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Self-driving car companies aim to offer hands-off journeys with Automated Vehicle (AV) technology, but public skepticism persists on safety aspects of AVs. A proposed solution involves creating dedicated AV corridors with traffic control and interruptions. Realistically, full penetration of AVs is unlikely to occur in the near future while mixed traffic of AVs with human driven vehicles is more likely. This study applies this concept to a hypothetical scenario in NYC, proposing an AV corridor from the Staten Island Expressway to the Brooklyn Battery Tunnel. The goal is to assess the network-wide impacts of a limited AV deployment from a multi-modal perspective. The NYC transportation system is modeled using MATSim, an open-source agent-based modeling platform. Although each traveler is treated as an agent making multi-modal trip making decisions, it does not model microscopic behavior of cars. Thus, the modeling approach for AVs has to be a macroscopic one, where average headways are reduced on the AV (with mixed traffic) corridor portion of the overall network based on a link capacity relationship adopted from a study on Advanced Cruise-Assist Highway Systems [1]. Two reduced headway scenarios are simulated in this MATSim NYC model, 1.1 and.85 second headways. Results indicate that Scenario B (1.1-second headway) is preferable over Scenario A (0.85 second headway), mitigating potential congestion on connectors, especially on Victory Boulevard in Staten Island. This study suggests implementing Scenario B for a smoother corridor-wide introduction of AVs to avoid congestion on connectors of the AV corridor to the rest of the NYC network. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is an attractive method for tackling transportation optimization problems due to its ability to balance exploitation and exploration. However, scaling BO to solve high-dimensional problems is a major challenge which has remained unsolved. Since transportation problems can be high-dimensional, the use of BO to solve transportation problems has been limited. This paper explores the use of BO with Gaussian process (GP) models to tackle high-dimensional transportation problems. It proposes formulations of the prior mean function and covariance function of the GP that enable BO to incorporate problem-specific transportation information, while remaining computationally tractable. This is done through the use of an analytical surrogate model. We validate the method with the 1-D and 100-D Griewank functions. The impact of different forms of bias in the surrogate model was also evaluated using the 100-D Griewank function example. We then apply the method to a high-dimensional traffic signal control problem in New York City. The results indicate the added value of using the problem-specific information in the prior mean and/or the covariance function. Importantly, for surrogate models that are not accurate approximations of, but have significant (anti-)correlation with the true objective function, the better approach is to embed the information in the covariance function, rather than in the prior mean function. More generally, the use of problem-specific information in the covariance function is robust to the accuracy of the surrogate model. This opens the way for a variety of low-resolution analytical transportation models to be used to tackle high-dimensional simulation-based optimization problems. • Problem-specific information can be used for exploration and exploitation in BO. • Using the surrogate model in the GP prior mean function improves exploitation. • Using the surrogate model in the GP covariance function improves exploration. • The surrogate model-based covariance function is robust to biases in the model. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is of great significance to urban traffic control and public safety applications. The key challenge of traffic flow forecasting is how to capture the complex correlation of different time levels and learn time dependence. Some external information is closely related to traffic flow, such as accidental traffic accidents, weather, and Point of Interests (PoI) information. This paper proposes a deep learning-based model, called AttDeepSTN+, which is used to predict the inflow and outflow of each area of the entire city. Specifically, AttDeepSTN+ uses the structure of interactive attention and convolution to model the temporal closeness, trend, and periodicity of crowd flow, in the interactive attention layer, learn the importance of closeness to periodicity and trend respectively to model the long-term dependence of time, and then use feature fusion to capture complex correlations at different levels, thereby reducing model prediction accuracy. In addition, PoI information are combined with time factors to express the influence of location attributes on crowd flow, to learn prior knowledge of crowd flow. Finally, a new fusion mechanism is used to fuse the attention layer modules and PoI information and other information together into the module to capture the complex correlation between different levels of features, to predict the final crowd flow in each area, and further improve the prediction accuracy of the model. The New York City crowd flow experiment shows that the model is better than the current state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for "Hispanic" and "Male" groups; (2) the "hot spots" of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic. [ABSTRACT FROM AUTHOR]

As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study. [ABSTRACT FROM AUTHOR]

Highlights: [•] Uses large scale GPS traces of taxi cabs in New York City. [•] A model for estimating link travel times is developed using large scale data. [•] Developed insights into the congestion issues in New York City. [•] Rigorous numerical results are performed to test the approach. [ABSTRACT FROM AUTHOR]

The article discusses the modernization of traffic technologies of intelligent transportation systems (ITS) in New York City (NYC). The enhancement of ITS infrastructure in the city is aimed at addressing high-priority needs including recurring congestion, nonrecurring congestion and improved safety for travellers. Also discussed is the NYC advanced solid-state traffic controller (ASTC) project which shows how to purchase more advanced and cost effective traffic control equipment.

