Effects of intersection field of view on emergent collision avoidance performance at unsignalized intersections: analysis based on driving simulator experiments

Proper intersection sight distance can effectively lower the possibility of intersection accidents. American Association of State Highway and Transportation Officials (2011) provide a series of recommended dimensions of intersection sight triangles for uncontrolled and stop/yield‐controlled intersections. However, in reality, although the actual intersection design for unsignalized intersections satisfies the requirements of sight distance and clear sight triangle in American Association of State Highway and Transportation Officials' guideline, there are still a large number of crashes occurring at unsignalized intersections for drivers running stop/yield signs or failing to slow down. This paper presents a driving simulator study on pre‐crash at intersections under three intersection field of view (IFOV) conditions. The aim was to explore whether better IFOVs at unsignalized intersections improve their emergent collision avoidance performance under an assumption of valid intersection sight distance design. The experimental results show drivers' ability to identify potential hazards to be significantly affected by their IFOVs. As drivers' IFOV improved, drivers were more likely to choose braking actions to avoid collisions. Better IFOVs were also associated with significant increases in brake time to intersection and significant reductions in deceleration rate and crash rate, thus leading to a lower risk of traffic crash involvement. The results indicate that providing a better IFOV for drivers at intersections should be encouraged in practical applications in order to improve drivers' crash avoidance capabilities. Copyright © 2016 John Wiley & Sons, Ltd.     

Proposed collision warning system for right-turning vehicles at two-way stop-controlled rural intersections

At two-way stop-controlled (TWSC) rural intersections, a right-turning driver who is departing the minor road may select an improper gap and subsequently may be involved in a rear-end collision with another vehicle approaching on the rightmost lane on the major road. This paper provides perceptual framework and algorithm design of a proposed infrastructure-based collision warning system that has the potential to aid unprotected right-turning drivers at TWSC rural intersections. The proposed system utilizes a radar sensor that measures the location, speed, and acceleration of the approaching vehicle on the major road. Based on these measurements, the system’s algorithm determines if there will be any potential conflict between the approaching and the turning vehicles and warns the driver of the latter vehicle if such a conflict is found. The algorithm is based on realistic acceleration profile of the turning vehicle to estimate its acceleration rates at different times so that the system can accurately estimate the time and distance needed for the departing vehicle to accelerate to the same speed as for the approaching vehicle. That realistic acceleration profile is established using actual experimental data collected by a Global Positioning System (GPS) data logger device that was used to record the positions and instantaneous speeds of different right-turning vehicles at 1-s intervals. The algorithm also gives consideration to the time needed by the driver of the departing vehicle to perceive the message displayed by the system and react to it (to start departure) where it was found that 95% of drivers have a perception–reaction time of 1.89 s or less. A methodology is also illustrated to select the maximum measurement errors suggested for the detectors in measuring the locations of the approaching vehicle on the major road where it was found that the accuracy of the system significantly deteriorates if the errors in measuring the distance and the azimuth angle exceed 0.1 m and 0.2°, respectively. An application example is provided to illustrate the algorithm used by the proposed system.     

Coloured Petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions

The Traffic Alert and Collision Avoidance System (TCAS) is a world-wide accepted last-resort means of reducing the probability and frequency of mid-air collisions between aircraft. Unfortunately, it is widely known that in congested airspace, the use of the TCAS may actually lead to induced collisions. Therefore, further research regarding TCAS logic is required. In this paper, an encounter model is formalised to identify all of the potential collision scenarios that can be induced by a resolution advisory that was generated previously by the TCAS without considering the downstream consequences in the surrounding traffic. The existing encounter models focus on checking and validating the potential collisions between trajectories of a specific scenario. In contrast, the innovative approach described in this paper concentrates on quantitative analysis of the different induced collision scenarios that could be reached for a given initial trajectory and a rough specification of the surrounding traffic. This approach provides valuable information at the operational level. Furthermore, the proposed encounter model can be used as a test-bed to evaluate future TCAS logic changes to mitigate potential induced collisions in hot spot volumes. In addition, the encounter model is described by means of the coloured Petri net (CPN) formalism. The resulting state space provides a deep understanding of the cause-and-effect relationship that each TCAS action proposed to avoid an actual collision with a potential new collision in the surrounding traffic. Quantitative simulation results are conducted to validate the proposed encounter model, and the resulting collision scenarios are summarised as valuable information for future Air Traffic Management (ATM) systems.     

