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Despite an increasingly large body of research that focuses on the potential demand for autonomous vehicles (AVs), risk preference is an understudied factor. Given that AV technology and how it will interact with the evolving mobility system are highly risky, this lack of research on risk preference is a critical gap in current understanding. By using a stated preference survey of 1142 individuals from Singapore, this study achieves three objectives. First, it develops one measure of psychometric risk preference and operationalizes prospect theory to create two economic risk preference parameters. Second, it examines how these psychometric and economic risk preferences are associated with socioeconomic variables. Third, it analyzes how risk preference influences the mode choice of AVs. The study finds that risk preference parameters are significantly associated with socioeconomic variables: the elderly, poor, females, and unemployed Singaporeans appear more risk-averse and tend to overestimate small probabilities of losses. Furthermore, all three risk preference parameters contribute to the prediction of AV adoption. These modeling results have policy implications at both the aggregate and disaggregate levels. At the aggregate level, people misperceive probabilities, are overall risk-averse, and hence under-consume AVs relative to the social optimum. At the disaggregate level, the elderly, poor, female, and unemployed are more risk-averse and thus are less likely to adopt AVs. These results suggest that it might be valuable for governments to implement policies to encourage technology adoption, particularly for disadvantaged social groups, although caution remains due to uncertainty in the long-term effects of AVs. Individualized risk preference parameters could also inform how to design regulations, safety standards, and liability allocations of AVs since many regulations are essentially mechanisms for risk allocation. One limitation of the paper is that risk preference is measured and modeled only as individual-specific but not alternative-specific variables. Future studies should examine the relationship between the multiple components of risk preference and the multiple risky aspects of AVs. [ABSTRACT FROM AUTHOR]

The car fulfills not only instrumental transportation functions, but also holds important symbolic and affective meaning for its owners and users. In particular, owning and using a car can be a symbol of an individual's social status or personal image ('car pride'). This paper introduces and validates a standard measure of car pride estimated from 12 survey statements using a cross-sectional sample of 1236 commuters in New York City and Houston metropolitan statistical areas. We find that car pride is higher in Houston than in New York City. We then empirically examine the bidirectional relation between car pride (attitude) and household car ownership (behavior) using structural equation modeling. To identify the bidirectional relationship we use an individual's general pride as the instrumental variable (IV) for that same individual's car pride; in the opposite direction, we use the average household vehicle ownership in the respondent's census block group as the IV for the respondent's household car ownership. We find that positive and statistically significant relations exist from car pride to car ownership, while the relation in the reverse direction is not statistically significant. On average and in both city subsamples, the relation from car pride to household car ownership (attitude-to-behavior) is much stronger than the reverse (behavior-to-attitude). In fact, in our models car pride is more predictive of car ownership than most individual and household socio-demographics included in traditional ownership forecasting models, including income. Empowered with a well-validated, standard measure for car pride and a robust approach for exploring reciprocal attitude-behavior relations in cross-sectional data, future research can extend the current understanding presented in this paper to explore car pride's relation with other travel behaviors, the dynamics of these attitude-behavior relations over time, and their implications for policies to promote sustainable travel behavior. [ABSTRACT FROM AUTHOR]

Lotteries and auctions are common ways of allocating public resources, but they have rarely been used simultaneously in urban transportation policies. This paper presents a unique policy experiment in Guangzhou, China, where lotteries and auctions are used in conjunction to allocate vehicle licenses. Guangzhou introduced vehicle license regulations to control the monthly quota of local automobile growth in 2012. To obtain a license, residents are required to choose between the lottery and auction method. Since the introduction of the regulations, there has been heated debates on the distributional effects of lotteries and auctions; however, the debates have not been grounded in empirical studies. We analyze the distributional effects of such mixed mode of resource allocation in a positive manner based on individual behavioral choices. We conducted a survey in January 2016 (n = 1000 people ∗ 12 months), and used mixed logit models to analyze how socio-economic status, including income and household automobile ownership, determined people’s choices among lottery, auction, and non-participation alternatives. We find that income increased participation, but did not influence non-car owners’ choices between lotteries and auctions, which contrasts with the common notion that lotteries benefit the poor. Additionally, the positive impact of car ownership on participation indicates a car-dependent trajectory for automobile growth. The significant socio-economic differentiators between lotteries and auctions were age, gender, and education. Proxies of mobility needs were insignificant overall. The program attributes had a much larger impact than all other variables—people were more likely to choose lotteries with higher winning rates and more participants and more likely to choose auctions with higher prices and more participants. We concluded that for those who participated, the choice between lotteries and auctions did not depend on their income or mobility needs but, rather, the probability of winning plates and the opportunity for speculation. [ABSTRACT FROM AUTHOR]

• Examine how ICT use impacts an individual's travel behavior and ride-hailing use. • Study how ride-hailing use is associated with an individual's travel behavior. • Greater ICT use significantly predicts higher PMT and VMT. • ICT use is positively and substantively correlated with individual ride-hailing adoption. • Ride-hailing use of the passenger is slightly negatively correlated with PMT and VMT. Previous studies have explored the relationships between an individual's use of information and communication technology (ICT) and their travel. However, these studies often focus on one specific type of travel and have not considered new forms of mobility, such as ride-hailing, that are enabled by greater ICT penetration. This paper focuses on how ICT use impacts an individual's self-reported travel behavior—including total number of trips, personal miles traveled (PMT), and vehicle miles traveled (VMT) in a typical travel day—and ride-hailing use in the past month. Specifically, we investigate whether substitution or complementarity dominates the relationships between ICT use and an individual's net travel; how ICT impacts individual ride-hailing adoption and frequency of use; and how ride-hailing use is associated with an individual's overall travel behavior. Using data from the 2017 U.S. National Household Travel Survey (NHTS), we estimate a structural equation model that includes a robust set of individual, household, built environment, and travel characteristics, frequency of ICT use, and a hurdle model (two-part regression) of the adoption and frequency of ride-hailing use. Results reveal that greater ICT is not significantly related to the total number of trips that an individual takes, but it does significantly predict higher PMT and VMT. Greater ICT use is positively and substantively correlated with whether or not the individual has used ride-hailing in the past 30 days, but has no significant relationship with the frequency of ride-hailing use with this bounded outcome being controlled for. We further find that an individual's ride-hailing use has a small negative correlation with their PMT and VMT after controlling for other common factors. Our results indicate the importance of future research examining the mechanisms by which ICT use increases the distance individuals travel and the role that new ICT-enabled modes, such as ride-hailing, play in changing these mechanisms at both the individual and system levels. [ABSTRACT FROM AUTHOR]

Abstract Effective management of demand information is a critical factor in the successful operation of autonomous mobility-on-demand (AMoD) systems. This paper classifies, measures and evaluates the demand information for an AMoD system. First, the paper studies demand information at both individual and aggregate levels and measures two critical attributes: dynamism and granularity. We identify the trade-offs between both attributes during the data collection and information inference processes and discuss the compatibility of the AMoD dispatching algorithms with different types of information. Second, the paper assesses the value of demand information through agent-based simulation experiments with the actual road network and travel demand in a major European city, where we assume a single operator monopolizes the AMoD service in the case study area but competes with other transportation modes. The performance of the AMoD system is evaluated from the perspectives of travelers, AMoD operators, and transportation authority in terms of the overall system performance. The paper tests multiple scenarios, combining different information levels, information dynamism, and information granularity, as well as various fleet sizes. Results show that aggregate demand information leads to more served requests, shorter wait time and higher profit through effective rebalancing, especially when supply is high and demand information is spatially granular. Individual demand information from in-advance requests also improves the system performance, the degree of which depends on the spatial disparity of requests and their coupled service priority. By designing hailing policies accordingly, the operator is able to maximize the potential benefits. The paper concludes that the strategic trade-offs of demand information need to be made regarding the information level, information dynamism, and information granularity. It also offers a broader discussion on the benefits and costs of demand information for key stakeholders including the users, the operator, and the society. [ABSTRACT FROM AUTHOR]

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently. [ABSTRACT FROM AUTHOR]

• Car users informed of cars' emissions externality more likely to try green modes. • This effect persists but is dampened for car users in cities with polluted air. • Green travel subsidy is stronger boost to green modes, persists in polluted cities. • Green mobility policy more effective when combined with greening of other sectors. • Study uses stated preference experiment in Zhengzhou, China. Urban leaders in areas with high air pollution often face the dual task of reducing pollution levels while educating the public about the health impacts of pollution and preventive measures. Transportation policies to cut motorized personal vehicle use are often a key part of pollution reduction efforts. One type of these policies is information interventions educating commuters of the higher emissions impact of cars. This study evaluates the impact of such an information intervention on car commuters' intention to switch from car use to transit, biking, or walking in Zhengzhou, China. Further, it tracks how this greening impact evolves as drivers are given additional public health information regarding Zhengzhou's severe air pollution level, its health effects, and the reduced pollution exposure when driving compared with transiting, biking, or walking outside in polluted air. Using a randomized controlled survey experiment with a treatment group of 281 participants and a control group of 280 participants drawn from Zhengzhou workers who typically commute by car, this study applies multinomial logit models and difference-in-difference estimation to estimate the information intervention's impact. We find that while emissions information interventions are initially highly effective in reducing intended car usage, the effect is progressively dampened as respondents learn of the level of local air pollution and the risks of outdoor pollution exposure. Some greening effects do, however, remain even at the end of the experiment. The results suggest that governments in polluted areas must be aware of how policies in different segments of their portfolio interact and support broader initiatives like mixed-use planning and transit extension, simultaneously cutting transportation emissions and creating shorter commute durations with lower outdoor pollution exposure. [ABSTRACT FROM AUTHOR]

