Neha Arora
I lead the MobilityAI team at Google Research.
Mobility AI is focused on advancing AI to help cities tackle major transportation problems like traffic congestion, road safety, and emissions, enabling data-driven urban planning and operations.
Read more about our work here - https://research.google/blog/introducing-mobility-ai-advancing-urban-transportation/
My prior experience has been in search ranking algorithms.
Authored Publications
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Origin-destination travel demand estimation: an approach that scales worldwide, and its application to five metropolitan highway networks
Christopher Bian
Yechen Li
Willa Ng
Bin Yan
Janny Zhang
2025
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Estimating Origin-Destination (OD) travel demand is vital for effective urban planning
and traffic management. Developing universally applicable OD estimation
methodologies is significantly challenged by the pervasive scarcity of high-fidelity traffic
data and the difficulty in obtaining city-specific prior OD estimates (or seed ODs), which
are often prerequisite for traditional approaches. Our proposed method directly
estimates OD travel demand by systematically leveraging aggregated, anonymized
statistics from Google Maps Traffic Trends, obviating the need for conventional census
or city-provided OD data. The OD demand is estimated by formulating a single-level,
one-dimensional, continuous nonlinear optimization problem with nonlinear equality
and bound constraints to replicate highway path travel times. The method achieves
efficiency and scalability by employing a differentiable analytical macroscopic network
model. This model by design is computationally lightweight, distinguished by its
parsimonious parameterization that requires minimal calibration effort and its capacity
for instantaneous evaluation. These attributes ensure the method's broad applicability
and practical utility across diverse cities globally. Using segment sensor counts from
Los Angeles and San Diego highway networks, we validate our proposed approach,
demonstrating a two-thirds to three-quarters improvement in the fit to segment count
data over a baseline. Beyond validation, we establish the method's scalability and
robust performance in replicating path travel times across diverse highway networks,
including Seattle, Orlando, Denver, Philadelphia, and Boston. In these expanded
evaluations, our method not only aligns with simulation-based benchmarks but also
achieves an average 13% improvement in it's ability to fit travel time data compared to
the baseline during afternoon peak hours.
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Improving simulation-based origin-destination demand calibration using sample segment counts data
Arwa Alanqary
Yechen Li
The 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII), Okinawa, Japan (2025)
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This paper introduces a novel approach to demand estimation that utilizes partial observations of segment-level track counts. Building on established simulation-based demand estimation methods, we present a modified formulation that integrates sample track counts as a regularization term. This approach effectively addresses the underdetermination challenge in demand estimation, moving beyond the conventional reliance on a prior OD matrix. The proposed formulation aims to preserve the distribution of the observed track counts while optimizing the demand to align with observed path-level travel times. We tested this approach on Seattle's highway network with various congestion levels. Our findings reveal significant enhancements in the solution quality, particularly in accurately recovering ground truth demand patterns at both the OD and segment levels.
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Recent advances in large foundation models (FMs) have enabled
learning general-purpose representations in natural language, vi-
sion, and audio. Yet geospatial artificial intelligence (GeoAI) still
lacks widely adopted foundation models that generalize across
tasks. We argue that a key bottleneck is the absence of unified,
general-purpose, and transferable representations for geospatially
embedded objects (GEOs). Such objects include points, polylines,
and polygons in geographic space, enriched with semantic context
and critical for geospatial reasoning. Much current GeoAI research
compares GEOs to tokens in language models, where patterns of
human movement and spatiotemporal interactions yield contextual
meaning similar to patterns of words in text. However, modeling
GEOs introduces challenges fundamentally different from language,
including spatial continuity, variable scale and resolution, temporal
dynamics, and data sparsity. Moreover, privacy constraints and
global variation in mobility further complicates modeling and gen-
eralization. This paper formalizes these challenges, identifies key
representational gaps, and outlines research directions for build-
ing foundation models that learn behavior-informed, transferable
representations of GEOs from large-scale human mobility data.
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This position paper advocates for the development of a geospatial foundation model based on human mobility trajectories in the built environment. Such a model would be widely applicable across many important societal domains currently addressed independently, including transportation networks, data-driven urban planning and management, tourism, and sustainability. Unlike existing large vision-language models, trained primarily on text and images, this foundation model should integrate the complex spatiotemporal and multimodal data inherent to human mobility. This paper motivates this challenging research agenda, outlining many downstream applications that would be differentially impacted or enabled by such a model. It then explains the critical spatial, temporal, and contextual factors that the model must capture to effectively analyze trajectories. Finally, it concludes with several research questions and directions, laying the foundations for future exploration in this exciting and emerging field.
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Scalable Learning of Segment-Level Traffic Congestion Functions
Shushman Choudhury
Aboudy Kreidieh
Alexandre Bayen
IEEE Intelligent Transportation Systems Conference (2024)
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We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area. First, we pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. Second, we train a feed-forward neural network on this dataset, which we can then use on any segment in the area. We evaluate how well our framework identifies congestion functions on observed segments and how it generalizes to unobserved segments and predicts segment attributes on a large dataset covering multiple cities worldwide. For identification error on observed segments, our single data-driven congestion function compares favorably to segment-specific model-based functions on highway roads, but has room to improve on arterial roads. For generalization, our approach shows strong performance across cities and road types: both on unobserved segments in the same city and on zero-shot transfer learning between cities. Finally, for predicting segment attributes, we find that our approach can approximate critical densities for individual segments using their static properties.
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Traffic simulations: multi-city calibration of metropolitan highway networks
Yechen Li
Damien Pierce
The 27th IEEE International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada (2024)
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This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.
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This work develops a compute-efficient algorithm to tackle a fundamental problem in transportation: that of urban travel demand estimation. It focuses on the calibration of origin-destination travel demand input parameters for high-resolution traffic simulation models. It considers the use of abundant traffic road speed data.The travel demand calibration problem is formulated as a continuous, high-dimensional, simulation-based optimization (SO) problem with bound constraints. There is a lack of compute efficient algorithms to tackle this problem. We propose the use of an SO algorithm that relies on an efficient, analytical, differentiable, physics-based traffic model, known as a metamodel or surrogate model. We formulate a metamodel that enables the use of road speed data. Tests are performed on a Salt Lake City network. We study how the amount of data, as well as the congestion levels, impact both in-sample and out-of-sample performance. The proposed method outperforms the benchmark for both in-sample and out-of-sample performance by 84.4% and 72.2% in terms of speeds and counts, respectively. Most importantly, the proposed method yields the highest compute efficiency, identifying solutions with good performance within few simulation function evaluations (i.e., with small samples).
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On how traffic signals impact the fundamental diagrams of urban roads
Yechen Li
The 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 (MFTS2022), Dresden, Germany
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Being widely adopted by the transportation and planning practitioners, the fundamental diagram (FD) is the primary tool used to relate the key macroscopic traffic variables of speed, flow, and density. We empirically analyze the relation between vehicular space-mean speeds and flows given different signal settings and postulate a parsimonious parametric function form of the traditional FD where its function parameters are explicitly modeled as a function of the signal plan factors. We validate the proposed formulation using data from signalized urban road segments in Salt Lake City, Utah, USA. The proposed formulation builds our understanding of how changes to signal settings impact the FDs, and more generally the congestion patterns, of signalized urban segments.
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An Adversarial Variational Inference Approach for Travel Demand Calibration of Urban Traffic Simulators
Martin Mladenov
Sanjay Ganapathy Subramaniam
Proceedings of the 30th ACM SIGSPATIAL Intl. Conf. on Advances in Geographic Information Systems (SIGSPATIAL-22), Seattle, WA (2022) (to appear)
Preview abstract
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
Sanjay Ganapathy Subramaniam
Yechen Li
Yi-fan Chen
Ziheng Lin
(2021) (to appear)
Preview abstract
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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