System and process for determining recurring and non-recurring road congestion to mitigate the same

A computer of a system for identifying, classifying, mitigating, and root-causing a road network congestion, the system having a plurality of motor vehicles positioned in a plurality of locations in a road network, with each of the motor vehicles having at least one sensor for generating an input, and each of the motor vehicles further having a Telematics Control Unit (TCU) for generating at least one location signal for a location of the associated motor vehicle and at least one event signal for an event related to the associated motor vehicle, with the at least one location signal and the at least one event signal being corresponding to a High Speed Vehicle Telemetry Data (HSVT data) based on the input from the at least one sensor, the computer comprising:

• at least one processor coupled to the TCU of the associated motor vehicles and receiving the HSVT data from the TCU of the associated motor vehicles;
• and
• a non-transitory computer readable storage medium including instructions such that the at least one processor is programmed to:
• identify the road network congestion at a current time slot based on the HSVT data;
• determine that the road network congestion is at least one of a recurring congestion and a non-recurring congestion based on the HSVT data over a period of time;
• determine at least one of a source of the recurring congestion and a cause of the non-recurring congestion, wherein the cause of the non-recurring congestion is determined by using a causal inference model to determine a causal relationship between a causal factor in a first location at a first time slot and the non-recurring congestion in a second location at a second time slot according to a statistical equation:
• δ
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• (
• X
• →
• "\[Rule]"
• Y
• )
• =
• f
• ⁡
• (
• ❘
• "\[LeftBracketingBar]"
• n
• ❘
• "\[RightBracketingBar]"
• ,
• ❘
• "\[LeftBracketingBar]"
• o
• ❘
• "\[RightBracketingBar]"
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• g
• ⁡
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• where δ(X→Y) represents a causality determination function to determine that the non-recurring congestion event Y is caused by a contributing event X;
• where f(|n|, |o|) represents a function operation between a weighted vector n and a weighted vector o;
• where n represents a weighted vector of the contributing event vector;
• where o represents a weighted vector of the autocorrelation function between the contributing event vector and the non-recurring congestion event;
• where g(|m|) represents a function operation of the weighted vector m;
• and where m represents a weighted vector of the non-recurring congestion event;
• and
• generate a notification signal associated with at least one of the recurring congestion, the source and the location of the recurring congestion, the non-recurring congestion, and the cause and the location of the non-recurring congestion, such that a display device displays an associated one of the recurring congestion, the source and the location of the recurring congestion, the non-recurring congestion, and the cause and the location of the non-recurring congestion based on the HSVT data in response to the display device receiving the notification signal from the at least one processor.

A process of operating a system for identifying, classifying, mitigating, and root-causing a road network congestion, the system having a plurality of motor vehicles positioned in a plurality of locations in a road network, with each of the motor vehicles having at least one sensor for generating an input and a Telematics Control Unit (TCU) for generating at least one location signal for a location of the associated motor vehicle and at least one event signal for an event related to the associated motor vehicle, with the at least one location signal and the at least one event signal being corresponding to a High Speed Vehicle Telemetry Data (HSVT data) based on the input from the at least one sensor, the system further including a computer having at least one processor coupled to the TCU of the associated motor vehicles and a non-transitory computer readable storage medium including instructions, the process comprising:

• identifying, using the at least one processor, the road network congestion at a current time slot based on the HSVT data;
• determining, using the at least one processor, that the road network congestion is at least one of a recurring congestion and a non-recurring congestion based on the HSVT data over a period of time;
• determining, using the at least one processor, at least one of a source of the recurring congestion and a cause of the non-recurring congestion, wherein the cause of the non-recurring congestion is determined by using a causal inference model to determine a causal relationship between a causal factor in a first location at a first time slot and the non-recurring congestion in a second location at a second time slot according to:
• Y(pi,t)=Σk=1PmkY(pi,t−k)+Σk=0PnkX(pj,t−k)+Σk=0POkε(pi,t−k)+φ
• where Y(pi, t) represents the non-recurring congestion in the location pi at the time slot t;
• where Σk=1PmkY(pi, t−k) represents an historical congestion in location pi at [t−K, t−1];
• where mk represents a weighted vector function for element Y(pi, t−k) at time slot t−k;
• where Y represents a non-recurring congestion vector in an historical data;
• where k represents a kth historical time slot before a current time slot t;
• where Σk=0PnkX(pj, t−k) represents an historical non-recurring congestion in location pj at [t−K, t];
• where nk is associated with at least one causal factor;
• where X represents the causal factor that contributes to a formation of a non-recurring congestion event Y;
• where Σk=0POkε(pi, t−k) represents an autocorrelation function between a non-recurring congestion event vector Y and a causing factor vector X, which is normally to be assumed as a normal (Gaussian) distribution N(O, σ2);
• where Ok represents a weighted function for an autocorrelation function element ε(pi, t−k);
• where k represents the kth historical time slot before the current time slot t;
• and where φ represents a random Gaussian noise;
• determining, with the at least one processor, that the causal factor in the first location at the first time slot causes the non-recurring congestion in the second location at the second time slot according to a statistical equation:
• δ
• ⁡
• (
• X
• →
• "\[Rule]"
• Y
• )
• =
• f
• ⁡
• (
• ❘
• "\[LeftBracketingBar]"
• n
• ❘
• "\[RightBracketingBar]"
• ,
• ❘
• "\[LeftBracketingBar]"
• o
• ❘
• "\[RightBracketingBar]"
• )
• g
• ⁡
• (
• ❘
• "\[LeftBracketingBar]"
• m
• ❘
• "\[RightBracketingBar]"
• )
• where δ(X→Y) represents a causality determination function to determine that the non-recurring congestion event Y is caused by a contributing event X;
• where f(|n|, |o|) represents a function operation between a weighted vector n and a weighted vector o;
• where n represents a weighted vector of the contributing event vector;
• where o represents a weighted vector of the autocorrelation function between the contributing event vector and the non-recurring congestion event;
• where g(|m|) represents a function operation of the weighted vector m;
• and where m represents a weighted vector of the non-recurring congestion event;
• and
• generating, using the at least one processor, a notification signal associated with at least one of the recurring congestion, the source and the location of the recurring congestion, the non-recurring congestion, and the cause and the location of the non-recurring congestion, such that a display device displays an associated one of the recurring congestion, the source and the location of the recurring congestion, the non-recurring congestion, and the cause and the location of the non-recurring congestion based on the HSVT data in response to the display device receiving the notification signal from the at least one processor.