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Record W4415258162 · doi:10.1016/j.cstp.2025.101630

Automated enforcement of bus-only lanes and crossings with policy and implementation insights from a Calgary case study

2025· article· en· W4415258162 on OpenAlex
Bilal Dawood, Zaid Mujtaba, Pedram Akbari, Saeid Saidi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCase Studies on Transport Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsCalgary Laboratory ServicesSAIT PolytechnicUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMitacsIndian Institute of Science
KeywordsEnforcementLicenseProcess (computing)Global Positioning SystemAgency (philosophy)Transit (satellite)Public transportPedestrian crossing

Abstract

fetched live from OpenAlex

• Proposes cost-effective framework for bus-only lane and crossing enforcement. • Combines Raspberry Pi, GPS, and LiDAR for automated violation detection. • Uses YOLOv8 and PaddleOCR for vehicle, plate, and pedestrian recognition. • Field-tested in Calgary on 18 bus lanes and one bus-only crossing corridor. • Provides policy insights for scalable, reliable transit priority enforcement. With further attention to improving public transit service quality and reliability, transit priority measures such as bus-only lanes and bus-only crossings are becoming more common. These measures are only effective if priority can be maintained for transit, and access for regular vehicles is restricted. This can be only achieved by regulating their usage and enforcing these regulations. However, enforcement measures to prevent unauthorized vehicles from using bus-only lanes and bus-only crossings have been a manual process or employed costly systems such as bus traps in many cities. Automating this enforcement process through digital technologies can lead to substantial benefits and cost savings. Nevertheless, academic and professional literature on automated enforcement of bus-only lanes and bus-only crossings using advanced image and video processing techniques is limited. In this study, we propose a framework and guidelines for these automated enforcement systems that can be implemented by any transit agency at a low cost. We outlined our framework using three components: Hardware proof of concept, software design containing an image processing model, and user interface. We used a Raspberry Pi for hardware, PaddleOCR for license plate recognition and YOLOv8 for training our model. The operation of the system was further optimized using GPS (for bus-only lanes) and LiDAR (for bus-only crossings) sensors. To assess the applicability of our framework, we tested it on several bus-only lanes and a bus-only crossing in Calgary. The results showed a cost-effective solution while providing good performance in detecting violations and identifying unauthorized vehicles and pedestrians. The observations presented in the study can provide valuable insights to any transit agency for future implementations and policy-making process of automated bus-only lanes and bus-only crossings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.377
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it