CN112101677B - Public transport travel path planning method, device, equipment and storage medium - Google Patents
Public transport travel path planning method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, equipment and a storage medium for planning a public transport travel path. According to the technical scheme provided by the embodiment of the application, the duration time and the influence range of the rail transit emergency are judged according to the induction factors and the occurrence place of the rail transit emergency, the system operation states of all line segments and stations of the rail transit system in the duration time are determined according to the duration time and the influence range, the optimal path from the starting point to the end point is planned for passengers on the basis of the start-end point information set by the passengers and the current position information of the passengers in combination with the system operation states of the rail transit system, the influence of the rail transit emergency on the vehicle line segment and the station of the travel path is considered in the planned optimal path, the time consumed by the users in the rail transit is reduced, and the user experience is improved.
Description
Technical Field
The embodiment of the application relates to the field of public transportation, in particular to a public transportation travel path planning method, device, equipment and storage medium.
Background
Along with the development of rail transit, people's trip is more and more the mode of choosing to take the subway, and rail transit can reduce the jam of traffic, has brought very big facility for people's trip.
Meanwhile, with the gradual maturity of the rail transit network, subway lines are more and more complex, many people can use navigation software to plan a travel path when traveling, and travel and transfer schemes which take shorter time can be planned by setting origin-destination points, so that the travel of people is facilitated.
However, in the operation process of the rail transit system, an emergency situation may occur at a certain line segment or station, which may lead to a long time spent by people when traveling along a planned route, thereby affecting user experience.
Disclosure of Invention
The embodiment of the application provides a public transport travel path planning method, a public transport travel path planning device, equipment and a storage medium, so that a travel path is planned for a passenger again according to a rail transit emergency, and user experience is optimized.
In a first aspect, an embodiment of the present application provides a public transportation travel path planning method, including:
estimating the duration and the influence range of the rail transit emergency based on the induction factors and the occurrence place of the rail transit emergency, calibrating the duration at the time interval from the occurrence of the emergency to the system repair, and calibrating the influence range by a set of a vehicle line segment and a station point of which the running state is changed after the occurrence of the emergency;
determining the system running state of the rail transit system according to the duration and the influence range of the rail transit emergency;
and obtaining an optimal path from the starting point to the end point based on the set starting-destination point information and in combination with the system running state of the rail transit system.
Further, the inducing factors include one or more of signal factors, vehicle factors, track factors, power supply factors, platform door factors, and human factors.
Further, before estimating the duration and the influence range of the rail transit emergency based on the inducing factors and the occurrence location of the rail transit emergency, the method further comprises the following steps:
a time range prediction model is built based on a neural network, the induction factors and the occurrence places of the historical rail transit emergency are used as sample input, the duration time and the influence range of the historical rail transit emergency are used as sample output, and the time range prediction model is trained.
Further, the duration and the influence range of the rail transit emergency are estimated based on the induction factors and the occurrence place of the rail transit emergency, and the duration and the influence range comprise:
responding to the rail transit emergency, and determining an inducing factor and an occurrence place of the rail transit emergency;
and inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency according to the induction factors and the occurrence places by the time range prediction model.
Further, the system operation state of the rail transit system comprises one or more combination of vehicle line travel time, vehicle platform parking time, carriage congestion degree, station access flow and platform congestion degree.
Further, the determining the system operation state of the rail transit system according to the duration and the influence range of the rail transit emergency includes:
determining vehicle line sections and system repair speeds of stations within the influence range of the rail transit emergency;
and determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed.
Further, the determining the system operation state of the rail transit system in the duration of the rail transit emergency according to the system repair speed includes:
segmenting the duration of the rail transit emergency;
and determining the system running states of the rail transit system in different duration segments according to the system repairing speed.
Further, the determining the system operation state of the rail transit system in the duration of the rail transit emergency according to the system repair speed includes:
determining vehicle line sections and station recovery time within the influence range of the rail transit emergency according to the system repair speed;
and determining the system running states of each vehicle line segment and the station in the rail transit system before and after the corresponding recovery time based on the recovery time.
Further, the obtaining an optimal path from a starting point to an end point based on the set start-to-end point information and in combination with the system operation state of the rail transit system includes:
based on the set origin-destination information and in combination with the system running state of the rail transit system, obtaining a first optimal path from the starting point to the destination and a second optimal path from the starting point to the destination through the road surface bus;
and determining an optimal path according to the comparison condition of the first optimal path and the second optimal path.
