Research on Micro-Mobility with a Focus on Electric Scooters within Smart Cities
In the context of the COVID-19 pandemic, an increasing number of people prefer individual single-track vehicles for urban transport. Long-range super-lightweight small electric vehicles are preferred due to the rising cost of electricity. It is difficult for new researchers and experts to obtain information on the current state of solutions in addressing the issues described within the Smart Cities platform. The research on the current state of the development of long-range super-lightweight small electric vehicles for intergenerational urban E-mobility using intelligent infrastructure within Smart Cities was carried out with the prospect of using the information learned in a pilot study. The study will be applied to resolving the traffic service of the Poruba city district within the statutory city of Ostrava in the Czech Republic. The main reason for choosing this urban district is the fact that it has the largest concentration of secondary schools and is the seat of the VŠB-Technical University of Ostrava. The project investigators see secondary and university students as the main target group of users of micro-mobility devices based on super-lightweight and small electric vehicles.
The main objective of the article is to investigate the current state of the research, analysis, and design of a solution for the maximally efficient and comprehensive concept of urban E-mobility based on small lightweight passenger vehicles (including the possibility of transporting smaller loads). This would bring a qualitatively new level both in terms of the design and in terms the parameters of the vehicles themselves and, at the same time, in terms of their operation, charging, and management.
With the transition to the urban Micro-Mobility (MM) model in Smart-Cities (SCs)-as-a-service with sharing systems, Personal Lightweight Electric Vehicles (PLEVs) are becoming a popular means of transport in cities [1]. Micro–mobility affects first- and last-mile travel in urban areas [2]. In the aftermath of the emergency caused by the COVID-19 pandemic, it has become clear that urban mobility plans need to be modified to reduce the use of public transport and the crowding of people in traffic and, at the same time, avoid traffic congestion by, among other things, encouraging urban residents to stop using private motor vehicles. From this perspective, a reorganization of cities (e.g., Milan) is recommended, both in view of unpredictable environmental sustainability requirements and new mobility needs that require the promotion of bicycles and PLEVs, e.g., electric scooters [3]. PLEVs are a phenomenon that can be currently observed in cities and are intended to be an environmentally friendly form of transport. Analyses conducted show that the dynamic growth of PLEVs in cities will result in an increased demand for electricity distribution, which cities that are developing according to the concept of sustainable development must take into account [4]. Ciociola proposed an approach that uses open data to create a demand model for e-scooter sharing and charging (a flexible, data-driven demand model using modulated Poisson processes for time estimation and Kernel Density Estimation (KDE) for spatial estimation). This approach uses, together with a configurable e-scooter sharing simulator, other input information such as the effect of the number of scooters and the cost of managing their charging in the cities of Minneapolis and Louisville, USA [1]. However, E-Scooters (ESs) face challenges in urban management, such as traffic regulations, public safety, parking regulations, and liability issues for their operation [2]. The Rapid infiltration of stand-up ESs as a mobility option has left cities in a quandary, as they must deal with regulating them and planning for their sudden abandonment at unreserved locations [5]. Garikapati introduced a new paradigm for evaluating mobility options in an urban area using the Mobility and Energy Productivity (MEP) metric. The MEP measures accessibility and appropriately weighs it against the travel time, cost, and energy of each mode of transportation that provides access to options in a given location [6]. In the face of new “disruptive” mobilities, there is a need to (a) build on existing evidence to develop new regulations that address the “who, when, and where” rules for the introduction of new mobilities (such as e-bikes and e-scooters), whose health impacts can be easily predicted [7] Goli proposed a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. [8]. Diaz-Parra proposed a mathematical approach for resolving the electric School Bus Routing Problem (SBRP), which aims at minimizing the cost and optimizing the time and cost of transporting students [9]. Holyoak et al. scrutinized what it would take to turn a city into a world-class sustainable city through specific measures that would accelerate the use of active transportation, with a high number of households and industries equipped with solar and photovoltaic Smart fast-charging station systems at strategic locations for electric-assisted non-motorized transportation and for low-power transportation [10].
