An Informal Transit System Hiding in Plain Sight: Brooklyn's Dollar Vans and Transportation Planning and Policy in New York City

New York’s transit system serves millions of riders each day; the local newspapers complain about the lack of funding for infrastructure projects; and the City Council regularly hosts hearings about Bus Rapid Transit, bike-share, road safety, e-hail taxis, and gondolas. Transportation issues matter to New Yorkers, but these debates, at the policy level, often focus on technology, budgets, and regulations rather than the needs and experiences of passengers. This focus on “technical” matters allows planners and politicians to confine transportation debates to the realm of experts rather than engage the broader public in them. The failure to address the needs of passengers in Brooklyn and Queens has led to the development of dollar vans. Dollar vans are hybrid bus-taxis, also known as jitneys, that provide vital transportation links to more than 120,000 riders per day and operate beyond the control of the formal transit system governed by the Metropolitan Transportation Authority (MTA). While this ridership pales in comparison to the daily ridership on the subway or bus, it does rival bus ridership in cities like Dallas and Milwaukee and dwarfs the 50,000 peak ridership achieved by Citi Bike, New York’s celebrated bike-share system. More important, the durability of the vans reveals the failures of the existing formal system to serve all New Yorkers. I argue that this failure is important for three reasons. First, the vans respond to a geographically specific problem: adequate access to inadequate service. The vans thrive in busy transit corridors where MTA-owned buses come too infrequently, are overcrowded, or are regularly stuck in traffic. On these busy routes, the vans provide a more reliable ride and alternative for transit-dependent populations looking to bypass the faltering bus system. Second, regulations fail to reflect daily practice. This gap between practice and policy leaves van operators and passengers in an awkward limbo that criminalizes an industry and jeopardizes the mobility of entire neighborhoods. Third, since the vans operate outside of the formal system, traditional metrics, such as ridership, travel time, vehicle revenue miles, etc., are not collected and compared against the metrics of other modes operated by the MTA. As long as the vans remain an unknown quantity, it is impossible for the City and State to serve transit-dependent populations in Brooklyn and Queens. In this dissertation, I use a mixed-methods research design to probe the world of the vans and argue that continued regulatory uncertainty, long the friend of the vans, has the potential to upend them as development pressures and capital investment in Central Brooklyn intensifies

A point process framework for modeling electrical stimulation of the auditory nerve

Model-based studies of auditory nerve responses to electrical stimulation can provide insight into the functioning of cochlear implants. Ideally, these studies can identify limitations in sound processing strategies and lead to improved methods for providing sound information to cochlear implant users. To accomplish this, models must accurately describe auditory nerve spiking while avoiding excessive complexity that would preclude large-scale simulations of populations of auditory nerve fibers and obscure insight into the mechanisms that influence neural encoding of sound information. In this spirit, we develop a point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the proposed model consists of a cascade of linear and nonlinear stages. We show how each of these stages can be associated with biophysical mechanisms and related to models of neuronal dynamics. Moreover, we derive a semi-analytical procedure that uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. Throughout, we compare model predictions to published physiological data and explain differences in auditory nerve responses to high and low pulse rate stimulation. We close by performing an ideal observer analysis of simulated spike trains in response to sinusoidally amplitude modulated stimuli and find that carrier pulse rate does not affect modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi

The what and where of adding channel noise to the Hodgkin-Huxley equations

One of the most celebrated successes in computational biology is the Hodgkin-Huxley framework for modeling electrically active cells. This framework, expressed through a set of differential equations, synthesizes the impact of ionic currents on a cell's voltage -- and the highly nonlinear impact of that voltage back on the currents themselves -- into the rapid push and pull of the action potential. Latter studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic Hodgkin-Huxley equations. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly Matlab simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl

Mapeo de la red de tránsito no cartografiada de Bogotá, Colombia

New tools have enabled “civic mappers” and transportation researchers to map previously unmapped transit networks that have been historically the purview of locals and insiders. These new datasets and maps show the extent of these systems while also enumerating basic operating characteristics, such as travel speed, route distance, frequency, and fare data. In this paper, the authors detail their process of visualizing Bogotá’s entire transit network, both the centrally-planned system of buses and the decentralized network of jitneys. By seeing the entire network, they argue that they can disentangle the development patterns of the city and monitor who has access to reliable transit, which also happens to be one of the United Nations’ Sustainable Development Goals. Since this type of work is still in its infancy, it is critical that researchers go out into the field and add more examples of how to do this work and share their process so different methodologies can be tested in different types of cities. In Bogotá, the authors, researchers from NYU’s Marron Institute of Urban Management worked with researchers and students from the Universidad del Rosario and the civic mapping community in Bogotá, used smartphones, cloud-based data managements systems, and mapmaking software to bring Bogotá’s unmapped transit network out of the shadows and put it on an equal footing with the established network of buses

Analysis of responses of channel noise models for a fixed voltage trajectory.

<p>(A) Voltage trace obtained from the Markov chain model with no current input, 6,000 channels and 1,800 channels. Dynamics are characterized by a prolonged subthreshold period followed by a spontaneous, channel noise-induced spike at . (B) Means of fraction of open and channels for the voltage trace shown in (A), as computed from Equations 10 and 11. (C) Variance in the fraction of open channels. (D) Variance in the fraction of open channels. Left insets in (C and D) show magnified views of the period preceding the spike. Right inset in (C) shows magnified view during the spike. For (C and D), exact variances (black) were computed from Equation 12 and Equation 13 and all other variances were estimated from 5,000 repeated simulations of the channel noise models.</p