RampCast Phase II: Connected Vehicles Traffic Management Application on Indiana Highways

Mathew, Abin; Sundar, Agnideven Palanisamy; Peng, Juntong; Zhou, Jue; Chen, Yaobin; Li, Feng

doi:10.5703/1288284318619

i

RampCast Phase II: Connected Vehicles Traffic Management Application on Indiana Highways

2026-02-23
By Mathew, Abin ; Sundar, Agnideven Palanisamy ; Peng, Juntong ; ...

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English

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Creators:

Mathew, Abin ; Sundar, Agnideven Palanisamy ; Peng, Juntong ; Zhou, Jue ; Chen, Yaobin ; Li, Feng
Corporate Creators:

Purdue University. Joint Transportation Research Program
Corporate Contributors:

Indiana Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:

AI Decision Engine Artificial Intelligence Cellular Networks Connected Vehicles Context-aware Geofencing Decision Making Field Tests Geocast Highways Location Location-aware Prioritization Vehicle To Vehicle Communications
Publication/ Report Number:

FHWA/IN/JTRP-2026/11
DOI:

https://doi.org/10.5703/1288284318619
Resource Type:

Tech Report
Geographical Coverage:

United States ; Indiana
Edition:

Final Report
Corporate Publisher:

Purdue University. Joint Transportation Research Program
Abstract:

This project advances connected vehicle applications by developing and testing an enhanced RampCast system, a comprehensive traffic management system using C-V2X technology for Indiana highways. The system features a dual-mode architecture integrating both short-range (PC5) and long-range cellular (Uu) Cellular Vehicle-to-Everything (C-V2X) communication pathways, utilizing commercial-grade Cohda MK6 hardware and adhering to SAE J2735 standards to ensure interoperability. A key innovation is the integration of an AI-based prioritization framework, which leverages a large language model to enhance the contextual relevance of traffic messages. This AI system introduces two intelligent agents: one to dynamically estimate the appropriate display distance for an event based on its severity, and another to prioritize the order of messages based on urgency and potential driver impact. Field tests conducted on I-65 and I-70 in Indianapolis validated the system’s hybrid design. Results confirmed that the PC5 link provides very low latency (around 25 ms), ideal for time-critical alerts, while the Uu link ensures highly reliable coverage in complex environments, albeit with higher latency (around 45 ms). The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver. These findings support a hybrid C-V2X architecture as a robust model for future smart highway deployments.
Format:

PDF
Funding:

SPR-4934
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:53c837026f1b9f36c475eea648d9ac89e0e093b6d4b68776b55692677eb47759402dc88be1c567aa3c0f5cc933a01f8670a75f7fb57b8f448d5a102fa890069f
Download URL:

https://rosap.ntl.bts.gov/view/dot/92554/dot_92554_DS1.pdf
File Type:

[PDF - 11.33 MB ]

File Language:

English

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