@article{protogyrou_hajibabai_2024, title={A Lagrangian relaxation approach for resource allocation problem with capacity constraints}, volume={5}, ISSN={["1467-8667"]}, DOI={10.1111/mice.13223}, abstractNote={Abstract 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.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Protogyrou, Demetra and Hajibabai, Leila}, year={2024}, month={May} }
 @article{bagheri_samany_toomanian_jelokhani-niaraki_hajibabai_2024, title={A planar graph cluster-routing approach for optimizing medical waste collection based on spatial constraint}, volume={4}, ISSN={["1467-9671"]}, DOI={10.1111/tgis.13159}, abstractNote={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.}, journal={TRANSACTIONS IN GIS}, author={Bagheri, Keyvan and Samany, Najmeh Neysani and Toomanian, Ara and Jelokhani-Niaraki, Mohammadreza and Hajibabai, Leila}, year={2024}, month={Apr} }
 @article{li_atik_zheng_hajibabai_hajbabaie_2024, title={A relaxation-based Voronoi diagram approach for equitable resource distribution}, volume={9}, ISSN={["1467-8667"]}, DOI={10.1111/mice.13339}, abstractNote={Abstract 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).}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Li, Kuangying and Atik, Asya and Zheng, Dayang and Hajibabai, Leila and Hajbabaie, Ali}, year={2024}, month={Sep} }
 @article{niroumand_hajibabai_hajbabaie_2024, title={Advancing the white phase mobile traffic control paradigm to consider pedestrians}, volume={3}, ISSN={["1467-8667"]}, DOI={10.1111/mice.13178}, abstractNote={AbstractCurrent 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%.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Niroumand, Ramin and Hajibabai, Leila and Hajbabaie, Ali}, year={2024}, month={Mar} }
 @article{mirheli_hajibabai_2023, title={Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions}, volume={1}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2022.3227888}, abstractNote={This paper aims to investigate the electric vehicle (EV) charging network design and utilization management considering user-centric decisions. A hierarchical formulation is developed with the EV charging network design and demand-driven pricing scheme in the upper level and users’ charging decisions to minimize their own travel costs and charging expenses in the lower level. The model aims to minimize the facility deployment cost and maximize the charging income of the network operator while minimizing the user-centric costs. We have converted the proposed bi-level formulation into an equivalent single-level model using the lower-level objective function as complementary equations. Then, we have developed an iterative active-set based solution technique to determine the strategic decisions on charging network design. To partially overcome the computational burden, the arc travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a system-level bi-level optimization benchmark. Our experiments show a CPU time of $2.3~hr$ for the proposed approach compared to $173.1~hr$ of the benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Mirheli, Amir and Hajibabai, Leila}, year={2023}, month={Jan} }
 @article{atik_hajibabai_2023, title={Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation}, volume={5}, ISSN={["1467-8667"]}, DOI={10.1111/mice.13016}, abstractNote={AbstractIncident 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.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Atik, Asya and Hajibabai, Leila}, year={2023}, month={May} }
 @article{niroumand_hajibabai_hajbabaie_2023, title={White Phase Intersection Control Through Distributed Coordination: A Mobile Controller Paradigm in a Mixed Traffic Stream}, volume={24}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2022.3226557}, DOI={10.1109/TITS.2022.3226557}, abstractNote={This study presents a vehicle-level distributed coordination strategy to control a mixed traffic stream of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. We use CAVs as mobile traffic controllers during a newly introduced “white phase”, during which CAVs will negotiate the right-of-way to lead a group of CHVs while CHVs must follow their immediate front vehicle. The white phase will not be activated under low CAV penetration rates, where vehicles must wait for green signals. We have formulated this problem as a distributed mixed-integer non-linear program and developed a methodology to form an agreement among all vehicles on their trajectories and signal timing parameters. The agreement on trajectories is reached through an iterative process, where CAVs update their trajectory based on shared trajectory of other vehicles to avoid collisions and share their trajectory with other vehicles. Additionally, the agreement on signal timing parameters is formed through a voting process where the most voted feasible signal timing parameters are selected. The numerical experiments indicate that the proposed methodology can efficiently control vehicle movements at signalized intersections under various CAV market shares. The introduced white phase reduces the total delay by 3.2% to 94.06% compared to cooperative trajectory and signal optimization under different CAV market shares in our tests. In addition, our numerical results show that the proposed technique yields reductions in total delay, ranging from 40.2% – 98.9%, compared to those of a fully-actuated signal control obtained from a state-of-practice traffic signal optimization software.