The sharing economy is making its way into our everyday lives. One of its business models, car-sharing, has become highly popular. Can it help us increase our sustainability? Besides emissions and vehicle miles traveled, one key aspect in the assessment regards the effect of car-sharing on car ownership. Previous studies investigating this effect have relied almost exclusively on surveys and come to very heterogeneous results, partly suggesting spectacular substitution rates between shared and private cars. This study empirically explores the impact of car-sharing on noncorporate car ownership and car markets in 35 large German cities. The analysis draws on publicly available data for the years 2012, 2013, 2015, and 2017, including, among others, the number of shared cars per operating mode (free-floating and station-based) and the number of cars owned and registered by private individuals (i.e., excluding company cars). We find that one additional station-based car is associated with a reduction of about nine private cars. We do not find a statistically significant relation between car ownership and free-floating car-sharing. Neither type of car-sharing appears to impact the markets for used and new cars significantly. Given the measurable impacts on car ownership levels, this result is surprising and invites future research to study car-sharing’s impact on the dynamics of car markets.

This dataset is the output of an advanced vehicular simulation performed using the VEINS OMNeT++ framework. The simulation was designed to model realistic vehicle dynamics, sensor readings, and communication parameters over a network of urban roadways connecting major US cities. The dataset provides a comprehensive view of vehicular performance and environmental conditions over a 30-day period for 500 vehicles.

Authors: Muhammad Ali & Tariq Qayyum

Below is a detailed description of each feature included in the dataset:

Temporal and Geospatial Data

VehicleID: A unique identifier for each vehicle in the simulation. Timestamp: The exact date and time for each record, allowing temporal analysis of vehicle behavior. Day, Hour, Minute, Second: These fields break down the timestamp into finer granularity for detailed time-series analysis. Latitude and Longitude: The real-time geographic coordinates of the vehicle as it progresses along its route. StartingPointLatitude and StartingPointLongitude: The coordinates of the vehicle's origin (selected from major US cities). DestinationLatitude and DestinationLongitude: The target coordinates where the vehicle is headed. Mobility and Performance Metrics

Speed: The current speed of the vehicle (in km/h), adjusted based on simulated traffic, weather conditions, and road quality. Direction: The vehicle's heading in degrees, indicating its travel direction. Odometer: The cumulative distance traveled by the vehicle during the simulation. VehicleType: Categorical variable indicating the type of vehicle (Car, Truck, Bus, Motorcycle). VehicleAge: The age of the vehicle, measured in years. EngineTemperature: The simulated engine temperature (in °C), which responds dynamically to vehicle speed. FuelLevel: The remaining fuel level, tracking consumption over distance traveled. BatteryLevel: The current battery level, which decreases based on speed and usage. TirePressure, BrakeFluidLevel, CoolantLevel, OilLevel, WiperFluidLevel: These parameters simulate vehicle maintenance metrics, reflecting system status during operation. Environmental and Traffic Conditions

WeatherCondition: The prevailing weather conditions (Clear, Rain, Fog, or Snow) set for each simulation day. RoadCondition: A categorical assessment of road quality (Good, Moderate, or Poor). TrafficDensity: The simulated traffic level at each time step (Low, Medium, or High), affecting vehicle speed and behavior. Communication and Processing Attributes

CPU_Available and Memory_Available: Simulated computational resource levels that might impact on-board processing and task execution. NetworkLatency: The simulated network latency in milliseconds, indicating communication delays. SignalStrength: The wireless signal strength measured in dBm. GPSStatus, WiFiStatus, BluetoothStatus, CellularStatus: Boolean flags indicating the operational status of various communication systems on the vehicle. RadarStatus, LidarStatus, CameraStatus, IMUStatus: Status indicators for the vehicle’s sensor suite, providing situational awareness. TaskType: The category of a computational or communication task being executed (Navigation, Entertainment, DataAnalysis, or Safety). TaskSize: A numeric value representing the computational or data size requirement of the task. TaskPriority: The priority level of the task (High, Medium, or Low), potentially affecting its execution order. TaskOffloaded: A boolean flag indicating whether a task was offloaded, influenced by the vehicle’s battery level and simulated decision-making. Additional Vehicle Features

