arXiv subjects

Anu Kuncheria

Publications and source records attributed to Anu Kuncheria.

4 recordsLinked to original sources

arXiv2025

Beyond Centrality: Understanding Urban Street Network Typologies Through Intersection Patterns

The structure of road networks plays a pivotal role in shaping transportation dynamics. It also provides insights into how drivers experience city streets and helps uncover each urban environment's unique characteristics and challenges. Consequently, characterizing cities based on their road network patterns can facilitate the identification of similarities and differences, informing collaborative traffic management strategies, particularly at a regional scale. While previous studies have investigated global network patterns for cities, they have often overlooked detailed characterizations within a single large urban region. Additionally, most existing research uses metrics like degree, centrality, orientation, etc., and misses the nuances of street networks at the intersection level, specifically the geometric angles formed by links at intersections, which could offer a more refined feature for characterization. To address these gaps, this study examines over 100 cities in the San Francisco Bay Area. We introduce a novel metric for classifying intersections, distinguishing between different types of 3-way and 4-way intersections based on the angles formed at the intersections. Through the application of clustering algorithms in machine learning, we have identified three distinct typologies - grid, orthogonal, and organic cities - within the San Francisco Bay Area. We demonstrate the effectiveness of the metric in capturing the differences between cities based on street and intersection patterns. The typologies generated in this study could offer valuable support for city planners and policymakers in crafting a range of practical strategies tailored to the complexities of each city's road network, covering aspects such as evacuation plans, traffic signage placements, and traffic signal control.

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arXiv2022

Simulating the Impact of Dynamic Rerouting on Metropolitan-Scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this paper, we describe the Mobiliti traffic simulator, which includes mechanisms to capture congestion delays, timing constraints, and link storage capacity constraints. The simulator is designed to support distributed memory parallel execution and be scalable on high-performance computing platforms. We introduce a method to model dynamic rerouting behavior with the addition of vehicle controller actors and reroute request events. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day of the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present an analysis of system-level impacts when changing the dynamic rerouting penetration rate and examine the varying effects on different functional classes and geographical regions. Finally, we present a validation of the simulation results compared to real world data.

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arXiv2021

Socially-Aware Evaluation Framework for Transportation

Technological advancements are rapidly changing traffic management in cities. Massive adoption of mobile devices and cloud-based applications have created new mechanisms for urban traffic control and management. Specifically, navigation applications have impacted cities in multiple ways by rerouting traffic on their streets. As different routing strategies distribute traffic differently across the city network, understanding these differences across multiple dimensions is highly relevant for policymakers. In this paper, we develop a holistic framework of indicators, called Socially-Aware Evaluation Framework for Transportation (SAEF), that will assist in understanding how traffic routing and the resultant traffic dynamics impact city metrics, with the intent of avoiding unintended consequences and adhering to city objectives. SAEF is a holistic decision framework formed as an assembled set of city performance indicators grounded in the literature. The selected indicators can be evaluated for cities of various sizes and at the urban scale. The SAEF framework is presented for four Bay Area cities, for which we compare three different routing strategies. Our intent with this work is to provide an evaluation framework that enables reflection on the consequence of policies, traffic management strategies and network changes. With an ability to model out proposed traffic management strategies, the policymaker can consider the trade-offs and potential unintended consequences.

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arXiv2021

Quasi-Dynamic Traffic Assignment using High Performance Computing

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is two-fold: 1) to introduce a new traffic assignment approach - a quasi-dynamic traffic assignment (QDTA) model and 2) to describe how we parallelized the QDTA algorithms to leverage High-Performance Computing (HPC) and scale to large metropolitan areas while dramatically reducing compute time. We examine and compare different scenarios, including a baseline static traffic assignment (STA) and a quasi-dynamic scenario inspired by the user-equilibrium (UET). Results are presented for the San Francisco Bay Area which accounts for 19M trips/day and an urban road network of 1M links. We utilize an iterative gradient descent method, where the step size is selected using a Quasi-Newton method with parallelized cost function evaluations and compare it to using pre-defined step sizes (MSA). Using the parallelized line search provides a 16 percent reduction in total execution time due to a reduction in the number of gradient descent iterations required for convergence. The full day QDTA comprising 96 optimization steps over 15 minute intervals runs in about 4 minutes on 1,024 cores of the NERSC Cori computer, which represents a speedup of over 36x versus serial execution. To our knowledge, this compute time is significantly lower than other traffic assignment solutions for a problem of this scale.

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