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In this study, a time-dependent agent-based taxi simulation model was developed. Modeling of taxi system is a complex task because it is dependent on the spatiotemporal pattern of passenger demand. However, the role of taxi as a public transport cannot be under-estimated in the urban area. Not only as a paratransit for the elder and disabled but also as a public transport for complementing the mass transit systems such as subways and arterial buses, a taxi conveys huge number of passengers every day. In this context, a practical modeling method for a taxi system was developed based on equilibrium philosophy and an agent-based simulation framework. In this study, the authors developed a simulation framework for the evaluation of taxi service for enhancing the quality of urban taxi transportation system. The developed model was tested with various passenger travel patterns, and a meaningful policy-related issue for improving the service performance of a taxi system was found for dealing with an asymmetric demand pattern. Keywords: agent-based model, taxi simulation, first-in-first-out violation function, asymmetric passenger travel demand ···································································································································································································································

Stackable 3DFETs such as FinFET using hybrid Si/ MoS 2 channels were developed using a fully CMOScompatible process. Adding several molecular layers (3-16 layers) of the transition-metal dichalcogenide (TMD), MoS 2 to Si fin and nanowire resulted in improved (+25%) I on,n of the FinFET and nanowire FET (NWFET). The PFETs also operated effectively and the N/P device V th are low and matched perfectly. The proposed heterogeneous Si/TMD 3DFETs can be useful in future electronics.I. Introduction 3DFETs can improve sub-20 nm CMOS node performance and substantially reduce supply voltage and short channel effects [1]. However, the traditional silicon channel must be replaced by high-mobility materials in future VLSI applications [2]-[3]. Heterogeneous twodimensional atomic crystals, namely, transition-metal dichalcogenide (TMD), have atomically smooth surface without dangling bounds and good mobility in CVD deposited films of atomic scale thickness are very attractive enablers of ultimately scaled transistors and 3D ICs [1,4]. However, a manufacturing flow must be realized using lowtemperature semiconductor process [5] and TMD by chemical vapor deposition (CVD) [6]. This paper presents the first CMOS process compatible TMD 3D transistor technology using novel hybrid Si/MoS 2 channel FinFET and NWFET with improved I on,n and matched V th of N and P devices. In comparison to previously published TMD transistors, this work reports the shortest gate length, thinnest gate dielectric, and first high performance at low voltage (Table 1 and Fig. 1). II. Process Integration and Device FabricationThe reported work made use of a previously published low temperature Tri-gate FinFETs technology [7]. The fewlayer MoS 2 growth step was inserted after blocking oxide deposition and clean (see Figs. 2 and Fig. 4 for FinFET and Fig. 3 and Fig. 5 and for NWFET). The ~5nm SiO 2 on the surface of the Si fin is responsible for setting the desirable low and matched V th of N and P devices as shown later. A low-temperature activation process that involved microwave annealing [5] was used after hybrid MoS 2 deposition. In Figs. 6 and 7, the TEM images show the hybrid Si/MoS 2 channel trigate FinFET and NWFET respectively. Few-layer MoS 2 was successfully integrated into 3DFETs technology using low-temperature CVD with the number of MoS 2 layers determined by deposition duration (Figs. 8 and 9). The TiN gate over the hybrid Si/MoS 2 fins is 50 nm long (Fig. 10). III. Results and Discussion A. MoS 2 Material AnalysisMoS 2 material analysis was performed over flat regions of the wafer and over the hybrid Si/MoS 2 fins. Figs. 11(a) and (b) show the S L-edge and Mo M-edge features of the MoS 2 clearly observed for both the films deposited on the flat Si substrate and over the hybrid Si/MoS 2 fins using X-ray absorption spectroscopy. Fig. 12 shows the X-ray absorption near the edge spectrum, which revealed that the Mo M-edge spectrum exhibited a peak in films deposited on flat Si substrate and on the hybrid Si/MoS 2 channel. Strong separated features ...

The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie’s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions.