CA3106015A1 - System and transport for optimizing operations of trains travelling along a railway line - Google Patents
System and transport for optimizing operations of trains travelling along a railway line Download PDFInfo
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- CA3106015A1 CA3106015A1 CA3106015A CA3106015A CA3106015A1 CA 3106015 A1 CA3106015 A1 CA 3106015A1 CA 3106015 A CA3106015 A CA 3106015A CA 3106015 A CA3106015 A CA 3106015A CA 3106015 A1 CA3106015 A1 CA 3106015A1
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- 238000000034 method Methods 0.000 claims abstract description 21
- 230000007613 environmental effect Effects 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 description 8
- 210000003128 head Anatomy 0.000 description 8
- 238000005457 optimization Methods 0.000 description 6
- 230000009977 dual effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/16—Trackside optimisation of vehicle or train operation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/40—Handling position reports or trackside vehicle data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Platform Screen Doors And Railroad Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
ABSTRACT
System and related method for optimizing the operations of a train traveling along a railway line, wherein one or more cameras are installed at a platform of a first station located along the railway line and are adapted to capture images of the train relative to said platform and of passengers over the platform moving for boarding on/de-boarding from the train. An image processing system is configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras. A
first data elaboration system is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
Date Recue/Date Received 2021-01-19
System and related method for optimizing the operations of a train traveling along a railway line, wherein one or more cameras are installed at a platform of a first station located along the railway line and are adapted to capture images of the train relative to said platform and of passengers over the platform moving for boarding on/de-boarding from the train. An image processing system is configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras. A
first data elaboration system is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
Date Recue/Date Received 2021-01-19
Description
SYSTEM AND METHOD FOR OPTIMIZING OPERATIONS OF TRAINS TRAVELLING
ALONG A RAILWAY LINE
Field of the invention The present invention concerns a system and a method for optimizing the operations of trains travelling along a railway line.
The system and method according to the present invention are particularly suitable to be used in urban or metro rail lines, and they will be described hereinafter by making specific reference to such applications, without intending in any way to limit their possible application to other type of railway networks.
Background of the invention As it is known, railway transportation systems, and in particular urban rail transit systems, are widely and increasingly used worldwide.
During service, the various trains have to perform their operations based on a pre-established timetable generated by a Centralized Control Center ("CCC"); the timetable can be impacted by some events on the railway line that influences the real operating times of each train.
In particular, on-board Automatic Train Operation (ATO) systems might not be able to meet a desired headway due to the variable dwell time at each station, i.e.
the time needed for completing boarding/deboarding of passengers at the station platforms.
In fact, the dwell time depends on several variables and can vary over the time at each station and from station to station; for example, such variables include the peak/off-peak hours/seasons, the historic/tourist/industrial importance of places near each station, current climatic conditions, et cetera.
Hence, from one side it is difficult to respect timetables based on nominal dwell times uniform for all stations; on the other side, it is often difficult to generate timetables at centralized control centers with different dwell times due to uncertainties and variables varying anyhow in real time, as above mentioned.
Summary of the invention The present invention is aimed at facing such issues, and in particular at providing a system and a method for optimizing operations of trains travelling along a railway line, offering substantial improvements over known solutions, especially as regard to the capability of properly taking into account the dwell time at each station in relation to the real time operating conditions.
This aim is achieved by a system for optimizing the operations of a train travelling along a railway line, characterized in that it comprises at least:
Date Recue/Date Received 2021-01-19
ALONG A RAILWAY LINE
Field of the invention The present invention concerns a system and a method for optimizing the operations of trains travelling along a railway line.
The system and method according to the present invention are particularly suitable to be used in urban or metro rail lines, and they will be described hereinafter by making specific reference to such applications, without intending in any way to limit their possible application to other type of railway networks.
Background of the invention As it is known, railway transportation systems, and in particular urban rail transit systems, are widely and increasingly used worldwide.
During service, the various trains have to perform their operations based on a pre-established timetable generated by a Centralized Control Center ("CCC"); the timetable can be impacted by some events on the railway line that influences the real operating times of each train.
In particular, on-board Automatic Train Operation (ATO) systems might not be able to meet a desired headway due to the variable dwell time at each station, i.e.
the time needed for completing boarding/deboarding of passengers at the station platforms.
In fact, the dwell time depends on several variables and can vary over the time at each station and from station to station; for example, such variables include the peak/off-peak hours/seasons, the historic/tourist/industrial importance of places near each station, current climatic conditions, et cetera.
Hence, from one side it is difficult to respect timetables based on nominal dwell times uniform for all stations; on the other side, it is often difficult to generate timetables at centralized control centers with different dwell times due to uncertainties and variables varying anyhow in real time, as above mentioned.
Summary of the invention The present invention is aimed at facing such issues, and in particular at providing a system and a method for optimizing operations of trains travelling along a railway line, offering substantial improvements over known solutions, especially as regard to the capability of properly taking into account the dwell time at each station in relation to the real time operating conditions.
