CN115841745A - Vehicle scheduling method and device and electronic equipment - Google Patents
Vehicle scheduling method and device and electronic equipment Download PDFInfo
- Publication number
- CN115841745A CN115841745A CN202111098444.XA CN202111098444A CN115841745A CN 115841745 A CN115841745 A CN 115841745A CN 202111098444 A CN202111098444 A CN 202111098444A CN 115841745 A CN115841745 A CN 115841745A
- Authority
- CN
- China
- Prior art keywords
- passenger
- coefficient
- departure interval
- time
- target
- 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 40
- 238000012544 monitoring process Methods 0.000 claims abstract description 40
- 238000004904 shortening Methods 0.000 claims description 75
- 230000002596 correlated effect Effects 0.000 claims description 72
- 230000002350 accommodative effect Effects 0.000 claims description 15
- 230000002035 prolonged effect Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 230000000694 effects Effects 0.000 abstract description 11
- 238000010586 diagram Methods 0.000 description 5
- 230000004308 accommodation Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a vehicle scheduling method, a vehicle scheduling device and electronic equipment, and relates to the technical field of traffic, wherein the vehicle scheduling method comprises the following steps: predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line; determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow volume; acquiring real-time monitoring data of a camera arranged aiming at the target line on the target date; and adjusting the predicted operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted predicted operation information. The invention can improve the vehicle dispatching effect.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a vehicle dispatching method, a vehicle dispatching device and electronic equipment.
Background
In the prior art, when vehicles (such as buses, unmanned vehicles, etc.) are scheduled, each vehicle is usually scheduled manually, each vehicle is scheduled to be dispatched at a fixed time, and the running route, the stop and the route between stops of each vehicle are also fixed. However, the traffic conditions of roads are complex, manual scheduling is labor-consuming, complicated traffic conditions cannot be handled, and the vehicle scheduling effect is poor.
Disclosure of Invention
The embodiment of the invention provides a vehicle scheduling method, a vehicle scheduling device and electronic equipment, and aims to solve the problems that the conventional manual scheduling mode consumes manpower, cannot cope with complex traffic conditions, and is poor in vehicle scheduling effect.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a vehicle scheduling method, where the method includes:
predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line;
determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow volume;
acquiring real-time monitoring data of a camera arranged aiming at the target route on the target date;
and adjusting the estimated operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted estimated operation information.
Optionally, the real-time monitoring data includes at least one of the following:
station passenger flow, weather state coefficient, traffic flow coefficient;
the weather state coefficient is negatively correlated with the weather severity, and the traffic flow coefficient is positively correlated with the traffic jam degree.
Optionally, the predicted operation information includes departure time intervals;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
determining a passenger saturation coefficient of a destination line based on a station passenger flow volume and a passenger accommodative volume of the destination line, the passenger accommodative volume being indicative of a number of passengers currently able for the vehicle, the passenger saturation coefficient being positively correlated with the station passenger flow volume and negatively correlated with the passenger accommodative volume;
determining departure interval shortening time or departure interval prolonging time according to a target parameter, wherein the target parameter is any one of the passenger saturation coefficient, the weather state coefficient and the traffic flow coefficient;
and adjusting the departure interval time of the vehicle on the target line according to the departure interval shortened time or the departure interval prolonged time.
Optionally, the determining, according to the target parameter, a departure interval shortening duration or a departure interval lengthening duration includes:
if the passenger saturation coefficient is larger than a first decision parameter, determining a departure interval shortening time length according to the passenger saturation coefficient, wherein the departure interval shortening time length is positively correlated with the passenger saturation coefficient, the departure interval shortening time length is negatively correlated with the traffic flow coefficient, and the departure interval shortening time length is negatively correlated with the first decision parameter, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Optionally, the determining, according to the target parameter, a departure interval shortening duration or a departure interval lengthening duration includes:
determining a departure interval extension time according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is smaller than a second decision parameter, wherein the departure interval extension time is negatively correlated with the passenger saturation coefficient, and the departure interval extension time is positively correlated with the traffic flow coefficient, and the second decision parameter is determined based on a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and prolonging the departure time interval of the target line according to the departure interval prolonged duration.
Optionally, the determining, according to the target parameter, the departure interval shortened duration or the departure interval extended duration includes:
under the condition that the weather state coefficient is smaller than a first preset threshold value, determining departure interval shortening time according to the weather state coefficient, wherein the departure interval shortening time is positively correlated with the passenger saturation coefficient, the departure interval shortening time is negatively correlated with the traffic flow coefficient, the departure interval shortening time is negatively correlated with a first decision parameter, the departure interval shortening time is negatively correlated with the weather state coefficient, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Optionally, the real-time monitoring data includes the traffic flow coefficient, and the predicted operation information includes a driving route;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
and under the condition that the traffic flow coefficient between a first station and a second station of the target line is smaller than a second preset threshold value within a preset time period, adjusting the driving route of the vehicle between the first station and the second station, wherein the first station is any station on the target line, and the second station is a station behind the first station.
In a second aspect, an embodiment of the present invention provides a vehicle dispatching device, where the device includes:
the prediction module is used for predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line;
the determining module is used for determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow;
the acquisition module is used for acquiring real-time monitoring data of the target date of the cameras arranged aiming at the target line;
and the scheduling module is used for adjusting the predicted operation information according to the real-time monitoring data and scheduling the vehicle based on the adjusted predicted operation information.
Optionally, the real-time monitoring data includes at least one of the following:
station passenger flow, weather state coefficient and traffic flow coefficient;
the weather state coefficient is negatively correlated with the weather severity, and the traffic flow coefficient is positively correlated with the traffic jam degree.
Optionally, the predicted operation information includes departure time intervals;
the scheduling module includes:
a first determination unit configured to determine a passenger saturation coefficient of a destination line based on a station passenger volume and a passenger accommodative volume of the destination line, the passenger accommodative volume indicating a number of passengers that can be currently accommodated by the vehicle, the passenger saturation coefficient being positively correlated with the station passenger volume and negatively correlated with the passenger accommodative volume;
the second determining unit is used for determining departure interval shortening time or departure interval lengthening time according to a target parameter, wherein the target parameter is any one of the passenger saturation coefficient, the weather state coefficient and the traffic flow coefficient;
the adjusting unit is used for adjusting the departure interval time of the vehicle on the target line according to the departure interval shortened time or the departure interval prolonged time;
and the scheduling unit is used for scheduling the vehicle based on the adjusted predicted operation information.
Optionally, the target parameter is the passenger saturation coefficient, and the second determining unit is specifically configured to:
determining a departure interval shortening time length according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is larger than a first decision parameter, wherein the departure interval shortening time length is positively correlated with the passenger saturation coefficient, the departure interval shortening time length is negatively correlated with the traffic flow coefficient, the departure interval shortening time length is negatively correlated with the first decision parameter, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting unit is specifically configured to:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Optionally, the target parameter is the passenger saturation coefficient, and the second determining unit is specifically configured to:
determining a departure interval extension time according to the passenger saturation coefficient when the passenger saturation coefficient is smaller than a second decision parameter, wherein the departure interval extension time is negatively correlated with the passenger saturation coefficient, and the departure interval extension time is positively correlated with the traffic flow coefficient, and the second decision parameter is determined based on a historical average traffic flow coefficient;
the adjusting unit is specifically configured to:
and prolonging the departure time interval of the target line according to the departure interval prolonged duration.
Optionally, the target parameter is the weather condition coefficient, and the second determining unit is specifically configured to:
under the condition that the weather state coefficient is smaller than a first preset threshold value, determining departure interval shortening time according to the weather state coefficient, wherein the departure interval shortening time is positively correlated with the passenger saturation coefficient, the departure interval shortening time is negatively correlated with the traffic flow coefficient, the departure interval shortening time is negatively correlated with a first decision parameter, the departure interval shortening time is negatively correlated with the weather state coefficient, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting unit is specifically configured to:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Optionally, the real-time monitoring data includes the traffic flow coefficient, and the predicted operation information includes a driving route;
the scheduling module is specifically configured to:
when the traffic flow coefficient between a first station and a second station of the target route is smaller than a second preset threshold value within a preset time period, adjusting a driving route of a vehicle between the first station and the second station, wherein the first station is any station on the target route, and the second station is a station behind the first station;
and scheduling the vehicle based on the adjusted predicted operation information.
In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory and a program stored on the memory and executable on the processor, the program, when executed by the processor, implementing the steps of the vehicle scheduling method of the first aspect.
In a fourth aspect, the embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the vehicle scheduling method according to the first aspect.
In the embodiment of the invention, the passenger flow of the target line on the target date is predicted according to the historical passenger flow data of the target line; determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow volume; acquiring real-time monitoring data of a camera arranged aiming at the target line on the target date; and adjusting the estimated operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted estimated operation information. Therefore, vehicles are dispatched based on historical passenger flow volume data and real-time monitoring data, manual dispatching is not needed, and vehicle dispatching effect can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a vehicle scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a vehicle dispatch process provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a vehicle dispatching device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another vehicle dispatching device provided by the embodiment of the invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The embodiment of the invention provides a vehicle scheduling method, a vehicle scheduling device and electronic equipment, and aims to solve the problems that the conventional manual scheduling mode consumes more manpower, cannot cope with complex traffic conditions and has a poor vehicle scheduling effect.
Referring to fig. 1, fig. 1 is a flowchart of a vehicle dispatching method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
The target line may be any one of the operating lines. Historical video stream data of a camera arranged aiming at the target line can be obtained, the historical video stream data is analyzed, and historical passenger flow volume data of the target line is obtained. The historical traffic data may include historical site traffic, historical site pick-up and pick-up numbers, and the like. The target date may be any day in the future, and may be, for example, the next day.
In addition, historical passenger flow volume data and a target date of a target line can be input into a neural network model for predicting passenger flow volume to obtain the passenger flow volume of the target line on the target date; or, the passenger flow of the target line on the target date may be obtained through prediction according to a first preset rule, for example, historical passenger flow data indicates that the passenger flow of the xx station at the five points of friday per week is between 30 and 40, and the passenger flow of the xx station at the five points of friday may be (30 + 40)/2 = 35.
And step 102, determining the expected running information of the vehicle on the target route on the target date according to the predicted passenger flow.
