The growth of the urban population and the increase in e-commerce activities affect the complexity of package or mail delivery processes [1
]. Factors such as complex construction, population density in cosmopolitan cities [2
], rapid communication, and technological developments keep the package or mail delivery service sector alive [3
]. The postal delivery industry is developing to provide customers with a faster and higher quality service. The postal delivery industry must overcome many obstacles to respond instantly to its customers [4
]. These obstacles include many parameters such as energy, cost, environmental factors, and complex delivery networks [5
Today, the postal transportation sector has two significant problems: energy [6
] and time [2
]. The energy costs needed during the delivery period of the vehicles used in the postal delivery sector are increasing daily [8
]. Most traditional delivery vehicles are powered with gasoline (diesel or oil) [9
]. The traditional vehicle fleets of the postal delivery sector consist of vehicles that consume gasoline or diesel, such as minivans, buses, trucks, pickup trucks, combi vans, motorcycles, and automobiles. This sector is turning to different delivery vehicles to overcome energy costs. The number and types of vehicles that use electricity to meet energy needs are increasing daily. The postal delivery sector also wants to benefit from these means of transportation that provide positive advantages [10
]. For this reason, it will not be surprising for customers that electric vehicles, which reduce energy costs, are preferred for mail delivery.
Following the emergence of shared e-scooters among the opportunities offered by the new generation of technology and micro-mobility vehicles, participation in their use is increasing. These vehicles, primarily preferred for daily and short-distance travel, have started to be used for different purposes over time. E-scooter vehicles, among the micro-mobility vehicles for daily travel, are heavily preferred in different parts of the world. A study reported that approximately 2.5 billion trips were made in 2018 with e-scooter vehicles for travel in New York City, USA [11
]. As a similar finding, another study examined more than 425,000 trips in Indianapolis City, USA, emphasizing the increasing demand for e-scooter vehicles [12
]. Dias et al. claimed that more than 400,000 drivers in Spain make more than 1.5 million trips in a year with shared e-scooter vehicles across the country [13
]. In the same study, the researchers found that more than 1.8 million trips are made in a year in Lisbon, Portugal, where drivers prefer e-scooter vehicles for daily travel [13
]. Although the use of shared e-scooters also significantly impacts the e-scooter industry, it has been mentioned that there are 85,000 shared e-scooters in more than 100 cities in the USA [14
]. Another study contributed to keeping the e-scooter industry alive by emphasizing that shared e-scooter vehicles will operate in approximately 60 cities in Germany until 2021 [15
Other reasons why drivers prefer e-scooter vehicles are related to driver behaviors and are due to reasons such as difficulty in accessing other vehicles and parking problems. One study reviewed 417 articles and investigated the psychosocial characteristics of e-scooter drivers, focusing on behavioral and risk-related aspects [16
]. Another study emphasized that the usage rate increased with e-scooter corrals by sharing e-scooter usage and driver perceptions [17
]. Another reason why e-scooter vehicles are preferred is that the vast majority of drivers think that these vehicles are economical and environmentally friendly [18
]. Considering all these factors, the use of e-scooters is increasing exponentially in different parts of the world. As a result, it is not surprising that the use of shared and rentable e-scooter vehicles among micro-mobility vehicles, especially for daily trips, is widespread worldwide. Still, these vehicles significantly contribute to their users in terms of time, energy, and cost.
Many studies that revealed the difference between traditional and electric vehicles in terms of energy costs have been discussed. A significant portion of the costs (insurance, maintenance, personnel, etc.) incurred during postal or package delivery is due to energy costs. Trucks and derivative vehicles used in the package or mail industry account for 23% of the energy used for transportation [19
]. A study predicted that the preferred micro-mobility vehicles for transport can reduce energy consumption by 1% at the national level and 2.6% at the city-center level, and that with the widespread use of micro-mobility there will be significant decreases in the amount of energy required for transportation [21
]. With the use of e-scooter vehicles in transportation or logistics, approximately 65% of the operating costs are calculated as energy costs [22
]. The cost of the amount of energy required for these vehicles also varies depending on the type and size of the material used for the battery, and approximately 15% can be saved. In one study, Nocerino et al. reported that in trials with electric micro-mobility vehicles, they achieved energy savings of between EUR 0.036 and EUR 0.194 per km. These savings were calculated as the maximum daily energy cost of EUR 11 for each e-vehicle [23
]. Many studies have emphasized in detail that with the widespread use of micro-mobility vehicles in transportation or logistics (for specific package sizes and weights), significant savings in energy costs are achieved. This study emphasized that using packages with a particular weight and height to deliver to customers with e-scooter vehicles provides substantial energy cost savings for the Turkish postal service unit.
