Use Of Vehicles Research Articles

Vehicle Stock Numbers and Survival Functions for On-Road Exhaust Emissions Analysis in India: 1993–2018

Road transport plays a crucial role in sustaining all the personal and freight movement needs of residential, commercial, and industrial activities, and in Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads contribute to as much as 50% of particulate matter pollution in a year. Therefore, effective management of vehicle exhaust emissions is vital not only for improving current air quality but also for ensuring the long-term benefits from efforts to reduce air pollution. In the approved clean air action plans for 131 cities under the national clean air program (NCAP), more than 50% of the implementable actions are transport-centric. Having a reliable and replicable vehicle exhaust emissions inventory is essential for effective planning, which can help establish a baseline, support scenario analysis, and allow for tracking progress in the sector. This process begins with accessing accurate vehicle stock numbers, typically obtained from vehicle registration databases, traffic surveys, and other governmental records. Often, in low- and middle-income countries like India, these numbers require extensive data cleaning before they can be used for emissions and pollution analysis. This paper presents a cleaned, open-access vehicle stock database for India and outlines a methodology to build and maintain an in-use vehicle age-mix database for future years. The database covers the years 1993 to 2018 for the entire country and individual states, along with estimates of the age distribution of vehicles using survival functions. By offering a comprehensive and reliable data source, this paper aims to support sustainable national and urban air quality management efforts, helping policymakers and stakeholders make informed decisions to improve air quality and public health.

Urbanization inequality: evidence from vehicle ownership in Chinese cities

Unequal outcomes resulting from urbanization can pose a significant challenge to sustainable development. Vehicles are an important urbanization dimension as a critical component of urban infrastructure by providing mobility and accessibility to social services. China’s vehicle ownership (referred to as in-use vehicle stocks) has been growing quickly since 2000, but its per capita stocks are still much lower than that in developed economies. This raises the question of whether and when China’s vehicle stocks will reach a peak level close to that in the developed countries. By analyzing vehicle stocks in 283 Chinese cities during 2001–2018, we have the following findings: (1) vehicle stocks are predominantly distributed in northern and eastern coastal cities and provincial capital cities; (2) inequality in vehicle ownership rates between cities shows a declining trend at both national and region scales; (3) the growth of vehicle ownership rates follows an S-shape curve and most cities are still at the early stage of motorization; (4) China is likely to have a lower saturation level of vehicle ownership rate. These results could help to accurately forecast future vehicle demand in China, estimate the resulting environmental impacts, and explore strategies to achieve carbon neutrality in transportation.

Computer Vision-Based Lane Detection and Detection of Vehicle, Traffic Sign, Pedestrian Using YOLOv5

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method.

Charging Station Site Selection Optimization for Electric Logistics Vehicles, Taking into Account Time-Window and Load Constraints

In order to improve the efficiency of the “last-mile” distribution in urban logistics and solve the problem of the difficult charging of electric logistics vehicles (ELVs), this paper proposes a charging station location optimization scheme for ELVs that takes into account time-window and load constraints (TW-LCs). Taking the optimal transportation path as the objective function and considering the time-window and vehicle load constraints, a charging station siting model was established. For the TW-LC problem, an improved genetic algorithm combining the farthest-insertion heuristic idea and local search operation was designed. Three different types of standardized arithmetic examples, C type, R type, and RC type, were used to test the proposed algorithm and compare it with the traditional genetic algorithm. The results indicate that, under the same conditions, compared to the traditional genetic algorithm, the improved genetic algorithm reduced the optimal path length by an average of 11.12%. It also decreased the number of charging stations selected, the number of vehicles in use, and the algorithm complexity by 22.97%, 13.71%, and 46.81%. Building on this, a case study was conducted on the TW-LC problem in a specific area of Chongqing, China. It resulted in a 50% reduction in the number of charging stations and a 25% reduction in the number of vehicles selected. In terms of economic indicators, the proposed algorithm improves unit electricity sales by 73.88% and reduces the total annualized cost of the logistics company by 18.81%, providing a theoretical basis for the subsequent promotion of ELVs.

Large-scale automated emission measurement of individual vehicles with point sampling

Abstract. Currently, emissions from internal combustion vehicles are not properly monitored throughout their life cycle. In particular, a small share of vehicles (< 20 %) with malfunctioning after-treatment systems and old vehicles with outdated engine technology are responsible for the majority (60 %–90 %) of traffic-related emissions. Remote emission sensing (RES) is a method used for screening emissions from a large number of in-use vehicles. Commercial open-path RES systems are capable of providing emission factors for many gaseous compounds, but they are less accurate and reliable for particulate matter (PM). Point sampling (PS) is an extractive RES method where a portion of the exhaust is sampled and then analyzed. So far, PS studies have been predominantly conducted on a rather small scale and have mainly analyzed heavy-duty vehicles (HDVs), which have high exhaust flow rates. In this work, we present a comprehensive PS system that can be used for large-scale screening of PM and gas emissions, largely independent of the vehicle type. The data analysis framework developed here is capable of processing data from thousands of vehicles. The core of the data analysis is our peak detection algorithm (TUG-PDA), which determines and separates emissions down to a spacing of just a few seconds between vehicles. We present a detailed evaluation of the main influencing factors on PS measurements by using about 100 000 vehicle records collected from several measurement locations, mainly in urban areas. We show the capability of the emission screening by providing real-world black carbon (BC), particle number (PN) and nitrogen oxide (NOx) emission trends for various vehicle categories such as diesel and petrol passenger cars or HDVs. Comparisons with open-path RES and PS studies show overall good agreement and demonstrate the applicability even for the latest Euro emission standards, where current open-path RES systems reach their limits.

Организация энергетического и информационного взаимодействия берегового поста управления с гнпа при обслуживании подводных добычных комплексов

The creation of underwater stations and resident vehicles for use in underwater conditions, as well as technologies that provide the full range of measures necessary for the placement, maintenance, bringing, reception and release of an underwater vehicle is a new direction in underwater robotics. When developing such systems, it is necessary to solve a number of tasks, some of which are traditional, for example, the tasks of approaching an underwater vehicle to the station and underwater docking. Other tasks are new, such as providing contactless battery charging and high-speed contactless information exchange between the station and the device, as well as organizing high-speed communication of the station with a remote coastal control post. The domestic experience of research on such tasks is limited to solving particular issues. The representation of the energy and information interaction system of an underwater service station, an uninhabited underwater vehicle and a coastal control post in the form of a single complete complex is unknown from the literature. For this reason, mention task is relevant, the solution of which will ultimately improve the efficiency of hydrocarbon production. The present functional diagram, which combines the channels of energy and information interaction into a single complex. The principle of constructive implementation of the basic elements is defined. Accepted research methods include interrelated theoretical analysis, mathematical modeling, and experimental research.

Impacts of ethanol blended fuels and cold temperature on VOC emissions from gasoline vehicles in China

The Chinese central government has initiated pilot projects to promote the adoption of gasoline containing 10%v ethanol (E10). Vehicle emissions using ethanol blended fuels require investigation to estimate the environmental impacts of the initiative. Five fuel formulations were created using two blending methods (splash blending and match blending) to evaluate the impacts of formulations on speciated volatile organic compounds (VOCs) from exhaust emissions. Seven in-use vehicles covering China 4 to China 6 emission standards were recruited. Vehicle tests were conducted using the Worldwide Harmonized Test Cycle (WLTC) in a temperature-controlled chamber at 23 °C and −7 °C. Splash blended E10 fuels led to significant reductions in VOC emissions by 12%–75%. E10 fuels had a better performance of reducing VOC emissions in older model vehicles than in newer model vehicles. These results suggested that E10 fuel could be an option to mitigate the VOC emissions. Although replacing methyl tert-butyl ether (MTBE) with ethanol in regular gasoline had no significant effects on VOC emissions, the replacement led to lower aromatic emissions by 40%–60%. Alkanes and aromatics dominated approximately 90% of VOC emissions for all vehicle-fuel combinations. Cold temperature increased VOC emissions significantly, by 3–26 folds for all vehicle/fuel combinations at −7 °C. Aromatic emissions were increased by cold temperature, from 2 to 26 mg/km at 23 °C to 33–238 mg/km at −7 °C. OVOC emissions were not significantly affected by E10 fuel or cold temperature. The ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) of splash blended E10 fuels decreased by up to 76% and 81%, respectively, compared with those of E0 fuels. The results are useful to update VOC emission profiles of Chinese vehicles using ethanol blended gasoline and under low-temperature conditions.

Development of a regional-based predictive model of incidence of traumatic spinal cord injury using machine learning algorithms

ObjectiveTo develop a predictive model of incidence of traumatic spinal cord injury (TSCI). MethodsThe data for training the model included both the incidence data and the covariates. The incidence data were extracted from systematic reviews and the covariates were extracted from data available in the international road federation database. Then the feature processing measures were taken. First we defined a hyper-parameter, missing-value threshold, in order to eliminate features that exceed this threshold. To tackle the problem of overfitting of model we determined the Pearson correlation of features and excluded those with more than 0.7 correlation. After feature selection three different models including simple linear regression, support vector regression, and multi-layer perceptron were examined to fit the purposes of this study. Finally, we evaluated the model based on three standard metrics: Mean Absolute Error, Root Mean Square Error, and R2. ResultsOur machine-learning based model could predict the incidence rate of TSCI with the mean absolute error of 4.66. Our model found “Vehicles in use, Total vehicles/Km of roads”, “Injury accidents/100 Million Veh-Km”, “Vehicles in use, Vans, Pick-ups, Lorries, Road Tractors”, “Inland surface Passengers Transport (Mio Passenger-Km), Rail”, and "% paved” as top predictors of transport-related TSCI (TRTSCI). ConclusionsOur model is proved to have a high accuracy to predict the incidence rate of TSCI for countries, especially where the main etiology of TSCI is related to road traffic injuries. Using this model, we can help the policymakers for resource allocation and evaluation of preventive measures.

Charging Stations for Large-Scale Deployment of Electric Vehicles

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established.

Assessment of the possibility of making changes to the design of vehicles in operation

The article discusses the procedure for registering changes made to the design of vehicles in use. Data is provided on the distribution of accredited testing laboratories by region of the Russian Federation and the number of registered changes made to the design of vehicles. Algorithms have been developed for conducting preliminary technical examination and safety testing with the participation of regional divisions of testing laboratories. Keywords design change, safety, testing laboratory

Omnidirectional Battery Electric Vehicle for Use in Oil Palm Plantation

Concerns on the low labour shortage and the need to reduce greenhouse gas (GHG) generation prompted the industrial player to increase mechanisationduring operation and adopting low carbon footprint mobile platforms. In this paper, a design and analysis study was conducted and reported on a conceptual battery electric vehicle (BEV) for use in the oil palm plantations. With a maximum operating road speed of 30km/hr and harvesting load of 320kg, the BEV will require a minimum operational power of 44.5kW. In accommodating the soggy ground with the demand to navigate narrow and undulating terrains, the proposed BEV was designed to be equipped with an omnidirectional functionality with continuous track, without the use of conventional wheels. Major components arrangement and design for the omnidirectional mechanism are presented in this paper. This preliminary work will also serve as the foundation towards refining the BEV design, especially on the vehicle functionality and manoeuvrability in the plantation sites. Further recommendations are also outlined that will focus on validating the conceptual design and analysis on the total energy generation and consumption of the BEV.

Characteristics of NOX and NH3 emissions from in-use heavy-duty diesel vehicles with various aftertreatment technologies in China

Some in-use China IV and China V heavy-duty diesel vehicles (HDDVs) with selective catalytic reduction (SCR) systems probably fail to mitigate nitrogen oxide (NOX) emissions as expected. Meanwhile, these SCR-equipped HDDVs might emit excessive ammonia (NH3). To better understand the NOX and NH3 emissions from typical HDDVs in China, seventeen in-use vehicles with various emission-control technologies were tested by using laboratory chassis dynamometers. The results indicated that individual NOX and NH3 emissions from HDDV fleets widely varied owing to differences in aftertreatment performance. China V and VI HDDVs with effectively functioning SCRs could substantially control their NOX emissions to be below the corresponding emission limits (i.e., 4.0 and 0.69 g/kWh for China V and China VI vehicles, respectively) but with a potential risk of high NH3 emissions caused by diesel exhaust fluid (DEF) overdosing. Furthermore, higher vehicle speed and payload resulted in lower NOX emissions and possibly higher NH3 emissions from HDDVs with effectively functioning SCRs, while higher NOX emissions from tampered- and non-SCR HDDVs. NOX emissions from China VI HDDVs were more sensitive to cold starts compared to China V and earlier vehicles, but there was no significant discrepancy in NH3 emissions between cold- and hot-start tests.

Analytical Studies on Techniques and Algorithms of Automatic Number Plate Recognition

The city's efforts to improve traffic make a big step forward when license plates can be read. It explains how an intelligent transportation system should work and what steps should be taken for it to be successful. Because the number of cars is growing so quickly, ANPR is a must-have for managing traffic control. Automatic number plate recognition (ANPR) is mostly used to keep an eye on traffic and keep people safe. Number plate recognition uses image processing and the most recent advances in technology to automatically read the characters on a vehicle's license plate. In the last few years, there have been a number of technological advances in the field of studying how to read license plates. Image processing techniques like OCR make it possible for traffic surveillance to solve a wide range of problems that come up during criminal investigations, the collection of tolls, the monitoring of traffic, the regulation of speed, and the management of parking, among other things. If you want to control traffic and keep an eye on a large number of people in a transportation system, you need an ANPR system. Image processing techniques and the collection of photos of vehicles for use in the dataset have made it possible to keep an eye on traffic on a large scale. Automatic License Plate Reader (ANPR) is a way to take the steps needed to run a good intelligent transportation network. Traffic control has become an absolute must because the number of cars has grown so quickly. The main goal of ANPR is to keep an eye on and record traffic for defense purposes. To read the text on license plates, number plate recognition uses image processing, optical character recognition (OCR), and edge detection technology. The model is made up of three different parts called, respectively, the module for car detection, the module for license plate segmentation, and the module for recognition. Image processing made us more determined to stop a wide range of illegal activities, such as armed robberies of cars, breaking traffic rules, and the way law enforcement is handled. This review study looked at all of the different designs for reading license plates that have been put into use so far.

Mining and Analysis of In-use Vehicle Corrosion Data Based on Typical Environmental Conditions in China

As an important performance indicator for measuring product quality and safety, vehicle corrosion resistance has always been of great concern to industry enterprises and consumers. In this paper, we collect, processed and analysed exterior corrosion data from a total of 54 in-use vehicles (the vehicle that has been licensed and put into service) of nine models with high ownership in the coastal region of North China. Through the evaluation model of automobile corrosion resistance performance, data mining analysis was conducted on five key areas and the whole vehicle with different brands and ages (3-year and 6-year vehicles), key areas including exterior body, passenger compartment, luggage compartment, engine compartment, and chassis. It was found that the chassis and engine compartment of each vehicle model were severely corroded, with the corrosion surface of some chassis parts reaching 100%. The luggage compartment and exterior body were slightly corroded, and the luggage compartment parts of 8 models have not corroded after 3 years of use.

A real-time NOx emission inventory from heavy-duty vehicles based on on-board diagnostics big data with acceptable quality in China

With the phase-in of China VI emission standard for heavy-duty vehicle (HDV), increasing numbers of HDVs are mandatorily equipped with on-board diagnostic (OBD) systems, providing opportunity to remote monitoring and supervision of emissions from in-use vehicles. However, the quality problem of OBD data is serious and lack of comprehensive assessment. In this study, the quality of nearly 1.6 billion OBD data from 38,692 China VI diesel HDVs travelling inside the Tangshan city of China was evaluated using a proposed framework which consists of validity indicators and signal transmission indicators. More than 24% of vehicles had overall invalidity rate over 20%, and only 0.4% of vehicles met the transmission period requirement of 1 Hz, indicating that the necessity and urgency for supervising and improving the OBD data quality. In particularly, several specific vehicle models were more prone to data quality problems, especially in the inability to determine the emission-related information. A real-time street-level NOx emission inventory was developed based on the OBD data with acceptable quality, using the direct monitoring and calculation of instantaneous emissions from each HDV. Compared to those from the top-down method which relied on gridded emissions and spatial allocation proxy schemes, the temporal and spatial characteristics of our results reflected the distribution and variations of actual freight demand in the real world. The developed real-world emission inventory will provide accurate support for the policy-making of precise control measures on HDVs.

Implementation of Enhanced Spray Routing Protocol for VDTN On Surabaya Smart City Scenario

The application of smart-city, which promises better city management in helping to improve people's quality of life, is still inhibited due to the high cost of infrastructure investment. In several Smart Cities, it takes at least $ 30 - 40 billion to convert a conventional town into a smart city, including for data collection infrastructure. Alternatively, low-power wide-area networks (LPWANs) could be considered, but they need more bandwidth to serve data transmission in a smart city. Vehicle Delay Tolerant Network (VDTN) is one part of DTN that employs vehicles as a communication infrastructure that allows communication in challenging conditions and could make it an alternative network for Data Collection in a Smart City. This paper proposes a Surabaya Smart City scenario with VDTN as data collection. The scenario consists of 40 wireless sensors and 50 to 200 vehicles (car and bus) with five Road Side Units that forward data from the sensor to the monitoring server. Furthermore, to increase the VDTN performance, we improve our proposed routing protocol, Spray and Hop Distance (SNHD), with two sprays method (Adaptive and Simple) and data collection support from multiple sources and destinations. The evaluation was carried out using a simulation-based comparison with an increase in the number of vehicles to determine the impact of vehicle density on data collection performance in terms of delivery probability, latency average, and overhead ratio. Based on the simulation results, the simple spray method in SNHD and A-SNHD outperformed the well-known VDTN routing protocol, i.e., Epidemic and Spray and Wait. Furthermore, when the number of cars increases from 50 to 200, the performance of VDTN does not increase significantly as the density of the network increases. It means that VDTN only requires a small number of vehicles to be used as a low-cost alternative network for smart cities.
 

