@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={Abstract 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%.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Niroumand, Ramin and Hajibabai, Leila and Hajbabaie, Ali}, year={2024}, month={Mar} }
 @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{niroumand_bahrami_aashtiani_hajbabaie_2022, title={Battery Electric Vehicles Network Equilibrium With Flow-Dependent Energy Consumption}, volume={2677}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981221131813}, DOI={10.1177/03611981221131813}, abstractNote={Recent studies show that energy consumption of battery electric vehicles (BEVs) increases in traffic congestion. Therefore, it is important to consider the effect of link flow on BEV energy consumption. The flow-dependent energy consumption changes the route choice and user equilibrium conditions. In this paper, some shortcomings of available BEV flow-dependent energy consumption user equilibrium models are shown first. Then, “sufficient” as well as “sufficient and necessary” user equilibrium based on the generalized travel time of each path and sub-path penalties are defined and modeled for flow-dependent energy consumption. While it is difficult to solve the sufficient and necessary model, the sufficient model can be solved directly with commercial solvers for small to medium-sized networks by generating all paths. An iterative algorithm is also presented to generate paths as required to solve the problem for larger networks. Numerical examples demonstrate the model and proposed algorithm, and analyze the impact of flow-dependent energy consumption on equilibrium conditions.}, number={5}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Niroumand, Ramin and Bahrami, Sina and Aashtiani, Hedayat Z. and Hajbabaie, Ali}, year={2022}, month={Nov}, pages={444–462} }
 @article{tajalli_niroumand_hajbabaie_2022, title={Distributed cooperative trajectory and lane changing optimization of connected automated vehicles: Freeway segments with lane drop}, volume={143}, ISSN={0968-090X}, url={http://dx.doi.org/10.1016/j.trc.2022.103761}, DOI={10.1016/j.trc.2022.103761}, abstractNote={• Developing a formulation to couple the discrete lane changing decisions with the polynomial longitudinal and lateral equations of motion without assuming a predefined function for the lateral movement of vehicles. • Establishing cooperation among all vehicles on the road to promote system-level operational optimality while maintaining safety. • Introducing a vehicle-level distributed algorithm to reduce the cooperative problem’s complexity so that the algorithm can work with traffic demand flow rates as high as 2400 vehicles per hour per lane. • Improving mobility on freeway facilities by reducing the average travel time by up to 86.4% and increasing the throughput by at most 134.3% depending on traffic demand and lane configuration. This study presents a methodology for optimal control of connected automated vehicles (CAVs) in freeway segments with a lane drop. Lane drops can create bottlenecks with a considerable number of mandatory and discretionary lane-changing maneuvers when traffic volume is high, which can eventually lead to stop-and-go conditions. Proper motion planning aligned with optimal lane changing upstream of a lane drop can increase capacity and reduce the number of stops and the risk of collision. This paper introduces a vehicle-level mixed-integer program to control longitudinal and lateral movement of CAVs, provide a smooth flow of traffic, and avoid congestion in freeway segments with lane drops. To ensure the feasibility of vehicle-level decisions and promote system-level optimality, a cooperative distributed algorithm is established, where CAVs coordinate their decisions to find the optimal longitudinal and lateral maneuvers that avoid collisions among all vehicles. The proposed coordination scheme lets CAVs find their optimal trajectories based on predictive information from surrounding vehicles (i.e., future locations and speeds) and coordinate their lane-changing decisions to avoid collisions. The results show that optimal lane changing of CAVs smoothens the traffic flow and increases freeway capacity in congested traffic conditions. Compared with all-knowing CAVs simulated in Vissim, the proposed methodology reduced the average travel time by up to 86.4%. It increased the number of completed trips by up to 134.3% based on various traffic demands and lane drop layout combinations.}, journal={Transportation Research Part C: Emerging Technologies}, publisher={Elsevier BV}, author={Tajalli, Mehrdad and Niroumand, Ramin and Hajbabaie, Ali}, year={2022}, month={Oct}, pages={103761} }
 @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{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} }