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Dr. Chengxiang Zhuge
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

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0 autonomous vehicle
0 Big Data Analysis
0 agent-based modelling
0 Transportation electrification
0 Complex urban system

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Article
Published: 29 July 2021 in Transportation
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The market penetration rate of electric vehicle (EV) is on the rise globally. However, the use behaviors of private EVs have not been well understood, in part due to the lack of proper datasets. This paper used a unique dataset containing trajectories of over 76,000 private EVs (accounting for 68% of the private EV fleet) in Beijing to uncover trip, parking and charging patterns of private EVs, so as to better inform policy making and infrastructure planning for different EV-related stakeholders, including planners, vehicle manufacturers, and power grid and infrastructure companies. We conducted both statistical and spatiotemporal analyses. In terms of statistical patterns, most of the EV trip distances (over 71%) were shorter than 15 km. Also, most of parking events (around 76%) lasted for less than 1 h. From a spatial perspective, the densities of trip Origins and Destinations (ODs), parking events and charging events in the central districts tended to be much higher than those of the other districts. Furthermore, the number of intra-district trips tended to be much higher than the number of inter-district trips. In terms of temporal trip patterns, there were two peak periods on working days: a morning peak period from 7 to 9 AM, and an afternoon peak period from 5 to 7 PM; On non-working days, there was only one peak period from 9 AM to 5 PM; while the temporal charging patterns on working and non-working days had a similar trend: most of EV drivers got their EVs charged overnight. Finally, we demonstrated how to apply the observed statistical and spatiotemporal patterns into policy making (i.e., time-of-use tariff) and infrastructure planning (i.e., deployment of normal charging posts, enroute fast charging stations and vehicle-to-grid enabled infrastructures).

ACS Style

Mingdong Sun; Chunfu Shao; Chengxiang Zhuge; Pinxi Wang; Xiong Yang; Shiqi Wang. Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications. Transportation 2021, 1 -31.

AMA Style

Mingdong Sun, Chunfu Shao, Chengxiang Zhuge, Pinxi Wang, Xiong Yang, Shiqi Wang. Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications. Transportation. 2021; ():1-31.

Chicago/Turabian Style

Mingdong Sun; Chunfu Shao; Chengxiang Zhuge; Pinxi Wang; Xiong Yang; Shiqi Wang. 2021. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications." Transportation , no. : 1-31.

Journal article
Published: 24 July 2021 in Resources, Conservation and Recycling
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Technology innovations are expected to overcome several barriers to the uptake of Electric Vehicles (EVs). This paper explored the role of battery and charging technologies in the diffusion of EVs. Specifically, four groups of “what-if” scenario in Beijing were set up to assess the potential impacts of battery cost (i.e., EV price), battery capacity (i.e., driving range), battery swap stations and fast charging posts on the expansion of EV market. An agent-based spatial integrated model (SelfSim-EV) was used to simulate how vehicle consumers might respond to these technological innovations over time. The results suggested that 1) Plug-in Hybrid Electric Vehicle (PHEV) became competitive when its sale price decreased over time at a yearly rate of 8%, due to the decrease in battery cost; 2) Increasing the driving range of Battery Electric Vehicle (BEV) had little influence on the total number of vehicle purchasers, but did increase electricity consumption; 3) Deploying fast charging infrastructures, i.e., battery swap stations and fast charging posts, had little influence on the uptake of EVs at the macro level, suggesting that fast charging facilities might not be necessary at the early stage of EV development.

ACS Style

Chengxiang Zhuge; Chunjiao Dong; Binru Wei; Chunfu Shao. Exploring the role of technology innovations in the diffusion of electric vehicle with an agent-based spatial integrated model. Resources, Conservation and Recycling 2021, 174, 105806 .

AMA Style

Chengxiang Zhuge, Chunjiao Dong, Binru Wei, Chunfu Shao. Exploring the role of technology innovations in the diffusion of electric vehicle with an agent-based spatial integrated model. Resources, Conservation and Recycling. 2021; 174 ():105806.

