A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data

Abstract

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles.

1. Introduction

Electric vehicles (EVs) are gaining attention as an alternative to fuel-powered vehicles due to their growing importance to the environment [1,2,3,4]. As the importance of reducing CO₂ emissions has grown, the popularity of eco-friendly vehicles has increased [2]. Among environmentally friendly vehicles, micro-EVs are regarded as the most likely choice as a mainstream mode of transport in the future [1]. A micro-EV is a small electric car with one or two seats which is designed for short-to-medium-distance trips [5], and their usage has expanded in recent years. In the EU, the STEVE project piloted micro-EV-based mobility services from 2017 to 2021. In Japan, Toyota implemented a car-sharing service for short-distance urban travel using micro-EVs through the Ha:Mo project. In Korea, micro-EVs have been introduced and operated as part of the postal delivery service [6]. According to statistical data provided by the Ministry of Land, Infrastructure, and Transport, the registered number of micro-EVs has increased by 38.8% annually since 2019, the year when the registration count was first compiled (from 950 units in 2019 to 4893 units in 2024). Despite this, the proportion of micro-EVs compared to the total number of vehicles remains very small, at around 0.02% (as of 2024) [7]. In response, the Ministry of Trade, Industry, and Energy has conducted research and development (R&D) projects related to micro-EVs to expand the industry. These projects aim to provide various services using micro-EVs and identify the types of services where micro-EVs have advantages. The micro-EVs used in this project comprised a total of 106 units, consisting of six models, as shown in

Table 1. Micro-EVs used in the empirical experiment and their specifications.

Classification	Micro Electric Vehicles Used in the Empirical Experiment
	CEVO-C	D2	D2C	Twizy	MASTA MINI	MASTA VAN
Length (mm)	2430	2820	3095	2338	2545	3150
Width (mm)	1425	1530	1495	1237	1290	1297
Height (mm)	1550	1520	1705	1454	1570	1685
Battery capacity (kW)	10.2	17.3	17.4	6.1	10.0	10.0
Max. output (kW)	14.9	10.0	10.0	12.6	10.0	5.0
Max. speed (km/h)	80	80	80	80	80	78
Range (km)	75.4	92.6	101.0	100.0	150.0	100.0
Charging time (hours)	4.0	8.0~10.0	8.0~10.0	3.5	2.5~3.0	2.5~3.0
# of vehicles used in the experiment	50	21	4	1	1	29

. While the CEVO-C, D2, Twizy, and MASTA MINI are most commonly used for personal trips, the D2C and MASTA VAN are more suitable for cargo transport. Micro-EVs have both advantages and disadvantages. They produce relatively less pollution, such as fine dust and noise, compared to conventional vehicles. Additionally, their small size makes it easier for them to travel in narrow areas compared to other modes of transportation. Micro-EVs are also suitable for addressing social changes, such as an aging population and the increase in single-person households. However, micro-EVs have some drawbacks, including short driving distances per charge due to their low battery capacity, limited output and speed, and insufficient driving performance. There are also concerns regarding the safety of these vehicles [6].

Due to the advantages and disadvantages of micro-EVs, it has not yet been evaluated as to which type of service would best promote their use. Through the aforementioned R&D project, the Ministry of Trade, Industry, and Energy provided three types of services using micro-EVs: a shared transport service, delivery service, and public service [6]. The role of each service is explained in

Table 2. Micro-EV usage description by service type.

Service Type	Description
Shared transport service	Transportation service connecting with bus terminals and train stations
Delivery service	Food delivery service/postal and freight delivery service
Public service	Transportation service for local governmental public works

. The shared transport service provided micro-EVs to connect public transportation with departure and destination points in urban areas. The delivery service utilized micro-EVs for food and postal freight delivery. Additionally, the public service offered micro-EVs as a means of transportation for public affairs in rural areas.

In this study, GPS data were used to analyze the travel and driving characteristics of micro-EVs, as well as the roads they traveled, in order to identify the most suitable service type for promoting the use of micro-EVs. GPS data collected from all regions where micro-EVs were used, covering a period of 10 months from January 2021 to October 2021, was utilized. The scope of the study will be discussed in detail in the Section 3.

The proceeding sections of this study include the following contents. In the Section 2, the existing literature related to micro-EVs and methodologies was examined. Moreover, the key aspects to be referenced in this study were assessed and the strengths of the studies were identified. The Section 3 introduces the analytical methods used in this study. The Section 4 explains the findings based on the analysis methods introduced in the Section 3. Finally, the Section 5 summarizes the research content, derives implications, and outlines future research directions.

