Road Data Research Articles

A review on the impacts of connected vehicles on pavement management systems

ABSTRACT The advent of connected vehicles (CVs) starts a new era in the field of pavement evaluation. With CVs on roads, the wide fleet of vehicles will constantly transmit pavement data to central servers and also share the necessary information with one another. Such a system presents opportunities for continuous, crowdsourced, and cost-effective monitoring of pavement assets and the development of pavement management databanks. This study reviewed the published studies on CV-based pavement evaluation, summarised the findings, and identified research gaps. In the published resources, 9 different applications have been proposed for CVs in the field of pavement monitoring (some applications are discussed in more than 1 resource): roughness measurement (proposed in 37.5% of resources), distress detection (31%), gathering road weather data (22%), slippery pavement recognition (19%), distress classification (12.5%), road geometry/type evaluation (9%), road amenities detection (9%), skid resistance measurement (6%), and distress intensity measurement (4.5%). The performed case studies were also scrutinised based on utilised sensors, data types, analysis methods, and connectivity messages and networks. In the end nine research subjects with no attention in the previous studies and five research subjects with insufficient attention in the published articles were identified.

Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning

Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.

Hierarchical eco-driving control for plug-in hybrid electric vehicles under multiple signalized intersection scenarios

With the development of vehicle-to-everything (V2X) and autonomous driving technologies, plug-in hybrid electric vehicles (PHEVs) enable to absorb surrounding information to enhance economic driving. This study proposes a dynamic inverse hierarchical optimization method, which incorporates traffic-signal phase and timing as well as road data, to plan economic velocity and improve PHEV energy consumption. The hierarchical control framework determines the desired speed and arrival time in the upper layer using the shortest path faster algorithm. The lower level accounts for multi-objective velocity planning based on particle swarm optimization and Pareto theory. The inverse layering method solves the economic velocity optimization problem. With the support of V2X and autonomous driving technologies, the method enhances energy efficiency and computational efficiency in PHEVs through dynamic inverse hierarchical optimization. Simulation results highlight that the proposed algorithm leads to 7.43% improvement in energy consumption economy and the reduced calculation time, compared to the existing solutions. The hardware-in-the-loop experiments validate the real-time applicability of the proposed algorithm.

Overcoming driving challenges in complex urban traffic: A multi-objective eco-driving strategy via safety model based reinforcement learning

This study proposes a novel eco-driving control strategy for connected and automated hybrid electric vehicles, which utilizes deep reinforcement learning (DRL) to optimize various aspects of driving performance, including fuel economy, ride comfort, and travel efficiency, in complex urban traffic scenarios. The proposed strategy incorporates a driving safety model that predicts potential risk associated with the DRL agent's planned speed, thus ensuring the safety of the DRL based eco-driving strategy. Additionally, we propose a multi-objective composite reward function design scheme that considers various constraints caused by traffic elements, such as traffic lights, preceding vehicles, road curvature, and speed limit. This design scheme enables the proposed strategy to effectively adapt to diverse driving challenges in complex urban traffic scenarios. To evaluate the proposed strategy, we develop an urban traffic simulation model based on real-world road and traffic data from Shanghai, China. This model is used as the test scenario and can reflect real urban traffic conditions. The simulation results demonstrate the capability of the proposed strategy to safely and efficiently control vehicles to complete driving tasks in complex urban scenarios. Moreover, the proposed strategy excels in simultaneously optimizing the driving comfort and fuel consumption of the controlled vehicle.

Detecting School Zones on Florida’s Public Roadways Using Aerial Images and Artificial Intelligence (AI2)

Collecting up-to-date roadway geometry data is essential for transportation agencies so they can undertake planning, maintenance, design, and rehabilitation of roadways. Collection methods can be categorized into two distinct groups: land-based methods (e.g., field inventories, mobile mapping, and image logging); and aerial-based methods (e.g., satellite imagery, drones, and laser scanning). Because using land-based methods for thousands of miles of highways is tedious, costly, and risky for crew members, there is a need to develop better methodologies to collect these data faster, and more safely and cheaply. The increasing availability of high-resolution images and recent advances in computer vision and object detection techniques have enabled the automated extraction of roadway geometry features. This novel study proposes a computer vision-based methodology to detect school zone pavement markings from high-resolution aerial images and determine school zones on Florida’s public roadways. This is critical information for transportation agencies, and they use it for a variety of different purposes: identifying those markings that are old and invisible; comparing the school zone locations with other geometric features such as crosswalks; and analyzing crashes that occur around the zones. Compared with the ground truth data obtained for Leon County, Florida, 94% accuracy was observed at the 90% confidence level. The model was then used to detect school zones in Orange County, Florida, and approximately 500 school zone markings were identified automatically. The road geometry data extracted can be integrated with crash and traffic data to advise policymakers and roadway users.

