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1In this paper we present the development of an integrated microscopic mobility simulator, 2 SimMobility Short-Term (ST). "Integrated" as its models, inputs and outputs, simulated components and 3 code-base are integrated within a multi-scale agent-and activity-based simulation platform capable of 4 simulating different spatial-temporal resolutions and account for different levels of travelers' decision 5 making. "Microscopic" as both the demand -agents and its trips -and the supply -trip realization and 6 movements on the network -are microscopic, i.e. modeled individually. Finally, "Mobility", as it copes 7with the multimodal nature of urban networks and the need for the flexible simulation of innovative 8 transportation services such as on-demand and smart mobility solutions. This paper follows previous 9 publications that describe SimMobility's overall framework and models. SimMobility is a multi-scale 10 platform that considers land-use, transportation, and mobility-sensitive behavioral models. SimMobility ST 11 aims at simulating the high-resolution movement of agents (traffic, transit, pedestrians and goods) and the 12 operation of different mobility services and control and information systems.

Capacity drop, which is defined as discharge flow drop after bottleneck activation, has been frequently observed on urban highways, especially in merging sections. Maintaining high capacity on roadways is a main concern for traffic operators, theorists, and transportation modelers. Accordingly, many researchers have investigated capacity drop, yet highway capacity and discharge flow measurement methods vary, and results are not comparable. A systematic methodology is introduced for finding capacity drops by using detector data to estimate roadway capacity and discharge flow. The impact of the number of lanes on capacity drops at merging sections on highways is investigated. Results show that capacity drop is negatively related to the number of lanes. Detailed information is analyzed for individual lanes and off-ramp effect on capacity drop. Individual lane analysis supports the negative relationship between the amount of capacity drop and the number of lanes. A decrease in capacity drop is observed when the flow ratio of the off-ramp increases.

This paper presents a stochastic approximation framework to solve a generalized problem of off-line calibration of demand for a multimodal microscopic (or mesoscopic) network simulation using aggregated sensor data. A key feature of this problem is that demand, although typically treated as a continuous variable is in fact discrete, particularly in the context of agent-based simulation. To address this, we first use a discrete version of the weighted simultaneous perturbation stochastic approximation (W-DSPSA) algorithm for minimizing a generalized least squares (GLS) objective (that measures the distance between simulated and observed measurements), defined over discrete sets. The algorithm computes the gradient at each iteration using a symmetric discrete perturbation of the calibration parameters and a multimodal weight matrix to improve the accuracy of the gradient estimate. The W-DSPSA algorithm is then applied to the large-scale calibration of multimodal origin–destination (OD) flows (including private vehicle (PVT) and public transit (PT) trips) in a microscopic network simulation model of Singapore. The results indicate that an acceptable margin of error on the vehicle loop count (VLC) and bus passenger count (BPC) are achieved at convergence with an improvement of 60%~80% in root mean squared errors. Lastly, we validate the calibration results with observed travel times on the network. Statistical comparison shows good agreements on both point-to-point travel time (PTT) and public buses’ stop-to-stop ride-time (SRT) with the field observations.