We propose an approach to clustering and visualization of students' cognitive structural models. We use the self-organizing map (SOM) combined with Ward's clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept.

/pdf/using-self-organizing-neural-network-map-combined-with-ward-5dkq2adgtg.pdf

Using self-organizing neural network map combined with Ward's clustering algorithm for visualization of students' cognitive structural models about aliveness concept

The primary objective of this study is to identify the characteristics of vehicle headway on multi-lane freeway under lane management in China considering car–truck interaction. More specifically, ...

https://journals.sagepub.com/doi/pdf/10.1177/1687814016646673

Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction

The area of traffic flow modelling and analysis that bridges civil engineering, computer science, and mathematics has gained significant momentum in the urban areas due to increasing vehicular population causing traffic congestion and accidents. Notably, the existence of mixed traffic conditions has been proven to be a significant contributor to road accidents and congestion. The interaction of vehicles takes place in both lateral and longitudinal directions, giving rise to a two-dimensional (2D) traffic behaviour. This behaviour contradicts with the traditional car-following (CF) or one-dimensional (1D) lane-based traffic flow. Existing one-dimensional CF models did the inclusion of lane changing and overtaking behaviour of the mixed traffic stream with specific alterations. However, these parameters cannot describe the continuous lateral manoeuvre of mixed traffic flow. This review focuses on all the significant contributions made by 2D models in evaluating the lateral and longitudinal vehicle behaviour simultaneously. The accommodation of vehicle heterogeneity into the car-following models (homogeneous traffic models) is discussed in detail, along with their shortcomings and research gaps. Also, the review of commercially existing microscopic traffic simulation frameworks built to evaluate real-world traffic scenario are presented. This review identified various vehicle parameters adopted by existing CF models and whether the current 2D traffic models developed from CF models effectively captured the vehicle behaviour in mixed traffic conditions. Findings of this study are outlined at the end.

/pdf/simulation-strategies-for-mixed-traffic-conditions-a-review-2ojrerihku.pdf

Simulation Strategies for Mixed Traffic Conditions: A Review of Car-Following Models and Simulation Frameworks

The mixed traffic flow has complex dynamics by nature. The kinematic differences between automobiles and motorcycles result to distinct driving behaviors. Traditional automobile-based traffic flow theory is not always suitable for mixed traffic streams. The purpose of this study is to observe from actual data a clearance boundary, called Safety Space, drivers maintain from other vehicles, and use it as a spatial filter to determine conflicts in mixed traffic flows. Image data are collected from an Unmanned Aerial Vehicle (UAV), and microscopic characteristics such as vehicle type, position, velocity, and trajectory are extracted through computer vision techniques. The Histogram of Oriented Gradients (HOG) feature and the Support Vector Machine (SVM) classifier are utilized for the vehicle detection, while the Kalman Filter is employed for the derivation of vehicle trajectories. The Safety Space is then determined based on those trajectories. Validation data are collected at intersections in Taipei, Taiwan; Bangkok, Thailand; and Mumbai, India. The vehicle detection and tracking are satisfactory, and the Safety Space surrogate reveals risk zones caused by spatial proximity between vehicles.

Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences

This paper proposes an overall Multi-Controller Architecture (MCA) for safe and flexible navigation of autonomous navigation, under uncertainties in highway use-cases. In addition to the details given about the main modules (and their interactions) composing the proposed MCA, an important focus of the paper is made on the definition of a robust Two-Sequential Level Decision Network (TSLDN), which uses both: Extended Time-To-Collision (ETTC) metric and a new definition of a specific Predicted Inter-Distance Profile (PIDP, between vehicles during lane changes maneuvers) in order to estimate the maneuvers risks. The TSLDN is utilized for: the driving situation assessment, decision-making and for safety retrospection over the current maneuver risk. It allows us to have the best decision to achieve the vehicle navigation task while maximizing its safety. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for very risky scenarios.

Multi-Level Bayesian Decision-Making for Safe and Flexible Autonomous Navigation in Highway Environment

Ways to estimate the time-to-collision are explored. In the context of traffic simulation models, classical lane-based notions of vehicle location are relaxed and new, fast, and efficient algorithms are examined. With trajectory conflicts being the main focus, computational procedures are explored which use a two-dimensional coordinate system to track the vehicle trajectories and assess conflicts. Vector-based kinematic variables are used to support the calculations. Algorithms based on boxes, circles, and ellipses are considered. Their performance is evaluated in the context of computational complexity and solution time. Results from these analyses suggest promise for effective and efficient analyses. A combined computation process is found to be very effective.

/pdf/new-algorithms-for-computing-the-time-to-collision-in-36tewa332a.pdf

New algorithms for computing the time-to-collision in freeway traffic simulation models

This paper develops an improved cellular automaton model in congested traffic conditions that differentiates vehicle combinations to consider both vehicle physical performance and driving behavior difference in car-following behavior. Car-following is divided into four groups by the lead and lag vehicle type. The vehicle behavior, including steady space gap, acceleration, and behavior choice, was studied to find out the diversity of car-following behavior in different vehicle combinations. The results show that vehicles in different vehicle combinations have different car-following behaviors. In the proposed model, each vehicle combination has its particular update rules and parameters. The model was evaluated by comparison with the existing car-following models at the microscopic and macroscopic level and the stability was also analyzed. The results show that the improved cellular automata model has a satisfactory performance.

Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations

A self-organizing feature map (SOM) was used to represent vehicle-following and to analyze the heterogeneities in vehicle-following behavior. The SOM was constructed in such a way that the prototype vectors represented vehicle-following stimuli (the follower's velocity, relative velocity, and gap) while the output signals represented the response (the follower's acceleration). Vehicle trajectories collected at a northbound segment of Interstate 80 Freeway at Emeryville, CA, were used to train the SOM. The trajectory information of two selected pairs of passenger cars was then fed into the trained SOM to identify similar stimuli experienced by the followers. The observed responses, when the stimuli were classified by the SOM into the same category, were compared to discover the interdriver heterogeneity. The acceleration profile of another passenger car was analyzed in the same fashion to observe the interdriver heterogeneity. The distribution of responses derived from data sets of car-following-car and car-following-truck, respectively, was compared to ascertain inter-vehicle-type heterogeneity.

/pdf/analysis-of-vehicle-following-heterogeneity-using-self-43bmgwlfz7.pdf

Xiucheng Guo

Papers

New algorithms for computing the time-to-collision in freeway traffic simulation models

Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations

Analysis of vehicle-following heterogeneity using self-organizing feature maps