Materials and Methods
The PRISMA method and the Kofod-Petersen method were used to extract useful information from the systematic review. The PRISMA 2020 statement includes reporting guidance that reflects advances in methods to identify, select, appraise, and synthesize studies. The structure and presentation of the items were modified to facilitate implementation with a 27-item checklist [11]. A systematic review has three main phases: planning, conducting, and reporting. Each of these phases is divided into several steps. The first phase involves planning the review and can be broken down into the following steps: identification of the need for a review, commissioning a review, specifying the research question(s), developing a review protocol, and evaluating the review protocol. This second phase is the actual review of the literature and consists of the following steps: identification of research, selection of primary studies, study quality assessment, data extraction and monitoring, and data synthesis. The last phase deals with how to disseminate the newly acquired knowledge and consists of three steps: specifying the dissemination strategy, formatting the main report, and evaluating the report [12]. In addition, the authors asked the research questions, determined the search process, identified the inclusion and exclusion criteria for the publications, selected individual studies, performed the data extraction and synthesis, and determined the risk of bias, as outlined in the following subsections [13].
2.1. Research Questions
The aim of this systematic review is to determine the possible solutions for the development of long-range super-lightweight small electric vehicles for the intergenerational urban E-mobility in Smart Cities concept. The steps to determine the current status of the solution are as follows:
Analyze the requirements and solutions of the needs for the development of a micro-mobility concept in Smart Cities;
Analyze the requirements and solutions of the needs for the development of the concept of electric vehicle charging while driving;
Analyze the requirements and solutions of the needs for the development of the electric scooter charger concept;
Analyze the requirements and solutions of the needs for the development of the management and sharing of the electric scooter concept;
Analyze the requirements and solutions of the needs for the development of the concept of E-mobility within Smart Cities (Smart Homes).
RQ1: What technological solutions and innovations can be used to develop the concept of micro-mobility in Smart Cities?
RQ2: What technological solutions and innovations can be used for the development of the concept of electric vehicle charging while driving?
RQ3: What technological solutions and innovations can be used to develop the electric scooter charger concept?
RQ4: What technological solutions and innovations can be used to develop the management and sharing of the electric scooter concept?
RQ5: What technological solutions and innovations can be used to develop the E-mobility within Smart Cities (Smart Homes) concept?
2.2. Search Process
The Web of Science scientific database [13] was used for the search. The search process started on 16 March 2022 and ended on 4 July 2022. The search results were stored in the Web of Science database, and the selected publications were uploaded and imported into the Endnote online reference manager. The main search keywords were “Smart Cities”, “micro-mobility”, “electric vehicle charging while driving”, “charger for electric scooter”, “management and sharing of electric scooters”, and “E-mobility within Smart City (Smart Home)”.
2.3. Inclusion and Exclusion Criteria
In order to refine the search and select relevant literature, inclusion and exclusion criteria were used in the search (Table 1).
2.4. Study Selection
The criteria for article selection included a review of the document title, abstract, and skimming of the article. In addition, the inclusion and exclusion criteria were also used in accordance with the PRISMA flowchart (Figure 1).
Specifically, 3693 publications were found for the keywords “Smart Cities mobility”. After specifying “electric mobility”, 712 publications were found. After entering the keywords “micro-mobility”, 63 publications were chosen, from which 38 publications (Table 2) were selected for the study of the “Smart Cities micro-mobility” topic.
2.5. Data Extraction and Synthesis
General information for the evaluation of the articles should include information on a needs analysis and innovative solutions in the areas of:
For each of the identified topics, the necessity to address the needs and individual requirements of the above-described area is described in the context of the current state of the solution in the development of super-lightweight small electric vehicles with a long range for intergenerational urban E-mobility concepts within Smart Cities. Furthermore, a table was created in the text—an overview of the tasks addressed within each topic in the development of super-lightweight small electric vehicles with a long range for intergenerational urban E-mobility concepts within Smart Cities.
