This paper applies a classical physics-based model to control platooning autonomous vehicles (AVs) in a commercial traffic simulation software. In the spring–mass–damper (SMD) model, each vehicle is assumed as a mass coupled with its preceding vehicle with a spring and a damper: the spring constant and damper coefficient control spacing and speed adoption between vehicles. Limitations on the platooning-oriented communication range and number of vehicles in each platoon are applied to the model to reflect real-world circumstances and avoid overlengthened platoons. The SMD model controls both intra-platoon and inter-platoon interactions. Initial evaluation of the model reveals that it does not cause a negative spacing error between AVs in a harsh deceleration scenario, guaranteeing safety. Besides that, the SMD model produces a smaller positive average spacing error than the VISSIM built-in platooning module, which prevents maximum throughput drop. The simulation result for a regular highway section reveals that the proposed platooning algorithm increases the maximum throughput by 10%, 29%, and 63% under 10%, 50%, and full market penetration rate (MPR) of AVs, respectively, with a 0.5 s response time. A merging section with different volume combinations on the main section and merging section and different MPRs of AVs is also modeled to test inter-platoon spacing policy effectiveness in accommodating merging vehicles. Travel time reductions of 20% and 4% are gained under a low MPR of AVs on the main lane and merging lane, respectively. Meanwhile, a more noticeable travel time reduction is observed in both main lane and merging lanes and under all volume combinations in higher AV MPRs.

Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures.

During a disaster the requests for using ambulance services increases. Efficient assignment of the ambulances leads to lowering the patients' travel time. Simulating these environments is very complex and needs a solid framework. This paper uses a Deep Reinforcement Learning approach to better schedule ambulance dispatch problem during those disasters. The concept of a call and assignment of ambulances are illustrated and the elements of states, rewards, and actions in the formulations are described. The algorithm steps for solving this problem are also presented. This paper can help disaster planners to have a better idea for better scheduling ambulances.

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