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On-Demand Delivery Services Definition

Delivery services, also known as on-demand delivery services, food delivery services or crowdshipping, are a real-time local delivery solution for goods, typically prepared foods, groceries, or other consumer staples. Due to the rapid growth of online shopping, development of emerging technologies, and innovative forms of delivery services, have become more capable of handling a wide range of delivery needs, from small parcels to large-scale freight, with a level of precision and efficiency that was previously unattainable [1].

On demand delivery service businesses use platform technology to connect three parties in a marketplace: 1) a supplier of goods, often a restaurant, and 2) independent contractors or gig workers who can collect, transport, and deliver the goods to 3) a consumer who has ordered the goods.

New technologies such as crowdsourcing, location-based services, electric bikes and scooters, and advanced algorithms have empowered the courier services providers to offer faster, more environmentally friendly, and personalized delivery options to their customers. At the same time, to satisfy customers’ increasing and various demand of delivery services, new service forms are introduced, such as crowdsourced delivery (i.e., distributing delivery services to personal deliver instead of company staff) [2] and cross shipping (i.e., sending parcels to customers through an intermediate point instead of directly) [3].

Delivery services are popular globally, with top markets in China, the United States, and India [4]. Top companies in the United States are UberEats, and DoorDash [5].

A new trend in on-demand delivery service is to use robotic delivery services. The demand for robotic delivery services has increased quickly due to the technology development, challenges from traditional human delivery, and rising requests for contactless deliveries during COVID-19 [6]. As of 2021, cities located in 18 states in the US [7] had launched their robotic delivery pilot programs, such as Los Angeles, CA [8], Pittsburgh, Pennsylvania [9], and Redwood City, California [10]. The governments collaborate with emerging tech companies, including Uber, Starship, Kiwibot, Cruise, and so on. However, most of the programs are operating in small areas, indicating the experimental phase of these initiatives and the challenges in scaling up to wider service areas. When these systems are deployed at scale, several scenarios of concern and necessary considerations arise. Firstly, robotic delivery units could congest sidewalks, reducing accessibility for pedestrians and other users. This might require new urban planning strategies and dedicated pathways to ensure safe coexistence. Secondly, their widespread use could alter urban form and infrastructure, prompting cities to redesign pedestrian zones and potentially repurpose existing spaces.

References

  1. A. Rutter, D. Bierling, D. Lee, C. Morgan, and J. Warner, “How Will E-commerce Growth Impact Our Transportation Network,” PRC 17-79 F. Accessed: May 13, 2024. [Online]. Available: https://static.tti.tamu.edu/tti.tamu.edu/documents/PRC-17-79-F.pdf

  2. A. Alnaggar, F. Gzara, and J. H. Bookbinder, “Crowdsourced delivery: A review of platforms and academic literature,” Omega, vol. 98, p. 102139, Jan. 2021, doi: 10.1016/j.omega.2019.102139.

  3. A. I. Nikolopoulou, P. P. Repoussis, C. D. Tarantilis, and E. E. Zachariadis, “Moving products between location pairs: Cross-docking versus direct-shipping,” Eur. J. Oper. Res., vol. 256, no. 3, pp. 803–819, Feb. 2017, doi: 10.1016/j.ejor.2016.06.053.

  4. C. Li, M. Mirosa, and P. Bremer, “Review of Online Food Delivery Platforms and their Impacts on Sustainability,” Sustainability, vol. 12, no. 14, Art. no. 14, Jan. 2020, doi: 10.3390/su12145528.

  5. M. Kaczmarski, “Which company is winning the restaurant food delivery war?,” Bloomberg Second Measure. Accessed: Apr. 02, 2024. [Online]. Available: https://secondmeasure.com/datapoints/food-delivery-services-grubhub-uber-eats-doordash-postmates/

  6. S. Srinivas, S. Ramachandiran, and S. Rajendran, “Autonomous robot-driven deliveries: A review of recent developments and future directions,” Transp. Res. Part E Logist. Transp. Rev., vol. 165, p. 102834, Sep. 2022, doi: 10.1016/j.tre.2022.102834.

  7. Minnesota department of Transportation, “Personal Delivery Devices.” Accessed: May 13, 2024. [Online]. Available: https://dot.state.mn.us/automated/docs/personal-delivery-device-white-paper.pdf

  8. J. Fantozzi, “Uber launches delivery robot pilot program; adds Google voice ordering,” Nation’s Restaurant News. Accessed: May 13, 2024. [Online]. Available: https://www.nrn.com/technology/uber-launches-delivery-robot-pilot-program-adds-google-voice-ordering

  9. City of Pittsburgh Mobility and Infrastructure, “2021 Personal Delivery Device Final Pilot Evaluation.” Accessed: May 13, 2024. [Online]. Available: https://hdp-us-prod-app-pgh-engage-files.s3.us-west 2.amazonaws.com/9616/5540/2948/PDD_Final_Pilot_Evaluation_v2.pdf

  10. Staff, “Redwood City council renews pilot program for autonomous robot deliveries,” Climate Online. Accessed: May 13, 2024. [Online]. Available: https://climaterwc.com/2019/05/13/autonomous-robot-deliveries-returning-to-redwood-city-as-pilot-project/

Automated Vehicles Definition

Automated Vehicles (AVs) are vehicles equipped with technology that allows them to navigate and operate with varying degrees of human intervention. The Society of Automotive Engineers (SAE) defines AVs through a classification system that ranges from Level 0 to Level 5, based on the level of automation and the role of the human driver [1].

Advanced Driver Assistance Systems (ADAS) are found in Levels 1 and 2, and include features like adaptive cruise control, lane-keeping assistance, and automated emergency braking. They enhance driving safety and convenience but still require human oversight. Automated Driving Systems (ADS) are found in Levels 3 through 5, and can manage all driving tasks under certain conditions, enabling the vehicle to operate without human input.

The Mobility Center of Excellence (COE) focuses on Highly Automated Vehicles (Levels 4 and 5) due to their potential for large-scale deployment and significant impact on transportation systems. These vehicles promise to transform mobility by improving safety, reducing congestion, and providing transportation solutions for those unable to drive, but may be subject to unintended consequences that have plagued previous advancements in transportation technologies.

References

  1. SAE International, “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,” J3016_202104, Apr. 2021. [Online]. Available: https://www.sae.org/standards/content/j3016_202104/

Micromobility Definition

The term micromobility refers to small, low-speed vehicles intended for personal use, including bicycles, electric scooters (or e-scooters), and similar vehicles—whether powered or unpowered and both personally owned and deployed in shared fleets (as in bikesharing systems). SAE International developed a taxonomy of powered micromobility vehicles based on form factor (e.g. bicycle, standing or seated scooter) and physical characteristics such as width, curb weight, top speed, and power source [1]. The primary vehicle types deployed in shared fleets are human- or electric-powered bicycles in bikesharing, seated or standing e-scooters in scooter sharing, and mopeds.

Shared Micromobility - the shared use of a bicycle, scooter, or other low-speed mode - is an innovative transportation strategy that enables users short-term access to a transportation mode on an as-needed basis [2, Ch. 12]. Shared micromobility services may be docked (a station-to-station system in which users unlock vehicles from a fixed location, which also generally contains the IT infrastructure for reservation and payment, and in some cases facility for electric charging), dockless (with the IT infrastructure and locking mechanism integrated into the vehicles), or a hybrid of the two models [3].

References

  1. SAE International, “J3194_201911: Taxonomy and Classification of Powered Micromobility Vehicles.” 2019.  doi: https://doi.org/10.4271/J3194_201911.

  2. S. Shaheen and A. Cohen, A Modern Guide to the Urban Sharing Economy (Shared micromobility: policy and practices in the United States, Chapter 12). 2021. [Online]. Available: https://www.elgaronline.com/edcollchap/edcoll/9781789909555/9781789909555.00020.xml

  3. M. Hernandez, R. Eldridge, and K. Lukacs, “Public Transit and Bikesharing: A Synthesis of Transit Practice,” Transportation Research Board, TCRP Synthesis 132, 2018. doi: 10.17226/25088.

Ridehail/Transportation Network Companies Definition

Ride-hailing, also called ridesourcing, and codified in California law as Transportation Network Companies, are taxi-like commercial transportation services based on the use of an online platform that connects riders with drivers and automates reservations and payment. Ride-hailing services may offer a variety of service classes and vehicle sizes, generally using passenger vehicles or Sports Utility Vehicles (SUVs). In larger markets, a shared service class may be offered, in which unrelated passengers travel together for some part of their trip. Though the terms are often used interchangeably, ride-hailing is distinct from ridesharing, which refers to non-commercial sharing of journeys by drivers and passengers traveling to the same destination, as in carpooling, slugging, or vanpooling [1].

References

Car Sharing Definition

Carsharing can take different forms, as the model existed prior to the modern concept as monetized under the ‘sharing economy.’ First appearing in Europe in the 1940s, carsharing took the form of multiple individuals co-owning or sharing a car, mainly due to economic reasons. The model has since evolved to include a membership based system of sharing a fleet of cars, with no ownership rights conveyed [1]. Carshare participants gain the utility of a private vehicle without the costs associated with ownership [2]. Carsharing models vary depending on vehicle ownership and the technology underlying the system. Traditional or round-trip carsharing requires users to return a vehicle to the same location where they picked it up. One-way or free-floating allows users to drop off a vehicle at or within any of a number of designated locations or zones, regardless of where it was picked up. In peer-to-peer (or P2P) models, vehicles are made available for sharing by individual owners, rather than by a single fleet owner [3]. Advancements in reservation systems have improved system efficiency across models, and have been particularly important for P2P carsharing [4].

