Patent Publication Number: US-10773741-B2

Title: Traffic management method and traffic management system

Description:
This is a National Stage application of PCT international application PCT/EP2017/060806, filed on May 5, 2017 which claims the priority of European Patent Application No. 16305538.7 entitled “Traffic management method and traffic management system”, filed May 9, 2016, both of which are incorporated herein by reference in their entirety. 
     The present invention concerns a traffic management method and a traffic management system configured to perform the steps of said method. 
     The present invention relates in particular to the management of traffic, such as a set of trains, of a transportation network, such as a railway network. The operation of the traffic is performed based on a predetermined timetable. However, traffic disruption may occur, in case vehicles of the traffic are brought in conflict with other vehicles, with a disrupted element of the transportation network or with external elements. An automatic or semi-automatic traffic management system may be provided for solving the conflicts by proposing an optimal modified timetable for managing the traffic, thanks to which the consequences of the disruptions are mitigated as much as possible. 
     An example of such a traffic management system is disclosed in EP-A1-2 913 244. This particular disclosed traffic management system is configured for generating, when a timetable disruption has occurred, a timetable that prioritizes the time points of train arrivals and departures provided that the total cost, based on electrical power consumption required for train operation and the degree of the effect of delays in the form of monetary costs associated with train operation, is lower than that of a timetable that is anticipated to occur due to the timetable disruption. 
     However, in most known traffic management systems, the generation of an optimal instruction timetable often requires an important computing load and thus may take time, depending on the available computing resources, since the problem to be solved by the system is of exponential complexity. Thus, real-time generation of new timetables is hardly possible to achieve. 
     Consequently, the invention seeks to solve these aforementioned drawbacks of the prior art by providing a new traffic management method which requires little computation resources and is therefore especially fast to perform. 
     The object of the invention is a traffic management method according to claim  1 . 
     Combining a local solution with a complementary solution is faster than generating a conflict solution for the entire transportation network, since the sub-step of generating the local solution allows reducing the number of alternatives possibilities to be calculated for generating the complementary solution. Furthermore, the conflict solution is easier to understand for an operator willing to manage future traffic so as to avoid conflicts, since the generated conflict solution implies modifications of the instruction timetable which are local to the detected and/or predicted conflict. 
     Further optional and advantageous features of the invention are defined in claims  2  to  8 . 
     Another object of the invention is a traffic management system according to claim  9 . 
    
    
     
       The invention will now be explained in reference to the annexed drawings, as an illustrative example. In the annexed drawings: 
         FIG. 1  is a schematic architectural view or the traffic management system of the invention, and 
         FIG. 2  is a diagrammatic view of the traffic management method of the invention. 
     
    
    
