arXiv subjects

Ashish Bhaskar

Publications and source records attributed to Ashish Bhaskar.

6 recordsLinked to original sources

arXiv2026

Quantum computing for transport research: an introduction, systematic review, and perspective

Transport engineering has significant potential to benefit from quantum computing. The rise of intelligent transport systems, autonomous vehicles, and the Internet of Things has created an unprecedented demand for efficient information processing and computational optimisation. Accordingly, transport engineers and scientists have explored the ever-improving capabilities of quantum computers in an effort to meet this demand. Motivated by this growing interest, this paper sets out four aims: (1) to introduce the fundamental aspects of quantum computing relevant to the transport domain, (2) to identify transport-related problems which are suitable for quantum acceleration, (3) to develop a pipeline for solving these problems, and (4) to provide a systematic review of the existing literature. For the latter, a systematic search of the Scopus database (and supplemented by additional citation sources) identified 103 studies for inclusion following PRISMA 2020 guidelines. While a diverse set of use cases have been proposed, we conclude that future research should prioritise problems where quantum computation offers a clear practical benefit. To this end, we suggest promising directions to guide further work in this burgeoning subfield.

quant-ph↗

arXiv2025

Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic

This paper offers openly available microscopic vehicle trajectory (MVT) datasets collected using unmanned aerial vehicles (UAVs) in heterogeneous, area-based urban traffic conditions. Traditional roadside video collection often fails in dense mixed traffic due to occlusion, limited viewing angles, and irregular vehicle movements. UAV-based recording provides a top-down perspective that reduces these issues and captures rich spatial and temporal dynamics. The datasets described here were extracted using the Data from Sky (DFS) platform and validated against manual counts, space mean speeds, and probe trajectories in earlier work. Each dataset contains time-stamped vehicle positions, speeds, longitudinal and lateral accelerations, and vehicle classifications at a resolution of 30 frames per second. Data were collected at six mid-block locations in the national capital region of India, covering diverse traffic compositions and density levels. Exploratory analyses highlight key behavioural patterns, including lane-keeping preferences, speed distributions, and lateral manoeuvres typical of heterogeneous and area-based traffic settings. These datasets are intended as a resource for the global research community to support simulation modelling, safety assessment, and behavioural studies under area-based traffic conditions. By making these empirical datasets openly available, this work offers researchers a unique opportunity to develop, test, and validate models that more accurately represent complex urban traffic environments.

cs.CV↗

arXiv2024

Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contexts

Traffic data collection has been an overwhelming task for researchers as well as authorities over the years. With the advancement in technology and introduction of various tools for processing and extracting traffic data the task has been made significantly convenient. Data from Sky (DFS) is one such tool, based on image processing and artificial intelligence (AI), that provides output for macroscopic as well as microscopic variables of the traffic streams. The company claims to provide 98 to 100 percent accuracy on the data exported using DFS tool. The tool is widely used in developed countries where the traffic is homogenous and has lane-based movements. In this study, authors have checked the veracity of DFS tool in heterogenous and area-based traffic movement that is prevailing in most developing countries. The validation is done using various methods using Classified Volume Count (CVC), Space Mean Speeds (SMS) of individual vehicle classes and microscopic trajectory of probe vehicle to verify DFS claim. The error for CVCs for each vehicle class present in the traffic stream is estimated. Mean Absolute Percentage Error (MAPE) values are calculated for average speeds of each vehicle class between manually and DFS extracted space mean speeds (SMSs), and the microscopic trajectories are validated using a GPS based tracker put on probe vehicles. The results are fairly accurate in the case of data taken from a bird eye view with least errors. The other configurations of data collection have some significant errors, that are majorly caused by the varied traffic composition, the view of camera angle, and the direction of traffic.

cs.ET↗

arXiv2021

Continuum modeling of freeway traffic flows: State-of-the-art, challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles (CAVs) are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling. While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives (e.g., traffic efficiency and safety), there are uncertainties about the flow dynamics of CAVs in real-world traffic. The uncertainties are especially amplified for mixed traffic flows, consisting of CAVs and human-driven vehicles, where the implications can be significant from the continuum-modeling perspective, which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations. This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs. We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework. Wherever applicable, we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature. The paper then discusses some major problems inherent to continuum modeling of real-world (mixed) CAV traffic flows modeling by distinguishing between two major research directions: (a) modeling for explaining purposes, where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest, and (b) modeling for practical purposes, in which the focus is on the reliable predictions for operation and control. The paper proposes some potential solutions in each research direction and recommends some future research topics.

math.AP↗

arXiv2019

Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study

Origin-Destination matrix estimation is a keystone for traffic representation and analysis. Traditionally estimated thanks to traffic counts, surveys and socio-economic models, recent technological advances permit to rethink the estimation problem. Road user identification technologies, such as connected GPS, Bluetooth or Wifi detectors bring additional information, that is, for a fraction of the users, the origin, the destination and to some extend the itinerary taken. In the present work, this additional information is used for the estimation of a more comprehensive traffic representation tool: the link-origin-destination matrix. Such three-dimensional matrices extend the concept of traditional origin-destination matrices by also giving information on the traffic assignment. Their estimation is solved as an inverse problem whose objective function represents a trade-off between important properties the traffic has to satisfy. This article presents the theory and how to implement such method on real dataset. With the case study of Brisbane City where over 600 hundreds Bluetooth detectors have been installed it also illustrates the opportunities such link-origin-destination matrices create for traffic analysis.

eess.SP↗

arXiv2016

A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations.

math.OC↗