The predicted operation information may include information such as a station, departure time, the number of required vehicles, and predicted time. The vehicle may be a bus, an unmanned vehicle, a plug-in vehicle, an unmanned plug-in vehicle, or the like. The predicted passenger flow volume can be input into a neural network model for predicting predicted operation information to obtain the predicted operation information; or, the predicted operation information may be obtained according to a second preset rule, for example, if the passenger flow of the xx station at the five points of friday is 35, the predicted operation information may satisfy that a vehicle at the five points of friday arrives at the xx station, and the vehicle may accommodate 35 people at the xx station.
In addition, the predicted operation information of the vehicle on the target route on the target date may be stored as a plan. Taking the vehicle as an unmanned connection vehicle as an example, on the target date, the unmanned vehicles with the electric quantity meeting the target line can be screened out from the existing unmanned vehicles before departure, and if the screened unmanned vehicles are less than the required number of vehicles, the screened unmanned vehicles can be used as the unmanned vehicles of the target line, and the available vehicles can be monitored in real time to supplement the unmanned vehicles of the target line.
And 103, acquiring real-time monitoring data of the target date of the cameras arranged aiming at the target line.
The camera arranged for the target line may include a roadside camera arranged on a road section of the target line, and/or a camera mounted on a vehicle running the target line, and the like.
And 104, adjusting the predicted operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted predicted operation information.
Wherein the real-time monitoring data may include: the system comprises a station passenger flow, a weather state coefficient, a traffic flow coefficient and the like, wherein the weather state coefficient is inversely related to the weather severity, and the traffic flow coefficient is positively related to the traffic jam degree. The estimated operation information can be displayed on the display screen of each station, and after the estimated operation information is adjusted, the adjusted estimated operation information can be updated on the display screen, so that visual display can be realized.
It should be noted that, taking the scheduling of an unmanned vehicle as an example, historical video stream data can be analyzed to obtain road network traffic flow of each historical road segment, historical station passenger flow and historical station getting-on and getting-off quantity; forecasting operation information such as an operation route, stop stations, optimal departure time intervals in each period, the number of distributed unmanned vehicles and the like through the acquired data; the real-time video stream data can be analyzed, and the predicted operation information can be adjusted, for example, the operation route between stations can be adjusted, the unmanned vehicle running speed can be adjusted, the unmanned vehicle distribution quantity can be adjusted, and the like.
Taking the target route as the route 1 as an example, when the passenger flow of the route 1 is larger than the passenger capacity of the stop and the road traffic flow is smooth, the departure interval time of the route 1 can be shortened; when the cameras arranged on the line 1 monitor bad weather, the departure interval time of the line 1 can be shortened; when the cameras arranged on the line 1 monitor that accidents, obstacles, construction, temporary control and the like happen to a certain road section, the line of the line 1 can be re-planned. Therefore, in the running process, the vehicle is subjected to scene deployment under a specific scene by combining multipoint real-time video stream data and image recognition capability.
As shown in fig. 2, the decision-making planning and scheduling center issues the predicted operation route and departure time of each time period to the vehicle, and the vehicle runs to the station 1 according to the operation route, and the station can be considered as a station. When the vehicle runs to the platform 1, cameras arranged on a front door and a rear door of the vehicle carry out camera shooting, the cameras and a road side camera on the vehicle upload real-time video stream data to a decision-making planning and dispatching center, the decision-making planning and dispatching center analyzes the video stream data and issues a decision instruction to the vehicle according to the real-time video stream data, and the vehicle sends the state of the vehicle, the current position information of the vehicle, the electric quantity and the number of passengers in the vehicle to the decision-making planning and dispatching center at intervals in the running process.
In addition, taking the vehicle as an unmanned transfer vehicle as an example, for each unmanned transfer vehicle, the relevant information of the unmanned transfer vehicle can be displayed on a monitoring picture of a decision planning and scheduling center, and the monitoring information of the unmanned transfer vehicle can be displayed, wherein the monitoring information comprises camera pictures of the front of the vehicle, the front inside the vehicle, the door, the back inside the vehicle and the platform; the actual running speed and the planned running speed can also be displayed; the current previous station of the unmanned transfer vehicle, the number of passengers getting on the vehicle and the number of the remaining passengers can be displayed; the current next station of the unmanned transfer vehicle, the number of passengers at the next station and the predicted arrival time can be displayed; the number of unmanned transfer vehicles going to the next station and the number of vacant positions can be displayed; the percentage of the number of passengers in the vehicle in a previous period of time to the maximum number of passengers capable of being accommodated in the vehicle can be displayed; the number of the existing or not plug-in vehicles, the total travel of the running route, the estimated total running time and the station number can also be displayed.
As an embodiment, the traffic flow of the operation area road network, the passenger flow of each station in each line and the data of getting on or off the train can be collected and recorded; predicting traffic jam conditions and station passenger flow distribution conditions of a road network in each time period on the next day based on historical traffic flow, historical passenger flow and boarding and alighting data; determining estimated operation information such as departure time of each line on the next day, operation line stations, estimated number of people and estimated arrival time of the stations on the basis of the predicted traffic jam condition and station passenger flow distribution condition; and dispatching the vehicle according to the determined predicted operation information. Therefore, the operation route path and the station are given by comprehensively considering passenger flow distribution, the number of people getting on and off the train and the traffic flow condition of each daily time period in combination with multi-point historical video flow data, the distributed number of the vehicles and the departure time are reasonably and automatically allocated, the departure time of the vehicles on the next day can be predicted, the departure time and the departure route of the vehicles can be adjusted in real time, and the automation degree is high.