}, number={3}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Niroumand, Ramin and Hajibabai, Leila and Hajbabaie, Ali}, year={2023}, month={Mar}, pages={2993–3007} }
 @article{hajibabai_mirheli_2022, title={A Game-Theoretic Approach for Dynamic Service Scheduling at Charging Facilities}, volume={10}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2022.3212017}, abstractNote={Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging stations near highway ramps, or on parking lots next to office buildings, shops, airports, among other locations. Charging time and duration can be fixed and continuous or flexible and intermittent. EV user preferences of charging services depend on many factors (e.g., charging prices, choice of destinations), causing EV charging patterns to shift in real-time. Hence, there is a need for a highly flexible EV charging network to support the rapid adoption of the technology. This study presents a dynamic scheduling scheme for EV charging facilities considering uncertainties in charging demand, charger availability, and charging rate. The problem is formulated as a dynamic programming model that minimizes the travel and waiting costs and charging expenses while penalizing overcharging attempts. An integrated generalized Nash equilibrium technique is introduced to solve the problem that incorporates a Monte Carlo tree search algorithm to efficiently capture the uncertainties and approximate the value function of the dynamic program. Numerical experiments on hypothetical and real-world networks confirm the solution quality and computational efficiency of the proposed methodology. This study will promote EV adoption and support environmental sustainability by helping users lower the charging spot search burden via a real-time, user-adaptive optimizer. Stakeholders can retrieve charger utilization and pricing data and get feedback on their charging network policies.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Hajibabai, Leila and Mirheli, Amir}, year={2022}, month={Oct} }
 @article{niroumand_hajibabai_hajbabaie_tajalli_2022, title={Effects of Autonomous Driving Behavior on Intersection Performance and Safety in the Presence of White Phase for Mixed-Autonomy Traffic Stream}, volume={2676}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981221082580}, DOI={10.1177/03611981221082580}, abstractNote={ This paper studies the effects of different autonomous driving behaviors on an isolated intersection’s safety and mobility performance measures in a mixed-autonomy environment. The movement of vehicles through the intersection is controlled by green, red, and “white” signal indications. Traffic operations during green and red signals are identical to a typical intersection. However, in the presence of the white phase, connected human-driven vehicles (CHVs) should follow connected and autonomous vehicles (CAVs) to pass the intersection safely. Three levels of driving aggressiveness for CAVs are considered: (1) cautious behavior, (2) normal behavior, and (3) aggressive behavior. The mobility and safety impacts of these CAV behaviors are studied based on different CAV market penetration rates and demand levels. The results indicate that a more aggressive CAV driving behavior leads to a lower average delay while increasing the average number of stops for CAVs. Additionally, a more aggressive CAV driving behavior leads to more frequent activation of the white phase that contributes to significant reduction in the speed variance. Moreover, the total number of rear-end near-collision observations with a time to collision of less than 10 s decreases as the CAV penetration rate and aggressiveness level increase. The main reason for this observation is that aggressive CAVs have higher acceleration and lower deceleration values and, therefore, have more flexibility to avoid a crash. }, number={8}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Niroumand, Ramin and Hajibabai, Leila and Hajbabaie, Ali and Tajalli, Mehrdad}, year={2022}, month={Apr}, pages={112–130} }
