Metrolinx’s Toronto Electrification Project: Phase 1 — The Engineering Survey
Bibliographic record
Abstract
Metrolinx, Toronto’s rail authority currently has 200 engineering projects underway with a value of $16 billion. One of the largest projects is a $4 billion Electrification Project for the Toronto commuter rail lines. In support of the engineering design of the project, in November of 2015 Tulloch Engineering was contracted to provide a complete engineering survey of six Metrolinx railway commuter corridors originating from Union Station in Toronto, Canada. Tulloch used a unique combination of mobile LiDAR, static LiDAR, and conventional infill ground survey to complete the project. LiDAR, which stands for Light Detection and Ranging, is a surveying method that measures distance to a target by illuminating that target with a laser light. Using LiDAR technology provided significant advantages to the Electrification Project over using convention ground survey techniques. Metrolinx is a Canadian crown corporation responsible for the Greater Toronto and Hamilton Area’s GO Transit rail and bus commuter system. GO Transit trains currently carry 190,000 commuters per day. Electrification of Metrolinx GO Transit rail commuter rail corridors requires the upgrading of infrastructure and providing a means of getting the electricity to the trains which includes new electrical substations, overhead power lines and new equipment. The electrification is part of the GO Regional Express Rail program, which will expand the capacity of the GO rail network to provide customers with faster, more frequent and more convenient service to and from dozens of stations in core sections of the GO rail network throughout the day, evenings and weekends. Electrification is planned for most of Metrolinx commuter rail corridors by 2022–2024. The engineering technical and program management consultant for the Electrification Project is Gannett Fleming. An initial requirement for Metrolinx Electrification project is an up to date engineering survey to enable the preliminary engineering design. Our survey project involves surveying approximately 170 miles of railway corridor for 6 GO Transit tracks originating from Union Station in downtown Toronto. Our mobile LiDAR survey system was mounted on a GO Transit hi-rail truck; with most of the surveying occurring at night due to the heavy train traffic and since LiDAR is an active sensor. Tulloch provided a unique hybrid surveying approach, using mobile LiDAR surveying to collect all the visible features in the corridor, followed by conventional ground surveys to fill in missing features obscured from the LiDAR system’s field of view and static LiDAR surveys for some of the bridges inaccessible with mobile LiDAR. This is the first time Metrolinx has contracted an engineering survey using these multiple survey technologies. This survey approach reduces delivery timelines, limits track disruptions, and greatly improves safety. A major advantage of mobile LiDAR surveying for the GO-Transit rail corridors is that collection can occur at night when train activity is low and in a fraction of the time it takes to survey using conventional ground crews. This enabled project schedules to be advanced, as base mapping was completed in about 60% of the normal time required for the engineering survey. Using mobile scanning on the tracks reduced safety risks associated with on track field surveys. In addition, the resultant LiDAR point cloud can be revisited in the office, and additional features and critical information picked up without having to send field crews back to do so. The homogeneous nature of the point cloud, combined with the conventional in-fill survey provides a rich, full feature data set that can be used at various stages in the engineering design process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".