References

Demand-Responsive Transit & Microtransit Definition

Demand-Responsive Transit (DRT) is a flexible transportation service that adapts to the specific travel needs of its users, and is typically shared among users. Instead of following fixed routes and schedules, DRT services are typically booked in advance and operate within a defined area. DRT started decades ago as Dial-a-Ride or paratransit, which serves a specific population (e.g. elderly) or with a specific technology (e.g. phone calls for day-ahead reservations), but has been generalized recently to serve general populations and more advanced communication technologies (e.g., wireless and internet for real-time reservations). As a form of transit, it can serve multiple passengers on a journey, though the service may use anything from passenger cars to small buses to provide the service. It may also include deviated route service, in which an otherwise fixed-route service may make unscheduled stops within a corridor or service zone to pick up or drop off passengers.

Microtransit is a subset of DRT, often characterized by the use of new technologies to optimize and manage the public transit service with a specific focus on either population, spatial coverage or coordination with general public transit. It blends aspects of traditional public transit and private ride-hailing services, offering shared rides that are dynamically routed. Microtransit is generally operated within defined service zones or along a corridor, often with designated stops at key destinations like employment centers or transfer points to other transportation services [1].

References

Mobility-as-a-service Definition

The Shared-Use Mobility Center defines Mobility-as-a-Service (MaaS) as ”a practice that integrates the travel options available to a user and offers them in a single interface, with a single payment mechanism” [1]. MaaS is, in a simplistic form, a business model that allows multi-modal platform integration of transportation options. However, how the model is executed is still a point of debate, and recent failures of proposed systems throw MaaS further into doubt. In 2017, as a relatively new concept, there was much uncertainty in the core characteristics of MaaS [2]. This uncertainty continued through 2021 with no unified, agreed upon single definition of MaaS; pointing to an underestimation of what riders and users need and suggesting that an integrated trip planner may be enough to satisfy needs [3]. Currently, little progress has been made to unify MaaS as a concept and successfully launch a fully integrated system. Policy and regulatory barriers remain, and incorporating local characteristics will be crucial for MaaS to succeed [4].

In the US, cities like Pittsburgh [5], Minneapolis [6], and Tampa [7] have launched their pilot MaaS programs. These pilot programs, however, only included limited transport services, mainly due to difficulties with public private collaboration, funding, cyber security, and lack of attractiveness to transit users, auto users, and older populations [4].

References

  1. Shared-Use Mobility Center, “Towards the Promise of Mobility as a Service (MaaS) in the U.S.,” Chicago, IL, Jul. 2020. [Online]. Available: https://sharedusemobilitycenter.org/wp-content/uploads/2020/09/Towards-the-Promise-of-MaaS-in-the-US-July-2020-Shared-Use-Mobility-Center.pdf

  2. P. Jittrapirom, V. Caiati, A. M. Feneri, S. Ebrahimigharehbaghi, M. J. Alonso-González, and J. Narayan, “Mobility as a service: A critical review of definitions, assessments of schemes, and key challenges,” Urban Plan., vol. 2, no. 2, pp. 13–25, Jan. 2017, doi: 10.17645/up.v2i2.931.

  3. D. A. Hensher, C. Mulley, and J. D. Nelson, “Mobility as a service (MaaS) – Going somewhere or nowhere?,” Transp. Policy, vol. 111, pp. 153–156, Sep. 2021, doi: 10.1016/j.tranpol.2021.07.021.

  4. L. Butler, T. Yigitcanlar, and A. Paz, “Barriers and risks of Mobility-as-a-Service (MaaS) adoption in cities: A systematic review of the literature,” Cities, vol. 109, p. 103036, Feb. 2021, doi: 10.1016/j.cities.2020.103036.

  5. City of Pittsburgh Mobility and Infrastructure, “Move PGH Mid-Pilot Report.” Accessed: May 13, 2024. [Online]. Available: https://apps.pittsburghpa.gov/redtail/images/19169_Move_PGH_Mid_Pilot_Report_[FINAL]_v2.pdf

  6. C. of Minneapolis, “Minneapolis Mobility Hubs Pilot.” Accessed: May 13, 2024. [Online]. Available: https://www.minneapolismn.gov/government/programs-initiatives/transportation-programs/mobility-hubs/

  7. “City of Tampa Launches Mobility as a Service (MaaS) App | City of Tampa.” Accessed: May 13, 2024. [Online]. Available: https://www.tampa.gov/news/city-tampa-launches-mobility-service-maas-app-111716

Universal Basic Mobility Definition

Universal Basic Mobility (UBM) programs manifest the concept that all community members have a right to at least some degree of mobility, regardless of residence or income level [1]. The concept is the transportation equivalent of the Supplemental Nutrition Assistance Program (SNAP), which provides government-funded cash assistance to buy food. In the case of UBM, the government provides a mobility subsidy to eligible residents to address their transportation needs and allow them to choose more convenient trips. Several cities across the U.S. recently piloted UBM programs with the intention of removing the monetary cost barrier to transportation [2]. The pilot programs were envisioned to provide all residents with a baseline level of mobility, incentivize the use of shared travel modes, and improve access to employment opportunities [2].

References

  1. ITS America, “Universal Basic Mobility Primer.” Accessed: May 15, 2024. [Online]. Available: https://itsa.org/wp-content/uploads/2022/03/Universal-Basic-Mobility-One-Pager_Final.pdf

  2. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

Heavy Duty Applications of Automated Vehicles Definition

Based on EPA classifications, heavy duty vehicles include trucks over 8,500 pounds [8], as well as buses, shuttles, and specialized equipment like street sweepers. The level of automation of heavy-duty vehicles ranges from driver-assist technologies to driverless vehicles [9].
The Federal Motor Carrier Safety Administration (FMCSA) plays a crucial role in regulating and overseeing the deployment of automated heavy-duty vehicles. The FMCSA focuses on ensuring that these vehicles meet safety standards and operate within the regulatory framework. Their efforts include developing guidelines for testing and deployment, addressing cybersecurity concerns, and ensuring that automated systems can safely interact with other road users.

References

  1. OAR US Environmental Protection Agency, “How does MOVES Classify Light-Duty Trucks?” Accessed: May 15, 2024. [Online]. Available: https://www.epa.gov/moves/how-does-moves-classify-light-duty-trucks

  2. S. Clevenger, “Autonomous Trucks Reshaping the Freight Industry,” Transport Topics. Accessed: May 15, 2024. [Online]. Available: https://www.ttnews.com/articles/autonomous-trucks-reshaping-freight-industry

How Car Sharing affects Safety

Carshare may, relative to private auto travel, confer some safety benefits. For example,users generally have to go through a screening process to sign up for the programs and establish valid licenses. Safe driving behavior does, of course, vary by individual; a study of Australian carshare users found that infrequent users, users in households that owned other cars, and users that had fewer previous accidents, chose more expensive vehicle insurance, and had been licensed for longer, were less likely to be in a vehicle crash [1]. To enhance safety, the study recommended establishing incentives for carshare users with more driving experience and more extensive insurance [1].

More research may be necessary to better establish safety differences among carshare users, whether carshare users travel more safely relative to private vehicle owners, and if so, what the mechanisms are that promote additional precautions while driving.

How Universal Basic Mobility affects Social Equity

Inequality is embedded in our transportation systems and land use patterns, which reinforces unequal access to opportunities. Mobility inequality can be racialized, gendered, or based on income. The inequalities between those with and without private vehicles deepened during the COVID-19 pandemic [1], [2], [3]. Universal Basic Mobility (UBM) programs aim to address this and in turn create more equitable transportation systems. Based on qualitative evaluation of eight UBM programs and pilots, UC Davis researchers found that UBM pilot programs have had success in enrolling low-income people of color and increasing transit use [4].

Additional research related to equity impacts of mobility wallet pilot program outcomes is ongoing. For example, researchers at UCLA and UC Davis are evaluating the South LA mobility wallet pilot, where 1,000 people in South Los Angeles are receiving $150 per month for a year for use on transit needs [5]. Researchers at UC Davis are also evaluating pilot UBM programs in Oakland and Bakersfield, with a focus on economic, social, and environmental impacts [6]. However, there is little completed research on how effective university mobility programs are in addressing inequality in transportation access. Additional research is needed on the equity impacts of UBM programs, as well as how the programs compare to alternatives like free or reduced fare transit programs.

  1. E. Blumenberg, “En-gendering Effective Planning: Spatial Mismatch, Low Income Women, and Transportation Policy,” 2003, doi: 10.1080/01944360408976378.

  2. Mimí Sheller and M. Sheller, “Racialized Mobility Transitions in Philadelphia: Connecting Urban Sustainability and Transport Justice,” City Soc., vol. 27, no. 1, pp. 70–91, Apr. 2015, doi: 10.1111/ciso.12049.

  3. Isti Hidayati, I. Hidayati, Wendy Tan, W. Tan, Claudia Yamu, and C. Yamu, “Conceptualizing Mobility Inequality: Mobility and Accessibility for the Marginalized:,” J. Plan. Lit., vol. 36, no. 4, pp. 492–507, May 2021, doi: 10.1177/08854122211012898.

  4. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

  5. “Los Angeles launches nation’s largest UBM pilot, Lewis Center leads evaluation.,” UCLA Lewis Center for Regional Policy Studies., 2022. [Online]. Available: https://www.lewis.ucla.edu/project/2023-mb-01/

  6. A. Sanguinetti, E. Alston-Stepnitz, and M. C. D’Agostino, “Evaluating Two Universal Basic Mobility Pilot Projects in California.” [Online]. Available: https://www.ucits.org/research-project/2022-20/

How Automated Vehicles affects Health

The introduction and potential proliferation of highly automated vehicles (AVs) present the classic challenge of balancing the freedom of private manufacturers to innovate with the government's responsibility to protect public health. AVs raise many public health issues beyond their potential to improve safety, ranging from concerns about more automobile use and less use of healthier alternatives like biking or walking, to concerns that focusing on autonomous vehicles may distract attention and divert funding from efforts to improve mass transit. There are, additionally, issues of access, especially for the poor, disabled, and those in rural environments [1].