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters.

Exploring the impact of truck traffic on road segment-based severe crash proportion using extensive weigh-in-motion data

Fixed proportions by severity assumption in Highway Safety Manual could be violated since the proportions of severe crashes are likely to be affected by truck traffic characteristics. Previous studies often used truck proportion as the key indicator of truck traffic. However, it considered different trucks the same regardless of their actual weight. Therefore, this paper aimed to explore the impact of truck traffic characteristics, especially actual weight, on the proportions of severe crashes on road segments while controlling for other contributing factors. Extensive Weigh-in-Motion (WIM) data from five-year (2011–2015) 88 WIM stations in New Jersey were utilized to capture detailed vehicle weight information and other truck traffic-related characteristics. Road features, traffic volume, and crash data were also collected and aggregated for road segments. To account for the bounded nature of Fatality and Injury Proportion (FIP), one-part and two-part Fractional Regression Models (FRMs) were developed, and the link functions were appropriately selected based on corresponding statistical tests. The results show that the mean of vehicle weight was significant and positively related to the FIP of nonzero-FIP road segments while controlling for other contributing factors. For the road segment with a nonzero FIP, if the mean of vehicle weight increased by 1 kip, the total crash FIP, single-vehicle crash FIP, and multiple-vehicle crash FIP for the road segment with nonzero FIP increased by 3.3%, 3.4%, 2.2% respectively. This study contributes to the literature by building a link between actual vehicle weight measured in the traffic flow and road segment crash severity.

Site suitability analysis for waste to energy facility in Jos Metropolis, Plateau State, Nigeria

Solid waste management has emerged as a significant environmental challenge in developing countries, primarily driven by population growth, rapid urbanization, and the absence of effective sustainable strategies and policies. The city of Jos is not immune to these issues, as limited funding and expertise have hindered the local municipalities' ability to manage municipal solid waste efficiently. This study attempted to identify suitable sites for a waste-to-energy facility in Jos Metropolis, Plateau State, Nigeria. The study focused on estimating the amount of waste generated and determining a suitable site for siting a waste-to-energy facility in the study area. The data used were geographic coordinates of existing dumpsites which were acquired using Garmin eTrex R 20x Handheld GPS, topographic map which was obtained from Landsat (Glovis) website, landuse/landcover map which was obtained from Sentinel-2 ESRI website, road and waterbody data which were obtained from Open Street Map and administrative map of Jos metropolis which was obtained from ministry of lands, survey and town planning, Plateau state. Multi-criteria analysis, waste estimation formula, population projection formula, and the Analytic Hierarchy Process (AHP) in conjunction with a Geographic Information System (GIS) model were employed for analysis. The results revealed that an estimated 221 tons of waste is generated per day in Jos metropolis with a projected increase to 299 tons by 2032, reflecting a 35.29% growth in a decade. The multicriteria analysis revealed that only 2.8% (23.3 km²) of the area exhibited high suitability for a WtE facility, with Kuru 'A' identified as the optimal choice due to its advantageous proximity to power substations, well-established road networks, existing dumpsites, and minimal risks to people and economic activities. Based on the findings, the study recommends improving data collection efforts, expanding the suitability criteria, and involving stakeholders in decision-making processes to enhance waste management effectiveness.

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

One of the primary objectives of transportation engineering is to increase the safety of road infrastructure. This study seeks to determine the relationship between geometric design parameters in relation to road accident criteria based on accident hotspots on Jordan’s Desert Highway. The road accident data (from 2016 to 2019) were collected from the Jordan Traffic Department. The spatial pattern of hotspots was identified using a GIS tool named Getis-Ord Gi* based on the severity index of road accidents. A topographic survey was conducted to investigate the road alignment and intersections at hotspot locations. The study utilized the Structural Equation Modeling (SEM) technique via SmartPLS to highlight the correlation between geometric designs in relation to road accidents. The hotspot analysis (Gits-Ord Gi) discovered 80 road accident hotspots along the highway. The study found that horizontal alignment and road intersections significantly impact road accidents in hotspot locations. Furthermore, vertical alignment has no effect on road accidents in hotspot areas. The study enhanced the comprehension of the factors associated with road geometrics and intersections that affect the occurrence of road accidents.

