Method for predicting travel times using autoregressive models

• US 8,762,036 B2
• Filed: 11/15/2012
• Issued: 06/24/2014
• Est. Priority Date: 11/15/2012
• Status: Expired due to Fees

• Current Assignee: Mitsubishi Electric Research Laboratories (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Nikovski, Daniel Nikolaev
• Primary Examiner(s): ARTHUR JEANGLAUDE, GERTRUDE
• Application Number:
  US13/677,645
• Publication Number:
  US 20140136088A1
• Time in Patent Office: 586 Days
• Field of Search: 701/117, 701/118, 701/66, 701/423, 701/436, 701/484
• US Class Current: 701/117
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/01 Detecting movement of traffic to be counted or controlled G08G1/07 - G08G1/14 take precedence
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0145 for active traffic flow control

Abstract

Future travel times along links are predicted using training and prediction phases. During training, seasonal intervals, a seasonal component of the training inflows are learned. The seasonal component is subtracted from the training inflows to obtain training deviations from the training inflows to yield statistics, which along with the seasonal components form a model of traffic flow on the link. During prediction, current travel times on the link are collected for current seasonal intervals to determine current inflows. A most recent travel time is subtracted from a most recent inflow to obtain a current deviation. For a future time, a predicted deviation is estimated using the statistics. The seasonal component is added to the predicted deviation to obtain a predicted inflow from which the future travel time is predicted.

Claims

1. A method for predicting a future travel time on a link, comprising:

a training phase comprising the steps of;

collecting training travel times on the link for training seasonal intervals;

determining training inflows from the training travel times;

estimating a seasonal component of the training inflows;

subtracting the seasonal component from the training inflows to obtain training deviations from the training inflows;

determining statistics from the training deviations, wherein the seasonal components and the statistics form a model of traffic flow on the link; and

a prediction phase comprising the steps of;

collecting current travel times on the link for current seasonal intervals;

determine current inflows from the current travel times;

subtracting, for a most recent travel time from a most recent inflow to obtain a current deviation;

estimating, for a future time a predicted deviation using the statistics;

adding the seasonal component to the predicted deviation to obtain a predicted inflow; and

determining the future travel time from the predicted inflow.

2. The method of claim 1, wherein the predicting uses a seasonal auto-regressive stochastic process constrained by a fixed non-linear relationship between the travel times and the inflows on the link.

3. The method of claim 1, wherein a dependency of a particular travel time τ

(t) at a tune t and an aggregate estimate of a particular inflow u(t) is

w(t)=β

u(t)+(1−

β

)ν

(t+τ

(t)),

where ν

(t+τ

(t)) is a rate of outflow at time t+τ

(t), and β

is a constant weight.

4. The method of claim 3, wherein the inflow rate is

5. The method of claim 3, wherein the dependency is linear.

6. The method of claim 3, wherein the inflows u(t) originates from a seasonal autoregressive process of order one.

7. The method of claim 3, wherein the inflow u(t) is decomposed into the seasonal component s(t) and the deviation r(t), such that the deviation is an autoregressive process of order one.

8. The method of claim 7, wherein the autoregressive process is zero-mean.

9. The method of claim 2, where the travel times are observed at irregular intervals of arbitrary duration, and an autoregressive parameter is a mean-reversion rate, determined as an average ratio of a difference between natural logarithms of two consecutive random deviations and a difference between the times when corresponding travel times were observed.

10. The method of claim 5, where the dependency between aggregate estimate of the travel flows and travel times is linear.