Road Friction Estimation for Connected Vehicles using Supervised Machine Learning

In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.Comment: Published at IV 201

Managing Traffic Data through Clustering and Radial Basis Functions

Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT.This research was funded by The University of the Basque Country (UPV/EHU), Call for Innovation Projects “IKD i3 Laborategia” (Call 1-2020, 2019/20)

An artificial neural network model for predicting freeway work zone delays with big data

Lane closures due to road reconstruction and maintenance have resulted in a major source of non-recurring congestion on freeways. It is extremely important to accurately quantify the associated mobility impact so that a cost-effective work zone schedule and an efficient traffic management plan can be developed. Therefore, the development of a sound model for predicting delays or road users is desirable. A comprehensive literature review on existing work zone delay prediction models (i.e., deterministic queuing model and shock wave model) is conducted in this study, which explores the advantages, disadvantages, and limitations of different modeling approaches. The performance of those models seems restricted to predict congestion impact under space-varying (i.e., road geometry, number of lanes, lane width, etc.) and time-varying (i.e., traffic volume) conditions. To advance the delay prediction accuracy, a multivariate non-linear regression (MNR) model is developed first by incorporating big data to capture the relationship of speed versus the ratio of approaching traffic volume to work zone capacity for work zone delay prediction. The MNR model demonstrates itself able to predict spatio-temporal delays with reasonable accuracy. A more advanced model called ANN-SVM is developed later to further improve the prediction accuracy, which integrates a support vector machine (SVM) model and an artificial neural network (ANN) model. The SVM model is responsible to predict work zone capacity, and the ANN model is responsible to predict delays. The ultimate goal of ANN-SVM aims to predict spatio-temporal delays caused by a work zone on freeways in the statewide of New Jersey subject to road geometry, number of lane closure, and work zone duration in different times of a day and days of a week. There are 274 work zones with complete information for the proposed model development, which are identified by mapping data from different sources, including OpenReach, Plan4Safety, New Jersey Straight Line Diagram (NJSLD), New Jersey Congestion Management System (NJCMS), and INRIX. Big data analytics is used to examining this massive data for developing the proposed model in a reliable and efficient way. A comparative analysis is conducted by comparing the ANN-SVM results with those produced by MNR, RUCM (NJDOT Road User Cost Manual approach), and ANN-HCM (the ANN model with work zone capacity suggested by Highway Capacity Manual). It is found that ANN-SVM in general outperforms other models in terms of prediction accuracy and reliability. To demonstrate the applicability of the proposed model, an analysis tool, which adapts to ANN-SVM, is developed to produce graphical information. It is worth noting that the analysis tool is very user friendly and can be easily applied to assess the impact of any work zones on New Jersey freeways. This tool can assist transportation agencies visualize bottlenecks and congestion hot spots caused by a work zone, effectively quantify and assess the associated impact, and make suitable decisions (i.e., determining the best starting time of a work zone to minimize delays to the road users). Furthermore, ANN-SVM can be applied to develop, evaluate, and improve traffic management and congestion mitigation plans and to calculate contractor penalty based on cost overruns as well as incentive reward schedule in case of early work competition

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Cost Prediction for Roads Construction using Machine Learning Models

Predicting conceptual costs is among the essential criteria in project decision-making at the early stages of civil engineering disciplines. The cost estimation model availability that may help in the early stages of a project could be incredibly advantageous in respect of cost alternatives and more extraordinary cost-effective solutions periodically. There is a lack of case datasets. Most of the proposed dataset was inefficient. This study offers a new data set that includes the elements of road construction and economic advantages in the year of project construction. Real project data for rural roads in the State of Iraq / Diyala Governorate for the years 2012 to 2021 have use to train a predictive model with a high rate of accuracy based on machine learning (ML) methods. Ridge and Least Absolute Shrinkage and Selection Operator (LASSO) Regressions, K Nearest Neighbors (k-NN), and Random Forest (RF) algorithms have employ to create models for estimating road construction costs based on real-world data. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) coefficient of determination are utilize to assess the models\u27 performance. The analysis indicated that the RR is the best model for road construction costs, with results R2 = 1.0, MAPE =0.00, and RMSE=0.00. The results showed that the cost estimates were accurate and aligned with the project bids

Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results