Simulated Automated Vehicle Traffic and Emissions in San Francisco's Downtown

A simulation dataset modeling traffic flow, vehicle miles traveled, and greenhouse gas emissions in San Francisco's central business district under various automated vehicle scenarios. The study by Huajun Chai from UC Davis uses a microscopic road traffic model with local travel activity data to explore effects of changing drop-off/pick-up demand, parking supply, and curbside pricing. It provides insights into the potential impacts of automated vehicles on urban traffic and emissions.

Use Cases

• Modeling traffic congestion patterns based on simulated drop-off/pick-up event volumes.
• Estimating greenhouse gas emissions based on simulated vehicle miles traveled (VMT).
• Analyzing the relationship between parking supply and traffic flow under automated vehicle scenarios.
• Evaluating the impact of curbside space pricing on demand for parking versus drop-off/pick-up travel.

Strengths

• Based on a microscopic road traffic model with local travel activity data.
• Simulates multiple policy-relevant scenarios for automated vehicle integration.
• Focuses on a specific, high-impact urban area (San Francisco's downtown central business district).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of California, Davis

Collection Method

Microscopic road traffic simulation using local travel activity data.

Geography

San Francisco's downtown central business district