An Empirical Assessment of Traffic Volume Influence on Roadside NO₂ Concentration: A Micro-Scale Statistical Analysis in Makassar, Indonesia

: An Empirical Assessment of

Roadside PM10 concentrations in relation to traffic volume and meteorological conditions in Makassar, Indonesia

This study aimed to quantify the combined influence of traffic activity and meteorological conditions on roadside PM₁₀ in Makassar, Indonesia, and to evaluate whether an integrated model provides better predictive performance than traffic-only and nonlinear alternatives. Field measurements were conducted at 30 roadside points between 25 February and 20 March 2025. PM₁₀ concentrations were measured using a high volume air sampler, traffic was represented by total vehicle volume, and temperature, relative humidity, air pressure, wind direction, and wind velocity were recorded concurrently. The analysis included the Shapiro–Wilk test, Pearson correlation, simple linear regression, multiple linear regression, fifth-order polynomial regression, Random Forest benchmarking, leave-one-out cross-validation, residual diagnostics, Moran’s I, and exploratory inverse distance weighting. Total vehicle volume was positively associated with PM₁₀ (r = 0.752, p < 0.001), while relative humidity showed a strong negative association (r = −0.740, p < 0.001). The final multiple linear regression model using total vehicle volume and relative humidity achieved R² = 0.720 and adjusted R² = 0.699. Under leave-one-out cross-validation, it outperformed the other models with RMSE = 24.79 µg/m³, MAE = 18.62 µg/m³, cross-validated R² = 0.650, and observed–predicted r = 0.807. Random Forest ranked second but did not surpass the multiple linear model. No significant global spatial autocorrelation was detected in measured PM₁₀ or model residuals, and IDW showed weak out-of-location predictive skill, so the spatial surface was retained only as an exploratory visualization. The study is limited by 30 point-level observations, selected one-hour monitoring periods, and the absence of external seasonal validation. Practically, the model provides an interpretable screening framework for identifying roadside locations where traffic intensity and humidity jointly indicate elevated PM₁₀. Its originality lies in combining traf - fic, meteorology, internal validation, robustness diagnostics, a machine-learning benchmark, and explicitly con - strained spatial interpretation within a single roadside assessment framework for Makassar.

Linking traffic congestion, spatiotemporal pollutant patterns, and LEZ siting in a megacity: a case study of Jakarta

Assessing the Relationship Between Traffic Activity and Urban Air Pollution in Dhaka Using QGIS and Emission Factor Methods

Bad air quality is one of the most serious issues cities are struggling with today, especially in rapidly growing megacities such as Dhaka. Increasing traffic volume and congestion make vehicular emissions one of the major sources of urban air pollution. This study examines the spatial correlation between traffic activity and air pollution concentration levels on selected road segments in megacity Dhaka. Traffic volume data were collected from six road segments along Mirpur Road and Kazi Nazrul Islam Avenue using videography-based surveys. Carbon monoxide (CO), nitrogen oxides (NOx) and particulate matter (PM2. were simulated by using emission factor methods. We extracted air pollutant concentration data from the Open-Meteo Air Quality API for each of these respective times and locations. Spatial emission distribution was visualized using a grid framework of 250 m × 250 m by utilizing Quantum Geographic Information System (QGIS) tools. The relationship between emissions and pollutant concentration were analyzed using regression. The results show that there is a strong correlation between the daily traffic emissions and the concentrations of air pollutants, with R² = 0.97 for CO (carbon monoxide) and R² = 0.93 for PM2.5 (particulate matter). The study highlights pollution hotspots on some major road segments where traffic density is high. The study emphasizes the significance of both traffic control methods and sustainable transportation planning especially for Dhaka in facilitating urban air quality challenges.

Quantifying Impact of Rainfall on Road Traffic: A Case Study of Highway 4024 in Phuket

Phuket frequently experiences road flooding due to heavy rainfall and inadequate drainage systems. Preceding studies show that urban rainfall and flooding can disrupt traffic flow. This study aims to quantify the impact of rainfall on the traffic performance of Highway 4024 in Vichit Subdistrict, Phuket, Thailand. This research evaluates how weather conditions influence traffic volume, average speed, and traffic operating conditions, as reflected by the level of service (LOS). Traffic data were manually collected at 15-minute intervals on 17th June 2025 for the rainy day and 24th June 2025 for the dry day. Data collection was carried out during the late afternoon hours. The Passenger Car Unit (PCU) was used to standardize mixed traffic flow, and LOS analysis was applied to evaluate road performance. The results revealed that the average traffic speed decreased from 15.39 km/h under dry conditions to 13.91 km/h under rainy conditions, representing a 9.64% decrease. The average traffic volume was slightly higher during the rainy survey, at 1,572.95 PCU/h compared with 1,356.65 PCU/h under dry conditions, although this difference should be interpreted with caution because the observation windows were not identical. LOS analysis indicated that all observed traffic states fell under LOS F in both weather conditions, showing that Highway 4024 was operating under highly congested conditions and traffic efficiency was further reduced during the rainy survey. These findings suggest that rainfall can impair urban traffic performance and should be considered in transport planning and traffic management for climate-sensitive urban areas.

The Relationship Between Traffic Characteristics on Road Segments in Commercial Areas (Case Study: Basuki Rahmat Street, Palu City)

Commercial areas generate substantial traffic movements that influence traffic flow characteristics on urban roads. Basuki Rahmat Street in Palu City is one of the city's primary commercial corridors, experiencing increasing traffic activity that affects the relationships among traffic volume, speed, and density. This study aimed to analyze the relationships between traffic volume and speed, traffic volume and density, and speed and density, as well as to determine the most appropriate traffic flow model for Basuki Rahmat Street, Palu City. Traffic volume and speed data were collected through field surveys conducted during peak periods and were analyzed using the Greenshields, Greenberg, and Underwood traffic flow models. Road performance was evaluated in accordance with the Indonesian Road Capacity Guidelines (Pedoman Kapasitas Jalan Indonesia [PKJI]) 2023 using road capacity, degree of saturation, and Level of Service (LOS) indicators. The results indicated that the peak traffic volume reached 1,015 passenger car units (pcu)/hour, while the road capacity was 5,630.4 pcu/hour, with a degree of saturation of 0.18, corresponding to Level of Service (LOS) B, which indicates stable traffic flow conditions. The analysis of traffic flow characteristics showed that increases in traffic volume and density resulted in a reduction in vehicle speed. Among the three traffic flow models evaluated, the Greenshields model provided the closest representation of the observed traffic conditions and the PKJI 2023 parameters, with a maximum traffic volume of 1,505.51 pcu/hour, an optimum speed of 57.96 km/h, and a traffic density of 28.98 pcu/km. Therefore, the Greenshields model was identified as the most appropriate model for describing the traffic flow characteristics of the commercial corridor along Basuki Rahmat Street, Palu City.