Road Characteristics Research Articles

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility <200 m, where the severe weather and visibility <200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two-wheeled vehicles in China.

Understanding the route choice preferences of private and dock-based public bike users using GPS data in Seoul, South Korea

Analyzing route preferences is crucial for comprehending how individuals choose their travel paths, assessing road facilities, and anticipating upcoming traffic conditions. This study aimed to explore the factors influencing route selections made by private and public bike users and compare their decisions during peak and off-peak hours. Global positioning system (GPS) traces of 12,106 private bike users and 74,397 public bike users from a single weekday in Seoul, South Korea, were used. A map-matching process aligned the GPS data with a comprehensive cycling network encompassing bikeways, sidewalks, neighborhood roads, and their street attributes such as urban, park, and river streets. Choice sets composed of the shortest and 10th alternative routes were created to compare various road features with actual routes. By employing a path-size logit model to address overlapping issues among the routes, the results showed that buffered bike lanes separated from car flows positively influenced the route selection of bicyclists. Bike boulevards and shared sidewalk bikeways negatively impacted route decisions made by public bike users, with a more pronounced effect during peak hours. Furthermore, private bike users showed a decreased preference for routes in residential areas during peak hours, while an increased preference during off-peak hours. Moreover, public bike users preferred selecting routes in the presence of shopping-stores or amenities as well as public transit stations, with these effects being more pronounced during peak hours. These findings are expected to contribute to capturing valuable insights into the route preferences of bicyclists for the improvement of bicycle infrastructures.

Proactive safety assessment of urban through-roads based on GPS data

Road safety is a worldwide issue, while urban roads account for a high share of serious road injuries, especially involving vulnerable road users, such as pedestrians or cyclists. Specifically, the safety of major roads through built-up areas (through-roads) is insufficient due to mixed traffic conditions including vulnerable road users, varying driving behaviour, and many disruptions, which are combined with excessive speed.. In this context, various traffic calming measures have been implemented to improve road safety, such as gateways or pedestrian refuge islands. However, the specific safety impacts of traffic calming combined with specific characteristics of through-roads are often unknown, since most traditional evaluations have been limited by small sample sizes of crash data, as well as wide variations in physical and road characteristics. To overcome the limitations of crash-based evaluations, we used the GPS-based data from a sample of 21 Czech and 12 Polish through-roads to develop the Speed-Safety Index, which combines speed, speed variance, and traffic volume. Our study has three novelty features: (1) To assess safety, we used speed and speed variance simultaneously. (2) To complete the missing link between specific traffic calming measures and safety, we validated the statistical relationship between the developed Speed-Safety Index and crash history. (3) To prove the usefulness of the developed index, we also showed its practical interpretation by proving the effect of spacing between traffic calming measures on safety. The index proved to be well correlated to crash frequency and it also proved the effect of spacing between traffic calming measures: the longer spacing, the smaller speed-reducing effect. The paper concludes with a discussion on the limitations, which we plan to address in further research, by moving from the current macro-perspective (Speed-Safety Index on the level of through-roads) to the micro-perspective (focusing on individual directions, locations, and traffic calming measures). We also plan to investigate the method’s applicability in different contexts. If the approach proves feasible, with reliable and valid results, it may become an alternative for a proactive network-wide road assessment, as called for by the European Road Infrastructure Safety Management Directive.

Using the ISO Detection response task to measure the cognitive load of driving four separate vehicles on two distinct highways

The ISO Detection Response Task (DRT) is a standard tool for assessing drivers’ cognitive load and it has primarily been used to measure the cognitive load of completing non-driving tasks and interacting with vehicle systems. In this study we use the DRT to measure the workload of driving four separate vehicles (a 2019 Tesla Model 3, a 2018 Cadillac CT6, a 2018 Volvo XC90, a 2019 Nissan Rogue) in manual mode and on two distinct roadways (US Interstate Highway 15 and 80) in and around Salt Lake City, UT. Results showed that the unique road characteristics of I-80 resulted in higher levels of cognitive load as demonstrated by the slower DRT response times. Likewise, different levels of workload were found across the four vehicles, with higher workload levels found for one of the four vehicles. This study expands the use of the DRT outside its original area of application, and advances it as a tool to assess the cognitive demand induced by varying road and vehicle characteristics.