2. Literature Review

2.1. Research Related to Micro-EVs

Mu and Yamamoto analyzed the impact of micro-cars on traffic safety and the environment. They conducted traffic simulations with and without micro-cars on an urban highway and arterial road, especially in mixed-traffic conditions. The impacts of introducing micro-cars to mixed-flow on these variables were as follows: the lane-changing frequency decreased, the instances of deceleration decreased, the speed variation became smaller, and HC/NO/NOx emissions increased [8]. Lee’s research team evaluated the effect of satisfaction with micro-EV sharing service elements on the decision of whether to use micro-EVs. The research team collected data about socio-demographics (age, gender, income, and car ownership) and service satisfaction (accessibility to infrastructure, driving, use procedure) for micro-EV sharing service users. According to their evaluation results, groups comprising elderly users demonstrated more intention to use the micro-EV sharing service than other age groups. Moreover, the impact of the driving factor was most significant among service satisfaction factors. In particular, micro-EV sharing service users were not satisfied with the driving safety and ride comfort of micro-EVs [1]. Loustric and Matyas evaluated the market potential of micro-EVs. Their study suggested that micro-EVs are an innovative mode of transportation that combines the advantages of electric vehicles and micro-mobility, with particularly high market potential in developing countries [9]. Cho and his research team indicated that micro-EVs have the potential to enhance transportation efficiency by integrating with public transportation and car-sharing services [10]. Jin and his colleagues proposed a methodology for assessing the service quality of micro-EVs based on factors such as speed, number of stops, and driving performance on inclines [11].

2.2. Research on Travel Data Using Location-Based Information

The collection of location-based data has been increasing recently with the integration of GPS data collection devices on vehicles, mobile phones, etc. Even for vehicles that do not come with this technology integrated by default, data collection happens through any mobile device connected to the vehicle. These technologies have made it easy for us to collect, analyze, and deduct results from location-based data. For example, we can now derive the places most visited this month by analyzing the number of devices choosing specific locations as their destinations. Furthermore, the collection of location-based data from vehicles helps us to determine areas of traffic congestion, collisions, etc. The information obtained in these cases is then analyzed by GPS technologies, and these routes are not suggested for a period until the traffic clears.

There have been studies conducted where data on driving patterns, driving behaviors, and stopping behaviors of trucks were collected and analyzed using machine learning methodologies to classify truck stop locations into two categories, primary and secondary [12]. Additionally, GPS data collection from public transport vehicles can help us to analyze the quality of services a transportation system provides. Research has been conducted to analyze the same by monitoring the routes the transportation system covers, monitoring the number of cycles that a public transport vehicle achieves in a day, and monitoring whether a bus operates on its schedule. This activity helped determine the areas of improvement that a new bus service needs to establish, and analyze the public use of the system on weekdays as compared to weekends. Furthermore, it helped in deciding the number of additional buses to be added to existing routes to improve the quality of the service delivered [13].

Interestingly, GPS data collection can help us accurately predict delivery times for goods in the logistics market. For example, information has been collected from transport trucks operating in Mexico to analyze the data and predict accurate delivery times and possible delays, and eliminate activities that cause potential delays in delivery operations. This can help industries to become more reliable and develop trust with customers, thereby improving export markets, along with providing a lean approach to industries [14].

2.3. Research on Travel Data Analysis Using GPS Data

Extraction of trip information and trip chain characteristics from GPS data requires a comprehensive examination of the GPS and spatiotemporal and land use data collected from the user’s trip. The chief characteristics of trips are the trip duration, length, mode, number of stops, and waiting time. With the help of this information, the trip purpose and trip chain data can be estimated. To gain deeper insights into commuters’ travel behavior, researchers often follow a trip chaining conceptual model; however, there are only limited studies available on deriving trip chain data [15].

To analyze users’ travel behavior, crucial data such as the mode and purpose of travel, which is not explicitly obtained from the GPS data, must be determined. Hence, most of the existing literature proposes a framework or algorithm to extract the required information. Clifford’s research team proposed a heuristic set of rules for determining the purpose of travel using GPS data based on the average, maximum, and minimum speed, along with the spatial characteristics of the trips. Information such as location, time, speed of movement, and direction of travel are collected every second to ensure result accuracy. The authors considered a trip as having ended when there was a stop lasting 120 s or more. Considering this, along with the location information, OD matrices were formed [16]. Hui and their research team conducted a study to observe the trip characteristics and usage patterns of round-trip carsharing based on GPS data [17].

Duan and their co-researchers established personalized service recommendations for users. Existing services had not considered diverse users’ demands for more and better travel, so the researchers developed a personalized service-trajectory correlation model to recommend the most appropriate services to users. GPS data and traffic information system data were used in this research to identify the points that users visited using dwell time at each point, and clustering radii were used to classify points by each spot [18].

