Assist Transportation Agencies Research Articles

AI-Driven Approach for Automated Real-Time Pothole Detection, Localization, and Area Estimation

Given the potential hazards and risks that potholes pose to road users, this study introduces an image-based system that utilizes a combination of a camera and GPS for real-time detection, georeferencing, and area estimation of potholes. The captured system data is processed in real-time using YOLOv8, a deep learning model proficient in object detection and segmentation. To enhance the precision and reduce the occurrence of false detections, the system is specifically trained to detect potholes, manholes, and patches. Additionally, the camera is calibrated to accurately estimate the area of identified potholes. The proposed system achieved a mean average precision of 91% in detecting potholes, 98% in detecting manholes, and 90% for detecting patches. A salient feature of this system is its capability to localize potholes with reference to pavement line lane markings. This ability could facilitate proactive lane closure planning by maintenance crews, further enhancing road safety measures. The study findings suggest that the system holds significant potential for practical implementation. Its deployment could assist transportation agencies in the prioritization of road repairs, resource allocation, and advance planning for lane closures, ultimately enhancing the efficiency of their maintenance workflows.

Identifying spatio-temporal pattern of electric vehicles involved traffic accidents

The increasing popularity of electric vehicles (EVs) has given rise to concerns regarding emerging safety challenges. To address this, an enhanced spatio-temporal kernel density estimation (STKDE) method is developed, which leverages GIS technologies to analyze the temporal evolution of EV accidents across different locations. Our approach introduces several innovations: (1) The incorporation of network distance to address overestimation associated with the conventional Euclidean distance; (2) Integration of a Severity Index (SI) to rationalize accident weighting; (3) Utilization of a likelihood cross-validation method to determine optimal bandwidths for simplified computation. Additionally, statistical significance tests and a ranking system based on cluster strength are implemented to enhance the accuracy and stability of hotspot identification compared to the traditional kernel density estimation (KDE) method. Empirical analyses reveal spatial trends, indicating a higher likelihood of EV accidents occurring near city administrative centers and along arterial roads. Temporally, our findings show increased daytime hotspot density with distinct morning and evening peaks compared to nighttime. Furthermore, a total of 209 hazardous locations, containing 907 accidents, representing 23.4% of all accidents are filtered. Our improved STKDE approach demonstrates a mean hit rate of 0.553 and a PAI of 5.573, which are 3.5% and 27.5% higher, respectively, than those achieved with SKDEKDE. These insights can assist transportation agencies in implementing targeted interventions and resource allocation strategies.

Investigating safety and cost-effectiveness of cable median barriers in Louisiana

Objectives Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage–only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana. Methods Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit–cost analysis was conducted to determine the cost-effectiveness of CMBs. Results The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit–cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit–cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety. Conclusion This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs). A comparison of the estimated models demonstrated that by utilizing the mixed distribution and allowing the posterior parameter estimates of explanatory variables to vary from one observation to another, the Random Parameters Negative Binomial-Lindley (RPNB-L) model outperformed the traditional NB and fixed coefficient NB-L models. The results of the proposed RPNB-L model indicated that the PTSU implemented sections experienced a lower number of traffic crashes compared to the non-PTSU freeway sections. Among the attributes related to PTSU operation and design elements, the usage of the leftmost shoulder lane as PTSU, the presence of emergency rest areas for damaged vehicles, and adequate shoulder width would significantly reduce crash frequency for the PTSU implemented freeways. Moreover, investigation of the identified hotspots revealed that the transition areas (start/end locations of PTSU) are the most critical sections. The findings from this research could assist transportation agencies to take appropriate countermeasures for preventing and reducing crash occurrences on PTSU implemented freeways.

Enhancing sustainability for pavement maintenance decision-making through image processing-based distress detection

Road maintenance sustainability entails implementing practices that reduce the impact of road accident. while simultaneously ensuring the durability and functionality of the road infrastructure. Pavement distress is a major concern for transportation agencies as it affects the safety and comfort of road users. This paper presents a novel approach for prioritizing pavement distress through the application of image processing techniques and the Analytic Hierarchy Process (AHP). The proposed method involves capturing images of the pavement surface using a high-resolution camera and analyzing them using image processing algorithms. The images are processed to identify different types of pavement distress such as cracks, potholes, and rutting. The severity of each type of distress is then quantified. AHP is utilized to prioritize the identified distress based on its severity and impact on road users. The proposed approach has been tested on real-world pavement images, and the results demonstrate its effectiveness in accurately identifying and prioritizing pavement distress. This method can assist transportation agencies in making informed decisions about maintenance and repair activities, leading to improved road safety and reduced maintenance costs. This automated method achieved an accuracy percentage of about 95%.

