SYSTEM FOR DETECTING, CHARACTERIZING AND MINIMIZING ROAD NETWORK OVERLOAD

Abstract

System (100) for detecting, characterizing and minimizing road network congestion, wherein the system (100) comprises: several motor vehicles (102), each of which has a telematics control unit (TCU, 112) for generating at least one location signal for a location of the associated motor vehicle (102) and at least one event signal for an event related to the associated motor vehicle (102);a display device (136) and a computer (128) that communicates with the display device (136) and the TCU 112 of the associated motor vehicles (102), the computer (128) comprising: at least one processor (134) that receives the location signal and / or the event signal of the TCU 112 of the associated motor vehicles (102) and a non-transitory computer-readable storage medium (132) containing instructions such that the at least one processor (134) is programmed to: identify a location of road network congestion in a current time step based on the location signal and the event signal of the TCU 112 of the associated motor vehicles (102); track the road network congestion based on the location signal and the event signal of the TCU 112 of the associated motor vehicles (102);Predicting road network congestion in the next time step based on the location signal and the event signal of the TCU 112 of the associated motor vehicles (102) and generating a notification signal associated with the road network congestion for at least one of the time steps, such that the display device (136) indicates the road network congestion in response to the display device (136) receiving the notification signal from the at least one processor (134); wherein the display device (136) is a monitoring device of a desktop computer used by a traffic authority to analyze road congestion and modify traffic control infrastructure to better manage urban traffic;characterized in that the at least one processor (134) is programmed to identify the location of the road network congestion by identifying a road edge congestion state and a road intersection congestion state for the associated motor vehicles (102) based on the location signal and the event signal of the TCU 112 of the associated motor vehicles (102), and the at least one processor (134) is further programmed to identify the location of the road network congestion by determining a aggregation of congested areas based on the road edge congestion state and the road intersection congestion state; wherein the at least one processor (134) is further programmed to determine the aggregation of congested roads by: aggregating an overloaded intersection (106) and an overloaded edge (104) connected to each other into a first subgraph (108);Combining two congested edges that are connected to each other into a second subgraph (110); Merging the first and second subgraphs (108, 110); Deleting at least one non-congested edge (104) and at least one non-congested intersection (106); Determining a congestion type and determining a congestion level; wherein the at least one processor (134) is programmed to track and predict a propagation of road network congestion in a temporal and a spatial domain based on a spatiotemporal discrete Markov process; wherein the at least one processor (134) is programmed to use the partial difference equation (PDE) according to: x(t) = Fx(t−1) + HBxB(t−1), where x(t) is a spatiotemporal regional congestion state at a time step t; xB is a congestion state at a boundary condition and F and HB are PDE diffusion matrix, which is assigned to a geographical neighboring influence.

Description

INTRODUCTION

[0001] The present disclosure relates to road network congestion and, in particular, to a computer that uses flowing vehicle data to identify, track, and predict road network congestion in order to minimize it.

[0002] Vehicle manufacturers and traffic authorities are constantly investigating systems to mitigate road congestion associated with urbanization trends. These trends can result in corresponding losses in productivity, wasted energy, and increased vehicle emissions. Traffic authorities are currently implementing dispersed monitoring systems, such as traffic cameras and inductive loop detectors embedded in road surfaces. However, these systems can be expensive, difficult to maintain, ineffective at tracking highly dynamic road conditions, limited in coverage, and / or prone to missing data. Other systems utilize smartphones and associated smartphone applications, such as Waze or Google Maps, to detect road geometry and estimate vehicle behavior, including speeds.However, these smartphones may not accurately detect their position on the road surface. Additionally, some smartphone applications may not be able to detect road events, such as vehicle collisions.