Han and their co-workers conducted research about point of interest (POI) recommendations. They proposed a new methodology for POI recommendation. The models’ performances were evaluated through experiments, and the proposed model was more flexible in incorporating contextual factors than other state-of-the-art methods [19]. Palma and co-researchers presented a methodology to discover points of interest that are not expected by analysts. The researchers used a method considering background geographic information to overcome the drawback of existing methods. The method used in this paper was an alternative to the algorithm Stops and Moves of Trajectories (SMoT). SMoT searches for intersections in trajectories and finds geographic objects or facilities related to the intersections. However, the method in this paper is a speed-based spatiotemporal clustering approach, so interesting points or places including unknown facilities can be detected, unlike in previous methodologies [20]. Luo and their co-workers developed an improved DBSCAN algorithm to extract stop locations from trajectory data. There are many methodologies to analyze trajectories and POI, and the DBSCAN algorithm was used frequently. Extracting stops from trajectory data is called ‘stop detection,’ and they added a ‘move ability’ to the existing DBSCAN algorithm. The performance of stop detection improved when using the upgraded DBSCAN algorithm [21].

Ohmori and their colleagues examined the advancements in developing systems for collecting travel behavior data using GPS, PHS, and GIS technologies. The authors further evaluated the effectiveness of the data gathered by these systems and their applicability to travel behavior surveys. The study presented an extensive overview of previous studies that have utilized GPS and PHS technologies to collect data on travel behavior. It emphasized the benefits of utilizing these technologies, such as their capacity to provide accurate and relevant data regarding individuals’ travel patterns [22].

2.4. Research on Travel Behavior

Krizek classified nine categories of tours by considering the combination of primary and complex tours and three different activity goals within each tour [23]. Frank and his colleagues categorized all trips into three different categories according to their primary purpose. Nevertheless, the analysis of travel patterns based on tour or trip chains has mainly focused on developed countries, neglecting to examine the connections between urban structure and trip chaining behavior [24].

Nishi and their research team conducted a study that aimed to analyze how individuals combine their stops in their travel routines, with a focus on workers and their non-work-related activities. The theoretical findings indicated that factors like the speed of travel, distance of commuting, cost of transportation, and density of areas had an impact on how people plan their trips. The empirical evidence from two regions and time periods supported these correlations. Interestingly, despite the stability in trip rates, the research observed changes in trip chaining behavior over time. For example, there was an increase in the number of office-based work activities being performed on foot between 1970 and 1980 [25].

Noland and Thomas examined whether the trip plan was influenced by the layout of areas. Specifically, it focused on whether areas with population density tend to have frequent instances of combining multiple trips into one and having more complex travel routes. By analyzing data from the “2001 National Household Travel Survey”, the study identified factors that influenced people’s tendency to combine trips into intricate tours with multiple stops. The results showed that when considering factors like household characteristics and traveler traits, areas with lower population density tend to rely more on trip chaining and have more stops in their travel tours [26].

Holguín and Patil explored the trip behavior of commercial vehicles, looking at their day-to-day travel. It was observed that these vehicles completed around 5.6 tasks each day, with trip chains being common. About one out of every four was involved in several trip chains on a given day. An interesting find was that the more tasks in a trip chain, the less trip chains a vehicle made in a day. Freight or goods being transported were the main reason for these trips. This was true even with vehicles which were normally considered as people carriers, not goods carriers. A noteworthy finding was that more valuable freight added to the length of a trip chain. The study urged more research to better comprehend the ins and outs of trip chains and the behaviors tied to them [27]. The FHWA study showed that 27% of people working during the week engaged in trip chaining in 2001. As presented by McGuckin and their research team, this behavior offered potential for Transportation Demand Management (TDM) programs. People who chained trips often lived far from their jobs and drove more miles in a year [28].

2.5. Summary

As a result of reviewing the research related to the subjects above, it was found that there is a lack of research regarding the advantages of micro-EVs compared to traditional modes of transportation, as well as the types of use that are most suitable for micro-EVs. On the other hand, there have been plenty of studies dealing with the use of GPS data to extract and analyze the trip behavior of vehicles. Moreover, there have been studies on assessing the travel behaviors that group trips into trip chains for analysis. This study aims to identify the strengths of micro-EVs, which have not been addressed in previous research, and to analyze the service types most suitable for their use. For the analysis, GPS data from micro-EVs were utilized to examine their travel characteristics. Based on a review of studies on travel behavior analysis, individual trips and also trip chains were analyzed to better understand overall travel characteristics. Additionally, this study investigated the advantages of micro-EVs over conventional vehicles on narrow roads, an aspect not covered in previous research, and analyzed the characteristics of the roads where micro-EVs are commonly driven. Therefore, this study provides valuable insights into the strengths of micro-EVs and the most appropriate service types for their utilization.

3. Methodology

This study aimed to determine the types of services in which micro-EVs can be more efficiently utilized in order to promote their active adoption. To achieve this goal, the travel characteristics of micro-EVs were compared across the three different service types. Moreover, the driving characteristics of micro-EVs and conventional vehicles were compared to assess the advantages that micro-EVs offer over traditional vehicles. For the last part of the analyses, the characteristics of roads traversed by micro-EVs were assessed. The methodologies for the analyses were as follows.