     As depicted on  FIG. 1 , the traffic management system  1  is configured for interacting with traffic  2  and with a transportation network  3  used by said traffic  2 . 
     In the present example, the traffic  2  comprises a set of trains, or any other similar vehicles, that use a set of resources of the transportation network  3 . In this example, the transportation network  3  is a railway transportation network. Thus, the resources may comprise sections of tracks, switches, stations, or any other component typical of a railway transportation network. Some of said resources are linked together, so that one of the trains may circulate between two linked resources. Two linked resources are for example formed by two successive sections of tracks. Two resources may also be linked through a third resource of the network  3 , as a first track section may for example be accessed by a train from a second track section through a third track section linked to both of the first and second track sections. Each of the resources is said to be “occupied” when one or more trains use the resource and that no more train may use said resource at the same time without conflicting the trains already using said resource. Thus, a conflict may occur between two or more trains that attempt to occupy the same resource. In addition, a conflict may occur between two or more trains that attempt to occupy two incompatible resources, namely different resources that cannot be used at the same time for safety constraints. 
     The system  1  depicted on  FIG. 1  comprises a traffic manager  4 , for managing the traffic  2 . The traffic manager  4  is configured to obtain and store data relating to the network  3  and to the traffic  2 , through suitable communication means  6 . The traffic manager  4  is also configured to manage the traffic  2  on the network  3  by sending orders to said network  3  and traffic  2 , through said communication means  6 . The orders are for example sent to the traffic  2 , for example by means of modifications of signals of the network  3 , like speed limitation signals, home signals or semaphore signals. Some orders are sent to the network  3 , like modifying the status of switch or other components. 
     The traffic manager  4  is configured to manage the traffic  2  and the network  3  according to a basis instruction timetable, which includes instructions about which route or action each train of the traffic  2  should take on the network  3 , and at what time such a route or action should be taken. 
     The manager  4  may also interact with a human operator  5 , for example through an interface  7  of the system  1 . The main task of the operator  5  is to take decisions relative to the management performed by the manager  4 . In particular, the operator  5  may validate and edit the instruction timetable to be used by the manager  4 . 
     The instruction timetable is obtained by computing means of the system  1 , including a conflict detection and solution module  8 . Thus, the manager  4  constitutes a decision support system through which the operator  5  may manage and monitor the traffic  2  as well as the network  3 . The operator  5  is helped and advised by the system  1  in the management and monitoring of the traffic  2  and the network  3 . 
     The conflict detection and solution module  8  interacts with the manager  4  through a communicator  9 . The communicator  9  is for example a data bus of the system  1 . The module  8  itself is preferably automatic and thus does not need human intervention for functioning. Data relative to the traffic  2 , the network  3  and the instruction timetable is sent to the module  8  through the communicator  9 . 
     The module  8  comprises an automatic conflict detector  10 , which is fed with said data. Conflict detector  10  is configured to detect conflicts in current traffic  2  based on said data. Based on said data, conflict detector  10  may also be configured to predict by calculation, in anticipation, future conflicts that may occur in said traffic  2 , in the knowledge that said traffic  2  is being managed by the instruction timetable. 
     The traffic management system  1  further comprises a conflict solver  12 , which is configured for generating a conflict solution in knowledge of the detected and/or predicted conflicts. The generation this conflict solution may be performed each time a conflict is detected and/or predicted by the conflict detector  10 , or at a predetermined time frequency. The conflict solver  12  is configured for generating the conflict solution over a scheduling period of time in the near future, starting from the current time. The duration of the scheduling time, for which the conflict solution is to be generated, may be chosen by the operator  5  through the manager  4 . The conflict solution comprises in particular a set of routes to be used by the vehicles on the transportation network  3 , these routes being programmed over the scheduling period. When the traffic  2  is managed according to the conflict solution, the detected conflict is supposedly avoided in the scheduling period of time considered, unless a disruption in the traffic  2  or the network  3  occurs, generating further conflicts. 
     After said conflict solution is generated, the manager  4  is provided with this conflict solution through the communicator  9 . Thus, the manager  4  manages the traffic  2  and the transportation network  3  with a modified instruction time table, based on the generated conflict solution and decisions of the operator  5 , who validates the conflict solution. Thus, the traffic  2  uses the network  3  with a reduced number of conflicts, or even no conflict. The conflict solver  12  generates conflict solutions so as to update the basis instruction time table of the manager  4  so that conflicts are avoided as much as possible. Preferably, the conflict solution is generated according to several parameters, for the modified time table to be the least costly in terms of drawbacks, such as the amount of violated service intentions, delays, energy consumption by the trains, failure of dependencies between trains, detours taken by trains, increase of travelling time, or the like. Thus, the conflict solution provided to the manager  4  aims to resolve conflicts of the traffic  2  with reduced economic, service and environmental impact. 
     The conflict solver  12  comprises a pre-processor  14 , included in the same architectural module than the conflict detector  10 . The conflict solver  12  further comprises an optimizer  16  separate from the conflict detector  10  and included in the module  8 . The pre-processor  14  is linked to the optimizer  16  by communication means  18 . 
     The pre-processor  14  comprises an automatic splitter  20 , an automatic local solver  22 , a dependency grouper  24 , a time range grouper  26  and a combiner  28 . 
     The automatic splitter  20  is configured for virtually splitting the transportation network  3 , for calculation purposes, into one or more local parts and one complementary part. 
     Each local part includes an area of the network  3  where one of the conflicts is involved. More precisely, each local part is defined where the trains of the traffic  2  in conflict are located, and includes local occupied resources of the network  3 , which are occupied by said trains involved in the conflict. In addition, each local part includes local linking resources, which link together the local occupied resources. The amount of linking resources included in each local part is determined in accordance to several parameters. The splitter  20  chooses which resource is to be included to the set of resources to be considered as local linking resources according to said parameters. In particular, choosing the local linking resources is done according to a depth value, which may be chosen by the operator  5  or chosen during initialization or setting of the system  1 . 
     Optionally, the grouper  24  determines groups of vehicles among the set of vehicles of the traffic  2 : this grouping is performed so that the vehicles of a same group are related by one or more traffic dependencies, while the vehicles belonging to different groups are not related by a dependency, or by dependency of importance considered negligible. A traffic dependency is a link between two or more trains, such as shared rolling stocks or shared crew members. In practice, trains linked by a dependency are dependent and are to be managed in dependence from one another on the network  3 . 
