Research InterestsMy research and teaching interests center primarily on advancing the modeling of transportation and energy systems by improving their social and behavioral realism. This includes:
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The following are research areas where I have experience or interest:
Travel Behavior. Social Interactions in Travel, Policy Implications of Varying Behavioral Models, Driverless Vehicles, Unmanned Aerial Vehicles for Freight, Traffic Simulation
Emerging Technologies. Connected and Autonomous Vehicles, Unmanned Aerial Vehicles, Shared Mobility Services, Plug-in Electric Vehicles
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Modeling Social Interactions. Social Influence, Social Capital, Random Graph Models, Agent-Based Models & Social Simulation, Diffusion of Innovation
Sustainable Transportation. Cycling and Pedestrian Behavior, Green Lifestyles, Clean Vehicles, Diffusion of Green Innovation & Connected Vehicles
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(Discrete) Choice Modeling. Latent Class & Latent Variables, Social Interactions, Bayesian Inference, Alternative Decision Rules, Discrete-Continuous, Dynamic Models
Travel Survey Methods. Web-based Survey Methods, Smartphones, Social Network Data, Stated Preference, Adaptive Survey Design, Experimental Economics
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Platooning Picture Source: Public Domain Image from USDOT http://www.its.dot.gov/image_gallery/image36.htm
Publications
- M. Maness and Z. Lin (2019). Free Charging: Exploratory Study of Its Impact on Electric Vehicle Sales and Energy. Transportation Research Record: Journal of the Transportation Research Board, 2673(9), 590-601.
- H. Aziz, H. Park, A. Morton, R. Stewart, M. Hilliard, and M. Maness (2018). A High Resolution Agent-based Model to Support Walk-Bicycle Infrastructure Investment Decisions: A Case Study with New York City. Transportation Research Part C: Emerging Technologies, 86, 280-299.
- M. Maness (2017). Comparison of Position Generators and Name Generators as Social Capital Indicators in Predicting Activity Selection. Transportation Research Part A: Policy and Practice, 106, 374-395.
- M. Maness (2017). A Theory of Strong Ties, Weak Ties, and Activity Behavior: Leisure Activity Variety and Frequency. Transportation Research Record: Journal of the Transportation Research Board, 2665, 30-39. (ResearchGate)
- C. Cirillo, Y. Liu, and M. Maness (2017). A Time-dependent Stated Preference Approach to Measuring Vehicle Type Preferences and Market Elasticity of Conventional and Green Vehicles. Transportation Research Part A: Policy and Practice, 100, 294-310.
- C. Calastri, S. Hess, A. Daly, M. Maness, M. Kowald, and K. Axhausen (2017). Modelling Contact Mode and Frequency of Interactions with Social Network Members Using the Multiple Discrete-continuous Extreme Value Model. Transportation Research Part C: Emerging Technologies, 76, 16-34. (Research Gate)
- M. Maness and C. Cirillo (2016). An Indirect Latent Informational Conformity Social Influence Choice Model: Formulation and Case Study. Transportation Research Part B: Methodology, Vol. 93, pp. 75-101.
- M. Maness, C. Cirillo, and E. Dugundji (2015). Generalized Behavioral Framework for Choice Models of Social Influence: Behavioral and Data Concerns in Travel Behavior. Journal of Transport Geography, Vol. 46, pp. 137-150.
- X. Jiang, Q. Gan, J. Bared, M. Maness, and D. Hale (2015). Traffic Performance Analysis of Dynamic Merge Control Using Micro-simulation. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2484, pp. 23-30.
- C. Cirillo, M. Maness, and N. Serulle (2014), Measuring Value of Travel Time in the Presence of Managed Lanes: Results from a Pilot Stated Preference Survey on the Capital Beltway. Transportation Letters, Vol. 6(1), pp. 23-35.
- M. Maness and C. Cirillo (2012). Measuring Future Vehicle Preferences: Stated Preference Survey Approach with Dynamic Attributes and Multiyear Time Frame. Transportation Research Record: Journal of the Transportation Research Board, No. 2285, pp. 100-109. (PDF version)
My Google Scholar profile can be accessed at: scholar.google.com/citations?user=hD8t6LEAAAAJ.
