Below, users can build custom reports that include multiple individual research synthesis by selecting one or more mobility technologies or business models and one or more impact areas.
Each individual research synthesis can also be accessed via a matrix view.
How Mobility-as-a-service affects Safety
Mobility-as-a-service business models rely on collection of personal and financial data, creating potential privacy and safety concerns for prospective users [1] [2]. There is little available research on how MaaS programs impact safety in practice.
How Universal Basic Mobility affects Transportation Systems Operations (and Efficiency)
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.
How Automated Vehicles affects Transportation Systems Operations (and Efficiency)
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].
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].
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 Mobility-as-a-service affects Transportation Systems Operations (and Efficiency)
Studies show that Mobility-as-a-Service (MaaS) could decrease the use and ownership of private vehicles and support a switch to active travel modes and transit [1], [2], [3]. However, the magnitude of this switch is not comprehensively explored among the literature [2]. According to one simulation study, MaaS could reduce emissions by up to 54 percent, depending on the modeling scenarios [4]. Another simulation study showed that MaaS could reduce transport-related energy consumption because of the introduction of car-sharing and bike-sharing services [5]. Another study suggested that MaaS could reduce vehicle miles traveled and related negative externalities [6].
Several research directions are promising for future studies. First, there are limited studies on what drives people to use MaaS, highlighting a need to explore user incentives to adoption. Understanding these factors can inform more targeted service design and marketing strategies. Second, modeling the integration of multi-modal travel within MaaS is crucial. This could offer insights into optimizing traffic flows and enhancing the environmental and social benefits of MaaS. Third, the collaborative mechanism between the public and private sectors in the MaaS ecosystem requires further examination. Investigating how these entities can better cooperate could foster the broader application of MaaS solutions.
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 Mobility-as-a-service affects Municipal Budgets
There is still disagreement regarding what defines Mobility-as-a-Service (MaaS) as a business model, and research on how the implementation of MaaS would affect municipal budgets is limited. Many argue that to be successful, MaaS will have to develop a model that will be able to balance public and private providers in a sustainable manner [1], [2], but currently no such path exists. Doubts around the implementation of MaaS have been exacerbated by the recent failure of MaaS global [3]. The limited existing research on the budgetary impact from MaaS is based on revenue allocation models of economic spillovers from the deployment of such systems globally, rather than the direct impact of the presence of a MaaS system in a specific municipality [4].
How On-Demand Delivery Services affects Land Use
The expansion of on-demand delivery services has been made possible by ghost kitchens and dark stores – grocery fulfillment centers which are located near consumers but are not open to customers [1]. These fulfillment centers have created new real estate opportunities. Several major ghost kitchen operators are known for building large portfolios out of warehouses, empty strip malls, or other storefronts near areas with growing on-demand food-delivery markets [1]. Restaurants are dispersing away from ground-floor locations in popular retail districts as ghost kitchens increase their urban real estate [1], [2].
One emerging area of study is the impact of on-demand delivery services on restaurant formation and viability. The services charge participating restaurants delivery fees as high as 30 percent of order value, though some cities have imposed caps of 15 percent [3].
How Connectivity: CV, CAV, and V2X affects Education and Workforce
Collectively referred to as connected and automated vehicles (CAVs), connected vehicles (CVs), which communicate wirelessly with one another, and automated vehicles (AVs), in which a computer partially or entirely replaces the driver, have the capacity to revolutionize road maintenance and transportation operations [1]. According to Egan Smith (Managing Director of the Intelligent Transportation Systems (ITS) Joint Program Office of the United States Department of Transportation), "Successful deployment and operation of these new technologies depend largely on a knowledgeable, trained, and skilled workforce to support them” [2].
According to the California Department of Transportation's (Caltrans) strategic strategy, workforce development is a key action plan for CAV deployment [3]. Caltrans emphasized the importance of identifying labor difficulties and needs, as well as encouraging state efforts to recruit and retain the future workforce, in order to continue CAV. It could necessitate developing proper job categories, role descriptions, hiring procedures, and competitive salary ranges. Another option is to create a pool of highly skilled individuals (such as data scientists and network engineers) who can be housed in one functional unit and then transferred to other functional units or districts to share their technical expertise.
As CV and V2X technology advances, the Intelligent Transportation Systems (ITS) transportation workforce will require advanced knowledge, skills, and abilities. As a result, new and modified training opportunities are important for the ITS workforce to develop the advanced skill sets required to maintain a transportation network populated by evolving technologies [2].
Workforce development is essential not just for CAV deployment, but also for maintenance and repair (M&R). To stay up with technological advances, employees in this field must be upskilled and trained on a regular basis [4]. Crane et al. [5] also acknowledged that there is an increasing need to comprehend middle-skill positions, such as technicians, engineers, systems architects, managers, and IT specialists (that require at least a bachelor’s degree).
According to Parikh et al. [1], the most significant expense associated with CV deployment is the cost of labor for CV installation/deployment and people training. According to the author, operations and maintenance expenditures only account for about 20 percent of time, while the complexity of personnel training accounts for the other 80 percent.