As the classic Code of Ethics for Public Health recommends [2], public health advocates can advocate for the rights of individuals and their communities while protecting public health by helping to establish policies and priorities through “processes that ensure an opportunity for input from community members.” Public health thought leaders can ensure that communities have the information they need for informed decisions about whether and how autonomous vehicles will traverse their streets, and they can make sure that manufacturers who test and deploy autonomous vehicles obtain “the community’s consent for their implementation.” Finally, public health leaders can work for the empowerment of the disenfranchised, incorporating and respecting “diverse values, beliefs, and cultures in the community” and collaborating “in ways that build the public’s trust” [2].

  1. J. Fleetwood, “Public Health, Ethics, and Autonomous Vehicles,” Am. J. Public Health, vol. 107, no. 4, pp. 532–537, Apr. 2017, doi: 10.2105/AJPH.2016.303628.

  2. J. C. Thomas, M. Sage, J. Dillenberg, and V. J. Guillory, “A Code of Ethics for Public Health,” Am. J. Public Health, vol. 92, no. 7, pp. 1057–1059, Jul. 2002.

How Micromobility affects Health

Emerging micromobility options such as e-bikes and e-scooters can improve accessibility and connectivity for vulnerable population groups, such as those with physical limitations or without access to a car [1], [2]. Compared to biking or walking, electric micromobility (EMM) vehicles are often more accessible to users with lower interest in or capacity for physical activity, while still providing exercise and outdoor enjoyment [1], [2], [3]. For instance, e-bikes are favored by older adults as a form of physical activity and can encourage micromobility use for distances over 3 miles typically covered by cars [4], [5], [6]. An observational study found that starting to e-bike may increase overall biking frequency among older adults, potentially extending the number of years they are able to bike [4], [5], [6]. Despite being less physically demanding than conventional biking, e-biking offers many of the same cardiovascular benefits [5], [7].
In addition to health benefits from access, physical activity, and outdoor enjoyment, increased EMM vehicle usage has the potential to reduce air pollution from cars by substituting car trips and improving access to public transit. EMM vehicles can address the first-mile-last-mile problem, supporting the use of public transit [8], [9]. They also provide an alternative mode of transportation for short trips, which can help alleviate overcrowding on public transport and support social distancing when necessary [8]. Moreover, EMM vehicles may contribute to noise pollution reduction, which is linked to adverse health effects such as cognitive impairment in children and sleep disturbance [9]. However, studies indicate that not all EMM vehicles have the same environmental health benefits; e-scooters, for instance, may have a negative environmental impact compared to the modes they replace (for example, they may replace pedestrian trips) [9], [10], [11]. Additionally, the collection vehicles used for relocating and charging EMM vehicles in shared vehicle programs can contribute to emissions, particularly in less densely populated areas [9].
Safety remains a primary concern for public health regarding EMM usage, and is discussed in more detail in the section devoted to safety impacts. Cyclists, including e-bike users, are vulnerable to injuries and fatalities from collisions with cars. Electric scooter usage can also result in serious injuries, especially head and limb injuries, exacerbated by low helmet usage [9], [12]. Injuries to pedestrians from e-scooter riders on sidewalks are another significant concern [9]. Providing separate, designated infrastructure for EMM can enhance safety [1].
Future research could include the development of best practices for maximizing public health benefits of micromobility programs, as well as further analysis of the health impacts of different micromobility modes.

  1. A. Bretones et al., “Public Health-Led Insights on Electric Micro-mobility Adoption and Use: a Scoping Review,” J. Urban Health, vol. 100, no. 3, pp. 612–626, Jun. 2023, doi: 10.1007/s11524-023-00731-0.

  2. T. G. J. Jones, L. Harms, and E. Heinen, “Motives, perceptions and experiences of electric bicycle owners and implications for health, wellbeing and mobility,” J. Transp. Geogr., vol. 53, pp. 41–49, May 2016, doi: 10.1016/j.jtrangeo.2016.04.006.

  3. Aslak Fyhri et al., “A push to cycling—exploring the e-bike’s role in overcoming barriers to bicycle use with a survey and an intervention study,” Int. J. Sustain. Transp., vol. 11, no. 9, pp. 681–695, May 2017, doi: 10.1080/15568318.2017.1302526.

  4. Jessica Bourne et al., “The impact of e-cycling on travel behaviour: A scoping review.,” J. Transp. Health, vol. 19, p. 100910, 2020, doi: 10.1016/j.jth.2020.100910.

  5. Taylor H Hoj et al., “Increasing Active Transportation Through E-Bike Use: Pilot Study Comparing the Health Benefits, Attitudes, and Beliefs Surrounding E-Bikes and Conventional Bikes.,” JMIR Public Health Surveill., vol. 4, no. 4, Nov. 2018, doi: 10.2196/10461.

  6. Jelle Van Cauwenberg, J. Van Cauwenberg, Bas de Geus, B. de Geus, Benedicte Deforche, and B. Deforche, “Cycling for transport among older adults : health benefits, prevalence, determinants, injuries and the potential of e-bikes,” pp. 133–151, Jan. 2018, doi: 10.1007/978-3-319-76360-6_6.

  7. Thomas Mildestvedt et al., “Getting Physically Active by E-Bike : An Active Commuting Intervention Study,” vol. 4, no. 1, pp. 120–129, 2020, doi: 10.5334/paah.63.

  8. Gabriel Dias et al., “The Role of Shared E-Scooter Systems in Urban Sustainability and Resilience during the Covid-19 Mobility Restrictions,” Sustainability, vol. 13, no. 13, pp. 7084–7084, Jun. 2021, doi: 10.3390/su13137084

  9. J. Glenn et al., “Considering the Potential Health Impacts of Electric Scooters: An Analysis of User Reported Behaviors in Provo, Utah,” Int. J. Environ. Res. Public. Health, vol. 17, no. 17, p. 6344, 2020, doi: 10.3390/ijerph17176344.

  10. Joseph A. Hollingsworth, J. A. Hollingsworth, Brenna Copeland, B. Copeland, Jeremiah X. Johnson, and J. X. Johnson, “Are e-scooters polluters? The environmental impacts of shared dockless electric scooters,” Environ. Res. Lett., vol. 14, no. 8, p. 084031, Aug. 2019, doi: 10.1088/1748-9326/ab2da8.

  11. Anne de Bortoli et al., “Consequential LCA for territorial and multimodal transportation policies: method and application to the free-floating e-scooter disruption in Paris,” J. Clean. Prod., vol. 273, p. 122898, Nov. 2020, doi: 10.1016/j.jclepro.2020.122898.

  12. T. K. Trivedi et al., “Injuries associated with standing electric scooter use,” JAMA Netw. Open, vol. 2, no. 1, pp. e187381–e187381, 2019.

How Demand-Responsive Transit & Microtransit affects Health

Demand-responsive transit and microtransit can benefit public health by improving accessibility. Microtransit services are often more direct or even door-to-door and can serve users with limited mobility. They typically target users whose transportation needs are not met by traditional public transit, including shift workers, low-income individuals, the elderly, disabled, and communities with low levels of fixed-route public transit service [1], [2]. A study on demand-responsive microtransit programs’ return on social investment found that social benefits can outweigh costs by 4 to 6 times, due to their ability to increase access to essential services, foster social inclusion, and improve sustainability [1].
While there are some case studies on microtransit programs, there is limited research on public health impacts. Additional research is needed to understand the extent to which microtransit can meet transportation needs that are not filled by public transit, and how it can best serve different populations and uses, and how it impacts public health. Some of this research is in progress. For example, the "Safety and Public Health Impacts of Microtransit Services" research initiative at the University of Massachusetts Amherst is currently evaluating safety and public health impacts of microtransit services [3].
Finally, on-demand transit/microtransit programs are often meant to improve equitable access, but there is little research on how to design programs to best meet that goal. Survey data from four US cities found that men, younger riders, the highly educated, and transit riders were more likely to be interested in using microtransit. Additional research is needed to understand who on-demand transit/microtransit most frequently serves, and how that impacts public health across demographic groups.

How On-Demand Delivery Services affects Health

A scoping review of public health impacts from on demand food and alcohol delivery published in SSM Population Health found that on-demand delivery services increase geographical access to food but tend to market unhealthy and discretionary foods, and are likely increasing existing health issues and inequities [1]. The review also highlighted concerns over poor age verification processes potentially allowing minors to access alcohol more easily [1].

How Micromobility affects Municipal Budgets

Budgetary impacts from micromobility include costs of permits, operating licenses and fines for risky behavior. The rise of shared dockless micromobility led to reactive policy making and regulations that largely constrained operations [1]. The use of such regulation has been motivated by the desire to control the presence of shared micromobility devices in cities, rather than viewing them as a promising line of municipal revenue. In fact, in many cases, municipalities are addressing the need to subsidize riders, especially when it comes to low-income users [2]. A 2024 study by the Transportation Research and Education Center assessed taxes and fees on micromobility, and found that they vary dramatically by city and are typically higher than taxes and fees on ride-hailing and private vehicles [3].

In general, the literature suggests that while micromobility has the potential to enhance quality of life and access to mobility [4], there are also externalities of social harm such as (mis)parking [5]. There is little available research related to how micromobility could influence the tax burden or base of a locality.

