{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T08:39:07Z","timestamp":1758357547582,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019627","name":"Bayern Innovativ","doi-asserted-by":"publisher","award":["DIK0314"],"award-info":[{"award-number":["DIK0314"]}],"id":[{"id":"10.13039\/100019627","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008769","name":"Julius-Maximilians-Universit\u00e4t W\u00fcrzburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008769","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The need for efficient and reliable logistics solutions has increased significantly in the last decade. Traffic forecasts are a promising source of information that can be used to improve the planning of delivery schedules. However, most existing traffic forecasting approaches only support a forecasting horizon of up to an hour, which is insufficient for per-day-based schedule planning. In this paper, we focus on short-term traffic forecasting for up to four hours. We first propose a data collection process integrating traffic speed, incidents, weather, and holiday information. We have used this process to collect real-world traffic data for 115 days. We then define and evaluate twelve models for vehicle traffic forecasting, including well-known time series forecasting approaches and state-of-the-art deep learning models. Our results show that the best model in our comparison improved the accuracy by approximately 30% compared to a naive forecaster that repeats the last known value. The evaluation also shows that LSTM-based approaches are competitive to state-of-the-art models. Overall, the proposed deep-learning-based models perform best while requiring a smaller input timeframe than statistical models.<\/jats:p>","DOI":"10.1007\/s10489-025-06565-4","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T06:06:50Z","timestamp":1750226810000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Telling fortunes? Evaluation of traffic forecasting models using traffic and context features"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3602-5790","authenticated-orcid":false,"given":"Marius","family":"Hadry","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Leppich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronika","family":"Lesch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Kounev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"6565_CR1","unstructured":"Cramer-Flood E (2022) Worldwide ecommerce forecast update 2022 - Insider intelligence trends, forecasts & statistics. https:\/\/www.insiderintelligence.com\/content\/worldwide-ecommerce-forecast-update-2022. Accessed 05 Dec 2023"},{"key":"6565_CR2","unstructured":"Spadafora J (2020) Pitney bowes newsroom - Pitney bowes parcel shipping index reports continued growth as global parcel volume exceeds 100 billion for first time ever. https:\/\/news.pb.com\/article_display.cfm?article_id=5958. Accessed 05 Dec 2023"},{"key":"6565_CR3","unstructured":"Farrag-Thibault A (2021) Road freight zero - World economic forum. https:\/\/www.weforum.org\/projects\/decarbonizing-road-freight-initiative. Accessed 05 Dec 2023"},{"issue":"8","key":"6565_CR4","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"6565_CR5","unstructured":"Oreshkin BN, Carpov D, Chapados N, Bengio Y (2020) N-beats: Neural basis expansion analysis for interpretable time series forecasting. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=r1ecqn4YwB"},{"key":"6565_CR6","doi-asserted-by":"crossref","unstructured":"Olivares KG, Challu C, Marcjasz G, Weron R, Dubrawski A (2022) Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with nbeatsx. Int J Forecast","DOI":"10.1016\/j.ijforecast.2022.03.001"},{"key":"6565_CR7","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=SJiHXGWAZ"},{"key":"6565_CR8","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1609\/aaai.v34i04.5758","volume":"34","author":"W Chen","year":"2020","unstructured":"Chen W, Chen L, Xie Y, Cao W, Gao Y, Feng X (2020) Multi-range attentive bicomponent graph convolutional network for traffic forecasting. Proceedings of the AAAI conference on artificial intelligence 34:3529\u20133536","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"issue":"01","key":"6565_CR9","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1609\/aaai.v33i01.3301890","volume":"33","author":"Z Diao","year":"2019","unstructured":"Diao Z, Wang X, Zhang D, Liu Y, Xie K, He S (2019) Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. Proc AAAI Conf Artif Intell 33(01):890\u2013897. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301890","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"6565_CR10","doi-asserted-by":"publisher","unstructured":"Peng H, Klepp N, Toutiaee M, Arpinar IB, Miller JA (2019) Knowledge and situation-aware vehicle traffic forecasting. In: 2019 IEEE international conference on big data (Big Data), pp 3803\u20133812. https:\/\/doi.org\/10.1109\/BigData47090.2019.9005599","DOI":"10.1109\/BigData47090.2019.9005599"},{"key":"6565_CR11","doi-asserted-by":"publisher","unstructured":"Zhao H, Yang H, Wang Y, Wang D, Su R (2020) Attention based graph bi-lstm networks for traffic forecasting. In: 2020 IEEE 23rd international conference on intelligent transportation systems (ITSC), pp 1\u20136. https:\/\/doi.org\/10.1109\/ITSC45102.2020.9294470","DOI":"10.1109\/ITSC45102.2020.9294470"},{"issue":"7","key":"6565_CR12","doi-asserted-by":"publisher","first-page":"6414","DOI":"10.1109\/JIOT.2020.2974494","volume":"7","author":"F Zhou","year":"2020","unstructured":"Zhou F, Yang Q, Zhang K, Trajcevski G, Zhong T, Khokhar A (2020) Reinforced spatiotemporal attentive graph neural networks for traffic forecasting. IEEE Internet Things J 7(7):6414\u20136428. https:\/\/doi.org\/10.1109\/JIOT.2020.2974494","journal-title":"IEEE Internet Things J"},{"key":"6565_CR13","first-page":"1082","volume-title":"Traffic flow prediction via spatial temporal graph neural network","author":"X Wang","year":"2020","unstructured":"Wang X, Ma Y, Wang Y, Jin W, Wang X, Tang J, Jia C, Yu J (2020) Traffic flow prediction via spatial temporal graph neural network. Association for Computing Machinery, New York, NY, USA, pp 1082\u20131092"},{"key":"6565_CR14","doi-asserted-by":"publisher","unstructured":"Zhang K, Jia B, Dong Y (2020) Short-term traffic flow prediction based on multi-auxiliary information. In: 2020 the 4th international conference on big data research (ICBDR\u201920). Association for Computing Machinery, New York, NY, USA , pp 30\u201335. https:\/\/doi.org\/10.1145\/3445945.3445951","DOI":"10.1145\/3445945.3445951"},{"key":"6565_CR15","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/978-3-030-30241-2_46","volume-title":"Progress in artificial intelligence","author":"H Yi","year":"2019","unstructured":"Yi H, Bui KHN (2019) VDS data-based deep learning approach for traffic forecasting using LSTM network. In: Moura Oliveira P, Novais P, Reis LP (eds) Progress in artificial intelligence. Springer, Cham, pp 547\u2013558"},{"issue":"1","key":"6565_CR16","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TVT.2019.2952605","volume":"69","author":"F Zhao","year":"2020","unstructured":"Zhao F, Zeng GQ, Lu KD (2020) EnLSTM-WPEO: Short-term traffic flow prediction by ensemble LSTM, NNCT weight integration, and population extremal optimization. IEEE Trans Veh Technol 69(1):101\u2013113. https:\/\/doi.org\/10.1109\/TVT.2019.2952605","journal-title":"IEEE Trans Veh Technol"},{"issue":"11","key":"6565_CR17","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2020","unstructured":"Cui Z, Henrickson K, Ke R, Wang Y (2020) Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21(11):4883\u20134894. https:\/\/doi.org\/10.1109\/TITS.2019.2950416","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6565_CR18","doi-asserted-by":"publisher","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence, IJCAI-18, pp 3634\u20133640. https:\/\/doi.org\/10.24963\/ijcai.2018\/505","DOI":"10.24963\/ijcai.2018\/505"},{"key":"6565_CR19","doi-asserted-by":"publisher","unstructured":"Liu