Conjoint Associate Professor Mick Hunter

An approach is described to apply the dynamic data-driven application systems (DDDAS) paradigm to reduce fuel consumption and emissions in surface transportation systems. This approach includes algorithms and distributed simulations to predict space-time trajectories of onroad vehicles. Given historical and real-time measurement data from the road network, computation resources residing in the vehicle generate speed/acceleration profiles used to estimate fuel consumption and emissions. These predictions are used to suggest energy-efficient routes to the driver. Because many components of the envisioned DDDAS system operate on mobile computing devices, a distributed computing architecture and energy-efficient middleware and simulations are proposed to maximize battery life. Energy and emissions modeling and mobile client power measurements are also discussed.

With ridership declining nationally and transit agencies looking for innovative ways to maintain and attract riders, a more complex understanding of transit riders and their satisfaction could provide additional insight and guidance to benefit the future of transit. This study challenged the traditional captive versus choice rider dichotomy and indicates the need for a more nuanced breakdown of transit riders based on the attributes most important to them. To conduct the analysis, the authors obtained rider survey data from nine agencies across the United States from varying geographic regions and representing various agency sizes. Agencies were selected based on their intentional use of demographic classifications and questions about satisfaction with various aspects of transit service. The authors then applied ordered logit regression across the 18,544 rider survey responses to predict the relative importance of service attributes on overall satisfaction. The findings suggested that different classifications of riders by gender, race, and income yielded diverse priorities, although certain service aspects such as reliability were important across demographics. In addition to the findings from the regression analysis, this study also offers a series of recommendations to facilitate future investigations by using more consistent, standardized data to further the breadth and depth of national transit rider analyses.

In recent years, several innovative intersections have been designed and implemented as an alternative to typical two-way intersections. The Restricted Crossing U-Turn (RCUT) is a type of innovative intersection design that limits left-turns from the minor road by directing cross-street traffic to one of two auxiliary U-turns. This design seeks to improve intersection safety by reducing conflict points, as well as increase the capacity and decrease travel time on certain movements, such as the mainline through traffic. However, the change in the cross-street vehicle paths through the intersection can affect acceleration profiles and emissions. This research develops a methodology to test emission differences through simulation. Models of an RCUT and a traditional intersection were constructed and tested under varying turn volumes and mainline speeds conditions. CO2 and NOx emissions were calculated using Vissim simulation vehicle trajectories and the Motor Vehicle Emissions Simulator (MOVES) model. The modeled emissions values were used to compare the overall emissions performance of the RCUT versus a traditional intersection layout. The RCUT intersection layout was associated with an increase in average cross-street through and left-turn movement emissions because of the longer travel distances. However, a reduction in emissions was seen on the main line, primarily as a result of the additional green-time afforded by the RCUT design. Where the mainline volume to side-street ratio becomes sufficiently large, the RCUT performed better in overall intersection emissions levels.

Background: The transition to adulthood is a major developmental milestone; a time of self-discovery and increased independence. For young adults (YA) with intellectual disabilities (ID), however, this period is especially challenging. The increased incidence of mental health disorders in this population, such as depression and anxiety, make this transition even more difficult, increasing caregiver burden at a time when the young adult would traditionally be gaining independence. It is not clear, however, why YA with ID are more susceptible and what factors may predict mental health symptoms. Method: Potential risk and protective factors (demographic variables, coping styles, sense of hopelessness, unmet achievement of adulthood milestones, self-reflection and insight) of anxiety and depression symptoms were assessed in 55 YA with ID and a sample of age-matched controls. Results: Insight was the strongest predictor of anxiety (with gender in the controls) for YA with and without ID, with increased insight predicting fewer anxiety symptoms. However, YA with ID had significantly less insight than their aged-matched counterparts and significantly higher levels of anxiety. They were also less likely to have achieved traditional adulthood milestones. Maladaptive coping was the strongest predictor of depression for YA with ID. In comparison, both maladaptive coping and insight predicted depression in controls. More maladaptive coping predicted increased depressive symptoms in both populations, whilst increased insight predicted fewer depressive symptoms in controls. Conclusions: Insight and maladaptive coping are potential targets in the treatment of anxiety and depression among YA with ID. Longitudinal intervention studies exploring the efficacy of such targeted programmes in reducing mental health symptoms and improving the transition to adulthood for these young people are recommended.

Promising advances in Autonomous Vehicle (AV) technology have fueled industry and research fields to dedicate significant effort to the study of the integration of AVs into the traffic network. This study focuses on the transition phase between all Human Driven Vehicles (HDVs) in the network to all AVs, where these different vehicle types coexist in a mixed traffic environment. This paper investigates the potential impacts of aggressive merging behaviors by human drivers on traffic performance in a mixed environment. For this, three vehicle types - AVs, HDVs, and Aggressive HDVs (AHDVs) - are modeled in an open-source microscopic traffic simulation model, SUMO. In the developed simulation, the AHDVs are modeled to emulate aggressive merging behaviors in front of AVs at a merge section of a freeway exit ramp. Several experiments are used to study the impact of such behavior. Results show travel time gains by AHDVs at the expense of AVs and HDVs.

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.