2019.1月

0窖铡、張德升的最新投稿VeMo文章壳快，基于高速公路場景的ETC訂單數(shù)據(jù)

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、同樣來自張德升早先的工作岸蜗，450G的深圳數(shù)據(jù)拗小，通過估測候車時間，預(yù)測基于路段單位的乘客需求

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徒仓、陳德彪推薦的文章??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

參考文獻(xiàn)

0、城市計算綜述（55頁全）

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誊垢、經(jīng)典推薦2010年掉弛，舊金山數(shù)據(jù)

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症见、kmeans聚類

Atev S,Miller G,Papanikolopoulos N P. Clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems,2010,11(3): 647-657.

3、找社區(qū)平臺的旅行伙伴殃饿，geolife項目上的

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谋作、還是Geolife項目，挖掘興趣位置

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乎芳、波爾圖441輛出租車

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遵蚜、缺陷城市規(guī)劃

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、殘差網(wǎng)絡(luò)預(yù)測流量奈惑，網(wǎng)格地圖思想

Zhang J,Zheng Y,Qi D. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction[C]//AAAI. 2017: 1655-1661.

8吭净、推斷行程時間

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、順風(fēng)車

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肴甸、軌跡數(shù)據(jù)挖掘概述寂殉，科普類型書籍

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輛車原在，杭州數(shù)據(jù)集友扰，特征工程，沒用過多軌跡信息庶柿，SVM機(jī)器學(xué)習(xí)

輸出：是否停泊等待或者巡航村怪，以及how far in distance距離

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、湖南科技大學(xué)兩篇畢設(shè)后的英文浮庐，geolife數(shù)據(jù)实愚，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、微軟亞研院兔辅，乘客雙向推薦

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腊敲、空出租車推薦，北京12,000輛维苔，找數(shù)據(jù)集啊啊啊啊碰辅。。介时。

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没宾、微軟T-Drive兩篇

? ? 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、推斷出租車狀態(tài)沸柔，北京數(shù)據(jù)集600輛循衰，帶0&1標(biāo)識，同樣要找數(shù)據(jù)集褐澎。会钝。。

Zhu Y,Zheng Y,Zhang L,et al. Inferring taxi status using gps trajectories[J]. arXiv preprint arXiv:1205.4378,2012.

17、基于云的最快路徑迁酸，北京數(shù)據(jù) & 新加坡數(shù)據(jù)

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先鱼、出租車目的地預(yù)測

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、廣告投放平臺

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奸鬓、所要實現(xiàn)的核心論文基線：牛老師論文焙畔，成都數(shù)據(jù)

Niu K,Cheng C,Jielin C,et al. Real-Time Taxi-Passenger Prediction with L-CNN[J]. IEEE Transactions on Vehicular Technology,2018.

21、地圖可視化應(yīng)用參考

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串远、路徑規(guī)劃宏多，客戶端和服務(wù)器的圖，南京數(shù)據(jù)

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澡罚、挖掘美食地點绷落，geolife數(shù)據(jù)，很創(chuàng)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始苇、利用時空網(wǎng)格砌烁，減少出租車巡航時間，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催式、Ge Y這老哥的三篇函喉，商用導(dǎo)航系統(tǒng)，繞路欺詐檢測荣月，挺厲害管呵，

挖掘潛在載客點，最小化載客路線距離哺窄，舊金山30天500輛車

????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捐下、比較早的2008文章，濟(jì)州島數(shù)據(jù)

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萌业、2009年北京30天數(shù)據(jù)集介紹坷襟，說明T-Drive數(shù)據(jù)集日期跨度短的缺點（對方郵件回復(fù)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、還是北京數(shù)據(jù)集生年，帶載客標(biāo)識

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婴程、地圖匹配

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、地圖分割

Yuan N J, Zheng Y, Xie X. Segmentation of urban areas using road networks[J]. MSR-TR-2012–65, Tech. Rep., 2012.

31抱婉、2008年档叔，數(shù)據(jù)集信息相當(dāng)全，臺北兩個月5輛車的

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蒸绩、上海3個月數(shù)據(jù)衙四，8000輛車，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患亿、2013年新加坡兄弟的传蹈，2個月10,000輛車，交通異常檢測

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年鄭宇文章卡睦，構(gòu)建異常因果關(guān)系樹宴胧，交通路網(wǎng)的潛在流量監(jiān)測漱抓，北京6個月33,000輛

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表锻、交通影像數(shù)據(jù)，深度卷積網(wǎng)絡(luò)

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乞娄、貴陽數(shù)據(jù)集瞬逊，部署為Web平臺，界面可參考

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仪或、北京3個月數(shù)據(jù)确镊，多種標(biāo)識

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、南京一個月數(shù)據(jù)集范删，采用極限學(xué)習(xí)機(jī)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蕾域、北京5個月數(shù)據(jù)集，基于公交車軌跡到旦，預(yù)測未來交通事件

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旨巷、交通擁堵的危害引自本文，日本部分城市數(shù)據(jù)