[0003] Thus, while existing systems for minimizing road congestion can achieve their intended purpose, there is a need for a new and improved system that directly uses vehicle data to help address these problems. SUMMARY

[0004] According to several aspects of the present disclosure, a system for minimizing road network congestion is created. The system comprises several motor vehicles, each of which has a telematics control unit (TCU). The TCU generates one or more location signals for the location of the assigned motor vehicle and one or more event signals for an event related to the assigned motor vehicle. The system further comprises a display device and a computer that communicates with the display device and the TCU of the assigned motor vehicles. The computer comprises one or more processors that receive the location signal and / or the event signal from the TCU of the assigned motor vehicles.The computer further includes a non-transitory computer-readable memory (CRM) containing instructions such that the processor is programmed to identify a location of road network congestion in the current time step based on the location signal and / or the event signal. The processor is further programmed to track the road network congestion based on the location signal and / or the event signal. The processor is further programmed to predict the road network congestion in the next time step based on the location signal and / or the event signal. The processor is further programmed to generate a notification signal associated with the road network congestion, such that the display device indicates the road network congestion in response to receiving the notification signal from the processor.

[0005] In one aspect, the processor is programmed to identify the location of road network congestion by identifying a roadside congestion state and a road intersection congestion state for the associated motor vehicles based on the location signal and / or the event signal. The processor is further programmed to identify the location of road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state.

[0006] In another aspect, the processor is further programmed to determine the aggregation of congested roads by combining a congested intersection and a congested edge that are connected to each other into a first subgraph. The processor is further programmed to combine two congested edges that are connected to each other into a second subgraph. The processor is further programmed to merge the first and second subgraphs, delete one or more uncongested edges, delete one or more uncongested intersections, determine an aggregation type, and determine an aggregation level.

[0007] In another aspect, the processor is programmed to track and predict the spread of road network congestion in a temporal and a spatial domain based on a spatiotemporal discrete Markov process.

[0008] In another aspect, the processor is programmed to use the partial difference equation (PDE) according to: x(t)=Fx(t−1)+HBxB(t−1), where x(t) is a spatiotemporal regional overload state at a time step t; x B an overload condition at a boundary condition and F and H B a PDE diffusion matrix that is assigned to a geographical neighbor influence.

[0009] According to several aspects of the present disclosure, a computer is provided for a system comprising multiple motor vehicles. Each of the motor vehicles contains a telematics control unit (TCU) for generating one or more location signals for a location of the assigned motor vehicle and one or more event signals for an event related to the assigned motor vehicle. The computer contains one or more processors for receiving the location signal and / or the event signal from the TCU of the assigned motor vehicles. The computer further contains a non-transitory computer-readable memory medium (CRM) containing instructions such that the processor is programmed to identify a location of road network congestion in a current time step based on the location signal and / or the event signal.The processor is further programmed to track road network congestion based on the location signal and / or the event signal. The processor is further programmed to predict road network congestion in a subsequent time step, again based on the location signal and / or the event signal. The processor is further programmed to generate a notification signal associated with the road network congestion, such that a display device, upon receiving the notification signal from the processor, will indicate the road network congestion.

[0010] In one aspect, the processor is programmed to identify the location of road network congestion by identifying a roadside congestion state and a road intersection congestion state based on the location signal and / or the event signal. The processor is further programmed to identify the location of road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state.

[0011] In another aspect, the processor is further programmed to identify the roadside congestion state by determining a probability density function pdf(v) based on several speeds of the motor vehicles driving on the assigned roadside.

[0012] In another aspect, the processor is further programmed to identify the roadside congestion state by selecting a predefined statistical metric h(pdf(v)) based on the probability density function pdf(v) as an indicator of a congestion level for the assigned roadside.

[0013] In another aspect, the processor is also programmed to identify the roadside congestion state by determining a reference non-congestion velocity value g(v) for the assigned roadside.

[0014] In another aspect, the processor is also programmed to identify the roadside congestion state by performing a statistical regression test R between the given statistical metric h(pdf(v)) and the reference non-congestion speed value g(v) as an estimated congestion value.

[0015] In another aspect, the processor is programmed to identify the road intersection congestion state based on a control delay, an average approach travel time for the assigned intersection, a travel time for an assigned motor vehicle over an approach to the assigned intersection, a flow traffic travel time for the approach, a count of all vehicles recorded in a time interval during the approach, and a set of all approaches at the intersection.

