Lankershim Boulevard Dataset : [fact sheet]
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Summary
This document serves as a fact sheet describing the Lankershim Boulevard dataset, a component of the Next Generation SIMulation (NGSIM) program. The primary motivation for the NGSIM program was to support the development of algorithms for driver behavior at the microscopic level. Stakeholders identified the collection of real-world, vehicle trajectory data as essential for understanding and researching this behavior. The Lankershim dataset is characterized as one of the most detailed and accurate field data collections available for traffic microsimulation research and development. The data were collected on June 16, 2005, on Lankershim Boulevard in the Universal City neighborhood of Los Angeles, California. The study area covered approximately 500 meters (1,600 feet) of bidirectional, three-to-four lane arterial segments and included complete coverage of three signalized intersections. Data collection utilized five video cameras mounted on the roof of a 36-story building adjacent to the U.S. Highway 101 and Lankershim Boulevard interchange. A customized software application called NG-VIDEO transcribed vehicle trajectory data from the video footage, providing the precise location of each vehicle every one-tenth of a second. This high-frequency data allowed for detailed analysis of lane positions and locations relative to other vehicles. The full dataset comprises 30 minutes of data, segmented into two 15-minute periods (8:30 a.m. to 8:45 a.m. and 8:45 a.m. to 9:00 a.m.), representing primarily congested conditions. In addition to trajectory data, the dataset includes computer-aided design files, geographic information system files, aerial ortho-rectified photos, loop detector data, raw and processed videos, signal timings, traffic sign information, windshield videos, weather data, and aggregate data analysis reports. The Lankershim dataset has been utilized by NGSIM researchers to estimate and validate the Arterial Lane Selection model. This model accounts for both preemptive lane positioning behaviors and aggressive overtaking behaviors when obstructions are encountered. The dataset enabled researchers to understand lane-changing behavior on arterials with a level of detail and accuracy previously unavailable. This model can be incorporated into traffic microsimulation models to assist transportation practitioners in making more reliable and valid decisions. Furthermore, the broader traffic simulation community uses the dataset to examine various driver behaviors, including responses to yellow intervals at traffic signals, cooperative and forced driving behaviors on congested arterials, queue moveup and discharge behaviors at signals, and starting behaviors at the beginning of green intervals. The dataset also serves to validate and calibrate existing microsimulation algorithms and models. The full dataset is freely available via the NGSIM website.
Key finding
The dataset captured each vehicle's position at 0.1-second resolution over a 500-meter arterial with three signalized intersections, yielding 30 minutes of congested-condition trajectories used to validate an Arterial Lane Selection model.
Methodology
dataset
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 3 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Methodological Resource: dataset resource, tool software
- Theoretical Contribution: computational model