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

Abstract

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.

Multi-Criteria Hydro-Economic Decision Tool for Rejuvenating Community Irrigation Tanks in Rural India

“…Past studies on minor irrigation projects have employed the conventional benefit-cost analysis (BCA) tool to evaluate economic viability and equity considerations of alternative water allocation measures under a variety of climate-related uncertainty [36][37][38][39]. BCA tool has also been widely used in other fields of investment decisions, for instance, renewable energy resource projects [40][41][42] and transportation projects [43,44]. Most of the studies on water resources limited their analysis on the benefit side to estimates of the increase in water use, increase in crop yields, and likely increase in crop areas.…”

“…Furthermore, as mentioned before, the future performance of local water resources projects is highly uncertain. Following Yang et al (2007) [43] and Khazraeian and Hadi (2018) [44], we consider multiple sources of uncertainty into the traditional BCA by expressing various model parameters as probability distributions instead of fixed values. The probabilistic BCA is conducted using the popular Monte Carlo simulation technique wherein the analysis is repeated with thousands of sets of parameters representing underlying variability over the life of the project [45,46].…”

“…This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). ), Ashimane(44), Bommanalli(43), Bugadimane(41), Nagarakura(39), Kibbali(36), Golikoppa(35), Dalavaayi(35), Keshinamane(35), Halige (34), Keresara(33), Konkana(33), Manthaguli (30), Chowdi-2nd (29), Chowdi-1st (29), Kadave-1st (28), Beerala(27), Nidagod(27), Hakkimane-1st(27), Shindigere(26)Earth and Environment Department, Florida International University, MM Campus, Miami, FL 33199, USA; nchar042@fiu.edu 2 College of Fisheries, Mangalore 575002, India; rcbhat@gmail.com 3 Private Farmer, Bhairumbe, Sirsi 581405, Karnataka, India; krishnamhegde@gmail.com…”

Black-Scholes Process and Monte Carlo Simulation-Based Options Pricing

“…This method can overcome the limitation of the dimensionality of some previous algorithms in solving American option pricing problems, and can quickly solve the high Victoria American option pricing problem [8]. In addition, we introduce several martingale process construction methods and then give the specific steps of the dual method to solve the American option pricing problem [9]. The results of numerical experiments show that the combination of the quasi-Monte Carlo method and the dual method is obtained, and the calculated results are accurate and require a short time, which can greatly improve the computational efficiency of Monte Carlo simulation, so this method is very effective in practical applications.…”

Utilizing an Analytical Hierarchy Process with Stochastic Return On Investment to Justify Connected Vehicle-Based Deployment Decisions

“…The upper and lower limits for these input variables were estimated based on the reported values in previous studies. Khazraeian and Hadi ( 5 ) reported that stochastic ROI analysis using a Monte Carlo simulation is a better approach compared with other methods such as the Black Scholes and Binomial Lattice in accounting for uncertainty in the parameters.…”