METHOD FOR PROVIDING A TYPICAL LOAD PROFILE OF A VEHICLE FOR A PUBLIC TRANSPORT SYSTEM

• US 20180039932A1
• Filed: 07/31/2015
• Published: 02/08/2018
• Est. Priority Date: 07/31/2015
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Europe Ltd (NEC Corporation)
• Inventors: Moreira-Matias, Luis, Cats, Oded
• Granted Patent: US 10,453,020 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A method for providing a typical load profile of a vehicle includes computing dwell times and maximum load stops, wherein a maximum load stop indicates a stop having a largest load on a route for a trip using route information and vehicle scheduling information based on automatic vehicle location data, computing one or more trip load profiles by identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops. The method further includes computing the typical load profile based on load profiles of computed trips.

Claims

1. :

A method for providing a typical load profile of a vehicle for a public transport system for a vehicle'"'"'s route, wherein the vehicle performs at least one trip along the route, wherein a trip includes a number of stops with respective dwell times, and wherein a trip load profile indicates capacity utilization on a trip along a route, the method comprising;

a) computing, by a first computing device, dwell times and maximum load stops, wherein a maximum load stop indicates a stop having a largest load on the route for a trip using route information and vehicle scheduling information based on automatic vehicle location data;

b) computing, by a second computing device, one or more trip load profiles by;

identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and

constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops; and

c) computing, by a third computing device, the typical load profile based on load profiles of one or more computed trips.

2. :

The method according to claim 1, wherein timetable information representing parameters of historic and/or current timetables for the vehicle is included into the vehicle scheduling information.

3. :

The method according to claim 2, wherein the timetable information is generated by evaluating timetable generation and extracting meta parameters of the evaluated timetable generation like desired vehicle occupancy, vehicle capacity or the like.

4. :

The method according to claim 1, wherein demand profiles used for generating the timetables are obtained and then these demand profiles per trip are decomposed using averaged real headways between two trips.

5. :

The method according to claim 1, wherein the dwell times are decomposed into passenger information and vehicle information, including at least one of;

a number of boardings, a number of deboardings, a vehicle dead time, a boarding time, and a deboarding time for the vehicle.

6. :

The method according to claim 1, wherein a maximum load stop is computed by determining a stop having the maximum dwell time during a trip.

7. :

The method according to claim 1, wherein the local regression procedure is based on LOESS.

8. :

The method according to claim 1, wherein the constraining of b) is performed using a bandwidth indicating minimum and maximum admissible loads and a progression rate function decomposing the dwell time into boarding time and deboarding time and using the bandwidth and the progression rate function to update the trip load profile incrementally.

9. :

The method according to claim 1, wherein for computing the typical load profile a Euclidean distance is computed between each of the trip load profiles and an average load profile and the one having the smallest distance is selected as typical load profile.

10. :

The method according to claim 1, wherein when decomposing the dwell times, the boarding time and the deboarding time per passenger for the first and last stop of a trip is computed separately using the dwell times and the maximum load stop.

11. :

The method according to claim 1, wherein the typical load profile is determined in a peak hour of interest.

12. :

A system for providing a typical load profile of a vehicle for a public transport system for a vehicle'"'"'s route, wherein the vehicle is adapted to perform at least one trip along the route, wherein a trip includes a number of stops with respective dwell times and wherein a load profile indicates capacity utilization on a trip along a route, the system comprising;

a) a first computing device adapted to compute dwell times and maximum load stops wherein a maximum load stop indicates a stop having a largest load on the route for a trip using route information and vehicle scheduling information based on automatic vehicle location data,

b) a second computing device adapted to compute one or more trip load profiles by;

identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and

constraining the indentified load progression rate to an admissible value by evaluating the load progression rate with regard to historical dwell times and the computed maximum load stops; and

c) a third computing device adapted to compute the typical load profile based on one or more load profiles of computed trips.

13. :

The system according to claim 12, wherein the computing devices are adapted to operate in parallel exchanging data for computations.

14. :

The system according to claim 12, wherein the system is connected to one or more databases for providing route information and schedule information.

15. :

A non-transitory computer readable medium storing a program configured to cause one or more computers to execute a process for providing a typical load profile of a vehicle for a public transport system for a vehicle'"'"'s route, wherein the vehicle is adapted to perform at least one trip along the route, wherein a trip includes a number of stops with respective dwell times, and wherein a load profile indicates capacity utilization on a trip along a route, the process comprising the steps of;

a) computing, by a first computing device, dwell times and maximum load stops, wherein a maximum load stop indicates a stop having the largest load on the route for a trip using route information and vehicle scheduling information based on automatic vehicle location data;

b) computing, by a second computing device, one or more trip load profiles by

identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and

constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops; and

c) computing, by a third computing device, the typical load profile based on load profiles of one or more computed trips.