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This paper aims at presenting up-to-date urban mobility and traffic related indicators for the city of Thessaloniki, Greece. Insights are provided on the modeling approach and the methodologies used for the calculation of the mobility indicators, in regard to travel demand estimation and assignment of traffic in the network. Travel demand and supply data used for modeling urban mobility are presented, together with the processes followed and their outputs. Car ownership, vehicle occupancy, modal split and hourly traffic volumes are among the examined indicators.  Index Terms-Mobility indicators, transport modeling, travel demand, urban mobility.

The sensor location problem is of particular importance when planning the allocation of limited field equipment intended to be used for advanced traffic management systems and traveller information services. The locations within a network that satisfy specific goals need to be carefully selected, based on predefined goals related to the effective collection of data and the subsequent estimation of traffic related information. The detection of traffic volumes is mainly associated with two purposes, the travel time and the Origin–Destination (O–D) trip matrix estimation. In this context, this paper presents a quadratic programing model, able to determine the optimal location of tracking sensors. The model is implemented in the urban road network of the city of Thessaloniki (Greece) in which specific number of sensors is installed and utilized for real-time travel time information provision. The proposed methodology models the sensor location problem under the general framework of a set covering problem, which is one of the most popular optimization problems and has been applied in many industrial problems. The results of the case study in Thessaloniki reveal that the proposed model defines the optimal location of the limited number of sensors in such a way that the network, which is created having all sensors as origin or destination of all possible paths, represents to great extent (87% of the traffic flow along the major paths) the traffic volumes of the whole road network of the city.

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Intelligent transportation systems (ITS) are a powerful technology, not only for gaining traffic and transport benefits, such as less congestion and shorter travel times, but also for economic growth. The European Union (EU) has already published a directive (2010/40/EU) to regulate the coordinated and coherent deployment of ITS in all EU-member states. However, ITS deployment in some EU member states has yet to be integrated. According to the European Commission, South East Europe (SEE) is an area in which ITS deployment is “fragmented and uncoordinated and cannot provide geographical continuity of ITS services throughout the region.” An overview is provided of the current deployment and implementation status of ITS in nine countries of the SEE area. The results highlight the strengths and weaknesses for further ITS deployment in these countries and the level of harmonization of their national laws with the European directive, a prerequisite that could alleviate the fragmentation of the ITS provision along different regions within the nations and also at cross-border areas. Afterward, the methodology for creating national roadmaps for ITS deployment is presented as well as the final roadmaps for each of the nine countries. The findings of these roadmaps are crucial for future ITS deployment in SEE countries; the level of maturity of ITS deployment in each country has been taken into account.