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Impact by overheight trucks on highways bridges has been identified to be a serious problem by numerous studies in the past, including a detailed study by the PI in 2011. Most of the countermeasures for preventing impact of trucks on bridges utilize monitoring for truck heights to warn truck drivers. However, despite these systems being installed, bridges are still being impacted and some bridges suffer serious damage, particularly to fascia girders and decks.
Within the dynamic field of transportation research, Reinforcement Learning (RL) has been recognized as a critical approach to monitor, model, and manage transportation systems. Among the diverse array of RL techniques, the Upper Confidence Bound (UCB) algorithm stands out for its potential in solving long-standing transportation problems.
This project constitutes a research effort to verify and calibrate a Dynamic Toll Lane (DTL) microsimulation model (Phase I) that will be used to test innovative pricing and control strategies aimed at reducing congestion (Phase II), specifically in a DTL and the main tollway lanes in the Metropolitan Area of San Juan, PR. The DTL was built recently and has already experienced congestion at the entrances and exits. The primary objective of this facility is to reduce, in some way, the time it takes to traverse the congested section of the freeway during peak hours.
The objective of this project is to assess the feasibility of an employee shuttle for Stony Brook University (SBU) campus employees to reduce car dependency and to expand employee access to more affordable housing choices. The ultimate aim of the project is to develop a demand responsive employee shuttle pilot through an online mobility platform for work-home commute, complemented by on-demand service for noncommute trips (e.g., grocery) and carpool matching.
This research, addressing the areas of Inclusive Advanced Technology Application and Climate Resilient Infrastructure, will evaluate a set of proof-of-concept transportation resilience measures to determine their utility and scalability as state and local performance measures. The research will review the latest scientific literature on risk and resilience measures to catalog methodologies scoring road network assets based on road segment attributes, hazard intersections, network centrality, and accessibility.
Rapid urbanization in cities like New York City (NYC) has spurred an overwhelming surge in consumer demand, with a consequential 80% of deliveries now aimed at residential customers. Predominantly facilitated by trucks, which account for 90% of deliveries, this has detrimentally impacted air quality, traffic congestion, and overall life quality. In response, NYC has initiated the concept of micro-distribution centers or microhubs—spaces designed to transition deliveries from larger trucks to sustainable modes such as electric vehicles or cargo cycles.
New innovations in transportation to improve mobility and solve problems such as congestion are not always equitably distributed and do not impact all travelers equally. This project proposes to develop equity-based performance measures for Intelligent Transportation Systems (ITS) and new mobility technology implementations that can be used to ensure inclusivity of all users. Best practices will be studied from across the nation, and interviews will be held with local stakeholders to gain feedback.
Signal optimization and coordination represent a cost-effective approach to mitigating congestion and improving traffic flow, obviating the need for expensive infrastructure upgrades or construction. By effectively optimizing traffic signals, delays, travel times, and stops experienced by drivers can be significantly reduced, resulting in decreased fuel consumption and improved safety. In addition to the immediate benefits of reduced congestion and improved traffic flow, signal optimization and coordination offer long-term advantages.
Stormwater flooding has emerged as a major challenge in urban areas due to its widespread and adverse impacts on transportation and the normal functioning of the economy. It can also cause loss of life. To predict the depth and duration of flooding at a specific locale, one could use the tools developed for river flooding (due to backwater). These include accurately mapping the terrain and running hydrologic software such as SWMM or HEC-HMS followed by hydraulic engineering software such as HEC-RAS. However, due to the high number of flooding locations, such a task is cost prohibitive.