arXiv subjects

Tai-Yu Ma

Publications and source records attributed to Tai-Yu Ma.

18 recordsLinked to original sources

arXiv2025

A hybrid large neighborhood search algorithm for the integrated dial-a-ride problem using electric vehicles

Integrating demand-responsive mobility services with transit systems is recognized as a practical and effective strategy to mitigate their impact on traffic congestion and the environment. This study develops an efficient hybrid metaheuristic to solve the integrated dial-a-ride problem by utilizing electric vehicles to minimize operational costs and customer travel time. Customer transfer inconvenience is restricted by a maximum intermodal transfer time to synchronize demand-responsive buses' arrival and transit departures. The proposed metaheuristic addresses the challenges of integrating demand-responsive vehicle routing and charging operations with fixed-route transit systems with capacitated charging stations and partial recharge. We benchmarked our algorithm against a state-of-the-art mixed-integer programming solver on instances with 10-50 customers and two transit lines. Our approach achieves solutions that are, on average, 23.8% better in solution quality within around 2 minutes, outperforming those obtained by the solver using an 8-hour computational time limit. We evaluate the impact of various system parameters to bridge the gap between theory and practice. The results suggest that, from the operator's perspective, while the integrated dial-a-ride service reduces vehicle kilometers traveled, the used fleet size may not necessarily be reduced when ensuring high-quality service for passengers. Moreover, operating the integrated systems is more beneficial in areas with dense transit networks, compared with increases in transit frequency. The findings provide valuable insights for developing integrated dial-a-ride services in practice.

math.OC↗

arXiv2025

An Integrated Optimization Framework for Smart Charging of Electric Bus Fleets under Dynamic Electricity Prices with On-Site Solar Generation, Energy Storage, and V2G operations

The rapid electrification of city bus fleets presents public transportation operators (PTOs) with the complex challenge of managing charging operations to minimize energy costs. Most existing studies on electric bus (EB) charging management rely on a discrete-time-based discretization approach, which is operationally unrealistic and limits their scalability for realistic applications. This study proposes a discrete-event optimization (DEO) approach for daily EB fleet charging management that considers peak power charges, photovoltaic (PV) generation with an energy storage system (ESS), vehicle-to-grid (V2G) operations, and battery degradation costs. We apply the DEO approach to a real-world case in Brussels involving 28 articulated EBs and 232 trips. A set of parametric instances is used to assess computational scalability. The results demonstrate that the DEO formulation can solve instances of realistic size within practical computation times with tight optimality gaps. A thorough cost analysis was conducted to evaluate the added value of V2G benefits and on-site PV generation. Key findings indicate that incorporating demand charges into the optimization reduces daily costs by 5% and decreases the share of peak power costs by 9%, underscoring the importance of load management. Integrating PV and ESS results in a total net cost reduction of up to 56%, with ESS primarily used for energy arbitrage rather than direct bus charging. V2G participation is highly sensitive to battery degradation costs and policy incentives. Combining all extensions results in a 58% reduction in total operational expenses compared to the baseline, demonstrating the significant value of smart (dis)charging tools for PTOs.

math.OC↗

arXiv2025

Optimal mixed fleet and charging infrastructure planning to electrify demand responsive feeder services with target CO2 emission constraints

Electrifying demand-responsive transport systems need to plan the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. Earlier studies assume a fully electrified fleet and overlook the planning issue in the transition period. This study addresses the joint fleet size and charging infrastructure planning for a demand-responsive feeder service under stochastic demand, given a user-defined targeted CO2 emission reduction policy. We propose a bi-level optimization model where the upper-level determines charging station configuration given stochastic demand patterns, whereas the lower-level solves a mixed fleet dial-a-ride routing problem under the CO2 emission and capacitated charging station constraints. An efficient deterministic annealing metaheuristic is proposed to solve the CO2-constrained mixed fleet routing problem. The performance of the algorithm is validated by a series of numerical test instances with up to 500 requests. We apply the model for a real-world case study in Bettembourg, Luxembourg, with different demand and customised CO2 reduction targets. The results show that the proposed method provides a flexible tool for joint charging infrastructure and fleet size planning under different levels of demand and CO2 emission reduction targets.

math.OC↗

arXiv2024

Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand

Charging-station capacity strongly affects the profitability of electric ride-hailing systems. In this study, we develop a dynamic charging scheduling method that anticipates vehicles' energy needs and coordinates their charging operations with real-time energy prices to avoid long waiting time at charging stations and increase the total profit of the system. A sequential mixed integer linear programming model is proposed to devise vehicles' day-ahead charging plans based on their experienced charging waiting times and energy consumption. The developed charging policy is tested on a Manhattan-like study area using synthetic data drawn from NYC yellow taxi data with a fleet size of 100 vehicles given the scenarios of 3000 and 4000 customers/day. The computational results show that our method outperforms different benchmark policies with up to +19.32% profit and +20.03% service rate for 4000 customers relative to the weakest benchmark; relative to the strongest benchmark (OptChg), the corresponding gains are +3.91% profit and +4.60% service rate. Sensitivity analysis is conducted with different system parameters and managerial insights are discussed.

