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Optimal Departure Windows Based on Historic Traffic, Environmental, and Social Conditions

IP.com Disclosure Number: IPCOM000243298D
Publication Date: 2015-Sep-18
Document File: 2 page(s) / 46K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method and system to reliably quantify people’s experiential knowledge about traffic patterns by analyzing historic data and calculating predictions for future traffic and route transit times. The method determines the best window(s) of time within which a traveler needs to depart to get from point A to point B in the shortest amount of travel time, or by a given arrival time.

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Optimal Departure Windows Based on Historic Traffic, Environmental, and Social Conditions

Many people living in cities have personal knowledge of the particular streets that receive heavy traffic, and the approximate times of traffic congestion. People often leave a location at varying times in order to avoid rush hour or other traffic en route to a destination. These times are often influenced by "local knowledge" of factors like traffic patterns, events, weather, etc.

A method is needed to quantify people's experiential knowledge about traffic patterns.

The novel contribution is a method and system to reliably quantify people's experiential knowledge by analyzing historic data and calculating predictions for future traffic and route transit times. The method determines the best window(s) of time within which a traveler needs to depart to get from point A to point B in the shortest amount of travel time, or by a given arrival time. This system allows people to accurately assess the impact of traveling during a specific window of time and select the optimal departure time in order to reach a destination within the desired length of time.

To implement the method and system in a preferred embodiment:

1. The system collects and combines historic traffic and environmental data to develop its robust history of traffic patterns. This data can include (but is not limited to):

    A. Traffic during different times of the day and days of the week B. Weather predictions
C. Scheduled construction projects
D. Calendar-specific data (e.g., holidays, school hours, special events, etc.) E. Data from social media about other special events 2. The user specifies a source location, a destination location, and one of the following:

A. Approximate departure date/time

    B. Approximate arrival date/time
C. Date/time window for departure
D. Date/time window for arrival
E. Calendar entry, including date/times and locations 3. The system uses the collected historic information to predict the expected traffic impacts between the source and destination locations during those times in the future, for example:

    A. 5:00 pm rush hour traffic in Raleigh-Durham on I-40E next Thursday B. Driving across the bridge leaving Cape Cod on the Monday afternoon of the July 4th holiday weekend 4. The system identifies the best windows of time for the user to leave the source location (within the designated time window constraints...