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Value of travel time savings of autonomous vehicle commuters: a segmented valuation for local and inter-city travel
Abstract
In this study we aim to identify how the adoption of autonomous vehicles (AVs) affects the value of travel time savings (VOTTS) for commuting trips under different trip distance scenarios (local and inter-city travel). Using a stated preference survey administered to Korean commuters in 2019, two multinomial logit (MNL) mode choice models are developed. These models are stratified by trip length and include four alternatives: AV with a manual driving option (AVMD), AV with a self-driving option (AVSD), shared AV (SAV), and public transit (PT). The results show that the value of in-vehicle travel time savings for AVSD (on average, 10.26/hr, SAV = $13.67/hr). It suggests that travelers tend to pay less to reduce travel time while using a private hands-free mode because it allows travelers to use their travel time more productively and/or relieve stress for driving. In addition, changes in the VOTTS for each mode vary by trip length. As travel distance increases, VOTTS for shared modes (SAV and PT) tends to decrease. The value of in-vehicle travel time for SAV in the local travel scenario is the highest among the four modes but is considerably lower than that in the inter-city scenario. These differences suggest that travelers require a certain amount of time to fully use their in-vehicle time, highlighting the importance of trip length when determining the VOTTS. Interestingly, the VOTTS for PT is lower than three AV modes, implying that it might be too early to expect substantial reduction in the VOTTS for AVs due to potential risks of accidents, unfamiliarity with AVs, or immature technology of AVs.
• Highlights
• Likelihood of choosing autonomous vehicles is modelled based on trip length.
• Changes in value of travel time savings for each mode vary by trip length.
• Value of travel time savings for public transit is lower than autonomous vehicles.
• Self-driving vehicles have a low value of travel time savings than other modes.
• People need a certain amount of time to fully perform activities while traveling.
... Thus, travel demand is likely to increase due to these new user groups. Additionally, since their passengers can engage in non-driving activities, AVs will decrease drivers' willingness to pay for reduced travel times, thus value of time reduction, for travelers [18,[27][28][29]. As a result, a significant mode shift to AVs from public transportation is anticipated [14]. ...
... This may induce additional demand for private transport and a shift from public transport to AVs [18]. In addition, travel patterns may change since AVs may be preferred for longer trips due to the lowered burden of driving [28]. If such changes can be reflected in future simulation models, the outputs may be more reliable and interpretable. ...
Autonomous vehicles (AVs) are expected to be seen easily on our roads, providing benefits to society in the future. However, due to the challenges faced such as technical and regulatory issues, it will take a long time to reach 100% AVs, suggesting the coexistence of AVs and human-driven vehicles (HVs) for a substantial time. This coexistence of the mixed traffic of AVs and HVs may affect network performances in urban areas. Under this circumstance, to promote the rapid advancement of AV technology, transportation authorities are considering introducing AV-exclusive lanes. This study evaluates AV lane operation scenarios with mixed traffic consisting of both AVs and HVs. We utilized a macroscopic traffic simulation-based approach to measure the effects of AV lanes on two urban expressways in Seoul, South Korea. We varied the market penetration rate (MPR) from 10 to 80% in 10% increments. The simulation results indicate that the impact of exclusive lanes can be either negligible or beneficial depending on the characteristics of the roads. The results also showed that, in terms of the efficient use of road capacity, AV lanes should be introduced when the MPR reaches at least 20 to 30%. The results also showed that the capacity gain effect is more sensitive over a lower MPR range. We expect this study to not only enhance policy makers’ and planners’ fundamental understanding of the early stages of AV-exclusive lanes, but also help them prepare a large-scale urban network structure in the future.
Problem definition: Large cities around the globe are facing an alarming growth in traffic congestion and greenhouse gas emissions, to which a significant contributor in recent years are on-demand cabs operated by ride-hailing platforms. Newly emerged pooled transportation options like shuttle services are cheaper and greener alternatives. However, those alternatives are still new to many customers and policy makers. The design of their promotion policies demands careful investigation. This paper studies how we can reduce the number of on-demand cabs on the road and, therefore, their GHG emissions by promoting pooled transportation such as shuttle services. Methodology/Results: In this work, we use detailed usage data and build a structural model to study customer preferences of price and service features when choosing between private cabs and a scheduled shuttle service. Using the estimated model, we identify and evaluate the efficacy of improving service features like reducing the walking distance to shuttle stops on customers’ choices of transport and, therefore, the number of ride-hailing vehicles on the road. We find that a 20% decrease in walking distance can achieve 40% of the benefits of commonly adopted congestion surcharge policies. It can also reduce up to 4.8 thousand tonnes of GHG emissions, which is worth over a million dollars per year. In addition, we demonstrate the implementability of walking distance reduction policies by adding stops on existing shuttle routes. Managerial implications: Reducing the number of ride-hailing vehicles on the road has become an important goal in many cities’ green transport policy design. For example, cities like New York have implemented congestion surcharge policies targeting ride-hailing vehicles in recent years. Our findings suggest that, by changing operations levers such as service features of pooled transport, cities can achieve a substantial amount of benefits from reducing congestion compared with congestion surcharge policies with essentially zero cost, leading to much more efficient green transport policies.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0569 .
