Installation of a Smart Roadway Testbed in Brooklyn, NY to Measure the Impact of Overweight Trucks

January 2020 newsletter highlights the work of one of our USDOT Tier 1 University Transportation Centers, Connected Cities with Smart Transportation (C2SMART). C2SMART has been researching the damage caused by illegal overweight trucks on bridges and pavements in urban areas. Associate Director and Professor Hani Nassif from the Rutgers Infrastructure Monitoring and Evaluation (RIME) Group leads this multi-year effort along with researchers from the New York University (NYU) Tandon School of Engineering, and the New York City Department of Transportation (NYCDOT). The goal is to establish and implement a new smart roadway testbed along a cantilevered section of the Brooklyn-Queens Expressway (Interstate 278) in Brooklyn, New York. This new testbed will allow the research team to collect real-time data on truck loads using embedded weigh-in-motion (WIM) sensors to measure their impact on bridges and pavements in an urban transportation infrastructure network

Using Video Feeds from Public Traffic Cameras and Computer Vision to Analyze Social Distancing and Travel Patterns during the COVID-19 Pandemic

Researchers at the Connected Cities for Smart Mobility towards Accessible and Reliable Transportation (C2SMART) University Transportation Center (UTC), led by Professor Kaan Ozbay at New York University, have developed a continuous, real-time pedestrian detection framework that uses public traffic camera feeds and deep learning-based video processing to analyze sidewalk and roadway density. This framework allows researchers to capture critical data on pedestrian, cyclist, and vehicle flows and densities without any additional infrastructure investment. It also provides data that assist with answering both traditional transportation planning questions, as well as novel questions such as how often pedestrians maintained the recommended \u201c6 feet\u201d of social distance during the COVID-19 pandemic. This research showcases the feasibility of tracking density and physical distancing, which have previously been more difficult to track than metrics like volumes and congestion. Using publicly available video footage from existing traffic cameras in New York City and Seattle, researchers from multiple consortium universities, including both graduate and undergraduate students, trained computer vision to identify vehicles, pedestrians, and other objects on city blocks where traffic cameras had previously been installed. Due to both the low-resolution nature of the existing camera feeds, and the conversion of vehicles, cycles, and pedestrians into untraceable objects, this privacy-preserving visual recognition process prevents collection and/or leak of any identifying information for the human subjects in the camera footage

Evaluating the Effectiveness of Computer Vision Systems Mounted on Shared Electric Kick Scooters to Reduce Sidewalk Riding

69A3551747119The objective of this study was to assess the impact of feedback and speed limitations on the riding behavior of e-scooter riders on sidewalks. To do this, we used data provided by Spin, a US-based micromobility company, on Santa Monica e-scooters that were equipped with an AI camera to monitor surface type. We conducted an experiment in which 50 e-scooters had their feedback mechanisms turned off, while the rest 50 had them on. The study was conducted from November 23, 2022 to February 14, 2023, during which time 488 trips were made within the city of Santa Monica, California. We analyzed the data by calculating the time and distance between consecutive events within a trip, and assuming the distance between two GPS coordinates in the events was the actual path taken by the rider. Empirical cumulative distribution function (ECDF) plots and Kolmogorov-Smirnov tests indicated a statistically significant reduction in the fractions of trip time and distance that were spent on sidewalks, and in the length and duration of individual segments of sidewalk riding. To assess whether the feedback decreased the likelihood of choosing the sidewalk as the next surface when the rider is on the street or bike lane, we used a binary logistic regression model. The models' results revealed a statistically significant reduction in transitions onto sidewalks when riders were on feedback-enabled scooters. This suggests that feedback mechanisms can be valuable tools in guiding e-scooter riders' decisions on where to ride, potentially reducing conflicts between pedestrians and scooter riders

A Multiscale Simulation Platform for Connected and Automated Transportation Systems

69A3551747119Traffic simulation is an important tool that can assist researchers, analysts, and policymakers in testing vehicle/traffic control algorithms, gaining insights into micro/macro traffic dynamics, and designing traffic management strategies. However, different implementations require different simulation scales, and no multiscale simulation platform satisfies all requirements. In this project, we proposed to establish a multiscale vehicle-traffic demand (VTD) simulation platform for connected and automated transportation systems (CATS). This is particularly meant for the control and management of CATS with varying penetration rates of connected and automated vehicles (CAVs). We built a microscopic vehicle-in-the-loop (VIL) simulation platform, which used Unity 3D to simulate/visualize vehicle operations/dynamics and Simulation of Urban Mobility (SUMO) to simulate traffic flow dynamics

Automated Lane Change and Robust Safety

69A3551747119Firstly, to guarantee stability and robustness in the face of parametric uncertainties, non-linearities, and modeling errors , we have proposed a data-driven optimal control algorithm to solve the lane-changing problem of AVs which is inspired by reinforcement learning and adaptive dynamic programming. Secondly, we have developed a lane change decision-making algorithm to ensure safe and efficient lane change. Thirdly, the lane change risk index (LCRI) is used to evaluate the AV lane change safety obtained by using the proposed data-driven optimal control algorithm. Fourthly, we have combined the data-driven optimal controller with the lane change decision-making algorithm by using control barrier functions (CBFs). Lastly, we have developed an experimental setup that includes prototypes of AV and highway lanes

Integration and Operation of an Advanced Weigh-in-Motion (A-WIM) System for Autonomous Enforcement of Overweight Trucks