Further, after obtaining an optimal path from a starting point to an end point based on the set start-to-end point information and in combination with the system operating state of the rail transit system, the method further includes:
and replanning an optimal path based on the mandatory station in response to the set operation event of the mandatory station.
In a second aspect, an embodiment of the present application provides a public transportation travel path planning apparatus, including a time range prediction module, an operation state prediction module, and a travel path planning module, where:
the time range prediction module is used for predicting the duration and the influence range of the rail transit emergency based on the induction factors and the occurrence place of the rail transit emergency, the duration is calibrated at the time interval from the occurrence of the emergency to the restoration of a system, and the influence range is calibrated by a set of a vehicle line segment and a station point of which the running state is changed after the occurrence of the emergency;
the operation state prediction module is used for determining the system operation state of the rail transit system according to the duration and the influence range of the rail transit emergency;
and the travel path planning module is used for obtaining an optimal path from the starting point to the end point based on the set starting-destination point information and in combination with the system running state of the rail transit system.
Further, the inducing factors include one or more of signal factors, vehicle factors, track factors, power supply factors, platform door factors, and human factors.
Furthermore, the device also comprises a prediction model establishing module which is used for establishing a time range prediction model based on the neural network, taking the induction factors and the occurrence places of the historical rail transit emergencies as sample input, taking the duration and the influence range of the historical rail transit emergencies as sample output, and training the time range prediction model.
Further, the time range prediction module is specifically configured to:
responding to the rail transit emergency, and determining an inducing factor and an occurrence place of the rail transit emergency;
and inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency according to the induction factors and the occurrence places by the time range prediction model.
Further, the system operation state of the rail transit system comprises one or more combination of vehicle line travel time, vehicle platform parking time, carriage congestion degree, station access flow and platform congestion degree.
Further, the operating state prediction module is specifically configured to:
determining vehicle line sections and system repair speeds of stations within the influence range of the rail transit emergency;
and determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed.
Further, when the operation state prediction module determines the system operation state of the rail transit system within the duration of the rail transit emergency according to the system repair speed, the operation state prediction module specifically includes:
segmenting the duration of the rail transit emergency;
and determining the system running states of the rail transit system in different duration segments according to the system repairing speed.
Further, when the operation state prediction module determines the system operation state of the rail transit system within the duration of the rail transit emergency according to the system repair speed, the operation state prediction module specifically includes:
determining vehicle line sections and station recovery time within the influence range of the rail transit emergency according to the system repair speed;
and determining the system running states of each vehicle line segment and the station in the rail transit system before and after the corresponding recovery time based on the recovery time.
Further, the travel path planning module is specifically configured to:
based on the set origin-destination information and in combination with the system running state of the rail transit system, obtaining a first optimal path from the starting point to the destination and a second optimal path from the starting point to the destination through the road surface bus;
and determining an optimal path according to the comparison condition of the first optimal path and the second optimal path.
Further, the travel path planning module is further configured to replan an optimal path based on the inevitable site in response to a setting operation event of the inevitable site.
In a third aspect, an embodiment of the present application provides a computer device, including: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of mass transit travel path planning as described in the first aspect.
In a fourth aspect, the present application provides a storage medium containing computer-executable instructions for performing the method for public transportation travel path planning according to the first aspect when executed by a computer processor.
According to the method and the device, the duration time and the influence range of the rail transit emergency are judged according to the induction factors and the occurrence place of the rail transit emergency, the system operation states of all line sections and stations of the rail transit system in the duration time are determined according to the duration time and the influence range, the optimal path from the starting point to the ending point is planned for passengers based on the starting-ending point information set by the passengers and the current position information of the passengers, the influence of the rail transit emergency on the vehicle line sections and the stations of the travel path is considered in the planned optimal path, the time consumed by the travel of the users on the rail transit is reduced, and the user experience is improved.
