The growth of cities has been influenced over the last decades by social, economic, and technological transformations, resulting in the formation of urban spaces adjusted to the social dynamics of movement guided by the intense use of the internet and technological innovations, which have influenced the flow and mobility of people, transport, objects, services, and information in cities.

From this context, the search for solutions focused on logistics, mobility, infrastructure, business, environment, society, pollution, quality of life, and city management, under the smart city theme’s prism, has attracted experts’ and scientists’ attention. The topic of smart cities is linked, among other aspects, to the treatment given to its six dimensions: smart economy, smart environment, smart governance, smart life, smart people, and smart mobility, as highlighted by Taniguchi (2001), Giffinger et al. (2007), Cohen (2012), Dameri (2017), and The Smart City Model (2018). These axes are fundamental to be considered for developing cities with smart initiatives and their relationship with urban logistics (Giffinger et al. 2007; Fernández-Güell et al. 2016; Dameri 2017).

In recent years, the increased flow of cars in urban areas has resulted in traffic congestion, environmental, air and noise pollution, traffic accidents, and greenhouse gas emissions (Dameri 2017). Forecasts show that the activities of transport companies concerning 2005 will have grown by around 40% in 2030 and over 80% in 2050. At the same time, passenger transport could also increase by 34% in 2030 and 51% in 2050. With the advancement of this mobility pattern, the problem could be even bigger in 2050, when passenger cars can contribute more than 60% of the total passenger transport (Kiba-Janiak 2016).

2. Logistics in the Smart City Dimensions

The review of some scientific works observed in the research by Cheshmberah and Beheshtikia (2020) in the area of supply chain management, a field of study related to logistics, has categorized the dimensions of maturity with the dimensions presented here. The research by Cheshmberah and Beheshtikia (2020) identifies, among other dimensions, human resources, environment, social responsibility, logistics, and planning (policy making). While Gonzalez-Feliu et al. (2020) presented in their study an analysis of the elements that must be included in the design of an urban logistics maturity model, which are practices, processes, stakeholders, and their relationships, and issues related to information flow management and physical, which can help in the planning and decision-making process. In this way, these studies can contribute to and expand the understanding of the dimensions embarked on in this work on smart cities, which seeks to discuss a model of logistical maturity in dimensions related to the economy, environment, governance, quality of life, mobility, and people. The logistics approach applied to the smart economy concept arises from the smart mobility project, specific to the city for the movement of people and industrial and commercial goods, and urban transport, supported using the internet, digitalization, and automation of transport processes (Kumar and Dahiya 2017). In the study by Moustaka et al. (2017), some correlations between smart economy and logistics are pointed out, highlighting the financial concern of stakeholders with the local government budget, the existence of urban planning for mobility, and its aspects related to the city, and organization and control of activities related to the area of logistics, transport, and urban mobility. Applying the smart city concept in some cities is not an isolated phenomenon but an integral part of a broader transition to a digital economy. The dimensions of smart cities were created based on aspects related to their needs, limitations, challenges, and growth prospects related to mobility, economy, governance, environment, people, and quality of life (Giffinger et al. 2007; Caragliu et al. 2011; Dameri 2017). Within the smart environment dimension, some aspects of urban life stand out, linked to efficiency and sustainability supported by technologies. For example, smart sensors help identify levels of air pollution caused by traffic and are equally used to solve the problem of garbage collection and recycling through route optimization and efficient use of container space of collection trucks, reducing traffic pollution (Giffinger et al. 2007; Giffinger and Gudrun 2010; Caragliu et al. 2011; Nam and Pardo 2011; Meeus et al. 2011; Cohen 2012; Neirotti et al. 2014; Angelidou 2017; Dameri 2017). Regarding the objective of smart governance, researchers seek to build public governance with a digital perspective, making the offer of public services dynamic and providing access to documents and public information simply and transparently (Giffinger et al. 2007; Giffinger and Gudrun 2010; Caragliu et al. 2011; Lombardi et al. 2012; Neirotti et al. 2014; Dameri 2017). To Kumar and Dahiya (2017), Albino et al. (2015), and Dameri (2017), there are other aspects of smart governance linked to logistics factors, which are: (i) local, national, and global regulations that affect city logistics (passenger and cargo traffic, environmental protection, road safety, etc.); (ii) transparency of public investments in logistics, transport, and mobility; and (iii) e-government. The quality of life in the context of logistics in a smart city stands out for (i) the promotion of collective or ecological individual transport among citizens; (ii) the promotion of ecological cargo transport between transport and logistics companies; (iii) the application of digital transport monitoring systems, intelligent transport systems; and (iv) monitoring of future trends in the field of city logistics (Kiba-Janiak 2016; Cohen 2012). From the people’s (smart people’s) point of view, some other aspects are found in the literature, which are: (i) social aspects, such as those related to safe road transport, availability of personnel, experience, and knowledge; (ii) the experience of the city’s logistics stakeholders in the implementation of ideas and solutions that allow the improvement of passenger and cargo traffic; (iii) the residents’ inclination to use environmentally-friendly transport in a city; for example, the use of eco-friendly vehicles, or bicycles for commuting (Kiba-Janiak 2016). Mobility is among the most discussed components in the smart city field. In this sense, Albino et al. (2015) define smart mobility as “the use of information and communication technology in modern transport technologies to improve urban traffic”. Vanolo (2014) refers to smart mobility as “local accessibility, availability of Information and Communication Technologies (ICTs), modern, sustainable, and safe transport systems”. The topic of mobility is an important aspect of growing cities. Transporting people and goods within the city is crucial for developing the economy and everyday life. This issue makes the concept of mobility greater than transport or traffic (Graser et al. 2019). The role of logistics in smart mobility is to rationalize traffic and better manage the fleet of different modes to organize the distribution of products and materials efficiently, seeking to alleviate the intense flow attributed to urban centers due to the growth of cities in decades (Thorne and Griffiths 2014; Albino et al. 2015; Caragliu et al. 2011). To Dameri (2017), smart mobility also seeks to achieve the following objectives: 1. reduce pollution; 2. reduce traffic congestion; 3. increase people’s safety; 4. reduce noise pollution; 5. improve transfer speed; 6. reduce transfer costs. In recent years, the smart city theme has aligned with the proposal of solutions to problems identified in cities due to their rapid growth, pointed out by various city sectors. In this sense, creating evaluative models that best meet these needs has become a challenge for most specialists and researchers. Some models aligned with this theme are found in the works by Caragliu et al. (2011), entitled ‘Smart Cities in Europe’, Giffinger et al. (2007), in ‘The role of rankings in growing city competition’, and Cohen (2012), with ‘The smartest cities in the world 2015: Methodology’.