math.OC↗

arXiv2024

Optimized electrified meeting-point-based feeder bus services with capacitated charging stations and partial recharges

Meeting-point-based feeder services using EVs have good potential to achieve an efficient and clean on-demand mobility service. However, customer-to-meeting-point, vehicle routing, and charging scheduling need to be jointly optimized to achieve the best system performance. To this aim, we assess the effect of different system parameters and configure them based on our previously developed hybrid metaheuristic algorithm. A set of test instances based on morning peak hour commuting scenarios between the cities of Arlon and Luxembourg are used to evaluate the impact of the set parameters on the optimal solutions. The experimental results suggest that higher meeting point availability can achieve better system performance. By jointly configuring different system parameters, the overall system performance can be significantly improved (-10.8% total kilometers traveled by vehicles compared to the benchmark) to serve all requests. Our experimental results show that the meeting-point-based system can reduce up to 70.2% the fleet size, 6.4% the in-vehicle travel time and 49.4% the kilometers traveled when compared to a traditional door-to-door dial-a-ride system.

math.OC↗

arXiv2024

A hybrid metaheuristic to optimize electric first-mile feeder services with charging synchronization constraints and customer rejections

This paper addresses the on-demand meeting-point-based feeder electric bus routing and charging scheduling problem under charging synchronization constraints. The problem considered exhibits the structure of the location routing problem, which is more difficult to solve than many electric vehicle routing problems with capacitated charging stations. We propose to model the problem using a mixed-integer linear programming approach based on a layered graph structure. An efficient hybrid metaheuristic solution algorithm is proposed. A mixture of random and greedy partial charging scheduling strategies is used to find feasible charging schedules under the synchronization constraints. The algorithm is tested on instances with up to 100 customers and 49 bus stops/meeting points. The results show that the proposed algorithm provides near-optimal solutions within less one minute on average compared with the best solutions found by a mixed-integer linear programming solver set with a 4-hour computation time limit. A case study on a larger sized case with 1000 customers and 111 meeting points shows the proposed method is applicable to real-world situations.

math.OC↗

arXiv2022

Dynamic charging management for electric vehicle demand responsive transport

With the climate change challenges, transport network companies started to electrify their fleet to reduce CO2 emissions. However, such an ecological transition brings new research challenges for dynamic electric fleet charging management under uncertainty. In this study, we address the dynamic charging scheduling management of shared ride-hailing services with public charging stations. A two-stage charging scheduling optimization approach under a rolling horizon framework is proposed to minimize the overall charging operational costs of the fleet, including vehicles' access times, charging times, and waiting times, by anticipating future public charging station availability. The charging station occupancy prediction is based on a hybrid LSTM (Long short-term memory) network approach and integrated into the proposed online vehicle-charger assignment. The proposed methodology is applied to a realistic simulation study in the city of Dundee, UK. The numerical studies show that the proposed approach can reduce the total charging waiting times of the fleet by 48.3% and the total charged the amount of energy of the fleet by 35.3% compared to a need-based charging reference policy.

math.OC↗

arXiv2021

Survey of charging scheduling, fleet management, and location planning of charging stations for electrified demand-responsive transport systems: methodologies and recent developments

The accelerated electrification of transport systems with EVs has brought new challenges for charging scheduling, fleet management, and charging infrastructure location and configuration planning. In this review, we have provided a systematic review of the recent development in strategic, tactical, and operational decisions for demand responsive transport system planning using electric vehicles (EV-DRT). We have summarized recent developments in mathematical modeling approaches and identified future research directions. A list of existing open-access datasets, numerical test instances, and software are provided for future research in EV-DRT and related problems.

cs.CE↗

arXiv2021

Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks

Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for multistep discrete charging occupancy state prediction. Unlike the existing LSTM networks, the proposed model separates different types of features and handles them differently with mixed neural network architecture. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of the city of Dundee, UK. The results show that the proposed method produces very accurate predictions (99.99% and 81.87% for 1 step (10 minutes) and 6 steps (1 hour) ahead, respectively, and outperforms the benchmark approaches significantly (+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). A sensitivity analysis is conducted to evaluate the impact of the model parameters on prediction accuracy.

cs.LG↗

arXiv2021

Optimal queueing-based rebalancing for one-way electric carsharing systems with stochastic demand