Constraint-Based snowplow optimization model for winter maintenance operations

In the annual winter route maintenance, transportation agencies often deploy multiple fleets of trucks for snow control and removal activities over a vast maintenance area which creates an operational problem in determining the optimal maintenance routes and fleet size. The objective of this paper is to develop a snowplow routing optimization model to enhance the efficiency of snow removal route planning. The routing optimization model was developed using vehicle routing problems, constraint-based programming, and geographic information system. The developed model was applied to optimize the snow removal route planning practice of a District in Kansas, United States, as a case study. The result of this study shows that the optimization model can help minimize the fleet size and increase the level of service for treating snow routes within the selected District. The results of this study are expected to assist transportation agencies in optimizing their snow route removal in winter maintenance operations.

Performance Measure–Based Framework for Evaluating Transportation Infrastructure Resilience

Despite many organizations recognizing the need for more comprehensive resilience planning for transportation infrastructure, the United States still lacks a standardized planning framework to guide these decisions and prioritize improvements. Therefore, this paper develops and demonstrates the application of a flexible resilience framework that supports transportation resilience decision-making across multiple operational and infrastructure systems. This Performance-based Resilience Evaluation Procedure (PREP) framework calculates comparable resilience scores and can be implemented for any type of transportation infrastructure to assist transportation agencies and community stakeholders in making informed decisions in relation to project prioritization, risk mitigation, asset management, and design for more reliable infrastructure. The PREP framework, comprised of 12 steps organized in five phases, calculates resilience as a weighted probability of hazard events and the impacts of such hazards on measures of performance. Having such a framework is crucial because it allows stakeholders to develop the data-driven knowledge necessary to deliver informed management and planning of the entire transportation infrastructure system against disruptive events.

Capacity characteristics of long-term work zones on signalized intersection approaches

This study explored the capacity characteristics of five long-term WZ configurations at a major signalized intersection in Toronto. In total, 1,766 cycles and 22,057 inter-vehicle headway observations were recorded using over 140 h of real-time traffic videos. The saturation flow rate ranged from 1318.7 to 1767.3 pc/hr/ln across the study sites. Interestingly, the Highway Capacity Manual WZ model was found to significantly overestimate the traffic capacity at the study sites. The analysis assessed the hypothesis that the saturation headway is further compressed when long queues are present on site. The average saturation headway during oversaturated conditions was found to be shorter by 5 to 11% across the study sites as compared to undersaturated conditions. Other influential factors such as day versus night and late-merge were assessed. The saturation flow under daytime conditions was higher by 3 to 5% as compared to nighttimes. Vehicles merging late from the closed lanes had a shorter headway as compared to vehicles approaching from the open lane by around 8 to 10%. Probability distribution functions of the saturation headway were developed, the best-fit curve for all cases was found to be the three-parameter lognormal distribution. The study also developed multiple regression equations to estimate WZ capacity reduction induced by heavy vehicles for a wide range of heavy vehicle percentages. On average, oversaturated conditions yielded a 5.4% additional flow reduction as compared to undersaturated conditions for the same HV%. Moreover, the impact of different snow conditions on the saturation flow was estimated. The fully slushy, partly slushy, and bare-and-wet snow categories resulted in headway elongation of 35.1 to 42%, 21.2 to 23.4%, and 6.2 to 12.6%, respectively. The study explains how the findings can assist transportation agencies and practitioners in improving the policies, practices, and guidelines related to WZ activities and planning.

Investigating snowplow-related injury severity along mountainous roadway in Wyoming

Snow removal and deicing using snowplow trucks assist transportation agencies to enhance roadway safety and mobility. However, due to slower travel speeds during these operations, motorists often end up in crashes for poor visibility and disturbance of the snow. Despite the risk associated with snowplows, no previous study was found that exclusively investigate the factors associated with injury severity in snowplow-involved crashes. Therefore, this paper presents an extensive exploratory analysis and fills this knowledge gap by identifying the significant contributing factors affecting the occupant injury severity from the aspects of crashes with snowplow involvement. The study utilized eleven years (2010-2020) of historical snowplow-related crash data from Wyoming. Both the binary logit model and mixed binary logit model were developed to investigate the impacts of the various occupant, vehicle, crash, roadway, and environmental characteristics on the corresponding occupant injury severity. As one of the important findings from this research concludes that other vehicle drivers are more responsible than snowplow drivers contributing to more severe injuries in crashes involving snowplows. Recommendations suggested based on the modeling results are expected to help transportation agencies and policymakers take necessary actions in reducing snowplow-involved crashes by targeting appropriate strategies and proper resource allocation.

