Search

With the increasing attention paid to environmental effects and sustainable infrastructure, transportation agencies are now seeking more cost-effective and environmentally friendly countermeasures against traffic congestion than the traditional roadway expansion. Managed lane (ML) systems, as an innovative strategy for managing the roadway conditions in real time, have been gaining increasing popularity in the recent decade. However, the unique characteristics of ML facilities, such as the frictional effects between general purpose lanes (GPLs) and their adjacent ML, are not well studied and modeled. This paper investigates the interaction between GPLs and MLs, as buffer-separated ML facilities are readily affected by congestion in the adjacent GPLs. The frictional effect is quantified through development of speed–flow curves for the ML facility. A traffic flow model is developed on the basis of the cell transmission model incorporating this frictional effect to model the traffic evolution on the ML facility. This model is capable of replicating the traffic flow pattern, including congestion onset, propagation, and dissipation at a macroscopic level. The model also captures the effect of GPL congestion on the ML. This model can serve as the underlying ML traffic flow model in future research for evaluating the effectiveness of different tolling strategies.

With the increasing concerns about environmental effects and the sustainability of roadway capacity expansion, transportation agencies are seeking alternative solutions to mitigate congestion. Managed lanes (MLs) promote person throughput on freeways and manage congestion through improving efficiency. The ML concept therefore has been gaining popularity in past decades. However, the lack of guidance on evaluating the performance of the ML facility poses challenges for agencies wanting to design and implement the strategy effectively. Many MLs are designed to be left-lane concurrent; vehicles entering the freeway from general purpose (GP) lane on-ramps need to cross weave over multiple GP lanes to access the ML. These weaving vehicles will have a negative effect on the operating performance of the parallel GP lanes. This paper investigates this cross-weaving effect as a function of different roadway geometric configurations as well as traffic conditions. A microscopic simulation model was built and calibrated on the basis of video data collected along I-635 in Dallas, Texas. Multiple scenarios were tested to explore the effect of the following parameters: number of GP lanes, cross-weave demand, and cross-weaving length. A set of capacity adjustment factors was determined to account for this effect as a function of those parameters. Results showed that the capacity-reducing effect was higher with a reduction in cross-weaving length, an increase in the number of GP lanes, or a rise in cross-weave demand volumes. The results are important in evaluating the operational performance of freeway segments in the presence of concurrent GP lanes and MLs in a Highway Capacity Manual context.

The building of managed lanes parallel to general purpose lanes is an increasingly common approach to optimizing freeway capacity. Managed lanes allow agencies to classify customers and assign a portion of the freeway capacity to them. With no methodology in the Highway Capacity Manual (HCM) for analyzing these facilities, analysts rely on more time-consuming simulation analyses. A methodology is presented for estimating the performance of a parallel system of general purpose and managed lane facilities in an HCM context based on NCHRP Project 3–96. The methodology defines new managed lane segment types to use in an HCM analytical framework and is associated with a new set of speed–flow curves. It is sensitive to the number of lanes and the type of separation between managed lanes and general purpose lanes. The method introduces the concept of parallel lane groups of general purpose and managed lanes and thus can account for speed reduction in managed lanes caused by congestion in adjacent general purpose lanes. The method was implemented in a computational engine, FREEVAL-ML, which was built on the freeway facilities method in HCM 2010 but which was updated to incorporate inputs and outputs of the managed lane components. The geometry of two existing managed lane facilities in Washington State is used to illustrate the method, demonstrating the applicability of the analytical framework to real-world facilities.

Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models’ specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers’ overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes.