The static or dynamic roadway travel time system to determine the path with least travel time between two places

• US 20060200303A1
• Filed: 02/24/2006
• Published: 09/07/2006
• Est. Priority Date: 02/24/2005
• Status: Abandoned Application

• Current Assignee: Jorge Salomon Fuentes, Raymond Moberly, Yuan Feng Ouyang
• Original Assignee: Jorge Salomon Fuentes, Raymond Moberly, Yuan Feng Ouyang
• Inventors: Moberly, Raymond, Ouyang, Yuan Feng, Fuentes, Jorge Salomon
• Application Number:
  US11/307,835
• Publication Number:
  US 20060200303A1
• Time in Patent Office: Days
• US Class Current: 701/200
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096827 where the route is computed onboard
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data

Abstract

The static or dynamic roadway travel time system determines the path with least travel time 26, 28 between two places by using user input 8 and traffic data input 14 in the form of volume and occupancy, or speed, or travel time to inform a user of the roadway network path that yields the least travel time 26, 28.

Claims

1. A static process for modeling a travel time system, comprising:

(a) Converting traffic data in the form of vehicles per hour, volume and occupancy, volume and density, speed, and travel time into Gaussian random variables of travel time

(b) Associating one or more Gaussian random variables from (a) with a link of a graph representing a roadway network

(c) Acquiring user input in the form of text, speech, or graphical to determine departure location and destination location, and identifying nodes in the graph from (b) corresponding to locations selected by user

(d) Providing an estimated travel time for each link of the graph from (b) using Gaussian random variables from (b)

(e) Simulating several scenarios of travel time using estimated travel time from (d) for each link connecting nodes identified in (c), and determining the shortest travel time path using shortest path algorithm for each scenario of travel time

(f) Estimating least travel time by selecting path most visited during simulation in (e)

(g) Providing results from (f) in speech and graphical output.

3. A process for modeling a travel time system, comprising:

(o) combination of static process from claim 1, dynamic process from claim 2, and deterministic model

(p) Simulating several scenarios of travel time using a combination of the methods in (o) to estimate travel time for each link connecting nodes identified in (j) or (c), sampling as needed Gaussian random processes from (h) to obtain Gaussian random variables at the time a node in the graph from (i) or (b) is visited, and determining the shortest travel time path using shortest path algorithm for each scenario of travel time

(q) Estimating least travel time by selecting path most visited during simulation in (p)

(r) Providing results from (q) in speech and graphical output.

2. A dynamic process for modeling a travel time system, comprising:

(h) Converting time dependent traffic data in the form of vehicles per hour, volume and occupancy, volume and density, speed, and travel time into Gaussian random processes of travel time

(i) Associating one or more Gaussian random processes from (h) with a link of a graph representing a roadway network, and sampling Gaussian random processes to obtain Gaussian random variables

(j) Acquiring user input in the form of text, speech, or graphical to determine departure location, departure time, and destination location, and identifying nodes in the graph from (i) corresponding to locations selected by user

(k) Providing a time dependent estimated travel time for each link of the graph from (i) using Gaussian random processes from (i)

(l) Simulating several scenarios of travel time using estimated travel time from (k) for each link connecting nodes identified in (j), sampling Gaussian random processes from (h) to obtain Gaussian random variables at the time a node in the graph from (i) is visited, and determining the shortest travel time path using shortest path algorithm for each scenario of travel time

(m) Estimating least travel time by selecting path most visited during simulation in (l)

(n) Providing results from (m) in speech and graphical output.