Modern urban-transport planning requires evidence-based insights into current transport flows to better understand the needs and impacts of policymaking. Urban transport includes passenger and freight vehicles, which have different behavior, and the need for such a separation is often ignored in research and practice [1]. New digital data sources provide an opportunity to better understand urban transport and identify where policy interventions are required. We review the literature on digital counting techniques to monitor transport flows, including loops, Automatic-Number Plate Recognition (ANPR) cameras and floating car data. We further investigate the potential of ANPR cameras, which are widely deployed, and which can be augmented with vehicle category information. This article presents the methodology that we follow for transforming raw augmented ANPR camera data into practical knowledge for city planners. Our is aim is to provide a better understanding of passenger and freight vehicle movements and stops, identifying similarities and differences between vehicle categories. We demonstrate our methodology on a case study for the Mechelen-Willebroek district in Belgium, encompassing augmented data from 122 ANPR cameras for a period of two weeks. Additionally, we also look at the car-reduced zone and how time restrictions affect the different vehicle categories’ actions. The findings are validated with GPS data from heavy-good vehicles in the same period. The potential of augmented ANPR camera data and promising themes and applications of this data source are illustrated through the case study.

Sustainability is a key word in modern transportation and logistics. It requires not only economic development but also environmental and social actions. The involvement of multiple stakeholders can express different perspectives and interests to achieve the balance between these three pillars. The multi-actor multi-criteria analysis (MAMCA) is a methodology that can include multiple stakeholders in the process of decision making. It is important in the field of transport and logistic project appraisal, as many projects fail to be implemented because of a lack of support from one or more stakeholders. In MAMCA, multiple stakeholders can use different criteria trees and express their own preferences. At the end of the analysis, the advantages and disadvantages of each of the proposed scenarios are highlighted. Possible consensuses are then being discussed. However, this last step often turns out to be a difficult task.The purpose of this paper is to propose a way to help the facilitator to identify this (these) consensus(es). This will be based on the use of a weight sensitivity analysis model that was recently developed in the context of the PROMETHEE methods and which is based on inverse mixed-integer linear optimization. This approach allows finding the minimum weight modification for each stakeholder in order to improve the position of a given alternative in the individual rankings and, in an ideal case, to the first position of all the rankings simultaneously. This approach is illustrated on two real MAMCA logistic project cases to seek sustainable mobility solutions.

The Multi-Actor Multi-Criteria Analysis has been a successful methodology to integrate multiple stakeholders in the decisionmaking process. Because MAMCA evaluates different alternatives based on the objectives of the stakeholders, decision-makers can increase the support for the alternative they will choose. Still, the application of the methodology can be complex to popularize this approach. The MAMCA software was therefore published in order to facilitate the use of the methodology. The development of that tool offers also new opportunities. Currently, the goal is to extend the MAMCA software as a mass participation tool, hence maximizing participation involvement. In order to facilitate the application of the methodology, the new MAMCA software was published. This contribution highlights how the MAMCA methodology was integrated into the software and how the data is being visualized. We focus on enhancing the concept of "Participation" in the development. A new data structure has been developed and an easier user interface makes the tool more accessible. An easy-understand evaluation method is integrated into the software. The interaction experience between participants is improved. Overall, the new MAMCA software is aimed to have a better performance in workshop settings.

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The Multi-Actor Multi-Criteria Analysis is a methodology that allows for the involvement of multiple stakeholders within a decision-making process. It reveals the consensus and conflicts between the different groups of people that are involved in the evaluation but hold different interests. Nowadays, the concept of the "stakeholder" in MAMCA gradually shifts to the "stakeholder group", and there is a need for involving more than one evaluator in the stakeholder group to make sure all the voices from the group will be heard instead of being represented by one. Especially when a stakeholder group contains a large variation in interests, concerns and socio-economic characteristics. Additionally, one group can have subgroups that might be hard to reach, and therefore are not or un-der-represented in the analysis. This is typically the case for the 'citizens' stakeholder group. In order to fulfill the needs of the involvement of many different stakeholders within stakeholder groups, the mass-participation function was developed in MAMCA and the MAMCA survey tool is designed. This tool allows the decision-maker to design the dedicated survey for the stakeholder group which needs the mass-participation function. The easy-to-understand evaluation process is used to avoid time-consuming elicitation. It is possible to check the homogeneity and heterogeneity of the stakeholders within the stakeholder group based on the socio-economic profiles collected in the survey.