Results
This section presents the results from the data collected from RQ1 to RQ5 listed in Section 2. RQ1 and RQ2 provide a general perspective on the issues analyzed. The FOCUS on the analyzed area of technological solutions and innovations for the development of the charger for electric scooters and management and sharing of electric scooter concepts is established by RQ3 and RQ4. The context of E-mobility solutions within Smart Homes in Smart Cities is asked by question RQ5.
In the following text, the technical terms PHEV, PLEV, BEV, EV, and HEV are used: “PHEV—Plug-in Hybrid Electric Vehicle”, “PLEV—Personal Light Electric Vehicle”, “BEV—Battery Electric Vehicle”, “EV—Electric Vehicle “, “HEV—Hybrid Electric Vehicle”. Electric Vehicles (EVs) move using an electric motor instead of using an Internal Combustion Engine (ICE). Electric vehicles require a charging port and outlet to charge their batteries fully (BEVs). In other vehicles, such as conventional hybrids (HEVs), the engine requires both fuel and electricity to run. This is the same for Plug-in Hybrid Electric Vehicles (PHEVs) [14].
3.1. Smart Cities and Micro-Mobility
Interest in publishing on the topic of Smart Cities and micro-mobility according to the number of publications began in 2011. The highest number of publications, 12, was reached in 2019 (Figure 2).
The total number of 63 publications covering the research area that FOCUS on the described topic includes, among others, the following disciplines: Computer Science, Engineering, Transportation, Telecommunications, Environmental Sciences Ecology, Science Technology Other Topics, and others (Table 3).
In terms of affiliations, the topic is, among others, addressed by: Delft University of Technology, Universidad de Malaga, Czech Technical University Prague, Enzo Ferrari Engn Dept, Univ Nacl Patagonia Austral, Universidad Publica de Navarra (Table 4).
Countries/regions that support research on the topic include Spain, the USA, Germany, Italy, England, Mexico, The Netherlands, and others (Table 5).
Resolving problems in city administration, such as traffic regulations, public safety, parking regulations, and liability issues of running MM in SCs [2,3,5];
Analyses of the use of PLEVs in public transport using quantitative and qualitative indicators in SCs [17,18];
Legislation and legitimization of the conflict within the framework of the operation of MM in SCs [25];
Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data [27]
Evaluation of alternative battery charging schemes for one-way electric vehicle Smart mobility sharing systems based on real urban trip data [35];
Consumers’ innovativeness and conspicuous consumption orientation as predictors of environmentalism [36];
Two-Layer Model Predictive Battery Thermal and Energy Management Optimization for Connected and Automated Electric Vehicles [43];
3.2. Smart Cities and Electric Vehicle Charging while Driving
The first article on Smart Cities and electric vehicle charging while driving was published in 2011. The number of publications peaked in 2020 (Figure 3).
Out of the total number of 108 publications covering the research area that focuses on the described topic are included the areas: Engineering, Computer Science, Energy Fuels, Transportation, Telecommunications, Science Technology Other Topics, Environmental Sciences Ecology, Automation Control Systems (Table 6).
In terms of affiliations, the topic is, among others, addressed by the United States Department of Energy, Polytechnic University of Turin, University of Zagreb, American University of Sharjah, Chinese Academy of Sciences, Concordia University Canada (Table 7).
Countries/regions that support research on the topic include the People’s Republic China, the USA, India, Italy, Canada, England, Japan, and South Korea (Table 8).
Figure 4 shows the keywords of selected publications for the topic of Smart Cities and electric vehicle charging while driving.
