2023 KDD论文接收情况：Research track（研究赛道）接收率：22.1%（313/1416），ADS Track（应用数据科学赛道）接收率：25.4%（184/725）

（蹭一下KDD 2024第一轮Rebuttal的热度，祝大家都Rebuttal顺利）

本文总结了在两个赛道时空数据学习的相关论文（如有疏漏，欢迎大家补充），ADS Track在次条

Research track中有3个session中与时空数据（城市计算）紧密相关，还有一些其余session中有一些做的时空数据任务。

Research Track Topic：时空预测，信控优化，轨迹表示学习，多模态，神经过程，迁移学习等
ADS track中有2个session中与时空数据（城市计算）紧密相关，还有一些其余session中有一些做的时空数据任务。
ADS Track Topic：交通模拟，多模态数据，ETA，物流外卖配送，强化学习，交通预测，生成模型等。

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目录

Spatiotemporal Data

1. Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning

2. Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning

3. Localised Adaptive Spatial-Temporal Graph Neural Network

4. Spatio-Temporal Diffusion Point Processes

5. ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM

6. On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness

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Urban Data Ⅰ

7. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
8. Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction
9. TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Mechanism via Gated Transformer
10. Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference
11. Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning
12. Spatial Heterophily Aware Graph Neural Networks

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Urban Data Ⅱ

13. LightPath: Lightweight and Scalable Path Representation Learning
14. Urban Region Representation Learning with OpenStreetMap Building Footprints
15. Multi-Temporal Relationship Inference in Urban Areas
16. A Study of Situational Reasoning for Traffic Understanding
17. Frigate: Frugal Spatio-temporal Forecasting on Road Networks

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其他

18. Graph Neural Processes for Spatio-Temporal Extrapolation
19. Deep Bayesian Active Learning for Accelerating Stochastic Simulation
20. Generative Causal Interpretation Model for Spatio-Temporal Representation Learning
21. MM-DAG: Multi-task DAG Learning for Multi-Modal Data with Application for Traffic Congestion Analysis
22. Transferable Graph Structure Learning for Graph-Based Traffic Forecasting Across Cities

Research Track

Spatiotemporal Data

1. Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599421

代码：https://github.com/zzyy0929/KDD23-CauSTG

作者：Zhengyang Zhou (University of Science and Technology of China), Qihe Huang (University of Science and Technology of China), Kuo Yang (University of Science and Technology of China), Kun Wang (University of Science and Technology of China), Xu Wang (University of Science and Technology of China), Yudong Zhang (University of Science and Technology of China), Yuxuan Liang (University of Science and Technology of China), Yang Wang (University of Science and Technology of China)

关键词：分布外泛化，时空OOD，因果学习，不变学习，动态图

2. Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599360

代码：https://github.com/microsoft/robustlearn

作者：Xin Qin (Beijing Key Lab. of Mobile Com., CAS), Jindong Wang (Microsoft Research Asia), Shuo Ma (Beijing Key Lab. of Mobile Com., CAS), Wang Lu (Beijing Key Lab. of Mobile Com., CAS), Yongchun Zhu (Beijing Key Lab. of Mobile Com., CAS), Xin Xie (Microsoft Research Asia), Yiqiang Chen (Beijing Key Lab. of Mobile Com., CAS)

关键词：普适计算，迁移学习

3. Localised Adaptive Spatial-Temporal Graph Neural Network

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599418

作者：Wenying Duan (Nanchang University), Xiaoxi He (University of Macau), Zimu Zhou (City University of Hong Kong), Lothar Thiele (ETH Zurich), Hong Rao (Nanchang University)

关键词：时空预测，时空图神经网络，稀疏图

4. Spatio-Temporal Diffusion Point Processes

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599511

代码：https://github.com/tsinghua-fib-lab/Spatio-temporal-Diffusion-Point-Processes

作者：Yuan Yuan (Department of Electronic Engineering, Tsinghua University), Jingtao Ding (Department of Electronic Engineering, Tsinghua University), Chenyang Shao (Department of Electronic Engineering, Tsinghua University), Depeng Jin (Department of Electronic Engineering, Tsinghua University), Yong Li (Department of Electronic Engineering, Tsinghua University)

关键词：扩散模型，点过程

5. ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599513

代码：https://github.com/mhu3/ST-Siamese-Attack

作者：Mingzhi Hu (Worcester Polytechnic Institute), Xin Zhang (Worcester Polytechnic Institute), Yanhua Li (Worcester Polytechnic Institute), Xun Zhou (University of Iowa), Jun Luo (Lenovo Group Limited)

关键词：稳健性，对抗攻击，驾驶员检测，异常检测

6. On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599448

作者：Jiayi Chen (University of Virginia), Aidong Zhang (University of Virginia)

关键词：多模态时空数据，无监督学习，知识表示和推理，时空解耦，缺失数据，自编码器

Urban Data I

7. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599492

代码：https://github.com/usail-hkust/RDAT

作者：Fan Liu (The Hong Kong University of Science and Technology (Guangzhou)), Weijia Zhang (The Hong Kong University of Science and Technology (Guangzhou)), Hao Liu (The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology)

关键词：交通预测、对抗网络，稳健性

8. Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599463

作者：Binwu Wang (University of Science and Technology of China), Yudong Zhang (University of Science and Technology of China), Xu Wang (University of Science and Technology of China), Pengkun Wang (Suzhou Institute for Advanced Research, University of Science and Technology of China), Zhengyang Zhou (Suzhou Institute for Advanced Research, University of Science and Technology of China), LEI BAI (Shanghai AI Laboratory), Yang Wang (University of Science and Technology of China)

关键词：交通预测、持续学习

9. TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Mechanism via Gated Transformer

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599530

代码：https://github.com/Smart-Trafficlab/TransformerLight

作者：Qiang Wu (University of Electronic Science and Technology of China), Mingyuan Li (Beijing University of Posts and Telecommunications), Jun Shen (University of Wollongong), Linyuan Lü(University of Science and Technology of China), Bo Du (University of Wollongong), Ke Zhang (Beijing University of Posts and Telecommunications)

关键词：信控优化

解读：https://mp.weixin.qq.com/s/3CSCGMOm8xhMOpny0EeNQQ

10. Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599459

代码：https://github.com/siddarth-c/KDD23-ADAC

作者：Mayuresh Kunjir (Amazon Web Services), Sanjay Chawla (Qatar Computing Research Institute, Hamad Bin Khalifa University), Siddarth Chandrasekar (Indian Institute of Technology Madras), Devika Jay (Indian Institute of Technology Madras), Balaraman Ravindran (Indian Institute of Technology Madras)

关键词：信控优化，离线强化学习

11. Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599528

作者：Xiao Han (City University of Hong Kong), Xiangyu Zhao (City University of Hong Kong), Liang Zhang (Shenzhen Research Institute of Big Data), Wanyu Wang (City University of Hong Kong)

关键词：信控优化，交通状态预测

解读:https://mp.weixin.qq.com/s/F4DDGaabm6Yfs3j5_CSCCg

12. Spatial Heterophily Aware Graph Neural Networks

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599510

代码：https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/SHGNN

作者：Congxi Xiao (University of Science and Technology of China; Baidu Research), Jingbo Zhou (Baidu Research), Jizhou Huang (Baidu Inc.), Tong Xu (University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence), Hui Xiong (The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology)

关键词：空间异质性、时空预测

Urban Data II

13. LightPath: Lightweight and Scalable Path Representation Learning

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599415

作者：Sean Bin Yang (Aalborg University), Jilin Hu (East China Normal University), Chenjuan Guo (East China Normal University), Bin Yang (East China Normal University), Christian Jensen (Aalborg University)

关键词：轨迹表示学习，自监督学习，轻量化

14. Urban Region Representation Learning with OpenStreetMap Building Footprints

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599538

作者：Yi Li (Nanyang Technological University), Weiming Huang (Nanyang Technological University), Gao Cong (Nanyang Technological University), Hao Wang (Nanyang Technological University), Zheng Wang (Nanyang Technological University)

关键词：表示学习，对比学习，OpenStreetMap，城市区域，地理数据挖掘

15. Multi-Temporal Relationship Inference in Urban Areas

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599440

作者：Shuangli Li (University of Science and Technology of China; Baidu Research), Jingbo Zhou (Baidu Research), Ji Liu (Baidu Research), Tong Xu (University of Science and Technology of China; State Key Laboratory of Cognitive Intelligenc), Enhong Chen (University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence), Hui Xiong (The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology)

关键词：关系推断，空间图

16. A Study of Situational Reasoning for Traffic Understanding

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599246

作者：Jiarui Zhang (USC/ISI), Filip Ilievski (USC/ISI), Kaixin Ma (CMU), Aravinda Kollaa (USC/ISI), Jonathan Francis (Bosch), Alessandro Oltramari (Bosch)

