scholarly journals Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 782
Author(s):  
Na Zhang ◽  
Zijia Wang ◽  
Feng Chen ◽  
Jingni Song ◽  
Jianpo Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Quantitative Research ◽  
Carbon Emission ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Low Carbon ◽  
Demand Forecast ◽  
Passenger Demand ◽  
Urban Rail

There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.

scholarly journals A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Miao Zhang ◽  
Yihui Wang ◽  
Shuai Su ◽  
Tao Tang ◽  
Bin Ning
Keyword(s):  
Urban Traffic ◽  
Urban Rail Transit ◽  
Mixed Integer ◽  
Rail Transit ◽  
Train Scheduling ◽  
Passenger Demand ◽  
Urban Rail ◽  
Train Services ◽  
Minlp Model ◽  
Beijing Subway

In urban rail transit systems, train scheduling plays an important role in improving the transport capacity to alleviate the urban traffic pressure of huge passenger demand and reducing the operation costs for operators. This paper considers the train scheduling with short turning strategy for an urban rail transit line with multiple depots. In addition, the utilization of trains is also taken into consideration. First, we develop a mixed integer nonlinear programming (MINLP) model for the train scheduling, where short turning train services and full-length train services are optimized based on the predefined headway obtained by the passenger demand analysis. The MINLP model is then transformed into a mixed integer linear programming (MILP) model according to several transformation properties. The resulting MILP problem can be solved efficiently by existing solvers, e.g., CPLEX. Two case studies with different scales are constructed to assess the performance of train schedules with the short turning strategy based on the data of Beijing Subway line 4. The simulation results show that the reduction of the utilization of trains is about 20.69%.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

2021 ◽  
pp. 2150461
Author(s):  
Xiang Li ◽  
Yan Bai ◽  
Kaixiong Su
Keyword(s):  
Flow Control ◽  
Programming Model ◽  
Flow Characteristics ◽  
Service Level ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Control Measures ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

scholarly journals Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

2019 ◽  
Vol 11 (16) ◽  
pp. 4387 ◽  
Author(s):  
Lin ◽  
Zhang ◽  
Wang ◽  
Yang ◽  
Shi ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Carbon Tax ◽  
Search Algorithm ◽  
Urban Traffic ◽  
Low Carbon ◽  
Carbon Emission Reduction ◽  
Urban Distribution ◽  
The Impact

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the basis of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution routes with and without carbon emissions cost are constructed. Fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit.

Research on Walking Time of Transfer Pedestrians in Urban Rail Transit Station

2014 ◽  
Vol 610 ◽  
pp. 1053-1056 ◽  
Author(s):  
Jin Chu Zheng ◽  
Chang Xu Ji ◽  
Long Gao
Keyword(s):  
Traffic Congestion ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Statistical Point ◽  
Practical Applications ◽  
Urban Rail ◽  
Basic Work ◽  
Transit Station ◽  
Walking Time

The urban rail transport is the backbone of urban public transport. It alleviates urban traffic congestion and plays an important role. Survey and determination of the passenger walking time in urban rail transit station passages is the basic work to realize train coordination between lines and to reduce the passenger waiting time in transferring. The paper focuses on the passenger walking time on transfer pedestrians from the statistical point of view. Finally, Beijing Dongdan transfer station is selected as an example. This study has some practical applications.

Prospective Prognosis of Beijing Urban Transportation Energy Consumption and Carbon Emission

2012 ◽  
Vol 253-255 ◽  
pp. 1121-1129
Author(s):  
Ying Yue Hu ◽  
Feng Chen ◽  
Yao Huang
Keyword(s):  
Energy Consumption ◽  
Carbon Emission ◽  
Road Traffic ◽  
Urban Transportation ◽  
Forecast Model ◽  
Calculation Model ◽  
Urban Rail Transit ◽  
Low Carbon ◽  
Transportation Energy ◽  
Urban Rail

In order to achieve the goal of low-carbon development of the Beijing transportation, the changing trend of the transportation energy consumption and carbon emission is analyzed based on the inertia development of the present situation or under different macroscopic regulations so as to provide theoretical reference for transportation department to formulate development strategies effectively. According to the characteristics of the Beijing urban transportation system, first a energy consumption and carbon emission calculation model for urban road traffic and urban rail transit was established separately. Then the inertia-based prediction model of Beijing urban transportation energy consumption and carbon emission was established, by analyzing the development law of the related basic parameters using regression and other methods. According to the current policies, the development and prediction of part of the parameters were limited, and a scenario forecast model has been thus established (suitable up to 2020). Four types of scenario prediction examples under different policies were presented, and the contribution rate of each policy for carbon emission was analyzed.

scholarly journals Short-term passenger flow forecast for urban rail transit based on multi-source data

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Li ◽  
Liying Sui ◽  
Min Zhou ◽  
Hairong Dong
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Urban Rail ◽  
Svm Model

AbstractShort-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

scholarly journals Urban Rail Transit Scheduling under Time-Varying Passenger Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xing Zhao ◽  
Zhongyan Hou ◽  
Jihuai Chen ◽  
Yin Zhang ◽  
Junying Sun
Keyword(s):  
Urban Rail Transit ◽  
Load Factor ◽  
Rail Transit ◽  
Time Interval ◽  
Train Timetable ◽  
Passenger Demand ◽  
Scheduling Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Transit Scheduling

In view of the conflict between the time-variation of urban rail transit passenger demand and the homogeneity of the train timetable, this paper takes into account the interests of both passengers and operators to build an urban rail transit scheduling model to acquire an optimized time-dependent train timetable. Based on the dynamic passenger volumes of origin-destination pairs from the automatic fare collection system, the model focuses on minimizing the total passenger waiting time with constraints on time interval between two consecutive trains, number and capacity of trains available, and load factor of trains. A hybrid algorithm which consists of the main algorithm based on genetic algorithm and the nested algorithm based on train traction calculation and safety distance requirement is designed to solve the model. To justify the effectiveness and the practical value of the proposed model and algorithm, a case of Nanjing Metro Line S1 is illustrated in this paper. The result shows that the optimized train timetable has advantage compared to the original one.

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