In the embodiment of the invention, the passenger flow of the target line on the target date is predicted according to the historical passenger flow data of the target line; determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow volume; acquiring real-time monitoring data of a camera arranged aiming at the target line on the target date; and adjusting the estimated operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted estimated operation information. Therefore, vehicles are dispatched based on historical passenger flow volume data and real-time monitoring data, manual dispatching is not needed, and vehicle dispatching effect can be improved.
Optionally, the real-time monitoring data includes at least one of the following:
station passenger flow, weather state coefficient, traffic flow coefficient;
the weather state coefficient is negatively correlated with the weather severity, and the traffic flow coefficient is positively correlated with the traffic jam degree.
Wherein, the worse the weather is, the smaller the weather state coefficient is; the more serious the degree of congestion of traffic, the larger the traffic flow coefficient.
In this embodiment, the predicted operation information is adjusted according to at least one of the station passenger flow volume, the weather state coefficient, and the traffic flow coefficient, and the vehicle is scheduled based on the adjusted predicted operation information, so that the vehicle can be flexibly scheduled, and the vehicle scheduling effect is further improved.
Optionally, the predicted operation information includes departure time intervals;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
determining a passenger saturation coefficient of a destination line based on a station passenger flow volume and a passenger accommodative volume of the destination line, the passenger accommodative volume being indicative of a number of passengers currently able for the vehicle, the passenger saturation coefficient being positively correlated with the station passenger flow volume and negatively correlated with the passenger accommodative volume;
determining departure interval shortening time or departure interval prolonging time according to a target parameter, wherein the target parameter is any one of the passenger saturation coefficient, the weather state coefficient and the traffic flow coefficient;
and adjusting the departure interval time of the vehicle on the target line according to the departure interval shortened time or the departure interval prolonged time.
The passenger saturation coefficient can be the ratio of the passenger flow of a station to the passenger accommodation capacity; or, a first sum of the passenger capacity and a first preset value can be calculated, and the passenger saturation coefficient can be a ratio of the station passenger flow and the first sum; or a second sum of the station passenger flow and a second preset value can be calculated, and the passenger saturation coefficient can be a ratio of the second sum to the passenger accommodation capacity; and the like, which are not limited in this respect by the embodiments of the present invention.
For example, the station passenger flow may be passger _ flow, the passenger capacity may be passger _ container, and the passenger saturation factor, saturation, may be:
wherein, when the station passenger flow volume is consistent with the passenger capacity, the saturation is 1; when the saturation is greater than 1, the passenger flow of the station exceeds the passenger carrying capacity of the current station, and at the moment, the larger the saturation value is, the longer the residence time of the passenger will be; when the saturation is less than 1, the passenger flow of the station is less than the passenger carrying capacity of the current station, and at this time, the smaller the saturation value is, the lower the vehicle passenger carrying rate is, that is, the lower the actual operation efficiency is.
In addition, the traffic flow coefficient may be characterized as a traffic congestion degree or a traffic flow rate, or the like. As an embodiment, the traffic flow coefficient jam level The traffic congestion degree can be characterized as the traffic congestion degree, and the traffic congestion degree can be used for measuring the traffic congestion degree and is divided into a grade I, a grade II, a grade III and a grade IV which respectively represent severe congestion, moderate congestion, light congestion and unblocked traffic. The traffic congestion degree is in the following value ranges under congestion degrees of different levels: stage I: jam level ∈[2.5，3](ii) a And II, stage: jam level E [2,2.5); and (3) stage III: jam level E [1.5,2); IV stage: jam level ∈[1，1.5)。
In the embodiment, the departure interval time of the target route is adjusted according to any one of the passenger saturation coefficient, the weather state coefficient and the traffic flow coefficient, so that the departure interval time of the target route can be flexibly adjusted, the situation that passengers wait too long or the running efficiency of vehicles is low is avoided, and the vehicle dispatching effect can be improved.
Optionally, the determining, according to the target parameter, a departure interval shortening duration or a departure interval lengthening duration includes:
determining a departure interval shortening time length according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is larger than a first decision parameter, wherein the departure interval shortening time length is positively correlated with the passenger saturation coefficient, the departure interval shortening time length is negatively correlated with the traffic flow coefficient, the departure interval shortening time length is negatively correlated with the first decision parameter, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Wherein the first decision parameter may be greater than 1. The first decision parameter may be positively correlated with the historical maximum passenger saturation factor, the first decision parameter may be negatively correlated with the historical average passenger saturation factor, and the first decision parameter may be positively correlated with the historical average traffic flow factor. By way of example, the first decision parameter threshold1 may be:
wherein, saturation max Saturation for historical maximum passenger saturation factor average For historical average passenger saturation factor, jam average Is a historical average traffic flow coefficient; lambda is a service quality coefficient, the larger lambda is, the better the service quality is represented, the specific representation is that the shorter the average waiting time of passengers is, lambda can be configured in advance, and the value range of lambda is (0)1); alpha is an introduced empirical parameter of a balanced traffic flow coefficient, and the value range is (0,0.5)]。
As a specific implementation mode, the departure interval is shortened by the time length t cut Can be as follows:
wherein λ is a quality of service coefficient, saturation is a passenger saturation coefficient, jam level As a traffic flow coefficient, t interval To adjust the departure time interval before, i.e., the departure time interval in the expected operation information, threshold1 is the first decision parameter.
