2019.1月
0、张德升的最新投稿VeMo文章，基于高速公路场景的ETC订单数据
Yang Y,Xie X,Fang Z,et al. VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration[J]. arXiv preprint arXiv:1812.02780,2018.
1、同样来自张德升早先的工作，450G的深圳数据，通过估测候车时间，预测基于路段单位的乘客需求
Zhang D,He T,Lin S,et al. Taxi-passenger-demand modeling based on big data from a roving sensor network[J]. IEEE Transactions on Big Data,2017,3(3): 362-374.
2、陈德彪推荐的文章 TripImputor
Chen C, Jiao S, Zhang S, et al. TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data[J]. IEEE Trans Intell Transp Syst, 2018, 99: 1-13.
2018.12月
参考文献
0、城市计算综述（55页全）
Zheng Y, Capra L, Wolfson O, et al. Urban computing: concepts, methodologies, and applications[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2014, 5(3): 38.
1、经典推荐2010年，旧金山数据
Ge Y,Xiong H,Tuzhilin A,et al. An energy-efficient mobile recommender system[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2010: 899-908.
2、kmeans聚类
Atev S,Miller G,Papanikolopoulos N P. Clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems,2010,11(3): 647-657.
3、找社区平台的旅行伙伴，geolife项目上的
Tang L A,Zheng Y,Yuan J,et al. On discovery of traveling companions from streaming trajectories[C]//Data Engineering(ICDE),2012 IEEE 28th International Conference on. IEEE,2012: 186-197.
4、还是Geolife项目，挖掘兴趣位置
Zheng Y,Zhang L,Xie X,et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th international conference on World wide web. ACM,2009: 791-800.
5、波尔图441辆出租车
Moreira-Matias L,Gama J,Ferreira M,et al. Predicting taxi–passenger demand using streaming data[J]. IEEE Transactions on Intelligent Transportation Systems,2013,14(3): 1393-1402.
6、缺陷城市规划
Zheng Y,Liu Y,Yuan J,et al. Urban computing with taxicabs[C]//Proceedings of the 13th international conference on Ubiquitous computing. ACM,2011: 89-98.
7、残差网络预测流量，网格地图思想
Zhang J,Zheng Y,Qi D. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction[C]//AAAI. 2017: 1655-1661.
8、推断行程时间
Wang Y,Zheng Y,Xue Y. Travel time estimation of a path using sparse trajectories[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2014: 25-34.
9、顺风车
Ma S,Zheng Y,Wolfson O. T-share: A large-scale dynamic taxi ridesharing service[C]//Data Engineering(ICDE),2013 IEEE 29th International Conference on. IEEE,2013: 410-421.
10、轨迹数据挖掘概述，科普类型书籍
Zheng Y. Trajectory data mining: an overview[J]. ACM Transactions on Intelligent Systems and Technology(TIST),2015,6(3): 29.
11、2009年10月15天的5350辆车，杭州数据集，特征工程，没用过多轨迹信息，SVM机器学习
输出：是否停泊等待或者巡航，以及how far in distance距离
Li B,Zhang D,Sun L,et al. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset[C]//Pervasive Computing and Communications Workshops(PERCOM Workshops),2011 IEEE International Conference on. IEEE,2011: 63-68.
12、湖南科技大学两篇毕设后的英文，geolife数据，重要
Zhang M,Liu J,Liu Y,et al. Recommending Pick-up Points for Taxi-drivers based on Spatio-temporal Clustering[C]//Cloud and Green Computing(CGC),2012 Second International Conference on. IEEE,2012: 67-72.
13、微软亚研院，乘客双向推荐
Yuan N J,Zheng Y,Zhang L,et al. T-finder: A recommender system for finding passengers and vacant taxis[J]. IEEE Transactions on knowledge and data engineering,2013,25(10): 2390-2403.
14、空出租车推荐，北京12,000辆，找数据集啊啊啊啊。。。
Yuan J,Zheng Y,Zhang L,et al. Where to find my next passenger[C]//Proceedings of the 13th international conference on Ubiquitous computing. ACM,2011: 109-118.
15、微软T-Drive两篇
1）Yuan J,Zheng Y,Xie X,et al. T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence[J]. IEEE Trans. Knowl. Data Eng.,2013,25(1): 220-232.
2）Yuan J,Zheng Y,Zhang C,et al. T-drive: driving directions based on taxi trajectories[C]//Proceedings of the 18th SIGSPATIAL International conference on advances in geographic information systems. ACM,2010: 99-108.
16、推断出租车状态，北京数据集600辆，带0&1标识，同样要找数据集。。。
Zhu Y,Zheng Y,Zhang L,et al. Inferring taxi status using gps trajectories[J]. arXiv preprint arXiv:1205.4378,2012.
17、基于云的最快路径，北京数据 & 新加坡数据
Yuan J,Zheng Y,Xie X,et al. Driving with knowledge from the physical world[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2011: 316-324.
18、出租车目的地预测
De Brébisson A,Simon É,Auvolat A,et al. Artificial neural networks applied to taxi destination prediction[J]. arXiv preprint arXiv:1508.00021,2015.
