Haomin Wen
Abstract
In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier’s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems alleviate customers’ waiting anxiety and improve their experience. We identify three main challenges of this problem. First, unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. Second, an intuitive idea for solving ETPA is to predict the pick-up route and then the time in two stages. However, it is difficult to accurately and efficiently predict couriers’ future routes in the route prediction step since their behaviors are affected by multiple complex factors. Third, furthermore, in the time prediction step, the requirement for providing a courier’s all unpicked-up packages’ ETPA at once in real time makes the problem even more challenging. To tackle the preceding challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models.
- [1] . 2019. Seq2Slate: Re-ranking and slate optimization with RNNs. In Proceedings of the Workshop on Negative Dependence in Machine Learning.Google Scholar
- [2] . 2019. Travel time estimation in the age of big data. Operations Research 67, 2 (2019), 498–515.Google ScholarDigital Library
- [3] . 2015. A robust algorithm of multiquadric method based on an improved Huber loss function for interpolating remote-sensing-derived elevation data sets. Remote Sensing 7, 3 (2015), 3347–3371.Google ScholarCross Ref
- [4] . 2020. CrowdExpress: A probabilistic framework for on-time crowdsourced package deliveries. IEEE Transactions on Big Data 8, 3 (2020), 827–842.Google Scholar
- [5] . 2016. crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2016), 1478–1496.Google Scholar
- [6] . 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1724–1734.Google ScholarCross Ref
- [7] . 2021. End-to-end prediction of parcel delivery time with deep learning for smart-city applications. IEEE Internet of Things Journal 8, 23 (2021), 17043–17056.Google Scholar
- [8] . 2008. Traffic estimation and prediction based on real time floating car data. In Proceedings of the 2008 11th International IEEE Conference on Intelligent Transportation Systems. 197–203.Google ScholarCross Ref
- [9] . 2020. CompactETA: A fast inference system for travel time prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 3337–3345.Google ScholarDigital Library
- [10] . 1999. MACS-VRPTW: A multiple colony system for vehicle routing problems with time windows. In New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover (Eds.). McGraw-Hill, London, UK, 63–76.Google ScholarDigital Library
- [11] . 2021. A deep learning method for route and time prediction in food delivery service. In Proceedings of the 27th ACm SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’21). 2879–2889.Google ScholarDigital Library
- [12] . 2000. Learning to forget: Continual prediction with LSTM. Neural Computation 12, 10 (2000), 2451–2471.Google ScholarDigital Library
- [13] . 2017. Vehicle routing problem and its solution methodologies: A survey. International Journal of Logistics Systems and Management 28, 4 (2017), 419–435.Google ScholarCross Ref
- [14] . 2020. Jointly masked sequence-to-sequence model for non-autoregressive neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 376–385.Google ScholarCross Ref
- [15] . 2018. Sequence to sequence mixture model for diverse machine translation. In Proceedings of the 2018 Conference on Natural Language Learning. 583–592.Google ScholarCross Ref
- [16] Google OR-Tools. n.d. Vehicle Routing Problem. Retrieved February 17, 2023 from https://developers.google.com/optimization/routing/vrp.Google Scholar
- [17] . 2020. Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks. In Proceedings of the 2020 IEEE 36th International Conference on Data Engineering. 1417–1428.Google ScholarCross Ref
- [18] . 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning. 448–456.Google ScholarDigital Library
- [19] . 2013. Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B: Methodological 53 (2013), 64–81.Google ScholarCross Ref
- [20] . 2017. A unified neural network approach for estimating travel time and distance for a taxi trip. arXiv preprint arXiv:1710.04350 (2017).Google Scholar
- [21] . 1938. A new measure of rank correlation. Biometrika 30, 1-2 (1938), 81–93.Google ScholarCross Ref
- [22] . 2019. Attention, learn to solve routing problems! In Proceedings of the 7th International Conference on Learning Representations.Google Scholar
- [23] . 2016. Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 521–526.Google ScholarCross Ref
- [24] . 2003. Local search with annealing-like restarts to solve the VRPTW. European Journal of Operational Research 150, 1 (2003), 115–127.Google ScholarCross Ref
- [25] . 2018. Multi-task representation learning for travel time estimation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1695–1704.Google ScholarDigital Library
- [26] . 2016. Intelligent logistics: Involving the customer. Computers in Industry 81 (2016), 105–115.Google ScholarDigital Library
- [27] . 2017. Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619 (2017).Google Scholar
- [28] . 2019. Nei-TTE: Intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Transactions on Industrial Informatics 16, 4 (2019), 2659–2666.Google ScholarCross Ref
- [29] . 2019. Re-route package pickup and delivery planning with random demands. In Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).Google Scholar
- [30] . 2010. Travel time estimation using floating car data. arXiv preprint arXiv:1012.4249 (2010).Google Scholar
- [31] . 2018. A deep recurrent neural network with BiLSTM model for sentiment classification. In Proceedings of the 2018 International Conference on Bangla Speech and Language Processing. 1–4.Google ScholarCross Ref
- [32] . 2021. Algorithm design for solving VRPTW problem in supermarket chain distribution. In Proceedings of the 2021 4th International Conference on Information Management and Management Science. 71–75.Google ScholarDigital Library
- [33] . 2014. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14). 3104–3112.Google Scholar
- [34] . 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30. 5998–6008.Google Scholar
- [35] . 2015. Pointer networks. In Advances in Neural Information Processing Systems 28. 2692–2700.Google Scholar
- [36] . 2018. When will you arrive? Estimating travel time based on deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- [37] . 2019. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology 10, 2 (2019), 1–22.Google ScholarDigital Library
- [38] . 2018. Learning to estimate the travel time. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 858–866.Google ScholarDigital Library
- [39] . 2019. DeepETA: A spatial-temporal sequential neural network model for estimating time of arrival in package delivery system. In Proceedings of the AAAI Conference on Artificial Intelligence. 774–781.Google ScholarDigital Library
- [40] . 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317 (2020).Google Scholar
- [41] . 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the 32nd International Conference on Machine Learning (ICML’15). 2048–2057.Google Scholar
- [42] . 2017. Fast and accurate image super resolution by deep CNN with skip connection and network in network. In Proceedings of the International Conference on Neural Information Processing. 217–225.Google ScholarCross Ref
- [43] . 2020. Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW. Expert Systems with Applications 145 (2020), 113151.Google ScholarDigital Library
- [44] . 2020. Order fulfillment cycle time estimation for on-demand food delivery. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2571–2580.Google ScholarDigital Library
Index Terms
- Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors
Recommendations
DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction
Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and ...
Modeling Spatial–Temporal Constraints and Spatial-Transfer Patterns for Couriers’ Package Pick-up Route Prediction
Couriers’ package pick-up route prediction is a fundamental task in the emerging intelligent logistics systems. It is beneficial for order dispatching and arrival-time estimation by leveraging the predicted routes to improve those downstream tasks. ...
Attention Enhanced Package Pick-Up Time Prediction via Heterogeneous Behavior Modeling
Algorithms and Architectures for Parallel ProcessingAbstractThe logistics industry has developed rapidly with the popularity of online-to-offline businesses in recent years. First-mile package pick-up is one of the most critical and expensive parts of the whole logistics service chain, which is finished by ...
Comments