A deep learning approach for network-wide dynamic traffic prediction during hurricane evacuation

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncertainties caused by sudden changes in projected hurricane paths and consequently populations’ evacuation behavior. Moreover, modeling spatiotemporal traffic flow patterns requires extensive data over a longer time period, whereas evacuations typically last for two to five days. In this paper, we present a novel data-driven approach for predicting evacuation traffic at a network scale. We develop a dynamic graph convolutional long short-term memory neural network (DGCN-LSTM) model to learn the network dynamics during hurricane evacuation. We first train the model for non-evacuation period traffic data and found that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when the model is applied for predicting evacuation traffic, the RMSE value increased to 1440.99. We overcome this issue by adopting a transfer learning approach with additional features related to evacuation traffic demand such as distance from the evacuation zone, time to landfall, and other zonal level features to control the transfer of information (network dynamics) from non-evacuation periods to evacuation periods. The final transfer learned DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE = 399.69). The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (up to 6-hour). It will assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes.

The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March–June 2019 and March–June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations.

Impact of the Great Flood of 2016 on the Asphaltic Concrete Road Infrastructure in Louisiana

Extreme weather events, including inundation, are the major causes of weather-related disruption to the transportation sector. Considerable research has been conducted in recent years to evaluate the impacts of inundation on pavement functional and structural conditions. Yet, limited studies quantified how inundation may affect the long-term pavement performance and pavement life-cycle costs. As such, the main objective of this study was to quantify the impact of the 2016 inundation event in Louisiana, U.S., on the long-term pavement performance (in relation to reduction in the pavement service life [PSL]) and on pavement life-cycle costs. To achieve this objective, a total of 10 inundated pavement sections were evaluated. Pavement conditions, fatigue, and rutting indices for these sections were analyzed over a monitoring period of up to 15 years (11 years before and 4 years after inundation). Results indicated that the 2016 inundation event reduced the PSL of the inundated sections to an extent that depended on their pre-flooding pavement condition, traffic level, and asphalt concrete thickness. This reduction was attributed to the accelerated fatigue, rutting damage, or both, that resulted from prolonged water submergence and heavy traffic loading during debris hauling that took place after the inundation event. A logistic regression model with adequate accuracy was developed to predict whether an inundated pavement would experience reduction in its PSL based on the project conditions. The study findings were incorporated into a simple tool to assist transportation agencies make effective decisions for the maintenance and rehabilitation of inundated pavements.

Road Network Vulnerability Based on Diversion Routes to Reconnect Disrupted Road Segments

The reliance on roads to provide fluent mobilization has raised great concern when facing functional degradation. Disruption of the critical segments of a road network may significantly increase the distance traveled by a community. This paper proposes a method for measuring road network vulnerability when facing disruption by assessing all road segments within a network. The assessment is based on two of the shortest disjointed diversion routes from one end of the segment to the other, supporting the strategy of reaching equilibrium flow in an emergency condition. To generate diversion routes for the purpose of reconnecting a disrupted segment, the shortest path patterns are generated through the formation of adjacent polygons using GIS. Accordingly, this paper proposes a segment vulnerability index based on the support of diversion routes. Additionally, the model introduces supporting vulnerability, a parameter for measuring the potential of a road segment becoming a supporting diversion route when its surrounding segments are disrupted. By adopting the Malaysian Peninsular road network as a case study, the developed index can assist transportation agencies in planning and maintaining road assets while prioritizing vulnerable road segments relative to the entire road network.

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

Pavement rutting refers to surface depression in the wheel-path along an asphalt road which causes loss of steering control and consequently leads to serious traffic accidents. Hence, it is necessary to develop powerful methods to accurately recognize pavement rutting during road condition survey. This study presents a novel computer vision-based model to automatically identify rutting on asphalt pavement road. The model is established based on a hybridization of image processing techniques (ITPs), least squares support vector classification (LSSVC), dynamic feature selection (FS) method, and forensic-based investigation (FBI). The ITPs, including Gabor filter and discrete cosine transform were employed to implement texture computation for image data. These techniques are used to generate an initial set of extracted features describing rutting and non-rutting states. The extracted features were then refined by a wrapper-based feature selection (FS) method to determine set of highly relevant features. LSSVC models were used to learn the categorization of rutting and non-rutting based on the refined features and hyper-parameters optimized by the FBI metaheuristic. The final LSSVC prediction model with the most desired prediction accuracy can be obtained once the process of the FBI’s optimization terminates. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city (Vietnam) to construct and evaluate the newly developed model. The statistical results obtained from a k-fold cross-validation have demonstrated that the hybrid FBI-LSSVC-FS model can achieve the most desired rutting recognition performance with accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Therefore, this paper contributes to the body of knowledge by proposing a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey.

Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

Snowplow Truck Performance Assessment and Feature Importance Analysis Using Machine-Learning Techniques

Snowplow trucks serve a crucial role in winter maintenance activities by removing, loading, and disposing of snow. A performance-monitoring and analysis process can assist transportation agencies in effectively managing snowplow trucks and maintaining the normal functioning of roadways. Previous literature suggests that most snowplow truck performance analysis is done through life-cycle cost assessment at the macro level to determine the optimal life cycle for the entire truck fleet. However, this can lead to a waste of resources and may incur bias due to ignorance of performance variations resulting from endogenous and exogenous features. More important, such analysis fails to identify the factors that contribute to performance deterioration. With the proliferation of operational data on snowplow operations, these concerns can be addressed through predictive machine-learning (ML) techniques in a data-driven fashion. In this study, we applied a popular ML technique, random forest (RF), to predict the performance of snowplow trucks, which was quantified via the rank of major repair times. Another ML technique, linear support vector machine (SVM), was also applied for benchmarking and comparison. Using the snowplow truck fleet managed by the Utah Department of Transportation (UDOT), both models were implemented and it was demonstrated that RF outperforms linear SVM with regard to prediction accuracy. Further, a feature importance analysis from the RF model can help transportation agencies to improve truck replacement strategies by identifying crucial performance factors. Lastly, a sample application of the developed prediction model using RF suggests the threshold of work intensity for preventing the rapid deterioration of truck performance in various working environments. Compared with the life-cycle cost analyses used in previous studies, the prediction model proposed here can help transportation agencies to better prioritize fleet replacement.

Development of Benefit Cost Analysis Tools for Evaluating Transportation Research Projects

Transportation research projects are a vital element for transportation agencies to accumulate new technology and knowledge to improve the efficiency and safety of transportation infrastructure. The purpose of this paper is to develop benefit cost analysis (BCA) tools to assist transportation agencies in evaluating transportation research projects. The BCA tools provide analysis methods for estimating the benefits for changes in the level of service (LOS) of a roadway, reductions in the vehicle travel time, changes in vehicle operating costs and reductions in the number of crashes. Three case studies were investigated to illustrate the usage of the BCA tools. The first case study evaluated the effectiveness of variable speed limit systems implemented along I-80 in southern Wyoming. The BCA tools estimated a benefit cost ratio of 6.64 indicating that the implementation of the project was economically justifiable. The second case study was a recent study investigating and developing mitigation strategies for the effects of truck traffic along the I-80 in southern Wyoming resulting in a benefit cost ratio of a 9.23 indicating that the project had the potential to be economically viable upon implementation. The final case study evaluated the effect of wildlife overpass and underpass crossings on the migrations of pronghorn and mule deer. The benefit cost ratio for the research project was 0.42 indicating that the project was not economically beneficial after the first year of implementation. The case studies indicated that the BCA tools are a beneficial tool for quantifying the economic benefits of a transportation research project.

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach.

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

Exploring the reach of departments of transportation tweets: What drives public engagement?

State Departments of Transportation (DOT) utilize Twitter frequently in order to disseminate critical information regarding traffic conditions (e.g., accidents, roadway closures, and congestion) to public. Even though factors driving the effectiveness and reach of private social media accounts have been studied extensively in the literature, they are still not fully understood by federal and state transportation agencies in the context of information dissemination using Twitter. In this paper, Twitter interaction analytics such as engagement rate and impressions, and other exogenous and endogenous variables are utilized to identify those factors that influence the effectiveness of this information dissemination. For this purpose, a methodological framework is proposed involving: (a) machine learning and naïve Bayesian techniques to classify and extract information from the DOT tweets, and (b) a selection model to identify the significant endogenous (e.g. time-to-post, tweet click, tweet engagement) and exogenous (e.g. demographical, socioeconomic, and land use characteristics) factors that drive the engagement rate as an indicator of information dissemination effectiveness. This framework is implemented via a case study application based on the Twitter account of Florida Department of Transportation (FDOT) District 3 region. Results reveal that factors such as tweets’ time-to-post, tweet analytics, roadway accident information, and demographic variables drive engagement to tweets. As such, this study can assist transportation agencies to calibrate their plans and policies towards providing better and faster information to the public, and improving the effectiveness and reach of their social media accounts.