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Research Article Open Access
Objective: This research intends to explore the correlation between pavement roughness and vehicle emissions, and classify the pavement roughness based on vehicle emissions and public health impacts.
Method: On-road tests were conducted to measure vehicle emissions by a Portable Emission Measurement System (PEMS), and collect the corresponding pavement roughness by a smartphone application. A total of 19,038 data pairs were collected during 325 km long test routes in the State of Texas. The correlation of the emissions and International Roughness Index (IRI) are analyzed and the roughness was classified into clusters by three pattern recognition algorithms.
Findings: The pavement roughness could be classified into four categories based on the clustering features of emission factors. The average of Normalized Emission Factor (ANEF) started with 0.051 at a level of IRI between 0+ and 1.99 m/km (category A), then dropped to 0.032 with IRI between 1.99 and 3.21 m/km (category B), followed by a slight decline to 0.030 with IRI between 3.21 and 6 m/km (category C). When the IRI was greater than 6 m/km (category D), the ANEF increased to 0.039. Driving on the pavement categorized to C and D may induce higher invehicle noise and driving stress indicated by drivers’ higher heart rates.
Conclusion: The relationship between pavement roughness and vehicle emissions is nonlinear. The even smoother (category A) and even rougher (category D) road surfaces may also induce higher vehicle emissions. The proposed categorization of pavement roughness for Texas incorporates the impacts on environment and public health. To minimize the ANEF, the roughness in categories B and C (IRI: 2-6 m/km) is optimal for pavement design. If the impacts on in-vehicle noises and drivers’ heart rates are concerned as supplemental factors, category B (IRI: 1.99-3.21 m/km) is the best. Switching a pavement from category A to B, up to 34% of vehicle emissions and fuel consumption could be achieved. This categorization can be used in the design, maintenance, and evaluation of highway pavements, as well as applied to other states and countries with further calibrations of clusters for local specific classification of roughness.
Human public health, Environmental impacts, Heart rates, In-vehicle noise, Vehicle emissions, Pavement roughness, Clustering algorithms, Driver Safety, Applied Ergonomics