THE ASSESSMENT OF SCENIC ATTRACTIVENESS ON COASTAL WAYS: A CASE STUDY OF PERSEMBE-BOLAMAN (ORDU-TURKEY)

The biophysical characteristics of the areas that can be seen while travelling on motorways have an impact on the perception of the landscape. Highways provide diverse landscape experiences to travellers according to their natural and cultural qualities. Especially coastal ways that combine with nature and the sea have a high potential for scenic attractiveness. This study aims to analyse the scenic attractiveness of coastal ways using GIS and RS techniques. Persembe-Bolaman coastal way in the Black Sea Region of Turkey was selected as a case study. Three road features and seven viewshed features that are assumed to affect landscape attractiveness on the Persembe-Bolaman coastal road were selected. The data set of these features was categorised into three clusters by k-means clustering, one of the unsupervised learning algorithms. The most attractive cluster in terms of scenic attractiveness was selected by determining the characteristics of the clusters. In conclusion, it was found that the scenic attractiveness was the highest in Cluster-1, which corresponds to 46.3% of the selected route.

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images.

Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people's lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

RDPGNet: A road extraction network with dual-view information perception based on GCN

Automatic road extraction from satellite images is a popular research hotspot topic in the field of remote sensing. However, the complexity of road features and structures hinders their precise extraction. Road features have different saliency due to the occlusion of other objects and diverse road types. Road structures are scattered across various locations and affected by geographical conditions, population distribution, and transportation demands. These issues render current methods based on convolutional neural networks prone to fragmented and incomplete road extraction results. To address these issues, a Road Extraction Network with Dual-View Information Perception Based on GCN (RDPGNet) is proposed, where the GCN-based dual-view perceptron (GDVP) uses the superior information interaction capability of the GCN to explore road information under a dual view. In the GDVP, we first design the road feature saliency graph reasoning module (RFSG) and road structure homogeneous space graph reasoning module (RSHS) based on the features and structures. The RFSG obtains the similarity of road feature information in different regions as a similarity matrix to participate in the graph reasoning process, ensuring that equal treatment is given to regions with varying saliency levels. The RSHS projects the road structure onto a corresponding homogeneous space while aggregating and interacting with information through graph convolution, thereby enhancing the network’s perception of roads in diverse locations. Second, because multi-view information usually shares a potential common representation, a multi-view information fusion and alignment strategy (MVFA) is designed to comprehensively model road information. Experimental results obtained from two public datasets indicate that RDPGNet outperforms other state-of-the-art networks.

Research on the influence of traffic conditions on noise level in the passenger compartment of motor vehicles

The research investigates the noise levels measured in the passenger compartments of several automobiles. These vehicles are powered by various engines, travel on various highways, and function at differing speeds and acceleration. The results are visually processed using specialist software. The study investigates the effects of three major sources of automotive noise: engine operation, tire-road contact, and friction between the car body and the air. The immis noise in the passenger compartment is reported to be mostly impacted by the engine’s operational characteristics (load and speed) at lower speeds. In contrast, as travel speed increases, the noise produced by tire-road contact becomes more substantial, and at greater speeds, the noise produced by the interaction between the car body and the air becomes more significant. As a result, the engine type, tire and road characteristics, vehicle speed, and passenger compartment acoustic insulation are all important elements in influencing noise emission in the passenger compartment.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Co-Optimization of Speed Planning and Power Management for Automated HEVs in Unstructured Road Scenarios

With the development of intelligent and connected vehicle technology, the simultaneous optimization of powertrain performance and vehicle motions provides an unprecedented perspective to further explore the energy-saving potential of hybrid electric vehicles (HEV). However, it is still very challenging because of the complex driving environment and large computational burdens, and hence, the co-optimization strategy under unstructured scenarios has rarely been studied. For unstructured road driving scenarios, this paper proposes a hierarchical optimal control system combing speed planning and energy management for an autonomous HEV. In the first layer, a vehicle stability constraints system is designed with consideration of both external road characteristics and vehicle dynamics to limit the longitudinal speed, preventing the vehicle from excessive yaw, sideslip, and rollover during a complex, unstructured road trip. Then, the improved gray wolf optimizer is designed to efficiently generate optimal speed and SOC trajectories to guide the vehicle motion behavior and power split while ensuring driver safety. In the second layer, a mechanical implementation system is applied to fulfill the control system framework. Finally, the performance of proposed co-optimization is comprehensively verified in terms of fuel economy, vehicle maneuverability, computational effectiveness, and adaptability. Compared with the sequential optimization, the improvements by co-optimization range from 9.38% to 13.03% in fuel economy and from 0.31% to 3.15% in vehicle maneuverability under different test cycles, while ensuring real-time capability.