69A3551747119The ultimate objective of this project is to assist and support the NYCDOT in establishing the legislation to operate the autonomous OW enforcement system and extend the service life of the BQE corridor. This project evaluates the effectiveness of the implemented enforcement system. The report first presents the work on the existing advanced weight-in-motion system (A-WIM) and proposed new A-WIM system, as well as the automated license plate recognition (ALPR) system. A new structural health monitoring (SHM) system was also implemented in the testbed to evaluate the responses of structures under the traffic. Then evaluations of the multiple systems in the testbed are presented by presenting the results of accuracy of different weighing sensors, and practices of automated enforcement. Lastly, reliability-based live load factors for bridge load rating are developed

Implementation and Effectiveness of Autonomous Enforcement of OW Trucks in an Urban Infrastructure Environment

USDOT 69A3551747124In this study, the team presented the effort to summarize different WIM standards, develop the calibration procedure for the A-WIM system, and implement the calibration procedure to prove that the A-WIM system is capable of complying with ASTM E1318-09 Type III accuracy. Three prevailing WIM standards were compiled and compared, ASTM E1318-09, COST 323, and OIML R134-1. At least three trucks are required for an excessive number of calibration/optimization tests to meet the accuracy and compliance level and the Type-Approval test requirement of the ASTM E1318-09. The calibration and optimization tests provided the accuracy and compliance required in ASTM E1318-09 even though the pavement conditions did not meet the ASTM E1318-09 requirement. Based on the preliminary analysis of the change in the number of trucks after the enforcement, direct enforcement would reduce the number of overweight trucks by up to 76.9% for > 10% overweight trucks. More in-depth study would be required to evaluate the efficiency of direct enforcement

Developing a Framework to Optimize Floodnet Sensor Deployments around NYC for Equitable and Impact-Based Hyper-Local Street-Level Flood Monitoring and Data Collection

69A3551747119This report culminates a year-long study on flood-monitoring sensor deployment in urban areas, with a specific application to the deployment of FloodNet sensors in New York City. The study presents a comprehensive method developed over the course of the year, which is based on two key components: stakeholder needs and equity considerations. Initially, the report describes the stakeholder elicitation process in which we engaged experts and urban stakeholders to identify metrics that would guide sensor deployment. These metrics were then subjected to the Analytical Hierarchy Process (AHP) to weight their relative importance. Different flooding scenarios were considered using available flood maps, with a particular focus on pluvial flooding. In the data analysis and metrics quantification stage, each identified metric was associated with a quantifiable proxy using publicly available data. Some metrics, however, were excluded due to lack of available data at the required geographic granularity. The metrics for stakeholders\u2019 needs and equity were then normalized and weighted using AHP-derived factors, aggregated within each set, and combined to derive a final prioritization metric for each Census Tract. This enabled the ranking of NYC Census Tracts for sensor deployment. Radar charts were used to illustrate the influence of each metric on the final combined metric, revealing that stakeholder metrics, specifically the presence of electricity substations, were most influential in the prioritization process. The report concludes by discussing the potential for further refinement of the method, such as adding or removing metrics, using different flood maps, and assigning different weights to stakeholder and equity metrics

Deployment and Tech Transfer of a Street-Level Flooding Platform: Sensing and Data Sharing for Urban Accessibility and Resilience

69A3551747119Of the many impacts that are predicted to accompany climate change, flooding is expected to have an outsized influence on public health, infrastructure, and mobility in urban areas. However, very little data exist on the frequency and extent of urban surface flooding, and there is an unmet need for hyperlocal information on the presence and depth of street-level floodwater. Therefore, previous work funded in 2020-2021 was focused on the design and assessment of robust, low-cost sensors deployed in diverse urban environments to track street-level flood occurrence and depth. Given the success and lessons learned from our previous research program, the goal of the work funded in 2021-2022 was to expand sensor deployment and transfer data to our stakeholders through the following objectives: (1) expand the flood sensor network (2) develop a public-facing data dashboard to transfer flood data to a range of stakeholders, and (3) evaluate feasibility of new flood sensor modalities. During this time, we have designed, tested and built two new ultrasonic prototypes, designed and implemented plans for Design for Manufacturing, deployed 23 prototypes across all five boroughs in New York City, and collected a total of 744 days of data, logging multiple flood events and their profiles, including the highly impactful floods accompanying the storms Henri and Ida in August 2021. We have maintained collaborations with research partners at CUNY and city agency partners at DEP, DOT, NYC MOR and NYC MOCTO, furthering the goals of the FloodNet.NYC consortium founded during our prior funding cycle. We collectively applied for additional funding and secured a commitment for $7M in funding from the City of New York\u2019s Department of Environmental Protection to deploy an additional 500 sensors over the next 5 years, as well as$250K from the Alfred P. Sloan Foundation to prototype methods for public engagement around flood data

Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed: Models for an Urban Freight and Parcel Delivery Virtual Testbed in NYC

69A3551747119This project explored routing app designs that can be of use to NYC DOT in informing truck drivers in NYC. This involved developing a prototype app and engaging in a hackathon in Fall 2022 to refine the visualization of the routing data. Second, we leveraged public data to construct a synthetic population of trucks that can be incorporated into a multiagent simulation that allows for dynamic passenger and commercial vehicle interactions. The synthetic truck population, which includes schedules of trip chains for each individual truck, will be incorporated into MATSim-NYC (He et al., 2021). Third, we proposed a new model for predicting zonal residential parcel delivery volumes and VMT that is applicable to large-scale scenarios and validate such a model using data from New York City (NYC)