ESTIMATING TIME TRAVEL DISTRIBUTIONS ON SIGNALIZED ARTERIALS

Abstract

A system is provided for estimating time travel distributions on signalized arterials. The system may be implemented as a network service. Traffic data regarding a plurality of travel times on a signalized arterial may be received. A present distribution of the travel times on the signalized arterial may be determined. A prior distribution based on one or more travel time observations may also be determined. The present distribution may be calibrated based on the prior distribution.

Description

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation and claims the priority benefit of U.S. patent application Ser. No. 14/323,352 filed Jul. 3, 2014, which claims the priority benefit of U.S. patent application Ser. No. 13/752,351 filed Jan. 28, 2013, which issued as U.S. Pat. No. 8,781,718 on Jul. 15, 2014, which claims the priority benefit of U.S. provisional application No. 61/591,758 filed on Jan. 27, 2012, the disclosures of which are incorporated herein by reference.

BACKGROUND

1. Field of the Invention

The present invention generally concerns traffic management. More specifically, the present invention concerns estimating time travel distributions on signalized arterials and thoroughfares.

2. Description of the Related Art

Systems for estimating traffic conditions have historically focused on highways. Highways carry a majority of all vehicle-miles traveled on roads and are instrumented with traffic detectors. Notably, highways lack traffic signals (i.e., they are not “signalized”). Estimating traffic conditions on signalized streets represents a far greater challenge for two main reasons. First, traffic flows are interrupted because vehicles must stop at signalized intersections. These interruptions generate complex traffic patterns. Second, instrumentation amongst signalized arterials is sparse because the low traffic volumes make such instrumentation difficult to justify economically.

In recent years, however, global positioning system (GPS) connected devices have become a viable alternative to traditional traffic detectors for collecting data. As a result of the permeation of GPS connected devices, travel information services now commonly offer information related to arterial conditions. Although such information is frequently available, the actual quality of the traffic estimations provided remains dubious.

Even the most cursory of comparisons between information from multiple service providers reveals glaring differences in approximated signalized arterial traffic conditions. The low quality of such estimations is usually a result of having been produced from a limited set of observations. Recent efforts, however, have sought to increase data collection by using re-identification technologies.

Such techniques have been based on be based on magnetic signatures, toll tags, license plates, or embedded devices. The sampling sizes obtained from such technologies are orders of magnitude greater than those obtained from mobile GPS units. Sensys Networks, Inc. of Berkeley, Calif., for example, collects arterial travel time data using magnetic re-identification and yields sampling rates of up to 50%. Notwithstanding these recently improved observation techniques, there remains a need to provide more accurate estimates of traffic conditions on signalized arterials.

SUMMARY OF THE PRESENTLY CLAIMED INVENTION

A system for estimating time travel distributions on signalized arterials includes a processor, memory, and an application stored in memory. The application is executable by the processor to receive data regarding travel times on a signalized arterial, estimate a present distribution of the travel times, estimate a prior distribution based on one or more travel time observations, and calibrate the present distribution based on the prior distribution.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a system for estimating time travel distributions on signalized arterials.

FIG. 2 is a series of graphs showing distributions of pace on a signalized arterial segment at the same time on over three consecutive days.

FIG. 3 is a graph showing variations in pace throughout different times periods in a day.

FIG. 4 is a block diagram of a device for implementing an embodiment of the presently disclosed invention.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system for estimating time travel distributions on signalized arterials. The system of FIG. 1 includes a client computer 110, network 120, and a server 130. Client computer 110 and server 130 may communicate with one another over network 120. Client computer 110 may be implemented as a desktop, laptop, work station, notebook, tablet computer, smart phones, mobile device or other computing device. Network 120 may be implemented as one or more of a private network, public network, WAN, LAN, an intranet, the Internet, a cellular network or a combination of these networks.

Client computer 110 may implement all or a portion of the functionality described herein, including receive traffic data and other data or and information from devices using re-identification technologies. Such technologies may be based on magnetic signatures, toll tags, license plates, or embedded devices. Server 130 may receive probe data from GPS-connected mobile devices. Server 130 may communicate data directly with such data collection devices. Server 130 may also communicate, such as by sending and receiving data, with a third-party server, such as the one maintained by Sensys Networks, Inc. of Berkeley and accessible through the Internet at www.sensysresearch.com.

Server computer 130 may communicate with client computer 110 over network 120. Server computer may perform all or a portion of the functionality discussed herein, which may alternatively be distributed between client computer 110 and server 130, or may be provided by server 130 as a network service for client 110. Each of client 110 and server computer 130 are listed as a single block, but it is envisioned that either be implemented using one or more actual or logical machines.