3.1. Travel Behavior Analysis

3.1.1. GPS Data Collection

In the experiment analyzing the travel characteristics of micro-EVs, the vehicles were utilized across three service types: a shared transport service, delivery service, and public service. The micro-EV service was planned around the service operation organization’s base, and accordingly, micro-EVs were operated in different areas depending on the service type. The shared transport service was provided in regions where high user demand was anticipated, such as Jeju Island, local governments in the Jeolla province (including Mokpo, Yeonggwang, Muan, etc.), and the capital city of Seoul and surrounding cities, with the purpose of integrating with public transportation. Specifically, in Jeju Island, the service was provided to improve the commuting convenience for students and staff of schools in the area. The delivery service was provided in the Yeonggwang and Muan regions of Jeollanam-do. Although the area is not large, the presence of industrial complexes and a nuclear power plant was expected to generate considerable service demand. The micro-EVs used for the delivery service were utilized by workers who previously performed deliveries using motorcycles. Micro-EVs for public service were provided to government employees working in various local governments, ensuring alignment with the service’s purpose.

The number of micro-EVs differed by service type in the empirical study; fifty-seven micro-EVs were used for the shared transport service, thirteen micro-EVs were used for the delivery service, and thirty-six micro-EVs were used for the public service. Every micro-EV was equipped with a GPS device, and various data, including the location, speed, and gyroscope of the micro-EVs, were collected every second. Among the micro-EVs, GPS data from a few vehicles were not collected due to device issues. As a result, vehicles without GPS data were excluded from the analysis. The number of vehicles for each service type and micro-EV model is shown in

Table 3. Number of micro-EVs by service type and micro-EV model used in the analysis.

Micro-EV Model	Number of Micro-EVs for Each Service Type and Micro-EV Model Used for the Analysis
	Shared Transport Service	Delivery Service	Public Service
CEVO-C	37	7	3
D2	17	2	2
D2C	-	1	3
Twizy	-	1	-
MASTA MINI	-	-	1
MASTA VAN	-	2	26
Total	54	13	35

. Specifically, two micro-EVs from the shared transport service and one micro-EV from the public service were excluded from the analysis.

The structure of the GPS data is shown in

Table 4. Data variables recorded by GPS devices.

Variable	Description
Time	The time at which pertaining data were recorded (Year/Month/Day/Hour/Minute/Second)
Identity	The identity number of the GPS device equipped in the micro-EV
Car name	The name of the micro-EV which the GPS device was equipped in
Car number	The car number of the micro-EV which the GPS device was equipped in
Longitude/Latitude	The longitude and latitude of the micro-EV at the time (◦)
Altitude	The altitude of the micro-EV at the time (m)
Roll/Pitch/Yaw	The value of Roll/Pitch/Yaw of the micro-EV (◦)
Acceleration	The acceleration of the micro-EV along its three axes (axes: x, y, z)
Gyroscope	The angular velocity of the micro-EV’s rotation along its three axes (◦/s, axes: x, y, z)

. Figure 1a shows the locations of the micro-EV every second in the area where the micro-EV was driven, and Figure 1b shows the enlarged part of the path where the micro-EV was driven. Every dot in Figure 1 indicates the position of the micro-EV every second. The GPS data of micro-EVs were collected from January to October 2021 for all service types and used in this study to derive the travel behaviors of the devices. The GPS data were transformed into the dataset to analyze micro-EVs’ travel behaviors because the dataset was initially inadequate for the purposes of directly exporting trip and trip chain characteristics. Details of the data transformation will be described in a later chapter.

3.1.2. Methods to Analyze GPS Data

In this study, GPS data were used to analyze the travel behavior of micro-EVs. Trip and trip chain characteristics were selected as the variables to explain the travel behaviors of micro-EVs because those variables are suitable for determining how much the micro-EVs were used. A trip is defined as a one-way movement from a point of origin to a point of destination. Furthermore, a trip chain is a set of linked trips. A methodology was devised to extract the trip information of the micro-EVs from GPS data. The methodology focused on finding the state in which the vehicle was stopped. This is because trip information can be found when a stationary state is found, as a trip exists between stationary states. The procedure of extracting the trip information of the micro-EVs from GPS data was conducted as below.

The first step was to determine the micro-EVs’ movement state. It was decided whether a micro-EV was stopped or not at all moments. Considering the error of GPS data, if the speed at every moment was less than 1 m/s, this was defined as the “stationary” state. On the other hand, if the speed was 1 m/s or more, it was determined that the micro-EV was moving, and this was defined as the “moving” state. Since trips were determined based on the stationary state, the stationary state needed to be accurately determined. If the micro-EV was determined to be moving due to an error in GPS data, although it had stopped, the number of trips was likely to be overestimated. To solve this problem, the stationary state was conservatively judged. If at one moment, the vehicle speed was 1 m/s or more, but there was a stationary state within two seconds before and after this moment, it was determined to be stationary.