Viability of electric vehicle car sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward Markovian stochastic demand with server relocation with queueing constraints. We propose a new model formulation based on a node-charge graph structure that extends the relocation model to include transshipment relocation flows. Computational tests with up to 1000 node (and 4000 node-charges) suggest promising avenues for further study.

math.OC↗

arXiv2020

Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services

Coordinating the charging scheduling of electric vehicles for dynamic dial-a-ride services is challenging considering charging queuing delays and stochastic customer demand. We propose a new two-stage solution approach to handle dynamic vehicle charging scheduling to minimize the costs of daily charging operations of the fleet. The approach comprises two components: daily vehicle charging scheduling and online vehicle-charger assignment. A new battery charge scheduling model is proposed to obtain the vehicle charging schedules by minimizing the costs of vehicle daily charging operations while satisfying vehicle driving needs to serve customers. In the second stage, an online vehicle-charger assignment model is developed to minimize the total vehicle idle time for charges by considering queuing delays at the level of chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the large-scale vehicle-charger assignment problem with small optimality gaps. The approach is applied to a realistic dynamic dial-a-ride service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under different charging infrastructure scenarios. Our computational results show that the approach can achieve significant savings for the operator in terms of charging waiting times (-74.9%), charging times (-38.6%), and charged energy costs (-27.4%). A sensitivity analysis is conducted to evaluate the impact of the different model parameters, showing the scalability and robustness of the approach in a stochastic environment.

math.OC↗

arXiv2020

Optimal fast charging station locations for electric ridesharing service with online vehicle-charging station assignment

Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet's daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.

math.OC↗

arXiv2020

Integrated ridesharing services with chance-constrained dynamic pricing and demand learning

The design of integrated mobility-on-demand services requires jointly considering the interactions between traveler choice behavior and operators' operation policies to design a financially sustainable pricing scheme. However, most existing studies focus on the supply side perspective, disregarding the impact of customer choice behavior in the presence of co-existing transport networks. We propose a modeling framework for dynamic integrated mobility-on-demand service operation policy evaluation with two service options: door-to-door rideshare and rideshare with transit transfer. A new constrained dynamic pricing model is proposed to maximize operator profit, taking into account the correlated structure of different modes of transport. User willingness to pay is considered as a stochastic constraint, resulting in a more realistic ticket price setting while maximizing operator profit. Unlike most studies, which assume that travel demand is known, we propose a demand learning process to calibrate customer demand over time based on customers' historical purchase data. We evaluate the proposed methodology through simulations under different scenarios on a test network by considering the interactions of supply and demand in a multimodal market. Different scenarios in terms of customer arrival intensity, vehicle capacity, and the variance of user willingness to pay are tested. Results suggest that the proposed chance-constrained assortment price optimization model allows increasing operator profit while keeping the proposed ticket prices acceptable.

econ.GN↗

arXiv2020

A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.

physics.soc-ph↗

arXiv2020

A node-charge graph-based online carshare rebalancing policy with capacitated electric charging

Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward non-myopic algorithms using queueing principles. We propose a new rebalancing policy using cost function approximation. The cost function is modeled as a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities on a static node-charge graph structure. The cost function is NP-complete, so a heuristic is proposed that ensures feasible solutions that can be solved in an online system. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 traffic analysis zones (TAZs)) and charging station location data (18 charging stations with 4 port capacities). The proposed non-myopic rebalancing heuristic reduces the cost increase compared to myopic rebalancing by 38%. Other managerial insights are further discussed.

cs.DS↗

arXiv2019

A user-operator assignment game with heterogeneous user groups for empirical evaluation of a microtransit service in Luxembourg

We tackle the problem of evaluating the impact of different operation policies on the performance of a microtransit service. This study is the first empirical application using the stable matching modeling framework to evaluate different operation cost allocation and pricing mechanisms on microtransit service. We extend the deterministic stable matching model to a stochastic reliability-based one to consider user's heterogeneous perceptions of utility on the service routes. The proposed model is applied to the evaluation of Kussbus microtransit service in Luxembourg. We found that the current Kussbus operation is not a stable outcome. By reducing their route operating costs of 50%, it is expected to increase the ridership of 10%. If Kussbus can reduce in-vehicle travel time on their own by 20%, they can significantly increase profit several folds from the baseline.

physics.soc-ph↗

arXiv2019

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted.

cs.CY↗

arXiv2012

A hybrid cross entropy algorithm for solving dynamic transit network design problem

This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the op-timal frequency of a multimodal transit network, which minimizes total users' travel cost and operation cost of transit lines. The problem is formulated as a bilevel programming problem with equilibrium constraints describing non-cooperative Nash equilibrium in a dynamic simulation-based transit assignment context. A hybrid algorithm combing the cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is proposed. Computational results are provided on the Sioux Falls network to illustrate the perform-ance of the proposed algorithm.

cs.NI↗