Results

Nice ride and e-scooter usage

The nice ride and e-scooter usage was compared initially by computing multiple statistics and summarizing metrics. The dataset for the Nice Ride bike share dataset for 2018 ranged from April 2018 to November 2018. There was a total of 824,846 trips with an average trip time of 3825 seconds. This averages about 117,835 trips per month. The month with the most bike share trips was July, and the least was November. The date with the most trip was in July. In comparison, the e-scooter dataset for 2018 ranged from July 2018 to December 2018. There was a total of 225,543 trips with an average trip time of 1127 seconds and average trip distance of 2157 meters. The month with the most e-scooter trip activity was October and the least was December. The date with the most trips was in October.

In terms of usage during weekdays, the most nice-ride trips occurred on Saturdays and these trips also had the longest average trip duration. The weekday with the least number of trips overall was Tuesday (fig 1). In comparison, most nice ride trips occurred on Thursdays and the longest trip durations were on Sundays. The day with the least number of e-scooter trips was Sunday (fig 2).

Examining all trips on an hourly level for each weekday, the activity percentage trend for each weekday is similar. Each weekday had a peak hour and showed more activity in the morning. The peak activity for each weekday is between 8:00-8:59 AM for both micro-vehicles and the peaks are similar throughout the weekdays (fig 1). Throughout the day, the activity decreased after the peak with lower hourly activity after 4 pm.

E-scooter and bikeshare trips were similar in their start and ending locations. The areas with the most starting and trips for both micro-vehicles appear to be downtown Minneapolis and the University of Minnesota campus (fig 2, 3). Also, locations for ending trips of both micro-vehicles compared to the starting trip locations appear to be dispersed throughout the City – shown through the increase of points and road centerlines with trip counts (fig 2, 3). ­

Proximity to Transit Stops

The percentage of trips within a 0.25 buffer of a transit stop for e-scooters was 100% and 93.85 for the nice-ride bikes.

Demographics

Median household income and percent non-white were observed for each census tract in Minneapolis. The tracts with the highest median household income appear to be in the south west of the city boundary and the lowest median household income tracts are in downtown and north of downtown Minneapolis. For percent non-white, the areas with higher non-white populations were in tracts of downtown and north of downtown Minneapolis, while the tracts with a lower non-white population were in the southern region and north east corner of Minneapolis (fig 4).

­Discussion

The usage counts for the nice ride bikes and the e-scooters are different. Nice ride bikes have a higher count and a larger average trip duration. The usage activity in terms of total counts for each weekday for scooters have the most on usage on Thursdays and for nice ride bikes it is Saturday. When examining the activity on an hourly level for each weekday, both micro-vehicles have a similar trend throughout each weekday. The start and end locations are similar for both as they have highest trip counts for start and end locations in the downtown Minneapolis area and the University of Minnesota Minneapolis campus (fig 2, 3). The demographics of the tracts where the most trips start and end for each micro-vehicle correspond with low median household income rates and high non-white population (fig 4).

The usage of both micro-vehicles does not appear to compete with one another. The differences in trips counts could be attributed to the number of bodies for each vehicle. The nice ride bikes share program launched much earlier than the scooter and may have more availability in general. Furthermore, there is already infrastructure such as docks for bikes which can hold more than 5 bikes while scooters are dockless, and they are typically found at corners of blocks. Additionally, when the activities are examined on an hourly level for each weekday, the pattern of activity is very similar. When there is a peak in usage for the nice ride bikes, there is a similar increase in the e-scooter usage (fig 1). This suggests that folks are not favoring one micro-vehicle over another. Instead, they are both being used similarly in the same locations.

The start and end locations of the bikes and e-scooters are very similar. It suggests users are traveling from and within the downtown and University of Minneapolis campus mostly. With typical city traffic and presence of students in these areas, the micro-vehicles are a great alternative to the typical city bus and helps users decrease on wait times for traveling. One observation about the ending locations for the nice rides bikes is the ending points may be outside of the Minneapolis boundary, suggesting that users are traveling further than users using the e scooters. This is also indicated by the average trip duration time. Most trips started and ended within 0.25 miles Euclidean distance of a transit stop. There are many transits stops in Minneapolis especially in the areas where the most e-scooter and bike share trips occurred. One way to improve the observation of users to transit stops is using a network instead of a Euclidean distance to determine the 0.25 buffer. However, because of the density of transit stops, it showed that most or all trips occurred within the buffer.

The people living in the areas where the most trips are starting and ending had lower median household income and a higher non-white population (fig 4). This implies that micromobility is helping folks of lower household income and of color to travel around the city. Of course, the areas where trips are occurring also coincides with high population density like the downtown area of Minneapolis and the UMN college campus. Overall micromobility has many benefits. The usage of the two micro-vehicles is similar and have similar starting and ending locations. The folks living in the tracts where the trips start, and end imply that there are some social benefits in helping folks with lower income and of color travel throughout the city. Further studies can incorporate a smaller scale like block groups, network analysis, and collected demographics of the micro-vehicle users to observe the effectiveness of micromobility in Minneapolis, MN.