The automotive industry is currently shifting from traditional fossil fuels to electrification. There is a growing need in the EV industry to provide new infrastructures, services, tools, and solutions to support the use of EVs [44]. It is expected that future EVs will increasingly be able to use a connected driving environment for efficient, comfortable, and safe driving. Due to the relatively slow dynamics associated with the state of charge and temperature response in large battery-electrified vehicles, a long prediction/planning horizon is required to achieve better energy efficiency benefits [45]. The deployment of a Battery Management System (BMS) unit is a key element for monitoring the battery status of an electric car. In turn, the development and assessment of electric vehicle models form the basis for BMS design, as it provides a fast and inexpensive solution for testing optimal battery control logic in the Loop software environment [46]. In modern Smart Cities, mobility is based on EVs and it is considered a key factor in reducing carbon emissions and pollution. However, despite worldwide interest and investment, user adoption is still low, mainly due to a lack of support for charging services [47].
Electrification will continue to expand
Over the past few years, electric bike purchases have soared. David King, associate professor of urban planning at Arizona State University, said that local and state incentives could spur more e-bike sales in the U.S., especially if e-bike continue to rise. He pointed to Denver’s e-bike rebate program, which allows residents to save up to 450,200 on an e-bike purchase. King said he “expect[s] more cities to follow suit.”
The electrification trend matters for shared micromobility, too, said Samantha Herr, executive director of the North American Bikeshare Scootershare Association. As cities increasingly build out electric vehicle charging infrastructure, officials should include infrastructure for micromobility as part of these projects, she said.
Will shared micromobility companies continue to struggle?
Last year, shared micromobility companies faced a flurry of trials, but it’s unclear if those issues will persist since much of it is beyond their control. Micromobility executives have blamed recession fears, inflation, a lack of venture capital funding, and regulatory challenges for 2022’s troubles.
“The era of free or cheap money — is probably at an end,” King said. Consequently, companies will prioritize profitability and shrink their footprints, he said.
But even if shared micromobility operators leave a city, “there’s now a well-known and obvious substitute for these services—which is you go buy your own scooter or electric bike,” King said.
However, shared micromobility ridership will increase in 2023, Herr said. “I am sure of that,” she added. Shared micromobility ridership rose between 2020 and 2021 and likely increased in 2022, Herr said. But nationwide data is not available yet.
According to Herr, the issues seen in 2022 may continue into 2023, but shared micromobility is “still a pretty young industry,” and therefore, “you’re going to have flux and change…as the industry adjusts and matures.”
To alleviate some of these growing pains, many cities are moving toward creating “more stable environments” for both micromobility providers and city residents,” said Alex Engel, senior communications manager at the National Association of City Transportation Officials. For instance, cities may only allow a few providers to operate instead of allowing a “free for all” of dockless vehicles or FOCUS more on public-private partnerships like many bikeshare services, Engel said.
Cities will confront questions about accessibility
Partly because shared micromobility providers FOCUS on profitability, the past few years have seen a Rapid increase in trip prices. According to a report by NACTO, shared scooter and e-bike trip have more than doubled since 2018.
“One thing we’re looking to see in the year ahead is how cities manage some of these pressures,” Engel said. Some shared micromobility trips have actually become more expensive than a comparable trip taken in a shared vehicle like Uber or Lyft, he added. Higher could push riders away from shared micromobility, especially the poorest riders, who use shared micromobility less than the public as a whole. According to the report, have increased the most in cities that do not regulate shared micromobility.
City leaders should prioritize safe infrastructure
Researchers and transportation professionals emphasize that safe infrastructure is crucial for micromobility’s future. Safe infrastructure means “actually creating safe spaces on the roads for people to ride,” Herr said. King agreed, noting that “you’re not going to see sustained growth in usage unless [cities] dramatically improve the infrastructure.”
Fan is confident that local governments will improve micromobility infrastructure. “Cities are going to shift from car-dominated mindsets,” he said, reallocating roads and parking spaces to support infrastructure like protected bike lanes.
