Browsing by Author "Chen, Rongsheng"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Generating Traffic Information from Connected Vehicle V2V Basic Safety Messages
    (Minnesota Department of Transportation, 2021-03) Chen, Rongsheng; Levin, Michael; Hourdos, John; Duhn, Melissa
    Basic Safety Message (BSM) containing data about the vehicle's position, speed, and acceleration. Roadside receivers, RSUs, can capture BSM broadcasts and translate them into information about traffic conditions. If every vehicle is equipped with awareness, BSMs can be combined to calculate traffic flows, speeds, and densities. These three key parameters will be post-processed to obtain queue lengths and travel time estimates. The project team proposed a traffic state estimation algorithm using BSMs based on the Kalman filter technique. The algorithm's performance was tested with BSMs generated from several arterial in a microscopic simulation model and BSMs generated with radar data collected on freeway sections. Then the project team developed a traffic monitoring system to apply the algorithm to a large-scale network with different types of roads. In the system, computers could remotely access the online server to acquire BSMs and estimate traffic states in real-time.
  • Thumbnail Image
    Item
    Improving intersection safety through variable speed limits for connected vehicles
    (Center for Transportation Studies, University of Minnesota, 2019-05) Levin, Michael; Chen, Rongsheng; Liao, Chen-Fu; Zhang, Tab
    Autonomous vehicles create new opportunities for innovative intelligent traffic systems. Variable speed limits, which is a speed management systems that can adjust the speed limit according to traffic condition or predefined speed control algorithm on different road segments, can be better implemented with the cooperation of autonomous vehicles. These compliant vehicles can automatically follow speed limits. However, non-compliant vehicles will attempt to pass the moving bottleneck created by the compliant vehicle. This project builds a multi-class cell transmission model to represent the relation between traffic flow parameters. This model can calculate flows of both compliant and non-compliant vehicles. An algorithm is proposed to calculate variable speed limits for each cell of the cell transmission model. This control algorithm is designed to reduce the stop-and-go behavior of vehicles at traffic signals. Simulation is used to test the effects of VSLs on an example network. The result shows that VSL is effective at reducing the energy consumption of the whole system and reduce the likelihood of crash occurrence.
  • Thumbnail Image
    Item
    Maximum-stability Distributed Control in Traffic Networks
    (2021-05) Chen, Rongsheng
    The max-pressure control is a distributed control algorithm that has the property of stabilizing the total queue length in the network theoretically. In spite of its good properties, some assumptions or requirements of the max-pressure control make it hard to be applied to traffic networks in reality: such as the data collection of queue length information for each movement and fixed route choices. Besides, traditional max-pressure control algorithms are only designed for signal-controlled intersections and are not applicable for signal-free intersections. Therefore, this thesis proposes max-pressure control algorithms and tests their performances in traffic networks while relaxing some of the assumptions used in existing studies. This thesis first explores mild assumptions for weight functions to incorporate alternative data sources in max-pressure control. This thesis also proposes an autonomous intersection management (AIM) algorithm considering pedestrians using the max-pressure control. Besides, the performance of max-pressure control is tested when road users' route choice is considered using dynamic traffic assignment, and a routing guidance algorithm is also developed to modify road users' route choices and to improve network efficiency.
  • Thumbnail Image
    Item
    Microscopic Simulation and Evaluation of the Roundabout Capacity Model in Highway Capacity Manual
    (2018-01) Chen, Rongsheng
    Shown to be an effective intersection design, the roundabout is receiving increasing attention and popularity. Several models, described in this work, have been developed to predict roundabout capacity. One of them, the roundabout capacity model included in the Highway Capacity Manual (HCM), is widely used in the US, using a gap-acceptance foundation based on data collected in US roundabouts. This study explored the accuracy of the two-lane variants of the roundabout capacity models in HCM 6th Edition and HCM 2010 by comparing them with an exponential regression model fitted on flow rate measurements collected at a two-lane roundabout in Richfield, Minnesota. Based on the same gap-acceptance foundation proposed in HCM, two other models were developed by recalculating coefficients. Each followed a different calibration strategy and compared with the Richfield model. It was found that calibration can significantly enhance the accuracy of the default HCM model and calibrating only the intercept of the default HCM model can produce a model with similar accuracy as the model resulting by calibrating both coefficients. To further assist traffic engineers, this work validated the capability of the popular traffic simulator AIMSUN to build a roundabout model with realistic capacities. A sensitivity analysis, exploring the impact of different simulation parameters, further assisted in proposing an efficient and reliable simulation calibration methodology. Initial safety margin, visibility along main stream, reaction time at stop, and max acceleration were selected to calibrate driver’s gap acceptance behavior. The result showed that if a calibrated model in AIMSUN could produce the same critical headway and follow-up headway as those in the HCM6 model, it will also result in similar capacities as the HCM6 model.
  • Thumbnail Image
    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, Di
    Connected 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.