Due to stochastic traffic conditions and fluctuated demand, transit passengers often suffer from unreliable services. Especially for buses, keeping on-time schedules is challenging as they share the right of way with non-transit traffic. With the advance of real-time interaction between passengers and operators, bus transit can be operated in a more flexible way, thereby resulting in an energy-efficient, eco-friendly, and cost-effective urban transportation mode.

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.

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.

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.

This research has two objectives:

1) To develop algorithms for plausible and legally-justifiable freeway car-following and arterial-street gap acceptance driving behavior for AVs

2) To implement these algorithms on a representative road network, in order to generate representative drive cycles for AVs that are both theoretically-grounded and based on empirical driving conditions.

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.

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.

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.

Currently dozens of U.S. cities are in the midst of planning and building modern streetcar systems. Though seemingly mobility investments, the intended impacts of these streetcar projects reach beyond transportation and represent a strong turn toward strategic spatial planning through transportation infrastructure. Proponents of modern streetcars argue that they are tools of place making as much as if not more than improvements for transit services.

We examine why American driving fell between 2004 and 2014, and consider how planners should respond. We weigh two competing explanations: that the driving downturn was caused by “Peak Car”— a voluntary shift away from driving, and that it was caused by economic hardship. We analyze an array of aggregate data on travel, incomes, debt, public opinion and Internet access. These data are imperfect, as they lack the precision of microdata, but they are available annually for the years before during, and after driving’s decline. We find little evidence supporting Peak Car.