Drawings
Fig. 1 is a flowchart of a public transportation travel path planning method according to an embodiment of the present application;
fig. 2 is a flowchart of another method for planning a travel path of public transportation according to an embodiment of the present application;
FIG. 3 is a partial schematic view of a rail transit system provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a public transportation travel path planning apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Fig. 1 is a flowchart of a public transportation travel path planning method according to an embodiment of the present disclosure, where the public transportation travel path planning method according to the embodiment of the present disclosure may be executed by a public transportation travel path planning device, and the public transportation travel path planning device may be implemented by hardware and/or software and integrated in a computer device.
The following description will be given by taking an example of a method for executing a public transportation travel path planning by a public transportation travel path planning device. Referring to fig. 1, the public transportation travel path planning method includes:
s101: and estimating the duration and the influence range of the rail transit emergency based on the induction factors and the occurrence place of the rail transit emergency.
The duration is calibrated according to the time interval from the occurrence of an emergency to the system repair, and the influence range is calibrated according to the line segment of the vehicle with the changed running state after the occurrence of the emergency and the set of stations.
Illustratively, after the induction factors and the occurrence points of the rail transit emergency are determined, the duration and the influence range of the rail transit emergency are estimated by combining empirical data of past rail transit emergency.
It is understood that, there are different system repair rates for different inducing factors or occurrence locations, and the system repair rate can be determined according to the specific inducing factors and occurrence locations, and the time interval from occurrence of an emergency to system repair, i.e. the duration, can be determined.
Further, according to experience data of the rail transit emergency, a set of vehicle line segments and stations of which the current inducing factors and the generating places cause the change of the running state is judged, and the set is determined as the influence range of the rail transit emergency.
S102: and determining the system running state of the rail transit system according to the duration and the influence range of the rail transit emergency.
After the duration and the influence range of the rail transit emergency are determined, the running states of the vehicle sections and the stations in the rail transit system are determined according to the duration and the influence range of the rail transit emergency, and therefore the system running state of the rail transit system is determined.
It can be understood that, by spending more time on a vehicle section or a station within the influence range of the rail transit emergency than when the rail transit emergency does not occur, the running state of the corresponding vehicle section or station is more tense.
S103: and obtaining an optimal path from the starting point to the end point based on the set starting-destination point information and in combination with the system running state of the rail transit system.
The origin-destination information provided by this embodiment includes origin information and destination information, and can be set by a user terminal (e.g., a mobile phone, a tablet, etc.). Wherein the start point information may be determined based on the current location information, e.g. by starting with the current location or a station near the current location. The current location information may be determined based on the location information of the ue, for example, the location information of the ue is obtained through a positioning manner such as base station positioning, WiFi positioning, GPS positioning, and the like. And after obtaining the origin-destination information, the user terminal sends the origin-destination information to the public transportation travel path planning device.
For example, after the origin-destination information is determined, path planning is performed according to the system operating state of the rail transit system, so as to obtain an optimal path from the starting point to the destination. The optimal path may be determined based on an existing path planning algorithm, such as Dijkstra algorithm (dixtre algorithm), a-x algorithm, and D-x algorithm, which is not limited in this application.
In a possible embodiment, while the origin-destination information is set, the current location information is used as the starting point, the station corresponding to the starting point information is used as the intermediate node, and based on the set origin-destination information and the current location information, in combination with the system operating state of the rail transit system, the optimal path from the starting point to the destination via the intermediate node is obtained.
Optionally, after the optimal path is obtained, the optimal path is returned to the user terminal, so that the user terminal displays the optimal path.
The duration time and the influence range of the rail transit emergency are judged according to the induction factors and the occurrence place of the rail transit emergency, the system running states of all line segments and stations of the rail transit system in the duration time are determined according to the duration time and the influence range, the optimal path from the starting point to the end point is planned for passengers according to the starting-destination point information set by the passengers and the current position information of the passengers, the influence of the rail transit emergency on the vehicle line segment and the stations of the travel path is considered in the planned optimal path, the time consumed by the users in the rail transit is reduced, and the user experience is improved.
Fig. 2 is a flowchart of another public transportation travel path planning method according to an embodiment of the present application, which is an embodiment of the public transportation travel path planning method. Referring to fig. 2, the public transportation travel path planning method includes:
s201: a time range prediction model is built based on a neural network, the induction factors and the occurrence places of the historical rail transit emergency are used as sample input, the duration time and the influence range of the historical rail transit emergency are used as sample output, and the time range prediction model is trained.
