Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its use in modeling connected and automated vehicles (CAVs). Initially, the paper focuses on understanding the evolution of traffic microsimulation and on examining the various commercial and open-source simulation platforms available and their importance in traffic microsimulation studies. Following this, current autonomous vehicle (AV) microsimulation strategies are reviewed. From the review analysis, it is observed that AVs are modeled in traffic microsimulation with two sets of strategies. In the first set, the inbuilt models are used to replicate the driving behavior of AVs by adapting the models’ parameters. In the second strategy, AV behavior is programmed with the help of externalities (e.g., Application Programming Interface (API)). Studies simulating AVs with inbuilt models used mostly VISSIM compared to other microsimulation platforms. In addition, the studies are heavily focused on AVs’ penetration rate impact on traffic flow characteristics and traffic safety. On the other hand, studies which simulated AVs with externalities focused on the communication aspects for traffic management. Finally, the cosimulation strategies for simulating the CAVs are explored, and the ongoing research attempts are discussed. The present study identifies the limitations of present CAV microsimulation studies and proposes prospects and improvements in modeling AVs in traffic microsimulation.

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Evolution of Traffic Microsimulation and Its Use for Modeling Connected and Automated Vehicles

Adaptive super-twisting sliding mode control for stabilization platform of laser seeker based on extended state observer

The present research work provides a methodology of calibration of the vehicle-following behavior in VISSIM using vehicle trajectory data obtained under non-lane-based mixed traffic conditions. In ...

Modeling following behavior of vehicles using trajectory data under mixed traffic conditions: an Indian viewpoint

This study mainly focuses on improving traffic flow efficiency of Bhathena intersection coupled with BRT system located in the city of Surat, India. With the help of field data, the study intersection is modeled in microscopic simulation software VISSIM 8.0. Based on this, a simulation model is calibrated and validated by comparing travel time of the probe-vehicle and the observed queue lengths among other observed data and simulation results in such way that absolute percentage error is limited to15 percent. In order to improve the traffic flow behavior at the intersection in terms of reducing travel time, traffic signal design is carried out using Webster’s approach of signal design. The various scenarios include the design of 2-phase, 3-phase, 4-phase signal systems. In each of the scenarios, the signal design is carried out based on Webster method and same traffic signal system is simulated in VISSIM model and the travel time results across the arms were compared with the base case. It was observed that the 3-phase system is suitable to the intersection keeping in consideration BRTS operation.

A Simulation Study for Improving the Traffic Flow Efficiency of an Intersection Coupled with BRT

This paper presents roll stabilization of three axis gimbal system (TAGS) using higher order sliding mode control (HOSMC). TAGS under consideration has mass imbalance due to it's structural design. To achieve robust performance in presence of mass imbalance, robust control is sought. A mathematical model which includes cross coupling effect is used. HOSMC is proposed for roll stabilization and to encounter mass imbalance, different sets of gains are used. Simulated results of HOSMC are compared with PI controller. Performance of HOSMC is tested experimentally.

Roll Stabilization in the Presence of Mass Imbalance using Higher Order Sliding Modes

The paper presents the performance analysis of a well-designed truck parking terminal, which is planned for regulating truck traffic over a commercial port. The designed truck parking terminal is modeled using microscopic traffic simulation, which is validated based on the movement of vehicles to the parking bays. After validation, various scenarios were created to evaluate parking terminal performance by varying the parking volumes and number of operational parking bays. The operational efficiency of the parking terminal for design scenarios was evaluated using parking performance measures that included parking load, average parking duration, parking turnover, and load-to-capacity ratio (parking index). For the design peak load of 4,200 vehicles/day with a uniform arrival rate, the operational efficiency was found to be about 73%. Interestingly, it was observed that with an increase in the number of operational parking bays, the parking efficiency decreased for the given volume level. Considering this phenomenon, a methodology was developed to identify the optimum number of parking bays under varying demand-supply scenarios. The developed methodology can help identify the optimum number of parking bays for existing and future (expansion) conditions. Furthermore, this study highlights the importance of using simulation in evaluating operational and design aspects of transportation facilities, where the need for repeated empirical observations is eliminated. As such, this study should be of interest to traffic engineers and practitioners interested in the efficient operation of parking terminals. 

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Experimental Design for Measuring Operational Performance of Truck Parking Terminal Using Simulation Technique

The diverse nature of vehicle categories and the resultant lane discipline in mixed (heterogeneous) traﬃc cause complex spatial interactions. As a result, the driving behavior process in mixed traﬃc conditions is meaningfully diﬀerent, where both longitudinal and lateral movements of the vehicles continuously occur. Under prevailing homogeneous traﬃc conditions in developed countries, driving behavior is partially discrete, where following longitudinal behavior and outboard lane-change models can model traﬃc behavior. However, the established car-following and lane-change models cannot be directly used in shaping mixed traﬃc conditions. Such conditions also warrant the use of high-quality microlevel vehicular trajectory data. Accordingly, realizing this need, vehicular trajectory data for diﬀerent traﬃc ﬂow conditions were developed. The data were used to extract the parameters required for modeling the vehicles’ positions using machine learning algorithms. Three established supervised machine learning algorithms (k-NN, random forest, and regression tree) and deep learning are selected to model mixed traﬃc conditions. The parameters which inﬂuence longitudinal and lateral movements are identiﬁed using Spearman correlation analysis. Fur-thermore, simulation runs are performed using the python language. The performance of the algorithms is evaluated both at the microscopic and macroscopic levels using relevant traﬃc indicators. The results show that a deep learning model and k-NN tend to replicate better-mixed traﬃc conditions than random forest and regression trees.

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Data-Driven Approach for Modeling the Nonlane-Based Mixed Traffic Conditions

System identification and Robust control for a Stabilization system of a two-axis gimbaled sensor

Investigating traffic safety reckoning hyperbolic driving following behavior using trajectory data

This study originated with the intent of qualifying traffic string stability from empirical observations. A new responsiveness angle measure was developed to assess driver instincts under vehicle-following conditions. In this measure, the degree of the follower vehicle’s attention towards its leader vehicle’s actions is quantified. In understanding string stability in the traffic stream and assessing the propagation of disturbances, the newly conceptualized measure was used along with a discrete Fourier transform to measure the frequencies associated with responsiveness angle sequences. In this transform, a higher frequency of the angle depicts unstable conditions and vice versa. In assessing string stability from the empirical observations, vehicular trajectory data were developed from three study sections. Two study sections tended to have homogeneous lane-wise traffic, whereas the third section had mixed (heterogeneous) traffic. The results of the string stability analysis over the study sections showed that string stability varied with the change in traffic flow conditions, road geometries, and traffic flow type. In the case of free-flow conditions, the traffic streams were found to be stable with marginal disturbances in the responsiveness angle. From the analysis, it was observed that, in the case of study Section 3, around 26 instances of the stream were extremely unstable conditions (frequency equal to 10). For study Sections 1 and 2, the traffic stream was unsteady for 4 and 13 instances, respectively. However, as the traffic flow level rose, string stability deteriorated. This study demonstrated a novel approach to analyzing string stability based on actual traffic conditions that can be implemented in real time for traffic stream monitoring. 

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N. Subba Raju

Papers

Experimental Design for Measuring Operational Performance of Truck Parking Terminal Using Simulation Technique

Data-Driven Approach for Modeling the Nonlane-Based Mixed Traffic Conditions

System identification and Robust control for a Stabilization system of a two-axis gimbaled sensor

Investigating traffic safety reckoning hyperbolic driving following behavior using trajectory data

Proposed Empirical Approach to Measuring Traffic String Stability