Andreas Nikiforiadis

Hydrogen-powered cargo bikes are gaining increasing attention in the framework of urban logistics, thanks to their enhanced agility to travel and park in congested areas, low carbon footprint, and extended traveling range. Nevertheless, deploying a fleet of hydrogen-powered cargo bikes can be challenging, necessitating the parallel assessment of the operation of the fleet, the H₂ refueling strategy, and eventually the design of the respective refueling infrastructure. The objective of this article is to demonstrate a systematic methodological framework for deriving strategic insights into the deployment of a fleet of cargo bikes equipped with a hydrogen fuel cell unit. Employing advanced longitudinal-based vehicle mathematical modeling, coupled with statistical techniques, the energy consumption of the vehicle was analyzed under a broad spectrum of operating conditions. Moreover, the refueling strategy was analyzed for different ranges of vehicle fleets, and enhanced options for designing the respective hydrogen stations were charted. Ultimately, this work provides strategic insights that could be used by cargo bike fleet operators and H₂ refueling infrastructure designers.

1. Parking in urban areas is connected to multiple challenges and impacts on traffic flow and urban dynamics. Studies have shown that up to 30% of traffic congestion in urban areas is caused by dri...

Despite that a lot of research has been carried out for the development of routing algorithms, research for multimodal trip planning is still limited. Even more scarce is the research on incorporating individual preferences in multimodal trip planning; yet, this incorporation is essential since not all users are willing (or they are not equally willing) or capable of using all the different modes of transportation. The main contribution of this paper is the conceptualization of how multimodal trip planners’ results can be customized based on: a) user’s responses in a specific set of questions that aim to capture their preferences, b) the revealed preferences of the users that are automatically collected. The first case is being demonstrated within this paper for the case of Thessaloniki, Greece. The developed multimodal trip planner identifies various alternative combinations of shared modes and walking and for each combination calculates the travel cost, travel duration and number of modes changes. The alternative combinations are being prioritized based on a generalized cost function. The outcomes of this paper can be of interest for researchers, who develop trip planners, and for MaaS operators and authorities that are intending to offer multimodal planning services.

The impact of cycling-related traffic crashes on public health has increased significantly in recent decades, with cyclists being among the most vulnerable road users. The risk of severe injury in traffic crashes is notably high for cyclists, especially when distracted. Research indicates that distraction while cycling significantly increases the crash risk. This study investigates cycling distraction through a field operational test involving 100 participants. Riders followed a predetermined course while being exposed to external visual and auditory stimuli, including alarms, advertising signs, and car horns. Distraction levels were measured using eye-tracking technology. Data were analyzed by means of descriptive statistics, cluster, and correlation analyses. Our findings showed that auditory stimuli distract a higher percentage of cyclists, while audiovisual stimuli from road-related factors cause longer-lasting distractions. Additionally, five distraction clusters were identified based on stimulus duration. Lastly, it was found that males were more likely to belong to high-distraction clusters, whereas females and daily cyclists were more likely to fall into the lowest-distraction group.