| 294 | 0 | 124 |
| 下载次数 | 被引频次 | 阅读次数 |
【背景】随着全球能源结构转型和“双碳”战略推进，新能源出租车成为城市低碳交通体系的重要组成部分。然而，当前出租车订单需求研究缺乏对车辆类型的细致区分，针对新能源出租车订单需求的影响机制研究仍显不足。【方法】构建了针对新能源出租车订单需求影响的MDCEV模型，以中距离出行为基准，剖析社会经济、建成环境与交通网络等多类因素在不同高峰时段对于新能源出租车短、长距离出行需求的影响，从而为分时段、分距离的运营策略和交通规划提供科学依据。【数据】研究基于天津市新能源出租车的实际运营订单数据，涵盖不同时间段、出行距离及多维环境变量。【结果】长距离订单在各高峰时段需求饱和阈值最高；高房价促进短距离出行，高人口密度抑制长距离需求；充电站和医疗类POI提升短距离订单，办公类POI在休息日抑制长距离订单；距市中心距离与短距离订单正相关，路网密度与长距离订单负相关。【结论】不同因素对新能源出租车出行需求的影响具有显著时段性和距离差异性。【应用】为新能源出租车分类运营策略制定和城市交通规划提供科学依据，助力绿色交通可持续发展。
Abstract：[Background] As the global energy structure transforms and the “dual carbon” strategy gains traction, new-energy taxis have emerged as an essential component of low-carbon transportation systems. However, current studies on taxi order demand lack detailed distinctions among vehicle types, resulting in insufficient influencing mechanisms of order demand for new-energy taxis.[Method] An MDCEV model of new-energy taxi order demand was developed using medium-distance travel as a benchmark to quantify how socioeconomic, built environment, and transportation network attributes shape short-and long-distance demand during peak periods, allowing for more targeted operational strategies and planning decisions. [Data] Real-world operational data for new-energy taxis in Tianjin were used to cover various time periods, trip distances, and multidimensional environmental variables. [Result] Long-distance orders showed the highest saturation threshold during peak periods: high housing prices increased short-distance demand, while high population density decreased long-distance orders. Points of interest(POI) such as charging stations and medical facilities,boosted short-distance trips, while office POI reduced long-distance demand during weekend peaks.Distance from the city center positively correlated with short-distance trips, whereas road density negatively affected long-distance trips. [Conclusion] The impact of various factors on new energy taxi travel demand varies significantly based on the time of day and trip distance. [Application] This paper can provide scientific support for targeted operational strategies for new-energy taxis and urban transport planning, with the aim of promoting sustainable green mobility.
[1]LIU Q, DING C, CHEN P. A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand[J]. Travel Behaviour and Society, 2020, 18:29-36.
[2]CHEN C, FENG T, DING C, et al. Examining the spatialtemporal relationship between urban built environment and taxi ridership:results of a semi-parametric GWPR model[J]. Journal of Transport Geography, 2021, 96:103172.
[3]DUAN Y, YUAN C, MAO X, et al. Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit[J]. PLoS One, 2023, 18(10):e0292363.
[4]NIAN G, HUANG J, SUN D. Exploring built environment influence on taxi vacant time in megacities:a case study of Chongqing, China[J]. Journal of Advanced Transportation, 2022(1):3096901.
[5]CHEN Q, LV B, HAO B, et al. Impacts of the feeder-related built environment on taxi-metro integrated use in Lanzhou, China[J]. Journal of Advanced Transportation, 2023(1):8251433.
[6]路庆昌,王琴,徐标,等.出租车空载率时空特征及影响因素分析:以上海市为例[J].地理研究, 2024, 43(1):149-163.LU Qingchang, WANG Qin, XU Biao, et al. Analysis of spatiotemporal characteristics and influencing factors of taxi empty-loading ratio:a case study of Shanghai[J]. Geographical Research, 2024, 43(1):149-163.
[7]SABOURI S, PARK K, SMITH A, et al. Exploring the influence of built environment on Uber demand[J]. Transportation Research Part D:Transport and Environment,2020, 81:102296.
[8]ZHU P, LI J, WANG K, et al. Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression[J].Transportation, 2024, 51(5):1963-1997.
[9]马健霄,赵飞燕,尹超英,等.建成环境和出租车需求对网约车出行需求影响的时空间分异模式[J].交通运输系统工程与信息, 2023, 23(5):136-145.MA Jianxiao, ZHAO Feiyan, YIN Chaoying, et al. Spatial-temporal heterogeneity effects of built environment and taxi demand on ride-hailing demand[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5):136-145.
