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This paper proposes an energy consumption optimization method of train operation by controlling multiple parameters for an urban rail interval. The parameters include the maximum operation speed, the ratio of the minimum coasting speed to the maximum speed, the maximum traction, and the braking force. Based on the mass belt model, an optimization model of train operation is built with these parameters as decision variables. The objective function is to minimize the energy consumption and the penalty on the running time delays for the observed rail interval. A simulated annealing algorithm is designed to solve the model, which adopts synergic adjustment of multiple parameters based on the influence of the running time with variation of each parameter. A numerical example of Guangzhou Metro Line 8 is adopted to test the method. The results show that the maximum operation speed has a decisive influence on the optimal train operation strategy, interval running time, and energy consumption; the train traction has a direct effect on energy consumption; the train braking force has multiple optimal solutions; moreover, the energy-saving strategy has a significant effect on energy saving in a longer running time.

The rapid development of metro transit systems brings very significant energy consumption, and the high service frequency of metro trains increases the peak power requirement, which affects the operation of systems. Train scheduling optimization is an effective method to reduce energy consumption and substation peak power by adjusting timetable parameters. This paper proposes a train timetable optimization model to coordinate the operation of trains. The overlap time between accelerating and braking phases is maximized to improve the utilization of regenerative braking energy (RBE). Meanwhile, the overlap time between accelerating phases is minimized to reduce the substation peak power. In addition, the timetable optimization model is rebuilt into a mixed integer linear programming model by introducing logical and auxiliary variables, which can be solved by related solvers effectively. Case studies based on one of Guangzhou Metro Lines indicate that, for all-day operation, the utilization of RBE would likely be improved on the order of 23%, the substation energy consumption would likely be reduced on the order of 14%, and the duration of substation peak power would likely be reduced on the order of 66%.

In China, urban bus energy consumption is an increasing concern due to system expansion and poor energy efficiency due to frequent stopping and starting by buses. This study develops a mesoscopic bus energy consumption model based on the U.S. Environment Protection Agency’s Motor Vehicle Emission Simulator (MOVES). To localize MOVES, link operating mode distribution is calculated by bus GPS data collected from nine routes in Shanghai, China. A comparison of bus fuel economy between the U.S.A. and China is conducted to determine the model years in U.S.A. and China which have similar fuel consumption performance for buses with a certain weight. After MOVES localization, link energy consumption factors are estimated, and then the impacts of average speed, vehicle stops, acceleration, and road facility on link energy consumption factors are explored. Based on this exploration of influential variables, this study develops link-level bus energy consumption factor look-up tables for a variety of bus types. Model validation indicates that using link-level indicators to estimate bus energy consumption can achieve acceptable accuracy, and that the link type classification method can influence the accuracy of the mesoscopic bus energy consumption model. This study is useful to estimate bus energy consumption when instantaneous speed data is unavailable. This study also explores the extended application of MOVES by offering a procedure for applying MOVES to develop a bus energy consumption model in regions beyond the U.S.A.

Algorithms for current automatic train operation (ATO) focus mainly on reducing the mechanical energy of motion for a single train within an existing timetable. However, the reuse of regenerative energy is another factor that contributes to energy consumption and conservation in multitrain networks. To improve regenerative energy receptivity and energy savings in a bidirectional metro transit network, this study formulated a coordinated train control algorithm that was based on genetic algorithm techniques. The energy saving potential of different station departure time intervals between two opposing trains (synchronization time) was tested. Simulation on the Visual C++ platform demonstrated that the algorithm could provide an optimal train speed profile with better energy performance while also satisfying operational constraints. Different synchronization times have different optimization ratios. This research was another step to facilitate the development of an ATO control algorithm that considers overall energy consumption. Increased knowledge of the influence of synchronization time at stations on energy consumption in regenerative multitrain networks will also aid in the design of more energy-efficient timetables.

Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel ...

El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.