[0016] In another aspect, the processor is further programmed to determine the aggregation of congested roads by combining a congested intersection and a congested edge that are connected to each other into a first subgraph. The processor is further programmed to combine two congested edges that are connected to each other into a second subgraph. The processor is further programmed to merge the first and second subgraphs and delete one or more uncongested edges and one or more uncongested intersections. The processor is further programmed to determine an overload type and an overload level.

[0017] In another aspect, the processor is programmed to track and predict the spread of road network congestion in a temporal and a spatial domain based on a spatiotemporal discrete Markov process.

[0018] In another aspect, the processor is programmed to use the partial difference equation (PDE) according to: x(t)=Fx(t−1)+HBxB(t−1), where x(t) is a spatiotemporal regional overload state at a time step t; x B an overload condition at a boundary condition and F and H B a PDE diffusion matrix that is assigned to a geographical neighbor influence.

[0019] According to several aspects of the present disclosure, a process for operating a computer of a system for minimizing road network congestion is provided. The system includes a display device and several motor vehicles, each containing a telematics control unit (TCU). The computer includes one or more processors and a non-transitory computer-readable memory (CRM) containing instructions. The process includes generating, using the TCU of the assigned motor vehicles, one or more location signals for a location of the assigned motor vehicles and one or more event signals for an event related to the assigned motor vehicles. The process further includes identifying, using the processor, a location of road network congestion in a current time step based on the location signal and / or the event signal.The process further includes tracking using the road network congestion processor based on the location signal and / or the event signal. The process further includes predicting using the road network congestion processor at the next time step based on the location signal and / or the event signal. The process further includes generating a notification signal associated with the road network congestion using the processor. The process further includes displaying the notification signal using the road network congestion indicator device in response to the indicator device receiving the notification signal from the processor.

[0020] In one aspect, the process further includes identifying, using the processor, a roadside congestion state and a road intersection congestion state for the associated motor vehicles based on the location signal and / or the event signal. The process further includes identifying, using the processor, the location of the road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state.

[0021] In another aspect, the process further includes summarizing an overloaded intersection and an overloaded boundary that are connected to each other into a first subgraph using the processor. The process further includes summarizing two overloaded boundaries that are connected to each other into a second subgraph using the processor. The process further includes merging using the processor of the first and second subgraphs. The process further includes deleting one or more non-overloaded boundaries and one or more non-overloaded intersections using the processor. The process further includes determining an overload type and an overload level using the processor.

[0022] In another aspect, the process also includes tracking and predicting the spread of road network congestion in a temporal and spatial domain based on a spatiotemporal discrete Markov process using the processor.

[0023] In another aspect, the process also includes use with the partial difference equation (PDE) processor according to: x(t)=Fx(t−1)+HBxB(t−1), where x(t) is a spatiotemporal regional overload state at a time step t; x B an overload condition at a boundary condition and F and H B a PDE diffusion matrix that is assigned to a geographical neighbor influence.

[0024] Further areas of application will become apparent from the description provided here. Of course, the description and specific examples are provided for illustrative purposes only and are not intended to limit the scope of this disclosure. List of characters

[0025] The drawings described here are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. Fig. Figure 1 is a schematic view of a road network having multiple road edges and multiple intersections, with an example of a system containing multiple motor vehicles traveling along the associated road edges and passing over the associated intersections. Fig. Figure 2 is a schematic view of the system of Fig. 1, illustrating the system which includes motor vehicles with associated telematics control units (TCUs) and a computer that communicates with the TCUs to minimize road network congestion. Fig. Figure 3 is a schematic model of the road network of Fig. 1, which illustrates the spread of road network congestion in both a temporal and a spatial domain. Fig. 4 is a flowchart of a non-restrictive example of a process for operating the system of Fig. 1. DETAILED DESCRIPTION

[0026] The following description is purely illustrative and is not intended to limit the present disclosure, application, or uses. Although the drawings represent examples, they are not necessarily to scale, and some features may be exaggerated to better illustrate a particular aspect of an illustrative example. One or more of these aspects may be used alone or in combination. Furthermore, the illustrative examples described herein are not intended to be exhaustive or otherwise to limit or restrict to the specific form and configuration shown in the drawings and disclosed in the detailed description below. Illustrative examples are described in detail with reference to the drawings as follows: It will be on Fig. 1 Referenced, which is a non-restrictive example of a system 100 that uses real-time vehicle telemetry data collected within the scope of a city to actively identify one or more locations of congestion, track the spread of congestion and predict the development of congestion, such that traffic authorities can manage traffic and / or motor vehicles can take alternative routes with regard to the congestion.