In addition to the positive contribution of electric vehicle use to the energy cost, electric energy-powered delivery vehicles also provide positive impacts on environmental parameters. A study revealed that electric tricycles are a more viable alternative from economic, ecological, and social aspects [1
]. One study suggested that traditional delivery vehicles used for mail or package delivery have too much impact on CO2
emissions. Some studies shared some statistical results that road transport increases CO2
]. Trucks and derivative vehicles used in the package or mail transport system are responsible for 24% of greenhouse gas emissions [19
]. As the size of the vehicles used for transportation and the distance of the delivery area increases, the environmental impacts of those vehicles become increasingly negative. A study has shown that air transport is approximately four times more carbon-intensive than truck transport and approximately ten times more carbon-intensive than rail transport [25
]. Preferring vehicles such as electrically powered e-scooters for package or mail delivery has positive environmental impacts. One study emphasized that using an e-scooter vehicle throughout its operating life cycle decreased emissions to 57 g CO2
]. Another study noted that e-scooters used in transportation (excluding the logistics sector) minimized CO2
emissions, energy costs, traffic volume, and congestion [27
Especially in cosmopolitan cities, with the proliferation of complex settlements and the complexity of road routes, negative results occur in the delivery times of mail or packages with traditional vehicles [28
]. Traditional logistics vehicles used for mail or package delivery cause 8–10% of congestion in urban traffic flow [29
]. However, the preference for micro-mobility cars in the transportation and logistics sector reduces the utilization rate of road capacities by 30% [30
]. A study has emphasized that the time required for a postal or package delivery is shorter in vehicles powered by electrical energy in micro-mobility vehicles than in traditional vehicles [23
]. Micro-mobility vehicles, such as e-scooters, are not seriously affected by factors such as morning and evening traffic jams, weather conditions, and road working conditions in postal or package delivery time zones, unlike traditional delivery vehicles [31
]. Lia et al. compared the capacity with the driver’s weight (kg), traffic speed (km/h), amount of emissions, range of usage (km), and transportation costs for cargo bikes (170–210, 20, zero, 50–70, and low, respectively), e-cargo bikes (1710–200, 20, low, 50–70, and high, respectively), e-scooters (180–250, 25, low, 50–120, and average, respectively), and vans (710–1490, 8–15, high, not applicable, and very high, respectively), which are all vehicles used in the transportation and logistics sectors [31
Researchers have presented different methods to measure the impact of e-scooter vehicles on cost, energy, and the environment. Hosseinzadeh et al. used a spatial analysis approach to measure the impact of demographics, density, diversity, design, urbanism scores, public transport distance, and transportation-related factors on e-scooter trips [32
]. Another study proposed a proper and effective procedure for designing a reluctance machine using a multi-objective optimization technique by working on the battery specifications of e-scooter vehicles [33
]. Another study investigated the factors affecting charging station locations using a new Pythagorean fuzzy multi-criteria decision-making methodology for e-scooter location selection [34
]. Ciociola et al. created a simulation approach that used open-access data to develop a demand model that supports and generalizes e-scooter vehicles in a center [35
]. Another article described an optimization model that will minimize the cost of owning an electric micro-mobility by working on using electric micro-mobility vehicles in transportation and working on energy-generating batteries [36
]. Most studies on e-scooter vehicles have used statistical methods to analyze environmental factors. Hollingsworth et al. used statistical methods to measure the effects of environmental loads associated with charging e-scooters on material and production loads of e-scooters and transporting scooters to overnight charging stations [37
]. Another study analyzed e-scooter riding in Austin and Minneapolis using GIS (geographic information system) hotspot spatial analysis and negative binomial regression models to analyze environmental factors [38
]. This study used a linear response optimization regression model to measure the effect of nine independent variables on energy costs and delivery times of e-scooter vehicles used for mail and package delivery. The statistical analyses were made, and the magnitude of the effect of the input variables on the output variables and the results (positive or negative) were analyzed in this study.