[10]杜明洋,李雪峰,徐家聪,等.夜间网约车出行需求影响因素的时空异质性分析[J].吉林大学学报(工学版),2024, 54(6):1-12.DU Mingyang, LI Xuefeng, XU Jiacong, et al. Spatial and temporal heterogeneity analysis of influential factors of nighttime ride-hailing travel demand[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6):1-12.
[11]毛新博,高歌,李甜,等.考虑交互效应的建成环境对网约车时空分异影响分析[J].交通运输工程与信息学报, 2025, 23(2):16-31.MAO Xinbo, GAO Ge, LI Tian, et al. Effect of built environment considering interaction effect on spatiotem poral differentiation of online car-hailing trips[J]. Journal of Transportation Engineering and Information,2025, 23(2):16-31.
[12]牟凤云,田甜,王俊秀,等.出租车优质客源时空特征及影响因素[J].科学技术与工程, 2023, 23(25):10664-10672.MU Fengyun, TIAN Tian, WANG Junxiu, et al. Spatial and temporal characteristics and influencing factors of high quality taxi sources[J]. Science Technology and Engineering, 2023, 23(25):10664-10672.
[13]陈光会,姬浩,靳富强,等.考虑能源类型的城市出租车运营效率评价研究[J].交通运输系统工程与信息,2022, 22(3):36-44.CHEN Guanghui, JI Hao, JIN Fuqiang, et al. Operation efficiency evaluation on urban taxi considering energy types[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3):36-44.
[14]WU Z, ZHUO J. Impact of urban built environment on urban short-distance taxi travel:the case of Shanghai[J].IOP Conference Series:Earth and Environmental Science, 2018, 153(6):062019.
[15]DU J, RAKHA H A, BREUER H. An in-depth spatiotemporal analysis of ride-hailing travel:The Chicago case study[J]. Case Studies on Transport Policy, 2022, 10(1):118-129.
[16]LI X, XU J, DU M, et al. Understanding the spatiotemporal variation of ride-hailing orders under different travel distances[J]. Travel Behaviour and Society, 2023, 32:100581.
[17]WANG T, ZHANG Y, FU X, et al. Finding taxi service management opportunities based on the analysis of choice behavior for passengers with different travel distances[J]. Research in Transportation Business&Management, 2019, 33:100457.
[18]焦萍,马宁远,段雅馨,等.基于出租车订单轨迹数据的城市居民出行需求区域挖掘[J].长安大学学报(自然科学版), 2022, 42(4):108-117.JIAO Ping, MA Ningyuan, DUAN Yaxin, et al. Regional mining of urban residents’travel demand based on taxi order trajectory data[J]. Journal of Chang’an University(Natural Science Edition), 2022, 42(4):108-117.
[19]卓健,吴卓烨,翟端强,等.更新背景下减少短距离机动化出行的规划策略:基于上海多源数据的实证研究[J].城市规划, 2021, 45(3):99-108.ZHUO Jian, WU Zhuoye, ZHAI Duanqiang, et al. Planning measures for reducing the short-distance motorized travels in the context of urban regeneration:an empirical study based on multisource data analysis of Shanghai[J].City Planning Review, 2021, 45(3):99-108.
[20]WANG S, SUN L, RONG J, et al. Transit traffic analysis zone delineating method based on Thiessen polygon[J].Sustainability, 2014, 6(4):1821-1832.
[21]BHAT C R. The multiple discrete-continuous extreme value(MDCEV)model:Role of utility function parameters, identification considerations, and model extensions[J]. Transportation Research Part B:Methodological,2008, 42(3):274-303.
[22]陈芋宏,李娟.出租车跨区经营方案研究:以成都中心城区、郫县为例[J].交通运输工程与信息学报, 2012,10(1):25-30.CHEN Yuhong, LI Juan. Study on taxi cross-regional operation scheme:taking Chengdu City center and Pixian as an example[J]. Journal of Transportation Engineering and Information, 2012, 10(1):25-30.
[23]成华,段力伟.重庆市主城区交通方式结构演变与预测分析[J].城市公共交通, 2021(3):41-46.CHENG Hua, DUAN Liwei. The Evolution and Prediction of Transportation Structure for Main Urban Area of Chongqing[J]. Urban Public Transport, 2021(3):41-46.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.07.010
中图分类号:U491
引用信息:
[1]孙少凡,卢浩,寇巳瑾,等.基于订单数据的新能源出租车出行需求影响因素分析[J].交通运输工程与信息学报,2026,24(02):106-116.DOI:10.19961/j.cnki.1672-4747.2025.07.010.
基金信息:
国家自然科学基金项目（52202387）
2025-08-05
2025-08-05
2025-08-05