Browsing by Author "Kang, Di"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item Non-linear spacing policy and network analysis for shared-road platooning(Center for Transportation Studies, University of Minnesota, 2019-08) Levin, Michael; Rajamani, Rajesh; Jeon, Woongsun; Chen, Rongsheng; Kang, DiConnected vehicle technology creates new opportunities for obtaining knowledge about the surrounding traffic and using that knowledge to optimize individual vehicle behaviors. This project creates an interdisciplinary group to study vehicle connectivity, and this report discusses three activities of this group. First, we study the problem of traffic state (flows and densities) using position reports from connected vehicles. Even if the market penetration of connected vehicles is limited, speed information can be inverted through the flow-density relationship to estimate space-and time-specific flows and densities. Propagation, according to the kinematic wave theory, is combined with measurements through Kalman filtering. Second, the team studies the problem of cyber-attack communications. Malicious actors could hack the communications to incorrectly report position, speed, or accelerations to induce a collision. By comparing the communications with radar data, the project team develops an analytical method for vehicles using cooperative adaptive cruise control to detect erroneous or malicious data and respond accordingly (by not relying on connectivity for safe following distances). Third, the team considers new spacing policies for cooperative adaptive cruise control and how they would affect city traffic. Due to the computational complexity of microsimulation, the team elects to convert the new spacing policy into a flow-density relationship. A link transmission model is constructed by creating a piecewise linear approximation. Results from dynamic traffic assignment on a city network shows that improvements in capacity reduces delays on freeways, but surprisingly route choice increased congestion for the overall city.Item Pressure-based dispatch for shared autonomous vehicles(Center for Transportation Studies, University of Minnesota, 2019-08) Levin, Michael; Kang, DiShared autonomous vehicle (SAV) technology is rapidly maturing, with two companies (Uber and Waymo) already testing SAV services in cities in the US. Due to the point-to-point service and lack of a driver, SAV service costs could be similar to that of personal vehicles, resulting in major mode choice changes for daily travel. A major issue for SAV operators is the SAV dispatch problem, i.e., how to optimally assign vehicles to waiting passengers. SAV dispatch is essentially a vehicle routing problem, which is NP-hard, and is complicated by fleets measured in thousands of vehicles in typical cities. Previous studies have attempted to quantify the number of passengers served per SAV using agent-based simulation studies on realistic networks, with a variety of results that highly depend on the heuristic chosen for SAV dispatch. Ideally, the optimal SAV dispatch strategy would serve as many passengers as any other policy. This project created a max-pressure dispatch policy, which was analytically proven by showing stability in the number of unserved passengers through a Lyapunov function. Essentially, the work analytically compared the serviceable demand from the max-pressure dispatch to the demand that could be served by any other dispatch policy. The max-pressure policy relied on a planning horizon; as the horizon grows to infinity, the policy becomes arbitrarily close to any sequence of SAV movements that can serve given demand rates.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.