Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

Abstract

With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich.In classifying highway asset items, it should be noted that state agencies have different classification. For example, California Department of Transportation (CalTrans) categorizes transportation assets in primary classes of pavements, bridges, culverts, and Intelligent Transportation Systems (ITS) [4]. However, North Carolina DOT classifies assets into seven primary groups of pavements, bridges, tunnels, roadside features, pavement markings, rest areas, and maintenance yards [10]. Among these categories, that are different from state to state, we reviewed prediction methods for a subset of assets, including pavements, pavement markings, traffic signs, barriers, and culverts.We focused mostly on recent papers published in the past 20 years. To the best of our knowledge, we included most of the significant research studies reporting the application of predictive analytics. The total number of the investigated papers versus years of publication is shown in Figure 2.

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

“…Results showed that the grace period was effective in reducing sign maintenance and replacement costs. Karimzadeh and Shoghli (2020) presented a detailed literature review of different predictive analytics used for highway maintenance activities as life expectancy and deterioration models, pavement marking prediction models, and sign retro and degradation models [27]. The survey of the included studies indicated that the deterministic models have mainly dominated the field of traffic sign deterioration, their life expectancy, and retro-reflectivity prediction.…”

Trend analysis and fatality causes in Kenyan roads: A review of road traffic accident data between 2015 and 2020

“…We will utilize machine learning approaches to explore the data. Machine learning has been applied in various aspects of traffic analysis ranging from infrastructure design to prediction models (Gichaga, 2017;Pakgohar & Kazemi, 2015;Hadji Hosseinlou et al, 2018;Karimzadeh and Shoghli, 2020;Sysoev et al, 2020;Heyns et al, 2019). To our knowledge, there has not been publications on the said dataset.…”

Estudo sobre a vida útil residual de duas passarelas de pedestres localizadas na rodovia BR-101/PE

“…Tais instrumentos, que devem fazer parte de um sistema mais amplo de gestão, estão relacionados aos conceitos de durabilidade, de confiabilidade estrutural e ao ciclo de vida para a estimativa de vida útil das OAEs. Manafpour et al (2018) afirmam que tais ações podem maximizar a vida útil e diminuir os custos aplicados ao longo do ciclo de vida dessas estruturas, contribuindo assim para o incremento ou desempenho mínimo exigido das rodovias (Karimzadeh & Shoghli, 2020).…”