A Lightweight Privacy-preserving Path Selection Scheme in VANETs

<p>With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure and efficient road navigation while protecting privacy is a crucial issue. In this paper, we propose a privacy-preserving path selection protocol that supports a token as the object in the oblivious transfers, which effectively reduces the communication overhead. In addition, a lightweight dual authentication and group key negotiation protocol is provided to support dynamic joining or leaving of group members. Moreover, it can guarantee the security of forward data. After experimental analysis, the proposed protocol has high security and efficiency.</p> <p> </p>

Reconciling Pavement Condition Data from Connected Vehicles with the International Roughness Index from Standard Monitoring Equipment Using Physics-Integrated Machine Learning

This study investigates the possibility for augmenting infrequent standard pavement survey data with spatiotemporally continuous sensor data crowdsourced from connected vehicles. A framework is proposed to leverage physics-integrated machine learning (ML) models to reconcile the two disparate data sources. The proposed approach is demonstrated for the International Roughness Index (IRI). Using the quarter-car simulation, vertical acceleration data records were generated based on random stratified sampling from probability distributions of vehicle suspension properties at different speed levels on elevation profiles collected from various pavement sections. An ML model was pretrained to approximate the quarter-car simulation. Using transfer learning, the parameters of the outer layers of the model were finetuned to adapt model output to the measured values, while the parameters of the inner layers were fixed to preserve the embedded knowledge of the physical behavior of the suspension. The resulting physics-integrated ML model can predict the standard IRI value of a pavement section using speed, suspension properties, and features of the acceleration response from vehicles traversing that road section. The evaluation results on independent training and test datasets showed high accuracy, precision, and generalization capability, especially on smoother road profiles. This is attributed to the limited training data for rougher roads. After averaging the predicted IRI from all vehicles passing a certain road segment, a correction function is used to rectify the model’s under-predicted output and estimate the actual IRI value for that segment. High-frequency data resulting from the proposed approach can enhance pavement design, maintenance management, and asset management practices.

Statical Analysis of Road Accident in Indapur City

Abstract: Accident analysis studies aim at the identification of high rate accident location, safety deficient areas and the accidents prone zones. For this purpose the road accident data for seven consecutive years, pertaining for a city has to be used. The road geometry will be measured in accident prone location to find out the cause of accident, based on these result, suggestion are to be provided to reduce the frequency of accidents at that particular place. Accidents prediction is done by regressing traffic volume with accident data to obtain an equation which could apprehend the forth coming accidents in the future. Preventive measures regarding the improvement of traffic condition for minimizing accidents rate are suggested

Bulungan-Lebak Road Service Period, Jepara Regency Post Repair

Traffic volume is one of the causes of road damage. With the increasing number of vehicles, it is possible that the road will be damaged in a relatively short time. The purpose of this study was to determine the service life of the road and the relationship between vehicle volume and the level of road damage on the Bulungan-Lebak road section. In this study, researchers used visual methods to see the type of damage and then measured the damaged road sections and counted the number of vehicles during peak hours and obtained road age data. In this way, 3 (three) data are obtained, namely road inventory data, damage value data and time data after the last road was repaired

Tire Wear Sensitivity Analysis and Modeling Based on a Statistical Multidisciplinary Approach for High-Performance Vehicles

One of the main challenges in maximizing vehicle performance is to predict and optimize tire behavior in different working conditions, such as temperature, friction, and wear. Starting from several approaches to develop tire grip and wear models, based on physical principles, experimental data, or statistical methods available in the literature, this work aims to propose a novel tire wear model that combines physical and statistical analysis on a large number of high-performance vehicle telemetries, tracks, and road data, as well as tires’ viscoelastic properties. Another contribution of this multidisciplinary study is the definition of the functional relationships that govern the tire–road interaction in terms of friction and degradation, conducting a thorough analysis of the car’s telemetry, the track and asphalt features, and the viscoelastic properties of the tires.

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller.

This study presents the effectiveness of an anti-jerk predictive controller (AJPC) based on active aerodynamic surfaces to handle upcoming road maneuvers and enhance vehicle ride quality by mitigating external jerks operating on the body of the vehicle. In order to eliminate body jerk and improve ride comfort and road holding during turning, accelerating, or braking, the proposed control approach assists the vehicle in tracking the desired attitude position and achieving a realistic operation of the active aerodynamic surface. Vehicle speed and upcoming road data are used to calculate the desired attitude (roll or pitch) angles. The simulation results are performed for AJPC and predictive control strategies without jerk using MATLAB. The simulation results and comparison based on root-mean-square (rms) values show that compared to the predictive control strategy without jerk, the proposed control strategy significantly reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling at the cost of slow desired angle tracking.