Effect of Fuel Quality, Vehicle Maintenance and Advanced Emission Control Technology on Pakistan In -Use Light Vehicle Emissions

Pakistan is suffering from some of the worst air quality in Asia. The transportation sector is a major contributor to air pollution along diesel powered vehicles and rickshaws being the primary pollutions source. However, the contribution from light duty vehicles cannot be ignored. In this paper, results from an in-use emission study of over 500 light duty vehicles are reported and discussed. A random geographically diversified survey is conducted to collect data regarding the emissions from in-use vehicles in Pakistan. A review of the government fuel quality policy is provided. Through this analysis, the effects of polices addressing vehicle maintenance and repair, advanced vehicle emission technology and fuel quality are evaluated. These results provide insight to guide further measures to ensure meaningful impact on urban air quality in Pakistan. The same study may also be conducted in other regional countries to support effective emission control policy from the light vehicles.

Experimental analysis of phase shift modulation methods effects on EMI in dual active bridge DC-DC converter

Dual active bridge DC-DC converters (DAB) are one of the bidirectional converters and mainly used in electric vehicles and micro grids. With the increasing number of electric vehicles in use, and the increasing amount of batteries in these vehicles and rapidly spanning micro grids, the importance of DAB converters has increased and they are expected to be smaller in size and operate at higher power levels. The reduction in size has increased the importance of power density and efficiency, which are two significant parameters of DAB circuits. One of the methods to increase efficiency and power density is to increase the switching frequency. However, with the increase at the switching frequency, electromagnetic interference (EMI) in the DAB circuit also increases.In this manuscript, the change in the EMI values emitted by conduction, according to the phase shift modulation methods at DAB converters are evaluated. Also the effect of type of MOSFETs on conducted EMI is examined. Prototypes of the DAB converters by using Si and SiC MOSFETs with power rating of 1 kW are designed and developed. Switching frequency of converter is 25 kHz and leakage inductance is determined 81uH and 75uH. Si based circuit is operated with %88.5 efficiency. SiC based circuit is also operated with %88 efficiency. The designed circuits are operated using the phase shift modulations SPS and DPS. Conducted emission noise emitted by these circuits are analyzed by dividing them into common mode and difference mode noises. It has been observed that the EMI value when using SPS method is 15–20 dBµV lower than the EMI when using DPS method. The relationship between the measured noises and the efficiency of the circuits was observed.

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

What is on the Bicycle Paths? A Detailed Vehicle Taxonomy with Mode Share Data for Off-Street Paths in Metropolitan Vancouver, Canada

The trend of increasingly multi-modal urban transportation has accelerated with the advent of low-power vehicles such as electric-assist bicycles, scooters, and skateboards. Despite increased attention, little is known about the actual usage of these vehicles, such as their mode shares in off-street and cycling facilities. The objectives of this study are to: (1) catalog all motorized and non-motorized vehicles in use on cycling facilities in metropolitan Vancouver, Canada; (2) create a taxonomy of the vehicles using visually identifiable features; and (3) determine volumes and mode shares for each vehicle type. We develop and validate a field data collection method using pneumatic tubes synchronized with video cameras, and collect classified volume data at 12 strategically selected locations over four seasons. A total of 25,282 vehicles are classified from more than 450 h of video data, categorized into 27 types using 10 features, such as number of wheels, number of axles, and existence of a battery/motor. Conventional (non-shared) bicycles are still the dominant vehicle in use, with a mode share of 90%. Although a variety of motorized personal mobility devices are present in cycling facilities, their mode shares are still extremely small. Electric bicycles are the most common motorized vehicle, with a mean mode share of 4% (ranging from 0% to 20% by location-date). The proposed taxonomy enables consistent empirical data collection to evaluate mode share trends and patterns, and to study the impacts of regulatory changes, incentive programs, and new infrastructure.