Port Authority of New York and New Jersey

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.

Many seaports worldwide attempt to gain energy efficiency towards reducing their energy costs while mitigating climate change and environmental effects. In this pa- per, a low-cost business-oriented seaport energy effective management (PERFFECT) platform is introduced. PERFFECT intends to gain knowledge on fundamental port operational and business processes and evaluate them against energy efficiency and environmental footprint. Exploiting all the proposed PERFFECT applications like monitoring, evaluation, forecasting, and optimization, the energy efficiency of the op- erational infrastructure of the port’s is achieved. Port’s energy balance is estimated and used to decide the ports optimal energy usage. Moreover, the traffic volume produced by the gate check is simulated and utilized for further actions. These actions include energy savings and emission reduction recommendations. Finally, the proposed platform is evaluated against real-life data, while the experimental results present the out- come and efficiency of the system. The suggested system results in up to 82.82% light energy savings after a specific suggested action and up to 33% reduction of vehicle emissions, respectively.

Unmanned Air Vehicles (UAVs) have potential strengths to be implemented in the battle fields. A major problem for UAVs is to plan an optimal trajectory path from its starting position to the target position in real time. For a path to be optimal it has to minimize: threat, path length, fuel consumption, flying time, and preserves dynamic constructs of the vehicle.

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.

Over two million trips are taken every weekday across the New York City Transit (NYCT) bus network. Revising the schedules for each of these routes is a labor-intensive process, and because of limited resources, fewer than half of all routes are examined each year. Traditionally, schedules have been revised on a first-in, first-out basis, with most schedules rewritten once every 2 years. This approach leaves no room for reviewing routes that need more frequent changes, meaning service may not catch up to changes in demand or traffic patterns for several years. It also requires staff to spend valuable staff time analyzing routes that may be inactive. To better address rapidly evolving bus corridors, NYCT developed a methodology to pinpoint the routes most in need of schedule revisions. This data-driven approach uses automatic vehicle location data and a ridership algorithm that combines automated fare collection data with other sources to infer stop-by-stop boardings and alightings for individual trips. Each route’s schedule is evaluated on service capacity versus actual ridership, and scheduled versus actual running times. Routes that show the greatest discrepancies are designated for later in-depth review. This methodology was applied to develop the 2018 list of schedule revisions. As this process identifies routes with too much capacity or running time, as well as those with too little, resource-costly schedule adjustments can be offset with resource-saving ones. Using this approach allows scheduling staff to react more quickly to changes in customer demand and new development, thereby providing better service to passengers.

This paper reports the results of a stated preference survey of regular transit users’ willingness to ride and concerns about driverless buses in the Philadelphia region. As automated technologies advance, driverless buses may offer significant efficiency, safety, and operational improvements over traditional bus services. However, unfamiliarity with automated vehicle technology may challenge its acceptance among the general public and slow the adoption of new technologies. Using a mixed logit modeling framework, this research examines which types of transit users are most willing to ride in driverless buses and whether having a transit employee on board to monitor the vehicle operations and/or provide customer service matters. Of the 891 surveyed members of University of Pennsylvania’s transit pass benefit program, two-thirds express a willingness to ride in a driverless bus when a transit employee is on board to monitor vehicle operations and provide customer service. By contrast, only 13% would agree to ride a bus without an employee on board. Males and those in younger age groups (18–34) are more willing to ride in driverless buses than females and those in older age groups. Findings suggest that, so long as a transit employee is onboard, many transit passengers will willingly board early generation automated buses. An abrupt shift to buses without employees on board, by contrast, will likely alienate many transit users.

The City of Little Rock’s Neighborhood Alert Center program assessment focuses on understanding program functions, how these centers serve the respective neighborhoods, and the roles and responsibilities of neighborhood facilitators (program directors stationed at each location). A case study approach is used to provide a holistic, rigorous evaluation of the program. Though much has changed for these centers since the inception of the program, this assessment reveals that citizens appreciate the Neighborhood Alert Centers and their neighborhood facilitators. Assessment results show that these centers are key components to sustaining healthy, vibrant Little Rock neighborhoods. The project demonstrates the usefulness of an outside evaluation for providing recommendations to enhance a program and increase its capacity.

The Port Authority of New York and New Jersey (PANYNJ) is an interstate authority that builds, owns, and operates a region-wide multi-modal transportation infrastructure which includes tunnels, bridges, airports, seaports, bus stations, and the World Trade Center. As custodian of infrastructure that is critical to the regional and national economy and the national logistics supply chain, the Authority faces difficult choices in allocating State of Good Repair (SGR) resources to its many assets, some of which are a century old. Furthermore, SGR imperatives often compete with emerging hazards (e.g, security, flooding) for scarce resources, creating a dichotomy of choice. These circumstances led the PANYNJ to investigate a process that would evaluate project priority on the basis of multi-hazard resilience metrics. Tunnels, Bridges &Terminal (TB&T) Department of the PANYNJ is currently investigating the development of a resilience management framework for critical agency infrastructure that focuses on resilience to all disruptions, incorporates lessons learned, and rationalizes upgrades across a broad spectrum of hazards. Building on the risk and resilience methodologies introduced by the Department of Homeland Security, Science and Technology Directorate (DHS-S&T), the PANYNJ just completed a pilot program in which these existing resilience management methodologies were customized to vehicular tunnels and bus terminals. The pilot effort applied the resilience management methodologies to the Holland Tunnel and the Port Authority Bus Terminal to evaluate their resilience to Superstorm Sandy-like flood hazards. This paper will summarize some of the key contributions in this pilot effort: the development and demonstration of a resilience-based framework for prioritizing projects. The large range in costs and benefits of the piloted projects highlighted the need to quantify the far-reaching downstream consequences of a project’s improvements, as well as the ability to capture sensitivity of hypercritical assets. Project performance metrics were developed that encompass measures of enhancement to resilience, the cascading effects and their extents, to provide a more comprehensive means of capturing project benefits.

Everyone agrees that the state of infrastructure in the United States is, at best, deplorable. The American Society of Civil Engineers gives America a cumulative grade point average of D+ and estimates that the country needs to invest $3.6 trillion by 2020 but that only $2 trillion in funding is expected to be available. Is $3.6 trillion really necessary? Who will pay for the remaining $1.6 trillion? What does it mean for the economy if the United States fails to fund needed projects? How do utility and infrastructure managers balance capital needs with economic reality? This article discusses the importance of infrastructure investment and how planning decisions are made at the national, state, and local levels.