representing f characteristic period, r k,f Indicating the passenger arrival rate at the kth station for the f-th characteristic period.
6. The method for non-uniform departure of an origin station in a bus schedule according to claim 1, wherein: in step T5, the w 1 ，w 2 The weighting coefficients for the objective optimization function satisfy: w (w) 1 +w 2 ＝1。
7. A system for non-uniform departure of an origin in a bus dispatch implementing the method of any one of claims 1-6, the system comprising:
the input module is used for inputting data parameters;
the data processing module is used for analyzing and processing data;
the storage module is used for storing historical data;
the output module is used for outputting data;
the display module is used for displaying different types of data;
the data processing module is electrically connected with the input module, the storage module, the output module and the display module.
8. The system for non-uniform departure of an origin in a bus dispatch of claim 7, wherein: the data parameters comprise historical passenger flow data, rated passenger capacity of buses, bus operation time, travel time of vehicles among stations, departure interval constraint, maximum value of differences between adjacent departure intervals, minimum average full load rate, fare, cost of vehicle operation units and total length of operation lines.
9. The system for non-uniform departure of an origin in a bus dispatch of claim 7, wherein: the output of the data comprises a basic driving schedule and arrival rates of passenger flows of all stations.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831767A (en) * | 2012-08-13 | 2012-12-19 | 东南大学 | Multi-mode station stopping method at optimized comprehensive cost for urban public transport |
| CN103413433A (en) * | 2013-07-26 | 2013-11-27 | 浙江工业大学 | Traffic-jam bus transferring method based on passenger flow volume information |
| CN106022577A (en) * | 2016-05-12 | 2016-10-12 | 西安电子科技大学 | On-line balanced scheduling for passenger vehicle |
| US9562785B1 (en) * | 2015-07-20 | 2017-02-07 | Via Transportation, Inc. | Continuously updatable computer-generated routes with continuously configurable virtual bus stops for passenger ride-sharing of a fleet of ride-sharing vehicles and computer transportation systems and computer-implemented methods for use thereof |
| 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 |
| CN108399468A (en) * | 2018-01-31 | 2018-08-14 | 华南理工大学 | It is a kind of based on vehicle when cost optimization operation Time segments division method |
| CN109859459A (en) * | 2019-02-22 | 2019-06-07 | 北京航空航天大学 | A kind of automatic Pilot bus dispatching optimization method |
| CN110232831A (en) * | 2019-06-21 | 2019-09-13 | 上海理工大学 | A kind of frequency optimization method based on demand response type public transport |
| CN111105141A (en) * | 2019-11-23 | 2020-05-05 | 同济大学 | Demand response type bus dispatching method |
| CN111160597A (en) * | 2019-10-29 | 2020-05-15 | 三峡大学 | Intelligent scheduling method based on comprehensive satisfaction degree of taxi driver |
| WO2021004115A1 (en) * | 2019-07-11 | 2021-01-14 | 北京航空航天大学 | Combined dispatching optimization method for manned buses and autonomous buses |
| CN112562377A (en) * | 2020-12-01 | 2021-03-26 | 厦门大学 | Passenger vehicle real-time scheduling method based on random opportunity constraint |
| JP2022003458A (en) * | 2020-06-23 | 2022-01-11 | 日産自動車株式会社 | Vehicle allocation management device and vehicle allocation management method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170011629A1 (en) * | 2014-10-09 | 2017-01-12 | Chun Ngok Lau | Public transit vehicle route schedule and notification system |
| CN106448233B (en) * | 2016-08-19 | 2017-12-05 | 大连理工大学 | Public bus network timetable cooperative optimization method based on big data |
| US20200349485A1 (en) * | 2019-07-11 | 2020-11-05 | Beihang University | Optimization method for joint scheduling of manned buses and autonomous buses |
-
2022
- 2022-08-31 CN CN202211063087.8A patent/CN115457793B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102831767A (en) * | 2012-08-13 | 2012-12-19 | 东南大学 | Multi-mode station stopping method at optimized comprehensive cost for urban public transport |
| CN103413433A (en) * | 2013-07-26 | 2013-11-27 | 浙江工业大学 | Traffic-jam bus transferring method based on passenger flow volume information |
| US9562785B1 (en) * | 2015-07-20 | 2017-02-07 | Via Transportation, Inc. | Continuously updatable computer-generated routes with continuously configurable virtual bus stops for passenger ride-sharing of a fleet of ride-sharing vehicles and computer transportation systems and computer-implemented methods for use thereof |
| CN106022577A (en) * | 2016-05-12 | 2016-10-12 | 西安电子科技大学 | On-line balanced scheduling for passenger vehicle |
| 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 |
| CN108399468A (en) * | 2018-01-31 | 2018-08-14 | 华南理工大学 | It is a kind of based on vehicle when cost optimization operation Time segments division method |
| CN109859459A (en) * | 2019-02-22 | 2019-06-07 | 北京航空航天大学 | A kind of automatic Pilot bus dispatching optimization method |
| CN110232831A (en) * | 2019-06-21 | 2019-09-13 | 上海理工大学 | A kind of frequency optimization method based on demand response type public transport |
| WO2021004115A1 (en) * | 2019-07-11 | 2021-01-14 | 北京航空航天大学 | Combined dispatching optimization method for manned buses and autonomous buses |
| CN111160597A (en) * | 2019-10-29 | 2020-05-15 | 三峡大学 | Intelligent scheduling method based on comprehensive satisfaction degree of taxi driver |
| CN111105141A (en) * | 2019-11-23 | 2020-05-05 | 同济大学 | Demand response type bus dispatching method |
| JP2022003458A (en) * | 2020-06-23 | 2022-01-11 | 日産自動車株式会社 | Vehicle allocation management device and vehicle allocation management method |
| CN112562377A (en) * | 2020-12-01 | 2021-03-26 | 厦门大学 | Passenger vehicle real-time scheduling method based on random opportunity constraint |
Non-Patent Citations (1)
| Title |
|---|
| 基于服务水平的区域公交协调调度优化研究;杨信丰;李引珍;何瑞春;;系统工程(第06期);全文 * |
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