Specifically, historical track traffic emergencies are recorded, induction factors, occurrence places, duration and influence ranges corresponding to the historical track traffic emergencies are recorded, and a training sample set is established based on the records of the historical track traffic emergencies.
When historical track traffic emergencies are recorded, the duration and the influence range are determined in a manual marking mode, the duration is calibrated at the time interval from the occurrence of the emergencies to the system repair, and the influence range is calibrated by a vehicle line segment and a station set, of which the running state is changed after the emergencies.
Furthermore, a time range prediction model is built on the basis of the neural network of deep learning, induction factors and occurrence places of historical rail transit emergencies are used as sample inputs, duration and influence ranges of the historical rail transit emergencies are used as sample outputs, and the time range prediction model is trained until the accuracy of the time range prediction model meets preset requirements.
S202: and determining the inducing factors and the occurrence place of the rail transit emergency in response to the rail transit emergency.
Specifically, when a rail transit emergency happens, the inducing factors and the happening place of the rail transit emergency are determined. The rail transit emergency can be confirmed through operation feedback information fed back by monitoring terminals in various vehicle sections and stations of the rail transit system, for example, when the vehicle sections or stations have the emergency, the inducing factors and the occurrence places are uploaded through the monitoring terminals; or arranging sensors in the vehicle line sections and the stations, judging whether rail traffic emergencies occur according to the output of the sensors, and determining induction factors and occurrence places according to the types and installation places of the sensors; and the inducers and the occurrence positions of the emergency events can be determined according to the feedback of the staff to the emergency events of the vehicle line segments and the stations.
In the present embodiment, the inducing factors include one or more of signal factors, vehicle factors, track factors, power supply factors, platform door factors, and human factors.
S203: and inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency according to the induction factors and the occurrence places by the time range prediction model.
After the induction factors and the occurrence points of the rail transit emergency are determined, the induction factors and the occurrence points are input into a time range prediction model, the input induction factors and the occurrence points are analyzed by the time range prediction model, and the duration and the influence range are output, so that the estimation of the duration and the influence range of the rail transit emergency is realized.
Furthermore, according to the induction factors and the occurrence points corresponding to the rail transit emergencies occurring on the rail transit system, the time range prediction model analyzes the induction factors and the occurrence points to obtain the duration and the influence range of the rail transit emergencies.
S204: and determining the vehicle line segment within the influence range of the rail transit emergency and the system repair speed of the station.
Specifically, each vehicle line segment and station within the influence range of the rail transit emergency are determined, and the system repair speed of the vehicle line segments and the stations on the corresponding induction factors is obtained.
The system repair speed can be determined and recorded according to the specific vehicle line segment and the repair capacity of the station to the rail transit emergency caused by different inducing factors. It is understood that the system repair speed of different vehicle segments and stations for rail transit emergencies caused by the same inducement may be the same or different.
Optionally, the corresponding system repair speed may be determined according to repair resources (manpower, equipment, etc.) configured by the rail transit system at each vehicle segment and station. It will be appreciated that there are situations where the system repair time is different at different times, such as normal work hours and holidays, daytime and night.
S205: and determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed.
The system operation state of the rail transit system comprises one or more combination of vehicle line travel time, vehicle platform parking time, carriage congestion degree, station entrance and exit flow and platform congestion degree. It can be understood that, the more serious the rail traffic emergency is, the longer the travel time of the corresponding vehicle line segment is, the longer the parking time of the vehicle platform is, the more serious the degree of congestion of the carriage is, the larger the station entrance and exit flow is, the more serious the degree of congestion of the platform is, that is, the more tense the system operation state is.
Illustratively, according to the system repair speed, the running state of each vehicle line segment and platform within the influence range within the duration is calculated, and the running state of the vehicle line segment and platform outside the influence range is combined to determine the system running state of the rail transit system for a set time length from the occurrence of the rail transit emergency (or the current time) to the completion of the system repair.
In one embodiment, the determination of the system operation state may be an evaluation calculation performed in time-sharing, specifically, the method includes steps S2051 to S2052:
s2051: and segmenting the duration of the rail transit emergency.
Specifically, the duration is segmented according to a set time interval or an average number to obtain a plurality of duration segments.
