arXiv subjects

Bahman Madadi

Publications and source records attributed to Bahman Madadi.

4 recordsLinked to original sources

arXiv2026

Measuring the State of Open Science in Transportation Using Large Language Models

Open science initiatives have strengthened scientific integrity and accelerated research progress across many fields, but the state of their practice within transportation research remains under-investigated. Key features of open science, defined here as data and code availability, are difficult to extract due to the inherent complexity of the field. Previous work has either been limited to small-scale studies due to the labor-intensive nature of manual analysis or has relied on large-scale bibliometric approaches that sacrifice contextual richness. This paper introduces an automatic and scalable feature-extraction pipeline to measure code and data availability in transportation research. We employ Large Language Models (LLMs) for this task and validate their performance against a manually curated dataset and through an inter-rater agreement analysis. We applied this pipeline to examine 10,724 research articles published in the Transportation Research Part series of journals between 2019 and 2024. Our analysis found that only 5% of quantitative papers shared a code repository, 4% of quantitative papers shared a data repository, and about 3% of papers shared both, with trends differing across journals, topics, and geographic regions. We found no significant difference in citation counts or review duration between papers that provided data and code and those that did not, suggesting a misalignment between open science efforts and traditional academic metrics. Consequently, encouraging these practices will likely require structural interventions from journals and funding agencies to supplement the lack of direct author incentives. The pipeline developed in this study can be readily scaled to other journals, representing a critical step toward the automated measurement and monitoring of open science practices in transportation research.

cs.DL↗

arXiv2023

An Agent-Based Discrete Event Simulation of Teleoperated Driving in Freight Transport Operations: The Fleet Sizing Problem

Teleoperated or remote-controlled driving complements automated driving and acts as transitional technology toward full automation. An economic advantage of teleoperated driving in logistics operations lies in managing fleets with fewer teleoperators compared to vehicles with in-vehicle drivers. This alleviates growing truck driver shortage problems in the logistics industry and saves costs. However, a trade-off exists between the teleoperator-to-vehicle ratio and the service level of teleoperation. This study designs a simulation framework to explore this trade-off generating multiple performance indicators as proxies for teleoperation service level. By applying the framework, we identify factors influencing the trade-off and optimal teleoperator-to-vehicle ratios under different scenarios. Our case study on road freight tours in The Netherlands reveals that for any operational setting, a teleoperation-to-vehicle ratio below one can manage all freight truck tours without delay, while one represents the current situation. The minimum teleoperator-to-vehicle ratio for zero-delay operations is never above 0.6, implying a minimum of 40% teleoperation labor cost saving. For operations where a small delay is allowed, teleoperator-to-vehicle ratios as low as 0.4 are shown to be feasible, which indicates potential savings of up to 60%. This confirms great promise for a positive business case for the teleoperated driving as a service.

cs.MA↗

arXiv2023

A connected and automated vehicle readiness framework to support road authorities for C-ITS services

Connected and Automated Vehicles (CAVs) can have a profound influence on transport systems. However, most levels of automation and connectivity require support from the road infrastructure. Additional support such as Cooperative Intelligent Transport Systems (C-ITS) services can facilitate safe and efficient traffic, and alleviate the environmental impacts of road surface vehicles. However, due to the rapidly evolving technology, C-ITS service deployment requirements are not always clear. Furthermore, the costs and benefits of infrastructure investments are subject to tremendous uncertainty. This study articulates the requirements using a structured approach to propose a CAV-Readiness Framework (CRF). The main purpose of the CRF is allowing road authorities to assess their physical and digital infrastructure readiness, define requirements for C-ITS services, and identify future development paths to reach higher levels of readiness to support CAVs by enabling C-ITS services. The CRF is intended to guide and support road authorities' investment decisions on infrastructure.

econ.GN↗

arXiv2023

A hybrid deep-learning-metaheuristic framework for bi-level network design problems

This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function evaluations of a genetic algorithm (GA) to approximate solutions for NDPs. Using three test networks, two NDP variants and an exact solver as benchmark, we show that on average, our proposed framework can provide solutions within 1.5% gap of the best results in less than 0.5% of the time used by the exact solution procedure. Our framework can be utilized within an expert system for infrastructure planning to determine the best infrastructure planning and management decisions under different scenarios. Given the flexibility of the framework, it can easily be adapted to many other decision problems that can be modeled as bi-level problems on graphs. Moreover, we foreseen interesting future research directions, thus we also put forward a brief research agenda for this topic. The key observation from our research that can shape future research is that the fitness function evaluation time using the inferences made by the GNN model was in the order of milliseconds, which points to an opportunity and a need for novel heuristics that 1) can cope well with noisy fitness function values provided by deep learning models, and 2) can use the significantly enlarged efficiency of the evaluation step to explore the search space effectively (rather than efficiently). This opens a new avenue for a modern class of metaheuristics that are crafted for use with AI-powered predictors.

cs.NE↗