This aim is achieved by a system for optimizing the operations of a train travelling along a railway line, characterized in that it comprises at least:
Date Recue/Date Received 2021-01-19
2 - one or more cameras which are installed at a platform of a first station located along the railway line and are adapted to capture images of the train relative to said platform and of passengers over the platform moving for boarding on/de-boarding from the train;
- an image processing system configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras;
- a first data elaboration system which is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
The above mentioned aim is also achieved by a method for optimizing the operations of a train travelling along a railway line, characterized in that it comprises at least the following steps:
- (a): capturing images of the position of the train relative to a platform of a first station and of passengers over the platform moving for boarding on/de-boarding from the train;
- (b): based on the images captured, measuring the actual dwell time and calculating an optimum dwell time for the train at the platform of the first station;
- (c): calculating a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising one or more of a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
Brief description of drawings Further characteristics and advantages will become apparent from the description of some preferred but not exclusive exemplary embodiments of a system and a method according to the present disclosure, illustrated only by way of non-limitative examples with the accompanying drawings, wherein:
Date Recue/Date Received 2021-01-19
- an image processing system configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras;
- a first data elaboration system which is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
The above mentioned aim is also achieved by a method for optimizing the operations of a train travelling along a railway line, characterized in that it comprises at least the following steps:
- (a): capturing images of the position of the train relative to a platform of a first station and of passengers over the platform moving for boarding on/de-boarding from the train;
- (b): based on the images captured, measuring the actual dwell time and calculating an optimum dwell time for the train at the platform of the first station;
- (c): calculating a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising one or more of a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
Brief description of drawings Further characteristics and advantages will become apparent from the description of some preferred but not exclusive exemplary embodiments of a system and a method according to the present disclosure, illustrated only by way of non-limitative examples with the accompanying drawings, wherein:
Date Recue/Date Received 2021-01-19
3 Figure 1 is a block diagram schematically representing a system for optimizing the operations of a train travelling along a railway line according to the invention;
Figure 2 is a flow chart illustrating a method for optimizing the operations of a train travelling along a railway line, according to the invention;
Detailed description of preferred embodiments of the invention It should be noted that in the detailed description that follows, identical or similar components, either from a structural and/or functional point of view, have the same reference numerals, regardless of whether they are shown in different embodiments of the present disclosure.
It should be also noted that in order to clearly and concisely describe the present disclosure, the drawings may not necessarily be to scale and certain features of the disclosure may be shown in somewhat schematic form.
Further, when the term "adapted" or "arranged" or "configured" or "shaped", is used herein while referring to any component as a whole, or to any part of a component, or to a combination of components, it has to be understood that it means and encompasses correspondingly either the structure, and/or configuration and/or form and/or positioning.
In particular, for electronic and/or software means, each of the above listed terms means and encompasses electronic circuits or parts thereof, as well as stored, embedded or running software codes and/or routines, algorithms, or complete programs, suitably designed for achieving the technical result and/or the functional performances for which such means are devised.
In figure 1 there is schematically illustrated an exemplary system for optimizing the operations of trains travelling along a railway line 10, according to the present invention, indicated by the overall reference number 100.
The railway line 10 can be for example an urban rail system, e.g. a subway, and comprises a plurality of line stations located along the railway line 10 at a certain distance from each other.
In particular, for the sake of simplicity, in figure 1 the railway line 10 is represented by a portion of a track and as including only two line stations, namely a line station 3 referred to in the following also as the "first station 3", and a further line station 4, referred to in the following also as the "upcoming station 4", which follows the first station 3 with reference to a train, schematically represented in figure 1 by the reference number 1, moving along the line 10 in the direction indicated by the arrow "A".
Clearly, the number and type of line stations, as well as the type and composition of each train, can vary according to the specific applications.
Date Recue/Date Received 2021-01-19
Figure 2 is a flow chart illustrating a method for optimizing the operations of a train travelling along a railway line, according to the invention;
Detailed description of preferred embodiments of the invention It should be noted that in the detailed description that follows, identical or similar components, either from a structural and/or functional point of view, have the same reference numerals, regardless of whether they are shown in different embodiments of the present disclosure.
It should be also noted that in order to clearly and concisely describe the present disclosure, the drawings may not necessarily be to scale and certain features of the disclosure may be shown in somewhat schematic form.
Further, when the term "adapted" or "arranged" or "configured" or "shaped", is used herein while referring to any component as a whole, or to any part of a component, or to a combination of components, it has to be understood that it means and encompasses correspondingly either the structure, and/or configuration and/or form and/or positioning.
In particular, for electronic and/or software means, each of the above listed terms means and encompasses electronic circuits or parts thereof, as well as stored, embedded or running software codes and/or routines, algorithms, or complete programs, suitably designed for achieving the technical result and/or the functional performances for which such means are devised.
In figure 1 there is schematically illustrated an exemplary system for optimizing the operations of trains travelling along a railway line 10, according to the present invention, indicated by the overall reference number 100.
The railway line 10 can be for example an urban rail system, e.g. a subway, and comprises a plurality of line stations located along the railway line 10 at a certain distance from each other.
In particular, for the sake of simplicity, in figure 1 the railway line 10 is represented by a portion of a track and as including only two line stations, namely a line station 3 referred to in the following also as the "first station 3", and a further line station 4, referred to in the following also as the "upcoming station 4", which follows the first station 3 with reference to a train, schematically represented in figure 1 by the reference number 1, moving along the line 10 in the direction indicated by the arrow "A".
Clearly, the number and type of line stations, as well as the type and composition of each train, can vary according to the specific applications.
Date Recue/Date Received 2021-01-19
4 Each station has one platform 2 associated to a track of the line 10 where trains, while performing their duty service, stop for on/off boarding of passengers.
As schematically illustrated in figure 1, the system 100 according to the present invention comprises one or more cameras 110 which are installed at the platform 2 of the first station 3 and are adapted to capture images of the train 1, and in particular of its actual position relative to the platform 2, and also images of passengers 5 over the platform 2 moving for boarding on/de-boarding from the train 1.
According to a possible embodiment, the system 100 comprises at least a first camera 111, placed at the platform 2, e.g. at the entrance thereof, with its field of vision oriented to capture image data of the train 1 entering into or leaving the platform 2, and at least a second camera 112, preferably a plurality of second cameras 112, mounted at the platform 2, e.g. at the roof or at a side thereof, with its/their field of vision oriented for example towards the expected position of the doors of the train 1 once the train has stopped at the platform 2, to capture image data of passengers moving into/out from the train 1.
The second cameras 112 can be properly positioned to cover the door-to-door distance of the train 1, and eventually the entire platform 2, possibly without any gap.