关键词：问答模型、交通知识理解

17. Frigate: Frugal Spatio-temporal Forecasting on Road Networks

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599357

代码：https://github.com/idea-iitd/Frigate

作者：Mridul Gupta (Indian Institute of Technology Delhi), Hariprasad Kodamana (Indian Institute of Technology Delhi), Sayan Ranu (Indian Institute of Technology Delhi)

关键词：交通预测

解读：https://mp.weixin.qq.com/s/EjwWCRqmS5eZY4Q_Ue1aXQ

其他

18. Graph Neural Processes for Spatio-Temporal Extrapolation

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599372

代码：https://github.com/hjf1997/STGNP

作者：Junfeng Hu (National University of Singapore), Yuxuan Liang (Hong Kong University of Science and Technology (Guangzhou)), Zhencheng Fan (University of Technology Sydney), Hongyang Chen (Zhejiang Lab), Yu Zheng (JD Intelligent Cities Research; JD iCity, JD Technology), Roger Zimmermann (National University of Singapore)

关键词：不确定性量化、神经过程、时空外推

19. Deep Bayesian Active Learning for Accelerating Stochastic Simulation

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599300

代码：https://github.com/Rose-STL-Lab/Interactive-Neural-Process

作者：Dongxia Wu (University of California, San Diego), Ruijia Niu (University of California, San Diego), Matteo Chinazzi (Northeastern University), Alessandro Vespignani (Northeastern University), Yi-An Ma (University of California, San Diego), Rose Yu (University of California, San Diego)

关键词：不确定性量化、神经过程，贝叶斯主动学习

20. Generative Causal Interpretation Model for Spatio-Temporal Representation Learning

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599363

代码：https://github.com/EternityZY/GCIM

作者：Yu Zhao (Beihang University), Pan Deng (Beihang University), Junting Liu (Beihang University), Xiaofeng Jia (Beijing Big Data Centre), Jianwei Zhang (Capinfo Company Limited)

关键词：生成因果模型、时空表示学习

21. MM-DAG: Multi-task DAG Learning for Multi-Modal Data with Application for Traffic Congestion Analysis

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599436

代码：https://github.com/Lantian72/MM-DAG

作者：Tian Lan (Tsinghua University), Ziyue Li (University of Cologne), zhishuai Li (SenseTime Research), Lei Bai (Shanghai AI Laboratory), Man Li (The Hong Kong University of Science and Technology), Fugee Tsung (The Hong Kong University of Science and Technology (Guangzhou)), Wolfgang Ketter (University of Cologne), Rui Zhao (SenseTime Research), Chen Zhang (Tsinghua University)

关键词：因果学习、交通拥堵，有向无环图，多任务学习，多模态数据

22.Transferable Graph Structure Learning for Graph-Based Traffic Forecasting Across Cities

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599529

作者：Yilun Jin (Hong Kong University of Science and Technology), Kai Chen (Hong Kong University of Science and Technology), Qiang Yang (Hong Kong University of Science and Technology; WeBank)

关键词：迁移学习，交通预测

ADS Track

ADS track中有2个session中与时空数据（城市计算）紧密相关，还有一些其余session中有一些做的时空数据任务。

Transportation I

23. CBLab: Supporting the Training of Large-Scale Traffic Control Policies with Scalable Traffic Simulation

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599789

代码：https://github.com/caradryanl/CityBrainLab

作者：Chumeng Liang (Shanghai Jiao Tong University), Zherui Huang (Shanghai Jiao Tong University), Yicheng Liu (Shanghai Jiao Tong University), Zhanyu Liu (Shanghai Jiao Tong University), Guanjie Zheng (Shanghai Jiao Tong University), Hanyuan Shi (Independent Researchers), Kan Wu (Research Center for Intelligent Transportation, Zhejiang Lab), Yuhao Du (Independent Researchers), FULIANG LI (Baidu), Zhenhui Jessie Li (Yunqi Academy of Engineering)

关键词：信控优化，交通模拟，大规模数据

24. M3PT: A Multi-Modal Model for POI Tagging

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599862

代码：https://github.com/DeqingYang/M3PT

作者：Jingsong Yang (Fudan University), Guanzhou Han (Alibaba Group), Deqing Yang (Fudan University), Jingping Liu (East China University of Science and Technology), Yanghua Xiao (Fudan University), Xiang Xu (Alibaba Group), Baohua Wu (Alibaba Group), Shenghua Ni (Alibaba Group)

