@article{hajbabaie_tajalli_bardaka_2023, title={Effects of Connectivity and Automation on Saturation Headway and Capacity at Signalized Intersections}, volume={8}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981231187386}, DOI={10.1177/03611981231187386}, abstractNote={ This paper analyzes the potential effects of connected and automated vehicles on saturation headway and capacity at signalized intersections. A signalized intersection is created in Vissim as a testbed, where four vehicle types are modeled and tested: (I) human-driven vehicles (HVs), (II) connected vehicles (CVs), (III) automated vehicles (AVs), and (IV) connected automated vehicles (CAVs). Various scenarios are defined based on different market-penetration rates of these four vehicle types. AVs are assumed to move more cautiously than HVs. CVs and CAVs are supposed to receive information about the future state of traffic lights and adjust their speeds to avoid stopping at the intersection. As a result, their movements are expected to be smoother with a lower number of stops. The effects of these vehicle types in mixed traffic are investigated in relation to saturation headway, capacity, travel time, delay, and queue length in different lane groups of an intersection. A Python script code developed by Vissim is used to provide the communication between the signal controller and CVs and CAVs to adjust their speeds accordingly. The results show that increasing CV and CAV market-penetration rate reduces saturation headway and consequently increases capacity at signalized intersections. On the other hand, increasing the AV market-penetration rate deteriorates traffic operations. Results also indicate that the highest increase (80%) and decrease (20%) in lane-group capacity are observed respectively in a traffic stream of 100% CAVs and 100% AVs. }, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Hajbabaie, Ali and Tajalli, Mehrdad and Bardaka, Eleni}, year={2023}, month={Aug} }
 @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{tajalli_al islam_list_hajbabaie_2022, title={Testing Connected Vehicle-Based Accident Mitigation for Red-Light Violation Using Simulation Strategies}, volume={2676}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981221075630}, DOI={10.1177/03611981221075630}, abstractNote={ Simulation is often suggested as a way to analyze the safety improvements of geometric changes and operational strategies. But the results from simulations are mixed. This paper presents new ideas about how to do such assessments, especially in the context of testing the value of vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to pedestrian (V2P) communications in preventing crashes because of red-light violation at signalized intersections. Algorithms are created that watch for impending collisions through sensing and then issue speed and trajectory changes to avoid accidents. Red-light violation is a primary focus because it increases the likelihood that collisions will occur. VISSIM is used to test these ideas, including new communication and control algorithms that link to vehicles, pedestrians, and signal controllers through the communication interface. The algorithms predict unsafe conditions, determine an appropriate crash remedial decision, and communicate those controls with the appropriate vehicles and pedestrians. The impacts of these algorithms are explored under various demand patterns, connected vehicle market penetration rates, and red-light violation rates in a hypothetical simulated environment. The simulation analysis suggests that the number of near-crash events can be reduced significantly if V2V and V2P communications are implemented. Moreover, adding V2I communication on top of these may further reduce the number of near-crash events. These results suggest that not only could such control strategies have significant impacts, but also those impacts can be assessed through simulation. }, number={6}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Tajalli, Mehrdad and Al Islam, S. M. A. Bin and List, George F. and Hajbabaie, Ali}, year={2022}, month={Mar}, pages={583–600} }
 @article{tajalli_hajbabaie_2022, title={Traffic Signal Timing and Trajectory Optimization in a Mixed Autonomy Traffic Stream}, volume={23}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2021.3058193}, DOI={10.1109/TITS.2021.3058193}, abstractNote={This study introduces a methodology for cooperative signal timing and trajectory optimization at intersections with a mix of connected automated vehicles (CAVs) and human-driven vehicles (HVs). We represent joint signal timing and trajectory control as a mixed-integer non-linear program, which is computationally complex. The developed methodology provides a balance between computational efficiency and solution quality by (a) linearizing the nonlinear constraints and reformulating the problem with a tight convex hull of the mixed-integer solutions and (b) decomposing the intersection-level program into several lane-level programs. Hence, a unique controller jointly optimizes the trajectories of CAVs on a lane and the signal timing parameters associated with that lane. This setting will allow finding near-optimal solutions with small duality gaps for complex intersections with different demand levels. Case study results show that the proposed methodology finds solutions efficiently with at most 0.1% duality gap. We compared the developed methodology with an existing signal timing and trajectory control approach and found 13% to 41% reduction in average travel time and 1% to 31% reduction in fuel consumption under different scenarios.}, number={7}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tajalli, Mehrdad and Hajbabaie, Ali}, year={2022}, month={Jul}, pages={6525–6538} }
 @article{islam_tajalli_mohebifard_hajbabaie_2021, title={Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System}, volume={2675}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.1177/03611981211013036}, DOI={10.1177/03611981211013036}, abstractNote={ The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability. }, number={10}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Islam, S M A Bin Al and Tajalli, Mehrdad and Mohebifard, Rasool and Hajbabaie, Ali}, year={2021}, month={May}, pages={800–814} }
 @article{tajalli_mehrabipour_hajbabaie_2021, title={Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles}, volume={22}, ISSN={1524-9050 1558-0016}, url={http://dx.doi.org/10.1109/TITS.2020.2994468}, DOI={10.1109/TITS.2020.2994468}, abstractNote={This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures near-optimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides near-optimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.}, number={11}, journal={IEEE Transactions on Intelligent Transportation Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tajalli, Mehrdad and Mehrabipour, Mehrzad and Hajbabaie, Ali}, year={2021}, month={Nov}, pages={6748–6759} }
 @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} }