Urban metro is developing in the direction of the network. With the increasing scale, dealing with the sudden accident of the metro network has brought significant challenges to the relevant departments. When the metro is disrupted, it is necessary to evacuate the passengers. Emergency bus bridging is an essential means to evacuate the passengers. Notably, the core of efficient emergency bus bridging strategy is how to plan the location of the emergency bus reserve bases. Hence, this paper aims to establish the site selection model based on the vulnerability of the complex metro network, and rationally lay out the source of the emergency bridging bus in the case of sudden disruption of the complex metro network. Firstly, the space L topology model of the metro network is established, and the performance indicators of the network are analyzed by grey relational analysis (GRA), to identify the key stations. Secondly, we use a reverse covering location model to optimize the bridging response delay time and the number of reserved buses. Taking Tianjin metro network as a case study scenario, the research results of this paper are compared with those of other scholars. By comparison, the location results are better in coverage, bridging response delay time and the number of reserved buses. Therefore, the research method of this paper provides new ideas and new methods for the location of the emergency bridge bus reserve base.
Autonomous driving vehicle will come reality more and more possible along with prosperity of relevant technologies. However, autonomous driving vehicles and artificial driving vehicles will coexist for a long time until full connected and autonomous traffic environment achieved. To modeling lane-changing decision of autonomous driving vehicles, which can be regard as non-cooperative static game under complete information, we establish the game theory-based lane-changing model with combined driving style (GLCD model). Then we conduct a series of simulation experiments to evaluate indicators of the GLCD model, which include the number of lane-changing, the number of accidents, the number of vehicles passes and, the average passing time, compared with the cellular automata model, also compared with the general game theory-based lane-changing model. Results show that the developed model (GLCD model) can improve the efficiency of vehicle driving and ensure stability on the road under the premise of ensuring safety. Furthermore, this model can provide a feasible vehicle lane-change decision in a mixed traffic flow environment in the future.
To evaluate the environmental benefits and operating efficiency of exit lanes for left-turn intersections, a signal optimization model considering emissions and delays is proposed. Considering the three pollutants of CO, HC, and NOx in cars and buses exhaust, VISSIM simulation software is used to obtain second-by-second data of vehicles. The data is used to calculate the second-by-second Vehicle Specific Power and to calibrate the two types of emission factors for each lane group. Cycle length, main-signal and pre-signal control are regarded as constraints, and minimum emissions and delays are regarded as optimization goals. The bi-objective optimization for exit lanes for left-turn intersections is transformed into a single-objective optimization problem through the linear weighted sum method, and the genetic algorithm is used to solve the optimal signal timing scheme. The results show that, compared with conventional intersections, the design of EFL intersections can not only improve operation efficiency, but also reduce emissions. The optimal signal timing scheme for exit lanes for left-turn intersection can reduce the delay by 6.27% and the total emissions by 7.47%; At the same time, when the proportion of left-turn traffic is 35%~40%, the signal optimization model proposed in this paper has the best optimization performance. Effective in relieving traffic pressure and has good environmental benefits.
Considering the subway, private cars, and cycling, the Personal Mobility Carbon Credits Trading scheme is explored in a super network with homogeneous travelers to discuss the impact on travel mode split rate and system carbon emissions. The Personal Mobility Carbon Credits Trading scheme includes the travel route choice behavior and the dynamic credits price evolution, which affect each other. The former is modeled in a super network. The Dial-MSA algorithm is used for flow distribution, and the results are fed back to the dynamic credits price evolution model. Accordingly, the result of dynamic credits price evolution, in turn, affects travel cost and finally governs the result of traffic flow allocation. The results show that the system will reach a stable state in a finite time range. The convergent equilibrium price has nothing to do with the initial price but is related to the number of total credits and the credits allocation scheme. Compared with the equivalent distribution scheme, proportional distribution is more equitable.
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