Superstreets are an unconventional at-grade intersection design that have significant potential to help with safety and delay. The purpose of this paper is to formally evaluate residential, commuter, and business owner opinions of superstreets across North Carolina through feedback from three separate surveys. Findings from each of the surveys provide interesting feedback. Residents living near superstreets agree the design helps them travel more safely through the intersection. Commuting drivers perceive superstreets to be more difficult to navigate, but feel strongly about savings in travel time and reductions in numbers of stopped vehicles. Business reactions varied greatly depending on the ability to make direct left turns from the arterial. In general, more business managers feel superstreets negatively impact business growth and operations. Based on the opinions of adjacent business owners/managers, access and confusion were identified as key problems in retaining the number of regular customers and attracting new customers near superstreets. Agencies contemplating superstreet projects should be aware of these perceptions and try to mitigate the negative ones if possible.

This paper investigated the impact that the 2013 United States federal government shutdown had on the transportation system in Northern Virginia. Transit ridership data before and during the shutdown were provided by three transit agencies operating in Washington, DC, and its Northern Virginia suburbs. A month of pre-shutdown road volumes and travel times were collected for comparison with the 2-week shutdown period and 2 weeks after the shutdown. As expected, roadway volumes, high occupancy vehicle (HOV) volumes, and transit ridership all saw reductions during the shutdown, with impacts being larger in the second week of the shutdown than the first week. The modes that saw the largest impacts were HOV and the local bus service in the counties surrounding Washington, DC. This is likely unsurprising since these modes are often used by work travelers for their daily commute. Changes were more modest for the Washington Metropolitan Area Transit Authority (WMATA) system, arterials, and freeway general purpose lanes. During the first week, volume changes were often not statistically significant on these facilities. The second week saw similar trends of around a 3 to 5% reduction in daily travel on both the roadway and WMATA systems. Reductions in average travel times as well as improvements in travel time reliability were also observed during the shutdown. However, these benefits were more significant for morning commute trips than evening commute trips as well as for general purpose lanes than HOV facilities. After federal workers went back to work, volumes largely returned to normal. However, some of the additional volume seen at midday during the shutdown was still present soon after the return to work, suggesting that the midday behavior changes took longer to dissipate. The post-PM peak period and weekends after the return to work showed some higher volumes than the pre-shutdown periods, suggesting that some recreation or leisure trips may have been postponed during the shutdown and took place after the return to work.

This study investigated the impacts of flashing green on dilemma zone (DZ) behavior at the high-speed intersections with insufficient yellow interval. 360 high-resolution vehicle trajectories, used in the study, were obtained from an extensive survey at a typical rural high-speed intersection in Shanghai. Results indicate that with the presence of a flashing green signal, the measured behavioral parameters related to DZ determination at the onset of yellow tend to be remarkably smaller. The vast majority of drivers reacted to the onset of flashing green, instead of yellow onset. Flashing green significantly reduced the area of Type I DZ at the onset of yellow, while enlarged the areas of option zone and Type II DZ. Time to stop line at the onset of flashing green positively affected the stopping probability. Flashing green induced conservative stops by 30%, while encouraged aggressive passes slightly.

Evaluation of travel-time data quality from traveler information systems (TISs) is a critical need for many transportation agencies. To measure data quality, observations of ground-truth travel time must be collected on links in a transportation network. Therefore, selecting which links in the network to monitor is a critical step in evaluating the quality of a TIS. This paper presents a maximum entropy sampling (MES) design that can be used to objectively select a subset of links from a road network for ground-truth data collection. This approach objectively identifies subsets of links that have the greatest variability in traffic conditions. This allows for a more rigorous evaluation of a TIS to determine the system’s ability to effectively capture changing traffic conditions. The MES method was evaluated using both a simulated and an empirical data set.

Accurate roundabout capacity models are essential for optimal roundabout designs, but there exist significant differences in the predicted capacities of various state-of-the-art models and in their included explanatory variables. An empirical study into roundabout lane entry capacity was thus performed in the U.K. using data from 35 roundabout entry lanes, where various model forms and explanatory variable sets were tested. Two regression models and an artificial neural network were developed. A negative exponential relationship with circulating flow predicted lane capacity better at high and low circulating flows, and better reflected the overall trends in the aggregated capacity data, compared to a linear model. The regression models performed relatively well and provided better information on the impacts of the variables than the neural network. The models consistently suggest that entry-exit separation and flows exiting on the same arm have stronger significant effects on capacity than variables such as entry angle and entry radius. These findings could thus contribute to an improved understanding of the variables that affect entry lane capacity and therefore the development of better roundabout capacity models.

The rapid growth of freight traffic is now taxing a significant number of the U.S. national freight corridors. Additional demand accompanied by trucks over legal weight limits has been accelerating pavement and bridge deterioration at a faster rate than anticipated. States do not collect sufficient revenues to offset pavement and bridge damage caused by overweight trucks. As increase in overweight permit fee may affect different stakeholders positively and/or negatively, decision makers must develop policy options considering multiple conflicting objectives simultaneously. A multiobjective analysis approach [i.e., the $\epsilon$ (epsilon)-constraint method] was applied to address conflicting objectives associated with overweight freight truck mobility and to identify rational overweight truck damage cost recovery fee options by generating detailed tradeoffs between these options. Bridge damage costs were estimated as fatigue damage using finite-element simulation models of bridge archetypes and pavement damage costs were estimated using a method based on equivalent single-axle load as per AASHTO standard. These costs were used to develop the mathematical relationship between the objectives and constraints in the multiobjective model. This paper presents a case study with two objectives, as follows: (1) minimization of unpaid pavement and bridge damage by overweight freight trucks, and (2) minimization of overweight damage cost recovery fees. A set of 10 overweight fee options and the associated tradeoffs are developed for four damage cost recovery fee types [i.e., (1) flat, (2) axle-based, (3) weight-based, and (4) weight-distance-based fee types]. The tradeoff analysis reveals that increasing the flat overweight damage cost recovery fee by $1 from $43 will reduce unpaid damages by $4.2 million annually in South Carolina with a high elasticity of demand. In the axle-based damage cost recovery fee type, increasing the average axle-based overweight damage cost recovery fee by $1 from $43 will reduce unpaid damages of $3.8 million annually in South Carolina. These types of tradeoff analyses provide valuable information to decision makers in selecting types and levels of fee for overweight trucks. Tradeoff analysis framework and results of the tradeoff analysis depicted in the paper contributes to assessing infrastructure damage due to overweight trucks, and developing damage recovery fee policies considering multiple conflicting objectives.