Investing in digital transformation turns out to be a strategic action to tackle contemporary issues and to improve competitiveness for enterprises. The high variability of options in the digital transformation process enforces a higher complexity level in configuring and setting up objectives and goals based on cities’ needs; hence, a systematic approach is required to assist decision makers for better and sustainable transformation. A reference model is described in this paper to support decision makers with comprehensive assessment data for digital transformation cities transport. The proposed reference model assesses the cities based on digital transformation of transport services to assist policy makers for better decisions in transforming the Mobility 4.0. The proposed model in this study functions as a knowledge-based systematic framework for assessing the capabilities of the cities, diagnosing their needs under given circumstances and identifying the best fitting workflow for digital transformation of urban transportation systems and related services. The reference model takes on board a group of smart city indices with respective assessment criteria in determining a smartness level of transportation components. A conceptual 4-tier smartness scale has been proposed to establish a consistent assessment subject to cities circumstances in many respects. The reference model has been formalised into a mathematical model to characterise the assessments. The mathematical model encompasses strategic assessments by experts to identify priorities of investments in the digitalization process, which are aligned with strategic goals and policies of cities’ management.

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver’s gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas.

Digital transformation of urban transportation services attracts significant attention within the scope of smart city studies. It is known as a strategic action with many investment opportunities for the future cities, therefore it attracts the attention of enterprises and institutions. A sound and sustainable digital transformation requires substantial decision support in order to customize the needs and priorities of cities, especially in developing countries, where a lack of knowledge and skills is the case. Mobility 4.0 is recognized as the most up-to-date state of art technology and vision for urban transportation in the age of smart cities. Mobility as a Service (MaAS) is one of the most prominent Mobility 4.0 components that needs to be developed with an integrated management approach, which is available for research and development investments that have defined conceptually yet. In this study, the smartness level of city transport services is identified with a four-level transition approach. The transition from Mobility 3.0 to Mobility 4.0 for cities has been carried out with conceptual integrated management design of MaAS and demonstrated with a case study. The study also adapted a series of new strategically targeted transformation priorities for cities with reference to research and development strategies.

For urban rail lines, there is a need for resources at all stages such as planning, construction and operation. In financing these structures with large budgets and requiring specialized construction, it is possible to implement not only the use of state resources but also many models of Public-Private Partnership (PPP). This method, which can be called alternative financing, will make a significant contribution to the realization of investment auctions and will also contribute to the rapid commissioning / finalization of investments. This model has been tried many times in the world and it has been evaluated that it will be more successful for all the types of the metro (cable car, light subway, monorail, street tram which can be called as a relatively light subway investment. For rail lines when alternative financing is required, it would be appropriate to increase the diversity of income items in feasibility reports. In this context, in addition to travel revenues, real estate value tax, Tax credit financing, goodwill and real estate development partnership can provide income. Real estate prices increase in the area where rail system investment is made and this situation is used as a direct marketing tool by construction companies. On the other hand, in cases where Station structures have to be constructed outside the public area, the cost of expropriation for the project is increasing and financing needs are increasing. For the transportation investment projects carried out as Public-Private Partnership Project (PPP), the use of value increase strategies other than passenger revenues will be beneficial in terms of financing. The demographic and geographic features of the project will be decisive in the determination of the Value Capture Strategies (VCS) to be used. Within the scope of this project; VCS strategies were evaluated in PPP models by using analytical hierarchy process (AHP) with expert opinion in rail system projects to be carried out in Istanbul.

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.