关键词：多模态、POI、POI标记

25. Understanding the Semantics of GPS-Based Trajectories for Road Closure Detection

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599926

作者：Jiasheng Zhang (University of Electronic Science and Technology of China), Kaiqiang An (Didi Chuxing Technology Co.), Guoping Liu (Didi Chuxing Technology Co.), Xiang Wen (Didi Chuxing Technology Co.), Runbo Hu (Didi Chuxing Technology Co.), Jie Shao (University of Electronic Science and Technology of China)

关键词：封闭道路检测、对比学习

26. A Data-Driven Region Generation Framework for Spatiotemporal Transportation Service Management

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599760

作者：Liyue Chen (Peking University), Jiangyi Fang (Huazhong University of Science and Technology), Zhe Yu (DiDi Chuxing), Yongxin Tong (Beihang University), Shaosheng Cao (DiDi Chuxing), Leye Wang (Peking University)

关键词：出行服务、空间数据管理

27. Hierarchical Reinforcement Learning for Dynamic Autonomous Vehicle Navigation at Intelligent Intersections

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599839

作者：Qian Sun (The Hong Kong University of Science and Technology), Le Zhang (Baidu Research), Huan Yu (The Hong Kong University of Science and Technology(Guangzhou); The Hong Kong University of Science and Technology), Weijia Zhang (The Hong Kong University of Science and Technology(Guangzhou)), Yu Mei (Baidu Inc.), Hui Xiong (The Hong Kong University of Science and Technology(Guangzhou); The Hong Kong University of Science and Technology)

关键词：信控优化、多智能体强化学习，动态车辆导航

28. Road Planning for Slums via Deep Reinforcement Learning

链接：https://dl.acm.org/doi/10.1145/3580305.3599901

代码：https://github.com/tsinghua-fib-lab/road-planning-for-slums

作者：Yu Zheng (Department of Electronic Engineering, BNRist, Tsinghua University), Hongyuan Su (Department of Electronic Engineering, BNRist, Tsinghua University), Jingtao Ding (Department of Electronic Engineering, BNRist, Tsinghua University), Depeng Jin (Department of Electronic Engineering, BNRist, Tsinghua University), Yong Li (Department of Electronic Engineering, BNRist, Tsinghua University)

关键词：路径规划，贫民窟改造

29. Large-Scale Urban Cellular Traffic Generation via Knowledge-Enhanced GANs with Multi-Periodic Patterns

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599853

代码;https://github.com/shirdy/TrafficGeneration/tree/master/Urban/

作者：Shuodi Hui (Tsinghua University), Huandong Wang (Tsinghua University), Tong Li (Tsinghua University), Xinghao Yang (Tsinghua University), Xing Wang (China Mobile Research Institute), Junlan Feng (China Mobile Research Institute), Lin Zhu (China Mobile Research Institute), Chao Deng (China Mobile Research Institute), Pan Hui (Hong Kong University of Science and Technology), Depeng Jin (Tsinghua University), Yong Li (Tsinghua University)

关键词：蜂窝流量、知识图谱、GAN

Transportation II

30. SAInf: Stay Area Inference of Vehicles using Surveillance Camera Records

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599952

作者：Zhipeng Ma (Southwest Jiaotong University; JD iCity, JD Technology), Chuishi Meng (JD iCity, JD Technology), Huimin Ren (JD iCity, JD Technology), Sijie Ruan (Beijing Institute of Technology), Jie Bao (JD iCity, JD Technology), Xiaoting Wang (JD iCity, JD Technology), Tianrui Li (Southwest Jiaotong University), Yu Zheng (JD iCity, JD Technology)

关键词：轨迹数据挖掘、停留事件检测

31. Uncertainty-Aware Probabilistic Travel Time Prediction for On-Demand Ride-Hailing at DiDi

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599925

作者：Hao Liu (The Hong Kong University of Science and Technology (Guangzhou)), Wenzhao Jiang (The Hong Kong University of * Science and Technology (Guangzhou)), Shui Liu (Didichuxing Co. Ltd), Xi Chen (Didichuxing Co. Ltd)

关键词：不确定性、ETA、概率预测

32. QTNet: Theory-Based Queue Length Prediction for Urban Traffic

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599890

作者：Ryu Shirakami (Sumitomo Electric System Solutions, Co., Ltd.), Toshiya Kitahara (Sumitomo Electric System Solutions, Co., Ltd.), Koh Takeuchi (Kyoto University), Hisashi Kashima (Kyoto University)

