This paper presents a hybrid short-term traffic speed prediction framework through empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA). The goals of this paper are to investigate (1) does the hybrid model provide better short-term traffic conditions (i.e. traffic speeds) than the traditional models? (2) how the performance of the hybrid model varies for varying scenarios such as mixed traffic flow and vehicle-type specific traffic prediction in a work zone, on-ramp, and off-ramp; and (3) why hybrid models provide better prediction than other single-staged models. Using empirical data from a work zone on interstate I91 in Springfield, MA and the on/off-ramp data from the Georgia State Route 400, the proposed hybrid EMD–ARIMA modelling framework is tested in the four distinct scenarios aforementioned. The prediction results of the hybrid EMD–ARIMA model are evaluated against the experimental data and also compared with the results from the traditional ARIMA, the Holt–Winters, the artificial neural network models, and a naive model. The evaluation results showed that the hybrid EMD–ARIMA model outperforms the traditional forecasting models in different scenarios.

The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

This research examines the overall performance achievement of social media marketing (SMM) in Bangladesh by determining whether social media is successful in creating brand consciousness (i.e., brand preference, brand attachment, brand association, and brand loyalty) toward online consumers, which in turn may lead to buying commitment. In total, 564 Bangladeshi consumers were surveyed to monitor their responsiveness toward social media-aided motivations. We selected the online buying environment in Bangladesh, which is an emerging market established less than one decade ago. We specifically choose the entire local fashion industry as our target market, excluding the websites of international fashion brands operated overseas. We used the holistic concept of the five aspects of SMM, namely, interaction, entertainment, customization, electronic word of mouth (eWOM), and trendiness. Moreover, we statistically calculated the performance of social media through the consequences of five measures, namely, brand loyalty, brand preference, brand attachment, brand association, and buying commitment. We used regular linear multiple regression, correlation, and descriptive statistics to obtain statistical results. The study found strong evidence that SMM efforts (SMMEs) of the local Bangladeshi fashion industry are successful in establishing consumer attachment and preference. However, they fail to secure committed buyers when the measurement scale is below 50%. In line with the results of previous studies on consumer loyalty, our results demonstrate that SMMEs fail to create committed buyers. Lack of loyalty and association drive consumers to become uncommitted buyers.

The continuous advancement of connected vehicle (CV) technologies and its increasing market penetration (MP) will change the nature of traffic flow fundamentals by upending the traditional traffic composition. The objective of this paper is to create an agent-based modeling framework to evaluate the impact of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) technology on the mobility performance (i.e., travel time and its reliability) of a two-lane highway work zone scenario. This paper studies when and at what MP will the mobility benefits of connected vehicles emerge. Results showed that the mobility benefits are highly dependent on the CVs MP levels and traffic flow rates. In addition, the study found that the higher the traffic flow rate is, the higher MP level is required to enable the mobility benefits in the mixed traffic environment. Research findings will inform agency professionals, traffic engineers, and planners the achievable benefits that could accompany the implementation of CV technologies in diverse roadway settings such as work zone. Moreover, the results will help the decision-makers in agencies as state DOTs to develop effective resource allocation strategies and evaluate the cost and benefit ratio of deploying this technology in highway work zone scenario.