The first camera 111 can be for example a dual head video camera, with the front head facing the entrance of the platform 2, and the rear head facing the last car of the train as shown in figure 1; such camera 111 can trigger for instance an automatic recording of video images by the second cameras 112 based on the arrival and departures of a train 1.
For the sake of ease of illustration, only the platform 2 of the first station 3 is shown equipped with the one or more cameras 110; clearly, more stations, preferably all stations located along the railway line 10, are provided with the various cameras 110 at each relevant platform 2.
As illustrated, the system 100 further comprises an image processing system which is configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform 2 of the first station 3, based on the image data received from the one or more cameras 110.
For example, the image processing system 120 comprises or is constituted by a video analytics server that is in operative communication with the one or more cameras 110, via respective communication devices 125.
According to a possible embodiment, the processing system or video analytics server 120 is centralized, namely it is located for example in a centralized control room devised to supervise the entire railway line 10, and is in operative communication and Date Recue/Date Received 2021-01-19
As schematically illustrated in figure 1, the system 100 according to the present invention comprises one or more cameras 110 which are installed at the platform 2 of the first station 3 and are adapted to capture images of the train 1, and in particular of its actual position relative to the platform 2, and also images of passengers 5 over the platform 2 moving for boarding on/de-boarding from the train 1.
According to a possible embodiment, the system 100 comprises at least a first camera 111, placed at the platform 2, e.g. at the entrance thereof, with its field of vision oriented to capture image data of the train 1 entering into or leaving the platform 2, and at least a second camera 112, preferably a plurality of second cameras 112, mounted at the platform 2, e.g. at the roof or at a side thereof, with its/their field of vision oriented for example towards the expected position of the doors of the train 1 once the train has stopped at the platform 2, to capture image data of passengers moving into/out from the train 1.
The second cameras 112 can be properly positioned to cover the door-to-door distance of the train 1, and eventually the entire platform 2, possibly without any gap.
The first camera 111 can be for example a dual head video camera, with the front head facing the entrance of the platform 2, and the rear head facing the last car of the train as shown in figure 1; such camera 111 can trigger for instance an automatic recording of video images by the second cameras 112 based on the arrival and departures of a train 1.
For the sake of ease of illustration, only the platform 2 of the first station 3 is shown equipped with the one or more cameras 110; clearly, more stations, preferably all stations located along the railway line 10, are provided with the various cameras 110 at each relevant platform 2.
As illustrated, the system 100 further comprises an image processing system which is configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform 2 of the first station 3, based on the image data received from the one or more cameras 110.
For example, the image processing system 120 comprises or is constituted by a video analytics server that is in operative communication with the one or more cameras 110, via respective communication devices 125.
According to a possible embodiment, the processing system or video analytics server 120 is centralized, namely it is located for example in a centralized control room devised to supervise the entire railway line 10, and is in operative communication and Date Recue/Date Received 2021-01-19
5 receive all image data from all cameras 110 installed in the various stations of the line 10 itself.
In practice, for each station, a virtual machine is identified to process the corresponding dedicated video data, and the system 120 performs video analytics to extract the count of passengers 5 at a platform 2 for boarding/de-boarding the train 1 based for example on the direction of movement of passengers, and also measures the actual dwell times and calculates the optimal dwell times.
For instance, since the video images are initially taken just before the train arrival time, all the passengers 5 waiting on the platform can be counted as the number of passengers boarding the train; based on the velocity of passengers 5 moving towards the platform, the de-boarding passengers can be identified and using the top view of heads, hairs and noses side pointing, the number of de-boarding passengers from the train 1 is calculated.
The "actual dwell time" is for example measured from the video data based on the time interval between the doors opening and the doors closing automatically;
the "optimal dwell time" is calculated from the video data based on the time interval between the doors opening and there is no movement of passengers before the doors closing automatically.
The "optimal dwell time" can be shorter or equal to the "actual dwell time".
According to the invention, a first elaboration system or unit 130, operatively connected to the image processing system 120, is configured to calculate a predicted dwell time for the train 1 at the platform 2 of an upcoming station 4 along the railway line 10, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train 1, number of passengers at the platform 2 of the upcoming station 4, measured actual dwell time and calculated optimum dwell time at the platform 2 of the upcoming station 4 for a preceding train travelling along the railway line 10 in the same direction A, data related to actual environmental conditions along the railway line 1 or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
According to an embodiment, the first elaboration system 130 comprises, or is constituted by, a neural-network-based first server 130 that is in operative communication with the video analytics server 120, and is also centralized, i.e. it is placed in a centralized control room for the entire line 10 and is unique for the various stations along the line 10 itself.
Since the number of passengers boarding/de-boarding a train depends also on the location of the station, a kind virtual machine is identified at the system 130 for each station i.e. a neural network-based model is defined for each station.
Date Recue/Date Received 2021-01-19
In practice, for each station, a virtual machine is identified to process the corresponding dedicated video data, and the system 120 performs video analytics to extract the count of passengers 5 at a platform 2 for boarding/de-boarding the train 1 based for example on the direction of movement of passengers, and also measures the actual dwell times and calculates the optimal dwell times.
For instance, since the video images are initially taken just before the train arrival time, all the passengers 5 waiting on the platform can be counted as the number of passengers boarding the train; based on the velocity of passengers 5 moving towards the platform, the de-boarding passengers can be identified and using the top view of heads, hairs and noses side pointing, the number of de-boarding passengers from the train 1 is calculated.
The "actual dwell time" is for example measured from the video data based on the time interval between the doors opening and the doors closing automatically;
the "optimal dwell time" is calculated from the video data based on the time interval between the doors opening and there is no movement of passengers before the doors closing automatically.
The "optimal dwell time" can be shorter or equal to the "actual dwell time".
