Road Length Research Articles

Investigating spatial and temporal characteristics and elements influencing running among residents of Nanchang city in green open areas

Understanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.

Linear Regression-Based Traffic Flow Simulation: Vehicle Density and Speed Analysis on Buah Batu Road

Traffic congestion has become an increasingly severe problem in many major cities around the world, including in the city of Bandung. Population growth and increased vehicle use exacerbate congestion. Jalan Buah Batu, one of the main roads in the city of Bandung, often experiences congestion due to high density. This study explains the traffic flow simulation using the Lighthill-Whitham-Richards (LWR) model with a speed-density function obtained from observation data on Jalan Buah Batu, Bandung. The data included the relationship between vehicle density and speed which was then analyzed using the linear regression method. The approximation of the velocity-density function obtained from linear regression is v(ρ)= -6.904+4.302. Traffic flow simulations were carried out with a road length of 60 meters, a total time of 5 minutes, and high resolution with 300 grid points. At the beginning of the simulation, a peak density of 0.70 occurred in a 15-25 meter road segment. Over time, the peak density shifted and decreased: 0.65 at 20-30 meters at 1.25 minutes, 0.60 at 25-35 meters at 2.5 minutes, and 0.50 at 30-50 meters at the end of the simulation (5 minutes). These results show the movement of vehicles that reduce congestion and improve the smooth flow of traffic. In conclusion, linear regression is effective in determining the velocity-density function.

Bayesian inference of forest road failure frequency depending on rainfall intensity for every prefecture in Japan

ABSTRACT To make a quantitative prediction on forest road failures in the future under the influence of climate change, forest road failure frequency depending on rainfall intensity needs to be clarified. The forest road failure frequency at a certain rainfall intensity should vary regionally because of the variability in susceptibility. In the current situation of a lack of nationwide detailed forest road failure inventory data and GIS-based forest road data, this study analyzed the relationship between rainfall intensity and forest road failure frequency based on Bayesian inference using data available for all the 47 prefectures in Japan; the annual number of forest road failures, forest road length, analyzed precipitation data and polygon data of forest areas. The predicted forest road failure frequency varied broadly by prefecture; 10−4–10−2 km−1 at a rainfall event of 100 mm/24 h, while 10−2–100 km−1 at that of 400 mm/24 h. The frequency at the same rainfall intensity tended to be lower in conventionally rainy prefectures. The results imply that the number of forest road failures in currently less rainy regions may show a larger increase in the future as intense rainfall events are predicted all over Japan due to climate change.

Annual soil erosion characteristics of unpaved roads in the Loess Plateau hilly and gully region, China

The problem of unpaved road erosion is prominent in the Loess Plateau hilly and gully region. Unpaved roads contribute substantially to watershed sediment due to their high soil bulk density, low infiltration rates and extensive network. In this study, a field investigation was conducted on typical unpaved roads within a typical watershed in this region, focusing on assessing the damage state, annual soil loss and the factors influencing erosion in a comparatively wet year. The results showed that the soil erosion from unpaved roads was very severe, with an annual erosion intensity of 470 t hm−2, following three heavy rain events and two rainstorm events in the summer of 2022. The main unpaved roads (MUR) suffered the most severe road erosion, with 22.2 % of road segments experiencing severe erosion with classical gullies. The erosion gullies on the road had an average depth of 16.1 cm and an average width of 36.5 cm, with the widest being 146.0 cm and the deepest being 174.0 cm. The road erosion intensity was significantly related to drainage area, road area, road length and coverage. Road erosion reduced significantly when the land use in the drainage areas of the road was covered with shrub or grass, or road surface was covered with grass or gravel. Our findings offer valuable insights for road construction and erosion prevention in similar terrains.

Index of development of transport infrastructure as a factor of sustainable development of the coastal zone of the Sea of Azov, Russia