Chicago/Turabian Style

Chengxiang Zhuge; Chunjiao Dong; Binru Wei; Chunfu Shao. 2021. "Exploring the role of technology innovations in the diffusion of electric vehicle with an agent-based spatial integrated model." Resources, Conservation and Recycling 174, no. : 105806.

Journal article
Published: 19 June 2021 in Transportation Research Part D: Transport and Environment
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This paper proposed a novel Station-to-Point (S2P) Battery Swap Mode for Shared Electric Vehicles (SEVs), under which Battery Swap Stations (BSSs) have dedicated delivery vehicles transporting new/used batteries between BSSs and Battery Swapping Demand (BSD) points. We further developed a data-driven BSS location optimization model and day-to-day operation strategy, using a one-month GPS trajectory dataset containing 514 actual SEVs in Beijing. We set up 53 scenarios to test the model. In the baseline scenario, we found that the SEV fleet needed 15 BSSs, and each SEV, on average, needed 1.202 batteries and 0.031 delivery vehicles with the centralized management strategy applied. Through “what-if” scenarios, we found that the key parameters Q (the coverage rate of BSD points), R (the service radius of a BSS), and AADT (the acceptable average delay time) were influential to the outputs of interest.

ACS Style

Xiong Yang; Chunfu Shao; Chengxiang Zhuge; Mingdong Sun; Pinxi Wang; Shiqi Wang. Deploying battery swap stations for shared electric vehicles using trajectory data. Transportation Research Part D: Transport and Environment 2021, 97, 102943 .

AMA Style

Xiong Yang, Chunfu Shao, Chengxiang Zhuge, Mingdong Sun, Pinxi Wang, Shiqi Wang. Deploying battery swap stations for shared electric vehicles using trajectory data. Transportation Research Part D: Transport and Environment. 2021; 97 ():102943.

Chicago/Turabian Style

Xiong Yang; Chunfu Shao; Chengxiang Zhuge; Mingdong Sun; Pinxi Wang; Shiqi Wang. 2021. "Deploying battery swap stations for shared electric vehicles using trajectory data." Transportation Research Part D: Transport and Environment 97, no. : 102943.

Journal article
Published: 16 June 2021 in Journal of Cleaner Production
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Dockless bike sharing (DBS) provides a sustainable and green travel mode, which also enhances the connections with other travel modes. Understanding the travel mobility and demand of DBS become an urgent task for government and operators to provide better service. In this paper, we propose a network-based method to detect the travel mobility of DBS users based on the actual trip data. The studied area is divided by square grid with same size. The grids with trips are considered as nodes and the connections between nodes are considered as edges. To gain the dynamic characteristics of DBS travel mobility, we construct several networks according to different time periods in a weekday. We build a data-driven framework to analyze DBS network including accessibility, spatial inequality, spatial autocorrelation and network-based indicators. The relationship between flow strength and point-of-interest (POI) is discussed. The results show that travel demands of DBS are higher in morning peak and evening peak on weekdays. The DBS networks are inequality, connections are concentrated on center area. From the network view, the DBS network are assortative and positive autocorrelated with evident communities. The results imply that the number of residence and transport facility have strong correlations with flow strength.

ACS Style

Hui Zhang; Chengxiang Zhuge; Jianmin Jia; Baiying Shi; Wei Wang. Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis. Journal of Cleaner Production 2021, 313, 127930 .

AMA Style

Hui Zhang, Chengxiang Zhuge, Jianmin Jia, Baiying Shi, Wei Wang. Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis. Journal of Cleaner Production. 2021; 313 ():127930.

Chicago/Turabian Style

Hui Zhang; Chengxiang Zhuge; Jianmin Jia; Baiying Shi; Wei Wang. 2021. "Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis." Journal of Cleaner Production 313, no. : 127930.

