Method for planning trips to transport passengers

• US 9,558,446 B2
• Filed: 12/05/2011
• Issued: 01/31/2017
• Est. Priority Date: 02/11/2011
• Status: Active Grant

• Current Assignee: INIT Innovative Informatikanwendungen In Transport- Verkehrs- und Leitsystemen GmbH
• Original Assignee: INIT Innovative Informatikanwendungen In Transport- Verkehrs- und Leitsystemen GmbH
• Inventors: Koenig, Georg
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Zidanic, Michael
• Application Number:
  US13/983,730
• Publication Number:
  US 20130311418A1
• Time in Patent Office: 1,884 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0137 for specific applications

Abstract

A method for planning trips to transport passengers on the basis of a preferably statistical predictive model, in particular to plan capacities, optimize the utilization of vehicle fleets, determine the routes to be taken, etc., preferably in short-distance passenger traffic, wherein the predictive model takes into account, using parameters, characteristic passenger patterns over the course of a day, week, and/or year, is characterized in that the predictive model additionally uses weather-specific parameters.

Claims

1. A method of capacity planning for planning trips to transport passengers, said method being based on a statistical forecasting model provided via a forecasting program running on a central computer containing one or more processors for the purpose of optimizing the deployment of vehicle fleets and for the purpose of planning test drives, wherein the method comprises the steps of:

accounting for, via execution of the forecasting program by the one or more processors, one or more parameters associated with the behavior of passengers over a profile of at least one of a period of days, weeks, or years so as to provide a plurality of repeated test runs, wherein the one or more parameters comprise one or more weather specific parameters associated with information relating to precipitation and an outside temperature;

determining the precipitation via a sensor system on the vehicle, wherein actuation of a windshield wiper on at least one vehicle in the vehicle fleet serves as the sensor system;

measuring the outside temperature at the at least one vehicle and using the measured temperature to at least, in part, determine the nature of the precipitation detected via the sensor system;

transmitting data detected at the vehicle to the central computer for incorporation into the statistical program; and

initiating an optimized deployment of vehicle fleets based at least in part upon said data from the plurality of repeated test runs being incorporated into the statistical forecasting model and via automatic execution of said forecasting program by said one or more computer processors, the optimizing occurring prior to the deployment of the vehicle fleets and eliminating stochastic disturbance values due to the incorporation of the one or more weather specific parameters.

2. A method according to claim 1, wherein said optimized deployment of vehicle fleets facilitates regional transit of passengers.

3. A method according to claim 1, wherein the one or more weather specific parameters are further associated with information relating to dry weather.

4. A method according to claim 1, wherein the precipitation is at least one of rain or snow.

5. A method according to claim 1, wherein the one or more weather specific parameters comprise the strength of the precipitation.

6. A method according to claim 5, wherein the one or more weather specific parameters further comprise a forecast duration of the precipitation.

7. A method according to claim 1, wherein the parameter relating to the precipitation is determined via data from the current relevant weather report.

8. A method according to claim 1, wherein the weather specific parameters further include information relating to the road conditions, particularly with respect to black ice.

9. A method according to claim 8, wherein the information relating to the road conditions comprises information relating to black ice conditions.

10. A method according to claim 3, wherein the transmission of data to the central computer is via at least one of radio or WLAN.

11. A method according to claim 1, wherein the forecasting model uses probabilities for weather specific parameters, taking into account the history.