SYSTEM AND METHODS FOR TARGETED RECOMMENDATIONS DURING GIG-ECONOMY ACTIVITY
Systems and methods relating to improving the experience of gig-economy workers are disclosed, with particular reference to gig-economy work involving vehicle use. Such systems and methods include automatically monitoring and evaluating activities such as driving during performance of gigs, as well as providing recommendations based thereupon. During or after performance of gig-economy work, data automatically collected may be used to generate and present recommendations or education points to the gig-economy worker. Such recommendations may include recommendations to optimize gig metrics of interest to the gig-economy worker. Such recommendations may be related to nearby products or services and may be displayed to gig-economy workers or customers in various embodiments.
1 . A system for displaying targeted recommendations for transportation network trips, the device comprising:
a transceiver;
one or more memories;
an electronic display disposed within a vehicle; and
one or more processors interfacing with the transceiver, the one or more memories, and the electronic display, and configured to:
receive availability data corresponding to a gig-economy worker and environmental data associated with an environment of the vehicle, wherein the availability data includes an indication the gig-economy worker is active on a gig-economy application and the environmental data includes location data indicating locations of the vehicle,
determine, by a machine learning model, a targeted recommendation comprising a survey based upon the availability data or the environmental data,
determining the gig-economy worker is unavailable to answer the survey at a first time when the gig-economy worker is actively performing a gig-economy task based upon the availability data and the environmental data indicating movement of the vehicle along a route,
determine the gig-economy worker is available to answer the survey at a second time when the gig-economy worker is not actively performing a gig-economy task based upon the received availability data and the environmental data indicating lack of movement of the vehicle along the route,
cause the electronic display to display the targeted recommendation at the second time when the gig-economy worker is determined to be available,
receive a user response to at least one question of the survey from the gig-economy worker, wherein the user response includes answers to one or more survey questions, and
determine, by the machine learning model, one or more additional survey questions to be presented to the gig-economy worker based upon the user response.
2 . (canceled)
3 . (canceled)
4 . The system of claim 1 , wherein the one or more processors are further configured to:
responsive to receiving the user response after displaying the targeted recommendation, determine one or more of (i) a benefit or (ii) an incentive associated with a policy associated with the gig-economy worker.
5 . The system of claim 1 , further comprising:
a proximity sensor; and
an imaging sensor configured to capture one or more images of the environment of the vehicle.
6 . The system of claim 5 , wherein the environmental data includes one or more of: (i) a road condition, (ii) a weather condition, (iii) a nearby traffic condition, (iv) a road type, (v) a construction condition, (vi) a presence of pedestrians, or (vii) a presence of other obstacles.
7 . The system of claim 1 , wherein the one or more processors are configured to receive the environmental data from one or more of (i) vehicle-to-vehicle (V2V) communication protocols, (ii) vehicle-to-infrastructure (V2I) communication protocols, or (iii) one or more mobile devices.
8 . The system of claim 1 , wherein the availability data includes one or more of a gig-economy worker purchasing history.
9 . A computer-implemented method for displaying targeted recommendations for transportation network trips, the method comprising:
receiving, at one or more processors, availability data corresponding to a gig-economy worker and environmental data associated with an environment of a vehicle, wherein the availability data includes an indication the gig-economy worker is active on a gig-economy application and the environmental data includes location data indicating locations of the vehicle;
determining, by the one or more processors, a targeted recommendation comprising a survey based upon analysis by a machine learning model of the environmental data;
determine, by the one or more processors, the gig-economy worker is unavailable to answer the survey at a first time when the gig-economy worker is actively performing a gig-economy task based upon the availability data and the environmental data indicating movement of the vehicle along a route;
determining, by the one or more processors, the gig-economy worker is available to answer the survey at a second time when the gig-economy worker is not actively performing a gig-economy task based upon the received availability data and the environmental data indicating lack of movement of the vehicle along the route;
displaying, on an electronic display disposed within the vehicle, the targeted recommendation at the second time when the gig-economy worker is determined to be available;
receiving, by the one or more processors, a user response to at least one question of the survey from the gig-economy worker, wherein the user response includes answers to one or more survey questions; and
determining, by the one or more processors, by the machine learning model, one or more additional survey questions to be presented to the gig-economy worker based upon analysis by the machine learning model of the user response.
