Arterial Roads Research Articles

Accident Analysis on the Arteri Yos Sudarso street

The Yos Sudarso arterial road, a critical ring road connecting industrial zones and ports in Central Java, experiences high accident rates (averaging 6 accidents/month from 2019 to 2022). Despite its economic significance, inadequate traffic signage and complex topography contribute to recurring accidents, necessitating a targeted safety analysis. This study investigates (1) the correlation between road complementary devices (e.g., traffic signs) and accident rates, and (2) the relationship between traffic volume and accident frequency, with a focus on high-risk vehicle groups. Field surveys and spatial analysis were conducted to evaluate road conditions, signage compliance, and accident hotspots. Data from 262 accidents were analyzed alongside traffic volume measurements and road equipment assessments. Key results include: (1) A strong correlation between insufficient traffic signs and accident occurrence, and (2) An inverse relationship between traffic volume and accidents, with Group 1 vehicles (e.g., cargo trucks) identified as most vulnerable. While compliant with regulations, road geometries revealed blind spots due to median vegetation. The study advocates for enhanced signage and ITS integration (e.g., adaptive signals) to mitigate accidents. It also calls for dedicated lanes for high-risk vehicles and further research into behavioral factors underlying the traffic volume paradox. These findings contribute to evidence-based policies for arterial road safety in urban-industrial corridors.

Impact of Road Network on Residential Property Values in Ojo Local Government Area, Lagos, Nigeria

This study investigated the impact of road network development on residential property values in Ojo Local Government Area (LGA) of Lagos, Nigeria. Road infrastructure development in a country has been considered an important predictor of economic development and prosperity. The relationship between road networks and property values has been widely studied, with findings suggesting that access to well-developed transport systems increases the value of properties. The study uses data from a survey of 473 respondents, conducted across nine major roads in the area, to analyze the relationship between road types, accessibility, and residential property values. The findings reveal that properties located within 100 meters of arterial roads have a 20% higher rental value compared to those situated over 100 meters away. Additionally, properties along well-connected roads exhibit a 15-25% increase in sales value. The study further shows that improvements in road infrastructure result in an average 30% increase in property values, particularly for residential properties near transport hubs or key road junctions. These findings suggest that road network accessibility plays a significant role in shaping the market value of residential properties, offering valuable insights for urban planners and real estate investors. Policymakers should consider the role of road networks in shaping urban growth patterns and property markets. By investing in infrastructure that improves accessibility and connectivity, local governments can foster sustainable urban development and increase property values in previously underserved areas.

Performance Evaluation of Fused Floating Car Data (FCD) and Bluetooth (BT) Data Speed Estimation on Urban Arterials

Abstract Access to reliable data is crucial, particularly in emerging economies, where data may be scarce. Various collection methods, such as commercial Floating Car Data (FCD) and Bluetooth (BT) traffic data, which are low-cost and widely used methods making them ideal for monitoring and analyzing networks, provide valuable insights. This study presents "Link-Based Generalized Data Fusion Method (LB-GDFM)", which integrates and fuses commercial FCD with BT data to assess and manage traffic congestion on urban arterial links. By leveraging the strengths of both data sources, this fusion method provides a more accurate speed estimation and traffic state assessment of urban links and offers a robust comparative method for determining average link speed using fused FCD and BT data, along with performance results. The method, which involves determining a BT-based fused FCD speed, was applied to selected links of an arterial road in Mersin, Turkiye. Two data collection methods were compared to assess the effectiveness of measuring speed reliability across the links over a corridor. Performance metrics such as $${\text{MAE}}_{FCD, BT}$$ MAE F C D , B T , $$MAP{\text{E}}_{FCD, BT}$$ M A P E F C D , B T , and $${\text{RMSE}}_{FCD, BT}$$ RMSE F C D , B T were used to evaluate the accuracy of both data sources, demonstrating that BT-based fused FCD and BT data provide reliable speed estimates in both heavy and normal traffic scenarios. Results indicated that even with limited data sources, effective traffic management strategies can be developed in emerging economies. This study, therefore, provides a valuable data fusion model for traffic monitoring and emphasizes the potential to enhance the used approach by incorporating additional data sources and broader traffic patterns in developing countries.