How Universal Basic Mobility affects Education and Workforce

Increased access to education and job opportunities are cited as benefits of Universal Basic Mobility (UBM), based on robust existing research demonstrating the relationship between mobility and access to opportunity and early research on UBM pilot programs [1], [2]. Research assessing how effectively UBM policies and programs improve access to education and job opportunities is sparse.

How Universal Basic Mobility affects Transportation Systems Operations

The University of California Institute of Transportation Studies recently released a technical report that summarizes Universal Basic Mobility (UBM) pilot programs in California along various design dimensions, including eligibility requirements, monetary assistance value, and allowable travel modes [1]. For example, Los Angeles, CA offered 2,000 residents $150 per month for use of public transit, private taxi, transportation network company (e.g., Uber), electric bikeshare, and carshare. The Pittsburgh, PA program gave 50 residents unlimited access to transit and bikeshare along with a monthly credit for scooter and carshare [2]. Other U.S. cities that have implemented a UBM pilot include Portland, OR; Sacramento, CA; Oakland, CA; and Stockton, CA.

Evaluations of most UBM programs are still underway, though some results are available for Oakland and Portland. The Oakland Department of Transportation and Alameda County Transportation Commission surveyed 66 participants pre-program and mid-program, and they observed that 66 percent of these participants used the extra mobility funds for commuting. They also found that 90 percent of funds were spent on transit, and the number of participants who self-reported driving as their primary mode declined by 6 percent for commuting trips [3]. Researchers at Portland State University also evaluated the Portland program based on surveys. Their results revealed that participants had positive UBM perceptions: 89 percent of participants reported greater travel flexibility and 66 percent of participants reported the ability to reach work-related activities that would have been otherwise unreachable. Regarding travel mode shift, over 50 percent of participants agreed that they increased their usage frequency of Uber/Lyft, taxi, bikeshare, and e-scooter [4].

In addition to survey results, policymakers would benefit from studies that analyze how UBM affects system-level efficiency, accessibility and equity. However, there is limited completed research to this end. Most studies focus on analysis based on surveys that are only reflective of stated preferences from participants. Those stated preferences may not be generalizable or accurate in practice, and they are limited to a small spatio-temporal scope. Research gaps lie in tracking and understanding the actual (revealed) preferences of UBM participants, in regards to how UBM, by various levels of support, enables those participants to select mobility options to improve efficiency, accessibility and equity. In particular, research is needed to understand how those improvements vary by neighborhood and population groups. This would help public agencies and private service providers to jointly design a UBM program that is tailored for population groups with a vital business model to scale/group in the future.

  1. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

  2. L. Beibei, L. Branstetter, and C. M. U. Mobility21, “Evaluating Pittsburgh’s Universal Basic Mobility Pilot Program,” Jun. 2022. Accessed: May 15, 2024. [Online]. Available: https://rosap.ntl.bts.gov/view/dot/68460

  3. Oakland Department of Transportation, “Universal Basic Mobility Pilot Overview Evaluation,” 2022. Accessed: May 15, 2024. [Online]. Available: https://cao-94612.s3.us-west-2.amazonaws.com/documents/Universal-Basic-Mobility-Pilot-Overview_Eval_2022-03-16-001945_yfow.pdf

  4. H. Tan, N. McNeil, J. MacArthur, and K. Rodgers, “Evaluation of a Transportation Incentive Program for Affordable Housing Residents,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2675, no. 8, pp. 240–253, Aug. 2021, doi: 10.1177/0361198121997431.

How Automated Vehicles affects Transportation Systems Operations

Many researchers have used agent-based simulation to assess the effects of Automated Vehicles (AV)s on transportation system operations and efficiency (e.g., congestion and Vehicle Miles Traveled (VMT)) [1], [2], [3], [4], [5], [6], [7]. For example, Yan et al. (2020) simulated and then evaluated the performance of a shared autonomous vehicle fleet serving requests across the Minneapolis-Saint Paul region [1]. Yan et al. [1], [2], [3], [4], [5], [6], [7] estimated that the average shared AV could serve at most 30 person-trips per day with less than a 5 minute wait time but generates 13 percent more VMT. Yan et al. [1], [2], [3], [4], [5], [6], [7] also concluded that dynamic ridesharing could reduce shared AV VMT by 17 percent on average and restricting shared AV parking on the busiest streets could generate up to 8 percent more VMT.
Other methods such as static traffic assignment models and scenario analysis, have also been used to to understand the effect of AVs on congestion and VMT [8], [9], [10], [11], [12], [13]. For example, Harper et al. (2016) estimated the upper bound increase in travel with AVs for the non-driving, elderly, and people with travel-restrictive medical conditions by creating demand wedges and assuming that these traditionally underserved populations would travel as much as younger and/or healthier populations [9]. Harper et al. (2016) estimated that vehicle automation addressing latent demand for underserved population could increase VMT by as much as 14 percent, with females and non-drivers making up most of this increase [9].

Most studies are in agreement that AVs are likely to increase VMT and congestion, due to increased trip making, the ability for AVs to search for more distant and cheaper parking, and the additional VMT generated from people switching from personally owned vehicles to shared autonomous vehicles, generating empty travel [5], [9], [14]. Current opportunities for future research in this area include: 1) simulating AVs considering a heterogeneous population of travelers with different values of travel time (VOTT) and 2) incorporating parking to estimate the impact of AVs on transportation system operations [15].

  1. H. Yan, K. M. Kockelman, and K. M. Gurumurthy, “Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations,” Transp. Res. Part Transp. Environ., vol. 89, p. 102577, Dec. 2020, doi: 10.1016/j.trd.2020.102577.

  2. W. Zhang, S. Guhathakurta, and E. B. Khalil, “The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation,” Transp. Res. Part C Emerg. Technol., vol. 90, pp. 156–165, May 2018, doi: 10.1016/j.trc.2018.03.005.

  3. M. Hyland and H. S. Mahmassani, “Operational benefits and challenges of shared-ride automated mobility-on-demand services,” Transp. Res. Part Policy Pract., vol. 134, pp. 251–270, Apr. 2020, doi: 10.1016/j.tra.2020.02.017.

  4. S. Shafiei, Z. Gu, H. Grzybowska, and C. Cai, “Impact of self-parking autonomous vehicles on urban traffic congestion,” Transportation, vol. 50, no. 1, pp. 183–203, Feb. 2023, doi: 10.1007/s11116-021-10241-0.

  5. D. J. Fagnant and K. M. Kockelman, “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios,” Transp. Res. Part C Emerg. Technol., vol. 40, pp. 1–13, Mar. 2014, doi: 10.1016/j.trc.2013.12.001.

  6. D. J. Fagnant and K. M. Kockelman, “Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas,” Transportation, vol. 45, no. 1, pp. 143–158, Jan. 2018, doi: 10.1007/s11116-016-9729-z.

  7. G. Gelauff, I. Ossokina, and C. Teulings, “Spatial and welfare effects of automated driving: Will cities grow, decline or both?,” Transp. Res. Part Policy Pract., vol. 121, pp. 277–294, Mar. 2019, doi: 10.1016/j.tra.2019.01.013.

  8. A. Millard-Ball, “The autonomous vehicle parking problem,” Transp. Policy, vol. 75, pp. 99–108, Mar. 2019, doi: 10.1016/j.tranpol.2019.01.003.

  9. C. D. Harper, C. T. Hendrickson, S. Mangones, and C. Samaras, “Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions,” Transp. Res. Part C Emerg. Technol., vol. 72, pp. 1–9, Nov. 2016, doi: 10.1016/j.trc.2016.09.003.

  10. Z. Wadud, D. MacKenzie, and P. Leiby, “Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,” Transp. Res. Part Policy Pract., vol. 86, pp. 1–18, Apr. 2016, doi: 10.1016/j.tra.2015.12.001.

  11. A. Talebpour, H. S. Mahmassani, and A. Elfar, “Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability,” Transp. Res. Rec. J. Transp. Res. Board, no. 2622, 2017, Accessed: May 13, 2024. [Online]. Available: https://trid.trb.org/View/1438766

  12. R. E. Stern et al., “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transp. Res. Part C Emerg. Technol., vol. 89, pp. 205–221, Apr. 2018, doi: 10.1016/j.trc.2018.02.005.

  13. Y. Zhao and K. M. Kockelman, “Anticipating the Regional Impacts of Connected and Automated Vehicle Travel in Austin, Texas,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018032, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000463.

  14. C. D. Harper, C. T. Hendrickson, and C. Samaras, “Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018043, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000488.

  15. Md. M. Rahman and J.-C. Thill, “Impacts of connected and autonomous vehicles on urban transportation and environment: A comprehensive review,” Sustain. Cities Soc., vol. 96, p. 104649, Sep. 2023, doi: 10.1016/j.scs.2023.104649.

How Mobility-as-a-service affects Energy and Environment

The environmental impact of Mobility-as-a-Service (MaaS) and related business models depends on how the services are offered, and the incentives of the operator [1]. For example, if ride hailing is incentivized over public transit and bike-shares, there would be fewer environmental benefits [2]. Additionally, private operated mobility services are generally focused on maximizing revenue, while public transport operators may focus more on public benefits including reduced environmental impact [3]. A study assessing welfare impacts of MaaS found that MaaS schemes with shared mobility have the potential to substantially reduce energy consumption, and even greater reductions were possible with improved cost transparency for use of cars and inclusion of externalities such as greenhouse gas emissions in the generalized cost [4].

Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.