S2052: and determining the system running states of the rail transit system in different duration segments according to the system repairing speed.
Specifically, according to the system repair speed, the operating states of the vehicle segments and the stations in the influence range at the computing nodes of the duration segments (for example, the middle time points or the end time points of the duration segments) are calculated, and the operating states of the vehicle segments and the stations outside the influence range are combined to determine the system operating state of the track traffic system for a set time length from the occurrence of the track traffic emergency (or the current time) to the completion of the system repair. By calculating the running state of the system corresponding to each duration segment of the rail transit system, the pressure of calculation resources is relieved, and the determining efficiency of the running state of the system is improved.
In one embodiment, the determination of the system operating state may be an evaluation based on the recovery time of the vehicle segment or the station, and specifically includes steps S2053-S2054:
s2053: and determining the vehicle line sections and the recovery time of the stations within the influence range of the rail transit emergency according to the system repair speed.
Specifically, according to the system repair speed of each vehicle section and each station, the duration of the rail transit emergency and the time demarcation point of the recovery of each vehicle section and each station in the influence range to the normal running state are calculated, and the time demarcation point is used as the recovery time of the corresponding vehicle section and each station.
S2054: and determining the system running states of each vehicle line segment and the station in the rail transit system before and after the corresponding recovery time based on the recovery time.
Specifically, the running states of each vehicle line segment and the station before and after the recovery time are determined, and the running states of the vehicle line segments and the station outside the influence range are combined to determine the running state of the system of the rail transit system for a set time length from the occurrence of the rail transit emergency (or the current time) to the completion of the system repair.
S206: and obtaining a first optimal path from the starting point to the terminal point and a second optimal path from the starting point to the terminal point through the road surface bus based on the set starting-destination information and in combination with the system running state of the rail transit system.
Specifically, based on the origin-destination information set by the user terminal, the route planning is performed in combination with the system running state of the rail transit system, the first optimal route from the starting point to the destination is calculated, and the first estimated time length of the first optimal route is determined. The stations through which the first optimal path provided by this embodiment passes are all track stations.
Furthermore, based on the origin-destination information set by the user terminal, the route planning is carried out by combining the system running state of the rail transit system, a second optimal route from the starting point to the destination through the road surface bus is calculated, and a second estimated time length of the second optimal route is determined. Wherein the transfer station for transferring from the track station to the road bus is located within or in front of the influence range.
It can be understood that, when an optimal path is planned, the intermediate time of reaching each station is judged, and the operation state of the corresponding station or the next vehicle station segment and station at the intermediate time is determined, if the operation state of the corresponding station or the next vehicle station segment and station indicates normal traffic, the next station can be planned continuously, and if the operation state of the corresponding station or the next vehicle station segment and station indicates no traffic or congestion, the transfer plan or road surface transfer plan in the track can be planned.
S207: and determining an optimal path according to the comparison condition of the first optimal path and the second optimal path.
Specifically, a first predicted time length and a second predicted time length corresponding to the first optimal path and the second optimal path are compared, when the first predicted time length is less than or equal to a second predicted time length, the first optimal path is determined as the optimal path, and when the first predicted time length is greater than the second predicted time length, the second optimal path is determined as the optimal path.
Fig. 3 is a partial schematic view of a rail transit system according to an embodiment of the present disclosure. Illustratively, as shown in fig. 3, a solid line in the figure is a track line, the track line comprises a line L1 and a line L2, the line L1 passes through track stations a1-a5 and a10, the line L2 passes through track stations A6-a10 and a5, the track stations a2 and a7 are combined and switched with each other, and a dashed line is a road bus line and passes through bus stations B1-B4, wherein a1 is located near B1, and a5 is located near B4. Assuming that the origin-destination points are track stations a1 and a5, respectively, since a track traffic emergency occurs at track station A3, the determined influence range is a2-a4, and the first optimal path determined by the path planning algorithm is S1: starting from a1, the path is changed to a2 and multiplied to a7, and then reaches a5 through a line L2, and the second optimal path is S2: from A1, a road bus is transferred from A2 to a bus stop B1, the road bus is walked to a track stop A5 after arriving at a bus stop B4, the first predicted time and the second predicted time of the first optimal path S1 and the second optimal path S2 are respectively T1 and T2, when T1 is less than or equal to T2, the first optimal path S1 is determined to be an optimal path, and when T1 is greater than T2, the second optimal path S2 is determined to be an optimal path.
