TY - JOUR
T1 - A Synthesis of emerging data collection technologies and their impact on traffic management applications
AU - Antoniou, Constantinos
AU - Balakrishna, Ramachandran
AU - Koutsopoulos, Haris N.
PY - 2011/11
Y1 - 2011/11
N2 - Purpose: The objective of this research is to provide an overview of emerging data collection technologies and their impact on traffic management applications. Methods: Several existing and emerging surveillance technologies are being used for traffic data collection. Each of these technologies has different technical characteristics and operating principles, which determine the types of data collected, accuracy of the measurements, levels of maturity, feasibility and cost, and network coverage. This paper reviews the different sources of traffic surveillance data currently employed, and the types of traffic management applications they may support. Results: Automated Vehicle Identification data have several applications in traffic management and many more are certain to emerge as these data become more widely available, reliable, and accessible. Representative examples in this field are presented. Furthermore, the fusion of condition information with traffic data can result in better and more responsive dynamic traffic management applications with a richer data background. Conclusions: The current state-of-the-art of traffic modeling is discussed, in the context of using emerging data sources for better planning, operations and dynamic management of road networks.
AB - Purpose: The objective of this research is to provide an overview of emerging data collection technologies and their impact on traffic management applications. Methods: Several existing and emerging surveillance technologies are being used for traffic data collection. Each of these technologies has different technical characteristics and operating principles, which determine the types of data collected, accuracy of the measurements, levels of maturity, feasibility and cost, and network coverage. This paper reviews the different sources of traffic surveillance data currently employed, and the types of traffic management applications they may support. Results: Automated Vehicle Identification data have several applications in traffic management and many more are certain to emerge as these data become more widely available, reliable, and accessible. Representative examples in this field are presented. Furthermore, the fusion of condition information with traffic data can result in better and more responsive dynamic traffic management applications with a richer data background. Conclusions: The current state-of-the-art of traffic modeling is discussed, in the context of using emerging data sources for better planning, operations and dynamic management of road networks.
KW - Data fusion
KW - Emerging data sources
KW - Traffic management
KW - Traffic surveillance
UR - http://www.scopus.com/inward/record.url?scp=80255138359&partnerID=8YFLogxK
U2 - 10.1007/s12544-011-0058-1
DO - 10.1007/s12544-011-0058-1
M3 - Review article
AN - SCOPUS:80255138359
SN - 1867-0717
VL - 3
SP - 139
EP - 148
JO - European Transport Research Review
JF - European Transport Research Review
IS - 3
ER -