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

Arafat, Mahmoud; Iqbal, Shahadat; Hadi, Mohammed

doi:10.1177/0361198120929686

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…SROI has also been used to measure the social value created by investing in risk-based transportation asset management systems in the state of Iowa [41]. Although there are studies that evaluate the monetary benefits of transportation programs through approaches such as CBA [42,43] and ROI [44], studies utilizing SROI to measure the social value created by transportation interventions remain few [45]. Considering the potential for societal gains, including in SDOH, that can result from investing in transportation services, SROI is an appropriate approach to capture social, economic, and environmental returns from such investments.…”

Section: Social Return On Investmentmentioning

confidence: 99%

Estimating Benefits of Microtransit for Social Determinants of Health: A Social Return on Investment System Dynamics Model

Maleki,

Smith-Colin

2023

Systems

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Lack of transportation services in low-income communities greatly affects people’s health and well-being, creating barriers to social determinants of health (SDOH). One potential solution that has gained the attention of US decision-makers in recent years is microtransit, a transportation intervention aimed at addressing this issue. Despite promising results from prior microtransit implementation, the extent to which these programs deliver social benefits remains uncertain. This study presents a novel model called Social Return on Investment System Dynamics (SROISD) to forecast the social benefits of a microtransit program in Holmes County, Mississippi. The SROISD model identifies the scope and key stakeholders, maps outcomes, and gives outcomes a value. A causal loop diagram is developed next based on mapped outcomes and a literature review, thereby conceptualizing the processes through which social benefits are gained from the microtransit program. Three stock and flow diagrams are then created from the causal loop diagram to formulate the system and produce results. Outcomes mapped relative to three SDOH areas (1) accessing healthcare, (2) accessing employment, and (3) social participation indicate an overall positive return from investing in microtransit within the low-income community of interest. Additionally, ridesharing demonstrates a significant positive correlation with the SROI ratio. These findings offer support for the advantages of investing in microtransit. Additionally, the SROISD methodology offers decisionmakers a dynamically responsive approach that integrates traditional return on investment methodologies with system dynamics to explore social benefits across a variety of impact categories.

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Section: Social Return On Investmentmentioning

confidence: 99%

Estimating Benefits of Microtransit for Social Determinants of Health: A Social Return on Investment System Dynamics Model

Maleki,

Smith-Colin

2023

Systems

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“…The CR was checked against each case to ensure the overall consistency of the result ( 76 ). Only the checks resulting in a value less than 0.1 were accepted as consistent ( 77 , 78 ). After the CR check, respondents who made inconsistent decisions were discarded owing to the violation of CR criteria.…”

Section: Data Collection and Analysismentioning

confidence: 99%

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

Aromal

Naseer

2022

Transportation Research Record: Journal of the Transportation R

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The existing pedestrian facilities in urban areas can be improved by prioritizing the areas based on their infrastructure performance, location, safety, mobility, and liveability aspects. This paper aims to present a combination of Delphi, analytic hierarchy process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing the improvement of pedestrian facilities in an urban area. The approach has been broken into three stages. Firstly, the significant factor assessment was done by the Delphi method. Secondly, AHP was applied to estimate the relative weightage of each factor. Finally, the TOPSIS method was used to rank the alternatives in preparation for the final decision. For better understanding, the proposed method was demonstrated by applying the framework in a case study. The analyses revealed that the proposed method is effective for prioritizing the improvement of pedestrian facilities in an urban area. The method described in this paper is intended to assist decision makers in ranking different sidewalk stretches in the urban areas that need to be improved based on various attributes. This paper makes a major impact by incorporating a novel approach for the decision making of pedestrian facilities improvement, which considered both subjective and objective judgments of multiple decision makers in the procedure at various stages using different multi-criteria decision making techniques. This approach can be combined with a scheduling methodology and optimization technique to increase the efficiency of the resource allocation process.

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“…ICS contains several types of control systems, such as supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs). ICSs are widely used in different critical infrastructures such as smart grids, power distribution, transportation systems, water treatment plants, and manufacturing [1,2]. In the power plants, ICS's key role is evident, and a multitude of automated systems are operating in a SCADA framework.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data

Abbaspour²,

et al. 2021

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Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed a novel solution to this problem based on measurement data in the supervisory control and data acquisition (SCADA) system. The proposed approach is called measurement intrusion detection system (MIDS), which enables the system to detect any abnormal activity in the system even if the attacker tries to conceal it in the system’s control layer. A supervised machine learning model is generated to classify normal and abnormal activities in an ICS to evaluate the MIDS performance. A hardware-in-the-loop (HIL) testbed is developed to simulate the power generation units and exploit the attack dataset. In the proposed approach, we applied several machine learning models on the dataset, which show remarkable performances in detecting the dataset’s anomalies, especially stealthy attacks. The results show that the random forest is performing better than other classifier algorithms in detecting anomalies based on measured data in the testbed.

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

Cited by 12 publications

References 15 publications

Estimating Benefits of Microtransit for Social Determinants of Health: A Social Return on Investment System Dynamics Model

Estimating Benefits of Microtransit for Social Determinants of Health: A Social Return on Investment System Dynamics Model

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data

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