In addition, the departure interval time of the target route is shortened according to the departure interval shortening duration, and the departure interval time t before adjustment may be interval Shorten the time t by subtracting the departure interval cut As the adjusted departure time interval.
In addition, when the departure interval time of the target link is shortened, the departure time can be dynamically reduced based on the next departure shift time. If no vehicle can be dispatched in the next dispatching shift of the target route, the non-dispatched vehicle of the route with the least passenger flow in the routes operated except the target route can be called to be supplemented to the target route.
In this embodiment, when the passenger saturation coefficient is greater than the first decision parameter, the departure interval shortening time is determined according to the passenger saturation coefficient, and the departure interval time of the target line is shortened according to the departure interval shortening time, so that when the actual passenger flow exceeds the carrying capacity of the station, the departure interval time is shortened, the passenger is prevented from waiting too long, and the riding experience of the passenger is improved.
Optionally, the determining, according to the target parameter, a departure interval shortening duration or a departure interval lengthening duration includes:
determining a departure interval extension time according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is smaller than a second decision parameter, wherein the departure interval extension time is negatively correlated with the passenger saturation coefficient, and the departure interval extension time is positively correlated with the traffic flow coefficient, and the second decision parameter is determined based on a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and prolonging the departure time interval of the target line according to the departure interval prolonged duration.
Wherein the content of the first and second substances,
additionally, the second decision parameter may be less than 1. The second decision parameter may be positively correlated with the historical average traffic flow coefficient, and the second decision parameter threshold0 may be, for example:
wherein, jam average Is a historical average traffic flow coefficient; lambda is a service quality coefficient, the larger lambda is, the better the represented service quality is, the shorter the average waiting time of passengers is, the lambda can be configured in advance, and the value range of lambda is (0,1); alpha is an introduced empirical parameter of a balanced traffic flow coefficient, and the value range is (0,0.5)]。
As a specific implementation, the departure interval is extended by a time period t extend Can be as follows:
t extend ＝(1-λ)·t interval ·(1-saturation)·jam level
wherein λ is a quality of service coefficient, saturation is a passenger saturation coefficient, jam level As a traffic flow coefficient, t interval To adjust the previous departure time interval, i.e., the departure time interval in the expected operation information.
In addition, the departure time interval of the target route is extended according to the departure interval extension duration, which may be the duration t of the departure interval extension duration extend And adjustingPrevious departure time interval t interval And adding the time intervals to obtain the adjusted departure time interval.
In this embodiment, when the passenger saturation coefficient is smaller than a second decision parameter, the departure interval extension duration is determined according to the passenger saturation coefficient, and the departure interval of the target line is extended according to the departure interval extension duration, so that when the actual passenger flow is smaller than the carrying capacity of the station, the departure interval duration is extended, and resource waste caused by no-load of a vehicle is reduced.
Optionally, the determining, according to the target parameter, the departure interval shortened duration or the departure interval extended duration includes:
determining a departure interval shortened time length according to the weather state coefficient under the condition that the weather state coefficient is smaller than a first preset threshold, wherein the departure interval shortened time length is positively correlated with the passenger saturation coefficient, the departure interval shortened time length is negatively correlated with the traffic flow coefficient, the departure interval shortened time length is negatively correlated with a first decision parameter, the departure interval shortened time length is negatively correlated with the weather state coefficient, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Wherein, can confirm weather condition coefficient according to the characteristic thing that the camera was monitored, or can confirm weather condition coefficient according to the ambient temperature value that temperature sensor surveyed, or can confirm weather condition coefficient etc. jointly according to the characteristic thing that the camera was monitored and the ambient temperature value that temperature sensor surveyed, this embodiment does not restrict this. The first preset threshold may be 0.4, or 0.3, or 0.2, etc. For example, when the camera monitors an umbrella, rain or snow, it may be determined that the weather condition coefficient is 0.1, which is smaller than the first preset threshold.
Furthermore, local weather conditions in recent years can be counted, and all the weather conditions are converted into weather state coefficients t with the values between 0 and 1 cur ，t cur ∈[0,1]. When the weather condition is good, t cur The trend is 1; in bad weather conditions, t cur Tending towards 0.
As a specific implementation mode, the departure interval is shortened by the time length t cut Can be as follows:
wherein λ is a quality of service coefficient, saturation is a passenger saturation coefficient, jam level Is the traffic flow coefficient, t interval For adjusting the preceding departure time interval, i.e. the departure time interval in the expected operating information, t cur The weather condition coefficient r is an empirical parameter value and can be set according to actual environment, for example, r can be 0.5, and threshold1 is a first decision parameter.
