Abstract
Mobility has become one of the most difficult challenges that cities must face. More than half of world’s population resides in urban areas and with the continuously growing population it is imperative that cities use their resources more efficiently. Obtaining and gathering data from different sources can be extremely important to support new solutions that will help building a better mobility for the citizens. Crowdsensing has become a popular way to share data collected by sensing devices with the goal to achieve a common interest. Data collected by crowdsensing applications can be a promising way to obtain valuable mobility information from each citizen. In this paper, we study the current work on the integrated mobility services exploring the crowdsensing applications that were used to extract and provide valuable mobility data. Also, we analyze the main current techniques used to characterize urban mobility. © Springer Nature Switzerland AG 2019.

Abstract
A rise in population, along with urbanization, has been causing an increase in demand for urban transportation services in the sub-Saharan Africa countries. In these countries, mobility of people is mainly ensured by bus services and a large-scale informal public transport service, which is known as paratransit (e.g., car rapides in Senegal, Tro Tros in Ghana, taxis in Uganda and Ethiopia, and Matatus in Kenya). Transport demand estimation is a challenging task, particularly in developing countries, mainly due to its expensive and time-consuming data collection requirements. Without accurate demand estimation, it is difficult for transport operators to provide their services and make other important decisions. In this paper, we present a methodology to estimate passenger demand for public transport services using cell phone data. Significant origins and destinations of inhabitants are extracted and used to build origin-destination matrices that resemble travel demand. Based on the inferred travel demand, we are able to reasonably suggest strategic locations for public transport services such as paratransit and taxi stands, as well as new transit routes. The outcome of this study can be useful for the development of policies that can potentially help fulfill the mobility needs of city inhabitants.

Abstract
Traditionally, travel demand modelling focused on long-term multiple socio-economic scenarios and land-use configurations to estimate the required transport supply. However, the limited number of transportation requests in demand-responsive flexible transport systems require a higher resolution zoning. This work analyses users short-term destination choice patterns, with a careful analysis of the available data coming from various different sources, such as GPS traces and social networks. We use a Multinomial Logit Model, with a social component for utility and characteristics, both derived from Social Network Analyses. The results from the model show meaningful relationships between distance and attractiveness for all the different alternatives, with the variable distance being the most significant.