AI 中文总结
本研究以加州高铁为例，提出量化航空转高铁的航班延误成本节约方法，经Lasso模型等分析，得出2029年、2033年的对应延误成本节约金额，为相关决策提供数据支撑。
AI 中文摘要
本研究从航班延误成本降低的视角，提供了一种量化将客运从航空转移至高铁所获益处的方法。我们首先依据《加州高铁2020商业计划》给出的高铁乘客量预测，估算出机场起讫点对的航班减少数量，再将这些减少的航班分配至15分钟的时间区间内。应用Lasso模型，估算旧金山国际机场（SFO）、洛杉矶国际机场（LAX）和圣迭戈国际机场（SAN）排队延误减少对全国核心29个机场到达延误的影响。随后，利用飞机运营成本和乘客时间价值，将这些延误减少转化为货币价值。我们评估了不同的机场容量和航班时刻表情景，以及概率性高铁乘客量预测的多个百分位。估算结果显示，按2018年美元计算，2029年的航班延误成本节约为5100万至8800万美元，2033年为2.35亿至3.92亿美元。
英文摘要
This study provides a method to quantify the benefits of shifting passenger traffic from air to high-speed rail from the perspective of flight-delay cost reduction. We first estimate the number of flight reductions for airport origin-destination pairs based on the high-speed rail ridership forecasts provided in the California High-Speed Rail 2020 Business Plan, and then distribute these flight reductions to quarter-hour intervals. Lasso models are applied to estimate the impact of reduced queuing delays at SFO, LAX, and SAN on arrival delays at the national Core 29 airports. These delay reductions are then monetized using aircraft operating costs and the value of passenger time. We evaluate alternative airport-capacity and flight-schedule scenarios, as well as multiple percentiles of probabilistic high-speed rail ridership forecasts. The resulting estimates indicate flight-delay cost savings of $51-88 million in 2018 dollars in 2029 and $235-392 million in 2018 dollars in 2033.
Comments9 pages, 3 figures, and 3 tables. Presented at the 10th International Conference on Research in Air Transportation (ICRAT 2022), University of South Florida, Tampa, Florida, USA, June 19-23, 2022
Journal refProceedings of the 10th International Conference on Research in Air Transportation (ICRAT 2022), Tampa, Florida, USA, 2022
