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Data Analytics & Modeling for EV Charging Demand

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State & Local

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The contract seeks to develop predictive models for electric vehicle charging demand by analyzing vehicle adoption trends, land use patterns, and travel behavior data. The successful bidder will be responsible for building analytical frameworks that forecast regional charging needs under varying scenarios, leveraging advanced data analytics to identify patterns and emerging trends. Outputs must include interactive visualizations and scenario planning tools that enable decision-makers to assess the impact of policy changes, infrastructure investments, and demographic shifts on future charging infrastructure requirements. The work is classified under NAICS code 541690 and is structured as a subcontract under the Transportation agency in Texas. Proposals are due by August 27, 2026, with no specific set-aside designation. The project requires deep expertise in data science, spatial analysis, and modeling, with emphasis on translating complex datasets into actionable insights. While no performance location is specified, the results are expected to support transportation planning efforts within Texas. Interested parties must submit responses through the provided online portal before the deadline.

General Info

Develop predictive models for Texas EV charging demand using data analytics and scenario planning tools.

Agency

Texas → TransportationView Agency

NAICS

541690 - Other Scientific and Technical Consulting ServicesView NAICS

Place of Performance

TX, USA

Set-Aside

NONE

Documents

(0)

No documents available

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Timeline

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Organization & Contact Information

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AgencyTexas → Transportation
ContactsNo contacts available
OfficeN/A
Organization / Agency
Texas → Transportation
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Office AddressN/A
ContactsNo contact information available

Full Description

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Develop predictive models for EV charging demand using vehicle adoption trends, land use, and travel behavior data, with visualization and scenario planning outputs.

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