A capacity maximization scheme for intersection management with automated vehicles

With the advent of connected and automated vehicle technology, in this paper, we propose an innovative intersection operation scheme named as MCross: Maximum Capacity inteRsection Operation Scheme with Signals. This new scheme maximizes intersection capacity by utilizing all lanes of a road simultaneously. Lane assignment and green durations are dynamically optimized by solving a multi-objective mixed-integer non-linear programming problem. The demand conditions under which full capacity can be achieved in MCross are derived analytically. Numerical examples show that MCross can almost double the intersection capacity (increase by as high as 99.51% in comparison to that in conventional signal operation scheme).     

Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance

The control of automated container terminals is complex since Quay Cranes (QCs), Automated Guided Vehicles (AGVs) and Automated Stacking Cranes (ASCs) interact intensively for transporting containers, while collision avoidance of equipment must be ensured. This paper proposes a methodology to generate collision-free trajectories of free-ranging AGVs in automated container terminals, while minimizing the makespan of the whole container handling system. A hierarchical control architecture is proposed to integrate the scheduling of interacting machines and trajectory planning of AGVs. Following a so-called overall graph sequence by a scheduler, the collision-free trajectories of AGVs are determined by solving a collection of mixed integer linear programming problems sequentially. Simulation results illustrate the potential of the proposed methodology.     

A causal encounter model of traffic collision avoidance system operations for safety assessment and advisory optimization in high-density airspace

The traffic collision avoidance system (TCAS) acts as a proverbially accepted last-resort means to resolve encounters effectively, while it also has been proven to potentially induce a collision in the hectic air traffic. Thus, new research considering the impact on safety is required to increase the airspace capacity based on a comprehensive analysis and accurate flight evaluation. In this paper, a causal encounter model is proposed to extend the TCAS logic considering the horizontal resolution manoeuvres, which could be used as the auxiliary supports when a potential collision is predicted in the vertical dimension. Based on the generated state space, the model developed in the graphical modelling and analysis software (GMAS), not only provides a better comprehension of the potential collision occurrences for risk assessment by representing the cause-effect relationship of each action, but also aids the pilots in the involved aircraft to make a cooperative and optimal option. Quantitative simulation results are conducted to validate the feasibility and effectiveness of the encounter model with horizontal resolution. The resulting collision scenarios are further investigated to illustrate that the risk rate of TCAS logic failures is expected to reduce by shortening the pilot's response delay, and the computational efficiency is competent in dealing with multi-threat scenarios.     

Infrastructure based approach for roadway departure risk assessment

This paper presents a warning device to prevent the roadway departure of light vehicles while cornering. The proposed risk assessment methodology is based on recent works from the authors (Rey et al., 2011b,a; Rey, 2010). Given the random variability arising from the driver, the vehicle and the infrastructure at the entrance to the curve, a probabilistic strategy is adopted to assess the roadway departure risk. The infrastructure-based methodology enables the real curve characteristics to be considered and an alarm triggering decision to be made. Two safety criteria are tested and the potential safety benefits of the proposed warning device are evaluated. Contrary to existing roadway departure warning systems, the proposed approach does not require extra equipment for vehicles; it only requires that the measuring and warning devices be part of the road infrastructure, which is a great advantage in terms of economic cost.     

Extending Time to Collision for probabilistic reasoning in general traffic scenarios

Vehicle-to-vehicle communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty and to loss of communication links. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method to calculate Time to Collision for unconstrained vehicle motion. This metric is gated using a novel technique based on relative vehicle motion that we call “looming”. Finally, these ideas are integrated into a probabilistic framework that accounts for uncertainty in vehicle state and loss of vehicle-to-vehicle communication. Together this work represents a new way of considering vehicle collision estimation. These algorithms are validated on data collected from real world vehicle trials.     

Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity.     

A route-based solution algorithm for dynamic user equilibrium assignments

The aim of this study is to establish a method to calculate good quality user equilibrium assignments under time varying conditions. For this purpose, it introduces a dynamic network loading method that can maintain correct flow propagation as well as flow conservation, and it shows a novel route-based solution algorithm. This novel algorithm turns out to be convenient and logically plausible compared to the conventional [Frank, M., Wolfe, P., 1956. An algorithm for quadratic programming. Naval Research Logistics Quarterly 3, 95–110] algorithm, because the former does not require evaluation of an objective function and it finds solutions maintaining correct flow propagation in the time-varying network conditions. The application of novel dynamic network loading method and solution algorithm to test networks shows that we can find high quality dynamic user equilibrium assignment. This is illustrated in an example network using the deterministic queuing model for a link performance function and associating costs and flows in a predictive way in discrete time.     

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.