Improving Active Mobility and Micromobility with Machine Learning and Bio-inspired Algorithms

Overnight rebalancing in dock-based bike-sharing systems requires routing a limited fleet of trucks before user activity begins. This article formulates static rebalancing problem under stochastic demand uncertainty as a bi-objective combinatorial optimization problem that selects truck routes and visited stations. The first objective minimizes total travel distance. The second objective minimizes scenario-weighted unmet demand under a finite set of demand scenarios derived from historical station-status data. A deterministic recourse evaluation simulates truck loads and station inventories along each route and computes unmet demand for visited and unvisited stations. The article applies two multi-objective evolutionary algorithms, NSGA-II and MOEA/D, using a permutation–partition encoding and relocate-based operators that implement a 1–0 relocate neighborhood between routes. A roulette-wheel-based relocation operator (BB2) biases move selection by the induced change in route distance. Experiments on the Barcelona Bicing network with 518 stations and on clustered subinstances show that NSGA-II attains higher hypervolume and larger non-dominated sets, whereas MOEA/D attains lower runtime; an ablation analysis shows that BB2 improves coverage and proximity indicators.

Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies static overnight rebalancing under demand uncertainty modeled as a tri-objective optimization problem. The objectives minimize total travel distance, expected unmet demand, and a robustness-oriented unmet demand measure over high-demand scenarios. Route plans are evaluated via a recourse simulation that enforces truck loads and station capacity constraints across multiple demand realizations. The robustness objective supports selecting plans that reduce peak-demand service degradation. Trade-off solutions are approximated with Non-dominated Sorting Genetic Algorithm II using a permutation-partition encoding and domain-specific relocation operators, including a biased best-improvement move for station relocation. Experiments on the real Barcelona Bicing system with 460 stations show well-distributed Pareto sets and substantial contributions to the reference non-dominated set. Greedy constructive baselines mainly yield extreme solutions and are often dominated.

This paper proposes a multi-criteria approach to optimize urban infrastructure for e-scooters mobility. The problem considers redesigning road infrastructure to integrate e-scooters into a city's multimodal transportation system. This research aims to improve cycle lane coverage and connectivity for e-scooters while minimizing installation costs. Two parallel multi-objective evolutionary algorithms are devised to solve this problem in a real-world instance based on Málaga. The results showed that the algorithms effectively explored the Pareto front, offering diverse trade-off solutions. Key solutions are analyzed to evaluate the trade-offs between travel time improvement, cycle lane connectivity, multimodality, and installation costs. Visualization of proposed infrastructure changes illustrates significant reductions in travel time.

This paper addresses the optimization of urban infrastructure for e-scooter mobility through a multi-criteria approach. The proposed problem considers redesigning road infrastructure to integrate e-scooters into a city’s multimodal transportation system. The objectives involve improving cycle lane coverage for e-scooters while minimizing installation costs. A parallel multi-objective evolutionary algorithm is introduced to solve this problem, applied to a real-world instance based on Málaga city data. The results showcase the algorithm’s effectiveness in exploring the Pareto front, offering diverse trade-off solutions. Key solutions are analyzed, highlighting different zones with varying trade-offs between travel time improvement and installation costs. Visualization of proposed infrastructure changes illustrates significant reductions in travel time and enhanced multimodality. Computational efficiency analysis indicates successful parallelization, achieving substantial speedup and high efficiency with up to 32 processing elements.

There is an increasing interest in alternative vehicle mobility, such as electric scooters (e-scooters). E-scooters are getting attention not only for their environmental impact but also because they are easy to ride on. However, could our current infrastructure support e-scooter trips? Could e-scooter offer a better way to move on our present streets and roads? Using current open data such as the census segments of Malaga city (Spain), the list of non-tertiary institutions, and a map of OpenStreetMap, this work explores the potential of e-scooter in a growing European city by simulating routes trips in Origin-Destination Matrix. The main results are 1) in the current streets, a person can reduce at least 40% of the time using an e-scooter to commute compared to walking; 2) the road infrastructure offered for e-scooters downtown is adequate, especially for routes of more than one kilomete; however this infrastructure needs to be improved in the outskirts of the city.