The novelty of this research emphasizes the necessity of using electric micro-mobility vehicles with the possibilities offered by the new generation technology, unlike the traditional delivery vehicles used in the postal service sector. One of the essential features that distinguish this study from other studies is that this study used a statistical optimization model developed in terms of cost, time, and environmental factors, suggesting that e-scooter vehicles, which are generally preferred for travel, should be used for mail or package delivery. Another feature is that a wide range of data belonging to more than one region were selected to verify the validity of the optimum and statistical values. In addition, this study has revealed a useful model by emphasizing that micro-mobility tools, such as e-scooters, should be used for different purposes.
This study consists of five main parts. In the literature review the cost, energy, and environmental effects of traditional delivery vehicles used for mail or package delivery and electric vehicles, such as e-scooters, are discussed. The rest of the paper is structured as follows. Detailed information about the method developed for the recognition and processing of actual data used in this study is given in the second section. The numerical results of the study are discussed in the third section. The effects of the numerical results obtained with the developed method are mentioned in the study’s Discussion. The technique used in this study to contribute to other studies, the results, and comments are discussed in the last part of the study.
E-scooter test drives were carried out in different regions to popularize the use of e-scooters in the post or mail delivery sector by the Turkish postal service unit. In this study, time and energy costs, two of the most important factors taken into account in the mail or package delivery sector, were calculated using data from these test drives. The data for these two factors were obtained by considering the trial driving numbers to gain statistical significance and the independent variables. The effects of both the number of trial runs and the energy and time parameters according to the desirability degrees of the optimization models of the optimum results obtained are shown in Figure 5
Considering the effects of independent variables, the best performance values of the optimum results obtained according to the desirability levels of the optimization models stood out in six different provinces. The data from the cities with optimum values are shown in Figure 6
(the graphic design of this map was retrieved from https://www.mapchart.net
(accessed on 19 December 2022) [57
]). According to the degree of desirability, the best results were obtained in the city of Trabzon, with a value of 0.71. The city of Trabzon is located in the northeast of Turkey. The most important feature of this region is that roads and settlements have more obstacles than other cities (for the areas considered in this study). Nine different drivers in this city made a total of 265 attempts. A driver made 13 trials with the e-scooter vehicle in one day at most out of 60 test runs on different days.
The desirability degrees of the Adana, Kayseri, Kocaeli, Konya, and Uşak regions, among the other provinces with high desirability degrees, were calculated as 0.613, 0.647, 0.603, 0.614, and 0.618, respectively. The minimum energy cost required for package delivery in these provinces was TRY 0.0123, which belonged to the Izmir region. Other parameters were calculated as the distance traveled by e-scooter vehicles in the areas where the optimum values were obtained. While the minimum value of the delivery time of the e-scooter vehicle used for delivering a package or mail belonged to the Kocaeli region, the delivery time taken to cover 0.098 m was calculated as 0.63 min. One study emphasized that e-scooter vehicles used for transportation can cover 0.77 miles in 7.55 min. This study revealed that it would take 5.97 min to cover 1 km with an e-scooter [58
]. The city of Konya is located in the central region of Turkey. Unlike Trabzon, the layout and roads are more regular and have fewer slopes, unlike the city of Trabzon.
Counterplots of decision (independent) variables are shown in Figure 7
, together with the statistical analyses performed to measure the effect of independent variables (without interactions of factors) on dependent variables. These plots dealt with the impact of independent variables on energy and time parameters. Counterplots showing the effects of independent factors on the degree of desirability resulting from interactions with each other are shared in Appendix A
The age factor, expressed as the independent demographic variable of e-scooter drivers, directly affected the degree of desirability. According to the counterplot, it can be observed that there was an increase in the degree of desirability with the rise of age above a certain level. This proportionality was not observed between the temperature factor and the degree of desirability. There was a discrete distribution in the results obtained in the interaction between temperature and desirability. There was a nonlinear relationship between wind speed and desirability degrees. While the continuous increase in wind speed increased the desirability levels to a certain level, an excessive increase in wind speed caused a decrease in the desirability level. The fluctuations between such results have a significant effect on obtaining optimum results. The situation created by the impact of wind speed on the degree of desirability was also observed in the distance factor.
Many factors, such as the spread of e-commerce, formation of complex logistics networks, space of complex constructions, and changes in the physical structure of roads, directly or indirectly affect mail or package delivery services. The postal service has to implement changes in its internal dynamics to find ways to eliminate the effects of such factors. Today, in postal or package delivery services, most products are delivered to customers using traditional vehicles such as trucks, vans, pickups, motorcycles, etc. Postal delivery companies tend to reduce their vehicle sizes and turn to micro-mobility vehicles that are faster and more cost-effective to overcome the abovementioned problems, especially over short distances and in dense urban centers.