Journal article
Published: 06 January 2021 in Transportation Research Part D: Transport and Environment
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The future Autonomous Vehicles (AVs) are likely to be electric. We started with a review of the adoption of AVs, Electric Vehicles (EVs) and Autonomous Electric Vehicles (AEVs), as well as the six associated urban sub-systems, namely transportation, land use, environment, energy, economy, and population systems, in order to find evidence about the linkages and interactions between the diffusion of AEVs and the six sub-systems. Based on the review, we argued that an integrated urban model, which takes the linkages and interactions into account, was needed to fully understand the adoption and impacts of AEVs. Furthermore, we conducted a conceptual design of an integrated model for AEVs (without explicit modelling), and demonstrated how to update an existing agent-based Land Use and Transport Interaction (LUTI) model by incorporating AEV components. The resulting integrated model of AEVs would help different AEV-related stakeholders (e.g., local authorities) in their decision-making.

ACS Style

Chengxiang Zhuge; Chunyan Wang. Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design. Transportation Research Part D: Transport and Environment 2021, 91, 102679 .

AMA Style

Chengxiang Zhuge, Chunyan Wang. Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design. Transportation Research Part D: Transport and Environment. 2021; 91 ():102679.

Chicago/Turabian Style

Chengxiang Zhuge; Chunyan Wang. 2021. "Integrated modelling of autonomous electric vehicle diffusion: From review to conceptual design." Transportation Research Part D: Transport and Environment 91, no. : 102679.

Journal article
Published: 24 February 2020 in Energy Policy
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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.

ACS Style

Chengxiang Zhuge; Binru Wei; Chunfu Shao; Yuli Shan; Chunjiao Dong. The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing. Energy Policy 2020, 139, 111328 .

AMA Style

Chengxiang Zhuge, Binru Wei, Chunfu Shao, Yuli Shan, Chunjiao Dong. The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing. Energy Policy. 2020; 139 ():111328.

Chicago/Turabian Style

Chengxiang Zhuge; Binru Wei; Chunfu Shao; Yuli Shan; Chunjiao Dong. 2020. "The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing." Energy Policy 139, no. : 111328.

Journal article
Published: 27 November 2019 in Journal of Cleaner Production
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Cost-related factors (e.g., subsides) play a vital role in the diffusion of Electric Vehicle (EV). However, it remains unclear how these factors would influence the diffusion and further the associated urban elements (e.g., infrastructures) at the micro scale. In response, this paper tried to quantify the influence of two types of cost-related factors on the adoption of Electric Vehicle (EV), namely upfront cost and usage-related cost, using purchase subsides and fuel prices as examples, respectively. An agent-based integrated micro-simulation model (SelfSim-EV) was used here to simulate how the EV market in Beijing might evolve from 2016 to 2020, within several “what-if” scenarios considering different Plug-in Hybrid Electric Vehicle (PHEV) subsides, petrol prices and electricity prices. The results suggested that 1) doubling the PHEV subsidy would make PHEV price competitive and thus increase the PHEV sale from around zero to 2500 in 2019. The PHEV sale price increases by around 3500 RMB (from around 261,000 to 264,500 RMB) due to the increase in the PHEV penetrate rate. This further gives rise to the changes in those urban elements connected with the EV market, including the urban environment, electricity and infrastructure systems, especially at the disaggregate level; 2) both electricity and petrol prices have little influence on the adoption of EVs at the macro level (i.e. the city level), but they do influence the spatial distributions of both CV and EV owners (based on the analyses of their residential locations) and further geographical distributions of vehicular emissions, EV-related facilities (e.g., charging posts) and electricity demand of EV at multiple resolutions, ranging from the facility level to district level.

ACS Style

Chengxiang Zhuge; Binru Wei; Chunfu Shao; Chunjiao Dong; Meng Meng; Jie Zhang. The potential influence of cost-related factors on the adoption of electric vehicle: An integrated micro-simulation approach. Journal of Cleaner Production 2019, 250, 119479 .