Clustering-Based Urban Driving Cycle Generation: A Data-Driven Approach for Traffic Analysis and Sustainable Mobility Applications in Ecuador

A representative urban driving cycle was developed for Quito, Ecuador, using the K-Means clustering method. From 64 samples and 188,713 geospatial and speed data points, a 2870 s driving cycle was constructed to capture real-world traffic characteristics. Key parameters include an average speed of 22.68 km/h, acceleration and deceleration rates of 0.55 m/s2 and −0.57 m/s2, and a dwell time of 9.66%. Due to Quito’s linear urban development, where mobility is limited to north–south/south–north corridors, the driving cycle reflects frequent accelerations and decelerations along congested arterial roads. A comparative analysis with international driving cycles revealed that Quito’s traffic follows a unique pattern shaped by its geographic constraints. The HK cycle in China showed the greatest similarities, although differences in instantaneous speeds highlight the need for localized models. While this study primarily focuses on methodological robustness, the developed driving cycle provides a foundational dataset for future research on traffic flow optimization, emissions estimation, and sustainable urban mobility strategies. These insights contribute to data-driven decision-making for improving transportation efficiency and environmental impact assessment in cities with similar urban structures.

Which vehicle type is more responsive to traffic information provided on local roads?

In general, the same traffic information is provided to all drivers. This study investigated different responses to traffic information based on vehicle type on a local road. A living lab-based experiment was conducted in Seoul, South Korea. This experiment examined changes in approach speed and braking distances of vehicles in response to safety alerts. Data in the form of video recordings were collected, processed, and statistically analyzed. Two key findings emerged. Firstly, approach speed and braking distances were significantly reduced in both passenger and non-passenger car groups. This suggests that traffic information can effectively manage traffic, even on low-speed local roads, not just on high-speed arterial roads. Secondly, while overall responses were more pronounced in the non-passenger car group, the difference in braking distances between vehicle types was minimal. This indicates that driver responses to the interventions vary depending on the metric employed. These findings were interpreted in the context of safety-enhancing strategies for local roads. Specifically, traffic information should be customized to individual users rather than uniformly provided to all drivers, with tailored practices being essential for effective implementation. Finally, two critical issues—the transferability of results and the need for future research—arising from the living-lab survey environment were discussed.

Traffic noise modelling on urban arterial roads using mathematical and machine-learning techniques

Simultaneous measurements of main arterial roads inside the urban perimeter of Kurukshetra City (Haryana, India) regarding noise levels, traffic flow, and vehicle composition were taken and then used to develop some Traffic Noise Models (TNMs) to predict equivalent noise levels ( L e q ) from traffic flow per hour. The performance of the developed mathematical model was compared with four widely used TNMs. The potential of machine-learning techniques (M5 model tree, random forest and support vector machines) was also explored and their performance was compared with the developed mathematical TNM. Finally, the versatility of the developed mathematical model was examined for predicting the standard noise descriptors ( L D , L N and L D N ). The model required tuning for enhanced performance in predicting L D N , which was achieved by introducing a model coefficient ( P n = 1.05) to compensate for the night-time penalty. The study exhibited the potential application of machine-learning techniques in developing TNMs. However, the developed mathematical TNM allows a simple representation of a mathematical formula for the determination of L e q for any time-duration from the corresponding traffic flow per hour.

Vehicle Trajectory Reconstruction Method for Urban Arterial Roads Based on Multi-Source Data Fusion.

Vehicle trajectory data contain extensive spatiotemporal information and are of great significance for analyzing the operational patterns of urban traffic networks, optimizing traffic signal control and achieving refined traffic management. However, due to the low penetration rate of probe vehicles and the limited coverage of fixed sensors, it remains challenging to obtain comprehensive full-sample vehicle trajectory data. To address this issue, this paper proposes a multi-source data fusion-based vehicle trajectory reconstruction method, which comprises vehicle trajectory state estimation and a self-optimization algorithm. First, the trajectory states of undetected vehicles are categorized into four types based on the trajectory states of adjacent probe vehicles. Four corresponding trajectory estimation models are then established using an extended Intelligent Driver Model to reconstruct the initial trajectories of undetected vehicles. Second, a particle filter-based trajectory self-optimization algorithm is proposed, integrating upstream and downstream fixed sensor data to iteratively correct and optimize the initial trajectories by minimizing errors, thereby enhancing trajectory accuracy and smoothness. Case studies demonstrate that the proposed method achieves outstanding performance under low PV penetration rates and in complex traffic environments. Compared to baseline methods, MAE, MAPE, and RMSE are reduced by 14.31%, 22.87%, and 13.36%, respectively. Furthermore, the reconstruction errors of the proposed method gradually decrease as traffic density and PV penetration rates increase. Notably, PV penetration has a more significant impact on model accuracy. These findings confirm the robustness and effectiveness of the proposed method in complex traffic scenarios, providing critical technical support for refined urban traffic management and optimized decision-making.

Jakarta Outer Ring Road (JORR) Toll Road Off-Ramp Connectivity Queuing With Local Roads: A Case Study of T.B. Simatupang Road and JORR Toll Off-Ramp

TB Simatupang Road is one of the main arterial roads in South Jakarta, Indonesia, connecting various important areas. Efficient connectivity between major expressways and local roads is crucial for facilitating smooth traffic flow in urban areas. However, in some cases, the connection between toll road off-ramps and local roads experiences queues and traffic delays. This study aims to analyze the impact of off-ramps on local roads by incorporating the JORR Toll Road off-ramp and the signalized intersection at Fatmawati Street into the model in detail. The research methodology combines data analysis, traffic simulation, and field observations. Relevant traffic data, such as vehicle volume, average speed, and vehicle movement patterns, will be collected from the areas surrounding the toll road off-ramp intersection, TB Simatupang Road, and Fatmawati Road intersection. The results of the VISSIM simulation indicate that the degree of saturation exceeds 1, and the queue length reflects severe congestion. The conclusion is that to reduce congestion and improve traffic efficiency, an underpass should be constructed in the direction of Kampung Rambutan or from the off-ramp to Blok M. Additionally, eliminating lane merging is recommended to achieve smoother and more effective traffic flow.

A Novel Machine Vision-Based Collision Risk Warning Method for Unsignalized Intersections on Arterial Roads

To address the critical need for collision risk warning at unsignalized intersections, this study proposes an advanced predictive system combining YOLOv8 for object detection, Deep SORT for tracking, and Bi-LSTM networks for trajectory prediction. To adapt YOLOv8 for complex intersection scenarios, several architectural enhancements were incorporated. The RepLayer module replaced the original C2f module in the backbone, integrating large-kernel depthwise separable convolution to better capture contextual information in cluttered environments. The GIoU loss function was introduced to improve bounding box regression accuracy, mitigating the issues related to missed or incorrect detections due to occlusion and overlapping objects. Furthermore, a Global Attention Mechanism (GAM) was implemented in the neck network to better learn both location and semantic information, while the ReContext gradient composition feature pyramid replaced the traditional FPN, enabling more effective multi-scale object detection. Additionally, the CSPNet structure in the neck was substituted with Res-CSP, enhancing feature fusion flexibility and improving detection performance in complex traffic conditions. For tracking, the Deep SORT algorithm was optimized with enhanced appearance feature extraction, reducing the identity switches caused by occlusions and ensuring the stable tracking of vehicles, pedestrians, and non-motorized vehicles. The Bi-LSTM model was employed for trajectory prediction, capturing long-range dependencies to provide accurate forecasting of future positions. The collision risk was quantified using the predictive collision risk area (PCRA) method, categorizing risks into three levels (danger, warning, and caution) based on the predicted overlaps in trajectories. In the experimental setup, the dataset used for training the model consisted of 30,000 images annotated with bounding boxes around vehicles, pedestrians, and non-motorized vehicles. Data augmentation techniques such as Mosaic, Random_perspective, Mixup, HSV adjustments, Flipud, and Fliplr were applied to enrich the dataset and improve model robustness. In real-world testing, the system was deployed as part of the G310 highway safety project, where it achieved a mean Average Precision (mAP) of over 90% for object detection. Over a one-month period, 120 warning events involving vehicles, pedestrians, and non-motorized vehicles were recorded. Manual verification of the warnings indicated a prediction accuracy of 97%, demonstrating the system’s reliability in identifying potential collisions and issuing timely warnings. This approach represents a significant advancement in enhancing safety at unsignalized intersections in urban traffic environments.

Two pores instead of one: Gating pore current and the electrical leak in autism and epilepsy.

Two pores instead of one: Gating pore current and the electrical leak in autism and epilepsy.

