Incentive-Based Simulation-Optimization Framework for Rebalancing Micromobility Systems

Transportation Research Record Journal of the Transportation Research Board · 2026-03-27

This study addresses the critical challenge of rebalancing electric scooters in rapidly expanding bike-sharing systems, driven by fluctuating demand. An innovative user-adaptive rebalancing strategy is introduced that motivates users to alter their destinations toward high-demand areas, specifically through user-centered incentives for destination and charging zone relocations. The strategy aims to streamline rebalancing operations, reduce unmet demand and costs associated with overnight repositioning, and improve overall system efficiency and profitability. To approach the proposed rebalancing problem, a particle swarm optimization (PSO) is integrated with a simulation framework. PSO optimizes the rebalancing problem by iteratively refining candidate solutions, minimizing the need for extensive simulation runs, and improving computational performance. The proposed framework improves the runtime of the optimization by 27%. Through comprehensive simulations, sensitivity analyses, and PSO runs, optimal discount rates and battery level thresholds are identified that effectively nudge users to drop off scooters at the system’s preferred locations. The results highlight a significant increase in system profits, emphasizing the effectiveness of the proposed rebalancing strategy. The numerical results highlight the potential of adaptive, incentive-driven approaches to enhance electric scooter-sharing networks’ operational efficiency, computational performance, and sustainability.

Anticipatory Monte Carlo tree search–based optimization for stochastic dynamic routing with time windows

Computer-Aided Civil and Infrastructure Engineering · 2026-04-22

This paper develops an anticipatory logistics optimization framework for non-profit food rescue operations to address the challenges of hunger and food waste. The study aims to distribute perishable surplus food from food banks to food-insecure households, taking into account uncertain volunteer availability, dynamic household requests, and limited transportation resources. The problem is formulated as a dynamic vehicle routing problem incorporating time windows. A Monte Carlo tree search (MCTS)-based approach is proposed that incorporates vehicle returns to depots for loading food packages. The framework utilizes stochastic rollouts to anticipate future customer arrivals and inform online routing and replenishment decisions. The numerical results indicate that the proposed MCTS framework can effectively solve the problem, outperforming conventional insertion heuristics. Compared to baseline heuristics, the proposed method achieves a 10–15% reduction in total routing cost while serving a larger number of newly arriving household requests under uncertainty.

Integrated column generation for volunteer-based delivery assignment and route optimization

Computer-Aided Civil and Infrastructure Engineering · 2025-02-12 · 4 citations

A Heuristic for Battery-Constrained Charging and Rebalancing of Micromobility Devices

Transportation Research Record Journal of the Transportation Research Board · 2025-10-03 · 1 citations

This study presents a heuristic approach to optimize the charging and rebalancing of automatic micromobility devices with battery constraints. The methodology integrates a vehicle routing problem to reposition and charge automatic micromobility devices and a facility location problem to ensure efficient deployment of charging locations. We first define density-based homogeneous regions through a clustering technique and then employ a continuous approximation technique to estimate the average distance between the nodes in each cluster, which is then used to assess the routing objective value. By estimating total travel distance and cost, the heuristic accommodates both known and potential repositioning needs. Using real-world data from Chicago, IL, our findings indicate that the heuristic achieves near-optimal solutions with substantial reductions in computational time, highlighting its practical applicability in real-world scenarios compared with traditional methods. Additionally, sensitivity analyses reveal the impact of battery levels and facility costs on overall performance, providing valuable insights for decision makers. The proposed approach offers a robust framework for enhancing the efficiency of micromobility systems, with promising applications in improving system resilience in disaster-affected areas and improving equitable access to underserved communities.