On-Demand Delivery Services Definition

Delivery services, also known as on-demand delivery services, food delivery services or crowdshipping, are a real-time local delivery solution for goods, typically prepared foods, groceries, or other consumer staples. Due to the rapid growth of online shopping, development of emerging technologies, and innovative forms of delivery services, have become more capable of handling a wide range of delivery needs, from small parcels to large-scale freight, with a level of precision and efficiency that was previously unattainable [1].

On demand delivery service businesses use platform technology to connect three parties in a marketplace: 1) a supplier of goods, often a restaurant, and 2) independent contractors or gig workers who can collect, transport, and deliver the goods to 3) a consumer who has ordered the goods.

New technologies such as crowdsourcing, location-based services, electric bikes and scooters, and advanced algorithms have empowered the courier services providers to offer faster, more environmentally friendly, and personalized delivery options to their customers. At the same time, to satisfy customers’ increasing and various demand of delivery services, new service forms are introduced, such as crowdsourced delivery (i.e., distributing delivery services to personal deliver instead of company staff) [2] and cross shipping (i.e., sending parcels to customers through an intermediate point instead of directly) [3].

Delivery services are popular globally, with top markets in China, the United States, and India [4]. Top companies in the United States are UberEats, and DoorDash [5].

A new trend in on-demand delivery service is to use robotic delivery services. The demand for robotic delivery services has increased quickly due to the technology development, challenges from traditional human delivery, and rising requests for contactless deliveries during COVID-19 [6]. As of 2021, cities located in 18 states in the US [7] had launched their robotic delivery pilot programs, such as Los Angeles, CA [8], Pittsburgh, Pennsylvania [9], and Redwood City, California [10]. The governments collaborate with emerging tech companies, including Uber, Starship, Kiwibot, Cruise, and so on. However, most of the programs are operating in small areas, indicating the experimental phase of these initiatives and the challenges in scaling up to wider service areas. When these systems are deployed at scale, several scenarios of concern and necessary considerations arise. Firstly, robotic delivery units could congest sidewalks, reducing accessibility for pedestrians and other users. This might require new urban planning strategies and dedicated pathways to ensure safe coexistence. Secondly, their widespread use could alter urban form and infrastructure, prompting cities to redesign pedestrian zones and potentially repurpose existing spaces.

References

  1. A. Rutter, D. Bierling, D. Lee, C. Morgan, and J. Warner, “How Will E-commerce Growth Impact Our Transportation Network,” PRC 17-79 F. Accessed: May 13, 2024. [Online]. Available: https://static.tti.tamu.edu/tti.tamu.edu/documents/PRC-17-79-F.pdf

  2. A. Alnaggar, F. Gzara, and J. H. Bookbinder, “Crowdsourced delivery: A review of platforms and academic literature,” Omega, vol. 98, p. 102139, Jan. 2021, doi: 10.1016/j.omega.2019.102139.

  3. A. I. Nikolopoulou, P. P. Repoussis, C. D. Tarantilis, and E. E. Zachariadis, “Moving products between location pairs: Cross-docking versus direct-shipping,” Eur. J. Oper. Res., vol. 256, no. 3, pp. 803–819, Feb. 2017, doi: 10.1016/j.ejor.2016.06.053.

  4. C. Li, M. Mirosa, and P. Bremer, “Review of Online Food Delivery Platforms and their Impacts on Sustainability,” Sustainability, vol. 12, no. 14, Art. no. 14, Jan. 2020, doi: 10.3390/su12145528.

  5. M. Kaczmarski, “Which company is winning the restaurant food delivery war?,” Bloomberg Second Measure. Accessed: Apr. 02, 2024. [Online]. Available: https://secondmeasure.com/datapoints/food-delivery-services-grubhub-uber-eats-doordash-postmates/

  6. S. Srinivas, S. Ramachandiran, and S. Rajendran, “Autonomous robot-driven deliveries: A review of recent developments and future directions,” Transp. Res. Part E Logist. Transp. Rev., vol. 165, p. 102834, Sep. 2022, doi: 10.1016/j.tre.2022.102834.

  7. Minnesota department of Transportation, “Personal Delivery Devices.” Accessed: May 13, 2024. [Online]. Available: https://dot.state.mn.us/automated/docs/personal-delivery-device-white-paper.pdf

  8. J. Fantozzi, “Uber launches delivery robot pilot program; adds Google voice ordering,” Nation’s Restaurant News. Accessed: May 13, 2024. [Online]. Available: https://www.nrn.com/technology/uber-launches-delivery-robot-pilot-program-adds-google-voice-ordering

  9. City of Pittsburgh Mobility and Infrastructure, “2021 Personal Delivery Device Final Pilot Evaluation.” Accessed: May 13, 2024. [Online]. Available: https://hdp-us-prod-app-pgh-engage-files.s3.us-west 2.amazonaws.com/9616/5540/2948/PDD_Final_Pilot_Evaluation_v2.pdf

  10. Staff, “Redwood City council renews pilot program for autonomous robot deliveries,” Climate Online. Accessed: May 13, 2024. [Online]. Available: https://climaterwc.com/2019/05/13/autonomous-robot-deliveries-returning-to-redwood-city-as-pilot-project/

Automated Vehicles Definition

Automated Vehicles (AVs) are vehicles equipped with technology that allows them to navigate and operate with varying degrees of human intervention. The Society of Automotive Engineers (SAE) defines AVs through a classification system that ranges from Level 0 to Level 5, based on the level of automation and the role of the human driver [1].

Advanced Driver Assistance Systems (ADAS) are found in Levels 1 and 2, and include features like adaptive cruise control, lane-keeping assistance, and automated emergency braking. They enhance driving safety and convenience but still require human oversight. Automated Driving Systems (ADS) are found in Levels 3 through 5, and can manage all driving tasks under certain conditions, enabling the vehicle to operate without human input.

The Mobility Center of Excellence (COE) focuses on Highly Automated Vehicles (Levels 4 and 5) due to their potential for large-scale deployment and significant impact on transportation systems. These vehicles promise to transform mobility by improving safety, reducing congestion, and providing transportation solutions for those unable to drive, but may be subject to unintended consequences that have plagued previous advancements in transportation technologies.

References

  1. SAE International, “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,” J3016_202104, Apr. 2021. [Online]. Available: https://www.sae.org/standards/content/j3016_202104/

Micromobility Definition

The term micromobility refers to small, low-speed vehicles intended for personal use, including bicycles, electric scooters (or e-scooters), and similar vehicles—whether powered or unpowered and both personally owned and deployed in shared fleets (as in bikesharing systems). SAE International developed a taxonomy of powered micromobility vehicles based on form factor (e.g. bicycle, standing or seated scooter) and physical characteristics such as width, curb weight, top speed, and power source [1]. The primary vehicle types deployed in shared fleets are human- or electric-powered bicycles in bikesharing, seated or standing e-scooters in scooter sharing, and mopeds.

Shared Micromobility - the shared use of a bicycle, scooter, or other low-speed mode - is an innovative transportation strategy that enables users short-term access to a transportation mode on an as-needed basis [2, Ch. 12]. Shared micromobility services may be docked (a station-to-station system in which users unlock vehicles from a fixed location, which also generally contains the IT infrastructure for reservation and payment, and in some cases facility for electric charging), dockless (with the IT infrastructure and locking mechanism integrated into the vehicles), or a hybrid of the two models [3].

References

  1. SAE International, “J3194_201911: Taxonomy and Classification of Powered Micromobility Vehicles.” 2019.  doi: https://doi.org/10.4271/J3194_201911.

  2. S. Shaheen and A. Cohen, A Modern Guide to the Urban Sharing Economy (Shared micromobility: policy and practices in the United States, Chapter 12). 2021. [Online]. Available: https://www.elgaronline.com/edcollchap/edcoll/9781789909555/9781789909555.00020.xml

  3. M. Hernandez, R. Eldridge, and K. Lukacs, “Public Transit and Bikesharing: A Synthesis of Transit Practice,” Transportation Research Board, TCRP Synthesis 132, 2018. doi: 10.17226/25088.

Ridehail/Transportation Network Companies Definition

Ride-hailing, also called ridesourcing, and codified in California law as Transportation Network Companies, are taxi-like commercial transportation services based on the use of an online platform that connects riders with drivers and automates reservations and payment. Ride-hailing services may offer a variety of service classes and vehicle sizes, generally using passenger vehicles or Sports Utility Vehicles (SUVs). In larger markets, a shared service class may be offered, in which unrelated passengers travel together for some part of their trip. Though the terms are often used interchangeably, ride-hailing is distinct from ridesharing, which refers to non-commercial sharing of journeys by drivers and passengers traveling to the same destination, as in carpooling, slugging, or vanpooling [1].

References

Car Sharing Definition

Carsharing can take different forms, as the model existed prior to the modern concept as monetized under the ‘sharing economy.’ First appearing in Europe in the 1940s, carsharing took the form of multiple individuals co-owning or sharing a car, mainly due to economic reasons. The model has since evolved to include a membership based system of sharing a fleet of cars, with no ownership rights conveyed [1]. Carshare participants gain the utility of a private vehicle without the costs associated with ownership [2]. Carsharing models vary depending on vehicle ownership and the technology underlying the system. Traditional or round-trip carsharing requires users to return a vehicle to the same location where they picked it up. One-way or free-floating allows users to drop off a vehicle at or within any of a number of designated locations or zones, regardless of where it was picked up. In peer-to-peer (or P2P) models, vehicles are made available for sharing by individual owners, rather than by a single fleet owner [3]. Advancements in reservation systems have improved system efficiency across models, and have been particularly important for P2P carsharing [4].