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添忘、微軟采呐，摩拜自行車，道路規(guī)劃問題搁骑，上海單車數(shù)據(jù)

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斧吐、仉尚航女士的學(xué)術(shù)報告，城市攝像頭的交通流量計數(shù)

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仲器、預(yù)測道路障礙 & 識別障礙類型（臺風(fēng)煤率、洪水等），廈門2016后半年的出租車軌跡數(shù)據(jù)乏冀，頻率1分鐘涕侈；

融合氣象數(shù)據(jù)集、風(fēng)速煤辨、降雨降水裳涛，谷歌地圖獲取的路邊樹木覆蓋標(biāo)簽，人群感應(yīng)平臺

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众辨、目的地預(yù)測的最新論文啦啦啦~ 僅使用到5組5組的起點終點坐標(biāo)對端三，kaggle比賽波爾圖數(shù)據(jù)集，模型為帶注意力機(jī)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鹃彻、目的地預(yù)測相關(guān)聯(lián)的上車點預(yù)測郊闯，依賴起點終點，紐約數(shù)據(jù)

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、運用ARIMA（整合滑動平均自回歸）模型团赁，預(yù)測熱點位置乘客的PUQ時空變化育拨，杭州1年數(shù)據(jù)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、線性加權(quán)分布欢摄，武漢數(shù)據(jù)

唐爐亮, 鄭文斌, 王志強(qiáng), 等. 城市出租車上下客的 GPS 軌跡時空分布探測方法[J]. 地球信息科學(xué)學(xué)報, 2015, 17(10): 1179-1186.

48熬丧、熱點路段圖，選取前top-k個路段怀挠，深圳數(shù)據(jù)

戚欣, 梁偉濤, 馬勇. 基于出租車軌跡數(shù)據(jù)的最優(yōu)路徑規(guī)劃方法[J]. 計算機(jī)應(yīng)用, 2017, 37(7): 2106-2113.

49析蝴、乘客候車時間，杭州數(shù)據(jù)

齊觀德, 潘遙, 李石堅, 等. 基于出租車軌跡數(shù)據(jù)挖掘的乘客候車時間預(yù)測[J]. 軟件學(xué)報, 2013, 24(S2): 14-23.

50绿淋、網(wǎng)格15*11闷畸，上海市數(shù)據(jù)，CSDN已下載

孫冠東, 張兵, 劉禹岍, 等. 基于載客數(shù)據(jù)的出租車熱門區(qū)域功能發(fā)現(xiàn)[J]. 計算機(jī)工程, 2017, 34(5): 16-22.

51吞滞、網(wǎng)格Ｓ悠小！裁赠！最全面網(wǎng)格定義殿漠，重慶數(shù)據(jù)

鄭林江, 趙欣, 蔣朝輝, 等. 基于出租車軌跡數(shù)據(jù)的城市熱點出行區(qū)域挖掘[J]. 計算機(jī)應(yīng)用與軟件, 2018, 1: 002.

52、實時的載客點評估標(biāo)準(zhǔn)组贺，北京數(shù)據(jù)帶載客標(biāo)識凸舵，已聯(lián)系吳老師，被拒絕失尖。啊奄。。

????1）吳濤, 韓星, 劉薇. 基于數(shù)據(jù)流聚類的出租車載客點實時推薦算法[J]. 軟件導(dǎo)刊, 2017 (2): 77-80.

????2）吳濤, 毛嘉莉, 謝青成, 等. 基于實時路況的 top-k 載客熱門區(qū)域推薦[J]. 華東師范大學(xué)學(xué)報 (自然科學(xué)版), 2017, 2017(5): 186-200.

53掀潮、背景介紹可用菇夸，文章3頁，北京2009年三天數(shù)據(jù)集

王亞飛, 楊衛(wèi)東, 徐振強(qiáng). 基于出租車軌跡的載客熱點挖掘[J]. 信息與電腦 (理論版), 2017, 16: 054.

54仪吧、湖南科技大學(xué)庄新，連續(xù)兩年畢設(shè)！參考語言薯鼠，geolife數(shù)據(jù)集

????1）張明月. 基于出租車軌跡的載客點與熱點區(qū)域推薦[D]. 長沙: 湖南科技大學(xué), 2013.

????2）李衢伶. 基于 GPS 軌跡的出租車載客路徑智能推薦[D]. 長沙: 湖南科技大學(xué), 2014.

55择诈、背景介紹可用

呂紅瑾, 夏士雄, 楊旭, 等. 基于區(qū)域劃分的出租車統(tǒng)一推薦算法[J]. 計算機(jī)應(yīng)用, 2016, 36(8): 2109-2113.

56、同樣使用成都數(shù)據(jù)（2014年8月3~4號）

李雪麗, 盛勇, 蘭小機(jī). 基于 Spark 的并行化出租車軌跡熱點區(qū)域提取與分析 Extraction and Analysis of Hotspot Region of Parallel Taxi Trajectory Based on Spark[J]. Computer Science and Application, 2018, 8(09): 1482.