METHOD FOR INFERRING TYPE OF ROAD SEGMENT

• US 20160102987A1
• Filed: 11/28/2014
• Published: 04/14/2016
• Est. Priority Date: 10/14/2014
• Status: Abandoned Application

• Current Assignee: Guangzhou HKUST Fok Ying Tung Research Institute
• Original Assignee: Guangzhou HKUST Fok Ying Tung Research Institute
• Inventors: Ni, Lionel M., Ding, Ye, Tan, Haoyu, Gao, Min
• Application Number:
  US14/555,743
• Publication Number:
  US 20160102987A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/32 Structuring or formatting of map data
• CPC Class Codes: G01C 21/3822 Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles

Abstract

Present invention provides a method for inferring the type of a road segment, comprising the steps of: collecting historical driving trajectory data of a plurality of vehicles for a target road segment, performing a statistic on the historical driving trajectory data, and obtaining statistical features of the target road segment; extracting topological features of the target road segment from topological structure data of the road network; merging the statistical features with the topological features of the target road segment, and obtaining an arrogated feature vector of the target road segment; building a logistic regression model according to the arrogated feature vector, and obtaining a first inferred type of the target road segment. The present embodiment considers both the driving trajectory data of a plurality of vehicles and the topological structure data of the road network, the accuracy of the present invention is high, and the inferred result is accurate.

Claims

1. A method for inferring a type of a road segment, comprising the steps of:

collecting historical driving trajectory data of a plurality of vehicles for a target road segment, performing a statistic on the historical driving trajectory data, and obtaining statistical features of the target road segment;

extracting topological features of the target road segment from topological structure data of a road network in which the target road segment locates ;

merging the statistical features with the topological features of the target road segment and obtaining an arrogated feature vector of the target road segment; and

building a logistic regression model according to the arrogated feature vector and obtaining a first inferred type of the target road segment.

2. The method according to claim 1, wherein after the steps of building the logistic regression model according to the arrogated feature vector and obtaining the first inferred type of the target road segment, the method further comprises the steps of:

obtaining a connection angle between the target road segment and its neighboring road segment from the topological structure data of the road network; and

obtaining a second inferred type of the target road segment based on the obtained connection angle and the type of the neighboring road segment.

3. The method according to claim 2, wherein after obtaining the second inferred type of the target road segment, the method further comprises the step of:

adopting an integrated algorithm to compute a final inferred type of the target road segment according to the first inferred type and the second inferred type of the target road segment.

4. The method according to claim 1, wherein the steps of collecting historical driving trajectory data of the plurality of vehicles for the target road segment, performing the statistic on the historical driving trajectory data, and obtaining the statistical features of the target road segment comprise the steps of:

collecting historical driving trajectory data of the plurality of vehicles;

adopting a ST-Matching algorithm to match the historical driving trajectory data onto the road network, and obtaining the historical driving trajectory data of the target road segment; and

performing the statistic on the historical driving trajectory data of the target road segment, thereby obtaining the statistical features of the target road segment.

5. The method according to claim 1, wherein after the steps of merging the statistical features with the topological features of the target road segment and obtaining the arrogated feature vector of the target road segment, the method further comprises the steps of:

adopting a principal component analysis to reduce the dimensionality of the arrogated feature vector, and obtaining principal components of the arrogated feature vector.

6. The method according to claim 2, wherein after the step of obtaining the connection angle between the target road segment and its neighboring road segment from the topological structure data of the road network, the method further comprises the step of:

adopting a Bayes classifier to learn and thus to obtain a polynomial distribution based on a road segment whose type have been known and the topological structure data of the road network;

wherein the polynomial distribution represents the probability distribution of a road segment type by a function that depends on the connection angle between the road segment and its neighboring road segment and the type of its neighboring road segment.

7. The method according to claim 6, wherein the step of obtaining the second inferred type of the target road segment based on the obtained connection angle and the type of the neighboring road segment comprises the step of:

adopting Bayesian algorithm to compute the second inferred type by using the polynomial distribution according to the obtained connection angle and the types of the neighboring road segment of the target road segment.

8. The method according to claim 3, wherein the integrated algorithm is any one of Stacked generalization algorithm, Support Vector Machine algorithm and random forests algorithm.

9. The method according to claim 1, wherein the plurality of vehicles refer to a plurality of taxis;

and the statistical features comprise an average speed of occupied taxis, a density of occupied taxis, a density of vacant taxis, and the number of pick-up events.

10. The method according to claim 1, wherein the topological features of the target road segment comprise a length of the road segment, a cumulative flutter value, neighboring road segments of the road segment and adjacent road segments of the road segment.