Further, after the optimal path is determined, the optimal path is sent to the user side, the optimal path is displayed on the user side, route navigation is carried out according to the optimal path, and the user is guided to travel according to the route navigation.
The duration time and the influence range of the rail transit emergency are judged according to the induction factors and the occurrence place of the rail transit emergency, the system running states of all line segments and stations of the rail transit system in the duration time are determined according to the duration time and the influence range, the optimal path from the starting point to the end point is planned for passengers according to the starting-destination point information set by the passengers and the current position information of the passengers, the influence of the rail transit emergency on the vehicle line segment and the stations of the travel path is considered in the planned optimal path, the time consumed by the users in the rail transit is reduced, and the user experience is improved. Meanwhile, a time range prediction model is established based on historical rail transit emergencies, the duration and the influence range of the current rail transit emergencies are predicted through the time range prediction model, the objectivity and the accuracy of prediction of the duration and the influence range are improved, the system running state of the rail transit system is determined by combining the system repairing speed, the running states of all vehicle segments and stations are effectively evaluated, the system repairing time is accurately judged, and the path planning effect is optimized.
In a possible embodiment, after obtaining the optimal path from the starting point to the end point, the method for planning a public transportation travel path according to this embodiment further includes: and replanning an optimal path based on the mandatory station in response to the set operation event of the mandatory station.
The setting operation of the mandatory site is carried out through the user terminal, for example, a dialog box for setting the mandatory site is provided in an interactive interface of the user terminal, and the mandatory site is determined through input or selection on the dialog box.
After the setting operation event of the inevitable site is detected, the inevitable site is fixedly set as one site in the path, and the optimal path is planned again to obtain the optimal path passing through the inevitable site, so that the flexibility of path planning is improved, and the user experience is further improved.
Fig. 4 is a schematic structural diagram of a public transportation travel path planning apparatus according to an embodiment of the present application. Referring to fig. 4, the public transportation travel path planning apparatus provided in this embodiment includes a time range prediction module 41, an operation state prediction module 42, and a travel path planning module 43.
The time range prediction module 41 is configured to predict a duration and an influence range of the rail transit emergency based on an induction factor and an occurrence location of the rail transit emergency, where the duration is calibrated at a time interval from the occurrence of the emergency to the system repair, and the influence range is calibrated by a set of a station and a vehicle line segment in which an operation state changes after the occurrence of the emergency; the operation state prediction module 42 is configured to determine a system operation state of the rail transit system according to the duration and the influence range of the rail transit emergency; and the travel path planning module 43 is configured to obtain an optimal path from the starting point to the end point based on the set start-to-end point information and in combination with the system operation state of the rail transit system.
The duration time and the influence range of the rail transit emergency are judged according to the induction factors and the occurrence place of the rail transit emergency, the system running states of all line segments and stations of the rail transit system in the duration time are determined according to the duration time and the influence range, the optimal path from the starting point to the end point is planned for passengers according to the starting-destination point information set by the passengers and the current position information of the passengers, the influence of the rail transit emergency on the vehicle line segment and the stations of the travel path is considered in the planned optimal path, the time consumed by the users in the rail transit is reduced, and the user experience is improved.
In one possible embodiment, the inducement includes a combination of one or more of a signal factor, a vehicle factor, a track factor, a power factor, a platform door factor, and an artifact.
In one possible embodiment, the device further comprises a prediction model establishing module, wherein the prediction model establishing module is used for establishing a time range prediction model based on a neural network, taking the inducing factors and the occurrence places of the historical rail transit emergencies as sample inputs, taking the duration and the influence range of the historical rail transit emergencies as sample outputs, and training the time range prediction model.
In a possible embodiment, the time range prediction module 41 is specifically configured to:
responding to the rail transit emergency, and determining an inducing factor and an occurrence place of the rail transit emergency;
and inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency according to the induction factors and the occurrence places by the time range prediction model.
In one possible embodiment, the system operation state of the rail transit system comprises one or more of the combination of vehicle line travel time, vehicle platform stop time, degree of car congestion, station ingress and egress traffic, and degree of platform congestion.
