Transportation systems require many challenges in providing seamless door-to-door mobility. The main initiatives are encouraging a shift from private to other transport modes by providing a fully integrated multimodal service in which the coordination and data sharing among different stakeholders are required. The idea of this paper is to analyze the mode choice, as well as the variables that affect the travelers’ airport access mode choice. For that purpose, we used multinomial logistics (MNL) regression to determine probability of mode choice given various multimodal chain alternatives. The inputs of the proposed model were based on the answers from the participants of the online survey which was disseminated in Europe. Through more than 2000 answers to the survey, we collected the data related to the factors that influence the airport access mode choice, travelers’ attitude, motives for traveling, as well as the socio-demographics of participants. Afterwards, we investigated the influence of the main factors that have an impact on the non-coordination in the multimodal travel chain. The obtained results highlight the impact of the factors “reliability” and “waiting time” in making mode choice.

Real-time information and software support systems are crucial points for performing efficient logistics operations. Recently, most of the logistics companies have been using green-logistics solutions that encourage the use of eco-friendly vehicles, especially cargo bikes. However, for evaluating logistics' business performance, driver's exposure to emissions has often been neglected. Therefore, we proposed a Decision Support System (DSS) that considers, on the one hand, the efficiency of logistics performance and, on the other, the possibility of e-cargo bike drivers to choose the optimal route path considering two options, such as minimum travel time and minimum emission exposure. We applied the proposed DSS in a numerical application that evaluates the customer's assignment to an e-cargo bike according to the hourly traffic flows and emissions. We developed a dynamic algorithm that evaluates the path choice comparison between two route options. The choice of the minimum emission path compared with the shortest travel time path leads to a slight increase in the total travel time. The final path choice, according to the driver's opinion, was obtained using the Fuzzy Inference System (FIS). Moreover, the proposed DSS serves as a general framework for a decision-making process that could be applied to various two-wheels light-duty vehicles for last-mile delivery.