According to the invention, a first elaboration system or unit 130, operatively connected to the image processing system 120, is configured to calculate a predicted dwell time for the train 1 at the platform 2 of an upcoming station 4 along the railway line 10, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train 1, number of passengers at the platform 2 of the upcoming station 4, measured actual dwell time and calculated optimum dwell time at the platform 2 of the upcoming station 4 for a preceding train travelling along the railway line 10 in the same direction A, data related to actual environmental conditions along the railway line 1 or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
According to an embodiment, the first elaboration system 130 comprises, or is constituted by, a neural-network-based first server 130 that is in operative communication with the video analytics server 120, and is also centralized, i.e. it is placed in a centralized control room for the entire line 10 and is unique for the various stations along the line 10 itself.
Since the number of passengers boarding/de-boarding a train depends also on the location of the station, a kind virtual machine is identified at the system 130 for each station i.e. a neural network-based model is defined for each station.
Date Recue/Date Received 2021-01-19
6 In practice, according to this embodiment, the neural-network-based first server 130 is pre-trained with various relevant data for each station and using suitable weights for the various parameters inputs above indicated, namely the number of passengers 5 inside a relevant train 1, number of passengers 5 at the platform 2 of the upcoming station 4, measured actual dwell time and calculated optimum dwell time at the platform 2 of the upcoming station 4 for a preceding train travelling along the railway line 10 in the same direction A, environmental or climatic data such as temperature, wind speed, rain, snow et cetera, data related to calendar dates, such as public holidays and festival days, vacation periods, which can be input by operators, peak/non-peak times which can be assigned automatically or input manually.
According to a possible embodiment, the environmental or climatic data such as the ones above indicated, are provided by one or more sensors of the system 100;
these sensors are placed at selected positions along the railway line 10 for detecting actual data of one or more corresponding environmental parameters to be supplied to the first server 130 for calculating the predicted dwell time for the train 1 at the platform 2 of an upcoming station 4. For example, such one or more sensors, schematically represented in figure 1 by the reference number 115 only for the first station 3, can be outdoor sensors mounted at each station.
Once the training of the elaboration system 130 is completed, it is validated using one or more set of real-time data, and then the elaboration system 130 is put in real time operations for predicting the dwell time at the various stations.
Since the whole system 100 processes and collects data throughout every day, a re-learning/re-calculation of the system 130 can be performed periodically, for example every night at a fixed time. If the weights previously used for whatever reason diverge substantially from a tolerable limit, new weights are uploaded into the system 130. This ensures the continuous learning of dwell time prediction over the period and improves the accuracy in the prediction.
As illustrated in figure 1, the system 100 according to the invention comprises a second elaboration system or unit 140, operatively connected to the first elaboration system or unit 130, which is configured to calculate, for the train 1 leaving the platform 2 of the first station 3, first the total travel time needed to reach and leave the platform 2 of the upcoming station 4 and then to generate, based on the calculated total travel time, at least one optimized driving profile to be followed by the train 1 from the platform 2 of the first station 3 up to stopping at the platform of the upcoming station 4.
In practice, the total travel time is calculated as the arrival time from the first station 3 at the upcoming station 4 plus the dwell time at the upcoming station 4 itself.
Date Recue/Date Received 2021-01-19
According to a possible embodiment, the environmental or climatic data such as the ones above indicated, are provided by one or more sensors of the system 100;
these sensors are placed at selected positions along the railway line 10 for detecting actual data of one or more corresponding environmental parameters to be supplied to the first server 130 for calculating the predicted dwell time for the train 1 at the platform 2 of an upcoming station 4. For example, such one or more sensors, schematically represented in figure 1 by the reference number 115 only for the first station 3, can be outdoor sensors mounted at each station.
Once the training of the elaboration system 130 is completed, it is validated using one or more set of real-time data, and then the elaboration system 130 is put in real time operations for predicting the dwell time at the various stations.
Since the whole system 100 processes and collects data throughout every day, a re-learning/re-calculation of the system 130 can be performed periodically, for example every night at a fixed time. If the weights previously used for whatever reason diverge substantially from a tolerable limit, new weights are uploaded into the system 130. This ensures the continuous learning of dwell time prediction over the period and improves the accuracy in the prediction.
As illustrated in figure 1, the system 100 according to the invention comprises a second elaboration system or unit 140, operatively connected to the first elaboration system or unit 130, which is configured to calculate, for the train 1 leaving the platform 2 of the first station 3, first the total travel time needed to reach and leave the platform 2 of the upcoming station 4 and then to generate, based on the calculated total travel time, at least one optimized driving profile to be followed by the train 1 from the platform 2 of the first station 3 up to stopping at the platform of the upcoming station 4.
In practice, the total travel time is calculated as the arrival time from the first station 3 at the upcoming station 4 plus the dwell time at the upcoming station 4 itself.
Date Recue/Date Received 2021-01-19
7 The headway between two trains can be calculated, for instance automatically, based on the arrival time at the same station using images captured by the dual head camera 111.
According to an embodiment, the second elaboration system or unit 140 comprises, or is constituted, by an optimization server 140 (hereinafter also referred to as the second server 140) that is in operative communication with the first elaboration system or unit 130 and is also centralized, i.e. it is placed in a centralized control room for the entire line 10 and is unique for the various stations along the line 10 itself.
In particular, according to a possible embodiment, the second elaboration system or server 140 is configured first to generate a plurality of optimized driving profiles based on the calculated total travel time, and then to select one driving profile among the plurality of driving profiles generated to be followed by the train 1 based on a selectable operative target desired to be achieved.
The selectable operative target can be for example punctuality, e.g. the respect of a prefixed timetable, or alternatively energy consumption, for example to achieve certain savings; of course, other operative targets and/or combinations thereof could be selected.
