| Citation: | SUN Lishan, JIA Lin, WEI Zhonghua, LI Junfeng. Demand Forecasting of Taxi Travel Based on GPS Data[J]. Journal of Transport Information and Safety, 2021, 39(5): 128-136. doi: 10.3963/j.jssn.1674-4861.2021.05.016
|
In recent years, the number of "car-hailing" keeps increasing, which leads to several problems gradually, such as long waiting time of "car-hailing"and large demands for hot spot areas. The experiences of "car-hailing" should be improved urgently. Based on GPS data of taxis in Chengdu, the distribution characteristics of taxi trips are studied by dividing working days into morning, evening, and night peak periods. A k-distance curve is used to improve the density-based spatial clustering of applications with noise(DBSCAN)algorithm. Cluster analysis is carried out on taxi pick-up and drop-down points, and the hot spot areas are obtained by data mining. The BP neural network is used to predict the travel demands in hot spot areas. The prediction results show that compared with the random forest model and ridge regression model, the MAPE of the BP neural network model increases by 3.25% and 5.87% in the morning peak, 2.98% and 4.32% in the evening peak, and 1.44% and 2.58% in the night peak, respectively, which verifies the feasibility of the BP neural network in demand forecasting of taxi travel.
| [1] |
曹梁. 基于出租车GPS数据的城市出行及碳排放时空变化研究[D]. 上海: 上海师范大学, 2018.
CAO Liang. Spatiotemporal variation of urban travel and carbon emissions based on taxi GPS data[D]. Shanghai : Shanghai Normal University, 2018. (in Chinese)
|
| [2] |
付鑫, 孙茂棚, 孙皓. 基于GPS数据的出租车通勤识別及时空特征分析[J]. 中国公路学报, 2017, 30(7): 134-143. doi: 10.3969/j.issn.1001-7372.2017.07.017
FU Xin, SUN Maopeng, SUN Hao. Taxi commuting recognition and spatiotemporal characteristics analysis based on GPS da- ta[J]. China Journal of Highway and Transport, 2017, 30(7): 134-143. (in Chinese) doi: 10.3969/j.issn.1001-7372.2017.07.017
|
| [3] |
张俊涛, 武芳, 张浩. 利用出租车轨迹数据挖掘城市居民出行特征[J]. 地理与地理信息科学, 2015, 3(6): 104-108. doi: 10.3969/j.issn.1672-0504.2015.06.020
ZHANG Juntao, WU Fang, ZHANG Hao. Mining travel characteristics of urban residents using taxi trajectory data[J]. Geography and Geo-Information Science, 2015, 31(6) : 104-108. (in Chinese) doi: 10.3969/j.issn.1672-0504.2015.06.020
|
| [4] |
ALVAREZ-GARCIA J A, ORTEGA J A, GONZALEZ-ABRIL, L, et al. Trip destination prediction based on past GPS log using a hidden markov model[J]. Expert Systems with Applications, 2010, 37(12): 8166-8171. doi: 10.1016/j.eswa.2010.05.070
|
| [5] |
林基艳, 张雅琼, 张慧. 基于出租车GPS轨迹数据挖掘的居民出行特征研究[J]. 计算机时代, 2017(5): 37-39+41. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJS201705012.htm
LIN Jiyan, ZHANG Yaqiong, ZHANG Hui. Research on residents' travel characteristics based on taxi GPS trajectory data mining[J]. Computer Era, 2017(5) : 37-39+41. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJS201705012.htm
|
| [6] |
何月, 工崇侣. 基于时空聚类的出租车载客热点区域挖掘研究[J]. 测绘与空间地理信息, 2020, 43(1): 99-102. doi: 10.3969/j.issn.1672-5867.2020.01.027
HE Yue, WANG Chongqi. Mining hot spots of taxi passengers based on spatiotemporal clustering[J]. Geomatics & Spatial Information Technology, 2020, 43(1): 99-102. (in Chinese) doi: 10.3969/j.issn.1672-5867.2020.01.027
|
| [7] |
TANG J J, WANG Y W, HAO W, et al. A mixed path size logit-based taxi customer-search model considering spatio-temporal factors in route choice[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4) : 1347-1358. doi: 10.1109/TITS.2019.2905579
|
| [8] |
ZHENG L J, XIA D, ZHAO X, et al. Spatial-temporal travel pattern mining using massive taxi trajectory data[J]. Physica A-Statistical Mechanics and its Applications, 2018, (501): 24-41. http://d.wanfangdata.com.cn/periodical/4c9e129e797251a1c4ac3136fc2aa28a
|
| [9] |
工明. 基于出租车GPS数据的载客热点可视化的研究与应用[D]. 太原: 中北大学, 2018.
