[TOC]
Origin-Destination Travel Time Oracle for Map-based Services. Yan Lin(BJTU), Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Youfang Lin, Christian S. Jensen. SIGMOD 2024. paper code
Data-Driven Modeling of Urban Traffic Travel Times for Short- and Long-Term Forecasting
When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
Uncertainty-Aware Probabilistic Travel Time Prediction for On-Demand Ride-Hailing at DiDi. Hao Liu(HKUST), Wenzhao Jiang, Shui Liu, Xi Chen. KDD 2023. paper
iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation. Jindong Han(HKUST), Hao Liu, Shui Liu, Xi Chen, Naiqiang Tan, Hua Chai, Hui Xiong. KDD 2023. paper
GBTTE: Graph Attention Network Based Bus Travel Time Estimation. Yuecheng Rong(XJTU), Juntao Yao, Jun Liu, Yifan Fang, Wei Luo, Hao Liu, Jie Ma, Zepeng Dan, Jinzhu Lin, Zhi Wu, Yan Zhang, Chuanming Zhang. CIKM 2023. paper
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps. Jizhou Huang(Baidu Inc.), Zhengjie Huang, Xiaomin Fang, Shikun Feng, Xuyi Chen, Jiaxiang Liu, Haitao Yuan, Haifeng Wang. CIKM 2022. paper
Spatial Semantic Learning for Travel Time Estimation. Yi Xu(BUAA), Leilei Sun, Bowen Du, Liangzhe Han. KSEM 2022. paper
--2021--
SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps. Xiaomin Fang(Baidu Inc.), Jizhou Huang, Fan Wang, Lihang Liu, Yibo Sun, Haifeng Wang. KDD 2021. paper
GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs. Qiang Wang(BUPT), Chen Xu, Wenqi Zhang, Jingjing Li. IEEE Signal Processing Letters 202. paper code
HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering. Oscar LiJen Hsu. 2021. paper code
--2020--
CompactETA: A Fast Inference System for Travel Time Prediction. Kun Fu(Didi Chuxing), Fanlin Meng, Jieping Ye, Zheng Wang. KDD 2020. [paper](CompactETA: A Fast Inference System for Travel Time Prediction)
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival. Huiting Hong(Didi Chuxing), Yucheng Lin, Xiaoqing Yang, Zang Li, Kun Fu, Zheng Wang, Xiaohu Qie, Jieping Ye. KDD 2020. paper code
ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. Xiaomin Fang(Baidu Inc.), Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, Haifeng Wang. KDD 2020. paper
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. Haitao Yuan(THU), Guoliang Li, Zhifeng Bao, Ling Feng. SIGMOD 2020. paper
TTPNet: A Neural Network for Travel Time Prediction Based on Tensor Decomposition and Graph Embedding. Yibin Shen(East China Normal University), Cheqing Jin, Jiaxun Hua. TKDE 2020. paper code
Road Network Metric Learning for Estimated Time of Arrival. Yiwen Sun(THU), Kun Fu, Zheng Wang, Changshui Zhang, Jieping Ye. ICPR 2020. paper
--2019--
Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation. Ning Wu(BUAA), Jingyuan Wang, Wayne Xin Zhao, Yang Jin. CIKM 2019. paper
Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach. Wuwei Lan(OSU), Yanyan Xu, Bin Zhao. IJCAI 2019. paper
Deep-Learning Approach, integrate the trajectory data with morphological layout images and use CNN to learn the travel delay during the query trajectory from gridding images
DeepETA: A Spatial-Temporal Sequential Neural Network Model for Estimating Time of Arrival in Package Delivery System. Fan Wu(Caoniao Ltd.), Lixia Wu. AAAI 2019. paper
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation. Tao-Yang Fu(PSU), Wang-Chien Lee. CIKM 2019. paper
The Phase Abstraction for Estimating Energy Consumption and Travel Times for Electric Vehicle Route Planning. Payas Rajan(UCR), Chinya V. Ravishankar. SIGSPATIAL 2019. paper
A Simple Baseline for Travel Time Estimation using Large-scale Trip Data. Hongjian Wang(Twitter Inc.), Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, Zhenhui Li. ACM TIST 2019. paper
历史均值法, Sub-Path-Based Approaches, use the travel time of neighboring trajectories, a seasonal ARIMA to predict the future travel speed but only the average travel speed in a whole city
STDR: A Deep Learning Method for Travel Time Estimation. Jie Xu(THU), Yong Zhang, Li Chao, Chunxiao Xing. DASFAA 2019. paper
Deep-Learning Approach, partition a trajectory into segments and obtain different representation of segments by an embedding method
Survey of ETA prediction methods in public transport networks. Thilo Reich(Bournemouth University), Marcin Budka, Derek Robbins, David Hulbert. arXiv 2019. paper
Survey
--2018--
Multi-task Representation Learning for Travel Time Estimation. Yaguang Li(USC), Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, Yan Liu. KDD 2018. paper
Deep-Learning Approach, predict the travel time of origin-destination
Learning to Estimate the Travel Time. Zheng Wang(Didi Chuxing), Kun Fu, Jieping Ye. KDD 2018. paper
DEEPTRAVEL: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. Hanyuan Zhang(FDU), Hao Wu, Weiwei Sun, Baihua Zheng. IJCAI 2018. paper
Deep-Learning Approach, partition into grids, a LSTM model to extract travel speed features, a large number of grids lack speed values in a short time interval(the data sparsity), map GPS points to the grids and uses a word embedding method to represent each grid with a low-dimensional vector
Predicting Vehicular Travel Times by Modeling Heterogeneous Influences Between Arterial Roads. Avinash Achar(Tata Consultancy Services), Venkatesh Sarangan, Rohith Regikumar, Anand Sivasubramaniam. AAAI 2018. paper
