This paper presents a review of the different models developed for the taxicab problem. The presented models are grouped in two categories, aggregated and equilibrium models. Each model is analyzed from different points of view, such as market organization, operational organization and regulation issues. Conclusions extracted by authors are presented, listed and compared, analyzing each affirmation in terms of market regulation and organization. Finally, a state of the practice is presented, analyzing the configuration of the taxi market regulations along the world, linking the conclusions obtained by the authors with the real market situations

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

Collaborative transportation systems are relatively new concepts in research, but are very popular in practice. In the last years, collaborative transportation seems a good city logistics alternative to classical urban consolidation centers, but it is still in a development stage. This paper proposes a framework to define and evaluate collaborative urban transportation systems. This framework is organized in the following modules: a knowledge management system, a scenario simulator, a transportation management system, an environmental module, a risk factor estimation module and a multi-criteria decision support method. Finally, five realistic scenarios for the urban area of Lyon (France) are simulated and discussed to illustrate the proposed framework possible applications

Transport systems are undergoing a change of paradigm that focuses on resource-sharing and collaboration of multiple and diverse stakeholders. This paper aims to present a state-of-the-art on the main research issues of multi-stakeholder collaboration in urban transport and address the main contributions of the Special Issue on Collaboration and Urban Transport to the field. To that end, it seems necessary to identify and address the complexity of the relations of the stakeholders in the field, beyond the traditional classification of private and public stakeholders. A functional classification of urban stakeholders related to the different land uses is proposed a refer to space users and space organizers, each with several sub-categories. Furthermore, the collaboration among those stakeholders can take different forms and can be developed at different levels: transactional, informational and decisional. Thus, the main research topics regarding multi-stakeholders’ collaboration are defined as: partnerships, resource sharing, resource pooling and Mobility-as-a-Service (MaaS) systems. A set of papers in this special issue focus on Urban Consolidation Centres (UCCs), partnerships in transport under a general perspective, multi-stakeholder cooperation and its barriers, collaborative decision-making, traffic prediction and urban congestion. In the papers, which deal with the field of multi-stakeholder collaboration in urban transport, there is a predominance on the use of surveys, but also a focus on data-driven techniques. As a result, this special issue contributes not only to the theoretical aspects, but adds value to technical and methodological issues.