Enhanced Methods for Utilization of Data to Support Multi-Scenario Analysis and Multi-Resolution Modeling

The success of analysis and simulation in transportation systems depends on the availability, quality, reliability, and consistency of real-world data and the methods for utilizing the data. Additional data and data requirements are needed to support advanced analysis and simulation strategies such as multi-resolution modeling (MRM) and multi-scenario analysis. This study has developed, demonstrated, and assessed a systematic approach for the use of data to support MRM and multi-scenario analysis. First, the study developed and examined approaches for selecting one or more representative days for the analysis, considering the variability in travel conditions throughout the year based on cluster analysis. Second, this study developed and analyzed methods for using crowdsourced data vii to estimate origin-destination demands and link-level volumes for use as part of an MRM with consideration of the modeling scenario(s). The assessment of the methods to select the representative day(s) utilizes statistical measures, in addition to measures and visualization techniques that are specific to traffic operations. The results of the assessment indicate that the utilization of the K-means clustering algorithm with four clusters and spatio-temporal segregation of the variables demonstrated superior performance over other tested approaches, such as the use of the Gaussian Mixture clustering algorithm and the use of different segregation levels. The study assessed methods for the use of third-party crowdsourced data from StreetLight (SL) as part of the Origin-Destination Matrix Estimation (ODME), which identifies the method resulting in the closest origin-destination demands to the original seed matrices and real-world link counts. The results of the study indicate that Method 3(b) produced the best performance, which utilized combined data from demand forecasting models, crowdsourced data, and traffic counts. Additionally, this study examined regression models between crowdsourced data and count station data developed for link-level estimation of the volumes. This study also examined the accuracy and transferability of the link-level estimation of the volumes to determine if the crowdsourced data combined with available volume data at several locations can be used to predict missing or unavailable volumes in different locations on different days and times within the network. Regression models produced low errors than the default SL estimates when hourly or daily traffic volumes were taken into account. For similar traffic conditions, the models predicted directional traffic volume close to the real-world value

Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysis

Ride-hailing services have gained popularity in recent years due to attributes such as reduced travel costs, traffic congestion, and emissions. However, with the impact of COVID-19, the ridehailing market is estimated to lose its fair share of an uprising as a transportation mode. During normal and critical circumstances, ride-hailing service users express their concerns, habits, and emotions through posting on social platforms such as Twitter. Hence, Twitter, as an emerging data source, is an effective and innovative digital platform to observe the rider\u27s behavior in ridehailing services. This study hydrates large-scale Twitter reactions related to shared mobility to perform comparative sentiment and emotion analysis to understand the impact of COVID-19 on transportation network services in pre-pandemic and during pandemic conditions. Amid pandemic, negative tweets (34%) associated with \u27sad\u27 (15%) and \u27anger\u27 (15%) emotions were most prevalent in the dataset

Durability Properties of Nanomodified FRP-Concrete Adhesive Joints

Externally bonded fiber-reinforced polymer (FRP) composites represent a simple and economical solution for many repair and strengthening applications in concrete structures. However, the potential occurrence of sudden and brittle debonding failure in such repairs becomes prominent when FRP-concrete bond undergoes environmental degradation induced by moisture. Ambient-cured low-viscosity Bisphenol A epoxy adhesives are most commonly utilized in the engineering practice to bond wet-layup FRP to the concrete substrate. This study aims to elucidate the effects of Bisphenol A-based epoxy modified with commercial surface-modified nanosilica (SMNS), core-shell rubber (CSR) nanoparticles and multi-walled carbon nanotubes (MWCNT) on the improvement of mechanical properties of the epoxy adhesives, and strength and durability of FRP-concrete adhesively bonded joints. Moisture ingress in epoxy, DSC, tensile test on epoxy and three-point bending beam bond tests were performed. To determine the effects of environmental degradation, all specimens were subjected to the following environments: control—23 °C at RH 50 ± 10% for 18 weeks; and accelerated conditioning protocol (ACP)—water immersion at 45 ± 1 °C for 18 weeks. Improvement in mechanical properties were observed in dogbone specimens modified with nanoparticles without any reduction in glass transition temperature (Tg). In control conditions, nanomodified epoxy groups exhibited enhanced mechanical properties compared to the neat epoxy. Following ACP, strength, elongation and modulus of elasticity of neat epoxy deteriorated significantly, while no significant deterioration was observed in the nanomodified group of adhesives. Among all the nanomodified adhesive groups CSR Type-1 showed most improvement in mechanical properties over neat epoxy group both in control condition and in ACP. CSR-modified adhesive joints experienced practically no degradation when subjected to ACP and showed the highest maximum bond strength retention of 100% among all the adhesive groups. The bond strength of neat epoxy adhesive joints degraded most dramatically (15%) following ACP

Hydrating Large-Scale Coronavirus Pandemic Tweets: A Review of Software for Transportation Research

The coronavirus (COVID-19) pandemic has challenged the established societal structure, and the transportation sector is not out of this new normal. The primary objective of this research is to analyze and review the performance of software models used for extracting and processing large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts of machine learning applications on social media by providing a review of contemporary tools, including their computing maturity, and their potential usefulness. The paper also provides an open data repository for the processed data frames to facilitate the swift development of new transportation research. Transportation researchers and the American Society of Civil Engineers (ASCE) community are believed to benefit from this study