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DTSTART;TZID=America/New_York:20211117T120000
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UID:69645-1637150400-1637154000@c2smart.engineering.nyu.edu
SUMMARY:Building a Decision Support Tool for Optimal & Equitable Distribution of EV Charging Stations in NYC
DESCRIPTION:Electric vehicles (EV) are key to the world’s decarbonization effort\, but access to charging infrastructure may become a prominent adoption barrier\, the burden of which will disproportionately affect low- and middle-income communities\, communities of color\, areas near multi-family housing\, and residential and rural areas. Researchers led by Professor Yury Dvorkin\, NYU Tandon\, and Professor Burçin Ünel\, Energy Policy Director at the Institute for Policy Integrity at NYU School of Law\, set out to address the accessibility of EV charging to build a decision-support tool to inform where and how to provide optimal\, equitable investments in EV charging infrastructure. In the first phase of their research\, they found that availability and affordability of EV charging stations in NYC are more strongly associated with median household income and the percentage of white population in each zip code\, rather than population density. \nThe team is hosting this update on their research so far\, seeking input from primary stakeholders to understand the technological gaps that prevents widespread expansion of EV charging infrastructure and to encourage their continued participation in this research to drive practically feasible and ethical research outcomes.
URL:https://c2smart.engineering.nyu.edu/event/building-a-decision-support-tool-for-optimal-equitable-distribution-of-ev-charging-stations-in-nyc/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Equity & Accessibility,Virtual Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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DTSTART;TZID=America/New_York:20211019T150000
DTEND;TZID=America/New_York:20211019T160000
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LAST-MODIFIED:20211015T153257Z
UID:69113-1634655600-1634659200@c2smart.engineering.nyu.edu
SUMMARY:Proactive Safety Management Empowered by Big Data
DESCRIPTION:In the last few decades\, data-driven methods have been used to assist with key tasks of road safety management like hotspot identification\, countermeasure development\, and before-after evaluation. These methods have traditionally relied heavily on historical crash data for safety assessment\, which can take a long time to collect. Professor Kun Xie will share a more proactive and time-efficient approach based on surrogate safety measures (SSMs)\, which can assess safety by capturing the more frequent “near-crash” situations. Massive amounts of data from emerging sources like GPS devices\, smartphone apps\, traffic cameras\, naturalistic driving\, and connected vehicles (CV) can be leveraged to extract SSMs on a large scale\, presenting new opportunities for proactive traffic safety management. Results will show that risk status is a reliable criterion for safety assessment\, and promisingly point towards the use CV data for proactive traffic safety management.
URL:https://c2smart.engineering.nyu.edu/event/proactive-safety-management-empowered-by-big-data/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Safety in Transportation Systems,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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DTSTART;TZID=UTC:20210908T150000
DTEND;TZID=UTC:20210908T160000
DTSTAMP:20260429T091758
CREATED:20210902T204704Z
LAST-MODIFIED:20210902T204704Z
UID:1480-1631113200-1631116800@c2smart.engineering.nyu.edu
SUMMARY:Incentive Design for Promoting Ridesharing
DESCRIPTION:Traffic congestion has become a serious issue around the globe\, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However\, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation\, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. At this event\, Neda Masoud will discuss a study which addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides\, and (2) encourage a few\, carefully selected set of travelers to change their travel behavior. We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian Relaxation-based algorithm with worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore\, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program. \nBio: Neda Masoud is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor’s of Science Degree in Industrial Engineering and a Master’s of Science degree in Physics. She received her Ph.D. in Civil and Environmental Engineering from the University of California Irvine. Her research focuses on devising operational and planning tools to facilitate the transition into the next generation of mobility systems\, which are envisioned to be connected\, automated\, electrified\, and shared.
URL:https://c2smart.engineering.nyu.edu/event/incentive-design-for-promoting-ridesharing/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Virtual Events,Webinars
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