Electric Power Generation from Road Humps

Energy serves as the fundamental element crucial for the survival of all life forms across the vastness of the universe. It acts as the cohesive force directing various processes shaping our environment. However, this delicate equilibrium is endangered by the depletion of traditional energy sources, worsened by the ever-growing global population. This dilemma leads to an imminent energy crisis expected to arise in the next five years due to excessive consumption. To navigate this challenging situation, there's a need for a significant shift in approach, urging the adoption of methods to maximize the use of existing energy sources. In our research, there's a promising opportunity to tap into kinetic energy, particularly from vehicles traversing over speed bumps. The proposal suggests converting these ordinary road features into dynamic generators, seamlessly transforming kinetic energy into electricity for the grid. This process involves sophisticated mechanisms like rack and pinion systems to efficiently convert kinetic energy into mechanical power, eventually generating electricity for storage and later use. This innovative solution not only addresses energy conservation but also promises economic benefits, utilizing stored energy to power street lights during the night, and enhancing urban landscapes with efficient illumination. Key Word: Generator; non-conventional energy source; rack and pinion mechanism; chain sprocket; speed breaker.

Deep Learning-Based Traffic Accident Prediction: An Investigative Study for Enhanced Road Safety

INTRODUCTION: Traffic accidents cause enormous loss of life as well as property, which is a global concern. Effective accident prediction is essential for raising road safety and reducing the effects of accidents. To increase traffic safety, a deep learning-based technique for predicting accidents was developed in this research study.
 OBJECTIVES: It gathers a large amount of data on elements including weather, road features, volume of traffic, and past accident reports. The dataset goes through pre-processing, such as normalization, to ensure that the scales of the input characteristics are uniform. Normalizing the gathered dataset ensures consistent scaling for the input features during the data processing step. This process enables efficient model training and precise forecasting. In order to track and examine the movement patterns of automobiles, people, and other relevant entities, real-time tracking and monitoring technologies, such as the deep sort algorithm, are also employed.
 METHODS: The model develops a thorough grasp of the traffic situation by incorporating this tracking data with the dataset. Convolutional Neural Networks (CNN), in particular, are utilized in this research for feature extraction and prediction. CNNs capture crucial road characteristics by extracting spatial features from images or spatial data. With its insights into improved road safety, this study advances the prediction of traffic accidents.
 RESULTS: A safer transport infrastructure could result from the developed deep learning-based strategy, which has the potential to enable pre-emptive interventions, enhance traffic management, and eventually reduce the frequency and severity of traffic accidents.
 CONCLUSION: The proposed CNN demonstrates superior accuracy when compared to the existing method.

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.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

ABSTRACT With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models.

Development of a Network-Level Road Safety Assessment Procedure Based on Human Factors Principles

Road safety is a central issue in the management and development of a road network. Road agencies must try to identify the most dangerous sections of their network and act on them to improve safety. The most used procedure for this purpose is about considering the indicators based on crashes. However, a mature road safety management system must be able to assess the safety of a road section before accidents occur. The European community is moving in this direction with the update of Directive 2008/96/EC (Directive 1936/2019). This paper proposes a new methodology for carrying out a network-wide road safety assessment on rural single-carriageways and two-lane two-way roads. This procedure accounts for the influence of road characteristics on drivers’ perceptions. The methodology has been developed based on the human factors concepts from PIARC, and it includes a series of checklists that guide an inspector in carrying out a visual inspection of single-carriageway roads. The results from the checklist are then processed into an algorithm, and the level of risk in the analyzed section is provided. The objectives of the procedure are (a) to account for the perceptive aspects that are one of the major causes of road accidents, (b) to provide a proactive procedure in line with the requirements of the European Directive, and (c) to provide a useful instrument that can be easily implemented by road agencies and integrated with other analysis procedures. The procedure has been applied and tested on a case study of six different stretches of two-lane, two-way rural highways in Italy, Germany, and Slovenia (about 65 km). The results show a high degree of concordance with a risk classification based on the accident rate, mainly considering high-risk sections. Therefore, the procedure demonstrated its potential to be a useful instrument to be included in network safety assessments. Road agencies should consider the use of this procedure in their network safety analysis and ranking.