As a zone of interaction between land and sea, coastal zones are becoming a strategic area for achieving a sustainable expansion of economic space. The object of study is the coastal zone of the Sea of Azov (Rostov Region and Krasnodar Territory). The subject of the study is an assessment of the dynamics of transport infrastructure development index as a factor in the sustainable development of the coastal zone of the Sea of Azov.Methodological approaches to assessing the sustainable development of the territory were based on a methodology based on methods of system analysis, mathematical modeling, and GIS technologies. An indicator has been formulated that considers the area of the territory, the population, the length of public roads of local importance, the shipment of goods of local production, the work and services performed on their own, in order to measure the ‘economic’ load on the transport infrastructure.The analysis of the state of the transport infrastructure of the regions of the coastal zone for the period from 2015 to 2021 was carried out. Based on an analysis of the transport infrastructure index, it was concluded that the attraction of municipalities to the Rostov agglomeration, together with the strengthening of social, trade and infrastructure contacts with megacities will lead to a further increase in the anthropogenic load on the territory. The areas studied of the Krasnodar Territory belong to the Northern Economic Zone – a territory with a diversified economy, characterised by cross‐border links with neighboring regions.Highways are the most important link in the transport and logistics system. The level of development and the technical condition of the road network have a significant and diverse impact on the economic and social development of both the state as a whole and it individual regions.

Soil reclamation of cranberry plantations on reclaimed lands in Arkhangelsk region

In the production conditions of the "Arkhangelsk cranberry" in the Arkhangelsk region, the land use analysis was carried out as a part of land use monitoring in this agricultural production. In 2015, the first cranberry plantations were planted, and in April 2023 about 40 hectares of peatlands were already in land use of the factory including 8 fields (checks) with an average area of 1 hectare. An assessment of the dynamics of change on the area of the created plantation was carried out for all indicators such as the length of roads, developed area and some separately designed structures (except of drained bogs). It was found that the soil indicators of peatlands are normal for this type of lands, and provide favorable conditions for growing cranberries. The analysis of the dynamics of land use in the period from 2015 to 2023 revealed the following dynamic indicators: an increase in the area of active land use by 418.97%, an increase in the area of drained swamps by 310.68%, 5.5 hectares were withdrawn from the forest fund, 42.31 hectares are under development (complex works), the total number of checks for planting increased by 23 parcels, an increase in the length of roads, including dirt roads, by 8.6 km, an increase in the number of fire ponds by 3 pieces, an increase in the number of sludge ponds by 3 pieces, and, finally, 1 recreational site was commissioned. The research has also shown that drainage does not lead to a significant change in the chemical composition of the organic part of peat and its agrochemical properties, so using drainage is not rational. In this case, a slight change in the water-air regime was observed together with the changes in the soil-forming process, and some activities of microorganisms, which was a prerequisite for an increase in the level of mineralization of the organic part of peat. Despite the gradual commissioning of peatlands, environmental sustainability has a trend towards an increase, which should be taken into account in the further development of the enterprise.

Analysis of the Effectiveness of Connectivity Infrastructure Spending in the Bangka Belitung Islands Province

The purpose of this study is to analyze the correlation between APBN and APBD expenditures on the achievement of connectivity indicators. Secondary data sources were obtained from the Directorate of Budget Implementation, the Sintesa application, the OMSPAN and OMSPAN TKD applications, the Ministry of PUPR, and the BPS of the Bangka Belitung Islands Province. Data analysis uses the Pearson correlation method and qualitative descriptive. The results of the study indicate that APBN expenditure is negatively and very strongly correlated with the length of national roads. In addition, APBN expenditure is positively and very weakly correlated with the length of national roads in stable condition and has a moderate positive correlation with the percentage of national road stability. APBD expenditure is very weakly correlated with the length of provincial roads and is moderately correlated with the length of provincial roads in stable conditions and the percentage of stability. Regency/City APBD expenditure also has a very weak correlation and is negatively directed to the total length of district/city roads in the Bangka Belitung Islands Province and has a moderate correlation with the length of district/city roads in stable condition and the percentage of stability. APBN expenditure has a positive and very strong correlation with the length of regional roads and is positively and strongly correlated with the length of regional roads in stable conditions. APBN spending also has a moderate correlation with the percentage of regional road stability. Based on the relationship analysis using Pearson correlation, it was found that the total length of the road has a strong correlation and has a positive relationship with the ADHK GRDP. The Bangka Belitung Islands Province has a higher road stability than the national road stability and road stability in the Sumatra Region.

