Patent Grant
9672741
Many drivers may utilize public or private parking spots when traveling to a destination. In an example, a driver may use on-street parking when traveling to a trendy new urban restaurant in a city. In another example, the driver may park in a parking lot or parking deck for work. During the day, such as from 8:00 am until 6:00 pm, the user may pay for parking. During the evening, such as from 6:00 pm until 8:00 am, parking may be free. The driver may waste significant time and fuel, which may increase pollution, while attempting to locate available parking spaces.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Among other things, one or more systems and/or techniques for estimating parking occupancy are provided herein. In an example of estimating parking occupancy such as for paid periods during the day, a zone encompassing a parking meter may be defined. Parking meter transaction data, for the parking meter, may be acquired (e.g., a parking meter identifier, a timestamp of a time with which the parking meter was paid, a paid parking duration for which parking was paid, etc.). The parking meter transaction data may be evaluated to determine status data for one or more parking spaces managed by the parking meter. For example, the status data may be calculated, from the parking meter transaction data, as an estimation as to whether one or more parking spaces may be available or occupied and/or an estimated availability time at which one or more occupied parking spaces are estimated to become available. A parking occupancy (e.g., a low occupancy indicating a high likelihood that parking spaces are available, a medium occupancy indicating a moderate likelihood that parking spaces are available, or a high occupancy indicating a low likelihood that parking spaces are available) may be estimated based upon the status data. The parking occupancy may be displayed through a user interface.
In an example of estimating parking occupancy such as for free periods during the night, vehicle flow data (e.g., a location of a vehicle, a speed of the vehicle, a heading of the vehicle, and/or other information such as global positioning system (GPS) data provided by the vehicle), associated with one or more vehicles, may be acquired. The vehicle flow data may be evaluated to determine a start trip count of vehicles that started trips from the zone. The zone may be defined to encompass a parking meter and/or one or more parking spaces. The zone may overlap with other zones. The zone may be defined accordingly any shape and/or size (e.g., about a 50 to about a 70 meter zone or any other size around the parking meter). The vehicle flow data may be evaluated to determine an end trip count of vehicles that ended trips in the zone. A net number of vehicles within the zone at a point of time may be determined based upon a difference between the end trip count and the start trip count. The net number of vehicles may be integrated over time to determine a net number of vehicles observed to be remaining in the zone (e.g., parking) compared to leaving the zone. The parking occupancy for the zone may be determined based upon the net number of vehicles observed to be remaining in the zone compared to leaving the zone. In an example, both vehicle flow data and parking meter transaction data may be used to determine the parking occupancy for the zone. For example, the parking meter transaction data may be matched to the vehicle flow data, such as the net number of vehicles observed to be remaining in the zone compared to leaving the zone (e.g., the integration of the net number of vehicles), during a transition between a paid-period and a free period (e.g., 6:00 pm) to obtain a scale factor and offset. The scale factor and offset may be used to correctly scale and/or offset the net number of vehicles observed to be remaining in the zone compared to leaving the zone for the free-period.
To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings.
The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are generally used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth to provide an understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are illustrated in block diagram form in order to facilitate describing the claimed subject matter.
One or more systems and/or techniques for estimating parking occupancy are provided herein. Parking meter transaction data, indicative of when and for how long drivers pay for parking at one or more parking spaces, and/or vehicle flow data, indicative of vehicles remaining/parking within the one or more parking spaces, may be evaluated to determine parking occupancy for the one or more parking spaces. The parking occupancy may be displayed through a user interface, such as a smart phone app, a website, a vehicle head-end or navigation unit, a wearable device, or any other device, so that a driver may quickly identify a likelihood that an available parking space is available or not at a location such as street-side parking. In this way, the user may reduce time, fuel consumption, and/or pollution otherwise wasted in searching for an available parking space.