In one embodiment, the system may utilize Bayesian Inference principles to update a prior belief based on new data. In such an embodiment, the system may determine the distribution of travel times y on a given signalized arterial at the present time T. The prior beliefs may include the shape of the travel time distribution and the range of its possible parameters θ_T (e.g., mean and standard deviation) that are typical of a given time of day, such that y follows a probability function p(y|θ_T) These parameters themselves may follow a probability distribution p(θ_T|α_T) called the prior distribution. The prior distribution may comprise its own set of parameters α_T, which are referred to as hyper-parameters.

The system may estimate the current parameters using a recent travel time observation of the arterial of interest. The system may also account for observations on neighboring streets. In still further embodiments, the system may consider contextual evidence such as local weather, incidents, and special events such as sporting events, one off road closures, or other intermittent traffic diversions. In one embodiment, y* may designate the current travel time observations. The system may determine the likeliest θ_T using a known y* and α_T.

The system 100 may account for one or more travel time variability components. First, there may be individual variations between vehicles traveling at the same time of day. These variations stem from diverse driving profiles among drivers and their varying luck with traffic signals. Second, there may be recurring time-of-day variations that stem from fluctuating traffic demand patterns and signal timing. Third, there may be daily variations in the distributions of travel times over a given time slot. System 100 may account for other time travel variability components.

In one exemplary embodiment, the system 100 may employ standard Traffic Message Channel (TMC) location codes as base units of space, and fifteen-minute periods as base units of time. In such an embodiment, the system approximates that traffic conditions remain homogeneous across a given TMC location code over each fifteen-minute period. The system 100 may also use other spatial or temporal time units depending on the degree of precision desired. For example, the system 100 may normalize travel time data into a unit of pace that is expressed in seconds per mile. The system 100 may also calculate the average pace as a linear combination of individual paces weighted by distance traveled. Such calculations may be more convenient than using speed values.

FIG. 2 is a series of graphs showing distributions of pace on a signalized arterial segment at the same time on over three consecutive days. More specifically, FIG. 2 shows an exemplary distribution of pace on a 2-km arterial segment in Seattle, Wash. for the same fifteen-minute time period on three consecutive days. As suggested in FIG. 2, determining an exact distribution shape for a given fifteen minute period on any given day may pose a difficult realistic objective. The presently described system can, however, directly observe three different states of an arterial segment and then calibrate the prior probabilities of being in either state from archived data. The system may also use real-time data to help refine a given brief regarding which of the multiple state applies to the real-time prediction.

FIG. 3 is a graph showing variations in pace throughout different times periods in a day. As shown in FIG. 3, the presently disclosed system may account for time-of-day variations. Notably, the box indicates the 25^th, 50^th, and 75^th percentile value while the dotted lines extend to extreme values. In such embodiments, the system may use data regarding regular patterns of increase and decrease in travel times to calibrate prior distributions by time of day.

FIG. 4 is a block diagram of a device 400 for implementing an embodiment of the presently disclosed invention. System 400 of FIG. 4 may be implemented in the contexts of the likes of client computer 110 and server computer 130. The computing system 400 of FIG. 4 includes one or more processors 410 and memory 420. Main memory 420 may store, in part, instructions and data for execution by processor 410. Main memory can store the executable code when in operation. The system 400 of FIG. 4 further includes a storage 420, which may include mass storage and portable storage, antenna 440, output devices 450, user input devices 460, a display system 470, and peripheral devices 480.

The components shown in FIG. 4 are depicted as being connected via a single bus 490. The components may, however, be connected through one or more means of data transport. For example, processor unit 410 and main memory 420 may be connected via a local microprocessor bus, and the storage 430, peripheral device(s) 480 and display system 470 may be connected via one or more input/output (I/O) buses. In this regard, the exemplary computing device of FIG. 4 should not be considered limiting as to implementation of the presently disclosed invention. Embodiments may utilize one or more of the components illustrated in FIG. 4 as might be necessary and otherwise understood to one of ordinary skill in the art.

Storage device 430, which may include mass storage implemented with a magnetic disk drive or an optical disk drive, may be a non-volatile storage device for storing data and instructions for use by processor unit 410. Storage device 430 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 410.

Portable storage device of storage 430 operates in conjunction with a portable non-volatile storage medium, such as a floppy disk, compact disk or Digital video disc, to input and output data and code to and from the computer system 400 of FIG. 4. The system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system 400 via the portable storage device.

Antenna 440 may include one or more antennas for communicating wirelessly with another device. Antenna 440 may be used, for example, to communicate wirelessly via Wi-Fi, Bluetooth, with a cellular network, or with other wireless protocols and systems including but not limited to GPS, A-GPS, or other location based service technologies. The one or more antennas may be controlled by a processor 410, which may include a controller, to transmit and receive wireless signals. For example, processor 410 execute programs stored in memory 412 to control antenna 440 transmit a wireless signal to a cellular network and receive a wireless signal from a cellular network.