 @article{mirheli_hajibabai_2022, title={Hierarchical Optimization of Charging Infrastructure Design and Facility Utilization}, volume={1}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2022.3142196}, abstractNote={This study proposes a bi-level optimization program to represent the electric vehicle (EV) charging infrastructure design and utilization management problem with user-equilibrium (UE) decisions. The upper level aims to minimize total facility deployment costs and maximize the revenue generated from EV charging collections, while the lower level aims to minimize the EV users’ travel times and charging expenses. An iterative technique is implemented to solve the bi-level mixed-integer non-linear program that generates theoretical lower and upper bounds to the bi-level model and solves it to global optimality. A set of conditions are evaluated to show the convergence of the algorithm in a finite number of iterations. The numerical results, based on three demand levels, indicate that the proposed bi-level model can effectively determine the optimal charging facility location, physical capacity, and demand-responsive pricing scheme. The average charging price in medium demand level is increased by 38.21% compared to the lower level demand due to the surge in charging needs and highly utilized charging stations.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Mirheli, Amir and Hajibabai, Leila}, year={2022}, month={Jan} }
 @article{hajibabai_atik_mirheli_2022, title={Joint power distribution and charging network design for electrified mobility with user equilibrium decisions}, volume={6}, ISSN={["1467-8667"]}, DOI={10.1111/mice.12854}, abstractNote={AbstractRapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV‐dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed‐integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG ) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real‐world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east‐coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Hajibabai, Leila and Atik, Asya and Mirheli, Amir}, year={2022}, month={Jun} }
 @article{hajibabai_hajbabaie_swann_vergano_2022, title={Using COVID-19 Data on Vaccine Shipments and Wastage to Inform Modeling and Decision-Making}, volume={56}, ISSN={0041-1655 1526-5447}, url={http://dx.doi.org/10.1287/trsc.2022.1134}, DOI={10.1287/trsc.2022.1134}, abstractNote={ Since the start of the COVID-19 pandemic, disruptions have been experienced in many supply chains, particularly in personal protective equipment, testing kits, and even essential household goods. Effective vaccines to protect against COVID-19 were approved for emergency use in the United States in late 2020, which led to one of the most extensive vaccination campaigns in history. We continuously collect data on vaccine allocation, shipment and distribution, administration, and inventory in the United States, covering the entire vaccination campaign. In this article, we describe some data sets that we collaborated to obtain. We are publishing the data and making them freely available to researchers, media organizations, and other stakeholders so that others may use the data to develop insights about the distribution and wastage of vaccines during the current pandemic or to provide an informed future pandemic response. This article gives an overview of vaccine distribution logistics in the United States, describes the data we obtain, outlines how they may be accessed and used by others, and describes some high-level analyses demonstrating some aspects of the data (for data collected during January 1, 2021–March 31, 2021). This article also provides directions for future research using the collected data. Our goal is two-fold: (i) We would like the data to be used in many creative ways to inform the current and future pandemic response. (ii) We also want to inspire other researchers to make their data publicly available in a timely manner. }, number={5}, journal={Transportation Science}, publisher={Institute for Operations Research and the Management Sciences (INFORMS)}, author={Hajibabai, Leila and Hajbabaie, Ali and Swann, Julie and Vergano, Dan}, year={2022}, month={Sep}, pages={1135–1147} }
 @article{atik_hajibabai_2021, title={Location and Allocation of Incident Respondents under Severity Levels and Capacity Constraints: Formulation, Methodology, and Application}, ISSN={["2153-0009"]}, DOI={10.1109/ITSC48978.2021.9565010}, abstractNote={This study investigates an optimal incident response plan to cover the demand considering the location and availability of respondents. A multi-objective mixed-integer linear formulation is proposed that aims to minimize the expected travel time to incidents and maximize the expected demand coverage under resource constraints. The proposed model is applied to an empirical case study network with one year of incident data in North Carolina and solved by Benders Decomposition. The numerical experiments indicate the performance of the proposed methodology based on various problem sizes. While CPLEX Optimization Studio fails to obtain solutions for large-scale instances, the proposed algorithm can provide near-optimal solutions to the largest instance in this paper (i.e., 1,024-node network) with a 2.4% optimality gap.}, journal={2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC)}, author={Atik, Asya and Hajibabai, Leila}, year={2021}, pages={2181–2186} }