In one possible embodiment, the operating state prediction module 42 is specifically configured to:
determining vehicle line sections and system repair speeds of stations within the influence range of the rail transit emergency;
and determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed.
In a possible embodiment, the operation state prediction module 42, when determining the system operation state of the rail transit system within the duration of the rail transit emergency according to the system repair speed, specifically includes:
segmenting the duration of the rail transit emergency;
and determining the system running states of the rail transit system in different duration segments according to the system repairing speed.
In a possible embodiment, the operation state prediction module 42, when determining the system operation state of the rail transit system within the duration of the rail transit emergency according to the system repair speed, specifically includes:
determining vehicle line sections and station recovery time within the influence range of the rail transit emergency according to the system repair speed;
and determining the system running states of each vehicle line segment and the station in the rail transit system before and after the corresponding recovery time based on the recovery time.
In a possible embodiment, the travel path planning module 43 is specifically configured to:
based on the set origin-destination information and in combination with the system running state of the rail transit system, obtaining a first optimal path from the starting point to the destination and a second optimal path from the starting point to the destination through the road surface bus;
and determining an optimal path according to the comparison condition of the first optimal path and the second optimal path.
In one possible embodiment, the travel path planning module 43 is further configured to re-plan the optimal path based on the inevitable site in response to a setup operation event of the inevitable site.
The embodiment of the application also provides computer equipment which can be integrated with the public transport travel path planning device provided by the embodiment of the application. Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application. Referring to fig. 5, the computer apparatus includes: an input device 53, an output device 54, a memory 52, and one or more processors 51; the memory 52 for storing one or more programs; when the one or more programs are executed by the one or more processors 51, the one or more processors 51 are enabled to implement the method for planning a public transportation travel path as provided in the above embodiments. Wherein the input device 53, the output device 54, the memory 52 and the processor 51 may be connected by a bus or other means, as exemplified by the bus connection in fig. 5.
The memory 52 is a storage medium readable by a computing device, and can be used for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the public transportation travel path planning method according to any embodiment of the present application (for example, the time range prediction module 41, the operation state prediction module 42, and the travel path planning module 43 in the public transportation travel path planning apparatus). The memory 52 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 52 may further include memory located remotely from the processor 51, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 53 may be used to receive input numeric or character information and generate key signal inputs relating to user settings and function control of the apparatus. The output device 54 may include a display device such as a display screen.
The processor 51 executes various functional applications of the device and data processing by running software programs, instructions and modules stored in the memory 52, namely, implements the public transportation travel path planning method described above.
The device, the system and the computer for planning the public transportation travel path can be used for executing the method for planning the public transportation travel path provided by any embodiment, and have corresponding functions and beneficial effects.
Embodiments of the present application further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for planning a public transportation travel path provided in the foregoing embodiments, where the method for planning a public transportation travel path includes: estimating the duration and the influence range of the rail transit emergency based on the induction factors and the occurrence place of the rail transit emergency, calibrating the duration at the time interval from the occurrence of the emergency to the system repair, and calibrating the influence range by a set of a vehicle line segment and a station point of which the running state is changed after the occurrence of the emergency; determining the system running state of the rail transit system according to the duration and the influence range of the rail transit emergency; and obtaining an optimal path from the starting point to the end point based on the set starting-destination point information and in combination with the system running state of the rail transit system.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application and containing computer-executable instructions is not limited to the public transportation travel path planning method described above, and may also perform related operations in the public transportation travel path planning method provided in any embodiment of the present application.