King, however, doesn’t expect broad national trends in micromobility adoption. He said some places, like college campuses, will excel, while others will face more challenges. “Even sustained investment in safe infrastructure,” King said, “is not going to be enough to get suburban drivers out of their cars necessarily.”
Rules and Safety
Use the guide below to know what you ride and where you ride, including for e-bikes, low-power scooters, toy vehicles, e-scooters and e-skateboards.
Riders are responsible for knowing where their device is allowed.
- Always yield to pedestrians and slower-moving traffic
- Use an audible signal before passing on the left
- Equip their device with lights if riding at night
- Except for accessibility purposes, must dismount in Dismount Zones
- Must follow speed limits
- Multi-use path: 15 mph
- Crosswalks: 8 mph
- Residential streets: 20 mph
E-bikes
E-bikes are sorted into three classes with different sets of rules. View legal definitions at the bottom of the page.
E-bikes do not require licensing or registration.
Class 1 and Class 2 e-bikes
What: These bikes can reach a speed of no greater than 20 mph.
The main different between Class 1 and 2 e-bikes is that Class 2 e-bikes are also equipped with a throttle. Class 2 e-bike riders don’t have to pedal to get assistance.
Where: They follow the same rules as traditional bicycles and are allowed on:
Class 3 e-bikes
What: These bikes are
- pedal assist only
- do not have a throttle.
- can reach a speed of no greater than 28 mph
Where: Streets and in bike lanes. They may not be operated on sidewalks and multi-use paths.
Who: Can only be operated by those who are aged 16 or older. Riders ages 16 and 17 must wear a helmet.
See legal definitions below.
Class 1, 2 and 3 e-bikes are allowed on roads and bike lanes. The same rules apply whether you’re on an e-bike or a conventional bike.
- You must ride close to the right side of the road or bike lane (when it is safe to do so), except when making a left turn or passing a slower vehicle
- Ride side-by-side with no more than one other cyclist
- Signal your intention to turn or stop (only if it is safe for you to take a hand off the bars)
- E-bike riders must obey speed limits and other road signs in the same way that conventional bike riders do
- Colorado’s Safety Stop applies to e-bikes — bike and e-bike riders can treat stop signs like yield signs and red lights like stop signs if it is safe to do so and as long as they do not take the right of way from another road user
Class 3 e-bikes cannot be operated on multi-use paths.
Class 1 and 2 e-bikes follow the same multi-use path rules as regular bikes:
- The speed limit on multi-use paths is 15 mph.
- Always yield to pedestrians and slower-moving traffic
- Pass slower-moving travelers on the left
- Before you pass, always use an audible signal to alert people, such as with a bike bell or by loudly saying, “passing on your left”
- Always pass at a slow speed to ensure courtesy, etiquette and safety
Class 3 e-bikes cannot be operated on trails.
Class 1 and Class 2 e-bikes are allowed on certain OSMP trails.
Low-power Scooters
Two wheels and electric power don’t equal a Class 1-3 e-bike.
What: Low-power scooters, as defined in the Boulder Revised Code:
Where: Streets only. They may not be operated on bike lanes, sidewalks or multi-use paths.
Who: Riders must:
Helmets are required for persons under the age of eighteen who operate or are a passenger on a low-power scooter.
Toy Vehicles
What: Electric-powered devices that do not have proper equipment to operate in the public right-of-way, for example, front and rear lights.
They are not considered e-bikes because they do provide a chain driver or pedals to operate the device.