Additionally, the first decision parameter may be greater than 1. The first decision parameter may be positively correlated with the historical maximum passenger saturation factor, the first decision parameter may be negatively correlated with the historical average passenger saturation factor, and the first decision parameter may be positively correlated with the historical average traffic flow factor. By way of example, the first decision parameter threshold1 may be:
wherein, saturation max Saturation for historical maximum passenger saturation factor average For historical average passenger saturation factor, jam average Is a historical average traffic flow coefficient; lambda is a service quality coefficient, the larger lambda is, the better the represented service quality is, the shorter the average waiting time of passengers is, the lambda can be configured in advance, and the value range of lambda is (0,1); alpha is flatThe value range of the introduced empirical parameters of the constant traffic flow coefficient is (0,0.5)]。
When the weather is bad, the departure interval time of the target route may be shortened based on the next departure shift time.
In this embodiment, when the weather state coefficient is smaller than the first preset threshold, the departure interval shortening time length is determined according to the weather state coefficient, and the departure interval time of the target line is shortened according to the departure interval shortening time length, so that the departure interval time can be dynamically reduced when the weather is severe, the waiting time of a passenger in severe weather is reduced, and the riding experience of the passenger is improved.
Optionally, the real-time monitoring data includes the traffic flow coefficient, and the predicted operation information includes a driving route;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
and under the condition that the traffic flow coefficient between a first station and a second station of the target line is greater than a second preset threshold value within a preset time period, adjusting the driving route of the vehicle between the first station and the second station, wherein the first station is any station on the target line, and the second station is a station behind the first station.
Under the condition that a traffic flow coefficient between a first station and a second station of the target route is greater than a second preset threshold value within a preset time period, if a feasible alternate route exists between the first station and the second station, a driving route of a vehicle between the first station and the second station can be adjusted according to the alternate route; if no feasible alternative route exists between the first station and the second station, the estimated time for reaching the second station can be determined according to a traffic flow coefficient between the first station and the second station, and the estimated time is updated to a display screen of the second station.
In addition, the preset time period may be 20 minutes, 30 minutes or 40 minutes, and the like. The traffic flow coefficient is larger than the second preset threshold value within the preset time period, and the traffic flow is considered to be seriously congested within the preset time period. And under the condition that the traffic flow coefficient between the first station and the second station is smaller than a third preset threshold value, vehicles can be dispatched according to the predicted operation information, and the condition that the traffic flow coefficient is smaller than the third preset threshold value can represent that the traffic flow is smooth. Under the condition that the traffic flow coefficient between the first station and the second station is larger than a fourth preset threshold and smaller than a fifth preset threshold, the estimated time of arriving at the second station can be determined according to the traffic flow coefficient between the first station and the second station, the estimated time is updated to the display screen of the second station, and the condition that the traffic flow coefficient is larger than the fourth preset threshold and smaller than the fifth preset threshold can represent that the traffic flow is relatively congested. The estimated time of arrival at the second site may be a sum of a product of the traffic flow coefficient and a reference coefficient and the estimated time in the predicted operation information, and the reference coefficient may be an empirical value.
In this embodiment, when the traffic flow coefficient between the first station and the second station of the target route is greater than a second preset threshold within a preset time period, the driving route of the vehicle between the first station and the second station is adjusted, so that the driving route can be automatically adjusted when the traffic between the two stations of the target route is always in a congestion state, and the vehicle scheduling effect can be improved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a vehicle dispatching device according to an embodiment of the present invention, and as shown in fig. 3, the vehicle dispatching device 200 includes:
the prediction module 201 is used for predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line;
a determining module 202, configured to determine expected operation information of the vehicle on the target route on the target date according to the predicted passenger flow volume;
an obtaining module 203, configured to obtain real-time monitoring data of the target date for the cameras arranged on the target line;
and the scheduling module 204 is configured to adjust the predicted operation information according to the real-time monitoring data, and schedule the vehicle based on the adjusted predicted operation information.
Optionally, the real-time monitoring data includes at least one of the following:
station passenger flow, weather state coefficient, traffic flow coefficient;
the weather state coefficient is negatively correlated with the weather severity, and the traffic flow coefficient is positively correlated with the traffic jam degree.
Optionally, the predicted operation information includes departure time intervals;
as shown in fig. 4, the scheduling module 204 includes:
a first determining unit 2041 configured to determine a passenger saturation coefficient of a destination link based on a station passenger flow volume and a passenger accommodative amount of the destination link, the passenger accommodative amount indicating the number of passengers that can be currently accommodated by the vehicle, the passenger saturation coefficient being positively correlated with the station passenger flow volume and negatively correlated with the passenger accommodative amount;
a second determining unit 2042, configured to determine a departure interval shortening time length or a departure interval extending time length according to a target parameter, where the target parameter is any one of the passenger saturation coefficient, the weather state coefficient, and the traffic flow coefficient;
an adjusting unit 2043, configured to adjust the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration;
a scheduling unit 2044 for scheduling the vehicle based on the adjusted predicted operation information.
