Shared mobility services have experienced significant growth in adoption since the introduction of Uber, a ride-hailing service, in 2010. Although business models to support the sharing of vehicles (e.g., carsharing) have been present in the United States for more than 15 years, their adoption has been somewhat limited to niche markets in dense, urban cities or college campuses. To date, carsharing has attracted over 1.5 million members in North America and close to 5 million globally. Conversely, new models of ‘shared mobility,’ are estimated to have grown to more than 250 million users within five years.

The rapid adoption of these new mobility services poses significant challenges for transportation researchers, policymakers, and planners, as there is limited information and data about how these services may affect travel decisions and usage patterns. Given the long-range business, policy, and planning decisions that are required to support transportation infrastructure (including transit, roads, and vehicles), there is an urgent need to collect data on the adoption of these new services, and in particular, their potential impacts on travel choices.

This paper presents findings from a comprehensive travel and residential survey deployed in seven major U.S. cities that included questions on the adoption and use of carsharing and ride-hailing services. The findings suggest that early adopters of ride-hailing services tend to be younger, more highly educated, have higher incomes, and are more likely to reside in dense urban areas.

Although we find that ride-hailing adopters have lower levels of vehicle ownership than non-adopters, they are more likely to own a vehicle than core transit users. Given that ride-hailing services are relatively new, the majority of individuals report few changes in travel behavior. However, we do find preliminary evidence that these services support the disposal of a personal vehicle (9% of ride-hailing adopters reported having doing so) and a reduction in personal driving (26% of ride-hailing adopters). Reported changes in transit use by adopters are minimal; however, here we find early evidence that ride-hailing serves as a substitute for bus services, and may serve as a complement for commuter rail. While further research is needed, this study presents early findings on the potential impacts that emerging shared mobility services may have on travel behavior.

The City of Munich, in cooperation with the local public transport provider MVG, is testing a pilot project of a Mobility Station (multimodal mobility hub connecting public transport and new sharing mobility offers). The project’s goal is to provide sustainable mobility options that allow citizens to be mobile without owning a car. 

To evaluate the acceptance of the offer, as well as short and long term effects on mobility behavior, we developed an online user survey in close cooperation with the stakeholders and experts in the field of sharing mobility.

The results provide insights on the awareness and perception of the Mobility Station among users, their mobility patterns, current degree of multimodality, as well as actual and potential changes on mobility behavior and travel preferences due to the multimodal mobility offer.

Most users are young, male, and highly educated individuals with access to multiple mobility options. Public transport plays a central role for daily mobility together with the services they were identified to be customers of. The high share of users that use different mobility services at least once a month indicates some degree of multimodality.

Actual and potential changes in mobility behavior towards multimodality were revealed. Some users declared to use other mobility services more often. They appreciate the availability of different mobility options and show interest in other services and intermodal connections indicating that there is still potential to increase multimodal behavior. Based on previous findings multimodality can contribute to reduce car use and car ownership.

Transportation Network Companies (TNCs) have been facing significant growth in the past few years. Due to remarkable effect of these ridesharing alternatives on people`s travel behavior, travel demand modelers need to modify the key assumptions of the current practical models that are likely to be changed as the new travel modes emerge. In order to better understand the TNCs as a new mode of transportation and to comprehend the characteristics, preferences, and behavior of the people who use ridesharing options, we have conducted an internet-based survey of Uber riders in Chicago area. To analyze the effect of this alternative on utilization of other modes of travel, a nested logit model is developed on the second choice of riders in the absence of ridesharing mode. The results of estimated model highlight the influencing factors in shifting mode of travel from passenger car or transit to Uber.

From literature, it is clear that the discussion about the potential benefits and drawbacks of autonomous vehicles is not finished. In order to provide insight into this discussion, this paper investigates different attitudes with respect to different key factors for the deployment of system of autonomous taxis. To this end, a stated adaptation experiment was carried out in Belgium in March 2016.  To investigate which factors influence the variables of interest, i.e. (i) the number of minutes one is prepared to wait before an autonomous taxi picks up the person, (ii) the ownership of a private car when autonomous taxis are available, (iii) the willingness to share an autonomous taxi, (iv) the permittance for the autonomous vehicle to take a detour when it is beneficial for the society, and (v) the willingness to share your private agenda to ensure a timely autonomous taxi, different regression models are constructed. The results show that the considered explanatory factors only capture a small part of the variability of the five variables of interest. This is as signal that market segmentation might be very challenging. Besides, this is an indication that a broader range of factors should be included such as life-style factors and psychological constructs. Finally, this acknowledges the need for some skepticism with regard to the potential benefits of autonomous vehicles. The results are interesting for providing realistic boundaries and cross-classification in further simulation studies that look at the benefits of autonomous vehicles.