Millennials, the demographic cohort born in the last two decades of the twentieth century, are reported to adopt information and communication technologies (ICTs) in their everyday lives, including travel, to a greater extent than older generations. As ICT-driven travel-based multitasking influences travelers’ experience and satisfaction in various ways, millennials are expected to be affected at a greater scale. Still, to our knowledge, no previous studies have specifically focused on the impact of travel multitasking on travel behavior and the value of travel time (VOTT) of young adults. To address this gap, we use an original dataset collected among Northern California commuters ( N = 2216) to analyze the magnitude and significance of individual and household-level factors affecting commute mode choice. We estimate a revealed-preference mode choice model and investigate the differences between millennials and older adults in the sample. Additionally, we conduct a sensitivity analysis to explore how incorporation of explanatory factors such as attitudes and propensity to multitask while traveling in mode choice models affects coefficient estimates, VOTT, and willingness to pay to use a laptop on the commute. Compared to non-millennials, the mode choice of millennials is found to be less affected by socio-economic characteristics and more strongly influenced by the activities performed while traveling. Young adults are found to have lower VOTT than older adults for both in-vehicle (15.0% less) and out-of-vehicle travel time (15.7% less), and higher willingness to pay (in time or money) to use a laptop, even after controlling for demographic traits, personal attitudes, and the propensity to multitask. This study contributes to better understanding the commuting behavior of millennials, and the factors affecting it, a topic of interest to transportation researchers, planners, and practitioners.
Combining cooperative vehicle driving behavior of Connected and Automated Vehicles with supporting information infrastructure, is expected to increase the capacity of roadway infrastructure, which in turn results in travel time savings and user benefits. Automated driving also relieves the driver from steering the car, allowing to conduct other activities during the trip, which is likely to generate further user benefits. In order to assess the magnitude of automated driving on travel time-related user benefits, a typical commuting relation is analyzed, considering three route options as well as level 4 and 5 vehicle automation. The impacts on travel times are estimated by microscopic traffic flow simulations. The simulations reveal that around 27% of the travel time can be saved on a commuting relation due to road automation according to level 5. For level 4 vehicles the travel time savings amount to up to 20%. User benefits that accrue from time savings and the passenger’s option of using travelling time for activities other than conducting the car, are expected at a relevant magnitude. Even under consideration of higher operating costs of an automated car, significant user benefits accrue: 1,310–2,240 € p.a. for level 4 and 2,770–3,440€ p.a. for level 5 vehicles during a passenger car’s typical depreciation period. Thus, automated driving will decrease the commuters’ generalized user costs for individual motorized mobility, which is likely to enhance the urban hinterland’s attractiveness as residential area. This pattern and inherent second-order effects pose challenges for transport, land use and urban planners. Furthermore, it represents a challenge for transport research: to elaborate appropriate concepts that allow for exploiting the benefits of use of automated vehicles while countervailing undesirable socio-economic effects, as well as strains on the transport system and land use.
Membership-based short-term car rental-more popularly referred to in the marketplace and popular media as "car-sharing"-has been expanding rapidly worldwide. The advent of self-driving vehicles is likely to facilitate the growth of car-sharing by addressing a couple of the current barriers, including limited dedicated parking and non-competitive access times. Despite the growing importance of this topic, however, the patterns emerging in the market penetration of automated vehicles in car-sharing programs have not received much attention in the literature because of the relative newness of the automated vehicle technologies enabling them. Towards addressing this research gap in this increasingly important area, this paper presents the results of an Australian survey with a focus on consumer preferences towards car-sharing. A stated preference (SP) methodology was adopted to elicit consumers' valuation of specified mode-choice related factors. In particular, vehicle self-driving capability , a factor rarely examined in the literature, was provided as an option to participants in the SP survey. To increase the realness of the experiment, SP tasks were pivoted from respondents' most recent trips. The travel preferences data were analyzed using a random parameter (mixed) logit model. To explore preference heterogeneity, socio-demographic and other factors were interacted with alternative-specific attributes, and their influences on marginal utilities were extracted using simulation analysis. Preference heterogeneity across individuals towards shared automated vehicles (SAVs) was identified. Consistent with the literature, consumers' experience of using car-sharing appears to have significant influence on household mode choices, increasing the probability of using a diversified mode tools (e.g., TWS and Taxi) and decreasing the likelihood of choosing to use privately owned travel tools, such as private car. Although it has been argued in the literature that females, non-drivers, and the elderly are the most likely to benefit from SAVs, the results of this study reveal that these user groups in fact hold negative opinions about SAVs. The findings highlight the challenges policymakers may encounter in maximizing the social and individual benefits of SAVs.
