Method for providing a typical load profile of a vehicle for a public transport system

• US 10,453,020 B2
• Filed: 07/31/2015
• Issued: 10/22/2019
• Est. Priority Date: 07/31/2015
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Moreira-Matias, Luis, Cats, Oded
• Primary Examiner(s): Heinle, Courtney D
• Application Number:
  US15/549,386
• Publication Number:
  US 20180039932A1
• Time in Patent Office: 1,544 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 determining a composite load profile of a vehicle, wherein the vehicle is configured to take a plurality of trips along a route, the route defines a plurality of stops, the vehicle pauses at each of the stops for a respective dwell time during each trip along the route, the vehicle defines a maximum passenger capacity, and the composite load profile indicates a composite passenger capacity utilization of the vehicle along the route;

the method comprising, for each of a plurality of trips along the route;

(a) collecting, with a computing system comprising one or more computing devices and from one or more sensors mounted to the vehicle, first data identifying a time-indexed location of the vehicle along the route during the trip;

(b) estimating, with the computing system and based on the collected first data, (i) a dwell time for each stop and (ii) at least one instance of a maximum load stop, a stop being designated as a maximum load stop at least when the vehicle, during the stop, is at the maximum passenger capacity; and

(c) computing, with the computing system, a load profile for the trip by

performing a local regression procedure in which the route is divided into subsections and capacity utilization of the vehicle between adjacent subsections is represented as a linear function of the estimated dwell times and the at least one maximum load stop;

the method further comprising computing, with the computing system, the composite load profile based on the plurality of computed trip load profiles.

2. The method according to claim 1, wherein timetable information representing parameters of historic and/or current timetables for the vehicle is included into vehicle scheduling information and each of the trip load profiles are computed based on the vehicle scheduling information.

3. The method according to claim 2, wherein the timetable information is generated by extracting meta parameters including one or both of desired vehicle occupancy and vehicle capacity.

4. The method according to claim 1, wherein the composite load profile of the vehicle indicates, for each of the stops along the route, a composite dwell time and the method comprises estimating, for each of the composite dwell times of the composite load profile, at least one of:

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

5. The method according to claim 1, comprising estimating, during each instance of (b), the at least one maximum load stop for the trip based on the dwell time for each stop of the trip.

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

7. The method according to claim 1, comprising constraining, during each instance of (c), the linear function for the route based on a minimum desired passenger capacity of the vehicle.

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

9. The method according to claim 1, comprising estimating, for each of the plurality of trips, boarding time and deboarding time per passenger for a first and last stop of the trip based on the estimated dwell times and the estimated maximum load stop.

10. The method according to claim 1, comprising determining the composite load profile in a peak hour of interest.

14. A non-transitory computer-readable medium comprising code, the code being configured to cause the computing system to perform the method of claim 1.

11. A computing system comprising one or more computing devices, the computing system being for determining a composite load profile of a vehicle, wherein the vehicle is configured to take a plurality of trips along a route, the route defines a plurality of stops, the vehicle pauses at each of the stops for a respective dwell time during each trip along the route, the vehicle defines a maximum passenger capacity, and the composite load profile indicates a composite passenger capacity utilization of the vehicle along the route;

the computing system being configured to, for each of a plurality of trips along the route;

(a) collect, from one or more sensors mounted to the vehicle, first data identifying a time-indexed location of the vehicle along the route during the trip;

(b) estimate, based on the collected first data, (i) a dwell time for each stop and (ii) at least one instance of a maximum load stop, a stop being as the maximum load stop at least when the vehicle, during the stop, is at the maximum passenger capacity;

(c) compute a load profile for the trip by performing a local regression procedure in which the route is divided into subsections and capacity utilization of the vehicle between adjacent subsections is represented as a linear function of the estimated dwell times and the at least one maximum load stop;

the computing system being further configured to compute the composite load profile based on the plurality of computed trip load profiles.

12. The system according to claim 11, wherein the computing devices are adapted to operate in parallel.

13. The system according to claim 11, wherein the system is connected to one or more databases for providing route information and schedule information.

15. A non-transitory computer-readable medium comprising code, the code being configured to cause a computing system comprising one or more computing devices to determine a load profile of a vehicle, wherein the vehicle is configured to take a plurality of trips along a route, the route defines a plurality of stops, the vehicle pauses at each of the stops for a respective dwell time during each trip along the route, the vehicle defines a maximum passenger capacity, and the composite load profile indicates a composite passenger capacity utilization of the vehicle along the route;

the code being configured to cause the computing system to, for each of a plurality of trips along the route;

(a) collecting, from one or more sensors mounted to the vehicle, first data identifying a time-indexed location of the vehicle along the route during the trip;

(b) estimating (i) a dwell time for each stop and (ii) at least one instance of a maximum load stop, a stop being designated as a maximum load stop at least when the vehicle, during the stop, is at the maximum passenger capacity; and

(c) computing a load profile for the trip by performing a local regression procedure in which the route is divided into subsections and capacity utilization of the vehicle between adjacent subsections is represented as a linear function of the estimated dwell times and the at least one maximum load stop;

the code further being configured to cause the computing system to compute the composite load profile based on the plurality of computed trip load profiles.