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The constant evolution of many urban areas ultimately reaches a point where the current infrastructure cannot further serve the needs of citizens. In the case of transport networks, congested roads, increased delay, and low level of service are among the indicators of a need for road infrastructure upgrade. Thessaloniki is the second-largest city in Greece with a population of over 1 million inhabitants in its metropolitan area. Currently, a significant share of the city’s traffic demand is served via its ring road, whose capacity is set to be enhanced through the construction of a flyover highway with the simultaneous upgrade of the existing ring road. The current study aims at investigating the key factors determining the final route choice of drivers between the two road axes. To that end, data from a combined revealed and stated preference survey targeting car drivers were collected, which were later exploited as the basis for the development of binary route choice regression and machine learning models. The results reveal that drivers’ choice is affected by criteria such as total travel time, the probability of accident occurrence, and closure time due to accident. The results of this paper could prove beneficial to transport researchers in forecasting drivers’ behavior in terms of route choice and to practitioners during the planning phase of similar infrastructure projects.

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.

Background COVID-19 pandemic is a challenge that the world had never encountered in the last 100 years. In order to mitigate its negative effects, governments worldwide took action by prohibiting at first certain activities and in some cases by a countrywide lockdown. Greece was among the countries that were struck by the pandemic. Governmental authorities took action in limiting the spread of the pandemic through a series of countermeasures, which built up to a countrywide lockdown that lasted 42 days. Methodology This research aims at identifying the effect of certain socioeconomic factors on the travel behaviour of Greek citizens and at investigating whether any social groups were comparatively less privileged or suffered more from the lockdown. To this end, a dynamic online questionnaire survey on mobility characteristics was designed and distributed to Greek citizens during the lockdown period, which resulted in 1,259 valid responses. Collected data were analysed through descriptive and inferential statistical tests, in order to identify mobility patterns and correlations with certain socioeconomic characteristics. Additionally, a Generalised Linear Model (GLM) was developed in order to examine the potential influence of socioeconomic characteristics to trip frequency before and during the lockdown period. Results Outcomes indicate a decisive decrease in trip frequencies due to the lockdown. Furthermore, the model’s results indicate significant correlations between gender, income and trip frequencies during the lockdown, something that is not evident in the pre-pandemic era.

The bloom of computer engineering and data science unveils new potential for several business sectors, regarding the enhancement of their operational functions. Transport stakeholders can benefit from computer science advances, such as Artificial Intelligence (ΑΙ) or Data Mining (DM) techniques, which can contribute to the utilization of existing data and maximize their productive efficiency. Reduced operating costs, decreased travel times, as well as faster freight delivery times are among several gains that can be achieved through the adoption of such techniques. Analysing transport related social media content could also provide useful insight into customers’ needs and preferences based on their social media behaviour. However, the aforementioned computer science fields are relatively new and require advanced hardware as well as necessary expertise. Thus, the take – up from transport operators is not significant yet. In this paper, we provide an overview of the currently applied AI and DM techniques in corporate decision making, planning, and management practiced by various transport operators worldwide. Our literature review highlights original findings and case studies from selected freight and passenger transport operators in Greece. To further investigate their activities, a questionnaire survey was conducted to examine the existence of a systematic data collection system, the processes of data mining and analysis applied (if any) and most importantly the fields where the information derived from data analysis is exploited. Survey findings reveal the relative maturity of Greek transport operators for applying AI and DM techniques.

The eve of the 2020s finds European cities in a process of shifting citizens’ mobility preferences towards more sustainable transport means. Cycling is an emerging alternative to motorised modes, particularly due to its lower operating cost and its contribution to healthier lifestyles and containment of climate deregulation from transport sector. Towards establishing cycling as a commuting mode and decreasing private car use, city authorities are increasingly investing in delivering integrated cycling route networks, pro-cycling policies and related services. However, poor and inadequate network design may act as a deterrent to cycling, due to safety, accessibility or quality concerns. To address this, the Greek Ministry of Transport in 2016 issued a set of technical guidelines for cycling network infrastructures in order to standardize their design process. In response to these new guidelines, this paper proposes a methodology for the assessment of existing cycling infrastructure through the utilization of a structured checklist. This checklist considers critical components of bicycle networks, such as their signage, width, inclinations, lighting, intersections, types of separation and interface with other modes and examines them against the minimum requirements described in national standards. The methodology was piloted in the existing cycling networks of Athens and Trikala, Greece. Results indicated certain design flaws and the attributes that did not comply with national standards. The proposed methodology could assist planning authorities into monitoring the quality of cycling infrastructure in Greek cities and indicating the aspects that do not meet the national set thresholds along with highlighting the appropriate interventions.

A congested intersection is one of the major contributors of traffic delay, air and noise pollution in an urban area. A possible redesign of such an intersection can considerably reduce the above mentioned impacts and therefore achieve environmental and economic profits. The signalized intersection, under examination is one of the most congested in the city of Thessaloniki, located in northern Greece. Within the framework of this study, alternative design infrastructure schemes are proposed and examined through the use of microsimulation techniques. Each scenario consists of the construction of an underground bypass as well as a set of various traffic management interventions. In phase A of the study a full horizontal and vertical alignment study was conducted in order to secure the applicability of the proposed scenarios. In the second phase, the alternative scenarios are evaluated through microsimulation techniques and the software platform AIMSUN was used for traffic simulation. In order to model and calibrate the existing situation, a video of the morning peak hour was recorded followed by additional in-situ measurements. The main traffic indicators that were taken into account for calibration and validation were: the GEH index, the mean section queue length per 15min intervals, the traffic volume per 15 min intervals and the average travel time. In the final phase of the study, the proposed interventions were evaluated by comparing the profits from delay time and air pollutant emissions, with their total cost. Additionally, sustainability is evaluated through a survey targeted on postgraduate students, researchers and professionals.

In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances.