Hossam Abdelgawad

The research community has been extensively contributing to micro-level planning of Electric Vehicle Charging
Infrastructure (EVCI); employing objective-oriented optimization techniques for the optimum location of EVCI
and their capacity. However, the complexity of those techniques and their unfamiliarity for local authorities
makes micro-level planning, not a practical option for cities to start EVCI planning. This research contributes to
meso-level planning of EVCI by implementing the praiseworthy practices in 25 Mega EV cities along with cities
characteristics in creating a dynamic framework for EVCI planning. The proposed framework is divided into a
government-target-driven framework for cities with explicit targets for e-mobility, and international-bestpractices-
driven framework for early adopters cities. The employed planning tools are applicable for design
approaches and serviceability assessment by quantifying EV served demand and the distribution of EVCI locations
as demand-driven networks. The outputs of the proposed framework are: EVCI potential locations, type,
capacity, and served EV demand. Policy and operational recommendations including: projected market categories
of consumers, targeted degree of electrification, and proposed demand-responsive programs are discussed.
The international-best-practices-driven framework is demonstrated in the New Administrative Capital of Egypt
and a phased plan of EVCI from (2022–2027) is developed to provide multiple design alternatives for decisionmakers

This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared—in terms of mean square error (MSE) and mean absolute error (MAE)—with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).