Accordingly, in one possible embodiment of the system 100, the second server is configured to select among the plurality of generated driving profiles, one driving profile to be followed by the train 1 based on a predefined travelling time to be respected by the train 1, i.e. according to the operative target of punctuality, if the predicted dwell time for the train 1 is longer than or equal to a predefined nominal dwell time at the platform 2 of the upcoming station 4; alternatively, the driving profile to be followed by the train 1 can be selected based on a desired level of energy savings to be achieved by the train 1 (i.e. the operative criteria of consumption is chosen) if the predicted dwell time for the train 1 is shorter than a predefined nominal dwell time at the platform 2 of the upcoming station 4.
The nominal dwell time is for example the typical one taken into consideration by the centralized control center, schematically represented in figure 1 by the reference number 150, when generating the overall timetable for the trains travelling over the railway line 10.
In practice, for each train, a virtual machine is identified to perform the optimization calculations based on various inputs, namely data from train and track database, such as data related to maximum acceleration/deceleration rates for each train, permanent speed restrictions for sections of tracks, et cetera. A suitable optimization algorithm of the second server 140 provides a set of optimized driving profiles wherein, for example, in one scenario, either acceleration-cruising (AC) or acceleration-cruising-coasting (ACC) times are optimized. Accordingly, a train can have different modes such as Motoring-Cruising-Coasting-Braking (MCCB) or Motoring-Cruising-Braking (MCB). Hence, the Date Recue/Date Received 2021-01-19
According to an embodiment, the second elaboration system or unit 140 comprises, or is constituted, by an optimization server 140 (hereinafter also referred to as the second server 140) that is in operative communication with the first elaboration system or unit 130 and is also centralized, i.e. it is placed in a centralized control room for the entire line 10 and is unique for the various stations along the line 10 itself.
In particular, according to a possible embodiment, the second elaboration system or server 140 is configured first to generate a plurality of optimized driving profiles based on the calculated total travel time, and then to select one driving profile among the plurality of driving profiles generated to be followed by the train 1 based on a selectable operative target desired to be achieved.
The selectable operative target can be for example punctuality, e.g. the respect of a prefixed timetable, or alternatively energy consumption, for example to achieve certain savings; of course, other operative targets and/or combinations thereof could be selected.
Accordingly, in one possible embodiment of the system 100, the second server is configured to select among the plurality of generated driving profiles, one driving profile to be followed by the train 1 based on a predefined travelling time to be respected by the train 1, i.e. according to the operative target of punctuality, if the predicted dwell time for the train 1 is longer than or equal to a predefined nominal dwell time at the platform 2 of the upcoming station 4; alternatively, the driving profile to be followed by the train 1 can be selected based on a desired level of energy savings to be achieved by the train 1 (i.e. the operative criteria of consumption is chosen) if the predicted dwell time for the train 1 is shorter than a predefined nominal dwell time at the platform 2 of the upcoming station 4.
The nominal dwell time is for example the typical one taken into consideration by the centralized control center, schematically represented in figure 1 by the reference number 150, when generating the overall timetable for the trains travelling over the railway line 10.
In practice, for each train, a virtual machine is identified to perform the optimization calculations based on various inputs, namely data from train and track database, such as data related to maximum acceleration/deceleration rates for each train, permanent speed restrictions for sections of tracks, et cetera. A suitable optimization algorithm of the second server 140 provides a set of optimized driving profiles wherein, for example, in one scenario, either acceleration-cruising (AC) or acceleration-cruising-coasting (ACC) times are optimized. Accordingly, a train can have different modes such as Motoring-Cruising-Coasting-Braking (MCCB) or Motoring-Cruising-Braking (MCB). Hence, the Date Recue/Date Received 2021-01-19
8 optimization algorithm provides the set of optimized driving profiles, i.e. an optimized time for AC/ACC and an optimized mode of operation for MCCB/MCB along with the output of "optimized total time" to be traveled (punctuality) and predicted energy consumption. The decision logic of the second server 140 selects the best-optimized driving profile from the set of generated profiles based on the chosen objective punctuality or energy saving, and transmits it to the on-board Automatic Train Operation (ATO) system of the train 1 for execution.
A method for optimizing the operations of a train 1 traveling along a railway line 10, according to the invention, will be now described with reference to figure 2.
In particular, the method, indicated by the overall reference number 200, comprises at least the following steps:
- 210: capturing, for example by means of the one or more cameras 110 which are installed at a platform 2 of the first station 3 located along the railway line 10, images of the train 1 relative to the platform 2, and of passengers 5 over the platform 2 moving for boarding on/de-boarding from the train 1;
- 220: based on the images captured by the one or more cameras 110, measuring the actual dwell time and calculating an optimum dwell time for the train 1 at the platform 2 of the first station 3, for example by means of the image processing system 120;
-230: calculating, for example by means of the first data elaboration system 130, a predicted dwell time for the train 1 at the platform 2 of an upcoming station 4 along the railway line 10, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train 1, number of passengers at the platform 2 of the upcoming station 4, measured actual dwell time and calculated optimum dwell time at the platform 2 of the upcoming station 4 for a preceding train travelling along the railway line 10 in the same direction, data related to actual environmental conditions along the railway line 1 or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
In one possible embodiment, the method 200 further comprises the following steps:
-240: calculating for the train 1 leaving the platform 2 of the first station 3, for example by means of the second elaboration system 140, a total travel time to reach and leave a platform 2 of the upcoming station 4; and then -250: generating, based on the calculated total travel time, and still for example by means of a second elaboration system 140, at least one optimized driving profile to be followed by the train 1 from the platform 2 of the first station 3 up to the platform 2 of the upcoming station 4.
Date Recue/Date Received 2021-01-19
A method for optimizing the operations of a train 1 traveling along a railway line 10, according to the invention, will be now described with reference to figure 2.
