  With the rising concern over transportation emissions and pollution on a
global scale, shared electric mobility services like E-cars, E-bikes, and
E-scooters have emerged as promising solutions to mitigate these pressing
challenges. However, existing shared E-mobility services exhibit critical
design deficiencies, including insufficient service integration, imprecise
energy consumption forecasting, limited scalability and geographical coverage,
and a notable absence of a user-centric perspective, particularly in the
context of multi-modal transportation. More importantly, there is no
consolidated open-source platform which could benefit the E-mobility research
community. This paper aims to bridge this gap by providing an open-source
platform for shared E-mobility. The proposed platform, with an
agent-in-the-loop approach and modular architecture, is tailored to diverse
user preferences and offers enhanced customization. We demonstrate the
viability of this platform by providing a comprehensive analysis for integrated
multi-modal route-optimization in diverse scenarios of energy availability,
user preferences and E-mobility tools placement for which we use modified Ant
Colony Optimization algorithm so called Multi-Model Energy Constrained ACO
(MMEC-ACO) and Q-Learning algorithms. Our findings demonstrate that Q-learning
achieves significantly better performance in terms of travel time cost for more
than 90\% of the instances as compared to MMEC-ACO for different scenarios
including energy availability, user preference and E-mobility tools
distribution. For a fixed (O, D) pair, the average execution time to achieve
optimal time cost solution for MMEC-ACO is less than 2 seconds, while
Q-learning reaches an optimal time cost in 20 seconds on average. For a
run-time of 2 seconds, Q-learning still achieves a better optimal time cost
with a 20\% reduction over MMEC-ACO's time cost.
