Analysis and comparison of microscopic traffic flow models with real traffic microscopic data

The ever more widespread use of microscopic traffic simulation in the analysis of road systems has re-focussed attention on sub-models, including car-following models. The difficulties of micro-simulation models in accurately reproducing real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacy of the sub-models themselves. These drawbacks both originate in the scant information available on real phenomena, due to the difficulty of gathering accurate field data. In this study, the use of K-dGPS instruments allowed trajectories of four vehicles in a platoon to be accurately monitored in real traffic conditions, both on urban and extra-urban roads. Some of these data were used to analyse the behaviour of four microscopic traffic flow models which differed greatly both in approach and complexity. The effect on model calibration results of the choice of performance measures was first investigated and inter-vehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared to previous studies (with peaks in cross-validations between urban and extra-urban experiments). This confirms the need for real traffic data. On comparison, all the models showed similar performances (i.e. similar deviations in validation). However, if surprisingly the simplest model performed on average better than the others, the most complex one was the most robust, never reaching particularly high deviations

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Modeling Interactions Between Human Factors and Traffic Flow Characteristics

To serve research needs for traffic flow model development and highway safety enhancement, we model interactions between human factors and traffic flow character- istics, this topic includes methods on collecting data, modeling impacts of parameters on flow, and calibrating parameters on observed data. An example of successful traf- fic data collection is NGSIM data, which contains location, speed, and acceleration information of vehicles. An algorithm was designed to match and extract vehicles’ trajectory records, and utilize the extracted information for pattern recognition of lane changing maneuvers. This algorithm reads records from an NGSIM data set, pick out vehicles executing lane changing maneuvers, and note the corresponding time stamps. Also through matching these records by vehicle ID and time stamp, we obtain a map of vehicles when a lane changing is happening, thus calculating gaps and relative speeds becomes possible. An example of utilizing these information is pattern recognition on lane changing maneuvers. We analyze lane changing maneu- vers with speed data and gap data. The approach with speed data shows convincing results, as most lane changing vehicles have a descending and then ascending pattern on their speed profiles before executing the maneuver. On the other hand we can use collected data for calibrating parameters in traffic flow models. A heuristic method- ology is implemented to provide results with high accuracy, high efficiency and high robustness. Techniques include data aggregation and bisection analysis are applied in this approach to ensure achieving these goals and further requirements. Two traf- fic flow simulation models, Longitudinal Control Model (LCM) and Newell’s Model are calibrated by applying this approach using traffic data collected at Georgia 400 highway in July, 2003, with satisfying accuracy and robustness produced in a running time of less than 2 seconds. Meanwhile we can enhance human factors by applying new technologies, and connected vehicle is a good example which is rapidly devel- oping. Future vehicles will be able to communicate with each other which greatly improves drivers’ situational awareness. Consequently, drivers may be able to re- spond earlier to safety hazards before they manifest themselves in forms of imminent danger. Therefore, the overall effect of this technology can be attributed to drivers’ enhanced perception-reaction (P-R) capability which, in turn, translates to improved flow and capacity. However, it is critical to quantify such benefits before large-scale investment is made. In our research, a statistical transformation model is formulated to predict the probability distribution function of flow. By entering distributions of P-R time and enhanced P-R time, this model helps compare before and after distri- butions of traffic flow, based on which benefits of connected vehicles on traffic flow can be analyzed

Longitudinal control for person-following robots

Purpose: This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology. Design/methodology/approach: Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control. Findings: A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios. Originality/value: This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research

Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework

A Detection and Mitigation System for Unintended Acceleration: An Integrated Hybrid Data-driven and Model-based Approach

This study presents an integrated hybrid data-driven and model-based approach to detecting abnormal driving conditions. Vehicle data (e.g., velocity and gas pedal position) and traffic data (e.g., positions and velocities of cars nearby) are proposed for use in the detection process. In this study, the abnormal driving condition mainly refers to unintended acceleration (UA), which is the unintended, unexpected, uncontrolled acceleration of a vehicle. It is often accompanied by an apparent loss of braking effectiveness. UA has become one of the most complained-about vehicle problems in recent history. The data-driven algorithm aims to use historical data to develop a model that describes the boundary between normal and abnormal vehicle behavior in the vehicle data space. At first, several detection models were created by analyzing historical vehicle data at specific moments such as acceleration peaks and gear shifting. After that, these models were incorporated into a detection system. The system decided if a UA event had occurred by sending real-time vehicle data to the models and comprehensively analyzing their diagnostic results. Besides the data-driven algorithm, a driver model-based approach is proposed. An adaptive and rational driver model based on game theory was developed for a human driver. It was combined with a vehicle model to predict future vehicle behavior. The differences between real driving behavior and predicted driving behavior were recorded and analyzed by the detection system. An unusually large difference indicated a high probability of an abnormal event. Both the data-driven approach and the model-based approach were tested in the Simulink/dSPACE environment. It allowed a human driver to use analog steering wheels and pedals to control a virtual vehicle in real time and made tests more realistic. Vehicle models and traffic models were created in dSPACE to study the influences of UA and ineffective brakes in various roadway driving situations. Test results show that the integrated system was capable of detecting UA in one second with high accuracy. Finally, a brake assist system was designed to cooperate with the detection system, which reduced the risk of accidents