Efthymis Papadopoulos

Mobility as a Service (MaaS) and, more recently, electric Mobility as a Service (eMaaS) have increasingly been put forward to meet the economic, social, and environmental challenges linked to mobility. First, however, monitoring and evaluating such a scheme’s performance is crucial, mainly through the definition of appropriate indicators. In this study, a standardised methodological approach is presented for the assessment of an eMaaS scheme. In addition, this methodological approach contains a range of innovative Key Performance Indicators (KPIs). The proposed KPIs refer to the evaluation of the scheme based on four pillars: (a) society, (b) users, (c) operators, and (d) internal operation. The methodology for evaluating the proposed KPIs includes identifying the available sources for data collection. For example, data can be collected through questionnaire surveys, focus group discussions, and the system’s central dashboard. An appropriate set of indicators to evaluate a system from various perspectives is necessary to assess an eMaaS scheme in real-life conditions. Furthermore, the evaluation of the overall operation of the scheme will contribute to drawing valid conclusions (e.g., user acceptance, economic viability) for the implementation of eMaaS in urban areas.

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.

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.

In this paper, we explore users’ intentions to use bike-sharing systems (BSS) compared to traditional competitive transport modes—private car, bus and walking. Fueled by the increasingly rampant growth of shared economy and Information and Communication Technology (ICT), shared mobility is gaining increasing traction. The numbers of shared mobility schemes are rapidly growing worldwide and are accompanied by changes in the traditional vehicle ownership model. In order to pinpoint the factors that strongly affect the willingness to use BSS, a stated preference survey among car and bus users as well as pedestrians was designed and conducted. Binary logit models of the choice between the currently preferred transportation modes and BSSs were developed, for short and long-duration trips, respectively. The results highlight a distinctive set of factors and patterns affecting the willingness to adopt bike-sharing: choice is most sensitive to travel time and cost of the competitive travel options. In general, users are more willing to make the switch to a BSS, especially for short trip durations, when their typical mode of transport becomes more expensive. Bike-sharing also seems to be a more attractive option for certain user socio-demographic groups per mode and trip duration (age, education level, employment status, household income). Trip characteristics such as trip purpose and frequency were also found to affect the willingness to choose BSS. In general, BSS seem to mainly attract bus users and pedestrians, while car users may use BSS more sparingly, mainly for commuting purposes.

In this paper we explore factors that affect Bike Sharing System (BSS) usage and how they differentiate between discrete groups of potential users. BSS have known a rampant growth during recent years, through technological advances, re-evaluated business models and reinvention of the mean’s utility. Yet, for a realized use of dockless BSS and a successful integration in the urban mobility ecosystem to be achieved, the factors that promote willingness to use them need to be explored. By using a sample of 500 stated preference data, classification trees and random forest models are built for three groups of potential BSS users; car users, bus users and pedestrians. Among the considered factors are BSS cost gains, BSS In Vehicle Time (IVT) and Out of Vehicle Time (OVT) gains, trip frequency, purpose and duration. More specific, it was found that BSS potential, increases for short duration trips of up to 21 min for car users. Bus users and pedestrians were found to be more likely to choose a BSS option for a higher cost up to 0,60 and 0,75 euros respectively. On the other side sociodemographic characteristics such as household income, gender, education level and occupation did not found to be the dominant factors for the mode choice decision. OVT is found only to be relatively important for bus users, while the cost gains are comparatively more significant for bus users and pedestrians.