Road Data Research Articles

Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road

How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00–8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images

Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas. Thus, the extraction of road network data in RSI is still a complex issue. In recent times, DL-based approaches have projected a famous development in image segmentation outcomes, but a lot of them still could not retain boundary data and attain high-resolution road segmentation maps while processing the RSI. Traditional convolutional neural networks (CNNs) demonstrate impressive performance in road extract tasks; however, they frequently encounter difficulties in capturing intricate details and contextual information. The study introduces a novel method, named Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), to tackle the challenges encountered in remote sensing images. The AOA-QDCNNRE technique aims to generate a high-resolution road segmentation map using DL with a hyperparameter tuning process. The AOA-QDCNNRE technique primarily relies on the QDCNN model, which integrates quantum technology (QC) with dilated convolutions to augment the network's capacity to capture local as well as global contextual information. In addition, the incorporation of the dilated convolutional technique effectively enhances the receptive field without sacrificing spatial resolution, enabling the extraction of precise road features. To develop the road extraction outcomes of the QDCNN approach, the AOA-based hyperparameter tuning process can be exploited. The AOA-QDCNNRE system's simulation results can be tested on benchmark databases, and the results indicate that the AOA-QDCNNRE method surpasses recent algorithms.

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.

Light Electric Vehicle Performance with Digital Twin Technology: A Comparison of Motor Types

Digital twin (DT) is proposed as a solution to reduce financial and time losses for vehicle manufacturers by streamlining the expensive and time-consuming processes of designing and implementing electric vehicle types and road assessments. The use of digital twins to monitor, evaluate, and optimize vehicle performance based on real-time road data is increasingly crucial in the DT concept. In this study, the digital twin of the CERYAN brand vehicle model has been employed to compare the performance of different motor types (PMSM, PMSM Brushless, BLDC/PMSM Brushless, and BLDC) in terms of energy consumption and acceleration at various inclination angles, utilizing real-time road data. According to the World Motorcycle Test Cycle (WMTC) standards, the motor type with the best performance parameters was determined as a PMSM brushless motor (5 kW). The main superior aspects of the proposed motor type are to achieve a 25% higher range than the BLDC Motor, 30% better grade ascending capability than the PMSM Motor, and 26% lower energy consumption than the PMSM brushless motor (6 kW).

Enhancing Traffic Flow and Safety at LMU Nurtanio Road Crossings - Abdul Rahman Saleh Intersection in Bandung

Bandung is the largest metropolitan city in West Java Province and serves as its provincial capital. One of the locations where vehicle queues or traffic congestion occurs is at the level crossings between the highway and railway tracks, including the direct crossing (JPL) at Andir on LMU Nurtanio Street - Abdul Rahman Saleh Street. The purpose of this research is to assess the performance of the existing road section, simulate it using the PTV VISSIM program, and find alternative solutions to the identified problems. In this study, primary data was obtained through direct field measurements, including vehicle volume, queue length, and road geometry data. Additionally, secondary data on the number of trains passing through the crossing was obtained from PT Kereta Indonesian Fire. The PTV VISSIM program used in this analysis is a student-licensed program, which implies limitations and simplifications in the analysis. The performance calculation of the road section yielded a degree of saturation (Ds) value of 0.73 for LMU Nurtanio Street and 0.77 for Abdul Rahman Saleh Street. According to the road service levels set by the government, both roads fall below service level C. The PTV VISSIM modeling results indicated a queue length of 66.59 m for LMU Nurtanio Street and 105.18 m for Abdul Rahman Saleh Street. Bandung study uses PTV VISSIM to assess LMU Nurtanio - Abdul Rahman Saleh Streets, revealing congestion issues. Queue lengths highlight improvement needs.

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

Simulating whole-body vibration for neonatal patients on a tire-coupled road simulator.

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

Analysis of The Relationship of Land Transportation With GRDP in Wholesale and Retail Trade Sector, Car and Motorcycle Reparation

Transportation Land transportation influences national economic growth. Due to the existence of land transportation modes, the national economy can continue to grow from year to year. This study analyzed the variable relationship between land transportation through road length data, road condition data, and GRDP in the wholesale and retail trade sectors. The results of this study show that good national road conditions have a very low positive correlation (0.079). And has an insignificant relationship to GRDP (0.665 0.05). National road conditions have a shallow level of positive correlation (0.074). And has a negligible relationship to GRDP (0.679 0.05). Damaged national road conditions have a superficial negative correlation (-0.040). And has an insignificant relationship to GRDP (0.823 0.05). National road conditions severely damaged did not pass the requirements test, normality test results (0.024 0.05). Provincial road conditions are good, with a positive correlation (0.681). And has a significant relationship to GRDP (0.00 0.05). Medium local roads have a good level of positive correlation (0.586). And has a substantial relationship to GRDP (0.00 0.05). The condition of damaged provincial roads has a low negative correlation (-0.103). It has an insignificant relationship to GRDP (0.563 0.05). The condition of the badly damaged national road does not pass the requirements test, the normality test (0.024 0.05). Moderate district/city road conditions have an average positive correlation (0.356). And has a significant relationship to GRDP (0.039 0.05). The condition of district/city roads is damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05). The condition of district/city roads is badly damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05). The condition of district/city roads is damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05). The condition of district/city roads is badly damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05). The condition of district/city roads is damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05). The condition of district/city roads is badly damaged and has a shallow correlation level of (0.105). It has an insignificant relationship with GRDP (0.555 0.05).

