Pre-Cooling and Pre-Heating Transportation Vehicles Using Predictive Crowd Estimation Techniques

Patent metadata

Field	Value
Publication number	US-2018201092-A1
Application number	US-201715407816-A
Country	US
Kind code	A1
Filing date	Jan 17, 2017
Priority date	Jan 17, 2017
Publication date	Jul 19, 2018
Grant date	—

Abstract

Methods, systems, and computer program products for pre-cooling and pre-heating vehicles using crowd estimation techniques are provided herein. A computer-implemented method includes estimating the number of individuals having travelled in a vehicle over sub-routes of a fixed route, learning temporal patterns pertaining to the number of individuals in the vehicle across each of the sub-routes, predicting the number of individuals that will be in the vehicle during the sub-routes based on the patterns, computing an expected amount of time for the vehicle to complete a sub-route and arrive at a location that commences a subsequent sub-route, determining an amount by which the energy required to maintain a temperature range in the vehicle is to be modified prior to the vehicle reaching the location based on said predicting and said computing, and utilizing energy to maintain the temperature range, based on said determining, prior to the vehicle reaching the location.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
estimating the number of individuals having travelled in a vehicle across each of multiple sub-routes of a fixed route travelled by the vehicle, wherein the vehicle is used for shared passenger transportation services; learning one or more temporal patterns pertaining to the number of individuals in the vehicle across each of the multiple sub-routes; predicting the number of individuals that will be in the vehicle during one or more of the multiple sub-routes based on the one or more learned temporal patterns; computing an expected amount of time for the vehicle to complete a current one of the sub-routes and arrive at a location that commences a subsequent one of the sub-routes; determining an amount by which the energy required to maintain a pre-determined temperature range in the vehicle is to be modified prior to the vehicle reaching the location that commences the subsequent one of the sub-routes, such that any individuals exiting the vehicle and any individuals boarding the vehicle at the location that commences the subsequent sub-route will not cause the temperature in the vehicle to fluctuate outside of the pre-determined temperature range, wherein said determining is based on (i) said predicting and (ii) said computing; and utilizing energy to maintain the temperature range in the vehicle, wherein the amount of energy is based on said determining, prior to the vehicle reaching the location that commences the subsequent one of the sub-routes; wherein the steps are carried out by at least one computing device.

2 . The computer-implemented method of claim 1 , comprising:
estimating the number of individuals in the vehicle at the conclusion of the current one of the sub-routes; and updating the determined amount by which the energy required to maintain a pre-determined temperature range in the vehicle is to be modified based on said estimated number of individuals in the vehicle at the conclusion of the current one of the sub-routes.

3 . The computer-implemented method of claim 1 , wherein the vehicle comprises one of a bus, a train, and a subway.

4 . The computer-implemented method of claim 1 , wherein said estimating comprises estimating the number of individuals having travelled in the vehicle after concluding each of the multiple sub-routes.

5 . The computer-implemented method of claim 1 , comprising:
creating a predictive model for carrying out said predicting under one or more variable conditions.

6 . The computer-implemented method of claim 5 , wherein the predictive model determines, for a given one of the sub-routes, a deviation from a current number of individuals in the vehicle.

7 . The computer-implemented method of claim 5 , wherein the predictive model comprises a rule-based model incorporating one or more attributes.

8 . The computer-implemented method of claim 7 , wherein the one or more attributes comprises the day of the week.

9 . The computer-implemented method of claim 7 , wherein the one or more attributes comprises the time of day.

10 . The computer-implemented method of claim 7 , wherein the one or more attributes comprises identification of one or more holidays.

11 . The computer-implemented method of claim 7 , wherein the one or more attributes comprises the number of alternative vehicles available along the fixed route at one or more different times.

12 . The computer-implemented method of claim 1 , wherein said computing is based on (i) the current location of the vehicle, (ii) the distance that the vehicle needs to travel to reach the location that commences a subsequent one of the sub-routes, (iii) one or more traffic conditions between the current location of the vehicle and the location that commences a subsequent one of the sub-routes, and (iv) one or more items of vehicle speed data.

13 . The computer-implemented method of claim 1 , wherein said determining is further based on the current number of individuals in the vehicle.

14 . The computer-implemented method of claim 1 , wherein said determining is further based on the capacity of a temperature control system of the vehicle.

15 . The computer-implemented method of claim 1 , wherein said determining comprises determining the distance from the location that commences the subsequent one of the sub-routes at which energy modification is to be commenced.

16 . The computer-implemented method of claim 1 , wherein said utilizing comprises temporarily increasing a set-point temperature associated with a temperature control system of the vehicle.

17 . The computer-implemented method of claim 1 , wherein said utilizing comprises temporarily decreasing a set-point temperature associated with a temperature control system of the vehicle.

18 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:
estimate the number of individuals having travelled in a vehicle across each of multiple sub-routes of a fixed route travelled by the vehicle, wherein the vehicle is used for shared passenger transportation services; learn one or more temporal patterns pertaining to the number of individuals in the vehicle across each of the multiple sub-routes; predict the number of individuals that will be in the vehicle during one or more of the multiple sub-routes based on the one or more learned temporal patterns; compute an expected amount of time for the vehicle to complete a current one of the sub-routes and arrive at a location that commences a subsequent one of the sub-routes; determine an amount by which the energy required to maintain a pre-determined temperature range in the vehicle is to be modified prior to the vehicle reaching the location that commences the subsequent one of the sub-routes, such that any individuals exiting the vehicle and any individuals boarding the vehicle at the location that commences the subsequent sub-route will not cause the temperature in the vehicle to fluctuate outside of the pre-determined temperature range, wherein said determining is based on (i) said predicting and (ii) said computing; and utilize energy to maintain the temperature range in the vehicle, wherein the amount of energy is based on said determining, prior to the vehicle reaching the location that commences the subsequent one of the sub-routes.

19 . A system comprising:
a memory; and at least one processor operably coupled to the memory and configured for:
estimating the number of individuals having travelled in a vehicle across each of multiple sub-routes of a fixed route travelled by the vehicle, wherein the vehicle is used for shared passenger transportation services;
learning one or more temporal patterns pertaining to the number of individuals in the vehicle across each of the multiple sub-routes;
predicting the number of individuals that will be in the vehicle during one or more of the multiple sub-routes based on the one or more learned temporal patterns;
computing an expected amount of time for the vehicle to complete a current one of the sub-routes and arrive at a location that commences a subsequent one of the sub-routes;
determining an amount by which the energy required to maintain a pre-determined temperature range in the vehicle is to be modified prior to the vehicle reaching the location that commences the subsequent one of the sub-routes, such that any individuals exiting the vehicle and any individuals

Assignees

• IBM

Inventors

• Ahuja Karan
• Bansal Heena
• Dey Kuntal
• Nagar Seema

Classifications

• B60H1/00371 Primary

  for vehicles carrying large numbers of passengers, e.g. buses · CPC title

• G05B13/0265

  the criterion being a learning criterion · CPC title

• B60H1/00771

  the input being a vehicle position or surrounding, e.g. GPS-based position or tunnel · CPC title

• B60H1/00742

  by detection of the vehicle occupants' presence; by detection of conditions relating to the body of occupants, e.g. using radiant heat detectors · CPC title

• B60H1/00735 Primary

  Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models · CPC title