3. Maturity Models

Maturity models are increasingly popular frameworks to support assessment and guide organizational improvement. Most maturity models have their roots in the quality management movement, particularly in Deming’s (1993) plan-make-check-act cycle. This conceptual model was originally conceived by Crosby, who suggested a five-level structure to assess the quality of organizational processes (Crosby 1979). In the same vein, using this five-level framework as a basis, one of the first and most widely recognized maturity models was the capability maturity one developed for software, known as the ‘capability maturity model’ (CMM), developed by researchers at Carnegie Mellon University (Paulk et al. 1993). Based on the maturity model introduced by the Capability Maturity Model (CMM) concept, maturity models have proliferated across a multitude of domains (De Bruin et al. 2005). The use of maturity models serves to identify the ideal path for the evolution of a process from an early stage to a more advanced stage, passing through several intermediate stages (Becker et al. 2009; Wendler 2012). These stages should be sequential and represent a hierarchical progression (Wendler 2012). At the earliest and least advanced stage, performance can be quite poor. As the stages progress, activities are carried out more systematically and are better defined and managed (Gimenez et al. 2017). Maturity approaches have been used in different fields, such as quality, processes, management, or software, showing the different purposes they can have (Fraser et al. 2002). Another characteristic of a theoretical maturity model is that it aims to improve the efficiency and qualification of processes, systems, and activities through indicators and translate indicators into strategic information for interested agents (stakeholders). They can also assist in deciding how, why, where, and when to invest and help identify the cause and effect of changing an organization or field of inquiry; they also help in the evaluation and understanding of current resources to strategically identify the desired resources and determine the improvement activities that will allow the realization of these capabilities (Duncan et al. 1998). Among the main advantages of using maturity models is the development of a governance structure, standardization, and integration of processes, use of performance metrics, control and continuous improvement of processes, commitment to project management, project prioritization and alignment with organizational strategy, use of criteria for continuation or termination of projects, measure the maturity of the organization’s project, program, and portfolio management. Gimenez et al. (2017) state that a maturity model can help public or private companies, industries, governments, or cities better understand their relationship with their external environment and stakeholders. Regarding the development of smart cities and considering the activities related to transport logistics, theoretical maturity models can contribute to the activities qualification of the organizations’ logistics system in interface with the smart city dimensions to better improve and balance the urban space dynamics and the efficiency of the logistics services offered in the cities, addressing more accurate decision-making for managers regarding investment strategies, positioning, and adequacy of logistics activities to the built scenarios from the perspective of smart cities (Burger et al. 2011; Jin et al. 2014; Gonçalves Filho and Waterson 2018). This way, progressing paths from current maturity levels to desired levels form a clear roadmap for closing existing gaps.