Energy consumption simulation and economic benefit analysis for urban electric commercial-vehicles

Sun, Daniel; Zheng, Yijia; Duan, R. C.

doi:10.1016/j.trd.2021.103083

Cited by 31 publications

(14 citation statements)

References 24 publications

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“…The fields of the trajectory data are vehicle number, time, longitude, latitude, instantaneous velocity, direction angle, and occupancy status (0: empty, 1: occupancy), and the time interval of the trajectory data positioning point is 15 s. This paper is based on ArcGIS platform for matching GPS location points and road network. The matching method is to match the GPS positioning points to the nearest road in the same coordinate system [45]. To ensure the accuracy of the data, the positioning points with an instantaneous velocity greater than 120 km/h and more than 15 m away from the nearest road were deleted [8], and finally, 90.4% of the positioning points were matched to the road.…”

Section: Datamentioning

confidence: 99%

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Nian

Sun

Huang

2021

IJGI

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Road traffic congestion is a common problem in most large cities, and exploring the root causes is essential to alleviate traffic congestion. Travel behavior is closely related to the built environment, and affects road travel speed. This paper investigated the direct effect of built environment on the average travel speed of road traffic. Taxi trajectories were divided into 30 min time slot (48 time slots throughout the day) and matched to the road network to obtain the average travel speed of road segments. The Points of Interest (POIs) in the buffer zone on both sides of the road segment were used to calculate the built environment indicators corresponding to the road segment, and then a spatial panel data model was proposed to assess the influence of the built environment adjacent to the road segment on the average travel speed of the road segment. The results demonstrated that the bus stop density, healthcare service density, sports and leisure service density, and parking entrance and exit density are the key factors that positively affect the average road travel speed. The residential community density and business building density are the key factors that negatively affect the average travel speed. Built environments have spatial correlation and spatial heterogeneity in their influence on the average travel speed of road segments. Findings of this study may provide useful insights for understanding the correlation between road travel speed and built environment, which would have important implications for urban planning and governance, traffic demand forecasting and traffic system optimization.

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Section: Datamentioning

confidence: 99%

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Nian

Sun

Huang

2021

IJGI

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“…The paper [14] shows how the energy consumption changes when variable speeds are applied to a highway in Perth, Australia. The authors of [15] created a real driving cycle based on the road driving in Shanghai to determine energy consumption and economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Analysis of factors affecting the energy consumption of an EV vehicle - a literature study

Skuza

Jurecki

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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One of the most requested information about an electric vehicle is its energy consumption. This parameter is particularly important for electric vehicles users who drive them in real conditions. This paper aims to conduct literature research on the factors affecting the energy consumption of a vehicle. The article contains a set of information concerning how the temperature, traffic conditions, or properties of an electric vehicle translate into energy consumption. The results of studies presented in various publications were compared. The literature analysis allowed for the creation of a list that can be used as a compendium of knowledge on the energy consumption of electric cars.

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“…Urban mass transportation, such as car-sharing, ride-sharing, and ridehailing, has been the most severely affected service types. Ride-sharing and taxi-hailing services, as well as urban electric commercial vehicles have been on the rise in recent years owing to AI-driven smart applications, and management and is favored due to a variety of factors, including minimized travel costs, traffic congestion, emissions and energy issues [12][13][14]. However, in the aftermath of the pandemic; for example, the ride-sharing market is predicted to have lost a quota share of 50% to 60% during 2020 [15], but this is anticipated to rise by 70% to 80% with new countermeasures, such as barriers between drivers and passengers to adhere to social distancing restrictions.…”

Section: Introductionmentioning

confidence: 99%

Impact of COVID-19 on Urban Mobility and Parking Demand Distribution: A Global Review with Case Study in Melbourne, Australia

Mesfin

Sun

Peng

2022

IJERPH

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The tremendous impact of the novel coronavirus (COVID-19) on societal, political, and economic rhythms has given rise to a significant overall shift from pre- to post-pandemic policies. Restrictions, stay-at-home regulations, and lockdowns have directly influenced day-to-day urban transportation flow. The rise of door-to-door services and the demand for visiting medical facilities, grocery stores, and restaurants has had a significant impact on urban transportation modal demand, further impacting zonal parking demand distribution. This study reviews the overall impacts of the pandemic on urban transportation with respect to a variety of policy changes in different cities. The parking demand shift was investigated by exploring the during- and post-COVID-19 parking policies of distinct metropolises. The detailed data related to Melbourne city parking, generated by the Internet of things (IoT), such as sensors and devices, are examined. Empirical data from 2019 (16 March to 26 May) and 2020 (16 March to 26 May) are explored in-depth using explanatory data analysis to demonstrate the demand and average parking duration shifts from district to district. The results show that the experimental zones of Docklands, Queensbery, Southbanks, Titles, and Princess Theatre areas have experienced a decrease in percentage change of vehicle presence of 29.2%, 36.3%, 37.7%, 23.7% and 40.9%, respectively. Furthermore, on-street level analysis of Princess Theatre zone, Lonsdale Street, Exhibition Street, Spring Street, and Little Bourke Street parking bays indicated a decrease in percentage change of vehicle presence of 38.7%, 56.4%, 12.6%, and 35.1%, respectively. In conclusion, future potential policymaking frameworks are discussed that could provide further guidance in stipulating epidemic prevention and control policies, particularly in relation to parking regulations during the pandemic.

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Energy consumption simulation and economic benefit analysis for urban electric commercial-vehicles

Cited by 31 publications

References 24 publications

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model

Analysis of factors affecting the energy consumption of an EV vehicle - a literature study

Impact of COVID-19 on Urban Mobility and Parking Demand Distribution: A Global Review with Case Study in Melbourne, Australia

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