In particular, the method, indicated by the overall reference number 200, comprises at least the following steps:
- 210: capturing, for example by means of the one or more cameras 110 which are installed at a platform 2 of the first station 3 located along the railway line 10, images of the train 1 relative to the platform 2, and of passengers 5 over the platform 2 moving for boarding on/de-boarding from the train 1;
- 220: based on the images captured by the one or more cameras 110, measuring the actual dwell time and calculating an optimum dwell time for the train 1 at the platform 2 of the first station 3, for example by means of the image processing system 120;
-230: calculating, for example by means of the first data elaboration system 130, a predicted dwell time for the train 1 at the platform 2 of an upcoming station 4 along the railway line 10, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train 1, number of passengers at the platform 2 of the upcoming station 4, measured actual dwell time and calculated optimum dwell time at the platform 2 of the upcoming station 4 for a preceding train travelling along the railway line 10 in the same direction, data related to actual environmental conditions along the railway line 1 or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
In one possible embodiment, the method 200 further comprises the following steps:
-240: calculating for the train 1 leaving the platform 2 of the first station 3, for example by means of the second elaboration system 140, a total travel time to reach and leave a platform 2 of the upcoming station 4; and then -250: generating, based on the calculated total travel time, and still for example by means of a second elaboration system 140, at least one optimized driving profile to be followed by the train 1 from the platform 2 of the first station 3 up to the platform 2 of the upcoming station 4.
Date Recue/Date Received 2021-01-19
9 According to an embodiment, the step 250 of generating at least one optimized driving profile comprises a first sub-step 252 of generating a plurality of optimized driving profiles based on the calculated total travel time, and a second sub-step 254 of selecting, among the plurality of generated driving profiles, one driving profile to be followed by the train 1 based on a selectable operative target, such as in particular punctuality or energy consumption.
More in particular, in one embodiment, the second sub-step 254 of selecting comprises selecting among the plurality of generated driving profiles, one driving profile to be followed by the train 1 based on a predefined travelling time to be respected by the train 1 if the predicted dwell time for the train 1 is longer than or equal to a predefined nominal dwell time at the platform 2 of the upcoming station 4; or alternatively, the one driving profile is selected based on a level of energy savings to be achieved by the train 1 if the predicted dwell time for the train 1 is shorter than a predefined nominal dwell time at the platform 2 of the upcoming station 4.
In one embodiment, the method 200 comprises a step 260 of supplying with actual data of one or more corresponding environmental parameters detected by one or more sensors 115 placed along the railway line 10, the first data elaboration system 130 for the calculation at step 230 of the predicted dwell time for the train 1 at the platform 2 of the upcoming station 4.
In practice, when a train 1 starts its service and enters the first station 3 of the line
More in particular, in one embodiment, the second sub-step 254 of selecting comprises selecting among the plurality of generated driving profiles, one driving profile to be followed by the train 1 based on a predefined travelling time to be respected by the train 1 if the predicted dwell time for the train 1 is longer than or equal to a predefined nominal dwell time at the platform 2 of the upcoming station 4; or alternatively, the one driving profile is selected based on a level of energy savings to be achieved by the train 1 if the predicted dwell time for the train 1 is shorter than a predefined nominal dwell time at the platform 2 of the upcoming station 4.
In one embodiment, the method 200 comprises a step 260 of supplying with actual data of one or more corresponding environmental parameters detected by one or more sensors 115 placed along the railway line 10, the first data elaboration system 130 for the calculation at step 230 of the predicted dwell time for the train 1 at the platform 2 of the upcoming station 4.
In practice, when a train 1 starts its service and enters the first station 3 of the line
10, the number of passengers inside the train can be considered equal to zero.
Once the train 1 arrives within the field of view (FOV) of the front head of first camera 111, the first camera 111 sends the alert to the second cameras 112 to start the recording.
Based on this alert, recording of the video images of crowd at the platform 2 is started to count a number of passengers 5 ready for boarding the train 1. Complete stop of the train 1 at the platform 2 is detected using the rear head of the camera 111, and dwell time counter starts based on the trigger from the data of the rear head of the camera 111.
At the same time, the second cameras 112 capture moments of people de-boarding the train 1. The video recording continues until the train 1 starts the movement leaving the platform 2 after the doors of the train 1 have been closed. The train movement is detected using the rear head of the first camera 111; this completes the calculation of the "actual dwell time" by the video analytics server 120. To this end, data are transmitted to the video analytics server 120 via the communication devices 125 and using for example high bandwidth wireless communication. Using the analyzed video data, the actual time at which passenger boarding is completed (irrespective of the actual dwell time) is calculated and represents the "optimum dwell time". When used, outdoor sensors 115 send Date Recue/Date Received 2021-01-19 environmental data to the first data elaboration system 130 via respective communication devices 125, using for example low bandwidth wireless connection. Based on the video data and sensors data at the upcoming station 4, the first data elaboration system 130 predicts the "optimum dwell time" using the neural network weights specific for the upcoming station 4. If the predicted dwell time is longer than the typical nominal dwell time, then the running profile for the train 1 is selected such that train 1 reaches the upcoming station few seconds ahead of the scheduled time of arrival. This gives additional time for the passengers to board/de-board the train 1. As the early time of arrival is utilized for dwell time, the overall timetable is not affected. The early arrival can be dynamically updated into the centralized control enter 150. If the predicted dwell time is substantially equal to the typical nominal dwell time, then the running profile is selected such that for example the scheduled time is strictly followed. In this case, punctuality is given primary importance over energy consumption. If the predicted dwell time is shorter than the typical nominal dwell time, then the running profile is selected such that energy consumption and hence savings is given more importance than punctuality. This may result in a profile with more coasting time, and hence can result in a slight delay for arrival time. For fewer passengers, a shorter dwell time is sufficient. Thus, only the additional unused dwell time is exploited in favour of energy savings. This will not affect the timetable but still, the delayed arrival by few seconds can be communicated to the control center 150 via the second elaboration system 140. The optimized profiles for each scenario are generated by the optimization server 140 using the train-track data and any constraint for the respective scenario, i.e. the trip time for punctuality, and a percentage of savings for energy consumption. Based on the optimized profiles, the driving profile is chosen and forwarded to train 1. The selected driving profile includes, inter alia, information about optimum motoring time-cruising time - coasting time or motoring time-cruising time. The on-board automatic train operation system drives the train 1 as per the Motoring-Cruising-Coasting-Braking (MCCB) or Motoring-Cruising-Braking (MCB) modes, based on the respective profile received from the optimization server 140. The various steps above are repeated at each station and for each train autonomously wherein, in sequence, the upcoming station 4 would represent the first station 3 once the train 1 arrives there, and the following station along the line 10 would represent the new upcoming station 4.