Optionally, the target parameter is the passenger saturation coefficient, and the second determining unit 2042 is specifically configured to:
determining a departure interval shortening time length according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is larger than a first decision parameter, wherein the departure interval shortening time length is positively correlated with the passenger saturation coefficient, the departure interval shortening time length is negatively correlated with the traffic flow coefficient, the departure interval shortening time length is negatively correlated with the first decision parameter, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting unit 2043 is specifically configured to:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
Optionally, the target parameter is the passenger saturation coefficient, and the second determining unit 2042 is specifically configured to:
determining a departure interval extension time according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is smaller than a second decision parameter, wherein the departure interval extension time is negatively correlated with the passenger saturation coefficient, and the departure interval extension time is positively correlated with the traffic flow coefficient, and the second decision parameter is determined based on a historical average traffic flow coefficient;
the adjusting unit 2043 is specifically configured to:
and prolonging the departure time interval of the target line according to the departure interval prolonged duration.
Optionally, the target parameter is the weather state coefficient, and the second determining unit 2042 is specifically configured to:
under the condition that the weather state coefficient is smaller than a first preset threshold value, determining departure interval shortening time according to the weather state coefficient, wherein the departure interval shortening time is positively correlated with the passenger saturation coefficient, the departure interval shortening time is negatively correlated with the traffic flow coefficient, the departure interval shortening time is negatively correlated with a first decision parameter, the departure interval shortening time is negatively correlated with the weather state coefficient, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting unit 2043 is specifically configured to:
and shortening the departure interval time of the target line according to the departure interval shortened duration.
Optionally, the real-time monitoring data includes the traffic flow coefficient, and the predicted operation information includes a driving route;
the scheduling module 204 is specifically configured to:
when the traffic flow coefficient between a first station and a second station of the target route is smaller than a second preset threshold value within a preset time period, adjusting a driving route of a vehicle between the first station and the second station, wherein the first station is any station on the target route, and the second station is a station behind the first station;
and scheduling the vehicle based on the adjusted predicted operation information.
As shown in fig. 5, an embodiment of the present invention further provides an electronic device 300, including: the vehicle scheduling method includes a processor 301, a memory 302, and a program stored in the memory 302 and capable of running on the processor 301, where the program, when executed by the processor 301, implements each process of the vehicle scheduling method embodiment, and can achieve the same technical effect, and is not described herein again to avoid repetition.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the processes of the embodiment of the vehicle scheduling method, and can achieve the same technical effect, and in order to avoid repetition, the computer program is not described herein again. The computer readable storage medium is, for example, ROM, RAM, magnetic disk or optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A vehicle scheduling method, characterized in that the method comprises:
predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line;
determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow volume;
acquiring real-time monitoring data of a camera arranged aiming at the target route on the target date;
and adjusting the predicted operation information according to the real-time monitoring data, and scheduling the vehicle based on the adjusted predicted operation information.
2. The method of claim 1, wherein the real-time monitoring data comprises at least one of:
station passenger flow, weather state coefficient, traffic flow coefficient;
the weather state coefficient is negatively correlated with the weather severity, and the traffic flow coefficient is positively correlated with the traffic jam degree.
3. The method of claim 2, wherein the projected operational information includes departure time intervals;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
determining a passenger saturation coefficient of a destination line based on a station passenger flow volume and a passenger accommodative volume of the destination line, the passenger accommodative volume being indicative of a number of passengers currently able for the vehicle, the passenger saturation coefficient being positively correlated with the station passenger flow volume and negatively correlated with the passenger accommodative volume;
determining departure interval shortening time or departure interval lengthening time according to a target parameter, wherein the target parameter is any one of the passenger saturation coefficient, the weather state coefficient and the traffic flow coefficient;
and adjusting the departure interval time of the vehicle on the target line according to the departure interval shortened time or the departure interval prolonged time.
4. The method of claim 3, wherein the target parameter is the passenger saturation factor, and wherein determining the departure interval shortening duration or departure interval extending duration based on the target parameter comprises:
determining a departure interval shortening time length according to the passenger saturation coefficient under the condition that the passenger saturation coefficient is larger than a first decision parameter, wherein the departure interval shortening time length is positively correlated with the passenger saturation coefficient, the departure interval shortening time length is negatively correlated with the traffic flow coefficient, the departure interval shortening time length is negatively correlated with the first decision parameter, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
5. The method of claim 3, wherein the target parameter is the passenger saturation factor, and wherein determining the departure interval shortening duration or departure interval extending duration based on the target parameter comprises:
determining a departure interval extension time according to the passenger saturation coefficient when the passenger saturation coefficient is smaller than a second decision parameter, wherein the departure interval extension time is negatively correlated with the passenger saturation coefficient, and the departure interval extension time is positively correlated with the traffic flow coefficient, and the second decision parameter is determined based on a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and prolonging the departure time interval of the target line according to the departure interval prolonged duration.
6. The method of claim 3, wherein the target parameter is the weather condition coefficient, and wherein determining the departure interval shortened duration or the departure interval extended duration based on the target parameter comprises:
under the condition that the weather state coefficient is smaller than a first preset threshold value, determining departure interval shortening time according to the weather state coefficient, wherein the departure interval shortening time is positively correlated with the passenger saturation coefficient, the departure interval shortening time is negatively correlated with the traffic flow coefficient, the departure interval shortening time is negatively correlated with a first decision parameter, the departure interval shortening time is negatively correlated with the weather state coefficient, and the first decision parameter is determined based on a historical maximum passenger saturation coefficient, a historical average passenger saturation coefficient and a historical average traffic flow coefficient;
the adjusting the departure interval time of the vehicle on the target route according to the departure interval shortened duration or the departure interval extended duration includes:
and shortening the departure interval time of the target line according to the departure interval shortening duration.