19、广告投放平台
Liu D, Weng D, Li Y, et al. Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations[J]. IEEE transactions on visualization and computer graphics, 2017, 23(1): 1-10.
20、所要实现的核心论文基线：牛老师论文，成都数据
Niu K,Cheng C,Jielin C,et al. Real-Time Taxi-Passenger Prediction with L-CNN[J]. IEEE Transactions on Vehicular Technology,2018.
21、地图可视化应用参考
Zhang J,Zheng Y,Qi D,et al. DNN-based prediction model for spatio-temporal data[C]//Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM,2016: 92.
22、路径规划，客户端和服务器的图，南京数据
Zhao D,Stefanakis E. Mining massive taxi trajectories for rapid fastest path planning in dynamic multi-level landmark network[J]. Computers,Environment and Urban Systems,2018,72: 221-231.
23、挖掘美食地点，geolife数据，很创新...
Wei Q,She J,Zhang S,et al. Using individual GPS trajectories to explore foodscape exposure: A case study in Beijing metropolitan area[J]. International journal of environmental research and public health,2018,15(3): 405.
24、利用时空网格，减少出租车巡航时间，增加盈利
Powell J W,Huang Y,Bastani F,et al. Towards reducing taxicab cruising time using spatio-temporal profitability maps[C]//International Symposium on Spatial and Temporal Databases. Springer,Berlin,Heidelberg,2011: 242-260.
25、Ge Y这老哥的三篇，商用导航系统，绕路欺诈检测，挺厉害，
挖掘潜在载客点，最小化载客路线距离，旧金山30天500辆车
1）Ge Y,Xiong H,Tuzhilin A,et al. An energy-efficient mobile recommender system[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2010: 899-908.
2）Ge Y,Xiong H,Liu C,et al. A taxi driving fraud detection system[C]//Data Mining(ICDM),2011 IEEE 11th International Conference on. IEEE,2011: 181-190.
3）Ge Y,Liu C,Xiong H,et al. A taxi business intelligence system[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2011: 735-738.
26、比较早的2008文章，济州岛数据
Lee J,Shin I,Park G L. Analysis of the passenger pick-up pattern for taxi location recommendation[C]//Networked Computing and Advanced Information Management,2008. NCM'08. Fourth International Conference on. IEEE,2008,1: 199-204.
27、2009年北京30天数据集介绍，说明T-Drive数据集日期跨度短的缺点（对方邮件回复啦）
Lian J, Zhang L. One-month beijing taxi GPS trajectory dataset with taxi IDs and vehicle status[C]//Proceedings of the First Workshop on Data Acquisition To Analysis. ACM, 2018: 3-4.
28、还是北京数据集，带载客标识
Jiang W, Zhang L. The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data[J]. IEEE Access, 2018, 6: 12438-12450.
29、地图匹配
Yuan J, Zheng Y, Zhang C, et al. An interactive-voting based map matching algorithm[C]//Proceedings of the 2010 Eleventh International Conference on Mobile Data Management. IEEE Computer Society, 2010: 43-52.
30、地图分割
Yuan N J, Zheng Y, Xie X. Segmentation of urban areas using road networks[J]. MSR-TR-2012–65, Tech. Rep., 2012.
31、2008年，数据集信息相当全，台北两个月5辆车的
Chang H, Tai Y, Chen H, et al. iTaxi: Context-aware taxi demand hotspots prediction using ontology and data mining approaches[J]. Proc. of TAAI, 2008.
32、上海3个月数据，8000辆车，hunting is better than waiting
Gao Y, Xu P, Lu L, et al. Visualization of taxi drivers’ income and mobility intelligence[C]//International Symposium on Visual Computing. Springer, Berlin, Heidelberg, 2012: 275-284.
33、2013年新加坡兄弟的，2个月10,000辆车，交通异常检测
Sen R, Balan R K. Challenges and opportunities in taxi fleet anomaly detection[C]//Proceedings of First International Workshop on Sensing and Big Data Mining. ACM, 2013: 1-6.
34、2011年郑宇文章，构建异常因果关系树，交通路网的潜在流量监测，北京6个月33,000辆
Liu W, Zheng Y, Chawla S, et al. Discovering spatio-temporal causal interactions in traffic data streams[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011: 1010-1018.
35、交通影像数据，深度卷积网络
Li Y, Ge R, Ji Y, et al. Trajectory-pooled Spatial-temporal Architecture of Deep Convolutional Neural Networks for Video Event Detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017.
36、贵阳数据集，部署为Web平台，界面可参考
Li R, Ruan S, Bao J, et al. Efficient Path Query Processing over Massive Trajectories on the Cloud[J]. IEEE Transactions on Big Data, 2018.
37、北京3个月数据，多种标识
Jing W, Hu L, Shu L, et al. RPR: recommendation for passengers by roads based on cloud computing and taxis traces data[J]. Personal and Ubiquitous Computing, 2016, 20(3): 337-347.
38、南京一个月数据集，采用极限学习机ELM
Wang R, Chow C Y, Lyu Y, et al. Taxirec: recommending road clusters to taxi drivers using ranking-based extreme learning machines[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(3): 585-598.
