Energy-saving technologies and methods for transportation systems
With the emergence of new transport concepts and the development of advanced transportation modes, the construction of integrated transport will not only meet the needs of national economic development, but satisfy the ecological principles of planning, construction and management, with low resources and energy consumptions and low pollution emissions, as well as harmony with the environment. Therefore, the transportation energy consumption is a key factor. This paper discusses the concept of integrated transport energy consumption indicators based on the entropy changecoupling model. With consideration of the situation of China’s environmental level and economic development, it also demonstrates harmonious relations of China’s integrated transportation energy consumption level between environmental level and economic level. The results indicate the status of China’s overall transportation energy consumption and provide new ideas for the sustainable development integrated transport in the future.
To identify the energy consumption driving factors and measure their corresponding contributions in energy consumption, the paper classifies the factors into scale effect, structure effect and technique effect according the driving mechanism. It then analyzes the correlations between these factors by ASIF data structural principles and the time series regression. The contributions of these factors are measured via the expanded LMDI decomposition model based on the LMDI method. The result indicate that: (1) the impacts from scale effect and technique effect are weakening, while that from structure effect is increasing; (2) the transport supply structure needs to be further optimized, and there exists plenty of space for reducing the impact on energy consumption; (3) the energy efficiency has increased a lot in recent years, but its contribution to energy consumption is shrinking. Improving the transport intensity exerts significant effect on saving energy. The reliability of the models and its conclusions is proved by empirical studies, which provides scientific supports for the policy making of regional lowcarbon integrated transport.
The contradiction between transportation and energy increased with the development of industrialization and urbanization. Current research indicates that energy consumption of transportation are related with traffic volume and energy consumption intensity, however, neglecting impaction of system structure of integrated transportation. This paper takes the integrated transportation system as a research object, and the complete decomposition model is built up. The influencing factors of energy consumption is divided into three parts, traffic volume, energy consumption intensity and system structure of integrated transportation. A calculation example is gave based on the statistical data of 1995–2011, to discuss thequalitative and quantitative relationship between energy consumption of transportation and its influencing factors, and find out the main promotion factor that effects energy consumption increasing. By analyzing the data，we can draw the conclusion that the energy consumption of transportation increase significantly with the increase of traffic volume. But it is necessary to point out that the effects of transport structure become more and more outstanding. Therefore, when government constructing integrated transportation system, should analysis comparative advantage energy consumption of each transportation ways and make the whole developing planning to realize the coordination of transport modes. In particular, should greatly develop transportation of railway and waterway which are low energy consumption and little effect on environment.
In order to reduce energy consumption of the transportation system and meet the trip demand of the residents as possible. It is necessary to consider improving traffic efficiency under the condition of reducing vehicle fuel consumption. For the question, the average fuel consumption of the vehicles and macroscopic fundamental diagram (MFD) are introduced to establish the double- objective programming model, which makes the trip completion flow high but the average fuel consumption low, and the model is solved. Fuel consumption-proportional integral (FC-PI) control method is proposed based on the feedback principle and the equilibrium equations of the traffic flow, reducing the average fuel consumption of the vehicles in the region through the FC-PI control method, and improving the trip completion flow. Finally, the fuel consumption control model is verified by the actual urban road network as a case study, which is compared with the control method of Bang-Bang. The result shows that the trip completion flow is improved and the average fuel consumption of the vehicles is reduced significantly. And the control effect of FC-PI is better than Bang-Bang.
In order to coordinate a contradiction between the energy consumption and economic growth in transportation, it’s important to study the decoupling relationship between the two with the quantitative analysis. Decoupling modal is used to study what decoupling status occurred, and refined LMDI modal is used to analyze main factors that influence the decoupling relationship. Taking Hebei Province as a case study shows that weak decoupling status occurred in most years, but there was a rebound trend in recent years. Transportation fixed assets investment and integrated transport structure factor play a negative role in decoupling status, and highway transportation is the greatest obstacle of the decoupling relationship. Meanwhile, technological progress is the main factor to promote the occurrence of decoupling, and highway transportation, railway transportation play a positive role in decoupling status.
Nowadays, society pays much attention to the problems of fuel consumption. This paper concerns about prediction of microcosmic energy consumption, and its purpose is to realize fuel consumptions of Beijing basic freeway section. Based on OBD/GPS terminal installation on taxis, we extract driving behavior’s data of taxi drivers, select main relevant indexes, set up the prediction model of fuel consumption, and realize accurate prediction of fuel consumption in Beijing basic freeway section. Results show that average speed, standard deviation of speed, max speed, rate of operating condition, average acceleration and deceleration, distance and energy have greater influence on fuel consumption; PCA and neural network combination model can realize energy consumption prediction effectively, and the accuracy of prediction can reach 92.46%. This research can provide strong supports on monitor and regulation of traffic energy consumption.
