Dataset Information

Measuring Road Network Vulnerability with Sensitivity Analysis.

ABSTRACT: This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery.

SUBMITTER: Jun-Qiang L

PROVIDER: S-EPMC5268385 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Measuring Road Network Vulnerability with Sensitivity Analysis.

Jun-Qiang Leng L Long-Hai Yang Y Liu Wei-Yi WY Zhao Lin L

PloS one 20170126 1

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the chang ...[more]

PMID: 28125706

Similar Datasets

Project description:Economic vulnerability is an important indicator to measure regional coordination, health and stability. Despite the importance of vulnerabilities, this is the first study that presents 26 indicators selected from the dimensions of the domestic economic system, external economic system and financial system in the Belt and Road Initiative (BRI) countries. A quantitative analysis is conducted to analyze the characteristics of spatial heterogeneity of vulnerability of the economic subsystems and the comprehensive economic system of the BRI countries and the main influencing factors of the comprehensive economic system vulnerability (CESV) are identified based on obstacle degree model. The results show that the CESV of the East Asia, South Asia and ASEAN countries are lower than that of the Middle Eastern Europe, Central Asia and West Asia countries. The CESV of the BRI countries are generally in the middle level and the average vulnerability index of highly vulnerable countries is twice as much as that of lowly vulnerable countries. In addition, in terms of the vulnerability of the three subsystems, the spatial distribution of vulnerability of the domestic economic system (DESV) and financial system (FSV) is basically consistent with the spatial distribution pattern of CESV, both of which are low in East Asia and South Asia and high in West Asia and Central Asia. While, the vulnerability of external economic system (EESV) shows a different spatial pattern, with vulnerability of West Asia, Central Asia and ASEAN higher than that of East Asia and South Asia. The main obstacle factors influencing the CESV of BRI countries include GDP growth rate, saving ratio, ratio of bank capital to assets, service industry level, industrialization level and loan rate. Therefore, the key way to maintain the stability and mitigate the vulnerability of the economic system of BRI countries is to focus on the macroeconomic development and operation, stimulate the economy and market vitality, promote the development of industries, especially the service and secondary industries, and optimize the economic structure, banking system and financial system.

Project description:The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies, e.g., to estimate health impacts.

Dataset Information

Measuring Road Network Vulnerability with Sensitivity Analysis.

Publications

Measuring Road Network Vulnerability with Sensitivity Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets