Browsing by Author "Barman, Simanta"
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Item Efficient Pedestrian and Bicycle Traffic Flow Estimation Combining Mobile-Sourced Data with Route Choice Prediction(2023-12-08) Barman, Simanta; Stern, Raphael; Levin, Michael W.; Lindsey, Greg; rstern@umn.edu; Stern, Raphael; University of Minnesota CAVe LabAccurate estimate of traffic flow measures like annual average daily traffic (AADT) is vital to making decisions about roadway planning, safety, maintenance, operation etc. Methodology to inexpensively obtain an accurate estimate of traffic flow especially for pedestrian and bicyclist traffic is lacking in the literature. High expenses of conducting household surveys and setting up traffic monitoring stations to collect data motivated us to look for cheaper solutions. In this study, we develop a methodology to inexpensively obtain a good estimate of pedestrian and bicyclist traffic flow from mobile data sources while avoiding the privacy issues associated with models based on household survey data. However, the accuracy of mobile data is unknown and may vary in different locations. To deal with erroneous data sources we use different techniques to estimate and keep improving an origin-destination (OD) matrix from the observed link flows to ultimately get the actual link flows. In our model, we enforce the consistency between the number of productions and attractions of trips for different regions with the OD-matrix. Using the network topology, we use trip distribution based on the gravity model to generate an initial OD-matrix. Then we use an optimization formulation to improve the initial OD-matrix so that the link flow obtained using the improved OD-matrix matches with the partially observable link flows. Furthermore, we present the performance of the solution algorithm for the Twin Cities' bicycle and pedestrian networks. We also compare the accuracy of our estimate with manually collected traffic flow data for the real networks.Item Performance, Throughput Properties, and Optimal Location Evaluation for Max-pressure Control(2022-11) Barman, SimantaMax pressure (MP) signal timing is an actuated decentralized signal control policy. Rigorous mathematical studies have proven stability or bounded total vehicle queues over a long period for all feasible demands. Those studies also established the theoretical benefits of different MP policies. However, the theoretical studies make some assumptions about traffic properties that may not represent reality, the effects of which are not explored much in the literature under realistic traffic conditions. The first portion of this study focuses on examining how different variations of MP control perform in realistic scenarios and finding the most practical policy among those for implementation in real roads. Microsimulation models of seven intersections from two corridors, County Road (CR) 30 and CR 109 from Hennepin County, Minnesota were created. Real life demand and current signal timing data provided by Hennepin County, Minnesota were used to make the simulations as close to reality as possible. Then, the performance comparisons of current actuated-coordinated (AC) signal control with an acyclic MP and two variations of cyclic MP policies are shown. The performance of different control policies in terms of delay, throughput, worst lane delay and number of phase changes are also presented. How different parameters affect performance of the MP policies is also presented. We found that better performance can be achieved with cyclic max pressure policy by allowing phase skipping when no vehicles are waiting. Findings from this study also suggest that most of the claimed performance benefits can still be achieved in real life traffic conditions even with the simplified assumptions made in the theoretical models. In most cases, MP control policies outperformed current signal control. The second portion of this study covers deployment strategies of MP control under limited budget and the associated stability properties. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections. Previous work did not consider a limited number of MP controlled intersections while proving the stability properties. Therefore, it is not clear whether a network can still be stabilized with a limited deployment of MP control. Using Lyapunov drift techniques, this thesis proves that even with a limited deployment, MP control can stabilize a network within feasible demand. Then, an optimization formulation to find the optimal intersections to install MP control given a limited budget is presented. We also present an efficient greedy algorithm to solve that optimization problem and prove that the algorithm solves the problem to optimality. Numerical results from simulations conducted on the downtown Austin network using an in-house custom simulator called AVDTA are then presented. The change in theoretical maximum servable demands for different amounts of deployments obtained from the optimization problem seemed to mostly match with simulation results. We found that limited deployment of MP control almost always performed better than random deployment of MP control in terms of servable stable demand. Average total queue length and link density were observed to decrease as the number of MP controls increased, which indicates better network performance. Average travel times per vehicle also decreased with installations of MP controls, which shows how the travelers would benefit from more MP controls.Item Towards Implementation of Max-Pressure Signal Timing on Minnesota Roads(Minnesota Department of Transportation, 2022-12) Barman, Simanta; Levin, Michael W.; Robbennolt, Jake; Hu, Jeffrey; Odell, Michael; Kang, DiMax-pressure control is a new adaptive method for signal timing that is mathematically proven to achieve maximum throughput for the entire city road network. This throughput guarantee is nevertheless achieved by a decentralized control algorithm that depends only on local traffic information and is easy to compute. These mathematical properties suggest high potential for use in Minnesota, but the method?s performance in practice is not well-known. Furthermore, it lacks some practical constraints on signal timing that could cause confusion to drivers. This project conducted methodological improvements and simulation experiments on a calibrated model of 7 intersections in Hennepin County. We modified the theory behind max-pressure control to model first-in-first-out behaviors on lanes shared by multiple turning movements, and to force max-pressure control to follow a signal cycle. After making these significant methodological improvements, we proved that the maximum throughput properties still hold. Then, we calibrated SUMO (Simulation of Urban MObility) microsimulation models of 2 Hennepin County corridors with 7 intersections using signal timing data and 15-minute observed counts, and compared different versions of max-pressure control with existing actuated-coordinated signals. We varied the maximum cycle length and the time step (signal phases can only change once per time step). The performance depended on the control parameters. Overall, for most intersections and demand periods, we were able to find max-pressure control settings that significantly improved over current signal timings. Large reductions in delay (sometimes over 50%) suggested that max-pressure signal timing both achieved higher throughput during peak demand and was more responsive to queues.