Transportation improvements affect technological and socio-economic development, and several scholars have researched various transportation problems. The current study aims to illustrate a thorough review of those transport problems, where the Analytic Hierarchic Process (AHP) is used for enumerating the related criteria and alternatives. A systematic review methodology, the PRISMA protocol, is applied in the review process. The contribution of this work is highlighted along with the extensions of the AHP improving decision-making support. To this end, current research demonstrates the relevant results of 58 papers published from 2003 to 2019. The results indicated that most researchers applied the conventional AHP method to deal with transportation issues, while the most critical issue was public transport, followed by logistics problems. On top of that, TOPSIS was integrated with AHP more than other MCDM methods when dealing with multi-criteria transportation problems. Moreover, the "Transportation Research Part A: Policy and Practice" journal achieved first place by publishing ten papers on the topic, and the highest number of articles was published in 2018. The results are discussed adequately, and in the conclusion policy implications are presented.

The present study focuses on the relationship between the Covid-19 pandemic and mobility patterns. By using data from an international survey, transport users’ socio-demographic features and travel characteristics during the pre-pandemic and pandemic time are analyzed.Afterward, the pandemic commuting travel behavior is modeled. The multinomial model shows that public transport users are 31.5, 10.6, and 6.9 times more likely to change their commuting transportation mode than car users, motorcycle users, and walkers, respectively. The results demonstrate that travel distance and income level are the two most influential factors in pandemic decision-making. These results confirm the reflection of spatial-economic inequalities during the pandemic. Active modes, motorcycle, and personal car are perceived by the participants as the least risky urban transportation modes during the pandemic. Thus, a considerable growth in individual transportation modes, up to 26% increase for commuting and up to 15% for leisure activities, can be recognized.

Mobility as a Service (MaaS) is a new transport concept which integrates, manages, and distributes private and public mobility alternatives by using intelligent digital technologies. Recently, research and implementations have been widely conducted. In order to reveal future implications, it is crucial to analyze the available MaaS services by using systematic methodology. Cluster analysis was applied to create typical groups of MaaS services and to define the common features of the systems, which may highlight future trends. In order to identify the most relevant MaaS initiatives, the typical parameters of the services were taken into account and a dataset was developed. More than 30 MaaS services from 14 countries were investigated, and the features and the functionalities of these services were analyzed. The findings demonstrate that there is potential for the development of the applications in terms of their payment features, their personalization, and the provision of all attainable elements of MaaS. The number of operators is constantly increasing. However, it is uncertain whether public or private MaaS operators will be dominant on the market. Three cluster groups were created with specific features and directions of development. The Route planners group involves a few modes of transport, but it provides an extensive service. While the Third parties group has primarily private MaaS operators, the Public systems group usually includes public MaaS operators. This comprehensive study might be useful to MaaS operators and regulators for understanding the typical features and the development directions of the market.

The currently available transport modeling tools are used to evaluate the effects of behavior change. The aim of this study is to analyze the interaction between the transport mode choice and travel behavior of an individual—more specifically, to identify which of the variables has the greatest effect on mode choice. This is realized by using a multinomial logit model (MNL) and a nested logit model (NL) based on a utility function. The utility function contains activity characteristics, trip characteristics including travel cost, travel time, the distance between activity place, and the individual characteristics to calculate the maximum utility of the mode choice. The variables in the proposed model are tested by using real observations in Budapest, Hungary as a case study. When analyzing the results, it was found that “Trip distance” variable was the most significant, followed by “Travel time” and “Activity purpose”. These parameters have to be mainly considered when elaborating urban traffic models and travel plans. The advantage of using the proposed logit models and utility function is the ability to identify the relationship among the travel behavior of an individual and the mode choice. With the results, it is possible to estimate the influence of the various variables on mode choice and identify the best mode based on the utility function.