This study analyzes air passenger route choice behavior for long-haul inter-continental travel. It employs the SP （state preference） technique and logit modeling to investigate the impact of route development via ne...This study analyzes air passenger route choice behavior for long-haul inter-continental travel. It employs the SP （state preference） technique and logit modeling to investigate the impact of route development via neighboring countries in the region. With the Japanese government pursuing an increase in international routes at Haneda International Airport, and the Chinese government planning to construct Beijing Capital Second International Airport by 2019, the competition among airports to serve as hubs in Northeast Asia will increase significantly. Korean passengers will have a greater number of route choices when traveling to North America or Europe, utilizing not only direct flights from Incheon International Airport but also flights via Tokyo or Beijing area airports including Haneda International Airport, Narita International Airport, Beijing Capital International Airport and Beijing Capital Second International Airport. Accordingly, passengers will choose among the alternatives by considering fares and flight times. As such, it is essential for airports to offer flights with competitive prices for transit passengers to become successful competitive airports in the region. Therefore, it will become more important for market decision makers to strive toward more attractive ticket prices and better route network quality.展开更多
运营突发事件下,向城市轨道交通(城轨)乘客有针对性地发布诱导信息,是实现高效疏导、确保城轨运营安全的重要举措。本文考虑乘客异质性,开展突发事件下城轨诱导信息发布策略研究。首先,基于潜在分类模型(Latent Class Model,LCM)构建城...运营突发事件下,向城市轨道交通(城轨)乘客有针对性地发布诱导信息,是实现高效疏导、确保城轨运营安全的重要举措。本文考虑乘客异质性,开展突发事件下城轨诱导信息发布策略研究。首先,基于潜在分类模型(Latent Class Model,LCM)构建城轨乘客在突发事件和诱导信息双重作用下的路径选择行为模型,分析乘客对诱导信息接受程度及路径选择偏好的差异。其次,以线网乘客总出行时间和线网客流分布均衡性基尼系数为优化目标,构建基于路径选择行为分析的信息诱导优化模型,并应用非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)求解,以获得突发事件下受影响起讫点(Origin and Destination,OD)间各有效路径的最优推荐指数。最后,以北京市工作日早高峰城轨区域线网进行案例分析。结果表明:城轨乘客在突发事件下对诱导信息存在“诱导服从型”“诱导中立型”“诱导无视型”这3类不同接受程度的群体。提供诱导信息后,线网乘客总出行时间减少3.906%,线网客流分布均衡性基尼系数下降4.063%,同时减少了高满载率区间数量。事件结束后,诱导信息的继续发布可降低突发事件受影响区间7.08%的满载率,并避免乘客在刚恢复正常运营区间上的再次聚集。展开更多
Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary...Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.展开更多
文摘This study analyzes air passenger route choice behavior for long-haul inter-continental travel. It employs the SP （state preference） technique and logit modeling to investigate the impact of route development via neighboring countries in the region. With the Japanese government pursuing an increase in international routes at Haneda International Airport, and the Chinese government planning to construct Beijing Capital Second International Airport by 2019, the competition among airports to serve as hubs in Northeast Asia will increase significantly. Korean passengers will have a greater number of route choices when traveling to North America or Europe, utilizing not only direct flights from Incheon International Airport but also flights via Tokyo or Beijing area airports including Haneda International Airport, Narita International Airport, Beijing Capital International Airport and Beijing Capital Second International Airport. Accordingly, passengers will choose among the alternatives by considering fares and flight times. As such, it is essential for airports to offer flights with competitive prices for transit passengers to become successful competitive airports in the region. Therefore, it will become more important for market decision makers to strive toward more attractive ticket prices and better route network quality.
文摘运营突发事件下,向城市轨道交通(城轨)乘客有针对性地发布诱导信息,是实现高效疏导、确保城轨运营安全的重要举措。本文考虑乘客异质性,开展突发事件下城轨诱导信息发布策略研究。首先,基于潜在分类模型(Latent Class Model,LCM)构建城轨乘客在突发事件和诱导信息双重作用下的路径选择行为模型,分析乘客对诱导信息接受程度及路径选择偏好的差异。其次,以线网乘客总出行时间和线网客流分布均衡性基尼系数为优化目标,构建基于路径选择行为分析的信息诱导优化模型,并应用非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)求解,以获得突发事件下受影响起讫点(Origin and Destination,OD)间各有效路径的最优推荐指数。最后,以北京市工作日早高峰城轨区域线网进行案例分析。结果表明:城轨乘客在突发事件下对诱导信息存在“诱导服从型”“诱导中立型”“诱导无视型”这3类不同接受程度的群体。提供诱导信息后,线网乘客总出行时间减少3.906%,线网客流分布均衡性基尼系数下降4.063%,同时减少了高满载率区间数量。事件结束后,诱导信息的继续发布可降低突发事件受影响区间7.08%的满载率,并避免乘客在刚恢复正常运营区间上的再次聚集。
基金the National Natural Science Foundation of China （Grant Nos. U1564212, 61672082 and 61572229）.
文摘Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.
