Micro-Mobility
Micro-mobility has taken the street by storm, some would say for better, some for worse.
Paper Abstracts
Mobility, Congestion, and Inclusion: The Case of E-Scooters, Lauren N. McCarthy and Parker Reynolds,
Paper presented at NARSC November 2019
E-scooters proliferated at an unexpected rate. Catching many local governments off guard, e-scooters sent them scrambling to catch up. At the same time public opinion is mixed. Many are voicing discontent with the vehicles as they lay strewn on the sidewalks of many cities. Others are voicing their support of e-scooters with their feet - cities are seeing tens of thousands of rides in the first few months of deployment. Given e-scooters are only the beginning of a mobility revolution, the policy decisions made now are critical for the survival of micro-mobility.
To thoughtfully make these policy decisions two important questions need to be asked to help clarify the role such devices have to address problems cities. First, is there any evidence scooters offsetting car usage during commuting hours? Second, who is benefiting from the introduction of scooters into the mobility landscape?
Previous studies indicate mobility options have been found to enable first and last-mile connections, may impact vehicle ownership as well as vehicle miles traveled (Clewlow and Mishra 2017) have a role in reducing congestion (Jin et al. 2017) and have the potential to bridge transit and other modes service gaps (Rayle et al. 2016; Hall et al. 2017). To this end, this paper uses Austin, Texas e-scooter trip from April 2018 to December 2018 to analyze 1) the temporal use of e-scooters to determine patterns of use relating to standard periods of commuting 2) maps of geographic relationships between e-scooter trip origins and demographic variables by census track and 3) applying spatial regression to the demographic rider profiles by census track by number of rides to determine if traditionally underserved groups are using e-scooters. To do this we create a model of inclusion and then test it against three regression models: OLS, spatial lag, and spatial error models to determine the model of best fit.
Results first indicate a uni-modal frequency in trips – the majority of e-scooter trips are occurring mid-day, with a greater bump in evening hours from Thursday to Saturday. Second, using OLS, spatial lag, and spatial errors models, with the spatial error model determined to be the model of best fit, inclusion indicators are promising. Rides are more likely to originate from census tracks with a greater number of Hispanic and black individuals, as well as those with lower income. The presence of public transportation options is negatively related, potentially signaling residents of tracks well served by public transit do not need e-scooters or are able walk, whereas those in tracks less served by transit may be using e-scooters to connect to more options farther away or are scooting the entire distance.