 @article{tajalli_mirheli_hajbabaie_hajibabai_2021, title={Utilization and Cost Estimation Models for Highway Fleet Equipment}, volume={2675}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981211032215}, DOI={10.1177/03611981211032215}, abstractNote={ Highway agencies need to manage the utilization of their highway equipment assets to reduce fleet management costs, balance equipment use, and provide the required services. Predictive equipment utilization and operational cost models are required for optimal management; however, there are no widely accepted models for this purpose. Although the utilization data is collected by state DOTs, the literature does not show any specific statistical model to predict equipment utilization as a function of contributing factors such as asset age, fleet size, costs, and demand for service. This study will bridge this gap and develop a predictive model to estimate the utilization of fleet equipment. The main objective of this paper is to develop a set of predictive models to estimate the annual utilization of seven non-stationary highway equipment types based on several explanatory variables including their annual fuel cost, downtime hours, age, and weight. Furthermore, another set of models are fit to predict the annual operational cost for these equipment types based on the most important contributing factors. The prediction models are developed after a nationwide data collection. Several years of collected data from seven states are processed and used for model development. This research has identified annual mileage as an appropriate and widely used utilization metric. Various model structures to predict annual mileage are considered. The logarithmic function of annual mileage has provided the most appropriate structure. The final annual mileage predictive models have R-squared values that are between 0.65 and 0.89, which indicates a good fit for all models. The models are validated by performing several statistical tests and they have satisfied all required assumptions of regression analysis. The result of modeling and statistical analysis showed that the proposed models accurately estimated the utilization and operational cost for highway equipment assets. }, number={12}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Tajalli, Mehrdad and Mirheli, Amir and Hajbabaie, Ali and Hajibabai, Leila}, year={2021}, month={Aug}, pages={1172–1186} }
 @article{niroumand_tajalli_hajibabai_hajbabaie_2020, title={Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream}, volume={116}, ISSN={0968-090X}, url={http://dx.doi.org/10.1016/j.trc.2020.102659}, DOI={10.1016/j.trc.2020.102659}, abstractNote={This study develops a novel mixed-integer non-linear program to control the trajectory of mixed connected-automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. The trajectory of CAVs is continuously optimized via a central methodology, while a new “white” phase is introduced to enforce CHVs to follow their immediate front vehicle. The movement of CHVs is incorporated in the optimization framework utilizing a customized linear car-following model. During the white phase, CAVs lead groups of CHVs through an intersection. The proposed formulation determines the optimal signal indication for each lane-group in each time step. We have developed a receding horizon control framework to solve the problem. The case study results indicate that the proposed methodology successfully controls the mixed CAV-CHV traffic under various CAV market penetration rates and different demand levels. The results reveal that a higher CAV market penetration rate induces more frequent white phase indication compared to green-red signals. The proposed program reduces the total delay by 19.6%–96.2% compared to a fully-actuated signal control optimized by a state-of-practice traffic signal timing optimization software.}, journal={Transportation Research Part C: Emerging Technologies}, publisher={Elsevier BV}, author={Niroumand, Ramin and Tajalli, Mehrdad and Hajibabai, Leila and Hajbabaie, Ali}, year={2020}, month={Jul}, pages={102659} }