HeadlightStatus: A boolean indicating if the headlights are on, based on the time of day and weather conditions. BrakeLightStatus: Indicates whether brake lights are activated when the vehicle is decelerating. TurnSignalStatus: A boolean showing if the vehicle’s turn signal is active. HazardLightStatus: Indicates the status of the hazard lights. ABSStatus: Reflects the functioning status of the Anti-lock Braking System. AirbagStatus: Indicates whether the airbag system is active (typically remains enabled during the simulation). Simulation Environment

The dataset encapsulates a realistic simulation of vehicular movement and behavior in urban settings. Key aspects include:

Dynamic Speed Adjustment: Vehicles adjust speeds based on time-of-day (e.g., rush hour), weather impacts, and traffic density. Resource Management: The simulation models the gradual depletion of battery, fuel, and other vehicular maintenance parameters. Sensor and Communication Systems: Real-time statuses of sensor suites and communication modules are recorded, emulating modern connected vehicles. Route Progression: Each vehicle's journey is tracked from a starting city to a destination, with geospatial progression computed using a distance estimation formula. Potential Applications

Researchers and practitioners can utilize thi...

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Micro Electric Vehicle Market Size 2023-2027

The micro electric vehicle market size is forecast to increase by USD1.54 th units at a CAGR of 8.02% between 2022 and 2027.

The market is experiencing significant growth, driven by several key trends. One major factor fueling market expansion is the establishment of dedicated consortiums for the development of micro electric vehicles, leading to innovative designs and vehicle platforms that cater to the unique needs of consumers. Additionally, advancements in Li-ion batteries, lighter construction materials, and increasing automation are enhancing the drivability and maneuverability of these vehicles. However, it is essential to note that the power grids serving as a source for charging these electric vehicles can indirectly contribute to environmental pollution. Despite this challenge, the market is poised for continued growth, with consumers increasingly seeking sustainable and eco-friendly transportation solutions.

What will be the Size of the Micro Electric Vehicle Market During the Forecast Period?

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The market is experiencing significant growth in the United States, driven by the demand for green transportation solutions in the context of smart cities. With increasing focus on emission reduction and the adoption of renewable energy, alternative fuels such as electric vehicles (EVs) are gaining popularity. Micro electric vehicles, including one-seater EVs and compact cars, offer sustainable transportation alternatives for urban mobility and last-mile delivery. Battery technology advancements and charging solutions have made EVs more accessible and convenient for consumers. Electric vehicle infrastructure, including charging stations, is being integrated into transportation infrastructure to support the growing demand for emission-free driving.Regulations and incentives are also playing a crucial role in the adoption of micro electric vehicles, with many cities and states implementing policies to promote the use of eco-friendly vehicles. Moreover, the integration of autonomous vehicles and mobility data analytics into the mobility ecosystem is expected to further drive the growth of the market. Urban planning initiatives are also focusing on traffic congestion solutions and emission reduction strategies, making micro electric vehicles an attractive alternative to traditional transportation methods. Overall, the market is poised for continued growth as a key component of the sustainable transportation landscape.

How is this Micro Electric Vehicle Industry segmented and which is the largest segment?

The micro electric vehicle industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD th units' for the period 2023-2027, as well as historical data from 2017-2021 for the following segments.TypeGolf and micro carsQuadricycleApplicationCommercialPersonalPublic utilitiesGeographyNorth AmericaCanadaUSAPACJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Type Insights

The Golf and micro cars segment is estimated to witness significant growth during the forecast period.

The market is primarily driven by the golf carts and micro cars segments, accounting for approximately 80% of the market share. This dominance is attributed to the rising sales of electric golf carts and personal utility vehicles in regions like North America and Europe. Additionally, micro cars with a maximum power rating of up to 15 kW are gaining popularity due to their low carbon footprint and cost-effectiveness for personal use. In commercial applications, such vehicles are increasingly utilized for cargo transportation, particularly in industries with large campuses or distribution centers. The electrification trend in the transportation sector is further propelling the market growth.Intelligent charging programs and electrification technologies are being integrated into these vehicles to enhance their functionality and efficiency. The market is expected to continue expanding as more businesses and consumers adopt sustainable and cost-effective transportation solutions.

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The Golf and micro cars segment accounted for USD 2127.62 th units in 2017 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 47% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is experiencing significant growth due to increasing demand for personal, affordable, and environmentally responsible transportation options. This trend is particularly noticeabl