Determining a spatiotemporal impact of a planned event on traffic

• US 8,892,343 B2
• Filed: 07/31/2012
• Issued: 11/18/2014
• Est. Priority Date: 07/31/2012
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Gupta, Chetan Kumar, Castellanos, Maria G, Serebryakov, Sergey
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): TESTARDI, DAVID A
• Application Number:
  US13/562,490
• Publication Number:
  US 20140039782A1
• Time in Patent Office: 840 Days
• Field of Search: 701117-119, 701/414, 701/423, 340/995.13
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P

Abstract

Systems and methods are provided for determining an impact of a planned event. Traffic flow velocity is measured at each of a plurality of traffic sensors over a period of time before the planned event, with each traffic sensor having an associated geographical location. A plurality of measured traffic flow velocities for each traffic sensor are stored on a non-transitory computer readable medium. A traffic flow velocity at each traffic sensor is measured at each of a plurality of representative times within a defined time period including a known start time and an expected end time of the event. A threshold velocity is calculated from the stored traffic flow velocities for each traffic sensor corresponding to each representative time. For each representative time, a set of traffic sensors is determined at which the measured traffic flow velocity during the defined time period is less than the threshold velocity.

Claims

1. A traffic monitoring system comprising:

a plurality of sensors to generate traffic data representing traffic flow at respective geographical locations; and

a system control comprising;

a processor configured to execute machine readable instructions from a non-transitory computer readable medium;

a sensor interface to receive information from the plurality of sensors;

a database to store historical data from the plurality of sensors and information about a planned event having a known start time, an expected end time, and a venue; and

an event analysis component that interacts with the processor and is configured to determine a spatiotemporal region, defined as a set of paired sensor and time values, in which traffic is affected by the planned event, a given paired sensor value and time value belonging to the set if, at the time represented by the time value, the sensor detects a delay at its associated location and is part of a continuous series of sensors, including a sensor associated with the venue, in which each sensor is detecting a delay, wherein the event analysis component is configured to generate a directed acyclic graph representing a subset of the plurality of sensors within a threshold distance of the venue.

2. The traffic monitoring system of claim 1, wherein the event analysis component is configured to search the directed acyclic graph at each of a plurality of times within a defined time period to determine the spatiotemporal region.

3. The traffic monitoring system of claim 2, wherein the defined time period encompasses a predetermined period of time before the known start time of the planned event, an expected duration of the planned event, and a predetermined period after the expected end time of the planned event, the plurality of times being evenly spaced within the defined time period.

4. The traffic monitoring system of claim 1, wherein the event analysis component is configured to compute a threshold velocity from the stored historical data and determines if a given sensor detects a delay by comparing a current traffic flow velocity to the threshold velocity.

5. A method to determine an impact of a planned event comprising:

measuring traffic flow velocity at each of a plurality of traffic sensors over a period of time before the planned event, each traffic sensor having an associated geographical location;

storing a plurality of measured traffic flow velocities for each traffic sensor on a non-transitory computer readable medium;

measuring a traffic flow velocity at each traffic sensor at each of a plurality of representative times within a defined time period including a known start time and an expected end time of the planned event;

calculating a threshold velocity from the stored traffic flow velocities for each traffic sensor corresponding to each of the plurality of representative times;

determining, for each of the plurality of representative times, a set of traffic sensors at which the measured traffic flow velocity during the defined time period is less than the threshold velocity; and

constructing a directed acyclic graph comprising a plurality of nodes representing the plurality of traffic sensors, with a traffic sensor associated with a venue of the planned event being a root node,

wherein the directed acyclic graph is utilized by a processor to determine the impact of the planned event.

6. The method of claim 5, wherein calculating a threshold velocity from the stored traffic flow velocities comprises calculating an average, comprising one of a median and a mean, of the stored traffic velocities associated with a given representative time for each traffic sensor.

7. The method of claim 6, wherein calculating a threshold velocity from the stored traffic flow velocities further comprises multiplying the calculated average by a weight, the weight being a value between zero and one.

8. The method of claim 5, further comprising:

retrieving a geographical location of a venue associated with the planned event; and

selecting the plurality of traffic sensors from a set of available traffic sensors as all traffic sensors within a threshold distance of the geographical location of the venue.

9. The method of claim 5, further comprising:

defining the defined time period such that the time period extends from a first time prior to the known start time to a second time after the expected end time; and

selecting the plurality of representative times at evenly spaced intervals within the defined time period.

10. The method of claim 5, wherein determining, for each of the plurality of representative times, a set of traffic sensors at which the current velocity is less than the threshold velocity comprises

exploring the directed acyclic graph for each of the plurality of representative times to determine a set of nodes representing the set of traffic sensors at which the current velocity is less than the threshold velocity.

11. The method of claim 10, wherein exploring the directed acyclic graph for each of the plurality of representative times comprises:

selecting a node of the plurality of nodes;

comparing the measured traffic flow during the defined time period at the traffic sensor associated with the selected node at the representative time with the threshold velocity for the traffic sensor for the representative time;

adding the traffic sensor represented by the selected node to the set of traffic sensors if the measured traffic flow during the defined time period is less than the threshold velocity; and

eliminating all nodes downstream of the selected node from further analysis if the measured traffic flow during the defined time period is not less than the threshold velocity.

12. The method of claim 10, wherein exploring the directed acyclic graph for each of the plurality of representative times comprises:

selecting the start time of the planned event;

exploring the directed acyclic graph for the start time of the planned event; and

iteratively repeating the following steps until either no delay is found at the root node of the directed acyclic graph or all representative times prior to the start time have been selected;

selecting a representative time immediately before a currently selected time; and

exploring the directed acyclic graph for the selected time.

13. The method of claim 12, wherein exploring the directed acyclic graph for each of the plurality of representative times further comprises:

selecting the end time of the planned event;

exploring the directed acyclic graph for the end time of the planned event; and

iteratively repeating the following steps until either no delay is found at the root node of the directed acyclic graph for a selected time or all representative times after the end time have been selected;

selecting a representative time immediately after a currently selected time; and

exploring the directed acyclic graph for the selected time.

14. A traffic monitoring system comprising:

a plurality of sensors to generate traffic data representing traffic flow at respective geographical locations; and

a system control comprising;

a processor configured to execute machine readable instructions from a non-transitory computer readable medium;

a sensor interface to receive information from the plurality of sensors;

a database to store historical data from the plurality of sensors and information about a planned event having a known start time, an expected end time, and a venue; and

an event analysis component that interacts with the processor and is configured to determine a spatiotemporal region, defined as a set of paired sensor and time values, in which traffic is affected by the planned event, a given paired sensor value and time value belonging to the set if, at the time represented by the time value, the sensor detects a delay at its associated location and is part of a continuous series of sensors, including a sensor associated with the venue, in which each sensor is detecting a delay, by generating a directed acyclic graph representing a subset of the plurality of sensors within a threshold distance of the venue and searching the directed acyclic graph at each of a plurality of times within a defined time period encompassing a predetermined period of time before the known start time of the planned event, an expected duration of the planned event, and a predetermined period after the expected end time of the planned event to determine the spatiotemporal region.