University Transportation Research Center

The reinforcement of cohesive soils for roadbed construction is critical to ensure long-term stability and performance of transportation infrastructure. This study explores the effect of fly ash, a byproduct of coal combustion, on the strength properties of cohesive soils, aiming to improve their suitability for use in roadbed applications. Laboratory experiments were conducted by mixing fly ash in varying proportions (ranging from 10% to 50% by weight) with locally sourced cohesive soil. Key geotechnical tests, including unconfined compressive strength (UCS), Maximum Dry Density, Atterberg limits, and compaction characteristics, were performed to evaluate changes in strength and consistency. Results indicated that the addition of fly ash led to a significant increase in UCS, especially at 20–35% fly ash content, suggesting improved load-bearing capacity and structural integrity. Furthermore, the plasticity index was reduced, and optimum moisture content slightly increased, reflecting favourable modifications in soil behaviour. These improvements are attributed to pozzolanic reactions between fly ash and soil particles, leading to the formation of cementitious compounds. The study concludes that fly ash is an effective and sustainable stabilizing agent for cohesive soils, offering both environmental and engineering benefits for roadbed reinforcement and broader civil engineering applications.

• Study assessed the longitudinal impact of automated speed enforcement (ASE) in NYC. • Fixed speed camera deployment reduced traffic crashes by 14 %. • Fixed speed cameras effective within six months, varying impacts across sites. • Short-term analysis shows ASE effectiveness, with some locations exhibiting a time-lag effect. • Long-term analysis reveals 75 % fewer speeding tickets, validating continuous ASE enhancements. Speeding is a leading contributor to fatal crashes. This longitudinal study examines the short- and long-term changes associated with an automated speed enforcement program’s expansion from 2019 to 2021 in New York City, including the COVID-19-induced surge on speeding behaviors and the complex nature of high volumes of pedestrians and non-motorized vehicles. Leveraging speeding tickets from 1,821 fixed speed cameras in school zones and crash data, this study employs interrupted time-series, spatial distribution, clustering analysis, and Survival Analysis with a random effect (SARE) to investigate if such a program brings about immediate and/or long-term change in speeding behaviors and crash reduction. The findings suggest a decrease in speeding tickets by an average of 18.4 %, 13.3 %, and 0.6 % in the second-, third- and fourth-month post-installation, demonstrating the program’s short-term efficacy in reducing speeding behavior. However, diminishing and time-lag effects were observed at some camera locations, indicating the need for further investigation and potential alternative safety interventions at these sites. Long-term analysis revealed a substantial 75 % reduction in speeding tickets by the end of 2021, despite a temporary surge during the pandemic. Four different long-term patterns were identified. Furthermore, crash analysis showed a statistically significant 14 % decrease in traffic crashes (pre-COVID) following speed camera implementation. Overall, the program has been largely successful in reducing speeding violations and traffic crashes, but its temporal effect varies across sites. Continuous monitoring, data-led adaptive strategies, and additional safety countermeasures are needed to optimize the program’s impact.

Road traffic injuries (RTIs) pose significant public health threats, particularly for vulnerable road users such as pedestrians and cyclists. While recent studies have revealed adverse impacts of heat exposure on RTI frequency among motorized road users, a research gap persists in understanding these impacts on non-motorized road users, especially in tropical regions where their vulnerability can be heightened due to differential thermal exposure, adaptive capacity, and biological sensitivity. In this study, we compared associations between high temperatures and RTIs across four different crash-involved modes of transportation—pedestrians, cyclists, motorcyclists, and car drivers in Taiwan. Leveraging data on RTI records and temperature conditions in Taiwan's six municipalities from 2018 to 2022, we conducted a city-time-stratified case-crossover analysis. We employed distributed lag non-linear models with conditional Poisson regression models to estimate temperature-RTI associations for each mode of transportation, adjusting for various weather factors and unmeasured spatio-temporal patterns. Our findings reveal that individuals using exposed, open transportation modes (i.e., pedestrians, cyclists, and motorcyclists) exhibited higher relative risks of heat-induced RTIs than car drivers, with non-motorized mode users showing greater susceptibility compared to their motorized counterparts. These elevated risks can be attributed to the absence of built-in cooling systems in open travel modes and the increased exertional heat stress implied in active travel. Our study contributes novel insights to a global concern related to climate change, extending its impact to road safety, a health outcome rarely studied in the context of a changing climate. Our findings are thus important, especially for regions where rising temperatures regularly approach or exceed human physiological limits related to heat tolerance in the coming decades. Additionally, our findings hold significance in the existing urban health literature, particularly within the context of the emerging era of micromobility—a category of low-speed, non-enclosed, and lightweight vehicles increasingly integrated into urban activities worldwide.

Sustainability in roadway engineering practices is essential for reducing the environmental impact of transportation infrastructure while ensuring the safety, durability, and efficiency of roads. With increasing concerns about climate change and resource depletion, there is a growing need for road designs that promote sustainability, reduce carbon emissions, and incorporate renewable materials. This article explores various strategies for integrating sustainability into roadway engineering, including the use of recycled materials, energy-efficient road construction techniques, and green infrastructure solutions. It also discusses the challenges of implementing sustainable practices in roadway engineering and the future directions of sustainable transportation infrastructure

This study demonstrates the application of machine learning for classifying the tax compliance level of manufacturing companies in Indonesia. The k-nearest neighbor (kNN) algorithm was utilized to develop the machine learning model. A dataset was collected through a survey conducted directly with finance personnel in charge of the manufacturing companies. Data collection proved challenging as not all companies were willing to participate, resulting in 209 data points. Accountants analyzed the collected data and identified three classes of tax compliance levels. The developed machine learning model successfully achieved a classification accuracy of 92.86%.