Sub-Path-Based Approach, introduce a dynamic Bayesian network to model traffic congestion state of various road segments and search for more optimal concatenation of road segments to predict the travel time
When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. Dong Wang(Duke University), Junbo Zhang, Wei Cao, Jian Li, Yu Zheng. AAAI 2018. paper code1 code2
Deep-Learning Approach, a spatio-temporal component to learn the characteristics of going straight and turning based on raw GPS points
Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network. Wenjing Wei(Shandong University), Xiaoyi Jia, Yang Liu, Xiaohui Yu. APWeb-WAIM 2018. paper
Deep-Learning Approach, predict the travel time of each road
Adaptive Travel-Time Estimation: A Case for Custom Predicate Selection. Robert Waury(Aalborg University), Christian S. Jensen, Kristian Torp. IEEE MDM 2018. paper
Sub-Path-Based Approach, split the query trajectory into k sub-paths and search for similar trajectories
An Arrival Time Prediction Method for Bus System. Chi-Hua Chen(FZU). IEEE ITJ 2018. paper
Application of Support Vector Machine in Bus Travel Time Prediction. Zhang Junyou(SDUST), Wang Fanyu, Wang Shufeng. International Journal of Systems Engineering 2018. paper
Research of bus arrival prediction model based on GPS and SVM. Yao Li(Dalian Neusoft University of Information), Chuanlin Huang, Jingjing Jiang. IEEE CCDC 2018. paper
--2017--
A prediction model of bus arrival time at stops with multi-routes. Tingting Yin(SEU), Gang Zhong, Jian Zhang, Shanglu He, Bin Ran. Transportation Research Procedia 2017. paper
Prediction of Bus Travel Time Using Random Forests Based on Near Neighbors. Bin Yu(BUAA), Huaizhu Wang, Wenxuan Shan, Baozhen Yao. Computer-Aided Civil and Infrastructure Engineering 2017. paper
Bus arrival time prediction using mixed multi-route arrival time data at previous stop. Xuedong Hua(SEU), Wei Wang, Yinhai Wang, Min Ren. Transport 2017. paper
Bus arrival time prediction at any distance of bus route using deep neural network model. Wichai Treethidtaphat(NECTEC), Wasan Pattara-Atikom, Sippakorn Khaimook. IEEE ITSC 2017. paper
Traveling time prediction in scheduled transportation with journey segments. Avigdor Gal(Technion – Israel Institute of Technology), Avishai Mandelbaum, François Schnitzler, Arik Senderovich, Matthias Weidlich. Information Systems 2017. paper
Performance Comparison of Data Driven and Less Data Demanding Techniques for Bus Travel Time on Prediction. A Kumar(Indian Institute of Technology Madras), V Kumar, L Vanajakshi, Shankar Subramanian. European Transport 2017. paper
--2016--
Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method. Jingyuan Wang(BUAA), Qian Gu, Junjie Wu, Guannan Liu, Zhang Xiong. IEEE ICDM 2016. paper
Individual TTE method
Real time prediction of bus arrival time: A review. Rubina Choudhary(Lovely Professional University), Aditya Khamparia, Amandeep Kaur Gahier. IEEE NGCT 2016. paper
Survey
--2015--
Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes. Cong Bai(SJTU), Zhong Ren Peng, Qing Chang Lu, Jian Sun. Computational Intelligence and Neuroscience 2015. paper
Prediction of Bus Travel Time Using ANN: A Case Study in Delhi. Johar Amita(CTRANS), Jain Sukhvir Singh, Garg Pradeep Kumar. International Journal for Traffic and Transport Engineering 2015. paper
--2014--
Travel Time Estimation of a Path using Sparse Trajectories. Yilun Wang(MSRA), Yu Zheng, Yexiang Xue. KDD 2014. paper
Individual TTE method, matrix/tensor decomposition to estimate the individual vehicle speed or travel time on different roads
Historical Data based Real Time Prediction of Vehicle Arrival Time. Santa Maiti(Tata Consultancy Services), Arpan Pal, Arindam Pal, Tanushyam Chattopadhyay, Arijit Mukherjee. ITSC 2014. paper
历史均值法
--2013--
Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models. Bin Yang(Aarhus University), Chenjuan Guo, Christian S. Jensen. ACM VLDB 2013. paper
Individual TTE method, Road-Segment-Based Approach, consider the relationship between road segments and use a spatio-temporal Hidden Markov model to obtain the correlations among adjacent roads
Travel time estimation for urban road networks using low frequency probe vehicle data. Erik Jenelius(KTH Royal Institute of Technology), Haris N. Koutsopoulos. Transportation Research Part B: Methodological 2013. paper
Collective TTE method, Road-Segment-Based Approach, consider the travel time of each road segment as a multivariate Gaussian distribution and predict the travel time by maximum likelihood estimation
--2012--
Utilizing Real-World Transportation Data for Accurate Traffic Prediction. Bei Pan(UCLA), Ugur Demiryurek, Cyrus Shahabi. IEEE ICDM 2012. paper
Individual TTE method
--2004--
A Simple and Effective Method for Predicting Travel Times on Freeways. J. Rice(UC Berkeley), E. van Zwet. IEEE TITS 2014. paper
Road-Segment-Based Approach, depend on the loop detectors
Travel-Time Prediction With Support Vector Regression. Chun-Hsin Wu(National University of Kaohsiung), Jan-Ming Ho, D.T. Lee. IEEE TITS 2014. paper
Road-Segment-Based Approach, depend on the loop detectors