UNCERTAINTY MODELING IN TRAFFIC DEMAND PREDICTION

• US 20180357893A1
• Filed: 02/06/2018
• Published: 12/13/2018
• Est. Priority Date: 06/07/2017
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dong, Wei Shan, Gao, Peng, Sun, Wei, Yan, Jun Chi, Zhang, Shi Lei, Zhang, Xin, Zhu, Jun
• Application Number:
  US15/889,342
• Publication Number:
  US 20180357893A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data

Abstract

The disclosure involves a method comprising clustering a plurality of observation samples related to historical travel demands into one or more clusters; for each cluster, constructing an actual probability distribution of the historical travel demands corresponding to the observation samples in the cluster; for each cluster, inputting observation samples in the cluster into a prediction model for predicting future travel demands to produce a result of prediction; for each cluster, computing a predicted probability distribution of the historical travel demands corresponding to the observation samples in the cluster based on the result of prediction; for each cluster, evaluating a difference between the actual probability distribution and the predicted probability distribution of the cluster; and modifying the prediction model so that a statistical sum of the differences for the one or more clusters is decreased.

Claims

1. A method comprising:

receiving a plurality of observation samples of historical travel demands, wherein each observation sample of the plurality of observation samples includes an observation position and an observation time period of a historical travel demand corresponding to the respective observation sample and one or more factors affecting the historical travel demand, wherein the one or more factors affecting the historical travel demand are selected from a group consisting of;

travel origin, travel destination, traffic condition, weather, temperature, air pollution information, day of week, and point of interest;

determining similarity between observation samples of the plurality of observation samples, wherein the similarity between the observation samples included within a cluster is based on rules that include;

determining whether a first factor corresponding to a first observation sample is equal to a second factor corresponding to a second observation sample;

determining whether the difference between the first factor corresponding to the first observation sample and the second factor corresponding to the second observation sample is below a predetermined threshold;

determining whether an observation position of the first observation sample and the observation position of the second observation sample are adjacent;

determining whether an observation time period of the first observation sample and the observation time period of the second observation sample are adjacent; and

determining the similarity between the first observation sample and the second observation sample of the plurality of observation samples without considering an additional factor corresponding to each of the first observation sample and the second observation sample, such that the additional factor produces an identical effect to the historical travel demands of the plurality of observation samples as another factor affecting the historical travel demand;

generating, by the processor, one or more clusters including observation samples from the plurality of observation samples, determined similar by applying the rules;

constructing an actual probability distribution of the historical travel demands corresponding to the observation samples in each of one or more clusters by performing a non-parametric estimation method;

inputting the observation samples of each of the one or more clusters into a prediction model for predicting future travel demands to produce a result of prediction, wherein the prediction model includes a neuronal network;

generating a predicted probability distribution based on the historical travel demands corresponding to the observation samples for each of the one or more clusters;

determining differences between the actual probability distribution and the predicted probability distribution of the one or more clusters; and

modifying parameters of the neural network included in the prediction model, wherein a statistical sum of the differences between the actual probability distribution and the predicted probability distribution of each of the one or more clusters, is decreased.