The second step was to classify trips and trip chains based on the stationary state. Trips were classified based on the stationary states of micro-EVs. However, not all stationary states could be used as a criterion for distinguishing trips, because there were stationary states that were unintentional temporary stops due to traffic conditions and traffic signals, i.e., they did not mark the end of a trip. Therefore, trips were classified based on stationary states that lasted longer than a specific period. Most temporary stops that were not trip ends were caused by traffic control facilities such as traffic lights. Therefore, a minimum period for a stationary state was selected to distinguish trip ends from stationary periods in an area within 50 m from an intersection stop line. In order to separate most cases of stopping at an intersection from trip ends, a value corresponding to the 85th percentile of the distribution of time stopped near an intersection was selected as the minimum stationary period for classifying trips. The statistics regarding stationary periods near signal intersections and minimum stationary periods by service type are shown in

Table 5. Minimum stationary period for each service type.

Classification	Stationary Period Due to Traffic Signals by Service Type(s)
	Shared Transport Service	Delivery Service	Public Service
50th percentile	84	79	102
85th percentile	111	110	142
95th percentile	128	138	166
Stationary period standard for classifying trips	110	110	140

. A stationary reference time that was too short was likely to result in an overestimation of the number of trips, and a value that was too large was likely to underestimate the number of trips. Therefore, the stationary reference time values were selected as near the 85th percentile of the stationary periods due to traffic signals. Moreover, trip chains were classified based on whether there was enough of a time difference between trips. Multiple destinations can be planned before a trip begins. Since a trip occurs for a single purpose, travel involving multiple destinations between the initial departure point and the final destination can be classified as a single group [29]. Therefore, in this study, multiple trips were grouped into a trip chain. It would be ideal to know the multiple purposes planned by the driver before the trip begins, but GPS data do not include the driver’s trip purposes. To differentiate between trip chains, the time gap between trips was utilized. A suitable criterion was needed to distinguish between trip chains, and the 30 min criterion, used in previous studies [30], was applied. Figure 2 shows the concept of how trips and trip chains were classified. Squares and circles represent the positions of the micro-EV every second, and each shape represents a stationary state and a moving state. Stationary states above the minimum stationary period classified trips, and temporarily stationary states that did not meet the minimum stationary period were not used to distinguish trips. In addition, stationary states of a period of 30 min or more were used to distinguish a trip chain.

The last step was to extract the average values of the travel behavior variables. Trips and trip chains were classified based on the stationary state defined in the previous steps. This step derived trip characteristics from the divided trips and trip chains, as shown in

Table 6. Variables to analyze travel behavior.

Criteria		Description
Trip	Average number of trips	Average number of trips per day (# of trips/day)
	Average distance per trip	Average travel distance per trip (km/trip)
	Average speed per trip	Average travel speed per trip (km/hour/trip)
Trip chain	Average number of trip chains	Average number of trip chains per day (# of trip chains/day)
	Average distance per trip chain	Average travel distance per trip chain (km/trip chain)
	Average speed per trip chain	Average travel speed per trip chain (km/hour/trip chain)
Daily usage	Average usage time per day	Average usage time per day (hours/day)
	Average distance per day	Average travel distance per day (km/day)

. The number of trips per day, travel distance, and travel speed were derived for each trip. In addition, the number of trip chains per day, travel distance per trip chain, and travel speed per trip chain were derived for each trip chain. Finally, the usage time per day and the travel distance per day were derived to understand how much micro-EVs were used during the day.

3.2. Driving Characteristics Comparison of Micro-EVs and Conventional Vehicles

The micro-EV is smaller in size compared to regular vehicles, making it more suitable for driving on narrow roads. To support this claim, we evaluated the driving performance of both the micro-EVs and conventional vehicles on narrow roads. Using GPS data, we examined the roads traversed by the micro-EVs and selected sections with a road width of 5 m or less for the experiment. The selected sites for the experiment were divided into three sections, as shown in Figure 3. Section A consists of both normal and narrow road sections with a road width of 3 m. Section B is also composed of both normal and narrow road sections, but unlike Section A, it includes segments with road widths of 2 m and 3 m. In contrast, Section C consists solely of a narrow road segment with a road width of 2 m. Section A is situated in a residential area, while Section B is located near fields and farmlands with minimal vehicular access. Additionally, Section C is surrounded by commercial establishments and residential buildings, which attract a high volume of pedestrians.

To compare the driving capabilities of the micro-EVs and conventional vehicles, a representative car was selected to represent conventional vehicles in South Korea. According to the Motor Vehicle Management Act, vehicles are categorized into five types: passenger vehicles, motor vehicles for passengers and freight, freight motor vehicles, special motor vehicles, and two-wheeled motor vehicles. According to statistical data provided by the Ministry of Land, Infrastructure, and Transport, passenger vehicles account for the largest proportion among registered vehicles (82.8% as of 2024). Furthermore, when comparing the number of registered vehicles based on engine displacement, the highest proportion is found in vehicles with a displacement of 1600–2000 cc (32.0% as of 2024) [7]. The market share of Hyundai and Kia in the domestic automobile market is very high (91.8% as of 2024) [31], and among the vehicles manufactured by Hyundai and Kia, a typical passenger vehicle within the 1600–2000 cc range is the Sonata. Therefore, in this study, the Hyundai Sonata was selected as a representative vehicle of conventional automobiles in South Korea. A Hyundai Sonata equipped with a GPS device was used to traverse the selected roads. The 11 participants drove the selected roads twice each. Using the GPS device, the speeds of both the micro-EVs and the conventional vehicle were compared as they passed through the narrow road sections. The results demonstrated the strengths of the micro-EV on narrow roads.