Factors influencing the road accidents in low and middle-income countries: a systematic literature review

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

UnetEdge: A transfer learning-based framework for road feature segmentation from high-resolution remote sensing images

Topological information is a crucial factor affecting road feature extraction using semantic segmentation. Many segmentation models have recently been developed for road feature extraction from high-resolution remote sensing imagery but have yet to achieve accurate predictions when occluded by shadows. This study proposes a transfer learning-based framework, called UnetEdge, which is designed to effectively propagate topological information into the feature map. The novel Edge module transmits edge level topological information along with contextual spatial information through the decoder layer to the final feature map. This essentially enhances the continuous flow of semantic road pixel information into the network. Moreover, to integrate heterogeneous road structure information into the network, we have leveraged the transfer learning approach to produce accurate road segmentation maps. Extensive experiments on five standard public datasets and comparative analysis against the state-of-the-art networks establish the robustness and efficiency of the model. Furthermore, the experimental results on our acquired Indian drone dataset achieved an IoU score of 70.22 and a mIoU score of 82.45% with an overall accuracy of 95.27%, validating the model's effectiveness for real-world applications.

Experimental analysis of road surface flow characteristics

The rapid increase of impervious surfaces in urban areas has significantly altered the rainwater runoff landscape, necessitating an evaluation of the capacity of urban drainage systems as a part of global rainwater management plans (encompassing both gray and green infrastructures). However, no clear standard has been established for the appropriate analysis of surface flow characteristics that take into account the topographical and rainwater runoff conditions of urban roads. In this study, a hydraulic model experiment was performed, parameterized by road slope conditions (longitudinal slope: 2 to 10 %, transverse slope: 2 %), grate inlet geometric conditions (50 to 150 cm), and rainfall intensity conditions (design frequency: 5 to 30 years), to develop a method for estimating road surface flow. The results indicated that the grate inlet interception efficiency varied from 62.43 to 85.46 % for a 50 cm inlet length, 65.43 to 89.73 % for 100 cm, and 68.14 to 91.44 % for 150 cm. Similarly, the flow width in the gutter ranged from 82 to 129 cm, which is contingent on road slope and rainwater runoff conditions. Using the experimental results, a regression analysis was conducted to develop formulas for calculating the grate inlet interception efficiency considering flow width. The developed empirical formulae exhibit significant probabilities of ≤0.05 and high coefficients of determination, such as 0.99 for the grate inlet interception efficiency and 0.96 for flow width. Compared to the empirical formulas proposed in previous research, the developed formulas in this study demonstrate superior precision and broader applicability, thereby providing a reliable tool for the design and construction of urban drainage facilities.

High streets, ageing and well-being

ABSTRACT Despite their perceived decline, local high streets in the UK remain valuable central and well-connected places that can foster ageing in place, yet their potential to sustain well-being in old age has been overlooked. Using qualitative methods, the paper explores what features of local high streets support older people’s well-being in three local town centres in Edinburgh, Scotland. The findings show there are three main domains of local high streets’ public realm that enable older adults’ well-being, namely the streetscape, the spatial organization and accessibility of amenities and services, and the provision of housing that can foster town centre living.

Deciphering urban bike-sharing patterns: An in-depth analysis of natural environment and visual quality in New York's Citi bike system

Bike-sharing offers a convenient and sustainable mode of transportation. Numerous studies have investigated the influence of temporal variations in the natural environment on cycling, as well as the impact of physical street characteristics like networks and infrastructures. However, few studies integrated and compared the effects of natural environment and street visual quality on cycling in the spatial dimension. As a case study, we focused on the impact of these two factors on Citi Bike system on weekdays and weekends in New York City, while accounting for sociodemographic and functional factors. This study employed machine learning and multiscale geographically weighted regression models at both station and neighborhood scales for a comprehensive analysis of their relationships. The results reveal that the natural environment factors, particularly visibility, are more important factors associated with bike-sharing use. Among the visual quality factors, motorized traffic has a negative impact on both weekday and weekend cycling. When considering geographical location, sky openness exhibits an unfavorable influence on weekday cycling in specific areas. By combining natural environment and visual quality factors, our study promotes optimal resource allocation and the development of bike-friendly cities.