Combined with the actual situations, the generalized costs which considering the energy consumption and congestion pricing of private car and comfort consumption of bus are established, respectively. Furthermore, regarding the travel time as the upper objective function, the bi- level programming model based on energy consumption constraint is built. The objective of the upper model is to minimize the delay of the whole system, and the lower one is stochastic user equilibrium with dual-mode choice. The genetic algorithm and Frank-Wolfe algorithm are used to solve the proposed model. The traffic management strategies are abstracted and imported into the model as two simple examples. The transportation energy consumption change before and after the toll road and the effect of road pricing with different energy efficiency goals in multiple circumstances are derived and discussed. The calculation results show that the implementation of road congestion charge is beneficial to reduce traffic energy consumption when the traffic demand is large. When the energy saving target is less than 25% and the road charges are taken at the same time, the travel time of the road network is reduced accordingly.
With increasing of the vehicles and deteriorating of the environment, controlling the use of vehicle fuel has become one of the important measures in Beijing transport energy saving and emission reduction. The private vehicle energy-consumption cannot count from the whole sample, because there have too large number and the adding oil for the private vehicle is subjective. It only can count from the sampling survey method. Therefore, in order to obtain the reliable data of fuel consumption which accurately reflects the city's private vehicle energy consumption, it is necessary to explore the key factors of energy consumption and design a scientific stratified sampling framework. based on the data of Beijing private vehicle energy consumption survey, this paper constructs a econometric regression model to study the key determinants of energy consumption of private vehicle. This paper proposes the stratified sampling frame and threshold divided model through introducing the dummy variables which uses in econometrics, eventually form a stratified sampling framework.
Aiming at the problem of high energy consumption of vehicles at oversaturated signalized intersection, taking oversaturated traffic state of signalized intersection as the research period, the queue length, the number of stops and the time of pass through are analyzed by using the fixed number theory. It is determined that the vehicle travel time of deceleration, idle speed, acceleration and constant speed at signalized intersections. According to the vehicle energy consumption rate under different working conditions, the average energy consumption model for all vehicles at oversaturated intersection of the first stop to the stop line is established. In order to verify the accuracy of the model, taking a two phase oversaturated signalized intersection as an example, and the proposed model is used to calculate the vehicle energy consumption under different traffic flow. Then the calculation results of the proposed model are compared with the results of VISSIM simulation. The result shows that the model is reasonable for research vehicle energy consumption at the oversaturated signalized intersection. At the same time, this paper analyzes the signal timing influence on vehicle energy consumption at oversaturated intersection based on the model. It is shown that optimal timing parameters are of great significance for the vehicle energy saving at oversaturated signalized intersections.
The running time and operation method are two important factors for realizing high-speed train energysaving operation. In this paper, a multi-interstation energy-saving operation method for high-speed train with adjustable running time supplement is constructed. Considering the requirement difference in the arrival time of high-speed train arriving at hub station and non-hub station, the model added to the constraint that the train arrival time is the same as the fixed arrival time in timetabling for hub station, and the constraints in the time range of the arrival time of the train arriving at non-hub station. In order to avoid the situation that a number of unfeasible solutions unsatisfied the timing constraints in the solution space might have defect on the algorithm efficiency, a three layer coded genetic algorithm is designed to solve the model in the paper. Through the verification of a highspeed railway line including 3 hub stations and 3 non-hub stations, the results show that the multi-interstation energy-saving operation method for high-speed train proposed in the paper could obtain the optimal multiinterstation energy-saving speed trajectory, when ensure that the arrival time of hub station is the same as the time in timetabling and the arrival time of the non-hub train station is within a certain time range. Compared with the calculation results based on the acceleration-cruising method and single interstation energy-saving operation method in the paper under the running time in timetabling, the proposed method could save more than 16% and 4% energy respectively.
The energy consumption quota standard for urban rail transit (URT) stations can provide an important reference for energy consumption evaluation and energy saving. This paper analyzes the compositions of energy consumption in a URT system in consideration of the energy needs for urban transportation development. Using the URT stations in Beijing for case studies, this paper analyzes the impacts of major factors on energy consumptions, including the compositions of equipment energy consumptions, station characteristics, floor area, daily passenger volumes, station layouts, and number of exits at different stations. Using the quota evaluation method, this paper proposes the quota standard for different station energy consumption levels: basic, luxury, and comfortable. The energy consumption quota standard is also proposed for the season with air-conditioning on and the season with air- conditioning off. The energy saving strategies are also discussed for different types of URT stations.
In recent years, the problem of energy consumption and emission pollution caused by fast growing private cars is becoming more and more serious. How to build a fuel accounting model for private passenger cars and analyzing the macro energy saving effect of different TDM (Traffic Demand Management) policies are essential to urban traffic related energy saving and emission reduction. Considering the problem that the accuracy of energy consumption data derived from conventional survey for private passenger cars is poor and unable to meet the delicacy management requirements of urban traffic energy conservation and emission reduction. In this study, using the existing survey data and monitoring data and based on the method of “OLS( Ordinary Least Square) + Robust standard deviation”, the significant influencing factors are analyzed，and an accounting model of energy consumption for private passenger car based on traffic big data and applies it to the macro analysis of energy saving effect of TDM policy is proposed. The reliability and effectiveness of the proposed model are then verified by using the measured data of Beijing. Finally, the macro effect of private car's energy saving under different TDM policies (including combined policies) are analyzed. The result shows that when the policy effect indicators change at the same rate, the total fuel consumption reduction resulted from the policy combination of congestion charging and controlling the number of large-displacement passenger cars is largest.
