Research on travel time prediction based on neural network

doi:10.3969/j.issn.1000-5641.2023.02.012

Abstract

Abstract:

The popularity of positioning devices has generated a large volume of vehicle driving data, making it possible to use historical data to predict the driving time of vehicles. Vehicle driving data consists of two parts: the sequence of road segments that the vehicle travels through, the departure time, the total length of the path, and other external information. The questions of how to extract sequence features in road segments and how to effectively fuse sequence features with external features become the key issues in predicting the travel time. To solve the aforementioned problems, a transformer-based travel time prediction model is proposed, which consists of two parts: a road segment sequence processing module and a feature fusion module. First, the road segment sequence processing module uses the self-attention mechanism to process the road segment sequence and extract the road segment sequence features. The model can not only fully consider the spatiotemporal correlation of road speeds between each road segment and other road segments, but also ensures the parallel input of data into the model, avoiding the low efficiency problem caused by sequential input of data when using recurrent neural networks. The feature fusion module fuses the road segment sequence features with external information, such as departure time, and obtains the predicted travel time. On this basis, the number of road segments connected by the intersection is determined by the upstream and downstream intersection features of the road segment, and the input model is combined with the road segment characteristics to further improve the prediction accuracy of the driving time. Comparative experiments with mainstream prediction methods on real data sets show that the model improves prediction accuracy and training speed, reflecting the effectiveness of the proposed method.

Key words: travel time predition, location-based services, attention mechanism, spatial-temporal data

CLC Number:

TP311

Zhaoyang WU, Jiali MAO. Research on travel time prediction based on neural network[J]. Journal of East China Normal University(Natural Science), 2023, 2023(2): 106-118.

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References 31

1	WANG Z, FU K, YE J P. Learning to estimate the travel time [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 858-866.
2	WANG D, ZHANG J B, CAO W, et al. When will you arrive? Estimating travel time based on deep neural networks [C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI, 2018: 2500-2507.
3	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017). Red Hook, NY, United States: Curran Associates Inc., 2017: 6000-6010.
4	DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16 × 16 words: Transformers for image recognition at scale [EB/OL]. (2020-10-22)[2022-11-26]. https://arxiv.org/abs/2010.11929.
5	YING C X, CAI T, LUO S J, et al. Do transformers really perform bad for graph representation? [EB/OL]. (2021-11-24)[2021-11-26]. https://arxiv.org/abs/2106.05234.
6	DE SOUZA PEREIRA MOREIRA G, RABHI S, LEE J M, et al. Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation [C]// Proceedings of the 15th ACM Conference on Recommender Systems. ACM, 2021: 143-153.
7	WANG H J, TANG X F, KUO Y H, et al. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology (TIST), 2019, 10 (2): 19:1- 19:22. doi: 10.1145/3293317
8	JINDAL I, QIN Z W, CHEN X W, et al. A unified neural network approach for estimating travel time and distance for a taxi trip [EB/OL]. (2017-10-12)[2021-11-26]. https://arxiv.org/pdf/1710.04350.pdf.
9	LI Y G, FU K, WANG Z, et al. Multi-task representation learning for travel time estimation [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1695-1704.
10	OUNOUGHI C, YEFERNY T, YAHIA S B. ZED-TTE: Zone embedding and deep neural network based travel time estimation approach [C]// 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. DOI: 10.1109/IJCNN52387.2021.9533456.
11	ASGHARI M, EMRICH T, DEMIRYUREK U, et al. Probabilistic estimation of link travel times in dynamic road networks [C]// Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2015: Article number 47. DOI: http://dx.doi.org/10.1145/2820783.2820836.
12	JIA Z F, CHEN C, COIFMAN B, et al. The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors [C]// 2001 IEEE Intelligent Transportation Systems Proceedings. IEEE, 2001: 536-541. DOI: 10.1109/ITSC.2001.948715.
13	PETTY K F, BICKEL P, OSTLAND M, et al. Accurate estimation of travel times from single-loop detectors. Transportation Research Part A, 1998, 32 (1): 1- 17.
14	TANG J J, ZOU Y J, ASH J, et al. Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system. PloS One, 2016, 11 (2): e0147263. doi: 10.1371/journal.pone.0147263
15	JENELIUS E, KOUTSOPOULOS H N. Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B, 2013, 53 (4): 64- 81.
16	HUNTER T, HERRING R, ABBEEL P, et al. Path and travel time inference from GPS probe vehicle data [EB/OL]. (2009-01-29)[2022-11-25]. https://snap.stanford.edu/nipsgraphs2009/papers/hunter-paper.pdf.
17	WANG Y L, ZHENG Y, XUE Y X. 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.
18	ALEXANDER-RAKHLIN. CNN-for-sentence-classification-in-Keras [EB/OL]. (2017-07-17)[2022-11-26]. https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras.
19	HENG-TZE C, LEVENT K, JEREMIAH H, et al. Wide & deep learning for recommender systems [C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.
20	FU K, MENG F L, YE J P, et al. CompactETA: A fast inference system for travel time prediction [C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2020: 3337-3345.
21	FANG X M, HUANG J Z, WANG F, et al. ConSTGAT: Contextual spatial-temporal graph attention network for travel time estimation at Baidu maps [C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2020: 2697-2705.
22	SUN Y, WANG Y, FU K, et al. FMA-ETA: Estimating travel time entirely based on FFN with attention [C]// ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 3355-3359.
23	FANG X M, HUANG J Z, WANG F, et al. SSML: Self-supervised meta-learner for en route travel time estimation at Baidu maps [C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, 2021: 2840-2848.
24	HONG H T, LIN Y C, YANG X Q, et al. HetETA: Heterogeneous information network embedding for estimating time of arrival [C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2020: 2444-2454.
25	FU T Y, LEE W C. DeepIST: Deep image-based spatio-temporal network for travel time estimation [C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 2019: 69-78.
26	XU J, ZHANG Y, CHAO L, et al. STDR: A deep learning method for travel time estimation [C]// International Conference on Database Systems for Advanced Applications, DASFAA 2019, Lecture Notes in Computer Science, vol 11447. Cham: Springer, 2019: 156-172.
27	XIONG R B, YANG Y C, HE D, et al. On layer normalization in the transformer architecture [EB/OL]. (2020-06-29)[2021-11-26]. https://arxiv.org/abs/2002.04745.
28	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016: 770-778.
29	LOSHCHILOV I, HUTTER F. Decoupled weight decay regularization [EB/OL]. (2019-01-04)[2021-11-26]. https://arxiv.org/abs/1711.05101v3.
30	KE G L, MENG Q, FINLEY T, et al. LightGBM: A highly efficient gradient boosting decision tree [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017). Red Hook, NY, USA: Curran Associates Inc., 2017: 3149-3157.
31	GUO H F, TANG R M, YE Y M, et al. DeepFM: A factorization-machine based neural network for CTR prediction[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI, 2017: 1725-1731.

模型	MAE/s	MAPE/s
TEMP	137.2380	0.2574
Simple ETA	97.2830	0.1720
LightGBM	94.0360	0.1683
DFM	91.8010	0.1632
LF_multi-head	90.0800	0.1605
WDR	89.6700	0.1581
本文的LF	88.9340	0.1566

模型	MAE/s	MAPE/s
LF	89.4850	0.1623
LF + 路口特征	88.9340	0.1566

特征	MAE/s	MAPE/s
h	96.0890	0.1909
h + time	92.7380	0.1689
h + time + driver	91.2930	0.1624
h + time + driver + weather	89.5470	0.1608

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