Design of Energy-Saving Control System for Intelligent Street Lamps Based on STM32

In this paper, an energy-saving control system for intelligent street lamps based on STM32F103C8T6 microcontroller is designed, and dynamic lighting control is realized by integrating D80NK infrared sensor, LD2451 radar sensor and KDT331 photoresistor. According to the requirements of the "Urban Road Lighting Design Standard" (CJJ45-2015), the system adopts three operation modes: maintaining the lowest illumination (5lx) when there is no car, "lighting with the vehicle" when the traffic flow is sparse, and full power lighting when the traffic flow is dense. The hardware design includes a multi-protocol communication interface (I2C/SPI), a parameter adjustable module (8 buttons + OLED display) and a dual-stage regulated power supply (LM7805+AMS1117), which supports real-time traffic perception and adaptive dimming. Measurements show that the system can reduce energy consumption by about 58% (0.44 kWh per lamp per day), while extending the life of the equipment and reducing light pollution. Its low-cost and easy-to-deploy characteristics are suitable for the promotion of urban arterial roads, helping smart cities and achieve the "double carbon" goal.

Economic and environmental benefits of cool pavements: a case study of Bhubaneswar city.

The change of land use from natural lands to built-up areas is one of the key reasons for the urban heat island (UHI) effect, because of absorbance and storing of heat energy. Roads and streets cover a significant fraction of the urban fabric and are continuously exposed to solar radiation. This study examines the impact of pavement surface temperature on urban air temperature. Measurements were made across the Bhubaneswar city to capture the temperature and relative humidity along the major arterial roads. The study quantified the UHI effect and evaluated the benefits of cool pavements in reducing air temperature and improving energy efficiency. The study reveals a strong relation between pavement surface temperature and near-surface air temperature. It was found that up to 1.5m above the pavement surface, the impact of pavement surface temperature on the air temperature is substantial. On a particularly hot summer day, the air temperature just above the pavement surface and at 1.5m above the surface was observed to be higher than the surrounding ambient air temperature by up to 7.4°C and 2°C, respectively. Based on the measurements taken during this limited period, the peak UHI intensity in Bhubaneswar city was found to be ~ 1.9°C, which is high, if the current developments of the city are taken into consideration. This study also indicates that significant economic and environmental benefits can be achieved with the adoption of cool pavement technologies.

Analysis of Flood Discharge and Water Level of the Babon River in Semarang

This study aims to determine the toll rates desired by the people of Gresik Regency based on the value of Ability to Pay (ATP) and Willingness to Pay (WTP) on the Krian – Legundi – Bunder (KLB) Toll Road. Through traffic count surveys and Road Side Interviews (RSI) at toll gates and arterial roads, data on vehicle population, payment ability, and user preferences for existing toll rates were obtained. The analysis employed a Logit model to evaluate the relationship between the ATP and WTP values and their correlation with the current toll rates. The results revealed that both the WTP and ATP of the community were lower than the existing toll tariff. Specifically, the study found that the optimal toll rate for users would be Rp. 1,100/km, which is 26.96% lower than the current rate of Rp. 1,506/km. This adjustment not only aligns better with the financial capacities of the users but also presents an opportunity for revenue optimization. By lowering the toll rate, it is predicted that increased traffic volume could compensate for the reduced fee, ultimately benefiting both the toll operator and the community. In conclusion, establishing a toll rate that reflects user preferences is crucial for enhancing accessibility and ensuring sustainable revenue generation for infrastructure development.

Analysis of the Value of Ability to Pay & Willingness to Pay Toll Tariffs in the People of Gresik Regency Based on the Facilities and Infrastructure of the Krian – Legundi – Bunder Toll Road

This study aims to determine the toll rates desired by the people of Gresik Regency based on the value of Ability to Pay (ATP) and Willingness to Pay (WTP) on the Krian – Legundi – Bunder (KLB) Toll Road. Through traffic count surveys and Road Side Interviews (RSI) at toll gates and arterial roads, data on vehicle population, payment ability, and user preferences for existing toll rates were obtained. The analysis employed a Logit model to evaluate the relationship between the ATP and WTP values and their correlation with the current toll rates. The results revealed that both the WTP and ATP of the community were lower than the existing toll tariff. Specifically, the study found that the optimal toll rate for users would be Rp. 1,100/km, which is 26.96% lower than the current rate of Rp. 1,506/km. This adjustment not only aligns better with the financial capacities of the users but also presents an opportunity for revenue optimization. By lowering the toll rate, it is predicted that increased traffic volume could compensate for the reduced fee, ultimately benefiting both the toll operator and the community. In conclusion, establishing a toll rate that reflects user preferences is crucial for enhancing accessibility and ensuring sustainable revenue generation for infrastructure development.