Hence, it is evident from the foregoing description that the system 100 and method 200 according to the present invention achieve the intended aim since they allow to optimize the operations of trains along a railway line, in particular by taking into account, in real time, the dwell time at each station and then by dynamically adjusting the actual Date Recue/Date Received 2021-01-19
Once the train 1 arrives within the field of view (FOV) of the front head of first camera 111, the first camera 111 sends the alert to the second cameras 112 to start the recording.
Based on this alert, recording of the video images of crowd at the platform 2 is started to count a number of passengers 5 ready for boarding the train 1. Complete stop of the train 1 at the platform 2 is detected using the rear head of the camera 111, and dwell time counter starts based on the trigger from the data of the rear head of the camera 111.
At the same time, the second cameras 112 capture moments of people de-boarding the train 1. The video recording continues until the train 1 starts the movement leaving the platform 2 after the doors of the train 1 have been closed. The train movement is detected using the rear head of the first camera 111; this completes the calculation of the "actual dwell time" by the video analytics server 120. To this end, data are transmitted to the video analytics server 120 via the communication devices 125 and using for example high bandwidth wireless communication. Using the analyzed video data, the actual time at which passenger boarding is completed (irrespective of the actual dwell time) is calculated and represents the "optimum dwell time". When used, outdoor sensors 115 send Date Recue/Date Received 2021-01-19 environmental data to the first data elaboration system 130 via respective communication devices 125, using for example low bandwidth wireless connection. Based on the video data and sensors data at the upcoming station 4, the first data elaboration system 130 predicts the "optimum dwell time" using the neural network weights specific for the upcoming station 4. If the predicted dwell time is longer than the typical nominal dwell time, then the running profile for the train 1 is selected such that train 1 reaches the upcoming station few seconds ahead of the scheduled time of arrival. This gives additional time for the passengers to board/de-board the train 1. As the early time of arrival is utilized for dwell time, the overall timetable is not affected. The early arrival can be dynamically updated into the centralized control enter 150. If the predicted dwell time is substantially equal to the typical nominal dwell time, then the running profile is selected such that for example the scheduled time is strictly followed. In this case, punctuality is given primary importance over energy consumption. If the predicted dwell time is shorter than the typical nominal dwell time, then the running profile is selected such that energy consumption and hence savings is given more importance than punctuality. This may result in a profile with more coasting time, and hence can result in a slight delay for arrival time. For fewer passengers, a shorter dwell time is sufficient. Thus, only the additional unused dwell time is exploited in favour of energy savings. This will not affect the timetable but still, the delayed arrival by few seconds can be communicated to the control center 150 via the second elaboration system 140. The optimized profiles for each scenario are generated by the optimization server 140 using the train-track data and any constraint for the respective scenario, i.e. the trip time for punctuality, and a percentage of savings for energy consumption. Based on the optimized profiles, the driving profile is chosen and forwarded to train 1. The selected driving profile includes, inter alia, information about optimum motoring time-cruising time - coasting time or motoring time-cruising time. The on-board automatic train operation system drives the train 1 as per the Motoring-Cruising-Coasting-Braking (MCCB) or Motoring-Cruising-Braking (MCB) modes, based on the respective profile received from the optimization server 140. The various steps above are repeated at each station and for each train autonomously wherein, in sequence, the upcoming station 4 would represent the first station 3 once the train 1 arrives there, and the following station along the line 10 would represent the new upcoming station 4.
Hence, it is evident from the foregoing description that the system 100 and method 200 according to the present invention achieve the intended aim since they allow to optimize the operations of trains along a railway line, in particular by taking into account, in real time, the dwell time at each station and then by dynamically adjusting the actual Date Recue/Date Received 2021-01-19
11 driving profile of each train. Accordingly, it is possible to obtain flexibly either energy savings and/or punctuality based on the selected driving profiles.
The system 100 and method 200 thus conceived are susceptible of modifications and variations, all of which are within the scope of the inventive concept, as defined in particular by the appended claims; for example, the various systems or units 120, 130, 140 previously described can be preferably positioned in a unique operative room where also the central control room 150 is located, each and any of them can comprise an assembly of HW and SW components, such as one or more workstations and related control displays, elaboration units or processor based devices, et cetera, as schematically represented by the graphical symbols depicted in figure 1.
Date Recue/Date Received 2021-01-19
The system 100 and method 200 thus conceived are susceptible of modifications and variations, all of which are within the scope of the inventive concept, as defined in particular by the appended claims; for example, the various systems or units 120, 130, 140 previously described can be preferably positioned in a unique operative room where also the central control room 150 is located, each and any of them can comprise an assembly of HW and SW components, such as one or more workstations and related control displays, elaboration units or processor based devices, et cetera, as schematically represented by the graphical symbols depicted in figure 1.