 @article{mirheli_tajalli_mohebifard_hajibabai_hajbabaie_2020, title={Utilization Management of Highway Operations Equipment}, volume={2674}, ISSN={["2169-4052"]}, url={http://dx.doi.org/10.1177/0361198120927400}, DOI={10.1177/0361198120927400}, abstractNote={ This paper presents fleet utilization management processes for highway operations equipment based on actual tracked and reported usage data obtained from transportation agencies. The objective is to minimize total fleet utilization costs, including operational, purchase, and relocation expenses that yield the optimal utilization values and fleet composition of specific equipment types within each region in a year. The framework includes utilization prediction and optimization models, rather than relying on pre-determined utilization thresholds in existing strategies, to avoid under-utilization, over-utilization, or both. The prediction models are structured using equipment explanatory variables with their significant contributing factors, for example, annual equipment usage, annual fuel cost, downtime hours, age, and class code, to predict operational costs. The optimization model is formulated as a set of mathematical formulations, with embedded predictive models, that minimizes the total costs of (i) keeping an asset in-service using predictive annual operational cost functions, (ii) purchasing new assets in a region in the following year, and (iii) relocating assets by capturing the distance between regions. The costs include equipment purchase, operation, maintenance, and transportation expenses. The proposed framework captures the remedial actions to balance under-/over-utilized assets in the fleet in a cost-efficient manner. The proposed methodology is applied to utilization management of a set of operations equipment, and the findings of the dump trucks are presented. Several scenarios are designed to analyze the sensitivity of the costs to various decisions and parameters. The numerical experiments reveal that the proposed framework can facilitate the utilization prediction and management of highway operations equipment and save up to 16.6% in operational costs considering different demand scenarios. }, number={9}, journal={TRANSPORTATION RESEARCH RECORD}, author={Mirheli, Amir and Tajalli, Mehrdad and Mohebifard, Rasool and Hajibabai, Leila and Hajbabaie, Ali}, year={2020}, month={Sep}, pages={202–215} }
 @article{mirheli_tajalli_hajibabai_hajbabaie_2019, title={A consensus-based distributed trajectory control in a signal-free intersection}, volume={100}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060455583&partnerID=MN8TOARS}, DOI={10.1016/j.trc.2019.01.004}, abstractNote={This paper develops a distributed cooperative control logic to determine conflict-free trajectories for connected and automated vehicles (CAVs) in signal-free intersections. The cooperative trajectory planning problem is formulated as vehicle-level mixed-integer non-linear programs (MINLPs) that aim to minimize travel time of each vehicle and their speed variations, while avoiding near-crash conditions. To push vehicle-level solutions towards global optimality, we develop a coordination scheme between CAVs on conflicting movements. The coordination scheme shares vehicle states (i.e., location) over a prediction horizon and incorporates such information in CAVs’ respective MINLPs. Therefore, the CAVs will reach consensus through an iterative process and select conflict-free trajectories that minimize their travel time. The numerical experiments quantify the effects of the proposed methodology on traffic safety and performance measures in an intersection. The results show that the proposed distributed coordinated framework converges to near-optimal CAV trajectories with no conflicts in the intersection neighborhood. While the solutions are found in real-time, the comparison to a central intersection control logic for CAVs indicates a maximum marginal objective value of 2.30%. Furthermore, the maximum marginal travel time, throughput, and average speed do not exceed 0.5%, 0.1%, and 0.5%, respectively. The proposed control logic reduced travel time by 43.0–70.5%, and increased throughput and average speed respectively by 0.8–115.6% and 59.1–400.0% compared to an optimized actuated signal control, while eliminating all near-crash conditions.}, journal={Transportation Research Part C: Emerging Technologies}, publisher={Elsevier}, author={Mirheli, A. and Tajalli, M. and Hajibabai, L. and Hajbabaie, A.}, year={2019}, pages={161–176} }
 @article{mehrabipour_hajibabai_hajbabaie_2019, title={A decomposition scheme for parallelization of system optimal dynamic traffic assignment on urban networks with multiple origins and destinations}, volume={34}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85066156002&partnerID=MN8TOARS}, DOI={10.1111/mice.12455}, abstractNote={AbstractThis paper presents a decomposition scheme to find near‐optimal solutions to a cell transmission model‐based system optimal dynamic traffic assignment problem with multiple origin‐destination pairs. A linear and convex formulation is used to define the problem characteristics. The decomposition is designed based on the Dantzig–Wolfe technique that splits the set of decision variables into subsets through the construction of a master problem and subproblems. Each subproblem includes only a single origin‐destination pair with significantly less computational burden compared to the original problem. The master problem represents the coordination between subproblems through the design of interactive flows between the pairs. The proposed methodology is implemented in two case study networks of 20 and 40 intersections with up to 25 origin‐destination pairs. The numerical results show that the decomposition scheme converges to the optimal solution, within 2.0% gap, in substantially less time compared to a benchmark solution, which confirms the computational efficiency of the proposed algorithm. Various network performance measures have been assessed based on different traffic state scenarios to draw managerial insights.