Using the clustering method to find the final environmental parameters coefficients in road construction projects

Nowadays, more and more attention is being paid to environmental issues due to the development of road transportation, particularly the construction of arterial roads. Despite the existence of diverse methods to determine convenient criteria for their assessment, determining the projects with the least harmful effects on the environment and ranking them for purposes of budget allocation and prioritization are remarkably important. The case is more highlighted in regions where roads go through diverse areas with different climatic and geographical distributions. In the present study, a new method consisting of two phases was proposed to determine the optimal coefficient of environmental parameters in road construction parameters. In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. During this stage, similar coefficients were clustered together. In the second phase, an environmental index for various projects was determined based on the obtained results, and the proposed projects were ranked based on that. According to the results obtained concerning environmental parameters during the pre-implementation stage, polluting water resources was the most influential parameter, with a coefficient determined at 3.59. Moreover, the most significant parameter during the implementation was noise pollution, with a coefficient of 5.89, while damaging the ecosystem was the most significant one during the stage of land use change (5.25). Furthermore, soil pollution was the most remarkable parameter during the stage of maintenance (5.81), while damaging the local climate pollution was the most important one during the stage of road implementation (5.67). The above findings can be helpful for researchers in road construction projects.

Comparative analysis of vehicle speed on arterial roads with the greenshield model approach

Abstract Vehicle speed plays an important role in the traffic flow and road density. This parameter is often researched to obtain a model of the characteristics of an existing road section. This study aims to analyze vehicle speed on arterial roads using the Greenshield Model approach, which is a fundamental traffic model that describes the relationship between traffic density and vehicle speed. Through the collection of field data and statistical analysis, vehicle speed patterns and factors influencing them can be identified. The method used is t distribution analysis with the Greenshield Model approach to see the comparison of vehicle speed values on the Kenjeran highway and Kertajaya Indah road sections. The results show that the Greenshield Model can be effective in predicting traffic speeds on arterial roads, providing insights for better transportation planning and road design. Furthermore, the mathematical equation that can be produced on arterial roads is with an average speed value of 26.9 km/h and a standard error of 0.504 and an R2 correlation of 0.658. The conclusions of this study offer guidance for urban planners and transportation engineers in optimizing traffic flow and improving road user safety y=32.282-0.0347x.

Method for Identifying Homogeneous Periods to Evaluate Urban Travel Time Reliability

Travel time reliability influences drivers’ decisions in urban trips as they adapt to uncertainty levels of congestion. While measures of central tendency have traditionally characterized travel time reliability, recent research has expanded to consider alternative metrics for describing this feature of traffic flow. This paper proposes a method to determine time slots of homogeneous periods to assess travel time reliability with a case study on arterial streets of Belo Horizonte, Brazil. The method comprises an initial data collection stage through an application programming interface (API) on selected street segments and a second stage to group periods into hourly time slots with homogeneous travel time distributions to calculate reliability metrics identified in an extensive literature review. The results refer to travel time reliability on streets in Belo Horizonte during different times of the day and days of the week, highlighting low reliability between 5 and 10 a.m. on Monday and Tuesday from the neighborhood to the downtown direction and between 4 and 8 p.m. on Wednesday and Friday from the downtown to the neighborhood direction.

