Browsing by Author "Liu, Yue"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item AN INTEGRATED TRAFFIC CONTROL SYSTEM FOR FREEWAY CORRIDORS UNDER NON-RECURRENT CONGESTION(2009) Liu, Yue; Chang, Gang-Len; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This research has focused on developing an advanced dynamic corridor traffic control system that can assist responsible traffic professionals in generating effective control strategies for contending with non-recurrent congestion that often concurrently plagues both the freeway and arterial systems. The developed system features its hierarchical operating structure that consists of an integrated-level control and a local-level module for bottleneck management. The primary function of the integrated-level control is to maximize the capacity utilization of the entire corridor under incident conditions with concurrently implemented strategies over dynamically computed windows, including diversion control at critical off-ramps, on-ramp metering, and optimal arterial signal timings. The system development process starts with design of a set of innovative network formulations that can accurately and efficiently capture the operational characteristics of traffic flows in the entire corridor optimization process. Grounded on the proposed formulations for network flows, the second part of the system development process is to construct two integrated control models, where the base model is designed for a single-segment detour operation and the extended model is designated for general network applications. To efficiently explore the control effectiveness under different policy priorities between the target freeway and available detour routes, this study has further proposed a multi-objective control process for best managing the complex traffic conditions during incident operations. Due to the nonlinear nature of the proposed formulations and the concerns of computing efficiency, this study has also developed a GA-based heuristic along with a successive optimization process that can yield sufficiently reliable solutions for operating the proposed system in a real-time traffic environment. To evaluate the effectiveness and efficiency of the developed system, this study has conducted extensive numerical experiments with real-world cases. The experimental results have demonstrated that with the information generated from the proposed models, the responsible agency can effectively implement control strategies in a timely manner at all control points to substantially improve the efficiency of the corridor control operations. In view of potential spillback blockage due to detour operations, this study has further developed a local-level bottleneck management module with enhanced arterial flow formulations that can fully capture the complex interrelations between the overflow in each lane group and its impact on the neighboring lanes. As a supplemental component for corridor control, this module has been integrated with the optimization model to fine-tune the arterial signal timings and to prevent the queue spillback or blockages at off-ramps and intersections. The results of extensive numerical experiments have shown that the supplemental module is quite effective in producing local control strategies that can prevent the formation of intersection bottlenecks in the local arterial.Item WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND(2013) Liu, Yue; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better model fitted related variables, three candidate models multinomial logit (MNL), ordered probit logit (OP), and neural networks were chosen to examine in SAS. The results showed that the Multilayer Perceptron Model in neural networks performed the best and is the accident severity model of choice. During the model construction, eight factors related to weather condition were considered. They were: air temperature, average wind speed, total precipitation in the past 24 hours, visibility, slight, moderate, heavy precipitation and relative humidity. Based on the comparison criteria, we concluded that MNL regression is more interpretive than OP and Neural Networks models. All factors except visibility and heavy precipitation had significant impact on accident severity when considering the data from the entire Maryland highway system. Using MNL, a data subset with accident records only in a section of US route 50 was examined. After excluding the impact factors other than weather, a narrow significant variable set was obtained.