Wuping Xin

The Less-Than-Perfect Driver: A Model of Collision-Inclusive Car-Following Behavior

Unlike traditional car-following models that preclude vehicle collisions, a proposed model aims to emulate less-than-perfect everyday driving while capturing both safe and unsafe driver behavior. Most important, a realistic perception-response process is incorporated into the model on the basis of developments from visual perception studies. Driver inattention is characterized by a driver-specific variable called the scanning interval. This variable, when coupled with the drivers visual perception-response process, results in variable reaction times that are dependent not only on each drivers individual characteristics but also on instantaneous traffic conditions such as speed and density. This allows closer emulation of real-life human driving and its interactions with surrounding vehicles. Both inter- and intradriver variations in reaction time are captured in a plausible and coherent manner; in earlier studies, reaction time either was presumed fixed or was of limited variability. Furthermore, parameters of this model have a direct physical and behavioral meaning; this implies that vehicle collisions, if any, can be analyzed for behavioral patterns rather than simply being treated as numerical artifacts. In all, 54 detailed and accurate vehicle trajectories extracted from 10 real-life crashes were used to test the models capability of replicating freeway rear-end collisions. High-resolution crash-free trajectory data were used to validate the model against normal driving behavior. Test results indicate that the proposed model is able to replicate both normal and unsafe driving behavior that could lead to vehicle collisions. The feasibility of integrating the proposed model with existing microsimulators is discussed. The outcome of this work could facilitate studying crash mechanisms at a high-definition microscopic level and could enable safety-related system design improvements and evaluation through microsimulation software.

MINNESOTA'S NEW RAMP CONTROL STRATEGY: DESIGN OVERVIEW AND PRELIMINARY ASSESSMENT

Freeway ramp control has been successfully implemented in the Minneapolis-St. Paul area since the early 1970s. However, the recent ramp metering controversy highlighted the need for a less restrictive ramp control strategy that maximizes freeway capacity utilization while limiting ramp wait times. As a result, a new multilayer ramp control strategy called stratified ramp control was recently developed and deployed systemwide in the Twin Cities metropolitan area. This strategy determines the metering rates from freeway conditions as well as from real-time ramp demand and ramp queue size, indicating a shift of emphasis away from freeway flow toward the balance between both freeway efficiency and reduced ramp delays. Minnesotas new ramp control strategy is detailed along with a preliminary assessment of its effectiveness. The evaluation is accomplished by comparing the new strategy to its predecessor, ZONE metering, through rigorous micro-simulation. The preliminary results suggest that the stratified ramp control strategy is effective in reducing ramp delays and limiting ramp wait times below the prescribed value. However, peak-hour freeway congestion is extended in both time and space as opposed to that with the ZONE metering strategy and reveals a compromised freeway performance in favor of virtually reducing ramp delays.

Comprehensive Evaluation of New Integrated Freeway Ramp Control Strategy

A new integrated ramp control strategy, recently deployed by the Minnesota Department of Transportation in the Twin Cities metropolitan area, is evaluated. This strategy, stratified zone metering (SZM), takes into account real-time ramp demand and queue size information and aims to strike a balance between two competing objectives: improving freeway efficiency and preventing excessive ramp delays. In this study, the SZM strategy was compared to the earlier ZONE metering strategy as well as to the no-control alternative. Comprehensive metrics were generated through rigorous microsimulation to assess critical aspects of the strategys performance. The evaluation results are consistent with qualitative field observations and confirm that SZM strategy improves freeway efficiency when compared with the no-control alternative, reduces freeway travel time and delay, improves freeway speed, smooths freeway flow, and reduces the number of stops. More important, excessive queue spillbacks and ramp delays were signi...

Preprocessing Volume Input Data for Improved Traffic Simulation

Microsimulation has become an increasingly indispensable tool in demanding intelligent transportation systems and planning applications. To build reliable and realistic simulation models, high-quality input data, including roadway geometry, vehicle and driver characteristics, traffic volumes, and composition, are required. Volumes of these data are important but hard to obtain; even when collected with advanced surveillance systems, they are susceptible to miscounting, gaps in time and space, and other inaccuracies. Data that appear to be accurate often do not balance out (i.e., they are inconsistent in terms of maintaining conservation throughout the system). These problems could lead to anomalies or errors during the simulation, seriously tainting the reliability and accuracy of the outputs and weakening the credibility of the conclusions. A comprehensive methodology is proposed for improving the quality of freeway traffic volumes for simulation purposes. Established and enhanced procedures for checking...

Robust stability of a dynamic traffic assignment model with uncertainties

In this paper, a modified dynamic traffic assignment model is developed to explicitly formulate the impact of inaccuracy of cost measurement/estimation and the time-varying travel demand. The modified model is analyzed by using Lyapunov methods and robust stability results in non-linear control theory. A robust convergence property of the model is derived, and interestingly, is closely related to Sontags input-to-state stability (ISS) property. Simulation results are employed to validate the main result.

Multiregime Adaptive Signal Control for Congested Urban Roadway Networks

A hierarchical adaptive signal control was developed and implemented in New York City to manage congestion in a complex urban roadway environment. Control strategies, including strategically regulating traffic demand and balancing the queue–storage ratio at critical intersections, work in concert to systematically alleviate congestion and improve mobility. The high usage of electronic toll collection tags in this area allows large amounts of per trip travel time data to be collected (nearly 1 million per trip travel time records daily) and used in real time for effective control. Congestion levels are mapped to different control regimes. Various demand-regulating strategies are applied at the peripheral roadways of the target control zone. These strategies proactively employ signal offsets and splits to exert a tapering and rebalancing effect on the traffic. Demand regulation results in a better use of available network storage spaces while preserving the capacity of the target control zone. Inside the target control area, a dynamic queue-balancing strategy is implemented at selected critical intersections to prevent propagation of spillovers with stabilized or diminished queues. The initial implementation covered 110 intersections in the highly congested central business district of midtown Manhattan New York City. Results to date are summarized.