Pencarian Rute Terdekat Menghindari Jalan Rusak Menggunakan Algoritma A-Star

In our daily lives, we are often faced with the challenge of broken roads that can affect our travel. To solve this problem, a web-based map system has been developed. The system uses road data from OpenStreetMap and the A-star algorithm to find the shortest route that avoids bad roads. The website is built using JavaScript, PHP, HTML, and CSS, with the support of a MySQL database that stores information on damaged roads and user accounts. The process of using the system starts with registration and account login. Users can view the map on the main page, report the damaged road with photo evidence equipped with a time watermark and coordinate data, and view their initial route. The admin then validates the damaged road data, and the system automatically constructs an alternative route that avoids the verified damaged roads. The accuracy of the A-star algorithm in finding routes depends on the amount and correctness of the damaged road data entered by the admin. In further development, special attention is paid to admin validation, data security, integration with location service provider APIs, and performance optimization. Richer reporting features, multi-platform support, and notifications to users could also be valuable additions. With good documentation and thorough testing, the system is expected to provide an effective solution and be well received by users.

Road traffic noise exposure assessment based on spatiotemporal data fusion

The current static assessment methods for road traffic noise exposure have limitations in representing spatiotemporal variations and only account for the noise exposure of registered residents. To address these issues, this study proposes an assessment method by fusing spatiotemporal noise and population distributions. This approach estimates spatiotemporal noise distributions through real-time noise monitoring data and an update model of noise source intensity calibrated by historical noise monitoring data and road segment speed data. Meanwhile, spatiotemporal population distributions are generated by aggregating standardized spatiotemporal trajectories of users extracted from mobile phone data. The proposed method is applied to evaluate the population exposure to traffic noise in a Chinese city in different hourly periods. The results demonstrate that this method is a quantitative tool for assessing road traffic noise exposure dynamically, providing policymakers with valuable information to locate hotspots of traffic noise over short periods of time.

A review of icing prediction techniques for four typical surfaces in low-temperature natural environments

As a common phenomenon in nature and industrial fields, icing always adversely affects life, and plays negative effects. Although series of anti-/de-icing techniques are widely investigated, icing prediction techniques on surfaces under low-temperature natural environment are more important due to effectively reduce or even prevent icing caused harms. To understand the current research progress of icing prediction techniques, four typical static and moving surfaces that are prone to ice formation were selected as the research objects in this study, including road, transmission line, wind turbine blade and aircraft surfaces. As summarized, prediction methods mainly include physical models, statistical models, and machine learning. For road surface, statistical analysis, theoretical analysis and data mining methods are widely used, with the average prediction accuracy reaching 80%. For wind turbine blade and transmission line surfaces, both model-driven and data-driven methods were used, resulting in average prediction accuracies of 80% and 90%, respectively. For aircraft surface, the method of numerical analysis combined with machine learning is the mainstay, with a deviation of less than 20%. Summary and outlook are finally given, which are beneficial for the optimization of ice prediction technology in different industrial fields.

A New Urban Built-Up Index and Its Application in National Central Cities of China

Accurately mapping urban built-up areas is critical for monitoring urbanization and development. Previous studies have shown that Night light (NTL) data is effective in characterizing the extent of human activity. But its inherently low spatial resolution and saturation effect limit its application in the construction of urban built-up extraction. In this study, we developed a new index called VNRT (Vegetation, Nighttime Light, Road, and Temperature) to address these challenges and improve the accuracy of built-up area extraction. The VNRT index is the first to fuse the Normalized Difference Vegetation Index (NDVI), NPP-VIIRS Nighttime NTL data, road density data, and land surface temperature (LST) through factor multiplication. To verify the good performance of VNRT in extracting built-up areas, the built-up area ranges of four national central cities in China (Chengdu, Wuhan, Xi’an, and Zhengzhou) in 2019 are extracted by the local optimum thresholding method and compared with the actual validation points. The results show that the spatial distribution of VNRT is highly consistent with the actual built-up area. THE VNRT increases the variability between urban built-up areas and non-built-up areas, and can effectively distinguish some types of land cover that are easily ignored in previous urban indices, such as urban parks and water bodies. The VNRT index had the highest Accuracy (0.97), F1-score (0.94), Kappa coefficient (0.80), and overall accuracy (92%) compared to the two proposed urban indices. Therefore, the VNRT index could improve the identification of urban built-up areas and be an effective tool for long-term monitoring of regional-scale urbanization.

