Development and Deployment of GIS Tools to Facilitate Transit Network Design and Operational Evaluation
Bibliographic record
Abstract
INTRODUCTION With the widespread growth of geographic information systems (GIS) and greater application of GIS to transportation analysis, opportunities exist to utilize GIS functionality in transit planning. This paper presents two integrated GIS tools that were developed in conjunction with and have been successfully applied in the regional municipality of Waterloo, Ontario, Canada. The first tool formalizes the so-called desire line method--matching transportation facilities to existing or predicted major demand corridors--to solve the transportation network design problem (TNDP) for a transit network. First, GIS is used to identify transit-supportive land uses at the Traffic Analysis Zone (TAZ) level based on a user-defined combination of total population, total employment, or their densities. Next, the tool allows the user to interactively merge TAZs to create activity centers between which statistically meaningful travel volumes are expected. In the third step, the tool allows the user to merge travel data to create a visual representation of major flows between the activity centers generated. From these visual representations, trunk transit routes can be readily identified and input. Finally, the GIS application estimates the operating costs associated with candidate transit networks. The second tool also is designed to analyze travel patterns between activity centers concentrating on the need for and user costs of transfers. For any origin-destination pair (OD), the tool utilizes GIS functions to determine where transfers are required. The quality of the transfer then is evaluated by measuring the physical distance as well as the expected travel delay (based on scheduled times) to complete the transfer. The methodology may be automated to scan a series of origins and destinations to develop a range of transfer penalties associated with a given network layout and service schedule. The GIS tools were built using VBA scripting in ArcMap 9.3, part of ESRI's general ArcGIS suite. The tools incorporate specific GIS functionality, including spatial query, feature dissolve, and overlay functions, in addition to the use of the network analyst extension. The remainder of the paper is organized as follows. It reviews the literature focusing on technical methods to solve the TNDP, the concepts of transit-supportive land uses, and the issue of transfers in transit system performance. Next it presents the transit network design and transfer cost tools. Then it shows the application of these tools to the region of Waterloo. The conclusion presents comments on the transferability of these methods and means to improve these approaches. LITERATURE REVIEW This paper documents a method by which the TNDP may be addressed using GIS. Many methodologies have been proposed to solve the TNDP without the application of GIS; these methods are reviewed briefly here. Other researchers have posited methods by which GIS may be applied to the TNDP; these methods are reviewed as well. While nearly all approaches to solving the TNDP involve some level of spatial aggregation to reduce complexity, this paper suggests an aggregation method that emphasizes transit-supportive land uses. As such, it also briefly reviews the literature surrounding spatial aggregation and the development of activity centers. SOLVING THE TNDP WITHOUT GIS Non-GIS methods used to solve the TNDP can be classified into three categories: (1) constrained optimization models; (2) heuristic approaches; and (3) practical guidelines or ad hoc procedures (Tom and Mohan 2003, Fan and Machemehl 2004). These are reviewed here. Many of the optimization frameworks proposed to solve the TNDP are based on the works of Ceder and Wilson (1986) and Baaj and Mahmassani (1991). The objective functions of these formulations minimize some combination of passenger expenses and operator costs. Constraint sets include budgetary limits, fleet sizes, and human resources. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".