Education
Doctor of Philosophy (2015)
Master of Science (2010)
Bachelor of Science (2009)
- University of Maryland, Civil Engineering
- Dissertation: Choice Modeling Perspectives on Social Networks, Social Influence, and Social Capital in Activity and Travel Behavior
Master of Science (2010)
- University of Maryland, Civil Engineering
- Thesis: Modeling Vehicle Ownership Decisions in Maryland: A Preliminary Stated Preference Survey and Model
Bachelor of Science (2009)
- University of Maryland, Computer Science
- University of Maryland, Civil Engineering
Dissertation
Title: Choice Modeling Perspectives on Social Networks, Social Influence, and Social Capital in Activity and Travel Behavior
Abstract:
Understanding the determinants of activities and travel is critical for transportation policymakers, planners, and engineers to design and manage transportation systems. These systems, and their externalities, are interwoven with social systems in communities, cities, regions, and societies. But discrete choice models – the predominant modeling tool for researching travel behavior and planning transportation systems – are grounded in theories of individual decision-making. This dissertation expands knowledge about the incorporation of social interactions into activity-travel choice models in the areas of social capital and social network indicators; social influence motivations and informational conformity; and misspecification errors from social network data collection.
Incorporating social capital into activity choice models involves using social capital indicators from surveys. Using a position generator question type, the role of social network occupational diversity in activity participation was explored and the performance of models using name generator and position generator data was compared. Access to the resources embedded in diverse networks was found to positively correlate with leisure activity participation. Compared to core network indicators from name generators, position generator indicators were typically better at predicting activity participation in a cross-validation study.
Current models of social influence in travel do not account for varying motivations for social influence such as for accuracy, affiliation, and self-concept. To test for an accuracy motivation, a latent class discrete choice model was formulated that places individuals into classes based on information exposure. Contrasting with existing work, this model showed that “more informed” households are more likely to own bicycles due to preference changes causing less sensitivity to smaller home footprints and limited incomes. A Bayesian prediction procedure was used to derive distributions of local-level equilibria and social influence elasticity.
The effect of errors in social network data collection using name and position generators is not fully understood for choice models. In a case study, the social network occupational diversity measure was robust to varying position generator lengths. Simulation experiments tested the implications of social network structure, misspecification, and small samples on social influence choice models where sample size, social influence strength, and degree of misspecification had the greatest impact.
Abstract:
Understanding the determinants of activities and travel is critical for transportation policymakers, planners, and engineers to design and manage transportation systems. These systems, and their externalities, are interwoven with social systems in communities, cities, regions, and societies. But discrete choice models – the predominant modeling tool for researching travel behavior and planning transportation systems – are grounded in theories of individual decision-making. This dissertation expands knowledge about the incorporation of social interactions into activity-travel choice models in the areas of social capital and social network indicators; social influence motivations and informational conformity; and misspecification errors from social network data collection.
Incorporating social capital into activity choice models involves using social capital indicators from surveys. Using a position generator question type, the role of social network occupational diversity in activity participation was explored and the performance of models using name generator and position generator data was compared. Access to the resources embedded in diverse networks was found to positively correlate with leisure activity participation. Compared to core network indicators from name generators, position generator indicators were typically better at predicting activity participation in a cross-validation study.
Current models of social influence in travel do not account for varying motivations for social influence such as for accuracy, affiliation, and self-concept. To test for an accuracy motivation, a latent class discrete choice model was formulated that places individuals into classes based on information exposure. Contrasting with existing work, this model showed that “more informed” households are more likely to own bicycles due to preference changes causing less sensitivity to smaller home footprints and limited incomes. A Bayesian prediction procedure was used to derive distributions of local-level equilibria and social influence elasticity.
The effect of errors in social network data collection using name and position generators is not fully understood for choice models. In a case study, the social network occupational diversity measure was robust to varying position generator lengths. Simulation experiments tested the implications of social network structure, misspecification, and small samples on social influence choice models where sample size, social influence strength, and degree of misspecification had the greatest impact.