[0027] In this non-restrictive example, the system 100 contains several motor vehicles 102 traveling along associated road edges 104 and crossing associated intersections 106. As detailed below, the system 100 can determine that a congested intersection and a congested edge connected to each other form a first subgraph 108 of road congestion, and the system 100 can further determine that two congested edges connected to each other form a second subgraph 110 of road congestion.

[0028] With reference to Fig. 2 Each of the motor vehicles 102 contains a telematics control unit 112 (TCU) for utilizing telematics services to generate one or more location signals for a location of the assigned motor vehicle 102 and one or more event signals for an event related to the assigned motor vehicle. In this non-restrictive example, the TCU 112 is a microcontroller (a complete computer on a single electronic chip), a microprocessor, or a field-programmable gate array (FPGA). The TCU 112 wirelessly connects the assigned motor vehicle 102 to cloud services or other vehicles using V2X or P2P standards over a cellular network. The TCU 112 connects and communicates with various subsystems via data and control buses (CAN) in the motor vehicle 102 and collects telemetry data. This data includes elements such as position, speed, engine data, and connectivity quality.They can also provide in-vehicle connectivity via Wi-Fi and Bluetooth and enable an e-call function in relevant markets. The TCU 112 communicates with suitable components of the motor vehicle 102 to collect telemetry data. Non-restrictive examples of these components include a GPS unit 114, which logs the latitude and longitude values ​​of the motor vehicle 102 such that the TCU 112 can generate location signals based on these latitude and longitude values. Another non-restrictive example of these components includes an accelerometer 116 for detecting a collision, such that the TCU can generate event signals based on a collision, which is associated with the collision value.Other non-restrictive examples of these components may include a human-machine interface 118 (HMI), one or more cameras 120, a radar unit 122, a Li-DAR unit 124, and one or more mobile communication units 126 and an external interface for mobile communication (GSM, GPRS, Wi-Fi, WiMax, LTE, or 5G) that delivers the tracked values ​​to a centralized computer 128 or database server, as described below. The motor vehicles 102 also include a storage unit 130 for storing GPS values ​​in the case of mobile network-free zones or for intelligently storing information about a vehicle's sensor data. While in this non-restrictive example the TCU is a permanently installed control unit as part of a vehicle system, it is intended that the TCU could be corresponding components in a mobile communication device such as a smartphone.

[0029] As described in detail below, the System 100 can include a local street model using peer-to-peer (P2P) or edge computing, which utilizes the TCU 112 of motor vehicles communicating with the TCU 112 of other motor vehicles 102. Edge computing is a distributed computing paradigm that moves computation and data storage closer to the data sources to improve response times and save bandwidth. The System 100 can also include cloud computing with a global street model that uses a remote computer 128 or server. The computer 128 contains one or more processors 134 and a non-transitory, computer-readable storage medium 132 (“CRM”) containing instructions such that the processor 130 is programmed to receive the location signals and / or event signals from the TCUs 112 of one or more motor vehicles 102.Processor 134 is further programmed to identify a location of road network congestion in the current time step based on location signals and / or event signals. Processor 134 is programmed to identify the location of the road network congestion by identifying a roadside congestion state and a road intersection congestion state. Processor 134 is further programmed to identify the location of the road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state.

[0030] The processor 134 is further programmed to identify the road edge congestion state by determining a probability density function pdf(v) based on several speeds of the motor vehicles 102 traveling on the assigned road edge 104. The processor 134 is further programmed to identify the road edge congestion state by selecting a predefined statistical metric h(pdf(v)) based on the probability density function pdf(v) as an indicator of the congestion level of the assigned road edge 104 at time (t, t + 1). The processor 134 is further programmed to identify the road edge congestion state by determining a reference non-congestion speed value g(v) for the assigned road edge. The processor 134 is further programmed to identify the road edge congestion state by performing a statistical regression test R (e.g.,logistic or lasso regression) between the given statistical metric h(pd / (v)) and the reference non-overload velocity value g(v) as an estimated overload value Cong(i, t) according to:. Cong(i,t)=R[h(pdf(v(i,t))),g(v)]

[0031] For persons who are qualified in this domain of technical practice, of course any variants of Eq. 1 or further attempts at a solution can be used to solve the same problem.