Effect of adding dune sand to tuff in Saharan road construction

The south region of Algeria is characterized by a wide surface, a scattered population and a very small ratio of road length per habitant. To allow the development of agricultural, industrial and touristic activities between different cities in the south of Algeria, it is necessary to maintain and to develop the road infrastructure. However the development of these infrastructures necessitates the use of huge amount of certified aggregates from quarries which is not available in the vicinity of the need. For these raisons, in the framework of sustainable development, a strategy which consists in using local materials like fine sediments (dune sand) and other types of material is engaged. The materials constituting the road layers, until today, have been limited to certain so-called noble materials (gravel, aggregates, etc.), but these are being depleted as a result of the intensive exploitation and the scarcity of quality careers. Gypso-limestone encrustation tuffs, the most used materials in pavements (base course and base course) in the Saharan areas such as southern Algeria, have shown acceptable behavior for many years until these last days, when this type of material begins to present certain limits under the effect of the intensity of the traffic. In order to promote the abundant wind sands in these regions, we are interested in developing the dune sands in the pavement as a mixture with the tuffs. The present work presents a contribution to the study of the behavior of the tuff of the Adrar region (South of Algeria) alone and mixed with the sand dunes in different formulations. The aim is to evaluate the evolution of mechanical properties including resistance to simple compression, the ability to compaction and punching (RBC). The work also discusses the influence of the addition of cement in low levels on the performance of the mixture.

Measurement of Competitiveness and Determinants of Coal Output in East Kalimantan

The coal sector has the highest share and contribution to the economy in East Kalimantan Province. The importance of the existence of coal to the economy of East Kalimantan must be followed by high competitiveness and supportive policy direction. For this reason, this study aims to measure and identify the competitiveness and factors that affect the output of the coal sector in East Kalimantan Province using the competitiveness analysis (LQ) method and panel data regression. The results of the analysis identified that East Kalimantan occupies the first position with the highest coal sector competitiveness in Indonesia. This is evidenced by a consistent competitiveness index value of more than 1 from 2021 to 2024. Meanwhile, the results of the panel data analysis also identified that the variables of total investment and the number of workers in the coal sector with long roads are estimated to have a positive and significant effect on the output of the coal sector in East Kalimantan Province. Meanwhile, the road length variable is predicted to have a negative and significant effect on the output of the coal sector.

Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development.

Road Geometric Redesign used AutoCAD 2D A Case Jalan Majalengka-Rajagaluh

Majalengka-Rajagaluh road is a road located in several sub-districts which is a link to the city and also one of the connecting roads to Cirebon City. This design planning starts in Cigasong sub-district and continues to Rajagaluh sub-district with the length of Majalengka-Rajagaluh road is 5.3 kilometers. The geometric design results are visualized in the form of drawings using AutoCAD 2D. The design speed (Vd) was set at 60 km/h with a horizontal curve radius of 358 m, a maximum slope of 4%, and an elevation (e max) of 8%. Based on these design criteria, a spiral curve with a circular curve length (Lc) of 74.996 m was obtained. Furthermore, the results of the vertical alignment calculation of the length (L) of one type of convex curve in PPV were also calculated.

Research on the Markov-Chain State Interval Division Based on Predicted Data Correction

By 2022, the total length of roads in Tibet Autonomous Region reached 121,447 kilometers. Due to the unique geological conditions in Tibet, various natural disasters such as earthquakes, mudslides, landslides, avalanches, and strong winds frequently occur. Along the Sichuan-Tibet Highway alone, over 300 disasters happen each year, significantly impacting the region’s economic development. This study focuses on the complexity and randomness of natural disaster mechanisms and combines Markov chain theory to improve the accuracy of prediction data for mudslides, landslides, and earth subsidence etc. The main method is to modify the state interval of the prediction model parameter-Markov chain based on the distribution of discrete points on the number axis. The following state interval division methods are proposed: (1) If the relative error of the predicted value exceeds 50%, adjust the prediction model. (2) Obtain the lower bound of state E1 by taking the floor value downward. (3) The width of each interval does not need to be uniform. (4) Arrange continuous, dense, and close points on the number line in the same side in batches, and represent a state continuously, dividing it into one suitable interval or batches. Using this method, an improved RMSE of 0.28mm and MAPE of 0.87% were obtained for engineering examples, outperforming other models such as GM(1,1), Verhulst, DGM(2,1) with corresponding RMSE values of 0.86 mm, 0.69 mm, and 1.38 mm, and MAPE values of 2.75%, 2.53%, and 5.99%. The combined prediction results for five sets of data yielded an RMSE of 0.14 mm and MAPE of 0.56%, which are quite close to the results obtained using Markov selection correction with an RMSE of 0.37 mm and MAPE of 1.01%. Furthermore, comparing the four sets of case, the average reduction in RMSE and MAPE is 3.56mm and 1.72%, respectively, demonstrating that this method can further improve the performance of Markov chain prediction.