References

Demand-Responsive Transit & Microtransit Definition

Demand-Responsive Transit (DRT) is a flexible transportation service that adapts to the specific travel needs of its users, and is typically shared among users. Instead of following fixed routes and schedules, DRT services are typically booked in advance and operate within a defined area. DRT started decades ago as Dial-a-Ride or paratransit, which serves a specific population (e.g. elderly) or with a specific technology (e.g. phone calls for day-ahead reservations), but has been generalized recently to serve general populations and more advanced communication technologies (e.g., wireless and internet for real-time reservations). As a form of transit, it can serve multiple passengers on a journey, though the service may use anything from passenger cars to small buses to provide the service. It may also include deviated route service, in which an otherwise fixed-route service may make unscheduled stops within a corridor or service zone to pick up or drop off passengers.

Microtransit is a subset of DRT, often characterized by the use of new technologies to optimize and manage the public transit service with a specific focus on either population, spatial coverage or coordination with general public transit. It blends aspects of traditional public transit and private ride-hailing services, offering shared rides that are dynamically routed. Microtransit is generally operated within defined service zones or along a corridor, often with designated stops at key destinations like employment centers or transfer points to other transportation services [1].

References

Mobility-as-a-service Definition

The Shared-Use Mobility Center defines Mobility-as-a-Service (MaaS) as ”a practice that integrates the travel options available to a user and offers them in a single interface, with a single payment mechanism” [1]. MaaS is, in a simplistic form, a business model that allows multi-modal platform integration of transportation options. However, how the model is executed is still a point of debate, and recent failures of proposed systems throw MaaS further into doubt. In 2017, as a relatively new concept, there was much uncertainty in the core characteristics of MaaS [2]. This uncertainty continued through 2021 with no unified, agreed upon single definition of MaaS; pointing to an underestimation of what riders and users need and suggesting that an integrated trip planner may be enough to satisfy needs [3]. Currently, little progress has been made to unify MaaS as a concept and successfully launch a fully integrated system. Policy and regulatory barriers remain, and incorporating local characteristics will be crucial for MaaS to succeed [4].

In the US, cities like Pittsburgh [5], Minneapolis [6], and Tampa [7] have launched their pilot MaaS programs. These pilot programs, however, only included limited transport services, mainly due to difficulties with public private collaboration, funding, cyber security, and lack of attractiveness to transit users, auto users, and older populations [4].

References

  1. Shared-Use Mobility Center, “Towards the Promise of Mobility as a Service (MaaS) in the U.S.,” Chicago, IL, Jul. 2020. [Online]. Available: https://sharedusemobilitycenter.org/wp-content/uploads/2020/09/Towards-the-Promise-of-MaaS-in-the-US-July-2020-Shared-Use-Mobility-Center.pdf

  2. P. Jittrapirom, V. Caiati, A. M. Feneri, S. Ebrahimigharehbaghi, M. J. Alonso-González, and J. Narayan, “Mobility as a service: A critical review of definitions, assessments of schemes, and key challenges,” Urban Plan., vol. 2, no. 2, pp. 13–25, Jan. 2017, doi: 10.17645/up.v2i2.931.

  3. D. A. Hensher, C. Mulley, and J. D. Nelson, “Mobility as a service (MaaS) – Going somewhere or nowhere?,” Transp. Policy, vol. 111, pp. 153–156, Sep. 2021, doi: 10.1016/j.tranpol.2021.07.021.

  4. L. Butler, T. Yigitcanlar, and A. Paz, “Barriers and risks of Mobility-as-a-Service (MaaS) adoption in cities: A systematic review of the literature,” Cities, vol. 109, p. 103036, Feb. 2021, doi: 10.1016/j.cities.2020.103036.

  5. City of Pittsburgh Mobility and Infrastructure, “Move PGH Mid-Pilot Report.” Accessed: May 13, 2024. [Online]. Available: https://apps.pittsburghpa.gov/redtail/images/19169_Move_PGH_Mid_Pilot_Report_[FINAL]_v2.pdf

  6. C. of Minneapolis, “Minneapolis Mobility Hubs Pilot.” Accessed: May 13, 2024. [Online]. Available: https://www.minneapolismn.gov/government/programs-initiatives/transportation-programs/mobility-hubs/

  7. “City of Tampa Launches Mobility as a Service (MaaS) App | City of Tampa.” Accessed: May 13, 2024. [Online]. Available: https://www.tampa.gov/news/city-tampa-launches-mobility-service-maas-app-111716

Universal Basic Mobility Definition

Universal Basic Mobility (UBM) programs manifest the concept that all community members have a right to at least some degree of mobility, regardless of residence or income level [1]. The concept is the transportation equivalent of the Supplemental Nutrition Assistance Program (SNAP), which provides government-funded cash assistance to buy food. In the case of UBM, the government provides a mobility subsidy to eligible residents to address their transportation needs and allow them to choose more convenient trips. Several cities across the U.S. recently piloted UBM programs with the intention of removing the monetary cost barrier to transportation [2]. The pilot programs were envisioned to provide all residents with a baseline level of mobility, incentivize the use of shared travel modes, and improve access to employment opportunities [2].

References

  1. ITS America, “Universal Basic Mobility Primer.” Accessed: May 15, 2024. [Online]. Available: https://itsa.org/wp-content/uploads/2022/03/Universal-Basic-Mobility-One-Pager_Final.pdf

  2. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

Heavy Duty Applications of Automated Vehicles Definition

Based on EPA classifications, heavy duty vehicles include trucks over 8,500 pounds [8], as well as buses, shuttles, and specialized equipment like street sweepers. The level of automation of heavy-duty vehicles ranges from driver-assist technologies to driverless vehicles [9].
The Federal Motor Carrier Safety Administration (FMCSA) plays a crucial role in regulating and overseeing the deployment of automated heavy-duty vehicles. The FMCSA focuses on ensuring that these vehicles meet safety standards and operate within the regulatory framework. Their efforts include developing guidelines for testing and deployment, addressing cybersecurity concerns, and ensuring that automated systems can safely interact with other road users.

References

  1. OAR US Environmental Protection Agency, “How does MOVES Classify Light-Duty Trucks?” Accessed: May 15, 2024. [Online]. Available: https://www.epa.gov/moves/how-does-moves-classify-light-duty-trucks

  2. S. Clevenger, “Autonomous Trucks Reshaping the Freight Industry,” Transport Topics. Accessed: May 15, 2024. [Online]. Available: https://www.ttnews.com/articles/autonomous-trucks-reshaping-freight-industry

How Car Sharing affects Safety

Carshare may, relative to private auto travel, confer some safety benefits. For example,users generally have to go through a screening process to sign up for the programs and establish valid licenses. Safe driving behavior does, of course, vary by individual; a study of Australian carshare users found that infrequent users, users in households that owned other cars, and users that had fewer previous accidents, chose more expensive vehicle insurance, and had been licensed for longer, were less likely to be in a vehicle crash [1]. To enhance safety, the study recommended establishing incentives for carshare users with more driving experience and more extensive insurance [1].

More research may be necessary to better establish safety differences among carshare users, whether carshare users travel more safely relative to private vehicle owners, and if so, what the mechanisms are that promote additional precautions while driving.

How Universal Basic Mobility affects Social Equity

Inequality is embedded in our transportation systems and land use patterns, which reinforces unequal access to opportunities. Mobility inequality can be racialized, gendered, or based on income. The inequalities between those with and without private vehicles deepened during the COVID-19 pandemic [1], [2], [3]. Universal Basic Mobility (UBM) programs aim to address this and in turn create more equitable transportation systems. Based on qualitative evaluation of eight UBM programs and pilots, UC Davis researchers found that UBM pilot programs have had success in enrolling low-income people of color and increasing transit use [4].

Additional research related to equity impacts of mobility wallet pilot program outcomes is ongoing. For example, researchers at UCLA and UC Davis are evaluating the South LA mobility wallet pilot, where 1,000 people in South Los Angeles are receiving $150 per month for a year for use on transit needs [5]. Researchers at UC Davis are also evaluating pilot UBM programs in Oakland and Bakersfield, with a focus on economic, social, and environmental impacts [6]. However, there is little completed research on how effective university mobility programs are in addressing inequality in transportation access. Additional research is needed on the equity impacts of UBM programs, as well as how the programs compare to alternatives like free or reduced fare transit programs.

  1. E. Blumenberg, “En-gendering Effective Planning: Spatial Mismatch, Low Income Women, and Transportation Policy,” 2003, doi: 10.1080/01944360408976378.

  2. Mimí Sheller and M. Sheller, “Racialized Mobility Transitions in Philadelphia: Connecting Urban Sustainability and Transport Justice,” City Soc., vol. 27, no. 1, pp. 70–91, Apr. 2015, doi: 10.1111/ciso.12049.

  3. Isti Hidayati, I. Hidayati, Wendy Tan, W. Tan, Claudia Yamu, and C. Yamu, “Conceptualizing Mobility Inequality: Mobility and Accessibility for the Marginalized:,” J. Plan. Lit., vol. 36, no. 4, pp. 492–507, May 2021, doi: 10.1177/08854122211012898.

  4. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

  5. “Los Angeles launches nation’s largest UBM pilot, Lewis Center leads evaluation.,” UCLA Lewis Center for Regional Policy Studies., 2022. [Online]. Available: https://www.lewis.ucla.edu/project/2023-mb-01/

  6. A. Sanguinetti, E. Alston-Stepnitz, and M. C. D’Agostino, “Evaluating Two Universal Basic Mobility Pilot Projects in California.” [Online]. Available: https://www.ucits.org/research-project/2022-20/

How Automated Vehicles affects Health

The introduction and potential proliferation of highly automated vehicles (AVs) present the classic challenge of balancing the freedom of private manufacturers to innovate with the government's responsibility to protect public health. AVs raise many public health issues beyond their potential to improve safety, ranging from concerns about more automobile use and less use of healthier alternatives like biking or walking, to concerns that focusing on autonomous vehicles may distract attention and divert funding from efforts to improve mass transit. There are, additionally, issues of access, especially for the poor, disabled, and those in rural environments [1].