3.3. Characteristics of Roads Traversed by Micro-EVs

By analyzing the characteristics of the roads traversed by the micro-EVs, the conditions under which the micro-EV can be most effectively utilized were identified. For this purpose, we utilized road network data provided by the Korea Transport Database (KTDB). The road network data include information about the width and length of road segments. Using the road network data, roads located near the micro electric vehicle’s position were extracted. Since GPS devices sometimes exhibit errors, it is difficult to assume that GPS data accurately represent the location of a micro-EV at every moment. Therefore, potential errors of the GPS device must be taken into account. According to the GPS performance standards provided by the U.S. Department of Defense, the error of GPS devices ranges from 15 to 33 m at most [32]. Thus, in this study, roads within 30 m of the micro electric vehicle were considered as the roads traveled by the micro-EV. Subsequently, the road widths and lengths were categorized based on different types of micro-EV services, which helped to analyze the characteristics of the roads primarily used by micro-EVs.

4. Results

4.1. Travel Behaviors of Micro-EVs by Service Type

The travel behavior characteristics of the micro-EVs used for each service type are shown in

Table 7. Travel behavior characteristics of micro-EVs used for each service type.

Criteria			Classification
			Full Week	Weekday	Weekend
Shared transport service	Trip	Average number of trips (trips/day)	4.1	4.0	4.9
		Average distance per trip (km/trip)	3.5	3.4	3.8
		Average speed per trip (km/h/trip)	20.2	19.9	22.1
	Trip chain	Average number of trip chains (trip chains/day)	1.1	1.1	1.2
		Average distance per trip chain (km/trip chain)	14.0	13.1	17.8
		Average number of trips per trip chain (trips/trip chain)	3.6	3.6	4.3
	Daily usage	Average usage time per day (hours/day)	0.7	0.7	0.9
		Average distance per day (km/day)	15.6	14.7	19.7
Delivery service	Trip	Average number of trips (trips/day)	24.1	23.4	26.9
		Average distance per trip (km/trip)	1.8	1.8	1.7
		Average speed per trip (km/h/trip)	20.7	20.3	21.6
	Trip chain	Average number of trip chains (trip chains/day)	2.6	2.6	2.7
		Average distance per trip chain (km/trip chain)	17.7	17.2	18.4
		Average number of trips per trip chain (trips/trip chain)	9.4	9.0	10.1
	Daily usage	Average usage time per day (hours/day)	3.5	3.4	3.9
		Average distance per day (km/day)	38.5	37.8	42.0
Public service	Trip	Average number of trips (trips/day)	4.2	4.2	4.3
		Average distance per trip (km/trip)	2.4	2.4	2.2
		Average speed per trip (km/h/trip)	15.2	15.4	13.5
	Trip chain	Average number of trip chains (trip chains/day)	1.9	1.8	2.1
		Average distance per trip chain (km/trip chain)	5.4	5.6	4.1
		Average number of trips per trip chain (trips/trip chain)	2.3	2.3	2.1
	Daily usage	Average usage time per day (hours/day)	0.8	0.8	0.8
		Average distance per day (km/day)	11.0	10.9	14.2

. Furthermore, Figure 4 shows the graph comparing the travel behavior characteristics of micro-EVs by service type. The detailed results of the analysis of travel characteristics by service type for micro-EVs are as follows.

The shared transport service users used micro-EVs for an average of 0.7 h and drove an average of 15.6 km daily. Moreover, the average number of daily trips and trip chains were 4.1 and 1.1. In addition, the travel distances per trip and trip chain were 3.5 km and 14.0 km, respectively. The average speed per trip was 20.2 km/h, and there were 3.6 trips per trip chain. The micro-EVs used for the shared transport service were found to be more actively used on weekends than on weekdays because all variables related to trip, trip chain, and daily use were more significant on weekends than on weekdays. The micro-EVs used for the shared transport service had fewer trips and trip chains, which means that the micro-EVs were used less than the micro-EVs for other service types. However, the micro-EVs for the shared transport service had the highest average travel distance per trip compared to other service types. Moreover, the daily travel distance of the shared transport service’s micro-EVs was longer than those used for the public service. Also, the micro-EVs for the shared transport service visited more places than the micro-EVs for the public service, as the shared transport service micro-EVs’ number of trips per trip chain was more significant than the public service micro-EVs’ number of trips per trip chain.

The delivery service users used micro-EVs for 3.5 h a day and traveled 38.5 km on average daily. Furthermore, the daily number of trips and trip chains were 24.1 and 2.6, respectively. The travel distances per trip and trip chain were 1.8 km and 17.7 km, respectively. The average travel speed per trip was 20.7 km/h, and the number of trips per trip chain was 9.4. Micro-EVs used as part of the delivery service were also more actively used on weekends than weekdays. Excluding the average travel distance per trip, other index values were higher on weekends than during the weekdays. For the micro-EVs used as part of the delivery service, all other indicators were higher than other service types, except for the average travel distance per trip. Even if the average distance per trip was lower than that of other service types, the average daily usage time was higher, because the number of trips was about six times that of other service types’ micro-EVs. Therefore, the values of trip and trip chain indicators were higher than those of other service types.

The public service users used micro-EVs for an average of 0.8 h per day and traveled an average of 11.0 km, with an average of 4.2 daily trips and 1.9 daily trip chains. In addition, the travel distances per trip and trip chain were 2.4 km and 5.4 km, respectively. Also, the speed was an average of 15.2 km/h per trip, and the number of trips per trip chain was 2.3 times. The micro-EVs used as part of the public service had a longer travel distance per trip on weekdays than on weekends, which was judged to be a result of the higher number of trips per trip chain. The micro-EVs used as part of the public service had lower average travel speed and daily travel distance values than those of the other service types. The number of trips was larger than for the micro-EVs used as part of the shared transport service, but the number of trips per trip chain was smaller, meaning that the micro-EVs used for the public service visited fewer places than the micro-EVs used for the shared transport service.

The comparison of travel behavior characteristics across different micro-EV service types revealed that the micro-EVs used for the delivery service were the most actively utilized. Deliveries involve visiting multiple locations, resulting in a high frequency of trips, but each trip for a single purpose tended to be short in length. Additionally, due to the high frequency of trips, the number of trip chains planned for multiple purposes was relatively high for the micro-EVs used for the delivery service. In contrast, micro-EVs used for the shared transport service were primarily used as a means of connecting to public transportation, which resulted in longer trip distances per trip. However, because of this, it was more difficult to group the trips into chains, leading to fewer trips per chain compared to other service types.

4.2. Driving Characteristics Comparison of Micro-EVs and Conventional Vehicles

The results of evaluating the driving performance of the micro-EVs and the conventional vehicle on narrow roads are presented in

Table 8. Space mean speed of the micro-EVs and the conventional vehicle.

Section	Space Mean Speed (km/h) (n: # of Data)
	Narrow Road (Width: 3 m)			Narrow Road (Width: 2 m)
	Conventional Vehicle (a)	Micro-EV (b)	Difference (b − a)	Conventional Vehicle (a)	Micro-EV (b)	Difference (b − a)
Section A	20.6 (n = 22)	24.7 (n = 8)	4.1	-	-	-
Section B	21.0 (n = 21)	22.4 (n = 2)	1.4	19.7 (n = 22)	20.0 (n = 1)	0.3
Section C	-	-	-	15.5 (n = 22)	23.8 (n = 7)	8.3

. In all narrow road sections, the micro-EVs had a higher space mean speed compared to the conventional vehicle. Notably, the difference in space mean speed between the micro-EVs and the conventional vehicle was the greatest in Section C, with a difference of 8.3 km/h.

In Section B, there was less difference in the travel speeds between the micro-EVs and the conventional vehicle. Sections A and C are located in areas with high pedestrian volumes, where drivers had to be more cautious of pedestrians. In contrast, Section B is predominantly surrounded by fields and farmlands, with very few pedestrians to consider while driving. It can be inferred that when pedestrians are present along the road, micro-EVs, with their smaller body size, face less obstruction compared to the conventional vehicle with a larger body. Therefore, the minimal speed difference between micro-EVs and the conventional vehicle in Section B can be attributed to the lower presence of pedestrians that require attention while driving.

4.3. Characteristics of Roads Traversed by Micro-EVs

The results of analyzing the road links around the areas traversed by the micro-EVs are presented in

Table 9. Road length of the areas traversed by the micro-EVs by service type.

Road Width	Road Length of the Areas Traversed by Road Width (km)
	Shared Transport Service	Delivery Service	Public Service	Total
<5 m	1811 (42%)	6676 (68%)	2488 (47%)	10,975 (57%)
5–10 m	1099 (26%)	877 (9%)	521 (10%)	2497 (13%)
10–15 m	385 (9%)	365 (4%)	400 (7%)	1150 (6%)
15–20 m	73 (2%)	31 (0%)	57 (1%)	161 (1%)
≥20 m	901 (21%)	1830 (19%)	1882 (35%)	4613 (24%)
Total	4269 (100%)	9779 (100%)	5348 (100%)	19,396 (100%)

. Road links with a width of less than 5 m accounted for the largest proportion, making up approximately 57% of the total length. The micro-EVs used for the shared transport service were commonly located in areas where both narrow and wide roads coexisted, with a high concentration of points of interest (POIs). As a result, the distribution of road lengths across different road widths was relatively even. For the public service, the coexistence of large road-based public tasks and social welfare services targeting residential areas resulted in a higher proportion of travel on both narrow roads and wide roads (roads with a width of 20 m or more). Remarkably, in the case of the delivery service, the length of roads with a width of less than 5 m accounted for about 68% of the total length of roads that the micro-EVs traveled, suggesting that the micro-EVs were more frequently exposed to narrow roads compared to other service types. This is likely because the micro-EVs for the delivery service visited all detailed areas where deliveries could be made, leading to this observation.