关键词：交通预测，物理指导的深度学习

33. iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation

作者：Jindong Han (The Hong Kong University of Science and Technology), Hao Liu (The Hong Kong University of Science and Technology (Guangzhou); Guangzhou HKUST Fok Ying Tung Research Institute), Shui Liu (Didichuxing Co. Ltd.), Xi Chen (Didichuxing Co. Ltd.), Naiqiang Tan (Didichuxing Co. Ltd.), Hua Chai (Didichuxing Co. Ltd.), Hui Xiong (The Hong Kong University of Science and Technology (Guangzhou) ; Guangzhou HKUST Fok Ying Tung Research Institute)

关键词：增量学习、ETA、知识蒸馏，对抗训练

34. A Preference-Aware Meta-Optimization Framework for Personalized Vehicle Energy Consumption Estimation

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599767

代码：https://github.com/usail-hkust/Meta-Pec

作者：Siqi Lai (The Hong Kong University of Science and Technology (Guangzhou)), Weijia Zhang (The Hong Kong University of Science and Technology (Guangzhou)), Hao Liu (The Hong Kong University of Science and Technology (Guangzhou))

关键词：能量估计、元学习

35. Deep Transfer Learning for City-Scale Cellular Traffic Generation through Urban Knowledge Graph

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599801

作者：Zhang Shiyuan (Tsinghua University), Tong Li (Tsinghua University), Shuodi Hui (Tsinghua University), Guangyu Li (China Mobile Research Institute), Yanping Liang (China Mobile Research Institute), Li Yu (China Mobile Research Institute), Depeng Jin (Tsinghua University), Yong Li (Tsinghua University)

关键词：迁移学习、蜂窝流量，城市知识图谱

36. Practical Synthetic Human Trajectories Generation Based on Variational Point Processes

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599888

作者：Qingyue Long (Department of Electronic Engineering, Tsinghua University), Huandong Wang (Department of Electronic Engineering, Tsinghua University), Tong Li (Department of Electronic Engineering, Tsinghua University), Lisi Huang (China Mobile Research Institute), Kun Wang (China Mobile Research Institute), Qiong Wu (China Mobile Research Institute), Guangyu Li (China Mobile Research Institute), Yanping Liang (China Mobile Research Institute), Li Yu (China Mobile Research Institute), Yong Li (Department of Electronic Engineering, Tsinghua University)

关键词：轨迹生成，VAE

解读：

其他

37. Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599804

代码：https://github.com/tsinghua-fib-lab/KDD2023-ID546-UrbanInfra

作者：Jinzhu Mao (Tsinghua University), Liu Cao (Tsinghua University), Chen Gao (Tsinghua University), Huandong Wang (Tsinghua University), Fan Hangyu (Tsinghua University), Depeng Jin (Tsinghua University), Yong Li (Tsinghua University)

关键词：城市基础设置，强化学习，独立网络

38. ILRoute: A Graph-based Imitation Learning Method to Unveil Riders’ Routing Strategies in Food Delivery Service

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599844

作者：Tao Feng (Tsinghua University), Huan Yan (Tsinghua University), Huandong Wang (Tsinghua University), Wenzhen Huang (Tsinghua University), Yuyang Han (Tsinghua University), Hongsen Liao (Tsinghua University), Jinghua Hao (Tsinghua University), Yong Li (Tsinghua University)

关键词：外卖服务、模仿学习

39. DRL4Route: A Deep Reinforcement Learning Framework for Pick-Up and Delivery Route Prediction

链接：https://dl.acm.org/doi/abs/10.1145/3580305.3599811

代码：https://github.com/maoxiaowei97/DRL4Route

作者：Xiaowei Mao (Beijing Jiaotong University; Cainiao Network), Haomin Wen (Beijing Jiaotong University; Cainiao Network), Hengrui Zhang (Beijing Jiaotong University; Beijing Key Laboratory of Traffic Data Analysis and Mining), Huaiyu Wan (Beijing Jiaotong University; Beijing Key Laboratory of Traffic Data Analysis and Mining), Lixia Wu (Cainiao Network), Jianbin Zheng (Cainiao Network), Haoyuan Hu (Cainiao Network), Youfang Lin (Beijing Jiaotong University; Beijing Key Laboratory of Traffic Data Analysis and Mining)

关键词：物流配送，路线预测，强化学习

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