A GIS data realistic road generation approach for traffic simulation

4Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

Abstract

Road networks exist in the form of polylines with attributes within the GIS databases. Such a representation renders the geographic data impracticable for 3D road traffic simulation. In this work, we propose a method to transform raw GIS data into a realistic, operational model for real-time road traffic simulation. For instance, the proposed raw to simulation ready data transformation is achieved through several curvature estimation, interpolation/approximation, and clustering schemes. The obtained results show the performance of our approach and prove its adequacy to real traffic simulation scenario as can be seen in this video1

References Powered by Scopus

Procedural modeling of cities

275Citations
N/AReaders
Get full text

Interactive procedural street modeling

147Citations
N/AReaders
Get full text

Procedural generation of roads

99Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Map Matching Algorithm Based on Dynamic Programming Approach

6Citations
N/AReaders
Get full text

Raw GIS to 3D road modeling for real-time traffic simulation

4Citations
N/AReaders
Get full text

An Improved Map Matching Algorithm Based on Dynamic Programming Approach

3Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Amara, Y., Amamra, A., Daheur, Y., & Saichi, L. (2019). A GIS data realistic road generation approach for traffic simulation. In Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019 (pp. 385–390). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.15439/2019F223

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

50%

Professor / Associate Prof. 1

17%

Lecturer / Post doc 1

17%

Researcher 1

17%

Readers' Discipline

Tooltip

Computer Science 4

50%

Engineering 2

25%

Agricultural and Biological Sciences 1

13%

Economics, Econometrics and Finance 1

13%

Save time finding and organizing research with Mendeley

Sign up for free