Narrow Results

In order to investigate the collaborative physical effect between the micro vehicle system and the macro road transportation system, we propose a car-following model to describe the impacts of adjacent lanes on the driving behavior in the current lane under varying road condition at the macro level, and extend the model to explore the fuel consumption and exhaust emissions corresponding to the micro level. Numerical results at macro level indicate that during the starting and the braking processes, the interruption of vehicles on adjacent lanes can cause velocity fluctuation on the current lane; under small perturbation situations this influence becomes smaller (greater) when the road condition turns better (worse). The micro results involving fuel consumption and emissions show that, during the starting process the interruption from adjacent lanes can reduce the total fuel consumption and the total emissions on the current lane, but under small perturbation situations the trend is opposite; during the braking process, furthermore, the interruption has more complex influences on the total fuel consumption and exhaust emissions on the current lane.

Vehicle platoons were recently proposed to reduce energy consumption and emissions. However, the majority of platooning studies have focused on pure connected automated vehicle (CAV) environments with limited insights into energy consumption and emissions. This paper quantifies fuel consumption and emissions by studying the microscopic movements of CAVs and human driving vehicles (HDVs) in mixed traffic flows. With an extended cellular automaton model, a four-lane mixed traffic flow of CAVs and HDVs is investigated in detail. The results have shown that ad hoc platoon formation can lead to frequent acceleration and lane changing, resulting in more fuel consumption and emission. With a dedicated CAV lane (DCL) strategy, the interference of CAVs and HDVs is greatly reduced, thereby improving the fuel economy and emission reduction. The findings can benefit the organization of vehicle platoons in the persisting mixed traffic flow environment.

This study investigates an integrated model for the continuous berth allocation and quay crane scheduling problem by considering quay crane coverage range, ship fuel consumption, and transshipment costs. A nonlinear mixed-integer programming model is proposed. Some nonlinear parts in this model are linearized by approximation approaches. While the objective function aims to minimize waiting costs, it also seeks to minimize fuel consumption costs from the current port to the next port and housekeeping costs generated by transshipment between vessels. A local branching-based solution algorithm is designed to solve the proposed model. Computational experiments are conducted to validate the effectiveness of the proposed scientific programming model and efficiency of the algorithm.

Electric vehicle (EV) has become a potential traffic tool, which has attracted researchers to explore various traffic phenomena caused by EV (e.g. congestion, electricity consumption, etc.). In this paper, we study the energy consumption (including the fuel consumption and the electricity consumption) and emissions of heterogeneous traffic flow (that consists of the traditional vehicle (TV) and EV) under three traffic situations (i.e. uniform flow, shock and rarefaction waves, and a small perturbation) from the perspective of macro traffic flow. The numerical results show that the proportion of electric vehicular flow has great effects on the TV’s fuel consumption and emissions and the EV’s electricity consumption, i.e. the fuel consumption and emissions decrease while the electricity consumption increases with the increase of the proportion of electric vehicular flow. The results can help us better understand the energy consumption and emissions of the heterogeneous traffic flow consisting of TV and EV.

Currently, the parallel hybrid electric vehicle (PHEV) is the most common type of architecture on the hybrid vehicle market. Therefore, a PHEV can be a solution to reduce emission and fuel consumption. The main challenge in the development of HEVs is the power management between the components that ensure vehicle movement. Energy management is now highly necessary by applying a control strategy (CS) in the vehicle’s traction chain, which directly affects the PHEV emission and fuel economy. The CSs have different performances, namely the control of the different power sources operation mode and the control of the battery state of charge. For this purpose, we propose a fuzzy logic CS to optimize emissions (FLCS-em) for PHEV. To assess this approach, we compare it with the most commonly used and recent EMS, in particular the strategy to optimize fuel use (FLCS-f), the efficiency optimization strategy (FLCS-eff) and the electric assist CS (EACS), in urban and highway driving cycles. The results show that the elaborate FLCS-em, characterized by a limited number of rulers, provide significant advantage than CSs mentioned in terms of the efficiency of PHEV performance and emissions and fuel consumption minimization.

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time, gender, driving years and so on. Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior, and the drivers are generally divided into two classes (including aggressive drivers and careful drivers) or three classes (including aggressive drivers, normal drivers and careful drivers). Nevertheless, the classification approaches have not been verified, and the rationality of the classifications has not been confirmed as well. In this study, the trajectory data of drivers is extracted from the NGSIM datasets. By combining the K-Means method and Silhouette measure index, the drivers are classified into four clusters (named as clusters A, B, C and D, respectively) in accordance with the acceleration and time headway. The two-dimensional approach is applied to analyze the characteristics of different clusters. Here, one dimension consists of “Cautious” and “Aggressive” behaviors in terms of velocity and acceleration, and the other dimension consists of “Sensitive” and “Insensitive” behaviors in terms of reaction time. Finally, the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model. A surprising result indicates that overly “cautious” and “sensitive” behaviors may result in more fuel consumption and emissions. Therefore, it is necessary to find the balance between the driving characteristics.

This chapter introduces the basic working principles of genetic algorithm (GA). This method was tested on typical used products collection problem in the context of remanufacturing. It was observed that genetic algorithm was able to find the shortest travel plan, while keeping the fuel consumption rate at the lowest possible level. Furthermore, GA was used to update the finite element (FE) model to better reflect the measured data. The results obtained indicated that GA was able to successfully update the FE model resulting in better reflection of the measured data.

In order to reduce fuel consumption and pollutant emission of traffic system at the urban bus stop, the selecting method of the space between the bus stop and the intersection considering fuel consumption and pollutant emission indexes was put forward. VISSIM and MOVES were combined to compute traffic and environment evaluation indexes. The grey relational analysis model was adopted to evaluate the kinds of indexes and the relation between the grey relational grade and space was obtained. Subsequently, the best location of bus stop is at the space where the grey relational grade reached the maximum. Taking single intersection as an example, the selecting method was verified. The result demonstrates that it can obviously reduce fuel consumption and pollutant emission of the whole traffic system at intersection and improve operational efficiency.

The hybrid drive bulldozer adopts a dual-motor independent drive system with engine-generator assembly as its power source. The mathematical model of the whole system is constructed on the software platform of MATLAB/Simulink. And then according to the velocity data gained from a real test experiment, a typical duty cycle is build up. Finally the fuel consumption of the bulldozer is calculated under this duty-cycle. Simulation results show that, compared with the traditional mechanical one, the hybrid electric drive system can save fuel up to 16% and therefore indicates great potential for lifting up fuel economy.