Real‐time network‐level traffic signal and trajectory optimization with connected automated and human‐driven vehicles

Computer-Aided Civil and Infrastructure Engineering · 2025-11-15 · 3 citations

This paper introduces a real-time framework designed to optimize intersection signal timing and vehicles’ trajectories across a network of intersections in a mixed environment of human-driven and automated fleets. The network-level optimization model is decomposed into intersection-level sub-models, whose decisions are coordinated through information exchange, aiming to push them toward the network model's optimal solutions. At each intersection, a bi-level framework addresses both the signal timing and trajectory optimization models. A specialized greedy heuristic algorithm is developed for the lower-level problem where optimal connected and automated vehicles (CAVs) trajectories are constructed for a given signal timing plan. At the upper level, all the feasible signal timing plans are created, and the system selects the most effective one to implement. The study integrates the entire solution process into a receding horizon framework to ensure efficient handling throughout the study period. A case study demonstrated the system's capability to adjust signals and trajectories effectively under various traffic demands and CAV market shares. Results showed a reduction in overall arterial delay correlating with higher proportions of CAVs. The proposed system delivered solutions in less than 70 ms, which is significantly faster than the half-second solving time steps, ensuring decisions were made quicker than in real-time.

Advancing the white phase mobile traffic control paradigm to consider pedestrians

Computer-Aided Civil and Infrastructure Engineering · 2024-03-11 · 7 citations

Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human-driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced “white phase.” CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do-not-walk intervals for pedestrian movements. A receding horizon-based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%.

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

Transactions in GIS · 2024-04-24 · 11 citations

Abstract Medical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K ‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K ‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

A relaxation‐based Voronoi diagram approach for equitable resource distribution

Computer-Aided Civil and Infrastructure Engineering · 2024-09-17 · 4 citations

This paper introduces a methodology designed to reduce cost, improve demand coverage, and ensure equitable vaccine distribution during the initial stages of the vaccination campaign when demand significantly exceeds supply. We formulate an enhanced maximum covering problem as a mixed integer linear program, aiming to minimize the total vaccine distribution cost while maximizing the allocation of vaccines to population blocks under equity constraints. Block-level census data are employed to define demand locations, identifying gender, age, and racial groups within each block using population data. A Lagrangian relaxation technique integrated with a modified Voronoi diagram is proposed to solve the location–allocation problem efficiently. Empirical case studies in Pennsylvania, using real-world data from the Centers for Disease Control and Prevention and health department websites, were conducted for the first 4 months of the COVID-19 vaccination campaign. Preliminary results show that the proposed solution algorithm effectively solves the problem, achieving a 5.92% reduction in total transportation cost and a 28.15% increase in demand coverage. Moreover, our model can reduce the deviation from equity to 0.07 (∼50% improvement).

A Lagrangian relaxation approach for resource allocation problem with capacity constraints

Computer-Aided Civil and Infrastructure Engineering · 2024-05-31 · 3 citations

This study evaluates a capacitated facility location model enhanced with distance constraints for an emergency response problem, ensuring certain neighborhoods remain within an accessible range from facilities following a hurricane. The proposed model takes into account the capacity constraints for drones and vehicles. The model determines optimal locations for facilities and the distribution of supplies across the city. It also specifies which facilities should support the needs of each neighborhood and decides on the appropriate mode of transportation—ground vehicles if possible, or drones if roadways are obstructed. To solve the problem, a Lagrangian relaxation technique is employed, relaxing the constraints related to facility capacity and distance. The numerical results confirm the quality and efficiency of the solutions. The findings indicate that ground transportation is more frequently utilized than drones at each operational facility. A comprehensive set of sensitivity analyses is conducted to examine the impact of various variables and parameters on the solution.

Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation

Computer-Aided Civil and Infrastructure Engineering · 2023-05-04 · 6 citations

Incident response operations require effective planning of resources to ensure timely clearance of roadways and avoidance of secondary incidents. This study formulates a mixed-integer linear program to minimize the total expected travel time and maximize the demand covered. The model accounts for the location, severity, frequency of incidents, dispatching locations, and availability of incident respondents. An integrated methodology that includes column generation and Lagrangian relaxation with a density-based clustering technique that defines incident hot spots is proposed. The hybrid approach is applied to an empirical case study in Raleigh, NC. A network instance with 10,672 incident sites, clustered with a search distance (ε) of 5 min, is solved efficiently with an optimality gap of 1.37% in 2 min. A Benders decomposition technique is implemented to conduct benchmark analyses. The numerical results suggest that the proposed algorithm can solve the problem efficiently and outperform the benchmark solutions.