Sustainable Transportation for Maine’s Future

Maine is dependent on its transportation infrastructure for continued economic strength and growth, particularly on the 22,670 miles of public roads. Maine ranks fourteenth in the nation for the largest number of highway miles traveled annually per capita - 14,912 per year. Maine is highly reliant on its road system because large areas of the State lack transportation alternatives. This means that the current and future condition of the roadways is a major concern. How such a crucial infrastructure will continue to be supported and enhanced financially to meet the growing needs of the State must be considered carefully

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Safety and Operational Impact of Truck Platooning on Geometric Design Parameters

The most well-known benefits of heavy commercial vehicle (HCV) platooning are fuel savings and emission reductions. HCV platooning under SAE automation level 4 or 5 would also address the truck driver shortage by eliminating the driver from one or more HCVs in a platoon. This dissertation investigates the safety and operational implications of SAE level 4 HCV platooning on North American roadways. The research develops modified analytical models and micro-simulation models (PTV VISSIM) for analyzing impacts on two-lane rural highways, urban arterial roadways, and freeways. The study considers different time headways (0.6 sec and 1.2 sec) between the platooning vehicles, and three market penetration rates (0%, 5%, and 10%). The two-lane rural highways chapter investigates the passing sight distance (PSD) required to overtake an HCV platoon. The urban arterial roadways chapter compares existing traffic controls with traffic signal priority (TSP) for HCV platoons. The freeways chapter investigates freeway acceleration lane length on merging segments for HCV platooning operations. The findings suggest that two-HCV platooning with 0.6 sec time headway and a 5% market penetration rate can be allowed on designated North American roadways. With proper passing lanes, two-HCV platoons can be operated on two-lane rural highways that already permit long combination vehicle operations. Even with TSP, HCV platooning on urban arterial roadways at penetration rates higher than 5% at our selected intersection may, however, cause significant delays and overwhelm the traffic system. On freeways, two-HCV platooning at a 5% market penetration rate where the freeway acceleration lane is at least 600m long appear to be feasible. The study will assist transportation professionals and policymakers in understanding the consequences of HCV platoons and deciding whether to allow HCV platooning on North American roadways

Truck Characteristics for Use in Highway Design and Operation. Volume I: Research Report

DTFH61-87-C-00088Highway geometric design and traffic operations are based in part on consideration of vehicle characteristics. However, many of the current highway design and operational criteria are based on passenger car characteristics, even though truck characteristics may be more critical. This report reviews existing data for the truck characteristics that need to be considered in highway design, including truck dimensions, braking distance, driver eye height, acceleration capabilities, speed-maintenance capabilities on grades, turning radius and offtracking characteristics, suspension characteristics, and rollover threshold. The report also includes these truck characteristics. The highway design and operational criteria evaluated include sight distances, vertical curve length, intersection design, critical length of grade, lane width, horizontal curve design, vehicle change intervals at traffic signals, sign placement, and highway capacity. An assessment has been made of the need to change the current highway design and operational criteria to accommodate trucks. The cost effectiveness of proposed changes in design and operational criteria has been evaluated. This volume, Volume I, of the report presents the main findings of the study including recommended changes in highway geometric design and operational criteria to accommodate trucks. Volume II of the report contains appendixes documenting the detailed data collection and analysis activities

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

The End of Traffic and the Future of Access: A Roadmap to the New Transport Landscape

In most industrialized countries, car travel per person has peaked and the automobile regime is showing considering signs of instability. As cities across the globe venture to find the best ways to allow people to get around amidst technological and other changes, many forces are taking hold — all of which suggest a new transport landscape. Our roadmap describes why this landscape is taking shape and prescribes policies informed by contextual awareness, clear thinking, and flexibility

Gearing Up for Safety: Motor Carrier Safety in a Competitive Environment

This report contains the results of that analysis. A review of critical intergovernmental issues for the Department of Transportation and State Governments has been added to the basic questions about the adequacy of Federal standards and programs. During the course of the study, it became clear that the report would have to consider how policy is implemented, and consequently, the relationship between the Department of Transportation and the States, which have become important partners in Federal safety programs