Field and Laboratory Performance of Cementitious Partial Depth Repair Materials in Cold Climates

Incompressible debris accumulating in pavement joints or cracks restricts the free expansion of pavement slabs during warm weather and generates excessive compressive stresses along the joint or crack faces. These stresses cause deterioration and spalling of slab corners and edges and accelerate the rate of pavement degradation. Partial depth repair is a preventative maintenance treatment which is commonly used early in the pavement service life to repair spalls and shallow deterioration and restore the intact pavement surface typically before excessive deterioration occurs. The selected repair material must be compatible with the environmental and load conditions. Replacing the deteriorated concrete with a durable material restores the structure integrity, improves the ride quality, and reduces moisture infiltration to subsurface layers of the pavement. The objective of this paper is to evaluate the performance of several rapid setting cementitious concrete repair materials in cold climates and to develop selection criteria for partial depth repair materials. Six repair materials were applied to a test section on a major arterial road in the City of Winnipeg in the summer of 2010. Pre- and post installation detailed condition surveys were conducted at the repair sites and the field evaluation will continue for the next two years. The field performance of the repair materials was evaluated according to: presence of transverse and longitudinal cracks, separation between the material and concrete slab, surface finish of the repair area, and deterioration of the repair material. Laboratory tests were conducted to evaluate the impact of freeze-thaw and wet-dry cycling on the bond strength between repair materials and regular concrete. Concrete cylinders were prepared in the laboratory. The prepared cylinders had a concrete-repair material interface slanted at 30 degrees from the loading axis. The specimens were subjected to freeze-thaw and wet-dry cycling separately. The degradation of the bond strength between repair material and concrete was evaluated from the compressive testing of the bond cylinders at different levels of freeze-thaw and wet-dry conditioning. Results of laboratory and field evaluation will be used to develop performance-based selection criteria for cementitious partial depth repair materials.

Investigating temporal trends in risk factors related to injury severity of crashes with pedestrians in urban areas

Objective The implementation of road safety policy in urban areas can potentially change the severity profile of crashes, as well as how risk factors influence crash severity. In this sense, this study aims to empirically evaluate possible changes in the severity profile of crashes with pedestrians and in the influence of risk factors for pedestrian injuries after the efforts of the Decade of Action for Road Safety in the city of Fortaleza, Brazil. Methods This was done using data from crashes with pedestrians between 2009 and 2019; divided into three periods. Two categorical modeling analyses were performed using the mixed logit modeling approach, including sociodemographic, environmental, vehicle, road type, and traffic control device factors. In the first analysis, a single model was estimated, and time (period) was included as an explanatory variable; in the second one, models were estimated for each period. Results According to temporal analysis, a reduction was evident in the severity profile of crashes with pedestrians over the decade of action. In general, the safety interventions seemed to have little or no impact on pedestrian gender, young pedestrians (up to 15 years old), crashes at night, crashes during weekends and crossings near traffic lights. Regarding crashes on arterial roads, the results suggest an increase in the marginal effects for fatal crashes after the decade of action, while other variables, such as heavy vehicles and expressways, showed positive marginal effects in all periods, indicating that the direction of their effect did not change. This is a potential indication that the overall safety impact of policies during the decade were not effective for these types of crashes. It was possible to identify considerable reduction in the marginal effects for older pedestrians (60+) for both severe and fatal crashes. Conclusion Although it is not possible to claim that this change comes from specific actions or controlled factors, the results presented here indicate an improvement in road safety for these users, in line with the goals of the Safe Systems Approach and the Decade of Action for Road Safety to reduce severe and fatal traffic injuries.

Study Of Compressive And Flexural Strength Of Concrete Using Bima Cement For National Road Reconstruction In Kebumen

Concrete is an important aspect of building construction, one of which is road construction. Concrete is used for road construction because it has the advantages of high compressive strength, easy maintenance, ease of formation, and easy to obtain components. Road construction using concrete is applied to the reconstruction of the Kebumen South Ring Road, where the Kebumen South Ring Road is a 9.07 km long national road. National roads are arterial and collector roads in the primary road network system that connect provincial capitals, national strategic roads, and toll roads. Therefore, construction on national roads requires strong and good materials on the Kebumen South Ring Road to optimize road use. In this study, the concrete is composed of Bima cement as Portland cement, coarse aggregate from Kebumen, fine aggregate from the Progo River, and water from drilled wells. The admixture was obtained from a Conmix HK 2. Subsequently, all ingredients were mixed using a mixer truck. The concrete mixture was molded into cylinders and blocks. In this study, the average results of the 5-day concrete compressive strength test were obtainedwhich is 373.42 kg/cm2 and 28-day-old concrete is 442.32 kg/cm2. The average result of the 5-day concrete flexural test is50.65 kg/cm2 and that of 28-day-old concrete was 60.73 kg/cm2. The results are in accordance with the general specifications of the Bina Marga, namely, 45 kg.