Journal of Shanghai University(Natural Science Edition) ›› 2020, Vol. 26 ›› Issue (3): 328-341.doi: 10.12066/j.issn.1007-2861.2211
• Transportation Science and Computation • Previous Articles Next Articles
XI Yinfei, LIU Zhongkai, YANG Peiyun, YU Ye(), ZHANG Qi, LIU Zhiyuan
Received:
2020-02-10
Online:
2020-06-30
Published:
2020-07-07
Contact:
YU Ye
E-mail:yuyeseu@163.com
CLC Number:
XI Yinfei, LIU Zhongkai, YANG Peiyun, YU Ye, ZHANG Qi, LIU Zhiyuan. Travel demand forecast methods for Internet private hire vehicles[J]. Journal of Shanghai University(Natural Science Edition), 2020, 26(3): 328-341.
[1] | 梁婷婷. 智能出行平台下的城市出租车需求预测研究[D]. 长春: 吉林大学, 2016. |
[2] | 中商产业研究院. 中国网约车市场现状及发展前景研究报告 [EB/OL].[2018-03-23] https://www.askci.com/news/chanye/20180914/1600501132066_5.shtml. |
[3] | Zheng Y, Capra L, Wolfson O, et al. Urban computing: concepts, methodologies, and applications[J]. ACM Transaction on Intelligent Systems and Technology, 2014,5(3):38. |
[4] | 林玉川. 移动智能出行平台用户行为研究[D]. 厦门: 厦门大学, 2014. |
[5] | 唐军. 基于系统动力学的城市网约车出行需求发展趋势研究[D]. 西安: 长安大学, 2017. |
[6] | 林园. 网约车条件下居民出行方式选择研究[D]. 成都: 西南交通大学, 2018. |
[7] | 高永, 安健. 网络约租车对出行方式选择及交通运行的影响[J]. 城市交通, 2016,14(5):1-8. |
[8] | 王一帆. 基于打车软件的出租车服务模式优化研究[D]. 上海: 上海交通大学, 2014. |
[9] | 黎景壮. 基于 QPSO_RBF 神经网络的网约车需求量预测模型[J]. 广西大学学报 (自然科学版), 2018,43(2):700-709. |
[10] | 贾兴无. 基于网约车数据的居民出行需求特征分析及需求预测[J]. 交通工程, 2018,18(5):39-45. |
[11] | Dawes M. Perspectives on the ridesourcing revolution: surveying individual attitudes toward Uber and Lyft to inform urban transportation policymaking [D]. Cambridge, MA: Massachusetts Institute of Technology, 2016. |
[12] |
Farber H S. Why you cannot find a taxi in the rain and other labor supply lessons from cab drivers[J]. Quarterly Journal of Economics, 2014,130(4):1975-2026.
doi: 10.1093/qje/qjv026 |
[13] | Zhang K F, Chen S. A framework for passengers demand prediction and recommen-dation[C]// IEEE International Conference on Services Computing. 2016: 340-347. |
[14] | 贺国光, 李宇, 马寿峰. 基于数学模型的短时交通流预测方法探讨[J]. 系统工程理论与实践, 2000(12):51-56. |
[15] | Ahmed M S, Cook A R. Analysis of freeway traffic time-series data by using Box-Jenkins techniques[J]. Transportation Research Record, 1979,722:1-9. |
[16] | Nihan N L, Holmesland K O. Use of the box and Jenkins time series technique in traffic forecasting[J]. Transportation, 1980,2(9):125-143. |
[17] | Kim C, Hobeika A G. Short-term demand forecasting model from real-time traffic data[C]// Proceedings of the Infrastructure Planning and Management. 1993: 540-550. |
[18] | 王均, 关伟. 基于 Kalman 滤波的城市环路交通流短时预测研究[J]. 交通与计算机, 2006(5):16-19. |
[19] |
Chien S I, Kuchipudi C M. Dynamic travel time prediction with real-time and historic data[J]. Journal of Transportation Engineering, 2003,129(6):608-616.
doi: 10.1061/(ASCE)0733-947X(2003)129:6(608) |
[20] | 杨高飞, 徐睿, 秦鸣, 等. 基于 ARMA 和卡尔曼滤波的短时交通预测[J]. 郑州大学学报 (工学版), 2017,2:36-40. |
[21] |
Jiang X, Adeli H. Dynamic wavelet neural network model for traffic flow forecasting[J]. Journal of Transportation Engineering, 2005,131(10):771-779.
doi: 10.1061/(ASCE)0733-947X(2005)131:10(771) |
[22] | 曹征. 基于小波变换的交通流短时预测模型研究[D]. 北京: 北京交通大学, 2010. |
[23] | Disbro J E, Frame M. Traffic flow theory and chaotic behavior[J]. Transportation Research Record Journal of the Transportation Research Board, 1989,1225:109-115. |
[24] | 张勇. 交通流的非线性分析、预测和控制[D]. 北京: 北京交通大学, 2011. |
[25] |
Forbes G J, Hall F L. The applicability of catastrophe theory in modelling freeway traffic operations[J]. Transportation Research Part A: General, 1990,24(5):335-344.
doi: 10.1016/0191-2607(90)90046-9 |
[26] | 唐铁桥, 黄海军. 用燕尾突变理论来讨论交通流预测[J]. 数学研究, 2005,38(1):112-116. |
[27] |
Davis G A, Nihan N L. Nonparametric regression and short-term freeway traffic forecasting[J]. Journal of Transportation Engineering, 1991,117(2):178-188.
doi: 10.1061/(ASCE)0733-947X(1991)117:2(178) |
[28] | 丁涛杰. 基于GPS 数据的交通速度估计及短时预测研究[D]. 长沙: 国防科学技术大学, 2014. |
[29] | Smith B L, Demetsky M J. Short-term traffic flow prediction: neural network approach[J]. Transportation Research Record, 1994,1453:98-104. |
[30] | Dougherty M S, Cobbett M R. Short-term inter-urban traffic forecasts using neural networks[J]. International Journal of Forecasting, 1997,13(1):21-31. |
[31] |
Dia H. An object-oriented neural network approach to short-term traffic forecasting[J]. European Journal of Operational Research, 2001,131(2):253-261.
doi: 10.1016/S0377-2217(00)00125-9 |
[32] | 李松, 刘力军, 翟曼. 改进粒子群算法优化 BP 神经网络的短时交通流预测[J]. 系统工程理论与实践, 2012,32(9):2045-2049. |
[33] | 卢建中, 程浩. 改进 GA 优化 BP 神经网络的短时交通流预测[J]. 合肥工业大学学报 (自然科学版), 2015,38(1):127-131. |
[34] | 吕宏义. 基于支持向量回归机的路段平均速度短时预测方法研究[D]. 北京: 北京交通大学, 2008. |
[35] | 孙朝东, 梁雪春. 改进的花授粉算法优化 SVM 在交通流中的应用[J]. 计算机工程与设计, 2016,37(10):2717-2721. |
[36] |
Voort M V D, Dougherty M, Watson S. Combining kohonen maps with arima time series models to forecast traffic flow[J]. Transportation Research Part C:Emerging Technologies, 1996,4(5):307-318.
doi: 10.1016/S0968-090X(97)82903-8 |
[37] | Liu H. The forecast of dynamic traffic flow[C]// International Conference on Traffic and Transportation Studies Traffic and Transportation Studies. 2000: 507-512. |
[38] | 谭满春, 李英俊, 徐建闽. 基于小波消噪的 ARIMA 与 SVM 组合交通流预测[J]. 公路交通科技, 2009,26(7):127-132. |
[39] | Yu G, Hu J, Zhang C. Short-term traffic flow forecasting based on Markov chain model[C]// Proceedings of Intelligent Vehicles Symposium. 2003: 208-212. |
[40] | Tan G, Shi H, Wang F. Short-term traffic flow prediction based on parallel quasi-newton neural network[C]// IEEE 2009 International Conference on Measuring Technology and Mechatronics Automation. 2009: 305-308. |
[41] | 刘芹, 徐建闽. 基于云模型的短时交通流预测方法研究[J]. 计算机工程与设计, 2012,33(5):1953-1957. |
[42] | Moreira M L, Gama J, Feffeira M, et al. Predicting taxi-passenger demand using streaming data[J]. IEEE Transactions on Intelligent Transportation, 2013,14(3):1393-1402. |
[43] | 林永杰, 邹难. 基于运营系统的出租车出行需求短时预测模型[J]. 东北大学学报 (自然科学版), 2016 (9):1235-1240. |
[44] | 王芮. 基于 GPS 数据的城市出租车出行需求研究[D]. 济南: 山东大学, 2016. |
[45] | Chiang M F, Hoang T A, Lim E P. Where are the passengers: a grid-based gaussian mixture model for taxi bookings[C]// Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2015: 1-10. |
[46] | Deng D X, Shahabi C, Demiryurek U, et al. Latent space model for road networks to predict time-varying traffic[C]// Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 1525-1534. |
[47] | Tong Y, Chen Y, Zhou Z, et al. The simpler the better: a unified approach to predicting original taxi demands on large-scale online platforms[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 1653-1662. |
[48] |
Nie Y. How can the taxi industry survive the tide of ride sourcing evidence from Shenzhen, China[J]. Transportation Research Part C: Emerging Technologies, 2017,79:242-256.
doi: 10.1016/j.trc.2017.03.017 |
[49] | Yao H, Wu F, Ke J, et al. Deep multi-view spatial-temporal network for taxi demand prediction[J]. Machine Learning, 2018: 2588-2595. |
[50] |
Geng X, Li Y, Wang L, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019,33:3656-3663.