AMA Style

Chengxiang Zhuge, Binru Wei, Chunfu Shao, Chunjiao Dong, Meng Meng, Jie Zhang. The potential influence of cost-related factors on the adoption of electric vehicle: An integrated micro-simulation approach. Journal of Cleaner Production. 2019; 250 ():119479.

Chicago/Turabian Style

Chengxiang Zhuge; Binru Wei; Chunfu Shao; Chunjiao Dong; Meng Meng; Jie Zhang. 2019. "The potential influence of cost-related factors on the adoption of electric vehicle: An integrated micro-simulation approach." Journal of Cleaner Production 250, no. : 119479.

Journal article
Published: 22 November 2019 in Science of The Total Environment
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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.

ACS Style

Chengxiang Zhuge; Min Yu; Chunyan Wang; Yilan Cui; Yi Liu. An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China. Science of The Total Environment 2019, 708, 135086 .

AMA Style

Chengxiang Zhuge, Min Yu, Chunyan Wang, Yilan Cui, Yi Liu. An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China. Science of The Total Environment. 2019; 708 ():135086.

Chicago/Turabian Style

Chengxiang Zhuge; Min Yu; Chunyan Wang; Yilan Cui; Yi Liu. 2019. "An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China." Science of The Total Environment 708, no. : 135086.

Journal article
Published: 10 November 2019 in Journal of Computational Science
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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.

ACS Style

Chengxiang Zhuge; Chunfu Shao; Xiong Yang. Agent- and activity-based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China. Journal of Computational Science 2019, 38, 101046 .

AMA Style

Chengxiang Zhuge, Chunfu Shao, Xiong Yang. Agent- and activity-based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China. Journal of Computational Science. 2019; 38 ():101046.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao; Xiong Yang. 2019. "Agent- and activity-based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China." Journal of Computational Science 38, no. : 101046.

Article
Published: 05 September 2019 in Transportation
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Coupling activity-based models with dynamic traffic assignment appears to form a promising approach to investigating travel demand. However, such an integrated framework is generally time-consuming, especially for large-scale scenarios. This paper attempts to improve the performance of these kinds of integrated frameworks through some simple adjustments using MATSim as an example. We focus on two specific areas of the model—replanning and time stepping. In the first case we adjust the scoring system for agents to use in assessing their travel plans to include only agents with low plan scores, rather than selecting agents at random, as is the case in the current model. Secondly, we vary the model time step to account for network loading in the execution module of MATSim. The city of Baoding, China is used as a case study. The performance of the proposed methods was assessed through comparison between the improved and original MATSim, calibrated using Cadyts. The results suggest that the first solution can significantly decrease the computing time at the cost of slight increase of model error, but the second solution makes the improved MATSim outperform the original one, both in terms of computing time and model accuracy; Integrating all new proposed methods takes still less computing time and obtains relatively accurate outcomes, compared with those only incorporating one new method.

ACS Style

Chengxiang Zhuge; Mike Bithell; Chunfu Shao; Xia Li; Jian Gao. An improvement in MATSim computing time for large-scale travel behaviour microsimulation. Transportation 2019, 48, 193 -214.

AMA Style

Chengxiang Zhuge, Mike Bithell, Chunfu Shao, Xia Li, Jian Gao. An improvement in MATSim computing time for large-scale travel behaviour microsimulation. Transportation. 2019; 48 (1):193-214.

Chicago/Turabian Style

Chengxiang Zhuge; Mike Bithell; Chunfu Shao; Xia Li; Jian Gao. 2019. "An improvement in MATSim computing time for large-scale travel behaviour microsimulation." Transportation 48, no. 1: 193-214.