There are two crucial problems postal delivery companies face in delivering the products to the customer: the energy cost and delivery time. Delivery companies aim to shorten the delivery time and save energy costs with vehicles that require small batteries, such as e-scooters and e-bikes, which are micro-mobility vehicles. In addition, environmental sensitivity analyses of traditional vehicles used for package or mail delivery show a negative trend compared to micro-mobility delivery vehicles. In the analysis of some studies, micro-mobility vehicles, which provide many benefits in terms of the environment, economy, and energy use, also contribute to reducing traffic density. Daily traffic congestion in residential areas significantly increases the fuel consumption and carbon emissions of distribution vehicles and causes delays in the delivery of products to customers [59
This study analyzed the cost, energy use, and environmental contributions of e-scooter vehicles for postal or package delivery by the PTT, an official institution of Turkey. In this study, nine independent factors were examined, and the effects of these factors on e-scooter vehicle use, package or mail delivery time, and energy cost were examined. To popularize the use of e-scooters, in this study, a response optimization regression method was developed using data from test drives in 12 cities. This study concluded using a statistical analysis that driver age (p; 0.002), time zone (p; 0.001), distance (p; 0.001), wind speed (p; 0.043), and delivery region (p; 0.001) had a direct effect on delivery time, while time zone (p;0.053), distance (p; 0.001), area (or region) (p; 0.001), temperature (p; 0.0033), and rainfall (p;0.044) factors had a direct effect on the energy cost.
Based on the statistical analysis results of the study, the factors of year, age, distance, region, and wind speed directly affected the delivery time. Considering the cross-reference line of 2.0 for the delivery time, which was defined as the dependent variable, year (Coeff; −0.913), age (Coeff; −0.02061), temperature (Coeff; −0.0038), wind speed (Coeff; −0.210) and humidity (Coeff; −0.00884) parameters had a negative effect on the dependent variable. It was determined that the delivery time increased as the driver’s age increased and the delivery time decreased as the wind speed increased, with a directly proportional effect of other factors on the dependent variable. The other dependent variable, the energy cost, was positively affected by the year, distance, temperature, humidity, precipitation, and wind speed. The cross-reference line for this dependent variable was considered as 2.0. The parameters of year (Coeff; 0.000385), distance (Kats; 0.054207), temperature (Coeff; 0.000068), humidity (Coeff; 0.000022), precipitation (Coeff; 0.000038), and wind speed (Coeff; 0.000121) positively affected the dependent variable. Only the age (Coeff; −0.000015) and region (Coeff; −0.013091) parameters had a negative impact on the energy cost required for delivery.
By calculating the optimum data for the dependent and independent variables, the effects of the interactions of the variables on the energy cost and delivery time could be discussed. The optimal results were tested using desirability degrees to verify the validity of the optimum results of the mathematical models. The data concerning the optimum values of the objective functions in the mathematical models were calculated as 2.83 (rounded up to 3) for the number of tests, 2.87 for the delivery time, and 0.0208 for the energy cost required. The optimum energy cost for a distance of approximately 100 m (or per minute) was calculated as approximately TRY 0.021.
This study had some limitations and prerequisites. Although the data on the batteries of the e-scooter vehicles were not taken into account, batteries were full during the test drives. The charging time of the batteries was not taken into account in the trial runs. Another limit was the slope information of the roads used by e-scooter vehicles for testing in the regions considered in the study. The effects of these data on driving times and energy consumption could not be measured. The third limitation was that data on physical structures, such as the weight and height of e-scooter drivers, were not used in this research. A final limitation of this study was the absence of legal regulations regarding e-scooter transportation. For this reason, the safety of e-scooter drivers was not discussed in this study. There is a need for studies that argue that this problem is essential for drivers using electric vehicles, such as e-scooters, for negative situations that they may encounter during travel [60
As a result, in terms of the environment, economy, and energy consumption, using micro-mobility vehicles, such as e-scooters, provides significant advantages in the package and postal transportation sector in densely populated areas. This study concluded that micro-mobility vehicles would contribute in many areas due to test applications in 12 cities in terms of both delivery time and energy cost of the e-scooter vehicle in mail or package transportation.