This study did not investigate which roads micro-EV users traveled on, or the reasons behind those choices. Therefore, the exact reasons for micro-EVs being exposed to narrow roads more frequently cannot be determined. For an accurate comparison, it would be necessary to compare them with conventional vehicles used for each service type. However, given the small size of micro-EVs, it is expected that users would feel less hesitance in driving on narrow roads compared to conventional vehicles. Thus, it can be concluded that micro-EVs were more frequently exposed to narrow roads.

4.4. Summary of Results

This study analyzed the advantages of micro-EVs and performed three analyses to select the most suitable service type for promoting micro-EV use. First, the travel characteristics of micro-EVs across different service types were compared and analyzed. The results showed that micro-EVs used for the delivery service had the longest daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), as well as the highest average number of trips (24.1 trips/day), average number of trip chains (2.6 trip chains/day), average travel distance per trip chain (17.7 km/trip chain), and number of trips per trip chain (9.4 trips/trip chain) compared to other service types. This suggests that micro-EVs were used most frequently for the delivery service. Second, the driving performances of micro-EVs and a conventional vehicle on narrow roads were compared. The analysis revealed that micro-EVs had a higher average speed on narrow roads than the conventional vehicle. In particular, Section C showed the largest speed difference of 8.3 km/h. This suggests that micro-EVs have an advantage over conventional vehicles when driving on narrow roads. Finally, the characteristics of the roads on which micro-EVs were driven were analyzed. The results indicated that micro-EVs traveled on roads with a high proportion of narrow road links. Specifically, 57% of the total road length around the roads where micro-EVs were exposed had road links with a width of less than 5 m.

Based on the above analyses, it can be concluded that micro-EVs were most actively used for the delivery service. Micro-EVs are expected to exhibit the greatest advantages when used for delivery services, which often involve narrow roads leading to residential areas.

5. Conclusions

Micro-EVs, being an eco-friendly mode of transportation and compact in size, has the advantage of being highly useful in complex urban environments. However, despite these advantages, they have not yet gained significant attention in the competition among transportation systems. Therefore, in order to promote the use of micro-EVs, it is crucial to analyze their strengths and identify the types of services where they can be effectively utilized. In response to this demand, the Ministry of Trade, Industry, and Energy conducted an R&D project that provided three types of services using micro-EVs. Micro-EVs have pros and cons; therefore, their strengths compared to conventional vehicles, along with their suitable usage types, should be assessed. As part of the R&D project, this study analyzed the strengths of micro-EVs and conducted an analysis to identify the most suitable service type for using micro-EVs.

According to the analysis results of comparing micro-EVs’ travel characteristics across the three service types, it can be concluded that the micro-EVs were most actively utilized for the delivery service among all service types. The comparative analysis of driving performance between micro-EVs and conventional vehicles revealed the advantages of the micro-EVs on narrow roads. Furthermore, the micro-EVs used for delivery service were more exposed to narrow roads, where they have a distinct advantage over conventional vehicles, compared to the micro-EVs used for other service types. Therefore, it is recommended that plans to promote the use of micro-EVs should be focused on delivery services, especially in old town city areas where there are many narrow roads.

This study analyzes the strengths of micro-EVs compared to conventional vehicles and provides the most suitable service type where micro-EVs can be effectively utilized, contributing to the development of strategies to promote the use of micro-EVs. On the other hand, this study has the following limitations. When classifying the trips and trip chains, the location characteristics of the trip and trip chain ends were not considered. Considering the type of use of the land where trip and trip chain ends are located would enable trip and trip chains to be distinguished more accurately. In addition, no comparison was made between the travel behavior characteristics of trips and trip chains derived through GPS data and the actual use of micro-EVs. If the variable values derived from this study are corrected by comparing the actual use of micro-EVs with the characteristics of trips, more accurate results can be derived. Overall, more meaningful results can be derived if the limitations presented above are addressed.

Along with this study, further research will be necessary to promote the use of micro-EVs. First, research on the specific characteristics of micro-EVs is needed. By evaluating the functionality, safety, and stability of different micro-EV models, measures should be developed to alleviate users’ concerns regarding safety. Additionally, it is important to conduct a study on the satisfaction levels of users across different micro-EV service types and identify areas for improvement in micro-EVs.