Emergency evacuation is to transfer people from dangerous places to safe areas, so as to reduce or even avoid the potential harm to people. It is inherently a comprehensive system composed of evacuation managers, evacuees, road networks, shelters, etc. Security is one of the important indicators of such system. Moreover, in order to ensure the normal and efficient operation of evacuation system, each component should cooperate well with each other, thus making stability another important index of the evacuation system. In order to optimize evacuation safety, some residential areas may be arranged to stay much longer which is hard to be accepted, namely, the stability of evacuation system is low. In this paper, a system-based evacuation CSO model at residential level is proposed which compromises the security and stability of evacuation systems. The CSO model is a bi-level network optimization model, the upper level aims at minimizing the total risk of evacuation subject to the residential tolerance level and the lower level conveys a cell transmission-based dynamic traffic assignment problem. Using our model, we also study the impact of the number of shelters, the organizational form of road intersections, the uncertainty of evacuation demand and risk distribution on evacuation system. INDEX TERMS Evacuation management, system theory, constrained system optimal, dynamic traffic assignment, cell transmission model.
The objective of this study was to investigate how route familiarity affected drivers' eye movement features (fixation and saccade) and driving speed when driving in the entrance zone of highway tunnels with different spatial visual conditions. On-road tests were conducted on the drivers' visual characteristics and the speed were recorded in real time using an eye tracker and onboard diagnostic system. The variations in the eye movement features and speed in the entrance zone of the tunnels were analyzed. Then, statistical methods were conducted to examine the influence of the route familiarity and spatial visual conditions of tunnels on the driver behavior. The results demonstrated that the variations in the drivers' eye movements and speed were much more significant in the entrance zone of a tunnel without spatial intervisibility than in a tunnel with spatial intervisibility. The impact of this environmental transition on unfamiliar drivers was greater than that on familiar drivers. Road familiarity reduced the drivers' period of adaptation to the tunnel entrance environment and increased the driving speed.
Tunnels are critical areas for highway safety because the severity of crashes in tunnels tends to be more serious. Controlling vehicle speed is regarded as a feasible measure to reduce the accident rate in the tunnel entrance and exit areas. This paper aims to evaluate the effectiveness of three types of speed reduction markings (SRMs) in tunnel entrance and exit zones by conducting a driving simulation experiment. For this study, 25 drivers completed the driving tasks in the day and night scenarios. The vehicle speed and acceleration data were collected for analysing and the relative speed contrast, time mean speed and acceleration were adopted as indices to evaluate the effectiveness of SRMs. The repeated ANOVA test results revealed that SRMs have a significant effect in reducing vehicle speed, especially in the exit zone. Colour Anti-skid Markings (CASMs) produced a more obvious deceleration in the entrance zone. In the entrance zone, a similar downward trend was performed in the situation of NSRMs and SRMs, but a lower speed occurred in case of SRMs. Besides, CASMs work better and cause an obvious gap of 10 km/h in daytime and 5 km/h at night compared to the speed without SRMs. In the exit zone, the present study supports the conclusion that the drivers are prone to accelerate. Our results showed that the drivers accelerated in case of NSRMs, while they slowed down in case of SRMs. Thus, SRMs are necessarily implemented in the highway tunnel entrance and exit zones. Our study also indicates that though CASMs result in lower speed at night, the Transverse Speed Reduction Markings(TSRMs) have a better performance than CASMs in daytime. The investigation provides essential information for developing a new marking design criterion and intelligent driver support systems in the highway tunnel zones.
Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of cooperative lane changing, merging, and car-following actions for CAV control at a local merging point together with upstream points. The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. Uniting all agents, the coordination graph (CG) method is applied to seek the global optimum, overcoming the exponential growth problem in MARL. Using MATLAB and the VISSIM COM interface, an online simulation platform is established. The simulation results show that MARL is effective for online computation with in-timing response. More importantly, comparisons of the results obtained in various scenarios demonstrate that the proposed system obtained smoother vehicle trajectories in all controlled sections, rather than only in the merging area, indicating that it can achieve better traffic conditions in freeway work zone areas.
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