As the classic Code of Ethics for Public Health recommends [2], public health advocates can advocate for the rights of individuals and their communities while protecting public health by helping to establish policies and priorities through “processes that ensure an opportunity for input from community members.” Public health thought leaders can ensure that communities have the information they need for informed decisions about whether and how autonomous vehicles will traverse their streets, and they can make sure that manufacturers who test and deploy autonomous vehicles obtain “the community’s consent for their implementation.” Finally, public health leaders can work for the empowerment of the disenfranchised, incorporating and respecting “diverse values, beliefs, and cultures in the community” and collaborating “in ways that build the public’s trust” [2].

  1. J. Fleetwood, “Public Health, Ethics, and Autonomous Vehicles,” Am. J. Public Health, vol. 107, no. 4, pp. 532–537, Apr. 2017, doi: 10.2105/AJPH.2016.303628.

  2. J. C. Thomas, M. Sage, J. Dillenberg, and V. J. Guillory, “A Code of Ethics for Public Health,” Am. J. Public Health, vol. 92, no. 7, pp. 1057–1059, Jul. 2002.

How Micromobility affects Health

Emerging micromobility options such as e-bikes and e-scooters can improve accessibility and connectivity for vulnerable population groups, such as those with physical limitations or without access to a car [1], [2]. Compared to biking or walking, electric micromobility (EMM) vehicles are often more accessible to users with lower interest in or capacity for physical activity, while still providing exercise and outdoor enjoyment [1], [2], [3]. For instance, e-bikes are favored by older adults as a form of physical activity and can encourage micromobility use for distances over 3 miles typically covered by cars [4], [5], [6]. An observational study found that starting to e-bike may increase overall biking frequency among older adults, potentially extending the number of years they are able to bike [4], [5], [6]. Despite being less physically demanding than conventional biking, e-biking offers many of the same cardiovascular benefits [5], [7].
In addition to health benefits from access, physical activity, and outdoor enjoyment, increased EMM vehicle usage has the potential to reduce air pollution from cars by substituting car trips and improving access to public transit. EMM vehicles can address the first-mile-last-mile problem, supporting the use of public transit [8], [9]. They also provide an alternative mode of transportation for short trips, which can help alleviate overcrowding on public transport and support social distancing when necessary [8]. Moreover, EMM vehicles may contribute to noise pollution reduction, which is linked to adverse health effects such as cognitive impairment in children and sleep disturbance [9]. However, studies indicate that not all EMM vehicles have the same environmental health benefits; e-scooters, for instance, may have a negative environmental impact compared to the modes they replace (for example, they may replace pedestrian trips) [9], [10], [11]. Additionally, the collection vehicles used for relocating and charging EMM vehicles in shared vehicle programs can contribute to emissions, particularly in less densely populated areas [9].
Safety remains a primary concern for public health regarding EMM usage, and is discussed in more detail in the section devoted to safety impacts. Cyclists, including e-bike users, are vulnerable to injuries and fatalities from collisions with cars. Electric scooter usage can also result in serious injuries, especially head and limb injuries, exacerbated by low helmet usage [9], [12]. Injuries to pedestrians from e-scooter riders on sidewalks are another significant concern [9]. Providing separate, designated infrastructure for EMM can enhance safety [1].
Future research could include the development of best practices for maximizing public health benefits of micromobility programs, as well as further analysis of the health impacts of different micromobility modes.

  1. A. Bretones et al., “Public Health-Led Insights on Electric Micro-mobility Adoption and Use: a Scoping Review,” J. Urban Health, vol. 100, no. 3, pp. 612–626, Jun. 2023, doi: 10.1007/s11524-023-00731-0.

  2. T. G. J. Jones, L. Harms, and E. Heinen, “Motives, perceptions and experiences of electric bicycle owners and implications for health, wellbeing and mobility,” J. Transp. Geogr., vol. 53, pp. 41–49, May 2016, doi: 10.1016/j.jtrangeo.2016.04.006.

  3. Aslak Fyhri et al., “A push to cycling—exploring the e-bike’s role in overcoming barriers to bicycle use with a survey and an intervention study,” Int. J. Sustain. Transp., vol. 11, no. 9, pp. 681–695, May 2017, doi: 10.1080/15568318.2017.1302526.

  4. Jessica Bourne et al., “The impact of e-cycling on travel behaviour: A scoping review.,” J. Transp. Health, vol. 19, p. 100910, 2020, doi: 10.1016/j.jth.2020.100910.

  5. Taylor H Hoj et al., “Increasing Active Transportation Through E-Bike Use: Pilot Study Comparing the Health Benefits, Attitudes, and Beliefs Surrounding E-Bikes and Conventional Bikes.,” JMIR Public Health Surveill., vol. 4, no. 4, Nov. 2018, doi: 10.2196/10461.

  6. Jelle Van Cauwenberg, J. Van Cauwenberg, Bas de Geus, B. de Geus, Benedicte Deforche, and B. Deforche, “Cycling for transport among older adults : health benefits, prevalence, determinants, injuries and the potential of e-bikes,” pp. 133–151, Jan. 2018, doi: 10.1007/978-3-319-76360-6_6.

  7. Thomas Mildestvedt et al., “Getting Physically Active by E-Bike : An Active Commuting Intervention Study,” vol. 4, no. 1, pp. 120–129, 2020, doi: 10.5334/paah.63.

  8. Gabriel Dias et al., “The Role of Shared E-Scooter Systems in Urban Sustainability and Resilience during the Covid-19 Mobility Restrictions,” Sustainability, vol. 13, no. 13, pp. 7084–7084, Jun. 2021, doi: 10.3390/su13137084

  9. J. Glenn et al., “Considering the Potential Health Impacts of Electric Scooters: An Analysis of User Reported Behaviors in Provo, Utah,” Int. J. Environ. Res. Public. Health, vol. 17, no. 17, p. 6344, 2020, doi: 10.3390/ijerph17176344.

  10. Joseph A. Hollingsworth, J. A. Hollingsworth, Brenna Copeland, B. Copeland, Jeremiah X. Johnson, and J. X. Johnson, “Are e-scooters polluters? The environmental impacts of shared dockless electric scooters,” Environ. Res. Lett., vol. 14, no. 8, p. 084031, Aug. 2019, doi: 10.1088/1748-9326/ab2da8.

  11. Anne de Bortoli et al., “Consequential LCA for territorial and multimodal transportation policies: method and application to the free-floating e-scooter disruption in Paris,” J. Clean. Prod., vol. 273, p. 122898, Nov. 2020, doi: 10.1016/j.jclepro.2020.122898.

  12. T. K. Trivedi et al., “Injuries associated with standing electric scooter use,” JAMA Netw. Open, vol. 2, no. 1, pp. e187381–e187381, 2019.

How Demand-Responsive Transit & Microtransit affects Health

Demand-responsive transit and microtransit can benefit public health by improving accessibility. Microtransit services are often more direct or even door-to-door and can serve users with limited mobility. They typically target users whose transportation needs are not met by traditional public transit, including shift workers, low-income individuals, the elderly, disabled, and communities with low levels of fixed-route public transit service [1], [2]. A study on demand-responsive microtransit programs’ return on social investment found that social benefits can outweigh costs by 4 to 6 times, due to their ability to increase access to essential services, foster social inclusion, and improve sustainability [1].
While there are some case studies on microtransit programs, there is limited research on public health impacts. Additional research is needed to understand the extent to which microtransit can meet transportation needs that are not filled by public transit, and how it can best serve different populations and uses, and how it impacts public health. Some of this research is in progress. For example, the "Safety and Public Health Impacts of Microtransit Services" research initiative at the University of Massachusetts Amherst is currently evaluating safety and public health impacts of microtransit services [3].
Finally, on-demand transit/microtransit programs are often meant to improve equitable access, but there is little research on how to design programs to best meet that goal. Survey data from four US cities found that men, younger riders, the highly educated, and transit riders were more likely to be interested in using microtransit. Additional research is needed to understand who on-demand transit/microtransit most frequently serves, and how that impacts public health across demographic groups.

How On-Demand Delivery Services affects Health

A scoping review of public health impacts from on demand food and alcohol delivery published in SSM Population Health found that on-demand delivery services increase geographical access to food but tend to market unhealthy and discretionary foods, and are likely increasing existing health issues and inequities [1]. The review also highlighted concerns over poor age verification processes potentially allowing minors to access alcohol more easily [1].

How Micromobility affects Municipal Budgets

Budgetary impacts from micromobility include costs of permits, operating licenses and fines for risky behavior. The rise of shared dockless micromobility led to reactive policy making and regulations that largely constrained operations [1]. The use of such regulation has been motivated by the desire to control the presence of shared micromobility devices in cities, rather than viewing them as a promising line of municipal revenue. In fact, in many cases, municipalities are addressing the need to subsidize riders, especially when it comes to low-income users [2]. A 2024 study by the Transportation Research and Education Center assessed taxes and fees on micromobility, and found that they vary dramatically by city and are typically higher than taxes and fees on ride-hailing and private vehicles [3].

In general, the literature suggests that while micromobility has the potential to enhance quality of life and access to mobility [4], there are also externalities of social harm such as (mis)parking [5]. There is little available research related to how micromobility could influence the tax burden or base of a locality.