WANG Ming. Research and application of passenger hotspot visualization based on taxi GPS data[D]. Taiyuan: North University of China, 2018. (in Chinese)
|
| [10] |
LUO T, ZHENG XW, XUGL, etal. An improved DBSCAN algorithm to detect stops in individual trajectories[J]. ISPRS International Journal of Geo-Information, 2017, 6 (3): 2673-2681. http://www.mdpi.com/2220-9964/6/3/63/pdf
|
| [11] |
桂智明, 向宇, 李玉鉴. 基于出租车轨迹的并行城市热点区域发现[J]. 华中科技大学学报(自然科学版), 2012(增刊1): 187-190. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG2012S1045.htm
GUI Zhiming, XIANG Yu, LI Yujian. Discovery of parallel urban hot spots based on taxi trajectories[J]. Journal of Hua- zhong University of Science and Technology, 2012 (S1): 187-190. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG2012S1045.htm
|
| [12] |
工郑委. 基于大数据Hadoop平台的出租车载客热点区域挖掘研究[D]. 北京: 北京交通大学, 2016.
WANG Zhengwei, Research on hotspot area mining of taxi carrying passengers based on big data Hadoop platform[D]. Beijing : Beijing Jiaotong University, 2016. (in Chinese)
|
| [13] |
ZHANG L, LU J, ZHOU J, et al. Complexities' day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory[J]. Modern Physics Letters B, 2018, 32(9): 1850062. doi: 10.1142/S0217984918500628
|
| [14] |
工芮. 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.
WANG Rui. Study on urban taxi travel demand based on GPS data[D]. Jinan : Shandong University, 2016. (in Chinese)
|
| [15] |
席殷飞, 刘钟锴, 杨佩云, 等. 网约车出行需求预测方法[J]. 上海大学学报(自然科学版), 2020, 26(3): 328-341. https://www.cnki.com.cn/Article/CJFDTOTAL-SDXZ202003002.htm
XI Yinfei, LIU Zhongkai, YANG Peiyun, et al. Car-hailing travel demand prediction method[J]. Journal of Shanghai University (Natural Science), 2020, 26 (3) : 328-341. (in Chi - nese) https://www.cnki.com.cn/Article/CJFDTOTAL-SDXZ202003002.htm
|
| [16] |
张文胜, 郝孜奇, 朱冀军, 等. 基于改进灰狼算法优化BP神经网络的短时交通流预测模型[J]. 交通运输系统工稈与信息, 2020, 20(2): 196-203. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202002030.htm
ZHANG Wensheng, HAO Ziqi, ZHU Jijun, et al. Short-time traffic flow prediction model was optimized by BP neural network based on improved gray wolf optimizer[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20 (2): 196-203. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202002030.htm
|
| [17] |
叶秀秀, 马晓风, 钟鸣, 等. 基于局部路网空间结构特征的无检测器路段交通流预测方法[J]. 交通信息与安全, 2021, 39(2): 137-144. doi: 10.3963/j.jssn.1674-4861.2021.02.017
YE Xiuxiu, MA Xiaofeng, ZHONG Ming, et al. Traffic flow prediction method based on spatial structure characteristics of local road network without detector[J]. Journal of Transport Information and Safety, 2021, 39(2) : 137-144. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.02.017
|
| [18] |
YANG J, DONG X C, JIN S G. Metro passenger flow prediction model using attention-based neural network[J]. IEEE AC- CESS, 2020(8) : 30953-30959.
|
| [19] |
WANG J C, WANG Y, YANG J G. Forecasting of significant wave height based on gated recurrent unit network in the taiwan strait and its adjacent waters[J]. WATER, 2021, 13(1). http://www.researchgate.net/publication/348203282_Forecasting_of_Significant_Wave_Height_Based_on_Gated_Recurrent_Unit_Network_in_the_Taiwan_Strait_and_Its_Adjacent_Waters
|
| [20] |
李岩. 基于热点区域的出租车出行需求预测模型研究[D]. 西安: 长安大学, 2019.
LI Yan. Research on taxi travel demand forecasting model based on hot spots[D]. Xi' an: Chang' an University, 2019. (in Chinese)
|
Figures(8) / Tables(10)
DownLoad:
