Urban Travel Time Variability in New York City: A Spatio-Temporal Analysis within Congestion Pricing Context
Traffic congestion is an important aspect of quality of life, mobility and accessibility in urban areas. The economic cost of congestion is in the order of billions of dollars especially for dense urban cities. Besides the congestion which relates to the magnitude of travel time, travel time variability is also studied extensively by researchers as an additional measure for transportation network efficiency. In order to enhance the efficiency of urban traffic flow in New York City, numerous policies have been discussed, including different transportation pricing schemes.
Urban Travel Time Variability: Spatio-Temporal Analysis for New York City
Traffic congestion is an important aspect of quality of life, mobility and accessibility in urban areas. The economic cost of congestion is in the order of billions of dollars especially for dense urban cities. Besides the congestion which relates to the magnitude of travel time, travel time variability is also studied extensively by researchers as an additional measure for transportation network efficiency. In order to enhance the efficiency of urban traffic flow in New York City (NYC), numerous policies have been discussed, including different transportation pricing schemes.
Using Visual Information to Determine the Subjective Valuation of Public Space for Transportation: Application to Subway Crowding Costs in NYC
Subway demand in NYC has been on the rise, reaching 1.8 billion trips in 2014. Overcrowding delays have extended to non-peak hours and weekends; weekend overcrowding delays grew 141.2% in 2014-2015 (NY Daily News, 2015). In addition to delays, nonmonetary crowding costs include discomfort and a loss in security that play against overall perceptions of public transportation. In fact, subway crime has also been on the rise (NY Daily News, 2015).
Using Visual Information to Determine the Subjective Valuation of Public Space for Transportation: Application to Subway Crowding Costs in NYC
The objective of this project is to explore the role of visual information in determining the users’ subjective valuation of multidimensional trip attributes that are relevant in decision-making, but are neglected in standard travel demand models. More specifically, this project aims at analyzing overcrowding perceptions in discrete choice experiments, with the use of visualization of passenger density in subway cars. Data will be collected in New York City, but a pretest with a small sample size will be performed with international collaborators in the subway system of Santiago, Chile.
Reducing Incident-Induced Emissions and Energy Use in Transportation: Use of Social Media Feeds as an Incident Management Support Tool
Ubiquitous connected devices and microblogging platforms, such as Twitter, are providing a huge amount user-generated information that has a great potential for applications in transportation incident management (TIM) with minimal infrastructure required. In this study publicly posted Twitter posts were gathered using relevant keywords.
Impact of Optimization Strategy and Adoption Rate of V2X Technology on Environmental Impact
This research evaluated the effects of automated vehicle control strategies on system level emissions, travel time and wait time through a series of traffic lights. The study was conducted using traffic simulation and a realistic vehicle mix. Two control strategies were evaluated including a single vehicle control strategy and a multi-vehicle coordination heuristic. The performance of each control strategy was recorded under various levels of connected and autonomous vehicle technology (V2X technology) and 3 levels of traffic flow.
Inferring High-Resolution Individual’s Activity and Trip Purposes with the Fusion of Social Media, Land Use and Connected Vehicle Trajectories
Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. With the increasing advance of the Information Communication Technology (ICT), tremendous social media data becomes available. The goal of this report is to model and predict trip purpose with social media data.
Locating Portable Stations to Support the Operation of Bike Sharing Systems
Redistributing bikes has been a major challenge for the daily operation of bike sharing system around the world. Existing literature explore solution strategies that rely on pick-up-and-delivery routing as well as user incentivization approaches. The key contribution of this work is to introduce the use of portable bike stations to augment the capacity of fixed stations in the context of redistribution.
Bayesian Multilevel Models for Ridership Demand using Rainfall
The Northeast United States, particularly New York State has experienced an increase in extreme 24-hour precipitation during the past 50 years (Horton et al., 2011). Recent events such as Hurricane Irene and Superstorm Sandy have revealed vulnerability to intense precipitation within the transportation sector. Stronger knowledge of extreme events and the resultant simultaneous regional network vulnerabilities can support emergency management division in creating more effective response systems.