10 . (canceled)
11 . (canceled)
12 . The computer-implemented method of claim 9 , further comprising:
responsive to receiving the user response after displaying the targeted recommendation, determining, by the one or more processors, one or more of (i) a benefit or (ii) an incentive associated with a policy associated with the gig-economy worker.
13 . The computer-implemented method of claim 9 , wherein receiving environmental data associated with the environment of the vehicle further comprises:
capturing, with a proximity sensor, a set of distances corresponding to the environment; and
capturing, with an imaging sensor, one or more images of the environment.
14 . The computer-implemented method of claim 13 , wherein the environmental data includes one or more of: (i) a road condition, (ii) a weather condition, (iii) a nearby traffic condition, (iv) a road type, (v) a construction condition, (vi) a presence of pedestrians, or (vii) a presence of other obstacles.
15 . The computer-implemented method of claim 9 , wherein the one or more processors are configured to receive the environmental data from one or more of (i) vehicle-to-vehicle (V2V) communication protocols, (ii) vehicle-to-infrastructure (V2I) communication protocols, or (iii) one or more mobile devices.
16 . A tangible, non-transitory computer readable medium storing computer-readable instructions stored thereon for displaying targeted recommendations for transportation network trips that, when executed on one or more processors, cause the one or more processors to:
receive availability data corresponding to a gig-economy worker and environmental data associated with an environment of a vehicle, wherein the availability data includes an indication the gig-economy worker is active on a gig-economy application and the environmental data includes location data indicating locations of the vehicle;
determine, by a machine learning model, a targeted recommendation comprising a survey based upon the environmental data;
determine the gig-economy worker is unavailable to answer the survey at a first time when the gig-economy worker is actively performing a gig-economy task based upon the availability data and the environmental data indicating movement of the vehicle along a route;
determine the gig-economy worker is available to answer the survey at a second time when the gig-economy worker is not actively performing a gig-economy task based upon the received availability data and the environmental data indicating lack of movement of the vehicle along the route;
cause an electronic display disposed within the vehicle to display the targeted recommendation at the second time when the gig-economy worker is determined to be available;
receive a user response to at least one question of the survey from the gig-economy worker, wherein the user response includes answers to one or more survey questions; and
determine, by the machine learning model, one or more additional survey questions to be presented to the gig-economy worker based upon the user response.
17 . (canceled)
18 . The tangible, non-transitory computer readable medium of claim 16 , wherein the computer-readable instructions further cause the one or more processors to:
responsive to receiving the user response after displaying the targeted recommendation, determine one or more of (i) a benefit or (ii) an incentive associated with a policy associated with the gig-economy worker.
19 . The tangible, non-transitory computer readable medium of claim 16 , wherein the computer-readable instructions that cause the one or more processors to receive environmental data associated with the environment of the vehicle further cause the one or more processors to:
capture, with a proximity sensor, a set of distances corresponding to the environment; and
capture, with an imaging sensor, one or more images of the environment.
20 . The tangible, non-transitory computer readable medium of claim 19 , wherein the environmental data includes one or more of: (i) a road condition, (ii) a weather condition, (iii) a nearby traffic condition, (iv) a road type, (v) a construction condition, (vi) a presence of pedestrians, or (vii) a presence of other obstacles, and wherein the instructions when executed on one or more processors further cause the one or more processors to receive the environmental data from one or more of (i) vehicle-to-vehicle (V2V) communication protocols, (ii) vehicle-to-infrastructure (V2I) communication protocols, or (iii) one or more mobile devices.
21 . The system of claim 1 , wherein actively performing the gig-economy task comprises driving the vehicle.
22 . The computer-implemented method of claim 9 , wherein actively performing the gig-economy task comprises driving the vehicle.
23 . The tangible, non-transitory computer readable medium of claim 16 , wherein actively performing the gig-economy task comprises driving the vehicle.