39、北京5个月数据集，基于公交车轨迹，预测未来交通事件
Aoki S, Sezaki K, Yuan N J, et al. BusBeat: Early Event Detection with Real-Time Bus GPS Trajectories[J]. IEEE Transactions on Big Data, 2018.
40、交通拥堵的危害引自本文，日本部分城市数据
Song X, Kanasugi H, Shibasaki R. DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level[C]//IJCAI. 2016, 16: 2618-2624.
41、微软，摩拜自行车，道路规划问题，上海单车数据
Bao J, He T, Ruan S, et al. Planning bike lanes based on sharing-bikes' trajectories[C]//Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2017: 1377-1386.
42、仉尚航女士的学术报告，城市摄像头的交通流量计数
Zhang S, Wu G, Costeira J P, et al. Fcn-rlstm: Deep spatio-temporal neural networks for vehicle counting in city cameras[C]//Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017: 3687-3696.
43、预测道路障碍 & 识别障碍类型（台风、洪水等），厦门2016后半年的出租车轨迹数据，频率1分钟；
融合气象数据集、风速、降雨降水，谷歌地图获取的路边树木覆盖标签，人群感应平台
Chen L, Fan X, Wang L, et al. RADAR: Road Obstacle Identification for Disaster Response Leveraging Cross-Domain Urban Data[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 1(4): 130.
44、目的地预测的最新论文啦啦啦~ 仅使用到5组5组的起点终点坐标对，kaggle比赛波尔图数据集，模型为带注意力机制的LSTM模型（“represent each location as a single word”）
Rossi A, Barlacchi G, Bianchini M, et al. Modeling Taxi Drivers' Behaviour for the Next Destination Prediction[J]. arXiv preprint arXiv:1807.08173, 2018.
45、目的地预测相关联的上车点预测，依赖起点终点，纽约数据
Smith A W, Kun A L, Krumm J. Predicting taxi pickups in cities: which data sources should we use?[C]//Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 2017: 380-387.
46、运用ARIMA（整合滑动平均自回归）模型，预测热点位置乘客的PUQ时空变化，杭州1年数据集
Li X, Pan G, Wu Z, et al. Prediction of urban human mobility using large-scale taxi traces and its applications[J]. Frontiers of Computer Science, 2012, 6(1): 111-121.
47、线性加权分布，武汉数据
唐炉亮, 郑文斌, 王志强, 等. 城市出租车上下客的 GPS 轨迹时空分布探测方法[J]. 地球信息科学学报, 2015, 17(10): 1179-1186.
48、热点路段图，选取前top-k个路段，深圳数据
戚欣, 梁伟涛, 马勇. 基于出租车轨迹数据的最优路径规划方法[J]. 计算机应用, 2017, 37(7): 2106-2113.
49、乘客候车时间，杭州数据
齐观德, 潘遥, 李石坚, 等. 基于出租车轨迹数据挖掘的乘客候车时间预测[J]. 软件学报, 2013, 24(S2): 14-23.
50、网格15*11，上海市数据，CSDN已下载
孙冠东, 张兵, 刘禹岍, 等. 基于载客数据的出租车热门区域功能发现[J]. 计算机工程, 2017, 34(5): 16-22.
51、网格！！！最全面网格定义，重庆数据
郑林江, 赵欣, 蒋朝辉, 等. 基于出租车轨迹数据的城市热点出行区域挖掘[J]. 计算机应用与软件, 2018, 1: 002.
52、实时的载客点评估标准，北京数据带载客标识，已联系吴老师，被拒绝。。。
1）吴涛, 韩星, 刘薇. 基于数据流聚类的出租车载客点实时推荐算法[J]. 软件导刊, 2017 (2): 77-80.
2）吴涛, 毛嘉莉, 谢青成, 等. 基于实时路况的 top-k 载客热门区域推荐[J]. 华东师范大学学报 (自然科学版), 2017, 2017(5): 186-200.
53、背景介绍可用，文章3页，北京2009年三天数据集
王亚飞, 杨卫东, 徐振强. 基于出租车轨迹的载客热点挖掘[J]. 信息与电脑 (理论版), 2017, 16: 054.
54、湖南科技大学，连续两年毕设！参考语言，geolife数据集
1）张明月. 基于出租车轨迹的载客点与热点区域推荐[D]. 长沙: 湖南科技大学, 2013.
2）李衢伶. 基于 GPS 轨迹的出租车载客路径智能推荐[D]. 长沙: 湖南科技大学, 2014.
55、背景介绍可用
吕红瑾, 夏士雄, 杨旭, 等. 基于区域划分的出租车统一推荐算法[J]. 计算机应用, 2016, 36(8): 2109-2113.
56、同样使用成都数据（2014年8月3~4号）
李雪丽, 盛勇, 兰小机. 基于 Spark 的并行化出租车轨迹热点区域提取与分析 Extraction and Analysis of Hotspot Region of Parallel Taxi Trajectory Based on Spark[J]. Computer Science and Application, 2018, 8(09): 1482.