THE IMPACT OF LAND, SEA, AND AIR TRANSPORTATION INFRASTRUCTURE ON ECONOMIC GROWTH IN G7 COUNTRIES

This research analyzes the influence of road length, rail length, containers traffic, air freight, GFCF, and labor force on economic growth. This study uses quantitative methods with G7 countries as research objects during 2010-2021. Panel data consisting of 84 observations was analyzed with the help of Eviews12 software. The statistical analysis used in this research is the Ordinary Least Square (OLS) method, with the Fixed Effect Model chosen as the best model for interpreting the research results. The findings show that the road length, container traffic, air freight, GFCF, and the labor force have a positive and significant influence. Meanwhile, only the rail length does not have a significant effect on economic growth. Based on the results of this research, the theoretical implication of this research is the development of transportation infrastructure, especially road length, containers, air freight, GFCF, and labor force can increase economic growth in a country. So countries must consider spending on developing transportation infrastructure. Meanwhile, practically, this research can be used as study material in considering infrastructure development because the negative impact caused by errors in infrastructure development can be a waste of budget.

Tire Wear Chemicals in the Urban Atmosphere: Significant Contributions of Tire Wear Particles to PM2.5.

Tire wear particles (TWPs) containing tire wear chemicals (TWCs) are of global concern due to their large emissions and potential toxicity. However, TWP contributions to urban fine particles are poorly understood. Here, 72 paired gas-phase and PM2.5 samples were collected in the urban air of the Pearl River Delta, China. The concentrations of 54 compounds were determined, and 28 TWCs were detected with total concentrations of 3130-317,000 pg/m3. Most p-phenylenediamines (PPDs) were unstable in solvent, likely leading to their low detection rates. The TWCs were mainly (73 ± 26%) in the gas phase. 2-OH-benzothiazole contributed 82 ± 21% of the gas-phase TWCs and benzothiazole-2-sulfonic acid contributed 74 ± 18% of the TWCs in PM2.5. Guangzhou and Foshan were "hotspots" for atmospheric TWCs. Most TWC concentrations significantly correlated with the road length nearby. More particulate TWCs were observed than model predictions, probably due to the impacts of nonexchangeable portion and sampling artifacts. Source apportionment combined with characteristic molecular markers indicated that TWPs contributed 13 ± 7% of urban PM2.5. Our study demonstrates that TWPs are important contributors to urban air pollution that could pose risks to humans. There is an urgent need to develop strategies to decrease TWP emissions, along with broader urban air quality improvement strategies.

Spatial insights for sustainable transportation based on carbon emissions from multiple transport modes: A township-level case study in China

Understanding the CO2 emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO2 emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO2 emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and Spatial Error Model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (−0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO2 emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO2 emissions.

The integrated methodology for assessing the safety of the road network infrastructure in Romania.

Abstract Romania has the highest road fatality rate in the EU and the lowest perception of road quality. This study presents the implementation of a preliminary procedure and tools provided by the European Commission for the safety assessment of the DN2-E85 road infrastructure, which covers 472.23 km. This road was selected due to the high number of road deaths between 2017 and 2022. The research focuses on the application of both proactive and reactive methodologies as required by Directive (EU) 2019/1936, which requires full road network safety assessments by 2024, with subsequent assessments every five years. The proactive methodology assesses the built-in road safety of the road infrastructure based on design parameters, while the reactive methodology is based on historical data on accidents and traffic flows. The integration of these methodologies allows the classification of road segments into five risk levels. Preliminary results indicate that approximately 59% of the length of the DN2-E85 road is classified as “very high priority”, highlighting significant safety deficiencies, which require actions to immediately improve traffic safety. This study highlights the need for systematic road infrastructure safety assessments and prioritisation of high-risk segments to reduce road fatalities and improve overall road safety. The findings serve as crucial information for road safety auditors and the Romanian Road Authority - ARR, supporting the identification of high-risk areas and the allocation of resources to improve safety. In addition, this research provides a basic basis for future adaptations of road infrastructure safety assessment guidelines, considering the specific characteristics of national and county roads. By aligning with EU directives and national standards, the study aims to increase road safety and reduce the incidence of serious accidents in Romania. The insights gained from this research are essential for developing effective road safety strategies and implementing remedial measures that address the most dangerous segments of the road network.

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Trends in Emissions from Road Traffic in Rapidly Urbanizing Areas

The process of urbanization has facilitated the exponential growth in demand for road traffic, consequently leading to substantial emissions of CO2 and pollutants. However, with the development of urbanization and the expansion of the road network, the distribution and emission characteristics of CO2 and pollutant emissions are still unclear. In this study, a bottom-up approach was initially employed to develop high-resolution emission inventories for CO2 and pollutant emissions (NOx, CO, and HC) from primary, secondary, trunk, and tertiary roads in rapidly urbanizing regions of China based on localized emission factor data. Subsequently, the standard road length method was utilized to analyze the spatiotemporal distribution of CO2 emissions and pollutant emissions across different road networks while exploring their spatiotemporal heterogeneity. Finally, the influence of elevation and surface vegetation cover on traffic-related CO2 and pollutant emissions was taken into consideration. The results indicated that CO2, CO, HC, and NOx emissions increased significantly in 2020 compared to those in 2017 on trunk roads, and the distribution of CO2 and pollutant emissions in Fuzhou was uneven; in 2017, areas of high emissions were predominantly concentrated in the central regions with low vegetation coverage levels and low topography but expanded significantly in 2020. This study enhances our comprehension of the spatiotemporal variations in carbon and pollutant emissions resulting from regional road network expansion, offering valuable insights and case studies for regions worldwide undergoing similar infrastructure development.

Dynamic Evaluation of Road Network Resilience to Traffic Accidents: An Emergency Management Perspective for Sustainable Cities in China

When assessing road network resilience, emergency management behavior should be considered, as this represents the road network’s capacity to adapt to and recover from traffic accidents. Given the timeliness and variability of emergency management behavior, deterministic approaches seem inadequate to represent real road network performance. Thus, this paper innovatively designs an emergency management perspective-based dynamic evaluation method of road network resilience to traffic accidents. Firstly, based on four stages of emergency management, a road network resilience evaluation index system encompassing resilience capabilities, resilience attributes and traffic accident emergency management ability indicators is constructed. Afterwards, the gray relational technique for order preference by similarity to the ideal solution (GRA-TOPSIS) evaluation method based on combination weighting, which integrates factor analysis with hesitant intuitionistic fuzzy expert scoring, is designed to quantify resilience. Finally, the obstacle degree model is utilized for identifying resilience constraints as the input of a long short-term memory (LSTM) model to predict the resilience variation trend. The fast road network of Shanghai in China is adopted as a case study, and the results indicate that road network resilience embodies significant spatial distribution characteristics. Road length, number of tractors, perception and response and disposal time of traffic accidents cast notable effects on resilience. Additionally, some roads are forecast to show descending resilience. The proposed method is valuable for helping policymakers identify current and potential vulnerable roads and to formulate proposals to effectively improve the resilience of urban agglomerations and promote sustainable cities.

Estimating Urban Traffic Safety and Analyzing Spatial Patterns through the Integration of City-Wide Near-Miss Data: A New York City Case Study

City-wide near-miss data can be beneficial for traffic safety estimation. In this study, we evaluate urban traffic safety and examine spatial patterns by incorporating city-wide near-miss data (59,277 near-misses). Our methodology employs a grid-based method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran’s I and local Moran’s I. The study findings reveal that near-misses have the strongest correlation with observed crash frequency among all the variables studied. Interestingly, the ratio of near-misses to crashes is roughly estimated to be 1957:1, providing a potentially useful benchmark for urban areas. For other variables, an increased number of intersections and bus stops, along with a greater road length, contribute to a higher crash frequency. Conversely, residential and open-space land use rates show a negative correlation with crash frequency. Through spatial analysis, potential risk hotspots including roads linking bridges and tunnels, and avenues bustling with pedestrian activity, are highlighted. The study also identified negative local spatial correlations in crash frequencies, suggesting significant safety risk variations within relatively short distances. By mapping the differences between observed and predicted crash frequencies, we identified specific grid areas with unexpectedly high or low crash frequencies. These findings highlight the crucial role of near-miss data in urban traffic safety policy and planning, particularly relevant with the imminent rise of autonomous and connected vehicles. By integrating near-miss data into safety estimations, we can develop a more comprehensive understanding of traffic safety and, thus, more effectively address urban traffic risks.