Trajectory-based identification of critical instantaneous decision events at mixed-flow signalized intersections.

Mixed-flow intersections are prevailing in many developing countries such as China and India. At mixed-flow intersections, there is no clear lane discipline or regular trajectories within the intersection, especially for the non-motorized traffic. This leads to more interactions and encounters between the motorized traffic and the non-motorized traffic. Hence, critical instantaneous decision events such as abrupt accelerating, decelerating, jerking, swerving, and swinging, may occur more frequently, which result in potential traffic conflicts and crashes. This study presents a methodology to identify critical instantaneous decision events at the mixed-flow signalized intersections, based on the entropy theory and high-resolution vehicle trajectory data. A three-dimensional cube searching algorithm is firstly proposed to extract general traffic events by examining the proximity between trajectories. A novel model incorporating vehicle kinematics and Permutation Entropy is then developed to identify critical events, by quantifying driving volatility based on the time-serial trajectory data. Next, 3, 349 vehicle trajectories and 805 bicycle trajectories with a resolution of 0.12 s collected at a signalized intersection in Shanghai are used to demonstrate the proposed method. Results show that the proposed method is capable of identifying critical instantaneous decision events, and tends to produce a higher identification ratio comparing with the conventional method solely based on kinematic thresholds. A sensitivity analysis is also conducted to investigate the effects of model parameters on the performance of the proposed method. The presented work could be applied for traffic safety assessment, real-time driving alert systems, and early diagnosis of risk-prone road users at mixed-flow intersections.

Real-time energy-saving metro train rescheduling with primary delay identification.

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators.

Evaluation of pedestrian safety at intersections: A theoretical framework based on pedestrian-vehicle interaction patterns.

Pedestrians are the most vulnerable road users, and pedestrian safety has become a major research focus in recent years. Regarding the quality and quantity issues with collision data, conflict analysis using surrogate safety measures has become a useful method to study pedestrian safety. However, given the inequality between pedestrians and vehicles in encounters and the multiple interactions between pedestrians and vehicles, it is insufficient to simply use the same indicator(s) or the same way to aggregate indicators for all conditions. In addition, behavioral factors cannot be neglected. To better use information extracted from trajectories for safety evaluation and pay more attention on effects of behavioral factors, this paper develops a more sophisticated framework for pedestrian conflict analysis that takes pedestrian-vehicle interactions into consideration. A concept of three interaction patterns has been proposed for the first time, namely "hard interaction," "no interaction," and "soft-interaction." Interactions have been categorized under one of these patterns by analyzing profiles of speed and conflict indicators during the whole interactive processes. In this paper, a support vector machine (SVM) approach has been adopted to classify severity levels for a dataset including 1144 events extracted from three intersections in Shanghai, China, followed by an analysis of variable importance. The results revealed that different conflict indicators have different contributions to indicating the severity level under various interaction patterns. Therefore, it is recommended either to use specific conflict indicators or to use weighted indicator aggregation for each interaction pattern when evaluating pedestrian safety. The implementation has been carried out at the fourth crosswalk, and the results indicate that the proposed method can achieve a higher accuracy and better robustness than conventional methods. Furthermore, the method is helpful for better understanding underlying levels of safety from the behavioral perspective, which can also provide evidence for targeted traffic education on proper behaviors.

Stabilization strategies of a general nonlinear car-following model with varying reaction-time delay of the drivers.

In this paper, the stabilization strategies of a general nonlinear car-following model with reaction-time delay of the drivers are investigated. The reaction-time delay of the driver is time varying and bounded. By using the Lyapunov stability theory, the sufficient condition for the existence of the state feedback control strategy for the stability of the car-following model is given in the form of linear matrix inequality, under which the traffic jam can be well suppressed with respect to the varying reaction-time delay. Moreover, by considering the external disturbance for the running cars, the robust state feedback control strategy is designed, which ensures robust stability and a smaller prescribed H∞ disturbance attenuation level for the traffic flow. Numerical examples are given to illustrate the effectiveness of the proposed methods.

Estimating rear-end accident probabilities at signalized intersections: a comparison study of intersections with and without green signal countdown devices.

OBJECTIVE: Rear-end accidents are the most common accident type at signalized intersections, because the diversity of actions taken increases due to signal change. Green signal countdown devices (GSCDs), which have been widely installed in Asia, are thought to have the potential of improving capacity and reducing accidents, but some negative effects on intersection safety have been observed in practice; for example, an increase in rear-end accidents. METHODS: A microscopic modeling approach was applied to estimate rear-end accident probability during the phase transition interval in the study. The rear-end accident probability is determined by the following probabilities: (1) a leading vehicle makes a "stop" decision, which was formulated by using a binary logistic model, and (2) the following vehicle fails to stop in the available stopping distance, which is closely related to the critical deceleration used by the leading vehicle. Based on the field observation carried out at 2 GSCD intersections and 2 NGSCD intersections (i.e., intersections without GSCD devices) along an arterial in Suzhou, the rear-end probabilities at GSCD and NGSCD intersections were calculated using Monte Carlo simulation. RESULTS: The results suggested that, on the one hand, GSCDs caused significantly negative safety effects during the flashing green interval, especially for vehicles in a zone ranging from 15 to 70 m; on the other hand, GSCD devices were helpful in reducing rear-end accidents during the yellow interval, especially in a zone from 0 to 50 m. CONCLUSIONS: GSCDs helped shorten indecision zones and reduce rear-end collisions near the stop line during the yellow interval, but they easily resulted in risky car following behavior and much higher rear-end collision probabilities at indecision zones during both flashing green and yellow intervals. GSCDs are recommended to be cautiously installed and education on safe driving behavior should be available.

Simulating cooperative behavior in human collective search pattern.

In the world, great natural disasters frequently occur. Along with these disasters, large-scale cooperative searches for missing persons are exigent. Because of the lack of experiments to reproduce the disaster rescue processes, our understanding of how to regulate the collective cooperative searches is still elusive. Here we use an improved Lévy walk model to simulate the rescuers' movements in which direction choice is considered. In our study, we systematically analyze the diffusive mechanism of rescuers' movements, and find that the search pattern shows a high degree of spatial order which displays some inherent features. Our results also indicate that cooperative search promotes rescuers' movements to disperse determinately.

Relationship between microscopic dynamics in traffic flow and complexity in networks.

Complex networks are constructed in the evolution process of traffic flow, and the states of traffic flow are represented by nodes in the network. The traffic dynamics can then be studied by investigating the statistical properties of those networks. According to Kerner's three-phase theory, there are two different phases in congested traffic, synchronized flow and wide moving jam. In the framework of this theory, we study different properties of synchronized flow and moving jam in relation to complex network. Scale-free network is constructed in stop-and-go traffic, i.e., a sequence of moving jams [Chin. Phys. Lett. 10, 2711 (2005)]. In this work, the networks generated in synchronized flow are investigated in detail. Simulation results show that the degree distribution of the networks constructed in synchronized flow has two power law regions, so the distinction in topological structure can really reflect the different dynamics in traffic flow. Furthermore, the real traffic data are investigated by this method, and the results are consistent with the simulations.