Traffic prediction using precipitation
View Patent ↗Described herein is a framework to facilitate traffic prediction. In accordance with one aspect, training data including historical traffic information and precipitation data is received. An impulse response function may be determined based on the training data. One or more traffic parameters may be predicted by calculating a weighted linear system model based on the impulse response function.
1. A method of traffic prediction performed by a computer system, comprising:
receiving training data including historical traffic information and precipitation data;
determining an impulse response function based on the training data;
predicting one or more traffic parameters by calculating a weighted linear system model based on the impulse response function, wherein calculating the weighted linear system model comprises calculating y(t)= y (t)+α∫h(τ)r(t−τ)dτ, wherein α is a weight parameter, y (t) is a historical average of travel time at time t of the day, h(τ) is the impulse response function and r(t−τ) is a precipitation rate; and graphically presenting the one or more predicted traffic parameters.
2. The method of claim 1 wherein the traffic information comprises travel time data.
3. The method of claim 1 wherein the precipitation data comprises rainfall data.
4. The method of claim 1 wherein the precipitation data comprises snowfall data.
5. The method of claim 1 wherein predicting the one or more traffic parameters comprises predicting travel time.
6. The method of claim 1 wherein the weight parameter comprises
α
=
{
0
,
if
r
_
≤
ω
1
0.5
,
if
r
_
>
ω
1
and
r
_
≤
ω
2
1
,
otherwise
,
wherein r is an average precipitation rate, ω 1 and ω 2 are empirically determined thresholds.
7. A non-transitory computer-readable medium having stored thereon program code, the program code is executable by a computer to:
receive training data including historical traffic information and precipitation data;
determine an impulse response function based on the training data;
predict one or more traffic parameters by calculating a weighted linear system model based on the impulse response function, wherein the weighted linear system model comprises y(t)= y (t)+α∫h(τ)r(t−τ)dτ, wherein α is a weight parameter, y (t) is a historical average of travel time at time t of the day, h(τ) is the impulse response function and r(t−τ) is a precipitation rate; and graphically present the one or more predicted traffic parameters.
8. The non-transitory computer-readable medium of claim 7 wherein the traffic information comprises travel time data.
9. The non-transitory computer-readable medium of claim 7 wherein the precipitation data comprises snowfall data.
10. The non-transitory computer-readable medium of claim 7 wherein the precipitation data comprises rainfall data.
11. The non-transitory computer-readable medium of claim 7 wherein the program code is executable by the computer to predict the one or more traffic parameters by predicting travel time.
12. The non-transitory computer-readable medium of claim 7 wherein the weight parameter comprises
α
=
{
0
,
if
r
_
≤
ω
1
0.5
,
if
r
_
>
ω
1
and
r
_
≤
ω
2
1
,
otherwise
,
wherein r is an average precipitation rate, ω 1 and ω 2 are empirically determined thresholds.
13. A system comprising:
a non-transitory memory device for storing computer-readable program code; and
a processor in communication with the memory device, the processor being operative with the computer-readable program code to:
receive training data including historical traffic information and precipitation data;
determine an impulse response function based on the training data;
predict one or more traffic parameters by calculating a weighted linear system model based on the impulse response function, wherein the weighted linear system model comprises y(t)= y (t)+α∫h(τ)r(t−τ)dτ, wherein α is a weight parameter, y (t) is a historical average of travel time at time t of the day, h(τ) is the impulse response function and r(t−τ) is a precipitation rate; and graphically present the one or more predicted traffic parameters.
14. The system of claim 13 wherein the traffic information comprises travel time data.
15. The system of claim 13 wherein the precipitation data comprises rainfall data.
16. The system of claim 13 wherein the processor is operative with the computer-readable program code to predict the one or more traffic parameters by predicting travel time.
17. The system of claim 13 wherein the weight parameter comprises
α
=
{
0
,
if
r
_
≤
ω
1
0.5
,
if
r
_
>
ω
1
and
r
_
≤
ω
2
1
,
otherwise
,
wherein r is an average precipitation rate, ω 1 and ω 2 are empirically determined thresholds.