In order to reduce passenger delays and prevent severe overcrowding in the subway system, it is necessary to accurately predict the short-term passenger flow during special events. However, few studies have been conducted to predict the subway passenger flow under these conditions. Traditional methods, such as the autoregressive integrated moving average (ARIMA) model, were commonly used to analyze regular traffic demands. These methods usually neglected the volatility (heteroscedasticity) in passenger flow influenced by unexpected external factors. This paper, therefore, proposed a generic framework to analyze short-term passenger flow, considering the dynamic volatility and nonlinearity of passenger flow during special events. Four different generalized autoregressive conditional heteroscedasticity models, along with the ARIMA model, were used to model the mean and volatility of passenger flow based on the transit smart card data from two stations near the Olympic Sports Center, Nanjing, China. Multiple statistical methods were applied to evaluate the performance of the hybrid models. The results indicate that the volatility of passenger flow had significant nonlinear and asymmetric features during special events. The proposed framework could effectively capture the mean and volatility of passenger flow, and outperform the traditional methods in terms of accuracy and reliability. Overall, this paper can help transit agencies to better understand the deterministic and stochastic changes of the passenger flow, and implement precautionary countermeasures for large crowds in a subway station before and after special events.

This study proposed a hybrid optimization model for urban bus transit route network design problem (TRNDP). Although several mathematical methods had been developed to make the problem tractable, the methods relied on excessive assumptions, which resulted in over-simplification or idealization of the problems. In light of these considerations, a multi-level and multi-mode network design method was introduced in this study. A multi-level network consisted of three levels: skeleton network, arterial network, and feeder network. The different levels of network were associated with different modes of public transportation (such as subway, light rail transit, trolleybus, BRT, normal bus, and community branch) based on the features of bus routes, city sizes, etc. In addition, according to the respective features of urban transit route network structures, we developed different optimization models for different levels. Finally, the proposed methodology was applied to case studies of the city of Zhaoyuan in China and the transit network of Mandl benchmark. The results showed that the total travel time for the proposed method was significantly lower than that of the competing method, with a 21.51% reduction. In addition, the proposed method provided 85.23% direct travelers, 14.65% travelers with one transfer, 0.12% travelers with two transfers, and no unsatisfied demand, which were better than the results from the compared method.

In metro train systems, energy-saving operations can include timetable optimization and speed control. Timetable optimization aims to promote the effective utilization of regenerative energy, and speed control aims to minimize the tractive energy consumption. To effectively reduce the net energy consumption (i.e., the difference between tractive energy consumption and regenerative energy utilization), this paper introduces an integrated optimization model that incorporates timetable and speed profile optimization. A cataclysmic genetic algorithm was developed to solve the proposed optimization model. Data were collected from the No. 2 metro line in Nanjing, China; the results showed that the proposed model could reduce the practical energy consumption by 17.5% in comparison with the original timetable. In addition, compared with the cooperative scheduling model that optimized the timetable and speed separately, the proposed model reduced the practical energy consumption by 8.9% on average. Finally, sensitivity analyses were also conducted to investigate the effects of the number of stations on the energy-saving operations.

The primary objective of this study was to develop a quantitative approach to estimate bus dwell time and time lost serving stop, which included acceleration and deceleration time, dead time, and time for serving boarding and alighting passengers. A polynomial model incorporating kinematics of a particle was derived for estimating bus acceleration and deceleration time. In addition, descriptive statistics methods were used to analyze dead time. A case study was conducted to show the applicability of the proposed model with data collected from the seven most common types of bus stops in China. R-square and Mean Absolute Percentage Error (MAPE) were calculated to be 0.8840 and 13.20% for non-peak and 0.8387 and 13.46% for peak, indicating the method was well-validated and could be practically used in China. Further research can be conducted to investigate the effects of different weather conditions and locations on the performance of the proposed method.

Urban buses have energy and environmental impacts because they are mostly equipped with heavy-duty diesel engines, having higher emission factors and pollution levels. This study proposed a mean distribution deviation (MDD) method to identify bus pollutant emissions including CO, CO₂, HC, and NO_X at road sections, intersections, and bus stops for different fuel types; and explore the changes in emissions for different locations in the road sections, bus stops, and intersection influence areas. Bus speed, acceleration, and emissions data were collected from four fuel types in China. For different locations and fuel types, the differences in emissions were all statistically significant. MDD values for different locations indicated that there were more obvious differences in emissions between road sections and intersections. In addition, heat maps were applied in this study to better understand changes in bus emissions for different locations in the bus stop influence areas, intersection influence areas, and road sections.