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Research Projects

1) Advanced demand estimators for energy-efficiency in personal transportation 

Daziano

Sponsor: National Science Foundation, Faculty Early Career Development (CAREER) Award CBET-1253475 

Student: Esther Chiew 

Because internal combustion engines are highly inefficient, with a tank-to-wheel energy efficiency of only about 15%, an important step toward improved energy efficiency and decarbonization is the development and successful commercialization of ultra-low-emission vehicles and alternative fuels. To best promote industry conversion to and consumer acceptance of ultra-low-emission vehicles, Dr. Daziano and his research group are working in deriving nonparametric Bayes estimators of willingness-to-pay and consumer-surplus measures that account for behavioral uncertainties in the adoption of energy efficiency. The goal is to use these estimators to formulate a systematic Bayesian cost-benefit analysis of integrative counterfactual scenarios of low-emission vehicle deployment for informing policy, technology, engineering, and infrastructure planning decisions. For instance, the consumers’ tradeoffs identified in this research will help to answer high-priority questions such as ‘How much effort needs to be devoted to develop cheaper electric batteries to ensure a given expected penetration?’ or ‘Should companies invest in a denser recharging network or in more powerful batteries?’ 

Publications resulting from this research: 

  • Daziano, RA. 2013. Conditional-logit Bayes estimators for the valuation of electric vehicle driving range. Resource and Energy Economics 35(3), 429-450. 
  • Daziano, RA and M Achtnicht. 2013. Forecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulator. Transportation Science, DOI 10.1287/trsc.2013.0464. 
  • Daziano, RA and E Chiew. 2013. On the effect of the prior of Bayes estimators of the willingness-to-pay for electric-vehicle driving range. Transportation Research Part D: Transport and Environment 21, 7-13. 


2) Forecasting evacuation behaviors of coastal communities in response to storm hazard information

Smartphones

Sponsor: NOAA and Sea Grant’s Coastal Storm Awareness Program

PI: Ricardo Daziano
Co-PIs: Linda Nozick, Philip Liu, Jonathon Schuldt

Between October 28th and November 29th, 2012, 117 fatalities were caused by Hurricane Sandy in New York, New Jersey, and nearby areas. A number of these fatalities could have been prevented if residents had evacuated when mandated to; 45% of drowning deaths occurred in Evacuation Zone A, which had been identified as being at risk of flooding from any category of hurricane. This fact illustrates the key motivation behind studying evacuation behavior. Understanding the motives underlying survival actions is important because evacuation is the most effective way to decrease the number of fatalities that result from extreme weather events such as hurricanes.

The goal of this project is to collect, analyze, and model microdata on informed evacuation behavior within coastal communities in the tri-state areas impacted by Hurricane Sandy. The group will test the effectiveness of traditional and social media such as Facebook for disseminating storm information; visual vs. textual information; different versions of hurricane tracking maps; and different ways of framing information about the storm and its effects, such as anticipated severity. In a second phase, the team will leverage their understanding of best uses of social media and other messaging strategies. The group hopes to develop “a set of best practices or guidelines that one can use to advise officials who are creating these message strategies and make them more effective.”



3) Analyzing Willingness to Improve the Resiliency of New York City’s Transportation System

NYC

Sponsor: University Transportation Research Center Region II

PI: Ricardo Daziano
Co-PI: Linda Nozick

Hurricane Sandy revealed the higher-risk vulnerability to natural hazards of civil infrastructure systems in coastal megacities such as New York. In particular, critical deficiencies in the NYC metropolitan area’s transportation system emerged after Sandy. Unfortunately, experts predict that future sea level rise and storms will exacerbate the problems caused by these deficiencies. There are thus several challenges to improving strength and resilience of transportation systems. In particular, preparedness, survival, and recovery require the identification of adequate funding sources to collect revenue for public investments to improve resilience of the systems under threat.

The goal of this project is to determine the community’s willingness to pay for improvements in the resiliency to extreme events of the transportation system in New York City. This objective seeks to provide better tools for better informing planning investments to improve both resilience and security of transportation infrastructure and services.




4) Data collection and econometric analysis of the demand for non motorized transportation 

Daziano

Sponsor: Research and Innovative Technology Administration / USDOT (RITA) 

Students: Yutaka Motoaki, Chen Wang 

Fostering sustainable mobility for secure and livable communities is key to address the current environmental and energy crises. There are successful examples of cities for which bicycling is playing a major role in their paths toward sustainability. For example, 5.8% of commuters in Portland cycle to work. The percentage in New York City is only 0.6%, despite 345 miles of bicycle routes being added in the last decade. To encourage the use of non-motorized alternatives we need to better understand the motives underlying demand. Econometric travel demand models are highly valuable for assessing the effect of policies and incentives seeking to reduce the indiscriminate use of car. In fact, forecasting demand using discrete choice models has proved to be successful in the case of modal split among motorized alternatives. However, there are several challenges in applying choice modeling to non-motorized options. Users of the transportation system may be motivated to cycle or walk not because of the tradeoff between cost and time, but because of health and environmental benefits of these alternatives. At the same time, there are several factors that may discourage the use of non-motorized transportation, such as poor accessibility, safety concerns, and unfavorable route and weather conditions. For instance, it is often argued that the North East has poor climate to encourage the use of biking. 

This research project focuses on two related problems that are relevant for better informing policies targeting sustainable transportation as well as safer and more livable cities. The first research project is to exploit a latent segmentation approach to discrete demand to model non-motorized transportation choices and characterize both utilitarian and recreational cycling users. The second research project is to improve the analysis of cycling demand subject to weather conditions by analyzing time series of automatic cycling counts.