doi: 10.1609/aaai.v33i01.33013656 |
[51] | 聂笃宪, 林健英, 林奕敏, 等. “互联网+”时代的出租车资源配置建模与分析[J]. 东莞理工学院学报, 2016,23(1):24-31. |
[52] | 樊文壮, 胡文力. 基于模糊分析的区域出租车资源紧缺程度评价研究[J]. 电脑知识与技术, 2016,12(7):158-159. |
[53] | 张娟, 何启明. “互联网+”出租车资源配置优化研究[J]. 自动化与仪器仪表, 2016(7):240-241. |
[54] | 刘嘉琪, 邹泞憾, 周梓楠, 等. 互联网时代出租车供需匹配及补贴方案确定[J]. 经济数学, 2016,33(2):103-110. |
[55] | 逯强, 张岩. 基于供需分析的城市网约车资源配置模型[J]. 牡丹江师范学院学报 (自然科学版), 2016,4(97):12-15. |
[56] | 朱家明, 刘玲, 孟康, 等. 基于AHP-熵值的不同时空出租车资源供求匹配研究: 以上海市为例[J]. 太原师范学院学报 (自然科学版), 2016,15(1):52-58. |
[57] | 陈文龙. 网约车模式下基于轨迹数据的出租汽车运力规模测算[D]. 西安: 长安大学, 2018. |
[58] |
Yang C, Gonzales E. Modeling taxi trip demand by time of day in New York[J]. Transportation Research Record Journal of the Transportation Research Board, 2014,2429:110-120.
doi: 10.3141/2429-12 |
[59] | Daniel F G. Local exclusive cruising regulation and efficiency in taxicab markets[J]. Journal of Transport Economics and Policy, 2005,39(2):155-166. |
[60] |
杨英俊, 赵祥模. 基于小波神经网络的出租车保有量预测模型[J]. 公路交通科技, 2012,29(8):136-141.
doi: 103969/jissn1002-0268201208023 |
[61] | 李敏杰. 大型活动区域交通疏散模型及其应急策略研究[D]. 天津: 天津工业大学, 2009. |
[62] | 章辉. 大型活动交通需求预测和疏散方案研究[D]. 武汉: 武汉理工大学, 2007. |
[63] | John B. Strategic transport planning, demand analysis of transport infrastructure and transport services for the 27th summer Olympiad held in Sydney[J]. Journal of Transportation Engineering and Information, 2004,2(2):14-30. |
[64] | Latoski S P, Dunn W M, Wagenblast B. Managing travel for planned special events[J]. Special Events, 2003,11:14. |
[65] | 王晓光, 耿玲虹, 赵锋. 城市大型活动中的交通需求与疏散分析[J]. 交通科技与经济, 2009(4):10-13. |
[66] | 赵跃萍. 大型活动事件下的城市交通需求预测方法研究[D]. 武汉: 武汉理工大学, 2008. |
[67] | 赵会珍, 郝力文, 李中山. 城市大型活动交通需求预测研究[J]. 物流技术, 2014,33(19):305-307. |
[68] |
Yang C, Gonzales E. Modeling taxi trip demand by time of day in New York[J]. Transportation Research Record Journal of the Transportation Research Board, 2014,2429:110-120.
doi: 10.3141/2429-12 |
[69] | Tseng P J, Hung C C, Chang T H, et al. Real-time urban traffic sensing with GPS equipped Probe Vehicles[C]// International Conference on ITS Telecommunications. 2013: 306-310. |
[70] | 孙贵治. 基于出租车 GPS 轨迹数据的热点区域出行需求预测[D]. 北京: 北京交通大学, 2019. |
[71] | 陈红丽. 基于出租车 GPS 数据的居民出行时空规律和出行热点区域研究[D]. 昆明: 云南大学, 2016. |
[72] |
Ashbrook D S T. Using GPS to learn significant locations and predictmovement across multiple users[J]. Personal and Ubiquitous Computing, 2003,7(5):275-286.
doi: 10.1007/s00779-003-0240-0 |
[73] |
Gong L S, Yamamoto T. Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines[J]. Journal of Modern Transportation, 2015,23(3):202-213.
doi: 10.1007/s40534-015-0079-x |
[74] |
Zong F, Bai Y, Wang X, et al. Identifying travel mode with GPS data using support vector machines and genetic algorithm[J]. Information, 2015,6:212-227.
doi: 10.3390/info6020212 |
[75] | 李追日. 基于手机定位数据的城市道路交通需求推估及分析[D]. 北京: 北京大学, 2016. |
[76] | 邓博. 基于手机 App 调查数据的出行信息提取方法研究[D]. 武汉: 武汉理工大学, 2018. |
[77] | 余琳玲. 基于移动通信数据的城市行人定位及出行方式分析[D]. 北京: 北京邮电大学, 2018. |
[78] | 张晓鹏. 基于多源数据融合的城市出行需求预测方法研究[D]. 长春: 吉林大学, 2018. |
[79] | 张凯. 乘客出行预测与推荐方法研究[D]. 天津: 天津大学, 2017. |
[1] | GAO Jun-jun1,TAN Chong-li1,LIU Yue2,YIN Ya-feng2. Demand Forecast Using Support Vector Machine for a Product Category [J]. Journal of Shanghai University(Natural Science Edition), 2009, 15(1): 71-76. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||