{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:35:39Z","timestamp":1759970139796,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Province Transportation Science and Technology Project","award":["2023Y08","JKKJ-2022-16","23B298"],"award-info":[{"award-number":["2023Y08","JKKJ-2022-16","23B298"]}]},{"name":"Science and Technology Project of Anhui Transportation Holding Group","award":["2023Y08","JKKJ-2022-16","23B298"],"award-info":[{"award-number":["2023Y08","JKKJ-2022-16","23B298"]}]},{"name":"Research Project Initiation of Jiangsu University","award":["2023Y08","JKKJ-2022-16","23B298"],"award-info":[{"award-number":["2023Y08","JKKJ-2022-16","23B298"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>To address poor real-time performance and low accuracy in car-following risk identification, a model based on autoencoders is proposed. Using the SHRP2 natural driving dataset, this paper constructs a car-following risk identification model in two stages. In Stage 1, a deep feedforward neural network autoencoder reconstructs preprocessed multi-source heterogeneous indicators of human-vehicle-road-environment. The high-dimensional latent space feature representation is used as input for Stage 2, enhancing the basic model\u2019s performance. Eight basic models and sixteen models with autoencoders are compared using multiple evaluation indicators. A simulated driving test verifies the model\u2019s generalization and robustness. Results show improved accuracy in car-following risk identification, with the optimized AutoEncoder_LR performing best at 91.33% for risk presence and 70.14% for risk levels. These findings can aid in safe driving and rear-end accident prevention.<\/jats:p>","DOI":"10.3390\/systems13010041","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T04:54:08Z","timestamp":1736312048000},"page":"41","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion"],"prefix":"10.3390","volume":"13","author":[{"given":"Shuwei","family":"Guo","sequence":"first","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyu","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"ref_1","unstructured":"Traffic Management Bureau of the Ministry of Public Security (2023). There Are 435 Million Vehicles, 523 Million Drivers, and More than 20 Million New Energy Vehicles in China, Traffic Management Bureau of the Ministry of Public Security."},{"key":"ref_2","unstructured":"National Bureau of Statistics (2023). China Statistical Yearbook 2023, National Bureau of Statistics."},{"key":"ref_3","unstructured":"Yang, Z. (2021). Research on Causes of Rear-End Collision and Behavior of Avoiding Collision Based on Deep Data Analysis, Northeast Forestry University."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.physa.2019.03.023","article-title":"Impact of risk illusions on traffic flow in fog weather","volume":"525","author":"Tan","year":"2019","journal-title":"Physica A Stat. Mech. Appl."},{"key":"ref_5","first-page":"48","article-title":"Analysis and Identification of Drivers\u2019 Difference in Car-Following Condition Based on Naturalistic Driving Data","volume":"21","author":"Liu","year":"2021","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_6","first-page":"25","article-title":"Improving Car Following Model Before the Vehicle Changes Lanes Based on NGSIM Data","volume":"39","author":"Bai","year":"2023","journal-title":"Traffic Transp."},{"key":"ref_7","first-page":"27","article-title":"Car-Following Risk Behavior in Rainy Weather Based on Random Forest","volume":"38","author":"Huang","year":"2020","journal-title":"J. Transp. Inf. Saf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.aap.2016.07.006","article-title":"Chosen risk level during car-following in adverse weather conditions","volume":"95","author":"Hjelkrem","year":"2016","journal-title":"Accident Anal. Prev."},{"key":"ref_9","first-page":"118","article-title":"Literature review of driving risk identification research based on bibliometric analysis","volume":"3","author":"Ge","year":"2023","journal-title":"J. Traffic Transp. Eng. (Engl. Ed.)"},{"key":"ref_10","first-page":"843","article-title":"Prediction of Car-Following Risk Status Based on Car-Following Behavior Spectrum","volume":"49","author":"Wang","year":"2021","journal-title":"J. Tongji Univ. (Nat. Sci.)"},{"key":"ref_11","unstructured":"Transportation Research Board (TRB) (2010). Highway Capacity Manual 2010, Highway Research Board."},{"key":"ref_12","first-page":"524","article-title":"The Effect of Heavy Goods Vehicles and Following Behavior on Capacity at Motorway Roadwork Sites","volume":"37","author":"Paker","year":"1996","journal-title":"Traffic Eng. Control"},{"key":"ref_13","unstructured":"Gartner, N., Messer, C.J., and Raathi, A.K. (1997). Traffic Flow Theory (Update of TRB Special Report 1165), Transportation Research Board."},{"key":"ref_14","first-page":"129","article-title":"Calibrating and Validating Car-Following Models on Urban Expressways for Chinese Drivers Using Naturalistic Driving Data","volume":"31","author":"Wang","year":"2018","journal-title":"China J. Highw. Transp."},{"key":"ref_15","first-page":"1","article-title":"Sparse Autoencoder","volume":"72","author":"Ng","year":"2011","journal-title":"CS294A Lect. Notes"},{"key":"ref_16","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (July, January 28). Contractive auto-encoders: Explicit invariance during feature extraction. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA."},{"key":"ref_18","unstructured":"Kingma, D.P., and Welling, M. (2013, January 2\u20134). Auto-Encoding Variational Bayes. Proceedings of the International Conference on Learning Representations (ICLR), Scottsdale, AZ, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ge, H., Huang, M., Lu, Y., and Yang, Y. (2020). Study on Traffic Conflict Prediction Model of Closed Lanes on the Outside of Expressway. Symmetry, 12.","DOI":"10.3390\/sym12060926"},{"key":"ref_20","first-page":"1","article-title":"Research on Characteristics and Trends of Traffic Flow Based on Mixed Velocity Method and Background Difference Method","volume":"2020","author":"Ge","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_21","first-page":"168","article-title":"Gel Strength prediction in ultrasonicated chicken mince: Fusing near-infrared and Raman spectroscopy coupled with deep learning LSTM algorithm","volume":"14","author":"Nunekpeku","year":"2024","journal-title":"Foods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"76532","DOI":"10.1109\/ACCESS.2020.2989471","article-title":"Research on Calculation of Warning Zone Length of Freeway Based on Micro-Simulation Model","volume":"8","author":"Ge","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, T., Chen, T., Zhang, X., Taha, M.F., Yang, N., and Shi, Q. (2024). Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach. Agronomy, 14.","DOI":"10.3390\/agriculture14071155"},{"key":"ref_24","first-page":"1","article-title":"Classification of drinking and drinker-playing in pigs by a video-based deep learning method","volume":"196","author":"Chen","year":"2020","journal-title":"Animals"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"134763","DOI":"10.1109\/ACCESS.2019.2937898","article-title":"Construction and Simulation of Rear-End Conflicts Recognition Model Based on Improved TTC Algorithm","volume":"7","author":"Ge","year":"2019","journal-title":"IEEE Access"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/1\/41\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:24:53Z","timestamp":1759919093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/1\/41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,8]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["systems13010041"],"URL":"https:\/\/doi.org\/10.3390\/systems13010041","relation":{},"ISSN":["2079-8954"],"issn-type":[{"type":"electronic","value":"2079-8954"}],"subject":[],"published":{"date-parts":[[2025,1,8]]}}}