Sanjay Ganapathy Subramaniam
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An Adversarial Variational Inference Approach for Travel Demand Calibration of Urban Traffic Simulators
Martin Mladenov
Proceedings of the 30th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems (SIGSPATIAL-22), Seattle, WA (2022) (to appear)
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This paper considers the calibration of travel demand inputs, defined as a set of origin-destination matrices (ODs), for stochastic microscopic urban traffic simulators. The goal of calibration is to find a (set of) travel demand input(s) that replicate sparse field count data statistics. While traditional approaches use only first-order moment information from the field data, it is well known that the OD calibration problem is underdetermined in realistic networks. We study the value of using higher-order statistics from spatially sparse field data to mitigate underdetermination, proposing a variational inference technique that identifies an OD distribution. We apply our approach to a high-dimensional setting in Salt Lake City, Utah. Our approach is flexible—it can be readily extended to account for arbitrary types of field data (e.g., road, path or trip data).
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An Efficient Simulation-Based Travel Demand Calibration Algorithm for Large-Scale Metropolitan Traffic Models
Yechen Li
Yi-fan Chen
Ziheng Lin
(2021) (to appear)
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Metropolitan scale vehicular traffic modeling is used by a variety of private and public sector urban mobil-ity stakeholders to inform the design and operations of road networks. High-resolution stochastic traffic simulators are increasingly used to describe detailed demand-supply interactions. The design of efficient calibration techniques remains a major challenge. This paper considers a class of high-dimensional calibration problems known as origin-destination (OD) calibration. We formulate the problem as a continuous simulation-based optimization problem. Our proposed algorithm builds upon recent metamodel methods that tackle the simulation-based problem by solving a sequence of approximate analytical optimization problems, which rely on the use of analytical network models. In this paper, we formulate a network model defined as a system of linear equations, the dimension of which scales linearly with the number of roads in the network and independently of the dimension of the route choice set. This makes the approach suitable for large-scale metropolitan networks. The approach has enhanced efficiency compared with past metamodel formulations that are based on systems of nonlinear, rather than linear, equations. It also has enhanced efficiency compared to traditional calibration methods that resort to simulation-based estimates of traffic assignment matrices, while the proposed approach uses analytical approximations of these matrices. We benchmark the approach considering a peak period Salt Lake City case study and calibrate based on field vehicular count data. The new formulation yields solutions with good performance, reduces the compute time needed, is suitable for large-scale road networks, and can be readily extended to account for other types of field data sources.
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Quantifying the sustainability impact of Google Maps: A case study of Salt Lake City
Theophile Cabannes
Yechen Li
Marc Nunkesser
2021 (2021) (to appear)
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Google Maps uses current and historical traffic trends to provide routes to drivers. In this paper, we use microscopic traffic simulation to quantify the improvements to both travel time and CO2 emissions from Google Maps real-time navigation. A case study in Salt Lake City shows that Google Maps users are, on average, saving 1.7% of CO2 emissions and 6.5% travel time. If we restrict to the users for which Google Maps finds a different route than their original route, the average savings are 3.4% of CO2 emissions and 12.5% of travel time. These results are based on traffic conditions observed during the Covid-19 pandemic. As congestion gradually builds back up to pre-pandemic levels, it is expected to lead to even greater savings in emissions.
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