Investigating the factors influencing the uptake of Electric Vehicles in Beijing, China: Statistical and spatial perspectives

Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I = 0.10) and purchase restriction (Moran's I = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour

The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing

Policy is an influential factor to the purchase and usage of Electric Vehicles (EVs). This paper is focused on the license plate lottery policy, a typical vehicle purchase restriction in Beijing, China. An agent-based spatial integrated urban model, SelfSim-EV, is employed to investigate how the policy may influence the uptake of EVs over time at the individual level. Two types of “what-if” scenario were set up to explore how the methods to allocate the vehicle purchase permits and the number of permits might influence the EV market expansion from 2016 to 2020. The results suggested that 1) both the allocation methods and the number of purchase permits could heavily influence the uptake of EVs and further its impacts on vehicular emissions, energy consumption and urban infrastructures; 2) compared to the baseline, both scenarios got significantly different spatial distributions of vehicle owners, transport facilities, vehicular emissions and charging demand at the multiple resolutions; 3) SelfSim-EV was found as a useful tool to quantify the nonlinear relationships between the increase of EV purchasers and the demand for transport facilities and electricity, and also to capture some unexpected results coming out from the interactions in the complex dynamic urban system

Exploring the future Electric Vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China

This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs

Agent- and activity- based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China

This paper develops an agent- and activity-based large-scale simulation model for Beijing, China (MATSim-Beijing) to explicitly simulate enroute travel, enroute refuelling and parking behaviours, as well as the associated vehicular energy consumption and emissions, based on MATSim (Multi-Agent Transport Simulation), which is a typical integrated activity-based model. In order to take into account heterogeneous parking and refuelling behaviours, the MATSim-Beijing model incorporates several Multinomial Logit (MNL) models to predict individual choices about the maximum acceptable times of walking from trip destination to parking lot, of diverting to a refuelling station and of queuing at a station, using the data collected in a paper-based questionnaire survey in Beijing. A Sensitivity Analysis (SA) -based calibration method was used to estimate the model parameters by searching for an optimal parameter combination with the objective of minimize the gap between simulated and observed traffic flow data, exhibiting a relatively good performance of decreasing the Mean Absolute Percentage Error (MAPE) by around 23%. Further, the calibrated model was used to investigate whether and how the population scaling and network simplification, which were two commonly used approaches to speeding up large-scale traffic simulations, might influence model accuracy and computing time. The results indicated that both approaches could to some extent influence model outputs, though they could significantly reduce computing time

An agent- and GIS-based virtual city creator: A case study of Beijing, China

Many agent-based integrated urban models have been developed to investigate urban issues, considering the dynamics and feedbacks in complex urban systems. The lack of disaggregate data, however, has become one of the main barriers to the application of these models, though a number of data synthesis methods have been applied. To generate a complete dataset that contains full disaggregate input data for model initialization, this paper develops a virtual city creator as a key component of an agent-based land-use and transport model, SelfSim. The creator is a set of disaggregate data synthesis methods, including a genetic algorithm (GA)-based population synthesizer, a transport facility synthesizer, an activity facility synthesizer and a daily plan generator, which use the household travel survey data as the main input. Finally, the capital of China, Beijing, was used as a case study. The creator was applied to generate an agent- and Geographic Information System (GIS)-based virtual Beijing containing individuals, households, transport and activity facilities, as well as their attributes and linkages

Research on Public Transit Network Hierarchy Based on Residential Transit Trip Distance

To the problem of being lack of transit network hierarchy theory, a research on public transit network hierarchy optimization based on residential transit trip distance is conducted. Firstly, the hierarchy standard of transit network is given, in addition, both simulating electron cloud model and Rayleigh distribution model are used to fit the residential transit trip distance. Secondly, from the view of balance between supply and demand, the hierarchy step of transit network based on residential transit trip distance is proposed. Then, models of transit’s supply turnover and demand turnover are developed. Finally, the method and models are applied into transit network optimization of Baoding, Hebei, China

An initial implementation of multiagent simulation of travel behavior for a medium-sized city in China

Since the traditional four-step model is so simple that it cannot solve complex modern transportation problems, microsimulation is gradually applied for transportation planning and some researches indicate that it is more compatible and realistic. In this paper, a framework of agent-based simulation of travel behavior is proposed, which is realized by MATSim, a simulation tool developed for large-scale agent-based simulation. MATSim is currently developed and some of its models are under training, so a detailed introduction of simulation structure and preparation of input data will be presented. In practice, the preparation process differs from one to another in different simulation projects because the available data for simulation is various. Thus, a simulation of travel behavior under a condition of limited available survey data will be studied based on MATSim; furthermore, a medium-sized city in China will be taken as an example to check whether agent-based simulation of travel behavior can be successfully applied in China

An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China

Water and energy consumptions in the residential sector are highly correlated. A better understanding of the correlation would help save both water and energy, for example, through technological innovations, management and policies. Recently, there is an increasing need for a higher spatiotemporal resolution in the analysis and modelling of water-energy demand, as the results would be more useful for policy analysis and infrastructure planning in both water and energy systems. In response, this paper developed an agent-based spatiotemporal integrated approach to simulate the water-energy consumption of each household or person agent in second throughout a whole day, considering the influences of out-of-home activities (e.g., work and shopping) on in-home activities (e.g., bathing, cooking and cleaning). The integrated approach was tested in the capital of China, Beijing. The temporal results suggested that the 24-hour distributions of water and related energy consumptions were quite similar, and the water-energy consumptions were highly correlated (with a Pearson correlation coefficient of 0.89); The spatial results suggested that people living in the central districts and the central areas of the outer districts tended to consume more water and related energy, and also the water-energy correlation varies across space. Such spatially and temporally explicit results are expected to be useful for policy making (e.g., time-of-use tariffs) and infrastructure planning and optimization in both water and energy sectors