7. The method of claim 2, wherein the real-time monitoring data includes the traffic flow coefficient, the predicted operational information includes driving routes;
the adjusting the predicted operation information according to the real-time monitoring data comprises:
and under the condition that the traffic flow coefficient between a first station and a second station of the target line is smaller than a second preset threshold value within a preset time period, adjusting the driving route of the vehicle between the first station and the second station, wherein the first station is any station on the target line, and the second station is a station behind the first station.
8. A vehicle dispatching device, comprising:
the prediction module is used for predicting the passenger flow of the target line on a target date according to the historical passenger flow data of the target line;
the determining module is used for determining the predicted running information of the vehicle on the target route on the target date according to the predicted passenger flow;
the acquisition module is used for acquiring real-time monitoring data of the target date of the cameras arranged aiming at the target line;
and the scheduling module is used for adjusting the predicted operation information according to the real-time monitoring data and scheduling the vehicle based on the adjusted predicted operation information.
9. An electronic device, comprising: a processor, a memory and a program stored on and executable on the memory, which when executed by the processor implements the steps of the vehicle scheduling method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the vehicle scheduling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111098444.XA CN115841745A (en) | 2021-09-18 | 2021-09-18 | Vehicle scheduling method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111098444.XA CN115841745A (en) | 2021-09-18 | 2021-09-18 | Vehicle scheduling method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115841745A true CN115841745A (en) | 2023-03-24 |
Family
ID=85574231
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111098444.XA Pending CN115841745A (en) | 2021-09-18 | 2021-09-18 | Vehicle scheduling method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115841745A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485137A (en) * | 2023-04-20 | 2023-07-25 | 浪潮智慧科技有限公司 | Intelligent bus scheduling method, device and medium based on big data |
| CN117495059A (en) * | 2023-12-29 | 2024-02-02 | 天津交控科技有限公司 | Rail transit operation data analysis method and storage medium |
-
2021
- 2021-09-18 CN CN202111098444.XA patent/CN115841745A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485137A (en) * | 2023-04-20 | 2023-07-25 | 浪潮智慧科技有限公司 | Intelligent bus scheduling method, device and medium based on big data |
| CN116485137B (en) * | 2023-04-20 | 2023-10-13 | 浪潮智慧科技有限公司 | Intelligent bus scheduling method, device and medium based on big data |
| CN117495059A (en) * | 2023-12-29 | 2024-02-02 | 天津交控科技有限公司 | Rail transit operation data analysis method and storage medium |
| CN117495059B (en) * | 2023-12-29 | 2024-04-12 | 天津交控科技有限公司 | Rail transit operation data analysis method and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108932862B (en) | Automatic driving vehicle scheduling method and device | |
| CN107330547B (en) | Urban public transport dynamic scheduling optimization method and system | |
| CN109118758B (en) | Intelligent networking traffic management system for mobile sharing | |
| CN111371904B (en) | Cloud-side-end-coordinated highway cloud control system and control method | |
| KR100809558B1 (en) | System and method for controlling bus operation | |
| CN115841745A (en) | Vehicle scheduling method and device and electronic equipment | |
| US20130197790A1 (en) | Method and system for traffic performance analysis, network reconfiguration, and real-time traffic monitoring | |
| JP2006188150A (en) | Prediction system for rate of occupancy | |
| CN113628473B (en) | Intelligent bus response type stop plan and dynamic scheduling system | |
| Razavi et al. | Smart traffic light scheduling in smart city using image and video processing | |
| CN113159393A (en) | Passenger station passenger flow awareness early warning method and device, electronic equipment and storage medium | |
| CN113393137B (en) | Scheduling sharing system based on Internet of vehicles | |
| CN114743401B (en) | Data visualization bus dispatching management platform based on bus digital transformation | |
| US20200124435A1 (en) | Distributed route determination system | |
| CN113053142B (en) | Bus priority control system based on vehicle-mounted positioning and bus path cooperation technology | |
| US10157509B2 (en) | System for public transit incident rate analysis and display | |
| CN112465213B (en) | Auxiliary device and method of subway passenger information service system | |
| CN111047228A (en) | Bus station separating method and system | |
| CN116432956B (en) | Bus allocation method, system, equipment and medium based on passenger flow | |
| CN116844337A (en) | Real-time road condition information analysis system and method for road traffic based on vehicle-road cooperation | |
| CN115762131A (en) | Intelligent driving plan compiling method and system applied to public transportation | |
| CN116109081A (en) | Dynamic bus scheduling optimization method and device based on Internet of things | |
| CN113542364B (en) | Bus and network appointment linkage intelligent traffic system | |
| CN115909722A (en) | Road resistance scheduling method and system applied to intelligent bus | |
| CN114463102A (en) | Order in-route matching degree calculation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |