EV Driver Trips In London by Kaggle | Other

About this Dataset

EV Driver Trips In London

EV Driver Trips in London

Charging Bundle Optimization for EV Adoption

About this dataset

This dataset provides an in-depth look at electric vehicle (EV) driver trips in Westfield Shopping Center and Canary Wharf, London. It includes crucial information such as EV drivers' age, gender, marital status, employment status, income level and family size along with timing details of their travel patterns. This data also allows us to understand energy requirements for charging as well as charging rate estimates for different destinations based on walking distances from parking locations. Additionally the ChargingBundles.csv file can be used to find optimal charging rates and trip profiles that increase EV adoption enabling a more sustainable future. With this data set you can gain insight into the ever-growing EV market while optimizing ev driver journey's maximizing energy efficiency - all with just a few clicks!

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How to use the dataset

This dataset is a great tool for anyone interested in studying electric vehicle drivers trips and destinations in Westfield Shopping Center and Canary Wharf, London. It contains information on the EV driver’s age, gender, employment status, marital status, number of children, income, parking start and end times, walking distances from parking to destination(s), parking location(s), energy requirements for the trip(s), charging rates during the trip(s), whether or not the tour was work- related as well as optimal charging rates and times.

In order to start using this dataset effectively it is important to understand each of the columns present within it. Each of them provides a different type of insight into each EV driver’s details; some columns provide qualitative data while others hold quantitative insights into the EV driver's trips.

The “Dummy_Age” column represents the age group that an EV driver belongs to which can then be further broken down through other columns such as “Dummy_Gender” or “Dummy_EmploymentStatus. Dummy_children tells us about if/how many children an EV drive has while Energy_Required, Charging_Rate give quantitative information regarding energy requirements & charging rate respectively. Other columns such as ParkingLocation, Weekday & whether it was a work based tour give us more qualitative insights iinto each trip's specifics.
Once you are able to interpret this data you can explore relationships between different variables by performing statistical analysis or even create visualizations (eg: Scatter plots etc)to better comprehend any trends within this dataset

Research Ideas

EV Driver Usage Profiling: This dataset can be used to create comprehensive profiles of EV drivers to better understand their patterns of travel and charging behaviors. By studying the data points included in this dataset such as age, gender, income level, marital status, number of children, charging rates, energy requirements for trips and other behaviors, general trends and patterns in the usage of EVs can be identified.

Trip Planning: This dataset can be used by EV drivers for trip planning purposes by providing them with insights on optimal charging times & rates for their destination locations as well as walking distances from parking locations to destinations included in the ChargingBundles.csv file.

EV Demand Forecasting: The data contained within these datasets can provide invaluable insights into how EVs are being used and how usage may change over time based on external factors such as weather conditions or holidays etc., enabling researchers to construct predictive models that would allow them to accurately forecast future demand for electric vehicles in London or other cities where similar datasets are available or created through synthetic modelling techniques!

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: SyntheticTripsWestfield.csv

Column name	Description
Dummy_Age	Age group of the EV driver. (Categorical)
Dummy_Gender	Gender of the EV driver. (Categorical)
Dummy_EmploymentStatus	Employment status of the EV driver. (Categorical)
Dummy_MaritalStatus	Marital status of the EV driver. (Categorical)
Dummy_Children	Whether the EV driver has children or not. (Categorical)
Dummy_Income	Income level of the EV driver. (Categorical)
Parking_Start_time	Time when the EV driver started parking. (Time)
Parking_End_time	Time when the EV driver ended parking. (Time)
WalkingDistance_Parking1	Walking distance from the parking to the destination. (Numeric)
WalkingDistance_Parking2	Walking distance from the parking to the destination. (Numeric)
ParkingLocation	Location of the parking. (Categorical)
Energy_Required	Energy required for the EV driver's trip. (Numeric)
Charging_Rate	Charging rate for the EV driver's trip. (Numeric)
Dummy_WorkBasedTour	Whether the EV driver's trip was work related or not. (Categorical)
Weekday	Day of the week when the EV driver's trip took place. (Categorical)

File: ChargingBundles.csv

Column name	Description
Charging_Rate	Charging rate for the EV driver's trip. (Numeric)
Charging_FirstHour	Charging rate for the first hour of the EV driver's trip. (Numeric)
Charging_SecondHour	Charging rate for the second hour of the EV driver's trip. (Numeric)
Charging_ThirdHour	Charging rate for the third hour of the EV driver's trip. (Numeric)
Charging_FourthHour	Charging rate for the fourth hour of the EV driver's trip. (Numeric)
Parking	Location of the parking spot for the EV driver's trip. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Chargingbundles

@kaggle.thedevastator_ev_driver_trips_in_london.chargingbundles

6.14 KB
60 rows
7 columns


CREATE TABLE chargingbundles (
  "unnamed_0" BIGINT,
  "charging_rate" BIGINT,
  "charging_firsthour" BIGINT,
  "charging_secondhour" BIGINT,
  "charging_thirdhour" BIGINT,
  "charging_fourthhour" BIGINT,
  "parking" BIGINT
);

Synthetictripscanarywharf

@kaggle.thedevastator_ev_driver_trips_in_london.synthetictripscanarywharf

133.95 KB
2713 rows
16 columns


CREATE TABLE synthetictripscanarywharf (
  "unnamed_0" BIGINT,
  "dummy_age" DOUBLE,
  "dummy_gender" DOUBLE,
  "dummy_employmentstatus" DOUBLE,
  "dummy_maritalstatus" DOUBLE,
  "dummy_children" DOUBLE,
  "dummy_income" DOUBLE,
  "parking_start_time" DOUBLE,
  "parking_end_time" DOUBLE,
  "walkingdistance_parking1" DOUBLE,
  "walkingdistance_parking2" DOUBLE,
  "parkinglocation" DOUBLE,
  "energy_required" DOUBLE,
  "charging_rate" DOUBLE,
  "dummy_workbasedtour" DOUBLE,
  "weekday" DOUBLE
);

Synthetictripswestfield

@kaggle.thedevastator_ev_driver_trips_in_london.synthetictripswestfield

171.96 KB
3590 rows
16 columns


CREATE TABLE synthetictripswestfield (
  "unnamed_0" BIGINT,
  "dummy_age" DOUBLE,
  "dummy_gender" DOUBLE,
  "dummy_employmentstatus" DOUBLE,
  "dummy_maritalstatus" DOUBLE,
  "dummy_children" DOUBLE,
  "dummy_income" DOUBLE,
  "parking_start_time" DOUBLE,
  "parking_end_time" DOUBLE,
  "walkingdistance_parking1" DOUBLE,
  "walkingdistance_parking2" DOUBLE,
  "parkinglocation" DOUBLE,
  "energy_required" DOUBLE,
  "charging_rate" DOUBLE,
  "dummy_workbasedtour" DOUBLE,
  "weekday" DOUBLE
);