How Universal Basic Mobility affects Accessibility
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.
How Mobility-as-a-service affects Safety
Mobility-as-a-service business models rely on collection of personal and financial data, creating potential privacy and safety concerns for prospective users [1] [2]. There is little available research on how MaaS programs impact safety in practice.
How Universal Basic Mobility affects Transportation Systems Operations (and Efficiency)
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.
How Automated Vehicles affects Transportation Systems Operations (and Efficiency)
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].
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].
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 Mobility-as-a-service affects Transportation Systems Operations (and Efficiency)
Studies show that Mobility-as-a-Service (MaaS) could decrease the use and ownership of private vehicles and support a switch to active travel modes and transit [1], [2], [3]. However, the magnitude of this switch is not comprehensively explored among the literature [2]. According to one simulation study, MaaS could reduce emissions by up to 54 percent, depending on the modeling scenarios [4]. Another simulation study showed that MaaS could reduce transport-related energy consumption because of the introduction of car-sharing and bike-sharing services [5]. Another study suggested that MaaS could reduce vehicle miles traveled and related negative externalities [6].
Several research directions are promising for future studies. First, there are limited studies on what drives people to use MaaS, highlighting a need to explore user incentives to adoption. Understanding these factors can inform more targeted service design and marketing strategies. Second, modeling the integration of multi-modal travel within MaaS is crucial. This could offer insights into optimizing traffic flows and enhancing the environmental and social benefits of MaaS. Third, the collaborative mechanism between the public and private sectors in the MaaS ecosystem requires further examination. Investigating how these entities can better cooperate could foster the broader application of MaaS solutions.
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 Mobility-as-a-service affects Municipal Budgets
There is still disagreement regarding what defines Mobility-as-a-Service (MaaS) as a business model, and research on how the implementation of MaaS would affect municipal budgets is limited. Many argue that to be successful, MaaS will have to develop a model that will be able to balance public and private providers in a sustainable manner [1], [2], but currently no such path exists. Doubts around the implementation of MaaS have been exacerbated by the recent failure of MaaS global [3]. The limited existing research on the budgetary impact from MaaS is based on revenue allocation models of economic spillovers from the deployment of such systems globally, rather than the direct impact of the presence of a MaaS system in a specific municipality [4].
How On-Demand Delivery Services affects Land Use
The expansion of on-demand delivery services has been made possible by ghost kitchens and dark stores – grocery fulfillment centers which are located near consumers but are not open to customers [1]. These fulfillment centers have created new real estate opportunities. Several major ghost kitchen operators are known for building large portfolios out of warehouses, empty strip malls, or other storefronts near areas with growing on-demand food-delivery markets [1]. Restaurants are dispersing away from ground-floor locations in popular retail districts as ghost kitchens increase their urban real estate [1], [2].
One emerging area of study is the impact of on-demand delivery services on restaurant formation and viability. The services charge participating restaurants delivery fees as high as 30 percent of order value, though some cities have imposed caps of 15 percent [3].
How Connectivity: CV, CAV, and V2X affects Education and Workforce
Collectively referred to as connected and automated vehicles (CAVs), connected vehicles (CVs), which communicate wirelessly with one another, and automated vehicles (AVs), in which a computer partially or entirely replaces the driver, have the capacity to revolutionize road maintenance and transportation operations [1]. According to Egan Smith (Managing Director of the Intelligent Transportation Systems (ITS) Joint Program Office of the United States Department of Transportation), "Successful deployment and operation of these new technologies depend largely on a knowledgeable, trained, and skilled workforce to support them” [2].
According to the California Department of Transportation's (Caltrans) strategic strategy, workforce development is a key action plan for CAV deployment [3]. Caltrans emphasized the importance of identifying labor difficulties and needs, as well as encouraging state efforts to recruit and retain the future workforce, in order to continue CAV. It could necessitate developing proper job categories, role descriptions, hiring procedures, and competitive salary ranges. Another option is to create a pool of highly skilled individuals (such as data scientists and network engineers) who can be housed in one functional unit and then transferred to other functional units or districts to share their technical expertise.
As CV and V2X technology advances, the Intelligent Transportation Systems (ITS) transportation workforce will require advanced knowledge, skills, and abilities. As a result, new and modified training opportunities are important for the ITS workforce to develop the advanced skill sets required to maintain a transportation network populated by evolving technologies [2].
Workforce development is essential not just for CAV deployment, but also for maintenance and repair (M&R). To stay up with technological advances, employees in this field must be upskilled and trained on a regular basis [4]. Crane et al. [5] also acknowledged that there is an increasing need to comprehend middle-skill positions, such as technicians, engineers, systems architects, managers, and IT specialists (that require at least a bachelor’s degree).
According to Parikh et al. [1], the most significant expense associated with CV deployment is the cost of labor for CV installation/deployment and people training. According to the author, operations and maintenance expenditures only account for about 20 percent of time, while the complexity of personnel training accounts for the other 80 percent.
How Universal Basic Mobility affects Accessibility
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.