Browsing by Author "Oza, Pratham Rajan"

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    Predictable Connected Traffic Infrastructure
    Oza, Pratham Rajan (Virginia Tech, 2022-05-03)
    While increasing number of vehicles on urban roadways create uncontrolled congestion, connectivity among vehicles, traffic lights and other road-side units provide abundant data that paves avenues for novel smart traffic control mechanisms to mitigate traffic congestion and delays. However, increasingly complex vehicular applications have outpaced the computational capabilities of on-board processing units, therefore requiring novel offloading schemes onto additional resources located by the road-side. Adding connectivity and other computational resources on legacy traffic infrastructure may also introduce security vulnerabilities. To ensure that the timeliness and resource constraints of the vehicles using the roadways as well as the applications being deployed on the traffic infrastructure are met, the transportation systems needs to be more predictable. This dissertation discusses three areas that focus on improving the predictability and performance of the connected traffic infrastructure. Firstly, a holistic traffic control strategy is presented that ensures predictable traffic flow by minimizing traffic delays, accounting for unexpected traffic conditions and ensuring timely emergency vehicle traversal through an urban road network. Secondly, a vehicular edge resource management strategy is discussed that incorporates connected traffic lights data to meet timeliness requirements of the vehicular applications. Finally, security vulnerabilities in existing traffic controllers are studied and countermeasures are provided to ensure predictable traffic flow while thwarting attacks on the traffic infrastructure.
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    A Real-Time Server Based Approach for Safe and Timely Intersection Crossings
    Oza, Pratham Rajan (Virginia Tech, 2019-05-31)
    Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10--50 %.