Hybrid Approaches to Estimating Freeway Travel Times Using Point Traffic Detector Data

Xiao, Yan

doi:10.25148/etd.fi11042503

Cited by 3 publications

(2 citation statements)

References 75 publications

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“…The Standard Deviation of Percentage Error (SDPE) was used a measure to estimate the accuracy. The S P D E i with point traffic detection was calculated by assuming a random value between 10% and 20% based on a previous assessment of this parameter for estimating travel time during incident conditions ( 31 ). The SPDE i with CV data as a function of the stochastic CV market penetration was calculated using Equation 6 ( 25 ):…”

Section: Methodolgymentioning

confidence: 99%

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Khazraeian

Hadi

2018

Transportation Research Record

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Decisions to invest in alternative intelligent transportation system (ITS) technologies are expected to increase in complexity, particularly with the introduction of connected vehicles (CV) and automated vehicles (AV) in the coming years. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. In addition, these methods cannot account for agency preferences and constraints that cannot be converted to dollar values. This study utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied in a case study of the selection between using CV data and point detector data to support the freeway traffic data collection and monitoring service. The four objectives specified in the AHP analysis are providing the required functions, providing the required performance, minimizing the risks and constraints, and maximizing the return on investment. A stochastic return-on-investment analysis using a Monte Carlo simulation was used to calculate the return on investment values for input to the AHP method.

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Section: Methodolgymentioning

confidence: 99%

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Khazraeian

Hadi

2018

Transportation Research Record

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show abstract

“…The Standard Deviation of Percentage Error (SDPE) was used as a measure to estimate the reliability of the information since this measure was associated with the dollar value in a previous study. The SPDE with point traffic detection was calculated by assuming a random value between 10% and 20%, based on a previous assessment of this parameter for estimating travel time during incident conditions (Xiao 2011). The SPDE of travel time estimated based on CV data was calculated as a function of the stochastic CV market penetration using the following equation ( Table 3-2.…”

Section: Traffic Data Collection and Monitoring Benefitsmentioning

confidence: 99%

Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

Khazraeian¹

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Dynamical systems approach for travel time prediction in intermediate section under mixed traffic conditions

Anusha

Vanajakshi

Subramanian

2022

Journal of Intelligent Transportation Systems

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Hybrid Approaches to Estimating Freeway Travel Times Using Point Traffic Detector Data

Cited by 3 publications

References 75 publications

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

Dynamical systems approach for travel time prediction in intermediate section under mixed traffic conditions

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