In this research, we explore the acceptability of several new and emerging travel options by presenting them alongside more established measures like congestion pricing and improved public transport systems. These include shared taxi (where passengers have the option to share their ride with other travellers with similar routes, benefiting from lower fares), one-way car rental (where stackable light electric vehicles can be picked up and dropped off at different points), and school bus services for park and ride (where children younger than 10 can be dropped off to be picked up by professional tutors). The acceptability and willingness-to-pay for these modes have been tested in the context of Lisbon using stated preferences (SP) techniques. The survey was extremely challenging to design and analyse given the multi-dimensionality, heterogeneity in the type of the influencing variables and the large choice set (9 modes, 5 departure times, 2 occupancies leading to 135alternatives).A sequential choice set presentation approach was used. The survey administered over the internet and computer aided personal interviews included 2372 valid SP observations from 1248 respondents. Mixed logit models were used to capture the complex correlations introduced due to the non-traditional survey design. Results indicated a significant preference of one-way car rental and shared taxi for non-commute trips. For commute trips, improved versions of traditional public transport modes were favoured. These findings are expected to provide important information to transportation planners and policy makers working to achieve sustainable transportation systems in Lisbon as well as in other cities.

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014-2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.

Autonomous vehicles (AVs) potentially increase vehicle travel by reducing travel and parking costs and by providing improved mobility to those who are too young to drive or older people. The increase in vehicle travel could be generated by both trip diversion from other modes and entirely new trips. Existing studies however tend to overlook AVs’ impacts on entirely new trips. There is a need to develop a methodology for estimating possible impacts of AVs on entirely new trips across all age groups. This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia. A new methodology for estimating entirely new trips associated with AVs is proposed by measuring gaps in travel need at different life stages. Results show that AVs would increase daily trips by 4.14% on average. The 76+ age group would have the largest increase of 18.5%, followed by the 18-24 age group and the 12-17 age group with 14.6% and 11.1% respectively. If car occupancy remains constant in AV scenarios, entirely new trips and trip diversions from public transport and active modes would lead to a 7.31% increase in car trips. However increases in car travel are substantially magnified by reduced car occupancy rates, a trend evidenced throughout the world. Car occupancy would need to increase by at least 5.3% to 7.3% to keep car trips unchanged in AV scenarios.

This study provides a large-scale micro-simulation of transportation patterns in a metropolitan area reliant on a system of shared autonomous vehicles (SAVs). The six-county Austin, Texas region is used for its land use patterns, demographics, networks and general travel behaviors. The MATSim toolkit is applied here since it runs quickly on large networks while allowing modelers to track individual travelers and individual vehicles, with great temporal and spatial detail. The main purpose of using MATSim in this study is to get the executable travel plans for individuals competing resources of time and space of road networks in Austin area. Having such travel plans (with trip start time, locations, distances, and routes), SAVs were introduced to serve travelers who request SAVs. The SAV requests were simulated through a stochastic process of mode choices based on four possible fare rates of using a SAV ($0.2, $0.5, $0.75, and $1.0 per mile). The mode choice was discussed for travelers who have access to privately owned vehicles (including AVs), and for those who do not have the access. Mode choice results showed that lower fare rates gain preferences from the long-distance travelers because of the reduced burden of in-vehicle activities in SAVs, relative to human-driving vehicles (HVs). For travelers who do not own a vehicle, SAVs (with four possible fare rates) seem to be preferable for trips shorter than 10 miles which account for 80% of travels in Austin area. Therefore, public transit services may be easily replaced by SAVs once SAVs are on road in this area. Under four possible fare rates ($0.2, $0.5, $0.75, or $1.0 per mile), four levels of demand for SAVs were obtained: 36.6%, 12.1 %, 8.0%, and 6.4% of trips respectively requested SAVs. Then SAVs were simulated to serve these requests. Simulation results showed that with the increase in SAV fleet size, more SAV requests can be served within 10 minutes; and the fare rate $0.5 per mile is associated with the greatest vehicle replacement. Reasons are discussed in the paper. Further this study discussed the mobility and sustainability benefits of SAVs. If SAV fare rate is low enough (not necessarily lower than the cost of using a HV), SAV users can travel a few extra miles compared to HV travelers. Also, the extra VMTs by SAVs are not sizeable enough to trade off the benefits gained from AV’s advancements. This study offers insights for industrial innovators who seek to implement SAVs in a metropolitan area, as well as transportation planners and policy (or decision-) makers who must be ready for the arrivals of SAVs in near future.