This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).

The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

This shows the number of vehicles that were registered by Washington State Department of Licensing (DOL) each month. The data is separated by county for passenger vehicles and trucks. DOL integrates National Highway Traffic Safety Administration (NHTSA) data and the Environmental Protection Agency (EPA) fuel efficiency ratings with DOL titling and registration data to create this information.

As of 2023, there were a total of about 368,088 1,001 cc to 1,600 cc cars in Singapore. In comparison, there were approximately 20,284 cars with 1,000 cc and below in total in the same year.

Dataset

This dataset was created by Tarun Karli

Released under Apache 2.0

Contents

Dataset

This dataset was created by AbdulRafay911

Released under U.S. Government Works

Contents

As of 2023, there was a total of approximately 524 thousand private cars in Singapore, slightly decreasing from the previous year. In comparison, there were about 502 thousand private cars in Singapore in 2017.

The dataset contains year-wise data on population, number of vehicles registered, the total road length of highways and all roads, and number of vehicles per 1,000 population and per 1,00 kilometers of road length.

As of the end of 2023, there was a total of approximately 996 thousand motor vehicles in Singapore. In comparison, there were approximately 972 thousand motor vehicles in Singapore in 2014. The lowest level was reached in 2016, with about 956 thousand vehicles registered in the country. Singapore’s car population In 2023, Singapore recorded a large proportion of cars and station wagons in its vehicle population, with around 651 thousand of these vehicles on the road, the vast majority of which were for private use. Indeed, during this period, the country recorded around 524 thousand private cars, a slight increase from the previous year. The most common type of car among Singaporeans ranged from 1,001 cc to 1,600 cc. Sustainability of Singapore's vehicle fleet In 2023, there were only about five thousand cars over twenty years old on Singapore's roads. Over the same year, around 30 thousand cars were less than a year old, indicating a relatively young fleet. The Certificate of Eligibility (COE) system, introduced in May 1990, has played a key role in regulating the car fleet. Despite this, gasoline-powered cars still account for the largest share of cars in the city-state, with a total of about 559 thousand vehicles. Although this proportion has fallen slightly over time, petrol-powered cars remain the most popular choice. Looking ahead, Singapore remains committed to phasing out internal combustion engine (ICE) vehicles and aims to switch to cleaner energy sources for all vehicles by 2040.

Singapore Motor Vehicle Population: Passenger Car: 1600cc & Below data was reported at 315,863.000 Unit in Oct 2018. This records a decrease from the previous number of 316,501.000 Unit for Sep 2018. Singapore Motor Vehicle Population: Passenger Car: 1600cc & Below data is updated monthly, averaging 321,700.000 Unit from Jan 2005 (Median) to Oct 2018, with 166 observations. The data reached an all-time high of 342,088.000 Unit in Jan 2013 and a record low of 260,026.000 Unit in Feb 2005. Singapore Motor Vehicle Population: Passenger Car: 1600cc & Below data remains active status in CEIC and is reported by Land Transport Authority. The data is categorized under Global Database’s Singapore – Table SG.TA002: Motor Vehicle Population and Registrations.

Singapore Motor Vehicle Population data was reported at 956,428.000 Unit in Nov 2018. This records an increase from the previous number of 956,106.000 Unit for Oct 2018. Singapore Motor Vehicle Population data is updated monthly, averaging 799,373.000 Unit from Jan 1995 (Median) to Nov 2018, with 287 observations. The data reached an all-time high of 974,495.000 Unit in Jan 2014 and a record low of 614,734.000 Unit in Jan 1995. Singapore Motor Vehicle Population data remains active status in CEIC and is reported by Land Transport Authority. The data is categorized under Global Database’s Singapore – Table SG.TA002: Motor Vehicle Population and Registrations.

Singapore Motor Vehicle Population: Cars: 1,000 cc & Below data was reported at 5,388.000 Unit in 2017. This records an increase from the previous number of 4,821.000 Unit for 2016. Singapore Motor Vehicle Population: Cars: 1,000 cc & Below data is updated yearly, averaging 28,457.000 Unit from Dec 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 53,094.000 Unit in 1997 and a record low of 4,821.000 Unit in 2016. Singapore Motor Vehicle Population: Cars: 1,000 cc & Below data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.TA006: Motor Vehicle Population and Registrations (Annual).

Dataset

This dataset was created by Josia Given Santoso

Released under MIT

Contents

Philippines Population Density: Cordillera Administrative Region (CAR) data was reported at 87.000 Person/sq km in 2015. This records an increase from the previous number of 82.000 Person/sq km for 2010. Philippines Population Density: Cordillera Administrative Region (CAR) data is updated yearly, averaging 66.493 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 87.000 Person/sq km in 2015 and a record low of 47.000 Person/sq km in 1980. Philippines Population Density: Cordillera Administrative Region (CAR) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G005: Population Density.