Journal article
Published: 09 August 2019 in Energies
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An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

ACS Style

Chengxiang Zhuge; Chunfu Shao; Xia Li; Shao; Li. Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China. Energies 2019, 12, 3073 .

AMA Style

Chengxiang Zhuge, Chunfu Shao, Xia Li, Shao, Li. Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China. Energies. 2019; 12 (16):3073.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao; Xia Li; Shao; Li. 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China." Energies 12, no. 16: 3073.

Journal article
Published: 16 July 2019 in Sustainability
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A comparative study is carried out to investigate the differences among conventional vehicles (CVs), battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) in the maximum acceptable time of diverting to a refuelling station, maximum acceptable time of queueing at a refuelling station, refuelling modes and desirable electric driving ranges, using Beijing, China, as a case study. Here, several multinomial logit (MNL) models are developed to relate the diverting and waiting times to individual attributes. The results suggest that, (1) the diverting time roughly follows a normal distribution for both CVs and electric vehicles (EVs), but the difference between them is slight; (2) EVs tend to bear longer waiting time above 10 min; (3) the MNL models indicate that income and the level of education tend to be more statistically significant to both the diverting and waiting times; (4) the most preferred driving ranges obtained for BEVs and PHEVs are both around 50 km, indicating that EV drivers may just prefer to charge for a specific time ranging from 8 to 10 min. Finally, ways to apply the empirical findings in planning refuelling and charging stations are discussed with specific examples.

ACS Style

Chengxiang Zhuge; Chunfu Shao; Xia Li. A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China. Sustainability 2019, 11, 3869 .

AMA Style

Chengxiang Zhuge, Chunfu Shao, Xia Li. A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China. Sustainability. 2019; 11 (14):3869.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao; Xia Li. 2019. "A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China." Sustainability 11, no. 14: 3869.

Journal article
Published: 19 March 2019 in Transportmetrica B: Transport Dynamics
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ACS Style

Chengxiang Zhuge; Chunfu Shao. Agent-based modelling of office market for a land use and transport model. Transportmetrica B: Transport Dynamics 2019, 7, 1232 -1257.

AMA Style

Chengxiang Zhuge, Chunfu Shao. Agent-based modelling of office market for a land use and transport model. Transportmetrica B: Transport Dynamics. 2019; 7 (1):1232-1257.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao. 2019. "Agent-based modelling of office market for a land use and transport model." Transportmetrica B: Transport Dynamics 7, no. 1: 1232-1257.

Journal article
Published: 04 March 2019 in Journal of Cleaner Production
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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.

ACS Style

Chengxiang Zhuge; Binru Wei; Chunjiao Dong; Chunfu Shao; Yuli Shan. Exploring the future electric vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China. Journal of Cleaner Production 2019, 221, 710 -737.

AMA Style

Chengxiang Zhuge, Binru Wei, Chunjiao Dong, Chunfu Shao, Yuli Shan. Exploring the future electric vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China. Journal of Cleaner Production. 2019; 221 ():710-737.

Chicago/Turabian Style

Chengxiang Zhuge; Binru Wei; Chunjiao Dong; Chunfu Shao; Yuli Shan. 2019. "Exploring the future electric vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China." Journal of Cleaner Production 221, no. : 710-737.

Journal article
Published: 15 December 2018 in Journal of Cleaner Production
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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.

ACS Style

Chengxiang Zhuge; Chunfu Shao. Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives. Journal of Cleaner Production 2018, 213, 199 -216.

AMA Style

Chengxiang Zhuge, Chunfu Shao. Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives. Journal of Cleaner Production. 2018; 213 ():199-216.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao. 2018. "Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives." Journal of Cleaner Production 213, no. : 199-216.

