Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece.

Short-term demand forecasting is essential for the public transit system, allowing for effective operations planning. This is especially relevant in the highly uncertain environment created by the SARS­CoV­2 pandemic. In this paper, we attempt to develop accurate prediction models of transit ridership in Athens, Greece, using Autoregressive Fractional Integrated time series models enhanced with SARS­CoV­2-related exogenous variables. The selected exogenous variables are, from the one hand, the ratio of weekly SARS­CoV­2 infections over the infections 3 weeks before (capturing the dynamics of the pandemic, as a proxy for fear of transmitting the disease while commuting), and from the other hand, an index of the stringency of the government's SARS­CoV­2-related measures and regulations. The developed ARFIMAX models have been fitted separately on bus and metro ridership data and wield comparable and statistically significant results. In both models, the exogenous variables prove to be statistically significant and their values are intuitive, suggesting a linear interrelation between them and transit ridership.

Critical power two wheeler driving patterns at the emergence of an incident.

The paper proposes a methodology based on Bayesian Networks for identifying the power two wheeler (PTW) driving patterns that arise at the emergence of a critical incident based on high resolution driving data (100Hz) from a naturalistic PTW driving experiment. The proposed methodology aims at identifying the prevailing PTW drivers' actions at the beginning and during critical incidents and associating the critical incidents to specific PTW driving patterns. Results using data from one PTW driver reveal three prevailing driving actions for describing the onset of an incident and an equal number of actions that a PTW driver executes during the course of an incident to avoid a crash. Furthermore, the proposed methodology efficiently relates the observed sets of actions with different types of incidents occurring during overtaking or due to the interactions of the rider with moving or stationary obstacles and the opposing traffic. The observed interrelations define several driving patterns that are characterized by different initial actions, as well as by different likelihood of sequential actions during the incident. The proposed modeling may have significant implications to the efficient and less time consuming analysis of the naturalist data, as well as to the development of custom made PTW driver assistance systems.

Overview of critical risk factors in Power-Two-Wheeler safety.

Power-Two-Wheelers (PTWs) constitute a vulnerable class of road users with increased frequency and severity of accidents. The present paper focuses of the PTW accident risk factors and reviews existing literature with regard to the PTW drivers' interactions with the automobile drivers, as well as interactions with infrastructure elements and weather conditions. Several critical risk factors are revealed with different levels of influence to PTW accident likelihood and severity. A broad classification based on the magnitude and the need for further research for each risk factor is proposed. The paper concludes by discussing the importance of dealing with accident configurations, the data quality and availability, methods implemented to model risk and exposure and risk identification which are critical for a thorough understanding of the determinants of PTW safety.