The purpose of the present study is to examine whether mining and analyzing social media data can be a powerful tool in the transportation domain. A survey of the literature based on the existing uses of social media in transportation is conducted; opportunities and barriers are also presented for the subject. Analysis of social media can provide valuable information regarding incident detection, mobility, and activity patterns as well as users’ opinions about different transport modes. The issues that need to be addressed are not few, with most important ones being the advanced mining and linguistic techniques required for the extraction of information, the reliability of the data collected, and the sample bias. The study concludes with recommendations in relation to the existing gaps in the literature, such as the need to create a transport-oriented lexicon to facilitate the process of collecting transport-related information from social media, use of social media during transport planning and operation, and finally, potential use of qualitative indicators on public transportation issues regarding the perceived level of service.

Intersection safety and drivers’ behavior are strongly interrelated, especially when the latter are located in dilemma zone. This paper explores, among others, the main factors affecting driver behavior, such as distance to stop line, approaching speed and acceleration/deceleration, and two additional factors, namely, driver’s aggressiveness and driver’s relative position at the onset of the yellow signal. Field data were collected using unmanned aerial vehicle (UAV) technology. Two binary choice models were developed, the first relying on observed data and the latter enriched by the latent factor drivers’ aggressiveness and the vehicles’ relative position. Drivers were classified to aggressive and non-aggressive ones using a latent class model that combined approaching speed and acceleration/deceleration data. Drivers were further grouped according to their expected reaction/decision to stop or cross the intersection in relation to their relative position. Both models equally explain drivers’ decisions adequately, but the second one offers additional explanatory power attributed to aggressiveness. Being able to identify the level of aggressiveness among the drivers enables the calculation of the probability that drivers will cross the intersection even if caught in a dilemma zone or in a zone in which the obvious decision is to stop. Such findings can be valuable when designing a signalized intersection and the traffic time settings, as well as the posted speed limit.

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

Public transport systems’ effectiveness is a well-recognized pillar of their sustainability. In this study, we employed order-m efficiency estimators to investigate the effectiveness of 57 bus public transport operators that provide services in both large and medium sized European cities. Their effectiveness was simulated through a tailored production model and was evaluated against critical exogenous variables, which were mostly extracted from Eurostat database. Results showed that the effectiveness of the examined operators is generally satisfactory. Our research suggests that certain exogenous factors significantly affect operators’ effectiveness and thus create either advantageous or disadvantageous operational environments for maintaining public transport sustainability. Among these factors, household size, unemployment and car ownership rates were found to be unfavorable to bus public transport operations. Contrary to them, the presence of university students and metro systems in cities create a favorable operational environment for bus public transport effectiveness. These findings assist in the identification of sustainable development policies that would both contribute to public transport sustainability and to the fulfillment of wider community goals. Our findings also rationalize benchmarking exercises in the public transport industry, since they enable fair performance comparisons between systems that seek to incorporate successful management practices to improve their sustainability.