SYSTEMS AND METHODS FOR ESTIMATING LEAD TIME PREDICTION

1 . A method for estimating lead time, the method comprising:

receiving, by one or more processors, a user request to transport a good in a shipping platform, wherein the user request includes a pickup location and a delivery location;

identifying a plurality of drivers for transporting the good within a virtual area that includes the pickup location and the delivery location;

determining an expected response time to receive an acceptance of a job corresponding to the user request from a driver in the plurality of drivers using a stochastic process;

estimating an expected lead time using the expected response time and an expected travel time based on a location of the driver and the pickup location; and

transmitting the expected lead time to the user in the shipping platform.

2 . The method of claim 1 , the determining the expected response time further comprising:

determining a plurality of solicitation sequences based on the plurality of drivers;

estimating a duration for each solicitation sequence in the plurality of solicitation sequences;

determining a probability for each solicitation sequence in the plurality of solicitation sequences using a machine learning model; and

calculating the expected response time based on the probability and the duration for each solicitation sequence in the plurality of solicitation sequences.

3 . The method of claim 2 , further comprising:

constraining the plurality of solicitation sequences from an infinite number of solicitation sequences by applying one or more of a time-out threshold, a path-length threshold, and a probability threshold.

4 . The method of claim 2 , the determining the probability for each solicitation sequence further comprising:

calculating a plurality of adjusted acceptance probabilities based on a plurality of acceptance probabilities and a plurality of driver time-out probabilities;

calculating a plurality of adjusted rejection probabilities based on a plurality of rejection probabilities and the plurality of driver time-out probabilities; and

determining the probability for each solicitation sequence based on the plurality of adjusted acceptance probabilities and the plurality of adjusted rejection probabilities.

5 . The method of claim 4 , wherein:

each adjusted acceptance probability among the plurality of adjusted acceptance probabilities is calculated by multiplying a probability that the driver timed-out a predetermined number of times by an acceptance probability among the plurality of acceptance probabilities; and

each adjusted rejection probability among the plurality of adjusted rejection probabilities is calculated by multiplying a probability that the driver timed-out a predetermined number of times by a rejection probability among the plurality of rejection probabilities.

6 . The method of claim 2 , the determining the probability for each solicitation sequence further comprising:

determining a probability for at least one of the plurality of solicitation sequences using an iterative process comprising:

calculating a probability of a partial solicitation sequence at an iteration; and

determining the probability for at least one of the plurality of solicitation sequences based on the probability of the partial solicitation sequence.

7 . The method of claim 6 , wherein the probability of the partial solicitation sequence at the iteration is calculated in accordance with the probability of another partial solicitation sequence calculated at a previous iteration.

8 . The method of claim 2 , wherein the calculating the expected response time further comprising:

extracting a training set of data for each driver for the machine learning model, wherein the training set of data comprises driver acceptance history information as an acceptance probability, driver time-out history information as a time-out probability, and driver rejection history information as a rejection probability.

9 . The method of claim 2 , wherein, when a driver is identified as a new driver, preset driver information is used as a training set of data in the machine learning model.

10 . The method of claim 8 , further comprising:

prioritizing the plurality of drivers within the virtual area based on the training set, wherein the stochastic process is performed in the prioritized order of the plurality of drivers.

11 . The method of claim 1 , the identifying the plurality of drivers further comprising:

determining the plurality of drivers in the virtual area that satisfy one or more requirements of a certification for transporting the good.

12 . The method of claim 9 , further comprising, upon determining that no driver satisfies the one or more requirements in the virtual area:

searching for one or more drivers outside the virtual area for transporting the good.

13 . The method of claim 9 , further comprising, upon determining that no driver satisfies the one or more requirements in the virtual area:

requesting to one or more third-party agent drivers in search of transporting the good.

14 . The method of claim 13 , wherein, when one or more third-party agent drivers are identified as available drivers, the preset driver information is used in the machine learning model.

15 . The method of claim 1 , wherein the transmitting of the output comprises at least one of:

generating a visual output on a display of a user device;

generating an audible output on the user's device; or

generating a haptic output on the user's device.

16 . The method of claim 1 , wherein the user request further includes at least one of size information, content information, special care requirement information, or weight information of the good for transporting.

17 . The method of claim 1 , further comprising:

determining a probability that no driver among the plurality of drivers accepts the user request for transporting of the good.

18 . The method of claim 1 , further comprising:

determining an expected variance of the expected lead time.

19 . A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising:

receiving a user request for transporting of a good via a shipping platform, wherein the request includes a pickup location and a delivery location;

identifying a plurality of eligible drivers for transporting the good within a virtual area that includes the pickup location and the delivery location;

determining an expected response time to receive an acceptance of the request from a driver in the plurality of drivers using a stochastic process;

estimating an expected lead time using the expected response time and an estimated expected travel time that is derived based on a trained driver model for transporting of the good; and

transmitting the expected lead time to the user in the shipping platform.

20 . A system for estimating lead time comprising:

a memory; and

at least one processor coupled to the memory and configured to:

receive a user request for transporting of a good via a shipping platform, wherein the request includes a pickup location and a delivery location;

identify a plurality of eligible drivers for transporting the good within a virtual area that includes the pickup location and the delivery location;

determine an expected response time to receive an acceptance of the request from a driver in the plurality of drivers using a stochastic process;

estimate an expected lead time using the expected response time and an estimated expected travel time that is derived based on a trained driver model for transporting of the good; and

transmit the expected lead time to the user in the shipping platform.