Z, Zheng G, Yu Y (2023) Cross-city few-shot traffic forecasting via traffic pattern bank. In: Proceedings of the 32nd ACM international conference on information and knowledge management. CIKM \u201923, Association for computing machinery, New York, NY, USA, pp 1451\u20131460. https:\/\/doi.org\/10.1145\/3583780.3614829","DOI":"10.1145\/3583780.3614829"},{"key":"6565_CR20","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.future.2022.09.018","volume":"139","author":"Y Djenouri","year":"2023","unstructured":"Djenouri Y, Belhadi A, Srivastava G, Lin JCW (2023) Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Futur Gener Comput Syst 139:100\u2013108. https:\/\/doi.org\/10.1016\/j.future.2022.09.018","journal-title":"Futur Gener Comput Syst"},{"key":"6565_CR21","doi-asserted-by":"publisher","unstructured":"Liang G, U K, Ning X, Tiwari P, Nowaczyk S, Kumar N (2023) Semantics-aware dynamic graph convolutional network for traffic flow forecasting. IEEE Trans Veh Technol 72(6):7796\u20137809. https:\/\/doi.org\/10.1109\/TVT.2023.3239054","DOI":"10.1109\/TVT.2023.3239054"},{"key":"6565_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117921","volume":"207","author":"W Jiang","year":"2022","unstructured":"Jiang W, Luo J (2022) Graph neural network for traffic forecasting: A survey. Expert Syst Appl 207:117921. https:\/\/doi.org\/10.1016\/j.eswa.2022.117921","journal-title":"Expert Syst Appl"},{"key":"6565_CR23","doi-asserted-by":"publisher","unstructured":"Mallick T, Balaprakash P, Rask E, Macfarlane J (2021) Transfer learning with graph neural networks for short-term highway traffic forecasting. In: 2020 25th international conference on pattern recognition (ICPR), pp 10367\u201310374. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413270","DOI":"10.1109\/ICPR48806.2021.9413270"},{"key":"6565_CR24","doi-asserted-by":"publisher","unstructured":"Tedjopurnomo DA, Bao Z, Zheng B, Choudhury FM, Qin AK (2022) A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Trans Knowl Data Eng 34(4):1544\u20131561. https:\/\/doi.org\/10.1109\/TKDE.2020.3001195","DOI":"10.1109\/TKDE.2020.3001195"},{"key":"6565_CR25","doi-asserted-by":"publisher","unstructured":"Almeida A, Br\u00e1s S, Oliveira I, Sargento S (2022) Vehicular traffic flow prediction using deployed traffic counters in a city. Futur Gener Comput Syst 128:429\u2013442. https:\/\/doi.org\/10.1016\/j.future.2021.10.022","DOI":"10.1016\/j.future.2021.10.022"},{"key":"6565_CR26","unstructured":"Franconia ADL (2023) Regierungsbezirk unterfranken. https:\/\/www.regierung.unterfranken.bayern.de\/regierungsbezirk\/index.html. Accessed 30 Nov 2023"},{"key":"6565_CR27","unstructured":"Civic\u00a0Education GFA (2023) Bev\u00f6lkerung nach bundesl\u00e4ndern. https:\/\/www.bpb.de\/kurz-knapp\/zahlen-und-fakten\/soziale-situation-in-deutschland\/61535\/bevoelkerung-nach-bundeslaendern\/. Accessed 30 Nov 2023"},{"issue":"3","key":"6565_CR28","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0169-2070(01)00110-8","volume":"18","author":"RJ Hyndman","year":"2002","unstructured":"Hyndman RJ, Koehler AB, Snyder RD, Grose S (2002) A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast 18(3):439\u2013454. https:\/\/doi.org\/10.1016\/S0169-2070(01)00110-8","journal-title":"Int J Forecast"},{"key":"6565_CR29","unstructured":"Hyndman RJ, Athanasopoulos G (2017) Forecasting: Principles and practice. OTexts, Melbourne, Australia. https:\/\/OTexts.org\/fpp"},{"issue":"496","key":"6565_CR30","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1198\/jasa.2011.tm09771","volume":"106","author":"AMD Livera","year":"2011","unstructured":"Livera AMD, Hyndman RJ, Snyder RD (2011) Forecasting time series with complex seasonal patterns using exponential smoothing. J Am Stat Assoc 106(496):1513\u20131527","journal-title":"J Am Stat Assoc"},{"key":"6565_CR31","doi-asserted-by":"crossref","unstructured":"Bauer A, Z\u00fcfle M, Herbst N, Kounev S, Curtef V (2020) Telescope: An automatic feature extraction and transformation