Date Recue/Date Received 2021-01-19
Claims (10)
1. A system for optimizing the operations of a train traveling along a railway line, wherein the system comprises at least:
- one or more cameras which are installed at a platform of a first station located along the railway line and are adapted to capture images of the train relative to said platform and of passengers over the platform moving for boarding on/de-boarding from the train;
- an image processing system configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras;
- a first data elaboration system which is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
- one or more cameras which are installed at a platform of a first station located along the railway line and are adapted to capture images of the train relative to said platform and of passengers over the platform moving for boarding on/de-boarding from the train;
- an image processing system configured at least to measure the actual dwell time and to calculate an optimum dwell time for the train at the platform of the first station based on the image data received from said one or more cameras;
- a first data elaboration system which is configured to calculate a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers traffic-related characteristics for a calendar date or part thereof.
2. The system according to claim 1, wherein the system further comprises a second elaboration system which is configured to calculate, for the train leaving the platform of the first station, first a total travel time to reach and leave the platform of the upcoming station and then to generate, based on the calculated total travel time, at least one optimized driving profile to be followed by the train from the platform of the first station up to the platform of the upcoming station.
3. The system according to claim 2, wherein said second elaboration system is configured to generate a plurality of optimized driving profiles based on the calculated total travel time and to select one driving profile among said plurality of driving profiles to be followed by the train based on an operative target to be respected.
4. The system according to claim 3, wherein said second elaboration system is configured to select among said plurality of generated driving profiles, one driving profile to be followed by the train based on a predefined travelling time to be respected by the train if the predicted dwell time for the train is longer than or equal to a predefined nominal dwell time at the platform of the upcoming station, or based Date Recue/Date Received 2021-01-19 on a level of energy consumption to be achieved by the train if the predicted dwell time for the train is shorter than a predefined nominal dwell time at the platform of the upcoming station.
5. The system according to any one of claims 1 to 4, wherein the first data elaboration system is a centralized neural-network-based, pre-trained and periodically updated server, said centralized neural-network-based server comprising a neural network based model for each station situated along the railway line.
6. The system according to any one of claims 1 to 5, wherein said one or more cameras comprise at least a first camera with its field of vision oriented to capture image data of the train entering into or leaving the platform, and at least a second camera with its field of vision oriented to capture image data of passengers moving into/out from the train.
7. The system according to any one of claims 1 to 6, wherein the system further comprises one or more sensors placed at selected positions along the railway line for detecting actual data of one or more corresponding environmental parameters to be supplied to said first data elaboration system for the calculation of the predicted dwell time for the train at the platform of an upcoming station.
8. A method for optimizing the operations of a train traveling along a railway line, wherein the method comprises at least the following steps:
- capturing images of the position of the train relative to a platform of a first station and of passengers over the platform moving for boarding on/de-boarding from the train;
- based on the images captured, measuring the actual dwell time and calculating an optimum dwell time for the train at the platform of the first station;
- calculating a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising one or more of a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
- capturing images of the position of the train relative to a platform of a first station and of passengers over the platform moving for boarding on/de-boarding from the train;
- based on the images captured, measuring the actual dwell time and calculating an optimum dwell time for the train at the platform of the first station;
- calculating a predicted dwell time for the train at the platform of an upcoming station along the railway line, based on one or more parameters selected from the group comprising one or more of a calculated number of actual passengers inside the train, number of passengers at the platform of the upcoming station, measured actual dwell time and calculated optimum dwell time at the platform of the upcoming station for a preceding train travelling along the railway line, data related to actual environmental conditions along the railway line or parts thereof, data indicative of passengers-related traffic characteristics for a calendar date or part thereof.
9. The method according to claim 8, wherein the method further comprises the following steps:
Date Recue/Date Received 2021-01-19 calculating for the train leaving the platform of the first station, a total travel time to reach and leave a platform of the upcoming station; and then generating, based on the calculated total travel time, at least one optimized driving profile to be followed by the train from the platform of the first station up to the platform of the upcoming station.
Date Recue/Date Received 2021-01-19 calculating for the train leaving the platform of the first station, a total travel time to reach and leave a platform of the upcoming station; and then generating, based on the calculated total travel time, at least one optimized driving profile to be followed by the train from the platform of the first station up to the platform of the upcoming station.
10. The method according to claim 9, wherein said step of generating at least one optimized driving profile comprises a first sub-step of generating a plurality of optimized driving profiles based on the calculated total travel time, and a second sub-step of selecting, among said plurality of generated driving profiles, one driving profile to be followed by the train based on an operative target to be respected.
Date Recue/Date Received 2021-01-19
Date Recue/Date Received 2021-01-19
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|---|---|---|---|---|
| CN114954572A (en) * | 2022-06-14 | 2022-08-30 | 通号城市轨道交通技术有限公司 | Automatic train dispatching method and device based on traction energy consumption statistics |
| CN116757347A (en) * | 2023-06-19 | 2023-09-15 | 中南大学 | Railway line selection method and system based on deep learning |
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| KR20230041920A (en) * | 2021-09-17 | 2023-03-27 | 한국철도기술연구원 | Method And Apparatus for Real-Time Scheduling for Resolving Delay of Train Operation |
| FR3140188A1 (en) * | 2022-09-22 | 2024-03-29 | Thales | Method for creating a passenger exchange model, method for controlling at least one public transport vehicle, computer program product and system |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114954572A (en) * | 2022-06-14 | 2022-08-30 | 通号城市轨道交通技术有限公司 | Automatic train dispatching method and device based on traction energy consumption statistics |
| CN114954572B (en) * | 2022-06-14 | 2024-04-19 | 通号城市轨道交通技术有限公司 | Automatic train dispatching method and device based on traction energy consumption statistics |
| CN116757347A (en) * | 2023-06-19 | 2023-09-15 | 中南大学 | Railway line selection method and system based on deep learning |
| CN116757347B (en) * | 2023-06-19 | 2024-02-13 | 中南大学 | Railway line selection method and system based on deep learning |
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