In response to severe traffic congestion and air pollution, Beijing introduced a car ownership restriction policy to curb growth in the number of private cars in the city. However, Beijing residents can still purchase and register their cars in neighboring cities and this "leakage" may substantially reduce the policy's effectiveness. Using city-level data collected from the CEIC China Premium Database, we aim to quantify the spill-over effect: the impact of Beijing's policy on the growth of private car registrations in neighboring cities. We first deploy a synthetic control method to create a weighted combination of non-treated cities for each treated city. We then employ a difference-in-differences approach to estimate the policy leakage. Our models suggest that the policy resulted in additional 443,000 cars sold in the neighboring cities (within 500 km of Beijing) from 2011 to 2013, compared to if the policy had not been implemented. 35–40% of the car growth reduction stipulated by the policy simply spilled over to neighboring cities. The significance of the policy leakage necessitates positioning Beijing's urban transportation in a broader context and executing regional collaboration. [ABSTRACT FROM AUTHOR]

E-scooter sharing provides a last-mile solution to complement transit services, but less was known about its effectiveness in serving short-distance transit trips. We investigate the potential of using e-scooter sharing to replace short-distance transit trips of excessive indirectness, multiple transfers, and long access-egress walking. First, we conducted a stated preference survey on e-scooter users in the Central Area of Singapore and estimated mixed logit models to examine factors influencing the choice of e-scooters and transit. We then calculated the number of transit trips that can be replaced by e-scooters. Second, we analyzed the decision of e-scooter companies in terms of the trade-offs between serving more e-scooter trips and making more revenue under varying fares. The results show that fare, MRT transfer, and MRT access-egress walking distance have significantly negative impacts on mode utilities with random tastes among respondents. Male, young and high-income groups are more heterogeneous in e-scooter preferences compared with other groups. The loss of mode share can be nearly 17% if maximizing the revenue. We classify trade-off situations into five categories and provide suggestions of how to balance between mode share and revenue for each category. Several implications are drawn for better harnessing and regulating this new mobility service, including where to deploy e-scooters to satisfy the demand unmet by the transit and how to reach a proper balance between private operators and public welfare. [ABSTRACT FROM AUTHOR]

This paper explores the possibility of using subway maps as a planning tool to influence passenger route choice to mitigate congestion. Specifically, it tests whether extending the appearance of an overcrowded subway line on the Washington DC subway map would encourage passengers to use other underutilized lines. The experiment was conducted through the Mechanical Turk, a crowdsourcing platform, with 3056 participants, producing 21,240 route choice decisions on the official and six alternative maps. Results show that redesigned maps significantly affect participants’ route choices. Depending on the specific design, a 20% length increase of the overcrowded line could move 1.9–5.7 percentage points of ridership to an alternative, underutilized line. The change could remove up to 10 passengers per car during the highest peak, reducing the number of highly congested half-hour periods (max load = 100–120 passengers per car) on the overcrowded line from 4 to 1, and the number of crush periods (max load > 120 passengers per car) from 3 to 1. This is done at minimal or zero cost. The paper calls for more attention from transit agencies to the planning potential of transit maps. [ABSTRACT FROM AUTHOR]

To combat congestion, promote sustainable forms of transportation, and support the public transit system, Chicago introduced a congestion pricing policy targeting transportation network company (TNC) services on January 6, 2020. This policy aimed to discourage single-occupant and peak-period TNC travel, particularly in high-congestion areas. Using TNC trip record data collected from the Chicago Data Portal, we quantify the impacts of the congestion pricing policy on TNC ridership in Chicago, differentiating between shared and single-occupant trips. Employing a Difference-in-Differences identification strategy, we find that the implementation of the congestion pricing policy led to an increase in shared TNC trip counts and a much larger decrease in single-occupant trip counts. Overall, the policy implementation is associated with a 7.1% reduction of total TNC pickup trips, a 16.4% increase of shared TNC pickup trips and a 11% reduction of single TNC pickup trips. Given the estimated policy effects, we find that the price elasticity of the TNC trip volume in the downtown areas is roughly −0.48. In terms of spatial variation, we find that the lost TNC trips were mainly trips that began and ended in the central business district. The south side of Chicago, which has a high proportion of African-American and low-income residents, shows evidence of single trip reduction for trips that began or ended in the downtown areas due to the policy implementation, but the policy did not seem to incentivize pooling to or from the downtown areas as effectively in the south side as in other regions of Chicago. Regarding the time-of-day variation, we find that the policy is more effective in encouraging trip sharing for off-peak travels than for peak-time travels. Our research provides local planners and policymakers with valuable insights into the impacts of the congestion pricing policy. The method and findings of this research can also be used for other cities that are considering adopting congestion pricing policies on TNCs in the future. • We estimate the effect of a TNC congestion pricing policy on TNC ridership in Chicago. • A difference-in-differences identification strategy is adopted. • Results show 7.1% and 7.7% reductions of TNC pickup and dropoff trips, respectively. • The policy led to a growth in shared TNC trips and a decline in single TNC trips. • Results indicate a great spatial variation of the policy impacts. [ABSTRACT FROM AUTHOR]