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   Holland, Jordan; Schmitt, Paul; Feamster, Nick; Mittal, Prateek (November 2021, ACM Conference on Computer and Communication Security (CCS))
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3. Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions

   https://doi.org/10.1155/2021/8816540  

   Zhong, Zijia; Lee, Joyoung; Zhao, Liuhui (January 2021, Journal of Advanced Transportation)

   Meng, Meng (Ed.)

   Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping. 

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4. Traffic Volume Prediction with Automated Signal Performance Measures (ATSPM) Data

   https://doi.org/10.1109/ISCC55528.2022.9912469  

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   Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSMO) and the overall efficiency of traffic networks. The real-time data, collected from Internet of Things (IoT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learningbased approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes. 

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5. Integrating runtime verification into an automated UAS traffic management system

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   null (Ed.)