Where: Toy vehicles are not allowed to operate in the public right-of-way, including:
Estimated urban travel-time effects
We evaluate treatment effects in the urban centre for both recurring and event-based mobility. For recurring mobility in the Midtown Experiment, which measures travel-time impacts in the city centre, we find evidence of a congestion effect due to the banning policy of 0.241 (standard error 0.035) minutes per mile (Table 1). For an average commute in Fulton County, this translates to an estimated increase in evening commute times of 2.3 to 4.2 minutes per trip (between 373,000 and 679,000 additional hours for Atlanta commuters per year). For the typical commuter in Atlanta, this congestion effect due to the scooter ban translates to a 9.9% average increase in city travel time. Similarly, for the MARTA Experiment, which measures travel decisions around transportation hubs and with high levels of scooter use for last-mile transit, we find evidence of a congestion effect due to the policy ban of 0.255 (s.e. 0.051) minutes per mile. This translates to an estimated increase in evening commute times of 2.0 to 4.8 minutes per trip (between 327,000 and 784,000 additional hours for Atlanta commuters per year). For a typical commuter in Atlanta, this congestion effect due to the scooter ban translates to a 10.5% average increase in travel time. With these two different experimental designs, we find quantitatively similar congestion estimates for evening trips (for example, overlapping 95% confidence intervals). We infer that when scooters are not available, a statistically significant substitution between micromobility and personal vehicles occurs. For reference, based on the estimated US average commute time of 27.6 minutes in 2019 30. the results from our natural experiment imply a 17.4% increase in travel time nationally.
Some may wonder why the effect of the ban initially tapers off before stabilizing to our final reported estimate. We acknowledge that it is not possible to fully characterize this phenomenon without more inductive or qualitative methods. However, in terms of possible mechanisms, we believe that after experimenting with other micromobility substitutes (for example, walking, rail, bus or other micromobility), riders gradually settle on their preferred alternative after two to three weeks of experimentation at which time the effect reappears and stabilizes using multiple methods and approaches. This behaviour is consistent with the habit discontinuity hypothesis that micromobility riders disrupt mobility patterns but do not necessarily revert to other sustainability-enhancing travel modes. We have some suggestive survey evidence for this mode settling. According to the Atlanta e-scooter survey, 42% of scooter users self-report that they would have made their trips by using a personal vehicle/rideshare had a scooter not been available 11. Although a full investigation of behavioural persistence beyond the 90-day period is out of scope in this study, we note that longer-term monitoring of the policy implementation becomes more difficult to justify as source of exogenous variation. In future research, we suggest further study into scooter use volumes and mechanisms of mode substitution to better understand the relationship between short-run behavioural modification and long-run habit formation for micromobility use. Given that these types of policy interventions are becoming more prevalent, it will be critical for decisionmakers to weigh the relative priorities between public safety and traffic congestion, which is already estimated to cost up to US166 billion annually in the United States 34.
Critics of micromobility solutions point to the fact that scooters may not displace cars and hence do not achieve sustainability co-benefits 12. Contrary to this view, we find that commuters revert to car-based travel (for example, personal vehicles, ride sharing or ride hailing) once micromobility devices are not available, resulting in statistically significant increases in travel time not intended by the original policy. These findings are consistent with other studies in Seattle and Beijing, for example, which suggest that micromobility rides can replace up to 18% of short car trips in congested corridors or mitigate traffic around subway stations by up to 4%, respectively 35,36. We find that the dominant behavioural response by riders is to substitute micromobility with cars. Although we do not observe micromobility trips directly, 52% of surveyed micromobility users in Atlanta reported that they used a scooter from least a few times per month to several times per week during the period of our study 11. Our results also indicate that micromobility users were largely not driven by environmental considerations in their travel mode choice following the safety regulation. This is important because as the micromobility user base is growing and consumer preferences are shifting towards longer e-scooter trip distances 3. micromobility adoption presents increased opportunities to achieve emissions reductions from a broader set of consumers who are not necessarily environmentally conscious.
