Modeling And Optimizing Route Choice For Multimodal Transportation Networks
Bibliographic record
Abstract
Traffic congestion has been one of the major issues that most urban areas are facing and thus, many solutions have been developed and deployed in order to mitigate its negative effects. Advanced Traveler Information Systems (ATIS) have been used over the past two decades to provide travelers with pre-trip or real-time traffic information. Most of the efforts have focused on providing timely traffic information at locations with regularly occurring congestion. ATIS can be used to provide travelers with pre-trip and on-route travel information necessary to improve trip decision making with respect to various criteria (e.g. minimizing delay, constraining travel to specific modes). Many jurisdictions within Canada and the United States have implemented the 511 travel information system that provides traffic information, road conditions and closures, traffic cameras, etc. \nSeveral studies were conducted on vehicle routing optimization methods in ATIS. Most of them consider passenger vehicles as the only transportation mode in their routing algorithm. Others that include two transportation modes are mostly based on shortest path algorithms. However, a probabilistic based route optimization approach could better capture the stochastic characteristic of road traffic conditions. This research investigates an adaptive routing methodology for multi-modal transportation networks. A routing algorithm based on Markov decision processes is proposed to capture short-term traffic characteristics of transportation networks. Graph theory is used to model typical travel behavior within a multimodal network. This thesis proposes to use special network modeling elements, e.g. super nodes, to allow the integration of public transportation schedule into the model via the publicly available predefined timetables. The proposed routing algorithm applies an iterative function to select the optimal transportation mode/route through the network junctions along a given path. \nThe proposed methodology is applied to several real-world networks of motorized and non-motorized modes located in the central business district in Toronto, Ontario, and Montreal and Longueuil in Quebec. The networks include train, bus, streetcar, subway and bicycle transportation facilities. Microsimulation models of the networks developed in VISSIM and AIMSUN are used to estimate travel times along major arterials, for all transportation modes and for different traffic demands and congestion levels. The simulation models were calibrated using volume and speed data. The developed routing algorithm is applied to several scenarios in order to estimate optimal routes for a hypothetical traveler moving between two arbitrarily selected nodes in the network. The results identify the most efficient combination of transportation modes that the travelers have to use given specific constraints pertaining to traffic and transit service conditions. It is also shown that by applying the proposed algorithm to bus lines, transit agencies can have significant cost savings by rerouting their fleet. \nThe results of the proposed research have the potential to be integrated into various Intelligent Transportation Systems applications by combining available traveler information services. It can assist travelers in making more informed decisions regarding their travel plans and provide transportation agencies with an overall assessment of the system and its performance. For example, it can be used to minimize the impact of congested traffic conditions on the overall travel time and/or cost incurred by travelers as well as the operating cost of transit agencies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".