Preliminary communication
https://doi.org/10.7307/ptt.v30i3.2518
Upgrade Evaluation of Traffic Signal Assets: High-resolution Performance Measurement Framework
Igor Dakic
; ETH Zurich
Milos Mladenovic
orcid.org/0000-0002-3746-3573
; Aalto University
Aleksandar Stevanovic
orcid.org/0000-0003-1091-3340
; Florida Atlantic University
Milan Zlatkovic
; University of Wyoming
Abstract
Agencies that have large-scale traffic signal systems under their purview often have to face asset upgrade decisions. As one of the most advanced traffic control technologies, Adaptive Traffic Control Systems (ATCS) are among the options that must be taken into consideration. Having in mind the complexity of benefits and costs stemming from ATCS investments, there is a need for information-rich performance measures (PM) used in the evaluation and decision-making. However, individual PMs are often not suitable for evaluating the multidimensionality of ATCS operations, due the inherent variability of ATCS control parameters. To expand the range of PMs used in ATCS evaluation, this research develops a new PM, i.e., average arrivals on green ratio, and proposes a refinement of average delay PM to account for queue formation. The paper also presents an application framework for a multi-criteria analysis, assuming a combination of the proposed and existing PMs. In addition to presenting the analytical PM formulation, the evaluation methodology uses microsimulation for a case study comparison between actuated-coordinated and ATCS operations. The results include a comparison between previous and proposed PMs, based on the processed simulation data as well as field data. In conclusion, the proposed PMs have a high transferability potential, low data collection cost, and high data quality, thus being suitable for use in decision processes for signal asset investment. Finally, this research opens up further opportunities for advancing decision-support methods for traffic operations asset management.
Keywords
Hrčak ID:
203296
URI
Publication date:
27.6.2018.
Visits: 1.108 *