}, number={10}, journal={Computer-Aided Civil and Infrastructure Engineering}, publisher={Wiley Online Library}, author={Mehrabipour, M. and Hajibabai, L. and Hajbabaie, A.}, year={2019}, pages={915–931} }
 @article{mirheli_hajibabai_2020, title={Utilization Management and Pricing of Parking Facilities under Uncertain Demand and User Decisions}, volume={21}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85084749864&partnerID=MN8TOARS}, DOI={10.1109/TITS.2019.2916337}, abstractNote={Excessive search for parking spots in congested areas contributes to additional travel delays and negative socio-economic impacts. While managing parking utilization to address the agency’s objectives, it is often very beneficial to reflect the diversity of users’ behaviors and their travel choices. This paper develops a stochastic dynamic parking management model, under competitive user-agency perceptions and uncertain user demand and parking occupancy, to simultaneously minimize the total travelers’ costs and maximize the parking agency’s revenue. The problem is formulated into a dynamic programming model and solved using a stochastic look-ahead technique based on the Monte Carlo tree search algorithm to determine optimal actions on parking price assignment and spot utilization over time. The numerical experiments on a hypothetical and empirical case study are conducted to show the performance of the proposed algorithm and to draw managerial insights. The results are compared with those of benchmark algorithms, which indicate that the proposed methodology can determine near-optimal solutions efficiently.}, number={5}, journal={IEEE Transactions on Intelligent Transportation Systems}, author={Mirheli, A. and Hajibabai, L.}, year={2020}, pages={2167–2179} }
 @article{mirheli_hajibabai_hajbabaie_2018, title={Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment}, volume={92}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85047272483&partnerID=MN8TOARS}, DOI={10.1016/j.trc.2018.04.026}, abstractNote={Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.}, journal={Transportation Research Part C: Emerging Technologies}, publisher={Elsevier}, author={Mirheli, A. and Hajibabai, L. and Hajbabaie, A.}, year={2018}, pages={412–425} }
 @article{hajibabai_saha_2019, title={Patrol Route Planning for Incident Response Vehicles under Dispatching Station Scenarios}, volume={34}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050953450&partnerID=MN8TOARS}, DOI={10.1111/mice.12384}, abstractNote={AbstractTraffic incidents often contribute to major safety concerns, impose additional congestion in the neighboring transportation networks, and induce indirect costs to economy. As roughly a third of traffic crashes are secondary accidents, effective incident management activities are critical, especially on roadways with high traffic volume, to detect, respond to, and clean up incidents in a timely fashion, which supports safety constraints and restores traffic capacity in the transportation network. Hence, it is beneficial to simultaneously plan for first respondents’ dispatching station location and patrol route design to mitigate congestion. This article presents an optimal route planning for patrolling vehicles to facilitate quick response to potential accidents. A mixed‐integer nonlinear program is proposed that minimizes the respondents’ patrolling travel cost based on the expected maximum response time from each arbitrary location to all incident locations (a.k.a. hotspots) with various incident occurrence probabilities. We have developed a column generation‐based solution technique to solve the route optimization model under different station design scenarios. To investigate the impact of dispatching station design on the routing cost, an integrated genetic algorithm framework with embedded continuous approximation approach is developed that reduces the complexity of the hybrid location design and route planning problem. Numerical experiments on hypothetical networks of various sizes are conducted to indicate the performance of the proposed algorithm and to draw managerial insights. The models and solution techniques, developed in this article, are applicable to a number of network problems that simultaneously involve routing and facility location choices.}, number={1}, journal={Computer-Aided Civil and Infrastructure Engineering}, author={Hajibabai, L. and Saha, D.}, year={2019}, pages={58–70} }