Conclusions
Decisions that shape our cities can lead to unexpected effects. We have established that when scooters and e-bikes are banned, drivers experience statistically significant increases in traffic congestion as many riders revert back to passenger vehicles for last-mile transit. Due to the precise nature of the intervention, we observe effects that are greatest in the first few days after the micromobility ban but show durability for many subsequent weeks. The persistence of these effects may compound the economic costs of increased traffic congestion, which we estimate can be worth up to US536 million globally (Methods). It remains unclear whether greater public awareness of these unintended congestion effects could shift public pressure on micromobility bans. Metropolitan areas around the world such as Singapore, Montreal and West Hollywood have instituted bans and other restrictions on shared micromobility, which risks further economic costs of increased commuter travel times. To accelerate the adoption of micromobility and achieve its associated sustainability benefits, we argue that cities will need to make additional investments in both physical and digital infrastructure. For physical infrastructure, land use and space allocation will require longer-term planning such as converting lanes usually reserved for cars into bike lanes that can be used for micromobility. If further micromobility adoption happens at the expense of ‘pollutingʼ modes like private vehicles or other car-based travel, then these investments become even more critical for urban sustainability and will carry larger policy implications. We are already seeing evidence of this in large cities such as Milan, Brussels, Seattle and Montreal 3 and mid-sized cities such as Raleigh, NC, Alexandria, VA, and Tucson, AZ 10. With its potential to displace cars for personal travel and drive short-run emissions reductions, micromobility is poised to continue its strong growth as an urban mobility solution.
Geofencing policy
The micromobility ban was implemented in the city of Atlanta on 9 August 2019. We use high-resolution data from 25 June 2019 to 22 September 2019 from Uber Movement to measure changes in evening travel times between 7:00 p.m. and midnight, pre- and post-policy implementation. This allows for a window of analysis of 45 days pre- and post-policy implementation (Supplementary Fig. 1). We designed three quasi-experiments to evaluate both recurring mobility (for example, daily community patterns) and event-based mobility (for example, travel for special events). The policy zone covers a total land area of 136.8 square miles (354.3 square km) as shown in Fig. 1. Unlike other interventions such as fines or usage rules that might discourage but do not eliminate scooter riding, we are able to observe treatment effects with near perfect compliance. This is because the mobile apps digitally shut off access to all devices during non-operating hours automatically between 9:00 p.m. and 4:00 a.m. with mobile geofencing.
The travel time data, as provided by Uber Movement, are derived from anonymized and aggregated trip location data that are spatially resolved to the nearest census tract. We downloaded intra-day travel times at the highest resolution available that includes the start of the ban, which Uber defines as between 7 p.m. and midnight. Thus, we analysed evening peak hour congestion impacts before and after the policy, where there is a time overlapping of peak hours and policy implementation hours that could be leveraged for the analysis. Because the travel distance for every tract may differ, we normalized the travel time data by the distance between origin and destination tracts. This allows for direct comparisons between trips to different parts of the city. The dependent variable for analysis in the Midtown and MARTA experiments is therefore the daily evening travel time per mile (Supplementary Table 3 provides descriptive statistics). In the Mercedes-Benz Experiment, we normalize the travel time per mile by the number of attendees to each event during July and August. In this way, we mitigate the possibility that during post-ban dates there could be more people at the stadium than before.
The independent variables include location-based statistical controls such as census tract characteristics, proxy variables for number of transit alternatives and measures for common time trends that could impact travel times including daily precipitation and time dummies. The census tract characteristics are variables that impact traffic congestion in the area include the number of vehicles owned per tract, which measures residential density. Because the ban was implemented coincident with the academic school year, we include school enrolment per tract as a control for differential impacts on traffic due to school size. The transit alternative variables impact travel mode choices made by commuters and include the number of transit routes, Walk Score and number of bike-share hubs. We also considered other transit alternative variables such as the Transit Score, but these could not be used in the analysis due to high correlation with other features. Because travel patterns may differ during rainy weather, we include a dummy variable for daily precipitation during the evening. To merge precipitation data with the tract-level observations, we found the nearest weather station to each tract using published data from the National Oceanic and Atmospheric Administration 39. It is possible that there could be different congestion effects on weekdays and weekends. Additionally, general traffic congestion could increase during the summer months such as mass gatherings during summer events. To capture this and other unobserved time-varying factors, we include monthly and day-of-the-week dummies. We include descriptive statistics by area in Supplementary Table 4 and provide additional descriptors for our variables in Supplementary Table 5.