The system 400 as shown in FIG. 4 includes output devices 450 and input device 460. Examples of suitable output devices include speakers, printers, network interfaces, and monitors. Input devices 460 may include a touch screen, microphone, accelerometers, a camera, and other device. Input devices 460 may include an alpha-numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys.

Display system 470 may include a liquid crystal display (LCD), LED display, or other suitable display device. Display system 470 receives textual and graphical information, and processes the information for output to the display device.

Peripherals 480 may include any type of computer support device to add additional functionality to the computer system. For example, peripheral device(s) 480 may include a modem or a router.

The components contained in the computer system 400 of FIG. 4 are those typically found in computing system, such as but not limited to a desk top computer, lap top computer, notebook computer, net book computer, tablet computer, smart phone, personal data assistant (PDA), or other computer that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 400 of FIG. 4 can be a personal computer, hand held computing device, telephone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device. The computer can also include different bus configurations, networked platforms, multi-processor platforms, etc. Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Palm OS, and other suitable operating systems.

The foregoing detailed description of the technology herein has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the technology to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the technology and its practical application to thereby enable others skilled in the art to best utilize the technology in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the technology be defined by the claims appended hereto.

Claims

1. A method for estimating time travel distributions on signalized arterials, the method comprising:

receiving travel data about a signalized arterial collected by one or more reidentification devices, the travel data corresponding to data collected within a common time segment in each of a plurality of different days;

receiving real-time travel data about the signalized arterial collected by one or more reidentification devices; and

executing instructions stored in memory, wherein execution of the instructions by a processor: normalizes the travel data into a plurality of individual pace values, the pace values expressed as a ratio of time per distance, calculates an average pace value for the signalized arterial as a linear combination of the individual pace values weighted by distance traveled across the signalized arterial, estimates a distribution based on the average pace value, travel data, and store the estimated distribution in memory, calibrates the distribution based on the real-time travel data, and generates a real-time prediction of the traffic conditions of the signalized arterial based on the calibrated distribution.

2. The method of claim 1, wherein the normalized travel data is expressed in seconds per mile.

3. The method of claim 1, wherein the normalized travel data uses a base unit of time of 15 minutes.

4. The method of claim 1, wherein the normalized travel data uses a base unit of space corresponding to standard Traffic Message Channel (TMC) location codes.

5. The method of claim 1, wherein the estimated distributions include variations in pace throughout different time periods in a day.

6. The method of claim 5, wherein the estimated distributions are calibrated using patterns of increase and decrease in travel times.

7. The method of claim 1, wherein the received travel data for a particular signalized arterial also includes observations from neighboring streets.

8. The method of claim 1, wherein the received travel data for a particular signalized arterial also includes contextual evidence.

9. The method of claim 8, wherein the contextual evidence includes local weather, incidents, and special events.

10. A non-transitory computer-readable storage medium, having embodied thereon a program executable by a processor to perform a method for estimating time travel distributions on signalized arterials, the method comprising:

receiving travel data about a signalized arterial collected by one or more reidentification devices, the travel data corresponding to data collected within a common time segment in each of a plurality of different days;

receiving real-time travel data about the signalized arterial collected by one or more reidentification devices;

normalizing the travel data into a plurality of individual pace values, the pace values expressed as a ratio of time per distance;

calculating an average pace value for the signalized arterial as a linear combination of the individual pace values weighted by distance traveled across the signalized arterial;

estimating a distribution based on the average pace value, travel data, and store the estimated distribution in memory;

calibrating the distribution based on the real-time travel data; and

generating a real-time prediction of the traffic conditions of the signalized arterial based on the calibrated distribution.

11. The non-transitory computer-readable storage medium of claim 10, wherein the normalized travel data is expressed in seconds per mile.

12. The non-transitory computer-readable storage medium of claim 10, wherein the normalized travel data uses a base unit of time of 15 minutes.

13. The non-transitory computer-readable storage medium of claim 10, wherein the normalized travel data uses a base unit of space corresponding to standard Traffic Message Channel (TMC) location codes.

14. The non-transitory computer-readable storage medium of claim 10, wherein the estimated distributions include variations in pace throughout different time periods in a day.

15. The non-transitory computer-readable storage medium of claim 14, wherein the estimated distributions are calibrated using patterns of increase and decrease in travel times.

16. The non-transitory computer-readable storage medium of claim 10, wherein the received travel data for a particular signalized arterial also includes observations from neighboring streets.

17. The non-transitory computer-readable storage medium of claim 10, wherein the received travel data for a particular signalized arterial also includes contextual evidence.

18. The non-transitory computer-readable storage medium of claim 17, wherein the contextual evidence includes local weather, incidents, and special events.