     The complementary part of the network  3  is distinct from the local parts, and constitute the rest of the network  3  not considered as a local part. Thus, the local parts are complementary to the complementary part, so that all these parts together constitute an illustration of the whole transportation network  3 . 
     The automatic local solver  22  is configured for generating one local solution for each of the split local parts of the transportation network  3 . Every one of these local solutions is only relative to one of the local parts, virtually excluding the complementary part. Optionally, each local solution is generated for only the groups of vehicles comprising the vehicles involved in conflicts. Thus, the groups of vehicles not involved in conflicts are ignored by the pre-processor  14 . Consequently, each of these local solutions is very fast to be computed, considering the reduced number of possibilities by exclusion of the complementary part, and optionally of the groups of vehicles not involved in conflicts. 
     The local solutions generated by the pre-processor  14  are fed to an automatic complementary solver  30  of the optimizer  16 , through the communication means  18 . The complementary solver  30  is configured to generate a complementary solution for only the complementary part of the transportation network  3 , starting from the local solutions provided by the pre-processor  14 . The complementary solver  30  generates the complementary solution based on the set of routes that has already been fixed by the pre-processor  14  for the local parts of the network  3 . Advantageously, the complementary solution is performed for the group of vehicles which does not comprise vehicles involved in the conflict, depending on the local solutions found for the group of vehicles including vehicles involved in said conflicts. 
     The number of potential possibilities for rerouting the traffic  2 , that the complementary solver  30  needs to contemplate for solving, is reduced. Starting from this imposed set of local routes, the complementary solver  30  is able to generate quite easily the complementary solution. 
     Then, the complementary solver  30  sends back the generated complementary solution to the pre-processor  14  through the communication means  18 . The automatic combiner  28  of the pre-processor  14  is configured to automatically combine the local solutions with the complementary solution for obtaining the conflict solution, by connecting the set of routes of the local solutions and the set of routes of the complementary solution for forming the conflict solution to be sent to the manager  4 . In view of the above, the generation of the conflict solution is very fast and reliable, so that it can be advantageously performed in real-time. The instruction timetable may be optimized at a very high frequency. 
     As an optional feature, the time range grouper  26  is configured for automatically splitting the scheduling period into successive time rangers covering a part only of the scheduling period. The local solver  22  may be configured for generating local solutions sequentially for the split time ranges. Complementarily, the complementary solver  30  may be configured to generate complementary solutions for each of the split time ranges. 
     As an example, more precisely, the scheduling period may be split into successive first, second and third time ranges. In this example, the local solver  22  firstly generates a first local solution relative to only the first time range and to the local part. Then, the complementary solver  30  generates a first complementary solution relative only to the first time range and to the complementary part. The first complementary solution is generated in consideration of the first local solution. The combiner  28  automatically combines the first local solution with the first complementary solution for obtaining a first time range solution. The first time range solution corresponds to a set of routes related to the whole transportation network  3 , but for only the first time range of the scheduling period. Afterwards, a second local solution is generated automatically by the local solver  22 , relative to only the second time range and the local part and in consideration of the first time range solution. In other words, the second local solution is built starting from the set of routes of the first time range solution, determined for the first time range. A second complementary solution is then automatically generated by the complementary solver  30 , relative to the complementary part in consideration of the second local solution. The combiner  28  combines the second local solution with the second complementary solution for generating the second time range solution. In a similar manner, a third local solution is generated for the third time range and is combined with a third complementary solution generated for the third time range, into a third time range solution. In the end, the three time range solutions generated according to the above-mentioned pattern are automatically combined together by the combiner  28 , for forming the conflict solution. Thanks to the above-mentioned patterns, the computing time of the conflict solver  12  is further reduced. 
     The scheduling period may be split into two or more successive time ranges and that as much time range solutions may be generated and combined for obtaining the conflict solution. 
     The system  1  described above is preferably implemented by one or more computers and/or one or more servers linked together by a data network. 
     As a summary, the traffic management system  1  described above is configured to perform the steps of the traffic management method according to the invention, and depicted on  FIG. 2 . 
     This method comprises a step  101  of managing the traffic  2  and transportation network  3 , according to a basis instruction time table, a step  102  of automatically detecting and/or predicting at least one conflict in the traffic  2 , a step  103  of automatically generating a conflict solution, in knowledge of the conflict and a step  104  of managing the traffic  2  and the transportation network  3  with a modified instruction time table based on the generated conflict solution. 
     Step  103  comprises a sub-step  110  of choosing a scheduling period, or using a predetermined scheduling period, and a sub-step  111  automatically splitting the scheduling period into successive time ranges. 
     Step  103  comprises a sub-step  105  of automatically splitting the transportation network  3  into local parts and complementary part. The sub-step  105  includes an elementary step  113  of choosing which resources are to be considered as local linking resources. Step  103  also comprises a sub-step  106  of automatically identifying groups of vehicles among the set of vehicles. 
     For a considered time range, sub-step  106  is followed by a sub-step  107  of automatically generating at least one local solution relative only to the local part, for the group of vehicles comprising vehicles involved in the conflict. 
     Afterwards, for the considered time range, step  103  comprises a sub-step  108  of automatically generating at least one complementary solution relative only to the complementary part, the complementary solution being generated in consideration of the local solution, for the group of vehicles which does not comprise vehicles involved in the conflict. 
     After the sub-steps  107  and  108  are performed, a further sub-step  112  of automatically combining the local solution with the complementary solution for obtaining a time range solution. The sub-steps  107 ,  108  and  112  are repeated for each time range. 
     Step  103  further comprises a sub-step  109  of automatically combining the time range solutions together for obtaining the conflict solution. 
     The embodiments and features described above may be combined for generating further embodiments of the invention.