This page has only limited features, please log in for full access.

Unclaimed
Nopadon Kronprasert
Department of Civil Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 12 August 2021 in Sustainability
Reads 0
Downloads 0

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

ACS Style

Nopadon Kronprasert; Katesirint Boontan; Patipat Kanha. Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand. Sustainability 2021, 13, 9011 .

AMA Style

Nopadon Kronprasert, Katesirint Boontan, Patipat Kanha. Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand. Sustainability. 2021; 13 (16):9011.

Chicago/Turabian Style

Nopadon Kronprasert; Katesirint Boontan; Patipat Kanha. 2021. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand." Sustainability 13, no. 16: 9011.

Journal article
Published: 13 March 2021 in Sustainability
Reads 0
Downloads 0

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: (1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), (2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, (3) determining recovery thresholds based on land use and system functionality, and (4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.

ACS Style

Suchat Tachaudomdach; Auttawit Upayokin; Nopadon Kronprasert; Kriangkrai Arunotayanun. Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems. Sustainability 2021, 13, 3172 .

AMA Style

Suchat Tachaudomdach, Auttawit Upayokin, Nopadon Kronprasert, Kriangkrai Arunotayanun. Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems. Sustainability. 2021; 13 (6):3172.

Chicago/Turabian Style

Suchat Tachaudomdach; Auttawit Upayokin; Nopadon Kronprasert; Kriangkrai Arunotayanun. 2021. "Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems." Sustainability 13, no. 6: 3172.

Conference paper
Published: 08 September 2016 in Computer Vision
Reads 0
Downloads 0

Road traffic accidents are among the most pressing transportation-related issues; they have not yet been addressed in a satisfactory way in many countries. They can be viewed as failures of road safety systems caused by a set of contributing components. This paper proposes a belief fault tree analysis model based on road safety inspection for identifying road infrastructure deficiencies that influence an accident occurrence and guiding highway professionals in the implementation of proper correction actions. Fault Tree Analysis is used as a risk assessment technique to diagnose the failures of road safety systems, while evidence theory is used to represent the probabilistic-based information under uncertainty gathered from expert opinions. The proposed approach is applied to analyse a real-world high-accident intersection location. It provides a means for road safety engineers to elucidate the cause of accident occurrence and to conduct road safety inspection under uncertainty.

ACS Style

Nopadon Kronprasert; Nattika Thipnee. Use of Evidence Theory in Fault Tree Analysis for Road Safety Inspection. Computer Vision 2016, 84 -93.

AMA Style

Nopadon Kronprasert, Nattika Thipnee. Use of Evidence Theory in Fault Tree Analysis for Road Safety Inspection. Computer Vision. 2016; ():84-93.

Chicago/Turabian Style

Nopadon Kronprasert; Nattika Thipnee. 2016. "Use of Evidence Theory in Fault Tree Analysis for Road Safety Inspection." Computer Vision , no. : 84-93.