Bus bridging evacuation refers to transporting affected passengers to their destination stations during metro disruption by buses dispatched from depots. Planning bus bridging plays an important role in maintaining the level of service of metro rail and serving affected passenger demands. This paper presents an integrated planning and operational model for bus bridging evacuation, in which the varying bridging passenger demand at disrupted metro stations, the arrival and departure of bus fleets, the dwelling time of a bus fleet, and the number of alighting and boarding passengers at disrupted metro stations are captured in detail. Based on the dynamics of a bus bridging system, bus bridging routes are planned to stop at intermediate disrupted metro stations between their dispatched disrupted metro station and turnover stations. Results of a case study with Shanghai metro line 16 demonstrate the advantage of the proposed planning model in real-world application. The proposed model and its simulation-based solving algorithm yield economical bridging plans with fewer dispatched buses and lower operation cost compared with our previous model. Results of the case study show that total operation cost and computation time are contradictory with each other. The combination of larger population size, larger maximum number of generations, and larger crossover rate probably contributes to finding the globally optimal bus dispatch plan.

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has.

Vehicles trapped in dilemma zones on arterial streets are at high risk of rear-end collision or right-angle collision. Most of extant studies focus on dilemma zone protection at isolated signalized intersections. A coordinated arterial dilemma zone protection model at the lane group level, aiming to minimize the weighted combination of safety and efficiency performance indicators, is proposed to improve the safety of arterial traffic by dynamically optimizing the timing parameters of arterial signals. The problem is solved with the rolling optimization technique which enumerates and evaluates feasible green phase durations for all lane groups over each time horizon by embedding the cell transmission model for traffic prediction and the genetic algorithm for signal timing optimization. The effectiveness of the dynamic optimization method is verified by comparing it with actuated control and isolated and coordinated fixed-time control strategies. The results show that the proposed method is able to obtain better safety performance by tending to choose longer cycle lengths and that the proposed method is more suitable for situations where traffic demand changes drastically. With the aid of a traffic surveillance system, the proposed model can be used to provide coordinated dynamic dilemma zone protection for arterial traffic.

In this research, a multi-modal transit network will be integrated with a nested logit (NL) approach to study the effects of integrated multi-modal transit information service (IMTIS) on residents' travel mode choice behaviors. A traffic hyper-network is designed to analyze the reason why IMTIS is helpful to promote modal shift, and a bi-level NL model is developed to deal with the complicated mode choice decisions in the multi-modal network. Considering the variation of travel time under mixed traffic conditions in China's urban traffic network, a travel time reliability factor is introduced to correct values of vital attribute variables of modes. Finally, the NL model is calibrated using data from a RP survey and usages of various travel modes with and without IMTIS. The result shows that IMTIS is effective in improving share ratios of public transport in China's big cities.

This study examines the moderating effects of renewable and nuclear energy on the relationship between transportation index (air and land) and environmental sustainability from 1995 to 2022 across the top 27 polluting countries. The study employed a series of pre-estimation tests, along with the novel Method of Moments Quantile Regression (MMQR), to estimate heterogeneous effects across the lower, middle, and upper quantiles of environmental sustainability. The MMQR results indicate that environmental sustainability is hampered by transportation, whereas renewable and nuclear energy promote it. The moderation effect model shows that both renewable and nuclear energy development mitigate the negative environmental externality from the transportation sector. The controlling factors, GDP and inflation, are found to be harmful for environmental sustainability, while trade openness is found to be favourable. The robustness findings using Driscoll and Kray standard errors (DKse) yielded similar results; nonetheless, the magnitude of the coefficient varies substantially. Thus, think tanks and policymakers are recommended to integrate renewable and nuclear energy into the transportation sector’s energy portfolio to mitigate its negative environmental impacts.