An embodiment of estimating parking occupancy is illustrated by an exemplary method 100 of
At 108, the parking meter transaction data may be evaluated to determine status data for the one or more parking spaces. In an example, the status data may comprise an estimation as to whether one or more parking spaces are available or occupied, such as based upon the timestamp of when the parking meter was paid and the paid parking duration. In an example, the status data may comprise an estimated availability time at which one or more occupied parking spaces are estimated to become available, such as based upon the timestamp of when the parking meter was paid and the paid parking duration (e.g., the parking meter may manage multiple parking spaces, and thus parking meter transactions, specified by the parking meter transaction data, may be tracked over time to estimate how many parking spaces are likely to be occupied).
At 110, a parking occupancy for the zone may be estimated based upon the status data (e.g., a likelihood that a parking space is available; an estimated number of available parking spaces; and/or other information indicative of parking availability). In another example of determining the parking occupancy, parking occupancies may be estimated for one or more time periods (e.g., every 30 minutes or any other time period) based upon total paid parking durations for respective time periods. For example, a total paid parking duration for a first time period (e.g., 25 minutes of paid parking out of a 30 minute time window) may be determined. A first parking occupancy for the first time period may be estimated based upon the total paid parking duration (e.g., a relatively high parking occupancy, indicating a relatively low likelihood of an available parking space, may be determined). At 112, the parking occupancy may be displayed through a user interface. For example, the parking occupancy may be displayed as a textual notification (e.g., a likelihood of available parking spaces) or a visual notification (e.g., a parking space user interface element, representing a parking space within a map, may be color coded based upon the likelihood of available parking spaces). The parking occupancy may be displayed through a mobile device, a wearable device, a vehicle head-end or navigation unit, a website, an app, etc. In an example, the parking meter transaction data may be evaluated to determine a payment rate for the parking meter. The payment rate may be displayed through the user interface.
In an example, a set of parking occupancies may be estimated for one or more zones encompassing portions of a block of parking spaces (e.g., a city block comprising 25 parking spaces). A block parking occupancy for the block of parking spaces may be determined based upon the set of parking occupancies. The block parking occupancy may be displayed through the user interface (e.g., a block user interface element, representing the block, may be colored according to a likelihood that parking spaces within the block are available). Responsive to the block parking occupancy corresponding to a high occupancy threshold range (e.g., where little to no parking spaces are estimated to be available), a high occupancy status may be displayed for the block user interface element representing the block through the user interface. Responsive to the block parking occupancy corresponding to a medium occupancy threshold range (e.g., where a few parking spaces are estimated to be available), a medium occupancy status may be displayed for the block user interface element. Responsive to the block parking occupancy corresponding to a low occupancy threshold range (e.g., where numerous parking spaces are estimated to be available), a low occupancy status may be displayed for the block user interface element. Responsive to the block parking occupancy being indicative of a parking obstruction (e.g., construction may have restricted parking for the block), a parking obstruction status may be displayed for the block user interface element. Responsive to the block parking occupancy being indicative of a restriction (e.g., police may have shut off access to parking due to a parade), a restriction status may be displayed for the block user interface element.
In an example, a parking occupancy model may be generated based upon the parking occupancy. For example, the parking occupancy model may be trained based upon parking occupancies associated with various weather conditions, seasons, occurrences of events (e.g., a sporting event), and/or other variables. In this way, future parking occupancies and/or historic parking occupancies, having such conditions/variables, may be predicted for the zone using the parking occupancy model.
In an example, the parking occupancy may be updated based upon vehicle flow data, such as global positioning system (GPS) data provided by a vehicle (e.g., a location of the vehicle, a speed of the vehicle, a heading of the vehicle, etc.). For example, vehicle flow data, associated with one or more vehicles, may be acquired (e.g., through cellular or other communication mediums). The vehicle flow data may be evaluated to determine a start trip count of vehicles that started trips from the zone and an end trip count of vehicles that ended trips in the zone. A net number of vehicles within the zone at a point of time may be determined based upon a difference between the end trip count and the start trip count. The net number of vehicles may be integrated over time to determine a net number of vehicles observed to be remaining in the zone (e.g., parking) compared to leaving the zone. The parking occupancy for the zone may be updated based upon the net number of vehicles observed to be remaining in the zone compared to leaving the zone to create an updated parking occupancy for the zone. In an example, the net number of vehicles observed to be remaining in the zone compared to leaving the zone may be normalized based upon the parking meter transaction data. In an example, the parking meter transaction data may be matched to the net number of vehicles observed to be remaining in the zone compared to leaving the zone), during a transition between a paid-period and a free period (e.g., 6:00 pm), to obtain a scale factor and offset. The scale factor and offset may be applied to the net number of vehicles observed to be remaining in the zone compared to leaving the zone.