 @article{hajibabai_ouyang_2016, title={Dynamic Snow Plow Fleet Management under Uncertain Demand and Service Disruption}, volume={17}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84959096203&partnerID=MN8TOARS}, DOI={10.1109/TITS.2016.2520918}, abstractNote={It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic service disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.}, number={9}, journal={IEEE Transactions on Intelligent Transportation Systems}, author={Hajibabai, L. and Ouyang, Y.}, year={2016}, pages={2574–2582} }
 @book{hajibabai_ouyang_2016, title={Planning of resource replenishment location for service trucks under network congestion and routing constraints}, volume={2567}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85015433549&partnerID=MN8TOARS}, DOI={10.3141/2567-02}, abstractNote={ It is often very challenging to plan expedient and cost-effective operations for service trucks under network design constraints, particularly on congested urban roadways. Hence, it is beneficial to account simultaneously for decisions on truck facility location design and network expansion to mitigate the additional congestion caused by trucks and facilitate their routing. This study developed an integrated mathematical model for facility location design under network routing and congestion constraints. The model determines the optimal number and location of replenishment facilities, minimizes truck routing costs on the basis of proposed network design, assigns traffic in the network (for both general roadway users and service trucks), and selects candidate links for possible roadway capacity expansion. The model aims to minimize the total costs for new facility construction, truck routing, transportation infrastructure expansion, and transportation delay. A genetic algorithm framework was developed that incorporates a continuous approximation model for truck routing cost estimation and a traffic assignment algorithm. The numerical results show that the integrated solution technique can solve the problem effectively. }, journal={Transportation Research Record}, author={Hajibabai, L. and Ouyang, Y.}, year={2016}, pages={10–17} }
 @article{hajibabai_bai_ouyang_2014, title={Joint optimization of freight facility location and pavement infrastructure rehabilitation under network traffic equilibrium}, volume={63}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84897836858&partnerID=MN8TOARS}, DOI={10.1016/j.trb.2014.02.003}, abstractNote={Establishment of industry facilities often induces heavy vehicle traffic that exacerbates congestion and pavement deterioration in the neighboring highway network. While planning facility locations and land use developments, it is important to take into account the routing of freight vehicles, the impact on public traffic, as well as the planning of pavement rehabilitation. This paper presents an integrated facility location model that simultaneously considers traffic routing under congestion and pavement rehabilitation under deterioration. The objective is to minimize the total cost due to facility investment, transportation cost including traffic delay, and pavement life-cycle costs. Building upon analytical results on optimal pavement rehabilitation, the problem is formulated into a bi-level mixed-integer non-linear program (MINLP), with facility location, freight shipment routing and pavement rehabilitation decisions in the upper level and traffic equilibrium in the lower level. This problem is then reformulated into an equivalent single-level MINLP based on Karush–Kuhn–Tucker (KKT) conditions and approximation by piece-wise linear functions. Numerical experiments on hypothetical and empirical network examples are conducted to show performance of the proposed algorithm and to draw managerial insights.}, journal={Transportation Research Part B: Methodological}, author={Hajibabai, L. and Bai, Y. and Ouyang, Y.}, year={2014}, pages={38–52} }
 @book{hajibabai_nourbakhsh_ouyang_peng_2014, title={Network routing of snowplow with resource replenishment and plowing priorities formulation. Algorithm, and application}, volume={2440}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84938513511&partnerID=MN8TOARS}, DOI={10.3141/2440-03}, abstractNote={ The routing of snowplow trucks in urban and regional areas encompasses a variety of complex decisions, especially for jurisdictions with heavy snowfall. The main activities involve dispatching a fleet of snowplow trucks from a central depot or satellite facility to clean and spread salt and chemicals on the network links (i.e., snow routes). In this paper, a mixed integer linear program model is proposed to minimize the total operation time of all snowplow