Journal article
Published: 23 September 2018 in Journal of Computational Science
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This paper proposes an agent-based spatial social network model, which combines a utility function and heuristic algorithms, to formulate friendships of agents in a given synthetic population comprising individuals and households, as well as their attributes and locations. In order to better and explicitly represent the real social networks, the model attempts to generate both close and somewhat close social networks by linking agents with either close or somewhat close friendships, fitting both distributions of network degree and transitivity, which are two basic characteristics of a network. Here, a utility function, which incorporates the similarity between agents in individual attributes (e.g., sex), as well as the spatial closeness of their residential locations and workplaces, is developed to judge whether a friendship between a pair of agents can be built. Furthermore, the social network model is developed as a key component of an agent-and Geographic Information System (GIS)-based virtual city creator that is a set of synthesis methods used to generate spatially disaggregate urban data. Finally, Beijing, China is used as a case study. Both close and somewhat social networks are generated with the target and generated distributions well matched, and the generated networks are further analysed from a geographical perspective.

ACS Style

Chengxiang Zhuge; Chunfu Shao; Binru Wei. An Agent-based Spatial Urban Social Network Generator: A Case Study of Beijing, China. Journal of Computational Science 2018, 29, 46 -58.

AMA Style

Chengxiang Zhuge, Chunfu Shao, Binru Wei. An Agent-based Spatial Urban Social Network Generator: A Case Study of Beijing, China. Journal of Computational Science. 2018; 29 ():46-58.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao; Binru Wei. 2018. "An Agent-based Spatial Urban Social Network Generator: A Case Study of Beijing, China." Journal of Computational Science 29, no. : 46-58.

Journal article
Published: 29 August 2018 in International Journal of Environmental Research and Public Health
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Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation. In the two-step model, the predicting car ownership status in the first-step model is used as a mediating variable in the second-step car use model. Taking Changchun as a case study, this paper identifies the presence of spatial effects in influencing the effects of built environment on car ownership and use. Meanwhile, the direct and cascading effects of built environment on car ownership and use are revealed. The results show that the spatial autocorrelation exists in influencing the interaction between built environment and car dependency. The results suggest that it is necessary for urban planners to pay attention to the spatial effects and make targeted policy according to local land use characteristics.

ACS Style

Xiaoquan Wang; Chunfu Shao; Chaoying Yin; Chengxiang Zhuge. Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China. International Journal of Environmental Research and Public Health 2018, 15, 1868 .

AMA Style

Xiaoquan Wang, Chunfu Shao, Chaoying Yin, Chengxiang Zhuge. Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China. International Journal of Environmental Research and Public Health. 2018; 15 (9):1868.

Chicago/Turabian Style

Xiaoquan Wang; Chunfu Shao; Chaoying Yin; Chengxiang Zhuge. 2018. "Exploring the Influence of Built Environment on Car Ownership and Use with a Spatial Multilevel Model: A Case Study of Changchun, China." International Journal of Environmental Research and Public Health 15, no. 9: 1868.

Article
Published: 07 August 2018 in Networks and Spatial Economics
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This paper proposes an agent-based transport facility development model for both Conventional Vehicles (CVs) and Electric Vehicles (EVs), as a key component of an agent-based land use-transport model, SelfSim. The model attempts to simultaneously locate public parking lots, refuelling stations, charging stations and charging posts at parking lots with the consideration of competitions and interactions between the facilities. The facility development model is composed of a link-based model and node-based model that are used to simulate the development of link-based (e.g., replenishing stations) and node-based facilities (e.g., parking lots), respectively, based on the spatial and temporal disaggregate demand. The demand is extracted from the activity-based simulation with MATSim-EV that is an EV extension of MATSim (Multi-Agent Transport Simulation). In the model, facility agents are defined with several specific attributes and behavioural rules, and act the role of locating transport facilities to accommodate the demand. Finally, both global and local sensitivity analyses are applied to fully test the model in several experiments set up based on a Chinese medium-sized city, Baoding. The global SA that is based on Elementary Effect Method is firstly applied to quantify the extent to which the twelve model outputs of interest are sensitive to forty key model parameters, resulting in nine significantly important parameters; Then the Once-At-A-Time (OAT)-based local SA is used to provide further insight into how these important parameters influence the model outputs of interest over years. The SA results are expected to be useful for model calibration, and how the SA results can be used to calibrate the model is discussed.