New York, like many US cities has recently seen gentrification in previously industrial dominated neighborhoods. As a result, urban streets in the affected neighborhoods must now accommodate a range of street uses, including walking, cycling, and commercial vehicle trips. Through mapping, spatial analysis, and development of collision frequency models in two case study areas located in the Brooklyn and Queens Boroughs of New York City, this project aims to identify the built environment factors that affect collision frequencies and to understand if gentrification has any significant effect on local street safety. Results of this project can inform policy and planning decisions for street design, land use decisions, traffic control, and curb management decisions. • Identify factors influencing the frequency of multi-modal traffic conflicts in gentrifying, mixed use areas of New York City. • Model factors that affect collision frequencies using GIS, spatial analysis, and poisson regression. • Explanatory variables examined in the model include land use characteristics, infrastructure characteristics, and a gentrification ranking. • Commercial vehicle; non-motorized collisions cluster near residences. • All types of collisions are more frequent on truck routes. Bicycle facility and gentrification impacts vary by neighborhood. [ABSTRACT FROM AUTHOR]

The objective of this study is to measure the effectiveness of three commonly used changeable message sign (CMS) messages in reducing vehicular speeds and variances in highway work zones. This paper presents the results of a field study conducted on Interstate-90 in western New York State to evaluate the use of three different types of messages displayed on a CMS, as a speed control measure. The study included speed measurements of nearly 180,000 vehicles. The three types of CMS messages tested were: (1) RIGHT|LANE|CLOSED∼KEEP|LEFT; (2) WORK ZONE|MAX SPEED|45 MPH∼BE|PREPARED|TO STOP; and (3) LEFT|LANE|CLOSED∼KEEP|RIGHT. Of the three CMS messages tested, the CMS message stating “WORK ZONE|MAX SPEED|45 MPH∼BE|PREPARED|TO STOP” proved the most effective, significantly reducing vehicle speeds by 3.3–6.7 mi/h(5.3–10.8 km/h). The other two CMS messages were not as effective. The standard deviation of speed for the CMS message: WORK ZONE|MAX SPEED|45 MPH∼BE|PREPARED|TO STOP increased by 1.1–1.7 mi/h(1.8–2.7 km/h) in the driving lane and 0.2–2.0 mi/h(0.3–3.2 km/h) in the passing lane. The speed variances for the other two CMS messages ranged from -2.00 to 1.13 mi/h (from -3.23 km/h to 1.83 km/h). [ABSTRACT FROM AUTHOR]

A new packet routing model proposed by the Internet Engineering Task Force is MultiProtocol Label Switching, or MPLS [B. Davie and Y. Rekhter, MPLS: Technology and Applications, Morgan Kaufmann (Elsevier), New York, 2000]. Instead of each router's parsing the packet network layer header and doing its lookups based on that analysis (as in much of conventional packet routing), MPLS ensures that the analysis of the header is performed just once. The packet is then assigned a stack of labels, where the labels are usually much smaller than the packet headers themselves. When a router receives a packet, it examines the label at the top of the label stack and makes the decision of where the packet is forwarded based solely on that label. It can pop the top label off the stack if it so desires, and can also push some new labels onto the stack, before forwarding the packet. This scheme has several advantages over conventional routing protocols, the two primary ones being (a) reduced amount of header analysis at intermediate routers, which allows for faster switching times, and (b) better traffic engineering capabilities and hence easier handling of quality of service issues. However, essentially nothing is known at a theoretical level about the performance one can achieve with this protocol, or about the intrinsic trade-offs in its use of resources. This paper initiates a theoretical study of MPLS protocols, and routing algorithms and lower bounds are given for a variety of situations. We first study the routing problem on the line, a case which is already nontrivial, and give routing protocols whose trade-offs are close to optimality. We then extend our results for paths to trees, and thence onto more general graphs. These routing algorithms on general graphs are obtained by finding a tree cover of a graph, i.e., a small family of subtrees of the graph such that, for each pair of vertices, one of the trees in the family contains an (almost-)shortest path between them. Our results show tree covers of logarithmic size for planar graphs and graphs with bounded separators, which may be of independent interest. [ABSTRACT FROM AUTHOR]

Considers the exhibition "The Street" by Claes Oldenburg as a means of thinking about the possibilities for and limitations of New York City, in view of the giant, highly controversial, program of modernist urban renewal then reshaping much of the city. Significance of the book "The Steadyflow Traffic System," by Fritz Marcher to U. S. urban traffic engineering; Description of the show; Information on a symposium attended by Oldenburg.

The article examines key considerations for redesigning New York City's Frederick Douglass Circle. The project has been perceived as an opportunity to improve vehicular and pedestrian conditions in the area. Urban-design guidelines are set for development to encourage healthy retail and commercial development at the circle.

On a peak day, New York City's Department of Sanitation deploys 354 mechanical sweepers to pick up a substantial portion of the 100 tons of litter deposited daily on the city's 6,000 street miles. Field studies conducted by the department indicated that the major obstacle to doing a thorough job of cleaning was the number of illegally parked cars blocking the curb. We used Monte Carlo simulations to determine the increase in sweeping effectiveness that could be expected from significant reduction in illegally parked cars. The simulations provided information that was critical to the decision making process and resulted in the redeployment of 30% of the city's mobile traffic enforcement agents. The redeployment led to fewer illegally parked cars and more curb space available for cleaning. [ABSTRACT FROM AUTHOR]

The article announces the promotion by Sam Schwartz Engineering of Jeffrey Smithline to vice president of traffic engineering in New York City and Daniel Schack to associate.