trucks needed to complete a given set of snow routes with multiple plowing priorities and to reduce the longest individual truck operation time. Customized construction and local search solution algorithms are developed and used to design snow routes for an empirical application. The computational results show that the proposed solution approach is able to solve the problem effectively and the model result outperforms the current solution in practice. The proposed models and algorithms are also incorporated into the development of a state-of-the-art snowplow routing software that can help planners optimize snow routes and evaluate options for resource allocation. }, journal={Transportation Research Record}, author={Hajibabai, L. and Nourbakhsh, S.M. and Ouyang, Y. and Peng, F.}, year={2014}, pages={16–25} }
 @article{hajibabai_ouyang_2013, title={Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to the Biofuel Industry}, volume={28}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84874741231&partnerID=MN8TOARS}, DOI={10.1111/j.1467-8667.2012.00791.x}, abstractNote={Abstract:   As the biofuel industry continues to expand, the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply points to the refineries and biofuel shipment to the consumption locations, which increases traffic demand in the transportation network and contributes to additional congestion (especially in the neighborhood of the refineries). Hence, it is beneficial to form public‐private partnerships to simultaneously consider transportation network expansion and biofuel supply chain design to mitigate congestion. This article presents an integrated mathematical model for biofuel supply chain design where the near‐optimum number and location of biorefinery facilities, the near‐optimal routing of biomass and biofuel shipments, and possible highway/railroad capacity expansion are determined. The objective is to minimize the total cost for biorefinery construction, transportation infrastructure expansion, and transportation delay (for both biomass/biofuel shipment and public travel) under congestion. A genetic algorithm framework (with embedded Lagrangian relaxation and traffic assignment algorithms) is developed to solve the optimization model, and an empirical case study for the state of Illinois is conducted with realistic biofuel production data. The computational results show that the proposed solution approach is able to solve the problem efficiently. Various managerial insights are also drawn. It shall be noted that although this article focuses on the booming biofuel industry, the model and solution techniques are suitable for a number of application contexts that simultaneously involve network traffic equilibrium, infrastructure expansion, and facility location choices (which determine the origin/destination of multi‐commodity flow).}, number={4}, journal={Computer-Aided Civil and Infrastructure Engineering}, author={Hajibabai, L. and Ouyang, Y.}, year={2013}, pages={247–259} }
 @article{hajibabai_aziz_peña-mora_2011, title={Visualizing greenhouse gas emissions from construction activities}, volume={11}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79960686479&partnerID=MN8TOARS}, DOI={10.1108/14714171111149052}, abstractNote={PurposeConstruction activities, particularly related to transportation, have a considerable impact on the environment and air quality. This paper aims to present a geographic information systems (GIS) and computer‐aided design (CAD)‐based approach for visualizing, communicating and analysing greenhouse gas (GHG) emissions resulting from construction activities.Design/methodology/approachA methodology using GIS is developed to graphically represent spatial aspects of construction. The approach adopted involves use of a 3D model developed in CAD environment, which was synchronized with a construction schedule stored in Excel spreadsheets. GIS environment is used to link spatial and scheduling information relevant to GHG emissions from construction activities. A baseline was created to enable effective monitoring of construction emissions.FindingsThe presented GIS model has the potential to enhance visualisation of distribution and dynamic variations of GHG emissions and could help stakeholders better analyse and understand how construction activities impact the environment.Originality/valueThis paper presents a novel method of graphically presenting GHG and other hazardous air emissions from construction activities using a GIS‐based approach. The paper presents the result of comparing the 3D surface representation of simulated estimated and actual construction emissions to show the impact of construction activities on the environment to support the engineering analysis and decision‐making process.}, number={3}, journal={Construction Innovation}, author={Hajibabai, L. and Aziz, Z. and Peña-Mora, F.}, year={2011}, pages={356–370} }