[0032] Processor 134 is programmed to identify the intersection congestion condition based on a control delay d. k and an average approach travel time tt k for the assigned intersection k according to: dk=[∑∀∈J,t∑∀j∈n,ttti−f fttjn]j tt¯k=[∑∀∈J,t∑∀i∈n,tttin]j where tt ia travel time for an assigned motor vehicle i over an approach j to the assigned intersection is represented; fftt j a flow-through travel time for approach j; n represents a count of all vehicles recorded in a time interval t during approach j; and J is a set of all approaches at the intersection. For persons qualified in this domain of technical practice, of course, any variants of Eq. 2 and Eq. 3, or further attempts at a solution, may be used to solve the same problem.

[0033] Processor 134 is further programmed to determine the aggregation of congested roads by merging a congested intersection and a congested edge that are connected to each other into a first subgraph. Processor 134 is further programmed to merge two congested edges that are connected to each other into a second subgraph. Processor 134 is further programmed to merge the first and second subgraphs. Processor 134 is further programmed to delete one or more uncongested edges and one or more uncongested intersections. Processor 134 is further programmed to determine an aggregation type c. t and an overload level c l according to: ct=f(ncs,scs) where cl=g(CEOE,CVOV) n cs represents a number of overloaded subgraphs; s csA size of the overloaded subgraph is represented; CE represents the overloaded boundary; OE represents the total boundary; CV represents the overloaded vertex; and OV represents the entire vertex. For individuals qualified in this domain of technical practice, of course, any variants of Eq. 4 and Eq. 5, or further attempts at a solution, may be used to solve the same problem.

[0034] Processor 134 is programmed to track road network congestion based on location signals and / or event signals at multiple time steps. Processor 134 is programmed to predict road network congestion at the next time step. Specifically, Processor 134 is programmed to track and predict the propagation of road network congestion in both a temporal and a spatial domain based on a three-dimensional spatiotemporal discrete Markov process (TS-DMP). Processor 134 uses the TS-DMP to model congestion propagation in both the temporal and spatial domains.

[0035] With reference to Fig. 3. In a non-restrictive example, the TS-DMP can exhibit three state transitions. In particular, the TS-DMP can exhibit a self-transition, where area A becomes overloaded after an event such as a collision. The TS-DMP can also exhibit spatial propagation, where the overload of area A spreads to an upstream area B. The TS-DMP can also exhibit temporal propagation, where the overload of area A at time step t + 1 is likely to be equal to or similar to that at time step t.

[0036] The processor 134 is programmed to use a partial difference equation (PDE) according to: x(t)=Fx(t−1)+HBxB(t−1) x(t) is a spatiotemporal regional overload state at a time step t; x B an overload condition at a boundary condition and F and H Ba PDE diffusion matrix assigned to a geographical neighbor influence. For individuals qualified in this domain of technical practice, any variants of Eq. 6 or further attempts at a solution can, of course, be used to solve the same problem.

[0037] In a non-restrictive example for 3 area areas and 2 boundary area areas, the PDE captures the TS-DMP according to: [xt(A)xt(B)xt(C)]=[θ1θ2θ3θ4θ5θ6θ7θ8θ9][xt−1(A)xt−1(B)xt−1(C)]+[∝1∝2∝3∝4∝5∝6][xB,t−1(A)xB,t−1(C)] where (θ1, .... θ9) are the elements in the PDE diffusion matrix function F and (∝1, ..., ∝6) are the elements in the PDE diffusion matrix function H Bare within the limiting range. For persons qualified in this domain of technical practice, Eq. 7 is, of course, merely an example of how the partial difference equation could be used for overload prediction in a specific setup.

