Abstract
| Original language | English |
|---|---|
| Number of pages | 21 |
| Publication status | Published - 17 Jan 2019 |
| Event | 98th Annual Meeting of the TRB Transportation Research Board 2019 - Walter E. Washington Convention Center, Washington, United States Duration: 13 Jan 2019 → 17 Jan 2019 Conference number: 98 http://www.trb.org/AnnualMeeting/AnnualMeeting.aspx |
Conference
| Conference | 98th Annual Meeting of the TRB Transportation Research Board 2019 |
|---|---|
| Abbreviated title | TRB 2019 |
| Country | United States |
| City | Washington |
| Period | 13/01/19 → 17/01/19 |
| Other | Paper number: 19-05489 |
| Internet address |
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Robust stop-skipping at the tactical planning stage with Evolutionary Optimization. / Gkiotsalitis, Konstantinos .
2019. Paper presented at 98th Annual Meeting of the TRB Transportation Research Board 2019, Washington, United States.Research output: Contribution to conference › Paper › Academic › peer-review
TY - CONF
T1 - Robust stop-skipping at the tactical planning stage with Evolutionary Optimization.
AU - Gkiotsalitis, Konstantinos
PY - 2019/1/17
Y1 - 2019/1/17
N2 - The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from the expected ones. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies which are robust to travel time variations. In more detail, we show how historical travel time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of 5-month data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop skipping problem.
AB - The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from the expected ones. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies which are robust to travel time variations. In more detail, we show how historical travel time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of 5-month data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop skipping problem.
M3 - Paper
ER -