How Universal Basic Mobility affects Education and Workforce

Increased access to education and job opportunities are cited as benefits of Universal Basic Mobility (UBM), based on robust existing research demonstrating the relationship between mobility and access to opportunity and early research on UBM pilot programs [1], [2]. Research assessing how effectively UBM policies and programs improve access to education and job opportunities is sparse.

How Universal Basic Mobility affects Transportation Systems Operations

The University of California Institute of Transportation Studies recently released a technical report that summarizes Universal Basic Mobility (UBM) pilot programs in California along various design dimensions, including eligibility requirements, monetary assistance value, and allowable travel modes [1]. For example, Los Angeles, CA offered 2,000 residents $150 per month for use of public transit, private taxi, transportation network company (e.g., Uber), electric bikeshare, and carshare. The Pittsburgh, PA program gave 50 residents unlimited access to transit and bikeshare along with a monthly credit for scooter and carshare [2]. Other U.S. cities that have implemented a UBM pilot include Portland, OR; Sacramento, CA; Oakland, CA; and Stockton, CA.

Evaluations of most UBM programs are still underway, though some results are available for Oakland and Portland. The Oakland Department of Transportation and Alameda County Transportation Commission surveyed 66 participants pre-program and mid-program, and they observed that 66 percent of these participants used the extra mobility funds for commuting. They also found that 90 percent of funds were spent on transit, and the number of participants who self-reported driving as their primary mode declined by 6 percent for commuting trips [3]. Researchers at Portland State University also evaluated the Portland program based on surveys. Their results revealed that participants had positive UBM perceptions: 89 percent of participants reported greater travel flexibility and 66 percent of participants reported the ability to reach work-related activities that would have been otherwise unreachable. Regarding travel mode shift, over 50 percent of participants agreed that they increased their usage frequency of Uber/Lyft, taxi, bikeshare, and e-scooter [4].

In addition to survey results, policymakers would benefit from studies that analyze how UBM affects system-level efficiency, accessibility and equity. However, there is limited completed research to this end. Most studies focus on analysis based on surveys that are only reflective of stated preferences from participants. Those stated preferences may not be generalizable or accurate in practice, and they are limited to a small spatio-temporal scope. Research gaps lie in tracking and understanding the actual (revealed) preferences of UBM participants, in regards to how UBM, by various levels of support, enables those participants to select mobility options to improve efficiency, accessibility and equity. In particular, research is needed to understand how those improvements vary by neighborhood and population groups. This would help public agencies and private service providers to jointly design a UBM program that is tailored for population groups with a vital business model to scale/group in the future.

  1. C. Rodier, A. Tovar, S. Fuller, M. D’Agostino, and B. Harold, “A Survey of Universal Basic Mobility Programs and Pilots in the United States,” University of California Institute of Transportation Studies. [Online]. Available: https://doi.org/10.7922/G2N8784Q

  2. L. Beibei, L. Branstetter, and C. M. U. Mobility21, “Evaluating Pittsburgh’s Universal Basic Mobility Pilot Program,” Jun. 2022. Accessed: May 15, 2024. [Online]. Available: https://rosap.ntl.bts.gov/view/dot/68460

  3. Oakland Department of Transportation, “Universal Basic Mobility Pilot Overview Evaluation,” 2022. Accessed: May 15, 2024. [Online]. Available: https://cao-94612.s3.us-west-2.amazonaws.com/documents/Universal-Basic-Mobility-Pilot-Overview_Eval_2022-03-16-001945_yfow.pdf

  4. H. Tan, N. McNeil, J. MacArthur, and K. Rodgers, “Evaluation of a Transportation Incentive Program for Affordable Housing Residents,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2675, no. 8, pp. 240–253, Aug. 2021, doi: 10.1177/0361198121997431.

How Automated Vehicles affects Transportation Systems Operations

Many researchers have used agent-based simulation to assess the effects of Automated Vehicles (AV)s on transportation system operations and efficiency (e.g., congestion and Vehicle Miles Traveled (VMT)) [1], [2], [3], [4], [5], [6], [7]. For example, Yan et al. (2020) simulated and then evaluated the performance of a shared autonomous vehicle fleet serving requests across the Minneapolis-Saint Paul region [1]. Yan et al. [1], [2], [3], [4], [5], [6], [7] estimated that the average shared AV could serve at most 30 person-trips per day with less than a 5 minute wait time but generates 13 percent more VMT. Yan et al. [1], [2], [3], [4], [5], [6], [7] also concluded that dynamic ridesharing could reduce shared AV VMT by 17 percent on average and restricting shared AV parking on the busiest streets could generate up to 8 percent more VMT.
Other methods such as static traffic assignment models and scenario analysis, have also been used to to understand the effect of AVs on congestion and VMT [8], [9], [10], [11], [12], [13]. For example, Harper et al. (2016) estimated the upper bound increase in travel with AVs for the non-driving, elderly, and people with travel-restrictive medical conditions by creating demand wedges and assuming that these traditionally underserved populations would travel as much as younger and/or healthier populations [9]. Harper et al. (2016) estimated that vehicle automation addressing latent demand for underserved population could increase VMT by as much as 14 percent, with females and non-drivers making up most of this increase [9].

Most studies are in agreement that AVs are likely to increase VMT and congestion, due to increased trip making, the ability for AVs to search for more distant and cheaper parking, and the additional VMT generated from people switching from personally owned vehicles to shared autonomous vehicles, generating empty travel [5], [9], [14]. Current opportunities for future research in this area include: 1) simulating AVs considering a heterogeneous population of travelers with different values of travel time (VOTT) and 2) incorporating parking to estimate the impact of AVs on transportation system operations [15].

  1. H. Yan, K. M. Kockelman, and K. M. Gurumurthy, “Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations,” Transp. Res. Part Transp. Environ., vol. 89, p. 102577, Dec. 2020, doi: 10.1016/j.trd.2020.102577.

  2. W. Zhang, S. Guhathakurta, and E. B. Khalil, “The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation,” Transp. Res. Part C Emerg. Technol., vol. 90, pp. 156–165, May 2018, doi: 10.1016/j.trc.2018.03.005.

  3. M. Hyland and H. S. Mahmassani, “Operational benefits and challenges of shared-ride automated mobility-on-demand services,” Transp. Res. Part Policy Pract., vol. 134, pp. 251–270, Apr. 2020, doi: 10.1016/j.tra.2020.02.017.

  4. S. Shafiei, Z. Gu, H. Grzybowska, and C. Cai, “Impact of self-parking autonomous vehicles on urban traffic congestion,” Transportation, vol. 50, no. 1, pp. 183–203, Feb. 2023, doi: 10.1007/s11116-021-10241-0.

  5. D. J. Fagnant and K. M. Kockelman, “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios,” Transp. Res. Part C Emerg. Technol., vol. 40, pp. 1–13, Mar. 2014, doi: 10.1016/j.trc.2013.12.001.

  6. D. J. Fagnant and K. M. Kockelman, “Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas,” Transportation, vol. 45, no. 1, pp. 143–158, Jan. 2018, doi: 10.1007/s11116-016-9729-z.

  7. G. Gelauff, I. Ossokina, and C. Teulings, “Spatial and welfare effects of automated driving: Will cities grow, decline or both?,” Transp. Res. Part Policy Pract., vol. 121, pp. 277–294, Mar. 2019, doi: 10.1016/j.tra.2019.01.013.

  8. A. Millard-Ball, “The autonomous vehicle parking problem,” Transp. Policy, vol. 75, pp. 99–108, Mar. 2019, doi: 10.1016/j.tranpol.2019.01.003.

  9. C. D. Harper, C. T. Hendrickson, S. Mangones, and C. Samaras, “Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions,” Transp. Res. Part C Emerg. Technol., vol. 72, pp. 1–9, Nov. 2016, doi: 10.1016/j.trc.2016.09.003.

  10. Z. Wadud, D. MacKenzie, and P. Leiby, “Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,” Transp. Res. Part Policy Pract., vol. 86, pp. 1–18, Apr. 2016, doi: 10.1016/j.tra.2015.12.001.

  11. A. Talebpour, H. S. Mahmassani, and A. Elfar, “Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability,” Transp. Res. Rec. J. Transp. Res. Board, no. 2622, 2017, Accessed: May 13, 2024. [Online]. Available: https://trid.trb.org/View/1438766

  12. R. E. Stern et al., “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transp. Res. Part C Emerg. Technol., vol. 89, pp. 205–221, Apr. 2018, doi: 10.1016/j.trc.2018.02.005.

  13. Y. Zhao and K. M. Kockelman, “Anticipating the Regional Impacts of Connected and Automated Vehicle Travel in Austin, Texas,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018032, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000463.

  14. C. D. Harper, C. T. Hendrickson, and C. Samaras, “Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018043, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000488.

  15. Md. M. Rahman and J.-C. Thill, “Impacts of connected and autonomous vehicles on urban transportation and environment: A comprehensive review,” Sustain. Cities Soc., vol. 96, p. 104649, Sep. 2023, doi: 10.1016/j.scs.2023.104649.

How Mobility-as-a-service affects Energy and Environment

The environmental impact of Mobility-as-a-Service (MaaS) and related business models depends on how the services are offered, and the incentives of the operator [1]. For example, if ride hailing is incentivized over public transit and bike-shares, there would be fewer environmental benefits [2]. Additionally, private operated mobility services are generally focused on maximizing revenue, while public transport operators may focus more on public benefits including reduced environmental impact [3]. A study assessing welfare impacts of MaaS found that MaaS schemes with shared mobility have the potential to substantially reduce energy consumption, and even greater reductions were possible with improved cost transparency for use of cars and inclusion of externalities such as greenhouse gas emissions in the generalized cost [4].

Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.