Data availability
The datasets generated and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4924424. Spatial and neighbourhood features are downloaded from AllTransit, Walk Score, the Census American Community Survey and the National Oceanic and Atmospheric Administration’s National Center for Environmental Information. The raw travel time data for the city of Atlanta are publicly available from Uber Movement, 2022 Uber Technologies, Inc., at http://movement.uber.com. Source data are provided with this paper.
To support scientific replication, all computer code used to generate the study’s main findings are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4924424.
NACTO Guidelines for Regulating Shared Micromobility
NACTO’s Guidelines for Regulating Shared Micromobility outlines best practices for cities and public entities regulating and managing shared micromobility services on their streets. Its recommendations were developed to reflect the wide variety of experiences that North American cities have had in regulating and managing shared micromobility.
While many of the issues covered are applicable to all forms of shared micromobility, this document is explicitly meant to help cities establish guidelines for formal management of public-use mobility options that are not managed through traditional procurement processes (the management mechanism for most docked bike share programs in North America). The Rapid growth in the number of shared micromobility trips and the introduction of e-scooters has required cities to FOCUS new attention on how best to regulate these new services in order to achieve the best public outcomes.
The Guidelines are divided into two broad sections:
Best Practice Recommendations
- Regulations or policies that cities should include in their permits or require from their operators. By addressing these issues in a similar fashion across multiple jurisdictions, cities can create a level playing field for vendors and ensure a safer, more equitable experience for riders.
- For outstanding questions for which there is not yet a defined best practice, this document provides a discussion guide, which outlines options that cities may choose to take and context for future debate.
- Shows how different cities regulate shared micromobility systems in different ways, including by fleet size, customer service expectations, permit fees, service areas, and more.
In particular, Guidelines for Regulating Shared Micromobility covers:
- Options for regulation, including permits, pilots, and demonstrations;
- General provisions that should be included in all agreements with providers, such as insurance requirements, and when an operator is to be considered in breach of its agreement with a city;
- Infrastructure investments, including device parking options such as on-street corrals and docking points (pdf), and guidance on providing safe places to ride (pdf);
- Suggestions on operational requirements, including fleet size, device relocation, rebalancing and fleet distribution, equipment and vehicle maintenance, customer service, and staffing;
- Safety provisions, including vehicle speed, battery practices, and parking options that preserve the public-right-of-way;
- Practices for equity, including increased access to underserved communities;
- Fee structures that enable cities to recoup their costs for managing dockless mobility in their cities, as well as provide public benefits;
- Public engagement (pdf), including outreach materials, as well as pricing and discount programs;
- Data management (pdf), including how cities can ensure access to accurate, high-quality data while maintaining individual privacy;
- Technology recommendations, including the best uses for geofencing technology along with its limitations.
Guidelines for Regulating Shared Micromobility was developed for cities, by cities; recommendations are the result of city experience. As with the first version, released in 2018, thoughtful, Smart management of new mobility options is essential for cities as they work to protect the public right of way, increase mobility, and ensure that everyone benefits from new mobility options. Deriving guidelines from city staff ensures recommendations are vetted and relevant for practitioners regulating and managing shared micromobility on their cities’ streets.
This Guidance is made possible by Climate Works and the Better Bike Share Partnership. The Better Bike Share Partnership is a collaboration funded by The JPB Foundation to build equitable and replicable bike share systems. The partners include The City of Philadelphia, the Bicycle Coalition of Greater Philadelphia, the National Association of City Transportation Officials (NACTO) and the PeopleForBikes Foundation.