approach for time series forecasting on a level-playing field. In: Proceedings of the 36th IEEE international conference on data engineering (ICDE), IEEE, pp 1902\u20131905","DOI":"10.1109\/ICDE48307.2020.00199"},{"key":"6565_CR32","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"key":"6565_CR33","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32"},{"key":"6565_CR34","doi-asserted-by":"crossref","unstructured":"Golovin D, Solnik B, Moitra S, Kochanski G, Karro JE, Sculley D (2017) Google vizier: A service for black-box optimization, pp 1487\u20131495 . http:\/\/www.kdd.org\/kdd2017\/papers\/view\/google-vizier-a-service-for-black-box-optimization","DOI":"10.1145\/3097983.3098043"},{"issue":"3","key":"6565_CR35","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1111\/tgis.12644","volume":"24","author":"L Cai","year":"2020","unstructured":"Cai L, Janowicz K, Mai G, Yan B, Zhu R (2020) Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting. Trans GIS 24(3):736\u2013755","journal-title":"Trans GIS"},{"key":"6565_CR36","unstructured":"Wu H, Xu J, Wang J, Long M (2021) Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Adv Neural Inf Process Syst 34:22419\u201322430. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf"},{"issue":"12","key":"6565_CR37","doi-asserted-by":"publisher","first-page":"11106","DOI":"10.1609\/aaai.v35i12.17325","volume":"35","author":"H Zhou","year":"2021","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: Beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artif Intell 35(12):11106\u201311115. https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"6","key":"6565_CR38","doi-asserted-by":"publisher","first-page":"6989","DOI":"10.1609\/aaai.v37i6.25854","volume":"37","author":"C Challu","year":"2023","unstructured":"Challu C, Olivares KG, Oreshkin BN, Garza Ramirez F, Mergenthaler Canseco M, Dubrawski A (2023) Nhits: Neural hierarchical interpolation for time series forecasting. Proc AAAI Conf Artif Intell 37(6):6989\u20136997. https:\/\/doi.org\/10.1609\/aaai.v37i6.25854","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"9","key":"6565_CR39","doi-asserted-by":"publisher","first-page":"11121","DOI":"10.1609\/aaai.v37i9.26317","volume":"37","author":"A Zeng","year":"2023","unstructured":"Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? Proc AAAI Conf Artif Intell 37(9):11121\u201311128. https:\/\/doi.org\/10.1609\/aaai.v37i9.26317","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"6565_CR40","unstructured":"Das A, Kong W, Sen R, Zhou Y (2024) A decoder-only foundation model for time-series forecasting. https:\/\/arxiv.org\/abs\/2310.10688"},{"key":"6565_CR41","unstructured":"Zhang Y, Yan J (2023) Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: International conference on learning representations"},{"key":"6565_CR42","doi-asserted-by":"publisher","unstructured":"Manibardo EL, La\u00f1a I, Ser JD (2022) Deep learning for road traffic forecasting: Does it make a difference? IEEE Trans Intell Transp Syst 23(7):6164\u20136188. https:\/\/doi.org\/10.1109\/TITS.2021.3083957","DOI":"10.1109\/TITS.2021.3083957"},{"key":"6565_CR43","doi-asserted-by":"publisher","unstructured":"Grover A, Leskovec J (2016) Node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201916, Association for Computing Machinery, New York, NY, USA, pp 855\u2013864. https:\/\/doi.org\/10.1145\/2939672.2939754","DOI":"10.1145\/2939672.2939754"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06565-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06565-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06565-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:57:21Z","timestamp":1758290241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06565-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":43,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["6565"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06565-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"10 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"755"}}