In an example, the parking occupancy and/or the updated parking occupancy may be adjusted based upon a business type of a business within a threshold distance of the zone. For example, parking spaces near dinner restaurants may have high occupancy at night compared to morning. In another example, parking spaces near a hospital may have varying parking occupancies. In this way, parking occupancy may be estimated and provided to drivers. At 114, the method 100 ends.
The parking occupancy estimator 220 may acquire parking meter transaction data 218 for the one or more parking meters. For example, the parking meter transaction data 218 may comprise data generated during a paid parking period (e.g., during the day, such as from 8:00 am to 6:00 am). The parking occupancy estimator 220 may evaluate the parking meter transaction data 218 to determine status data for the parking spaces encompassed by the one or more zones. The status data may indicate a probability that a parking space is available or occupied based upon the parking meter transaction data 218 indicating when a parking meter was paid and for how long. Similarly, the status data may indicate an estimated availability time at which an occupied parking space is estimated to become available. The parking occupancy estimator 220 may take into account the types of businesses that are within a threshold distance of the one or more zones when determining the status data (e.g., parking spaces may be less likely to be occupied because the night club 222 and dinner restaurants are less likely to have patrons during the day). The parking occupancy estimator 220 may estimate a parking occupancy 228 for the one or more zones based upon the status data. The parking occupancy 228 may indicate how likely parking spaces are available within a zone. The parking occupancy estimator 220 may estimate a block parking occupancy for a block of parking spaces based upon parking occupancies estimated for the first zone 210 and the second zone 212 and/or for a second block of parking spaces based upon parking occupancies estimated for the third zone 214 and the fourth zone 216.
An embodiment of estimating parking occupancy is illustrated by an exemplary method 300 of
The parking occupancy estimator 412 may evaluate the vehicle flow data to determine a start trip count of vehicles that started trips from the zone 406 (e.g., vehicles that left the parking space 404) and an end trip count of vehicles that ended trips in the zone 406 (e.g., vehicles that parked at the parking space 404). The parking occupancy estimator 412 may determine a net number of vehicles within the zone 406 at a point in time based upon a difference between the end trip count and the start trip count. The parking occupancy estimator 412 may integrate the net number of vehicles over time to determine a net number of vehicles observed to be remaining in the zone 406 compared to leaving the zone 406. The parking occupancy estimator 412 may estimate a parking occupancy 414 for the zone 406 (e.g., a likelihood that the parking space 404 and/or other parking spaces within the zone 406 are available) based upon the net number of vehicles observed to be remaining in the zone 406 compared to leaving the zone 406. The parking occupancy estimator 412 may take into account the types of businesses that are within a threshold distance of the zone 406 when determining the parking occupancy 414 (e.g., irregular or sporadic parking may occur near the hospital 408).
The parking occupancy estimator 502 may display parking space user interface elements corresponding to the parking spaces near the mall. If a parking occupancy for a parking space corresponds to a low occupancy threshold range (e.g., a high likelihood that the parking space will be available for a driver of a vehicle 506), then a low occupancy status may be displayed for the parking space, such as the light dotted fill of a first parking space 514. If a parking occupancy for a parking space corresponds to a medium occupancy threshold range (e.g., a moderate likelihood that the parking space will be available for the driver of the vehicle 506), then a medium occupancy status may be displayed for the parking space, such as the medium dotted fill of a second parking space 512. If a parking occupancy for a parking space corresponds to a high occupancy threshold range (e.g., a low likelihood that the parking space will be available for the driver of the vehicle 506), then a high occupancy status may be displayed for the parking space, such as the dense dotted fill of a third parking space 508. If a parking occupancy for a parking space is indicative of a parking obstruction (e.g., a threshold amount of time where no vehicles are parking within a parking space but other nearby parking spaces have high occupancy), then an obstruction status may be displayed for the parking space, such as the black fill of a fourth parking space 510.
Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to implement one or more of the techniques presented herein. An example embodiment of a computer-readable medium or a computer-readable device is illustrated in
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some of the claims.
As used in this application, the terms “component,” “module,” “system”, “interface”, and/or the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.
Although not required, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments.
In other embodiments, device 712 may include additional features and/or functionality. For example, device 712 may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in
The term “computer readable media” as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. Memory 718 and storage 720 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by device 712. Computer storage media does not, however, include propagated signals. Rather, computer storage media excludes propagated signals. Any such computer storage media may be part of device 712.
Device 712 may also include communication connection(s) 726 that allows device 712 to communicate with other devices. Communication connection(s) 726 may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device 712 to other computing devices. Communication connection(s) 726 may include a wired connection or a wireless connection. Communication connection(s) 726 may transmit and/or receive communication media.
The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
Device 712 may include input device(s) 724 such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s) 722 such as one or more displays, speakers, printers, and/or any other output device may also be included in device 712. Input device(s) 724 and output device(s) 722 may be connected to device 712 via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s) 724 or output device(s) 722 for computing device 712.
Components of computing device 712 may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device 712 may be interconnected by a network. For example, memory 718 may be comprised of multiple physical memory units located in different physical locations interconnected by a network.
Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device 730 accessible via a network 728 may store computer readable instructions to implement one or more embodiments provided herein. Computing device 712 may access computing device 730 and download a part or all of the computer readable instructions for execution. Alternatively, computing device 712 may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device 712 and some at computing device 730.
Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. Also, it will be understood that not all operations are necessary in some embodiments.
Further, unless specified otherwise, “first,” “second,” and/or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first object and a second object generally correspond to object A and object B or two different or two identical objects or the same object.
Moreover, “exemplary” is used herein to mean serving as an example, instance, illustration, etc., and not necessarily as advantageous. As used herein, “or” is intended to mean an inclusive “or” rather than an exclusive “or”. In addition, “a” and “an” as used in this application are generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Also, at least one of A and B and/or the like generally means A or B and/or both A and B. Furthermore, to the extent that “includes”, “having”, “has”, “with”, and/or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising”.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4228519 | Pfeifer | Oct 1980 | A |
| 5910782 | Schmitt | Jun 1999 | A |
| 7026954 | Slemmer | Apr 2006 | B2 |
| 9087453 | Krivacic | Jul 2015 | B2 |
| 20050280555 | Warner | Dec 2005 | A1 |
| 20070050240 | Belani et al. | Mar 2007 | A1 |
| 20080048885 | Quinn | Feb 2008 | A1 |
| 20100052946 | Levine et al. | Mar 2010 | A1 |
| 20120095790 | Stefik | Apr 2012 | A1 |
| 20130141252 | Ricci | Jun 2013 | A1 |
| 20130265174 | Scofield et al. | Oct 2013 | A1 |
| 20140145862 | Wang | May 2014 | A1 |
| 20140350853 | Proux | Nov 2014 | A1 |
| 20160019779 | Tobolshi et al. | Jan 2016 | A1 |
| Number | Date | Country |
|---|---|---|
| 2105904 | Sep 2009 | EP |
| 2988284 | Feb 2016 | EP |
| 2014170506 | Oct 2014 | WO |
| Entry |
|---|
| Int. Search Report/Written Opinion cited in PCT Application No. PCT/US2016/036361 dated Sep. 1, 2016, 17 pgs. |
| Number | Date | Country | |
|---|---|---|---|
| 20160358473 A1 | Dec 2016 | US |
