Mobile work zones for various types of moving operations (e.g., striping, pothole patching, sweeping) are an important component in highway maintenance activities which have particular safety issues. To help mitigate the risk and severity of collisions, a truck-mounted attenuator (TMA) is attached to a construction vehicle, typically equipped with amber/white lights. Despite these visible warnings, collisions involving TMAs and traffic still occur. In an effort to improve upon the traditional amber/white lights, the use of green lights on TMAs was investigated. The study included the evaluation of four light-color configurations: amber/white, green only, green/amber, and green/white. Driving simulator tests obtained various driver behavior measures, including the first blinker distance, merge distance, work zone and arrow direction recognition distance, and disability glare. Vehicle speeds were captured in both a simulator study and a field study. In the simulator study, the use of the amber/white combination led to the highest work zone visibility but also created the greatest concern with disability glare. Although the green-only configuration led to the lowest disability glare, it also resulted in low visibility. The results showed an inverse relationship between visibility (awareness of work zone) and arrow board recognition (easy on eyes). Other findings from the field study include lower traffic speeds for the green light TMA and lower vehicle speeds for lower TMA vehicle speeds. Overall, the study showed that the four configurations had various tradeoffs and none stood out as clearly superior in terms of the performance measures.

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

e rising interest in sustainable modes of transportation has increased demand for the design and implementation of bicycle facilities in the United States. However, as compared to the vehicular mode, bicycle facilities have relatively less development, research, and understanding. e availability of a bicycling simulator has the potential to contribute to the understanding of bicycle facility design and bicyclist behavior. e design and construction of a bicycling simulator differs from a driving simulator in many ways. A bicycling simulator requires interfaces for bicycle speed, braking, and steering angle as well as a visual interface. In addition, a representation of a real-world network, including pavement, buildings, the sky and background, and fixed and moving objects, needs to be modeled using a simulator engine. is paper presents the details of the ZouSim bicycling simulator development and the tradeoffs associated with various design decisions, such as the choice of a steering sensor and graphical display. A sample application of a wayfinding and detection markings study illustrates the use of ZouSim. e authors hope that this article will encourage other researchers who conduct research in sustainable cities to explore the use of bicycle simulators for improving bicycle facility design and operations.

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