The public transportation travel path planning apparatus, the device, and the storage medium provided in the above embodiments may execute the public transportation travel path planning method provided in any embodiment of the present application, and refer to the public transportation travel path planning method provided in any embodiment of the present application without detailed technical details described in the above embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (7)
1. A public transport travel path planning method is characterized by comprising the following steps:
building a time range prediction model based on a neural network, inputting induction factors and occurrence places of historical rail transit emergencies as samples, outputting duration and influence ranges of the historical rail transit emergencies as samples, and training the time range prediction model;
responding to the rail transit emergency, and determining an inducing factor and an occurrence place of the rail transit emergency; inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency by the time range prediction model according to the induction factors and the occurrence places, wherein the duration is calibrated at the time interval from the occurrence of the emergency to the system repair, and the influence range is calibrated by a vehicle line segment and a station set, of which the running state is changed after the occurrence of the emergency;
determining vehicle line sections and system repair speeds of stations within the influence range of the rail transit emergency; determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed; the system operation state of the rail transit system comprises one or more combinations of vehicle line section travel time, vehicle platform parking time, carriage crowding degree, station access flow and platform crowding degree, and the system operation state is more tense as the vehicle line section travel time is longer, the vehicle platform parking time is longer, the carriage crowding degree is more serious, the station access flow is larger, the platform crowding degree is more serious; the determining the system operation state of the rail transit system in the duration of the rail transit emergency according to the system repair speed comprises the following steps: segmenting the duration of the rail transit emergency; determining the system running states of the rail transit system in different duration segments according to the system repairing speed; the system repairing speed is determined according to repairing resources configured on each vehicle line segment and station of the rail transit system, and the system repairing time is different at different time;
based on the set origin-destination information and in combination with the system running state of the rail transit system, obtaining a first optimal path from the starting point to the destination and a second optimal path from the starting point to the destination through the road surface bus; and determining an optimal path according to the comparison condition of the first optimal path and the second optimal path.
2. A method for mass transit travel path planning according to claim 1, wherein the causative factors include one or more of signal factors, vehicle factors, track factors, power factors, platform door factors and human factors in combination.
3. The method for mass transit travel path planning according to claim 1, wherein said determining the system operation status of the rail transit system for the duration of the rail transit emergency according to the system repair speed comprises:
determining vehicle line sections and station recovery time within the influence range of the rail transit emergency according to the system repair speed;
and determining the system running states of each vehicle line segment and the station in the rail transit system before and after the corresponding recovery time based on the recovery time.
4. The method for planning a travel path of public transportation according to claim 1, wherein after obtaining an optimal path from the starting point to the end point based on the set starting-destination information and in combination with the system operating status of the rail transit system, the method further comprises:
and replanning an optimal path based on the mandatory station in response to the set operation event of the mandatory station.
5. The utility model provides a public transport trip path planning device which characterized in that, includes time range prediction module, running state prediction module and trip path planning module, wherein:
the time range prediction module is used for responding to the rail transit emergency and determining an inducing factor and an occurrence place of the rail transit emergency; inputting the induction factors and the occurrence places into the time range prediction model, and predicting the duration and the influence range of the rail transit emergency by the time range prediction model according to the induction factors and the occurrence places, wherein the duration is calibrated at the time interval from the occurrence of the emergency to the system repair, and the influence range is calibrated by a vehicle line segment and a station set, of which the running state is changed after the occurrence of the emergency;
determining vehicle line sections and system repair speeds of stations within the influence range of the rail transit emergency; determining the system running state of the rail transit system in the duration of the rail transit emergency according to the system repairing speed; the system operation state of the rail transit system comprises one or more combinations of vehicle line section travel time, vehicle platform parking time, carriage crowding degree, station access flow and platform crowding degree, and the system operation state is more tense as the vehicle line section travel time is longer, the vehicle platform parking time is longer, the carriage crowding degree is more serious, the station access flow is larger, the platform crowding degree is more serious; the determining the system operation state of the rail transit system in the duration of the rail transit emergency according to the system repair speed comprises the following steps: segmenting the duration of the rail transit emergency; determining the system running states of the rail transit system in different duration segments according to the system repairing speed; the system repairing speed is determined according to repairing resources configured on each vehicle line segment and station of the rail transit system, and the system repairing time is different at different time;
the travel path planning module is used for obtaining a first optimal path from a starting point to a terminal point and a second optimal path from the starting point to the terminal point through a road bus based on set starting-destination point information and in combination with the system running state of the rail transit system; determining an optimal path according to the comparison condition of the first optimal path and the second optimal path;
the public transport travel path planning device is further used for building a time range prediction model based on the neural network, inputting the induction factors and the occurrence places of the historical rail transit emergencies as samples, outputting the duration and the influence range of the historical rail transit emergencies as samples, and training the time range prediction model.
6. A computer device, comprising: a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of mass transit travel path planning as claimed in any one of claims 1-4.
7. A storage medium containing computer executable instructions for performing the method of mass transit travel path planning according to any of claims 1-4 when executed by a computer processor.
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