DISTRICT ROAD DATABASE MANAGEMENT BASED ON GEOGRAPHIC INFORMATION SYSTEM TO SUPPORT REGENCY ROAD MAINTENANCE (CASE STUDY: SINTANG DISTRICT)

Road maintenance on the section of the road classified as a district road in the Sintang sub-district is the responsibility of the Sintang District Government. The financial constraints of the Sintang District Government, allocated annually in the Regional Revenue and Expenditure Budget (APBD) of Sintang District, pose a challenge in realizing the plan for district road maintenance, whereas funding for road maintenance needs to be budgeted regularly to maintain the road conditions in good shape, therefore, in this case, stakeholders or decision-makers are expected to be able to consider and decide which road sections should receive recommendations for "urgent," "immediate," and "postponed" maintenance. The chosen road maintenance points must not solely prioritize the lowest construction costs, accessibility, or strategic area value. Indeed, in this case, an analysis is required to integrate various criteria, thus serving as the basis for a decision as the best choice. The research method is presented descriptively, explaining the determination of the scale for recommending maintenance of district roads. The data collection technique is carried out using primary data in the form of documentation photos of each road section and road condition data assessed using the Road Condition Index (RCI) method or road roughness index, as well as secondary data consisting of road inventory data, road condition data, accident data, area size data, socioeconomic data, and maps. The fulfillment of the Minimum Service Standards (MSS) for roads is then calculated based on the collected data. This includes both the MSS for individual road sections and the MSS for the road network. The fulfillment of the road network's MSS is divided into three aspects: accessibility, mobility, and safety. Based on the analysis of the MSS, it is categorized into three recommendations: "urgent," "immediate," and "postponed." After obtaining the recommendations for each road section, they are presented as a map. After obtaining the research results, two road sections fall under the "urgent" recommendation for road maintenance. For immediate recommendations, there are 15 road sections. Meanwhile, 44 road sections are included in the delayed recommendations for road maintenance to be carried out.

Expressway ETC Transaction Data Anomaly Detection Based on TL-XGBoost

China’s widely adopted expressway ETC system provides a feasible foundation for realizing co-operative vehicle–infrastructure integration, and the accuracy of ETC data, which forms the basis of this scheme, will directly affect the safety of driving. Therefore, this study focuses on the abnormal data in an expressway ETC system. This study combines road network topology data and capture data to mine the abnormal patterns of ETC data, and it designs an abnormal identification model for expressway transaction data based on TL-XGBoost. This model categorizes expressway ETC abnormal data into four distinct classes: missing detections, opposite lane detection, duplicated detection and reverse trajectory detection. ETC transaction data from a southeastern Chinese province were used for experimentation. The results validate the model’s effectiveness, achieving an accuracy of 98.14%, a precision of 97.59%, a recall of 95.44%, and an F1-score of 96.49%. Furthermore, this study conducts an analysis and offers insights into the potential causes of anomalies in expressway ETC data.

Extracting multilane roads from OpenStreetMap through graph convolutional neural network and road mesh relationship analysis

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition.

Road network free flow speed estimation using microscopic simulation and point-to-point travel times

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation.This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently.Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable.

Research on Early Warning Technology for High-Grade Highway Pavement Damage

Road surface damage on high-grade highways has a serious impact on driving safety. Conventional road surface damage detection mainly adopts manual detection technology. The detection efficiency is low, and the detection results are easily disturbed by human factors, which is not conducive to objective and accurate evaluation of road damage. To solve the problems of accurate collection, efficient identification, and correct positioning of high-grade road surface damage data, the research group has studied the early warning technology of road surface damage. The proposed early warning technology scheme enables drivers to take preventive driving action in a timely manner, avoids the occurrence of traffic accidents, and provides drivers with reasonable avoidance schemes to effectively prevent the occurrence of traffic accidents.

An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques

AbstractInsufficient progress in the development of national highways and state highways, coupled with a lack of public awareness regarding road safety, has resulted in prevalent traffic congestion and a high rate of accidents. Understanding the dominant and contributing factors that may influence road traffic accident severity is essential. This study identified the primary causes and the most significant target‐specific causative factors for road accident severity. A modified partitioning around medoids model determined the dominant road accident features. These clustering algorithms will extract hidden information from the road accident data and generate new features for our implementation. Then, the proposed method is compared with the other state‐of‐the‐art clustering techniques with three performance metrics: the silhouette coefficient, the Davies–Bouldin index, and the Calinski–Harabasz index. This article's main contribution is analyzing six different scenarios (different angles of the problem) concerning grievous and non‐injury accidents. This analysis provides deeper insights into the problem and can assist transport authorities in Tamil Nadu, India, in deriving new rules for road traffic. The output of different scenarios is compared with hierarchical clustering, and the overall clustering of the proposed method is compared with other clustering algorithms. Finally, it is proven that the proposed method outperforms other recently developed techniques.

Map Reduce approach for road accident data analysis using data mining techniques

Map Reduce approach for road accident data analysis using data mining techniques