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.

Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Accurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regressive Integrated Moving Average model (ARIMA) is a classical statistical analysis model, which is suitable for processing non-stationary time-series data. Long- and Short-Term Memory (LSTM) is a special cyclic neural network that can learn long-term dependent information in time series. Both are widely used in the field of temporal prediction. In view of the lack of time-series prediction in the tunnel deformation field, the body of this paper uses historical data of the Xinjian Road and the Dalian Road tunnel in Shanghai to propose a new way of modeling based on single points and road sections. ARIMA and LSTM models are applied in comprehensive experiments, and the results show that: (1) Both LSTM and ARIMA models have great performance for settlement and convergence deformation. (2) The overall robustness of ARIMA is better than that of LSTM, and it is more adaptable to the datasets. (3) The model prediction performance is closely related to the data quality. ARIMA has more stable performance under the lack of data volume, while LSTM has better performance with high-quality data and higher upper limit.

Study on the influence of high and low temperature environment on the energy consumption of battery electric vehicles

In order to study the influence of high and low temperature environments on the energy consumption of battery electric vehicles, this paper takes Tianjin as an example to collect the actual road data of 8 battery electric passenger cars for eight months. It determines the correlation between each characteristic parameter and energy consumption based on the maximum mutual information coefficient of short stroke segments, of which the average driving speed correlation is the highest, which is 0.785. Given the two factors that have a greater impact on energy consumption in high and low temperature environments: driving resistance and air conditioning on motion and idle energy consumption analysis, the study shows that in terms of driving resistance, high temperature and low temperature energy consumption are reduced and increased by 5.4% and 13.3% respectively relative to normal temperature; The energy consumption of high temperature and low temperature air conditioner switching on increased by 17.12% and 47.48% respectively compared with the air conditioner off.

Empirical Analysis of the Impact of Road and Construction Capital Expenditure on Economic Growth: Evidence from Nigeria

This study examines the impact of road and construction capital expenditure on economic growth in Nigeria. The study covered the period of 1981 to 2020 and employed the Autoregressive Distributed Lag Model (ARDL) to analyze the annual time series data collected from National Bureau of Statistics, BudgiT and Central Bank of Nigeria Statistical Bulletin. While the data for road and construction capital expenditure were collected from NBS and BudgiT database, the data for gross domestic product and commercial banks’ credit to the construction sector were obtained from CBN statistical Bulletin. The findings reveal that government capital expenditure on road and construction has a negative and statistically insignificant impact on economic growth in Nigeria. The result further shows that commercial banks’ credit to the construction sector has a positive but statistically insignificant impact on economic growth in Nigeria. The study therefore recommends that the government should ensure that allocations to this sector are properly monitored in order to enhance economic growth. Government should also encourage banks to give more loans for infrastructural development by reducing the bank rate.

Understanding children's cycling route selection through spatial trajectory data mining

A sustainable alternative transportation mode to address growing transportation and environmental stress is cycling, which is eco-friendly and healthy for humans. Improving the quality of the bicycling experience is crucial for increasing bicycle use. Good bicycling experience is more critical for child bicyclists because they are less experienced and need more space for error. Therefore, a scientific assessment of child bicyclist perception of selected route safety, comfort, and environment is of great interest. Finding an effective way to learn child bicyclist behavior and help them reduce cycling risk is necessary. In response to this need, we utilize a data mining model to develop a methodology for measuring children's bicycling route safety conditions by evaluating multiple road safety-related features. The proposed method uses a set of route features representing the situation of street environments extracted from state data and first-hand children’s bicycle trajectory data collected using Global Positioning Systems (GPS) from volunteer children bicyclists. A random forest (RF), a well-known classifier, is adopted to predict child bicyclists' behavior. We extract the different route segments between children’s selected routes and the shortest path to learn the child bicyclists' behavior and use the selected best features to interpret their changing cycling behavior. The result shows that children bicyclists' behavior could be analyzed by giving trajectory and nearby road safety situation data. Our model achieves a promising accuracy with an average rate of 92% over multiple scenarios, demonstrating the proposed method's feasibility and the effectiveness of selected features. In addition, we compare our feature effectiveness with the state's generated road safety score data to evaluate the feature robustness. Our features outperform the safety score feature with an average of 10% improvement in prediction accuracy. Furthermore, our method proposes a model framework that can be applied to different study regions and adult bicycling behavior learning.