The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

In 2020, Brunei had the highest vehicle to population ratio among Southeast Asian countries, with around 997.8 vehicles per 1,000 population. In contrast, there were approximately 27.9 vehicles per 1,000 population in Cambodia that year.

Main motor vehicles markets in Southeast Asia

Indonesia leads the motor vehicle sales in Southeast Asia with more than one million vehicles sold in a year, closely followed by Thailand. However, in terms of production the opposite is true. Thailand leads the motor vehicle production in the region with under two million vehicles. And Indonesia follows with almost one and a half million vehicles produced in a year. In 2022, Southeast Asian countries were characterized by significant growth in annual motor vehicle sales, led by Malaysia, Vietnam, and the Philippines, with over 30 percent increase each.

What purchase aspects do Southeast Asian vehicle buyers value?

Almost half of the consumers in Southeast Asia believe that striking a good deal is an important aspect of the vehicle purchasing experience. When it comes to choosing a car brand, consumers in Southeast Asia appreciate quality, with 71 percent of the consumers treasuring product quality as an important factor driving brand choice. Nevertheless, a shift towards more sustainable preferences is present among the consumers in the region. Even though there are many concerns regarding battery electric vehicles in the region, consumers think that there are several reasons to switch to electric cars as the number of charging stations increases and EV performance equals or surpasses that of combustible-engine or hybrid cars.

Inputs for the publicly available EPA modeling utility MOVES (MOtor Vehicle Emissions Simulator), used to estimate air pollution emissions from mobile sources. Please contact dec.sm.MOVES@dec.ny.gov with questions.

Dataset

This dataset was created by Don D.M. Tadaya

Released under MIT

Contents

Singapore Motor Vehicle Population: Cars: 3,001 cc & Above data was reported at 16,160.000 Unit in 2017. This records a decrease from the previous number of 17,862.000 Unit for 2016. Singapore Motor Vehicle Population: Cars: 3,001 cc & Above data is updated yearly, averaging 6,215.000 Unit from Dec 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 19,641.000 Unit in 2014 and a record low of 1,062.000 Unit in 1987. Singapore Motor Vehicle Population: Cars: 3,001 cc & Above data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.TA006: Motor Vehicle Population and Registrations (Annual).

Background

Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.

A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).

https://doi.org/10.1016/j.trd.2023.103645

Methodology

This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.

This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).

Data description

The current data product covers seven data files.

(1) Agents' experienced activity plans

File name: 1_activity_plans.csv

Column	Description	Data type	Unit
person	Agent ID	Integer	-
act_id	Activity index of each agent	Integer	-
deso	Zone code of Demographic statistical areas (DeSO)1	String	-
POINT_X	Coordinate X of activity location (SWEREF99TM)	Float	meter
POINT_Y	Coordinate Y of activity location (SWEREF99TM)	Float	meter
act_purpose	Activity purpose (work, home, other)	String	-
mode	Transport mode to reach the activity location (car)	String	-
dep_time	Departure time in decimal hour (0-23.99)	Float	hour
trav_time	Travel time to reach the activity location	String	hour:minute:second
trav_time_min	Travel time in decimal minute	Float	minute
speed	Travel speed to reach the activity location	Float	km/h
distance	Travel distance between the origin and the destination	Float	km
act_start	Start time of activity in minute (0-1439)	Integer	minute
act_time	Activity duration in decimal minute	Float	minute
act_end	End time of activity in decimal hour (0-23.99)	Float	hour
score	Utility score of the simulation day given by MATSim	Float	-

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Travel trajectories

File name: 2_input_zip

Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.

Column	Description	Data type	Unit
time	Time in second in a simulation day (0-86399)	Integer	Second
type	Event type defined by MATSim simulation2	String	-
person	Agent ID	Integer	-
link	Nearest road link consistent with (5)	String	-
vehicle	Vehicle ID identical to person	Integer	-

² One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(3) Overnight charger access

File name: 3_home_charger_access.csv

Column	Description	Data type	Unit
person	Agent ID	Integer	-
home_charger	Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)	Integer	-

(4) Car fleet composition

File name: 4_car_fleet.csv

Column	Description	Data type	Unit
person	Agent ID	Integer	-
income_class	Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)	Integer	-
car	Car model class (B=40 kWh, C=60 kWh, D=100 kWh)	String	-

(5) Road network with slope information

File name: 5_road_network_with_slope.shp (5 files in total)

Column	Description	Data type	Unit
length	The length of road