[1]叶东.基于出行链的中小城市居民出行方式选择行为研究[D]. 重庆: 重庆交通大学,2018. [YE D. Research
on the travel mode choice behavior of residents in
small and medium-sized cities based on trip chain[D].
Chongqing: Chongqing Jiaotong University, 2018.]
[2]骆晨,董青,姚擎,等.突发公共卫生事件持续期居民中长距离出行方式选择行为研究[J].交通运输系统工程与信息,2020, 20(6): 57-62. [LUO C, DONG Q, YAO
Q, et al. Behavior of long-distance travel mode choice
under the duration of public health emergencies[J].
Journal of Transportation Systems Engineering and
Information Technology, 2020, 20(6): 57-62.]
[3]姚荣涵,梁亚林,刘锴,等.考虑合乘的共享自动驾驶汽车选择行为实证分析[J].交通运输系统工程与信息, 2020, 20(1): 228-233. [YAO R H, LIANG Y L, LIU
K, et al. Empirical analysis of choice behavior for shared
autonomous vehicles with concern of ride-sharing[J].
Journal of Transportation Systems Engineering and
Information Technology, 2020, 20(1): 228-233.]
[4]吴静娴,钱依楠,韩印.考虑群体异质性的建成环境与老年人慢行出行关系研究[J].交通运输系统工程与信息, 2022, 22(4): 194-201. [WU J X, QIAN Y N, HAN Y.
Relationship between built environment and elderly
active travel of based on group heterogeneity[J]. Journal
of Transportation Systems Engineering and Information
Technology, 2022, 22(4): 194-201.]
[5] ZHAO X L, YAN X, YU A, et al. Prediction and
behavioral analysis of travel mode choice: A comparison
of machine learning and Logit models[J]. Travel
Behaviour and Society, 2020, 20: 22-35.
[6] HAGENAUER J, HELBICH M. A comparative study of
machine learning classifiers for modeling travel mode
choice[J]. Expert Systems with Applications, 2017, 78:
273-282.
[7] SALAS P, DE LA FUENTE R, ASTROZA S, et al. A
systematic comparative evaluation of machine learning
classifiers and discrete choice models for travel mode
choice in the presence of response heterogeneity[J].
Expert Systems with Applications, 2022, 193: 116253.
[8] MARTÍN-BAOS J A,LÓPEZ-GÓMEZ J A, RODRIGUEZ
BENITEZ L, et al. A prediction and behavioural analysis
of machine learning methods for modelling travel mode
choice[J]. Transportation Research Part C: Emerging
Technologies, 2023, 156: 104318.
[9] ABBASZADEHM,SOLTANI-MOHAMMADIS,AHMED
A N. Optimization of support vector machine parameters
in modeling of IJU deposit mineralization and alteration
zones using particle swarm optimization algorithm and
grid search method[J]. Computers & Geosciences, 2022,
165: 105140.
[10] SUN Y L, DONG Y A, WAYGOOD E O D, et al.
Machine-learning approaches to identify travel modes
using smartphone-assisted survey and map application
programming interface[J]. Transportation Research
Record, 2023, 2677(2): 385-400.
[11] LUNDBERG S M, LEE S I. A unified approach to
interpreting model predictions[C]. Long Beach, CA:
Proceedings of the 31st International Conference on
Neural Information Processing Systems, 2017.
[12] NAKATSU R T. An evaluation of four resampling
methods used in machine learning classification[J]. IEEE
Intelligent Systems, 2021, 36(3): 51-57.
[13] HILLEL T, ELSHAFIE M Z E B, JIN Y. Recreating
passenger mode choice-sets for transport simulation:
A case study of London, UK[J]. Proceedings of the
Institution of Civil Engineers-Smart Infrastructure and
Construction, 2018, 171(1): 29-42.
[14] HU H, XU J G, SHEN Q, et al. Travel mode choices in
small cities of China: A case study of Changting[J].
Transportation Research Part D: Transport and
Environment, 2018, 59: 361-374.
[15] LUAN X, CHENG L, SONG Y, et al. Better
understanding the choice of travel mode by urban
residents: New insights from the catchment areas of rail
transit stations[J]. Sustainable Cities and Society, 2020,
53: 101968.
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