[0038] The processor 134 is programmed to generate a notification signal associated with road network congestion during at least one of the time steps. The system further includes a display device 136 that communicates with the processor 134 such that the display device 136 indicates the road network congestion in response to receiving the notification signal from the processor 134. In a non-restrictive example, the display device could be a display screen in the motor vehicle to inform the vehicle occupant about the road congestion, enabling the occupant to drive the motor vehicle along an alternative route that does not have road congestion.In another non-restrictive example, the display device could be a screen in an autonomous vehicle to inform the vehicle occupant about road congestion and indicate that the autonomous vehicle is traveling along an alternative route without the congestion. In yet another non-restrictive example, the display device could be a monitoring device on a desktop computer used by a traffic authority to analyze road congestion and modify traffic control infrastructure to better manage urban traffic. In yet another example, the display device could be a screen on a mobile communication device such as a smartphone.

[0039] With reference to Fig. 4 will be a non-restrictive example of a process 200 for operating the system 100 of Fig. 1 created. The process 200 begins in block 202 by the TCU 112 of the assigned motor vehicle 102 generating one or more location signals for a location of the assigned motor vehicle 102 and one or more event signals for an event that is related to the assigned motor vehicle 102.

[0040] In block 204, process 200 also includes an identification using processor 134 ( Fig.2) the location of the road network congestion in the current time step based on the location signal and / or the event signal. In particular, process 200 includes identifying, using processor 134, a roadside congestion state and a road intersection congestion state for the associated motor vehicles 102 according to equations 1-3 above. Process 200 further includes identifying, using processor 134, the location of the road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state.

[0041] In particular, process 200 includes a merging, using processor 134, of an overloaded intersection 106 and an overloaded edge 104 that are connected to each other in the first subgraph 108. Process 200 further includes a merging, using processor 134, of two overloaded edges 104 that are connected to each other in the second subgraph 110. Process 200 further includes a merging, using processor 134, of the first and second subgraphs 108 and 110. Process 200 further includes a deletion, using processor 134, of one or more non-overloaded edges 104 and one or more non-overloaded intersections 106. Process 200 further includes a determination, using processor 134, of the overload type and overload level according to equations 4 and 5 above.

[0042] In block 206, process 200 further includes tracking, using processor 134, of road network congestion in multiple time steps based on the location signal and / or the event signal. Process 200 also includes tracking and predicting, using processor 134, the propagation of road network congestion in the temporal and spatial domains based on the spatiotemporal discrete Markov process. Process 200 includes using the partial difference equation (PDE) with processor 134 according to equations 6 and 7 above.

[0043] In block 208, process 200 further includes a prediction using processor 134 of the road network congestion in a next time step based on the location signal and / or the event signal.

[0044] In block 210, process 200 further includes the generation, using processor 134, of a notification signal associated with road network congestion for at least one of the time steps.

[0045] In block 212, process 200 further includes a display using the display device 136 of the road network congestion in response to the display device 136 receiving the message signal from the processor 134.

[0046] Computers and computing devices generally contain computer-executable instructions, which can be executed by one or more computing devices, such as those listed above. Computer-executable instructions can be compiled or interpreted from computer programs created using a wide variety of programming languages ​​and / or techniques, including, without limitation, and either alone or in combination, Java, C, C++, MATLAB, Simuedge, Stateflow, Visual Basic, Java Script, Perl, HTML, Tenseorflow, Python, Pytorch, Keras, etc. Some of these applications can be compiled and executed in a virtual machine, such as the Java Virtual Machine, the Dalvik Virtual Machine, or similar. Generally, a processor (e.g., a microprocessor) receives instructions, for example, from main memory, a computer-readable medium, etc., and executes these instructions, thereby carrying out one or more processes that contain one or more of the processes described herein. Such instructions and other data can be stored and transmitted using a variety of computer-readable media. A file in a computing device is generally a collection of data stored on a computer-readable medium such as a storage medium, read / write memory, etc.