ACS Style

Chengxiang Zhuge; Chunfu Shao. Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles. Networks and Spatial Economics 2018, 18, 875 -908.

AMA Style

Chengxiang Zhuge, Chunfu Shao. Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles. Networks and Spatial Economics. 2018; 18 (4):875-908.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao. 2018. "Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles." Networks and Spatial Economics 18, no. 4: 875-908.

Journal article
Published: 01 July 2018 in Journal of Computational Science
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Residential Location Choice (RLC) and Real Estate Price(REP) have been considered as highly correlated and therefore have been jointly studied. This paper develops an agent-based RLC-REP joint model as a key component of an integrated land use-transport model, SelfSim. RLC-REP is capable of simultaneously simulating purchasing, renting and investing behaviour, considering the interactions and competitions between different agent types in the housing market, including renter, landlord, purchaser, seller and investor agents, resulting in new residential locations and real estate prices. In addition, the demographic evolution model in SelfSim that is directly linked to the RLC-REP model is also introduced. Next, both global and local sensitivity analyses (SAs), which employ the Elementary Effect Method (EEM) and Once-At-A-Time (OAT) Method, respectively, are carried out to fully test RLC-REP in a numerical example set up based on a Chinese medium-sized city, Baoding. The EEM-based global SAs identify four influential parameters (among the thirty-four) that could significantly influence the outputs of interests. The OAT-based local SAs further explore how these four important parameters influence the outputs, suggesting that the interactions between parameters could heavily influence the model sensitivity. Finally, the potential applications of the SA results to calibrate the model and to set up “what-if” scenarios are discussed.

ACS Style

Chengxiang Zhuge; Chunfu Shao. Agent-based modelling of purchasing, renting and investing behaviour in dynamic housing markets. Journal of Computational Science 2018, 27, 130 -146.

AMA Style

Chengxiang Zhuge, Chunfu Shao. Agent-based modelling of purchasing, renting and investing behaviour in dynamic housing markets. Journal of Computational Science. 2018; 27 ():130-146.

Chicago/Turabian Style

Chengxiang Zhuge; Chunfu Shao. 2018. "Agent-based modelling of purchasing, renting and investing behaviour in dynamic housing markets." Journal of Computational Science 27, no. : 130-146.

Journal article
Published: 01 July 2018 in Physica A: Statistical Mechanics and its Applications
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Hub stations and important lines play key roles in transfers between stations. In this paper, a node failure model is proposed to identify hub stations. In the model, we introduce two new indicators called neighborhood degree ratio and transfer index to evaluate the importance of stations, which consider neighborhood stations’ degree of station and the initial transfer times between stations. Moreover, line accessibility is developed to measure the importance of lines in the bus network. Xiamen bus network in 2016 is utilized to test the model. The results show that the two introduced indicators are more effective to identify hub stations compared with traditional complex network indicators such as degree, clustering coefficient and betweenness.

ACS Style

Hui Zhang; Chengxiang Zhuge; Xiaohua Yu. Identifying hub stations and important lines of bus networks: A case study in Xiamen, China. Physica A: Statistical Mechanics and its Applications 2018, 502, 394 -402.

AMA Style

Hui Zhang, Chengxiang Zhuge, Xiaohua Yu. Identifying hub stations and important lines of bus networks: A case study in Xiamen, China. Physica A: Statistical Mechanics and its Applications. 2018; 502 ():394-402.

Chicago/Turabian Style

Hui Zhang; Chengxiang Zhuge; Xiaohua Yu. 2018. "Identifying hub stations and important lines of bus networks: A case study in Xiamen, China." Physica A: Statistical Mechanics and its Applications 502, no. : 394-402.