Predicting Taxi Utilization Information

• US 20130144831A1
• Filed: 12/05/2012
• Published: 06/06/2013
• Est. Priority Date: 12/05/2011
• Status: Active Grant

• Current Assignee: Fasterfare, LLC
• Original Assignee: Fasterfare, LLC
• Inventors: Atlas, Steven Andrew
• Granted Patent: US 9,424,515 B2
• Time in Patent Office: Days
• US Class Current: 706/50
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

Techniques are described for automatically analyzing contingencies in information predicting taxi demand. This is to generate representative information regarding current or future taxi demand, and for using such generated representative taxi demand. Contingent demand information may be generated for a variety of types of useful measures of taxi demand rates, such as for projecting expected likelihood of finding a passenger at each of several road locations. Generated representative contingent taxi demand information may be used in various ways to assist taxi and livery service drivers plan optimal routes and schedules. The historical and/or recent contingent demand data may be used to generate the representative traffic flow information. This may include data readings from mobile data sensors in the one or more vehicles, data sensors in or near the roads, or aggregate data sources collected from one or more sensors or through publicly available data sets.

Claims

1. A method of predicting taxi utilization information, comprising:

storing in a database at least one of historical taxi utilization information and recent taxi utilization information;

receiving a request for future taxi utilization information;

identifying taxi aggregation categories based on the requested current or future taxi information;

applying the taxi aggregation categories using a computer processor, to at least one of the historical taxi utilization information and the recent taxi utilization information to generate summary information about data relevant to the aggregation categories; and

predicting the requested current or future taxi information based on generated summary information.

2. The method of claim 1, wherein the future taxi utilization information includes at least one of current or future taxi demand information and supply information.

3. The method of claim 1, further comprising providing taxi relevant conditions.

4. The method of claim 3, wherein the identifying step comprises identifying taxi aggregation categories relative to the taxi relevant conditions.

5. The method of claim 4, wherein the generated summary information includes information about the taxi aggregation categories relative to the taxi relevant conditions.

6. The method of claim 3, wherein the taxi relevant conditions comprise one or more of weather and event data.

7. The method of claim 1, wherein the step of predicting current or future taxi utilization information factors at least one of a) the expected number of pickups at a location and time, b) expected number of pickups at various locations regardless of time, c) expected number of pickups at various times regardless of location, and d) expected unmet demand based on predicted supply and predicted demand.

8. The method of claim 1, wherein the step of predicting comprises solving:

9. The method of claim 8, wherein the weighting function W comprises a weight placed on each type of historical or recent taxi demand information.

10. The method of claim 8, wherein the average historical or recent taxi utilization information for an aggregation category is standardized so that multiple measures of historical or recent taxi utilization information are comparable.

11. A taxi utilization apparatus comprising:

a memory storing at least one of historical taxi utilization information and recent taxi utilization information;

a computer processor configured to receive a request for current or future taxi utilization information, generate aggregation categories based on the request, and apply the taxi aggregation categories to at least one of the historical taxi utilization information and the recent taxi utilization information to predict the requested current or future taxi utilization information; and

a transmitter for conveying to a user the taxi demand information.

12. The taxi utilization apparatus of claim 11, wherein the transmitter transmits the taxi demand information to a graphical display.

13. The taxi utilization apparatus of claim 12, wherein the graphical display is disposed in a taxi for viewing by a taxi driver.

14. The taxi utilization apparatus of claim 13, wherein the graphical display is a map illustrating locations for probably fares.

15. The taxi utilization apparatus of claim 12, wherein the graphical display is provided to a dispatcher to dispatch taxis to locations for probable fares.

16. The taxi utilization apparatus of claim 11, wherein the memory further stores taxi relevant conditions.

17. The taxi utilization apparatus of claim 16, wherein the taxi relevant conditions comprise at least one of weather and event data.

18. The taxi utilization apparatus of claim 11, wherein the prediction is made by solving:

19. The method of claim 18, wherein the weighting function W comprises a weight placed on each type of historical or recent taxi demand information.

20. The method of claim 18, wherein the average historical or recent taxi utilization information for an aggregation category is standardized so that multiple measures of historical or recent taxi utilization information are comparable.