[0047] The CRM (also known as a processor-readable medium) is involved in providing data (e.g., instructions) that can be read by a computer (e.g., a computer's processor). Such a medium can take many forms, including but not limited to non-volatile and volatile media. Non-volatile media can include, for example, optical or magnetic storage media and other persistent storage devices. Volatile media can include, for example, dynamic read / write memory (DRAM), which typically forms main working memory. Such instructions can be transmitted through one or more transmission media, including coaxial cable, copper wire, and fiber optics, which comprise the wires that make up a system bus coupled to an ECU processor. Common forms of computer-readable media include, for example...a floppy disk, a flexible data carrier, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical media, punched cards, paper tape, any other physical media with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chips or cartridges, or any other media which a computer can read.

[0048] In some examples, system elements can be implemented as computer-readable instructions (e.g., software) to one or more computing devices, stored on computer-readable media (e.g., disks, main memory, etc.) allocated to them. A computer program product can include such instructions, stored on computer-readable media, for performing the functions described herein.

[0049] With regard to the media, processes, systems, procedures, heuristics, etc., described herein, it is understood that, although the steps of such processes, etc., have been described as occurring in a specific, ordered sequence, such processes may be practiced in which the described steps are carried out in a sequence other than that described herein. It is further understood that certain steps may be performed simultaneously, that further steps may be added, or that certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments and should in no way be construed as limiting the claims.

[0050] Accordingly, it is understood that the description above is intended to be illustrative and not limiting. Many embodiments and applications beyond the examples provided will be obvious to those skilled in the art upon reading the description above. The scope of the invention should not be determined by reference to the description above, but rather by reference to the appended claims together with the full scope of correspondences to which such claims entitle. It is expected and intended that future developments will occur in the field discussed herein and that the disclosed systems and methods will be incorporated into such future embodiments. In summary, it is understood that the invention is suitable for modifications and variations and is limited only by the following claims.

[0051] It is intended that all terms used in the claims retain their simple and conventional meanings as understood by those skilled in the art, unless expressly stated otherwise. In particular, the use of singular articles such as "a", "the", "said", etc., should be read as quoting one or more of the specified elements, unless a claim expressly limits this to the contrary.

Claims

[1] System for detecting, characterizing and minimizing road network congestion, the system comprising: several motor vehicles, each of which has a telematics control unit (TCU) for generating at least one location signal for a location of the assigned motor vehicle and at least one event signal for an event related to the assigned motor vehicle; a display device and a computer that communicates with the display device and the TCU of the associated motor vehicles, the computer comprising: at least one processor that receives the location signal and / or the event signal of the TCU of the assigned motor vehicles and a non-transitory, computer-readable storage medium containing instructions such that at least one processor is programmed to: Identifying a location of road network congestion in a current time step based on at least one location signal and at least one event signal; Tracking road network congestion based on at least one location signal and at least one event signal; Predictions of road network congestion in the next time step based on at least one location signal and at least one event signal and Generating a notification signal associated with road network congestion for at least one of the time steps, such that the display device indicates the road network congestion in response to the display device receiving the notification signal from the at least one processor. [2] System according to claim 1, wherein the at least one processor is programmed to identify the location of the road network congestion by identifying a roadside congestion state and a road intersection congestion state for the associated motor vehicles based on the at least one location signal and the at least one event signal, and the at least one processor is further programmed to identify the location of the road network congestion by determining a summary of congested areas based on the roadside congestion state and the road intersection congestion state. [3] System according to claim 2, wherein the at least one processor is further programmed to determine the aggregation of congested roads by: Combining a congested intersection and a congested boundary that are connected to each other into a first subgraph; Combining two congested borders that are connected into a second subgraph; Merging the first and second subgraphs; Delete at least one uncongested edge and at least one uncongested intersection; Determining an overload type and Determining an overload level. [4] System according to claim 3, wherein the at least one processor is programmed to track and predict the propagation of road network congestion in a temporal and a spatial domain based on a spatiotemporal discrete Markov process. [5] System according to claim 4, wherein the at least one processor is programmed to use the partial difference equation (PDE) according to: x(t)=Fx(t−1)+HBxB(t−1), where x(t) is a spatiotemporal regional overload state at a time step t; x Ban overload condition at a boundary condition is and F and H B a PDE diffusion matrix that is assigned to a geographical neighbor influence.

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