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Biswas, P K, Khan, S M, Piratla, K and Chowdhury, M (2023) Development and evaluation of statistical and machine-learning models for queue-length estimation for lane closures in freeway work zones. Journal of Construction Engineering and Management, 149(05).
- Type: Journal Article
- Keywords: machine learning; queue length; regression
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-12648
- Abstract:
Freeway maintenance and rehabilitation work usually require closing one or multiple lanes, interrupting traffic flows, and creating queues upstream of the work zone. Public agencies can use queue length as a criterion to determine the maximum duration of lane closures and necessary traffic diversions. Previous studies of estimating queue length due to work zone lane closures are data- and time-intensive. This study presents an efficient approach for estimating queue length estimation due to work zone lane closures by developing various statistical and machine-learning models. The inputs for these queue length estimation models were vehicle demand, lane closure duration, active work zone length, and heavy vehicle percentage. The extent of the queues caused by short-term work zones on freeways for 2-to-1 (one-lane closure on a two-lane freeway), 3-to-1 (one-lane closure on a three-lane freeway), and 3-to-2 (two-lane closure on a three-lane freeway) lane-closure configurations can be estimated with these models. The primary scientific contribution of this study is the applicability of the queue length estimation models in any freeway network with work zone configurations and geometric features such as those used for model development. This research evaluated the efficacy of both statistical and machine-learning models for estimating the queue length considering different work zone scenarios. The accuracy of the queue length estimation models was evaluated for a different network that the original models had not seen previously. Among the statistical models, the quantile regression model had the best accuracy based on mean absolute percentage error (MAPE) for the 2-to-1 lane-closure configuration (88%), and the multiple linear regression had the best accuracy for the 3-to-1 (76%) and 3-to-2 (72%) lane-closure configurations. Among the machine-learning models, the stacking regressor model had the best accuracy for 2-to-1 (95%), 3-to-1 (90%), and 3-to-2 (89%) lane-closure configurations. Based on the analysis, it was observed that machine-learning models performed better than the traditional statistical models in estimating queue lengths.
Chong, H Y and Cheng, M (2023) Smart contract implementation in building information modeling-enabled projects: Approach to contract administration. Journal of Construction Engineering and Management, 149(05).
Ding, S, Hu, H, Dai, L and Wang, W (2023) Blockchain adoption among multi-stakeholders under government subsidy: From the technology diffusion perspective. Journal of Construction Engineering and Management, 149(05).
Feng, Z, Chao, Q, Tan, C and Yang, Y (2023) Using bargaining model with loss aversion and a risk of breakdown to determine compensation for buyback of early terminating BOT highway projects. Journal of Construction Engineering and Management, 149(05).
Garg, S and Misra, S (2023) Framework for estimating quality-related incentive and disincentive in construction projects. Journal of Construction Engineering and Management, 149(05).
Gonsalves, N, Akanmu, A, Gao, X, Agee, P and Shojaei, A (2023) Industry perception of the suitability of wearable robot for construction work. Journal of Construction Engineering and Management, 149(05).
Han, S, Jiang, Y, Huang, Y, Wang, M, Bai, Y and Spool-White, A (2023) Scan2drawing: Use of deep learning for as-built model landscape architecture. Journal of Construction Engineering and Management, 149(05).
Jung, H, Seo, W and Kang, Y (2023) Differences in workers' safety behavior by project size and risk level of work in South Korea. Journal of Construction Engineering and Management, 149(05).
Labik, O, Nahmens, I, Ikuma, L and Harvey, C (2023) On-site versus in-factory installation of solar-plus-storage in modular construction. Journal of Construction Engineering and Management, 149(05).
Saqib, G, Hassan, M U, Zubair, M U and Choudhry, R M (2023) Investigating the acceptance of an electronic incident reporting system in the construction industry: An application of the technology acceptance model. Journal of Construction Engineering and Management, 149(05).
Shen, K, Zhu, Y, Pan, J and Li, X (2023) An intelligent decision-making model for the design of precast slab joints based on case-based reasoning. Journal of Construction Engineering and Management, 149(05).
Ullal, A (2023) Construction conditions and practices during war in Afghanistan. Journal of Construction Engineering and Management, 149(05).
Wang, C, Zhang, S, Gao, Y, Guo, Q and Zhang, L (2023) Effect of contractual complexity on conflict in construction subcontracting: Moderating roles of contractual enforcement and organizational culture distance. Journal of Construction Engineering and Management, 149(05).
Wang, J, Han, C and Li, X (2023) Modified streamlined optimization algorithm for time-cost tradeoff problems of complex large-scale construction projects. Journal of Construction Engineering and Management, 149(05).
Xue, G, Liu, S, Ren, L and Gong, D (2023) Adaptive cross-scenario few-shot learning framework for structural damage detection in civil infrastructure. Journal of Construction Engineering and Management, 149(05).
Zhang, Y, Minchin, R E, Flood, I and Ries, R J (2023) Preliminary cost estimation of highway projects using statistical learning methods. Journal of Construction Engineering and Management, 149(05).
Zhou, Y, Wang, X, Gosling, J and Naim, M M (2023) The system dynamics of engineer-to-order construction projects: Past, present, and future. Journal of Construction Engineering and Management, 149(05).