It is widely believed that vehicle automation will change how travelers perceive the value of travel time (VoTT), but the magnitude of this effect is still unknown. This study investigates how highly automated vehicles (AVs) may affect VoTT, using an existing mode—ridehailing services (RHS)—as an analogy for AVs. Both AVs and RHS relieve travelers from the effort of driving and allow them to participate in other activities while traveling. In a stated choice experiment, respondents chose between driving a personal vehicle or taking an RHS, with each mode characterized by a cost and travel time. Analysis results using a mixed logit model indicated that the VoTT was 13% lower when being driven in an RHS than when driving a personal car. We also told half the respondents (randomly selected) that the RHS was driverless; and for half (also randomly selected) we explicitly mentioned the ability to multitask while traveling in an RHS. Mentioning multitasking explicitly led to a much lower VoTT, approximately half that of driving oneself. However, the VoTT in a driverless RHS was 15% higher than when driving a personal car, which may reflect a lack of familiarity and comfort with driverless technology at present. These results suggest sizable reductions in VoTT for travel in future AVs, and point to the need for caution in making forecasts based on consumers’ current perceptions of AV technology.
From early studies of time allocation onward, it has been acknowledged that the "productive" nature of travel could affect its utility. Currently, at the margin an individual may choose transit over a shorter automobile trip, if thereby she is able to use the travel time more productively. On the other hand, recent advancements toward partly/fully automated vehicles are poised to revolutionize the perception and utilization of travel time in cars, and are further blurring the role of travel as a crisp transition between location-based activities. To quantify these effects, we created and administered a survey to measure travel multitasking attitudes and behaviors, together with general attitudes, mode-specific perceptions, and standard socioeconomic traits (N = 2229 Northern California commuters). In this paper, we present a revealed preference mode choice model that accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives. We find that the propensity to engage in productive activities on the commute, operationalized as using a laptop/tablet, significantly influences utility and accounts for a small but non-trivial portion of the current mode shares. For example, the model estimates that commuter rail, transit, and car/vanpool shares would respectively be 0.11, 0.23, and 1.18 percentage points lower, and the drive-alone share 1.49 percentage points higher, if the option to use a laptop or tablet while commuting were not available. Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow a high level of engagement in productive activities, the drive-alone share would increase by 1.48 percentage points. The results empirically demonstrate the potential of a multitasking propensity to reduce the disutility of travel time. Further, the methodology can be generalized to account for other properties of autonomous vehicles, among other applications.
Due to digitalization trends and rapid technological development, cars are becoming more technologically advanced with an on-going trend towards fully automated vehicles. Understanding possible changes in user preferences and the impact on mobility of autonomous driving is of great importance for policy and transport planning authorities in light of urbanization trends, demographic change, and environmental challenges. Despite the relevance of the topic, there are limited empirical insights on user preferences, once autonomous driving becomes available. To close this gap and analyze the potential changes in the value of travel time savings (VTTS) resulting from the availability of autonomous driving, an online survey using revealed and stated preference methods was conducted. In the survey user preferences toward currently available and future available modes of transportation were assessed using two discrete choice experiments. VTTS calculations are based on an estimated joint mixed logit model. The results of the study show an average VTTS reduction of 41% for autonomous driving compared to driving a conventional car, however, only for commuting trips. For leisure or shopping trips, no significant changes in the VTTS were found. Considering shared autonomous vehicles (SAV), the results indicate that using SAV is perceived as a less attractive option than using a privately owned autonomous vehicle. Translating the results into policy implications, a potential conflict between individual benefits of autonomous driving and societal goals is identified. Finally, policy recommendations are discussed.
Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers' intention to use AVs. Perceived behavioral control potentially stymie the travelers' intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.
With 36 ventures testing autonomous vehicles (AVs) in the State of California, commercial deployment of this disruptive technology is almost around the corner (California, 2017). Different business models of AVs, including Shared AVs (SAVs) and Private AVs (PAVs), will lead to significantly different changes in regional vehicle inventory and Vehicle Miles Travelled (VMT). Most prior studies have already explored the impact of SAVs on vehicle ownership and VMT generation. Limited understanding has been gained regarding vehicle ownership reduction and unoccupied VMT generation potentials in the era of PAVs. Motivated by such research gap, this study develops models to examine how much vehicle ownership reduction can be achieved once private conventional vehicles are replaced by AVs and the spatial distribution of unoccupied VMT accompanied with the vehicle reduction. The models are implemented using travel survey and synthesized trip profile from Atlanta Metropolitan Area. The results show that more than 18% of the households can reduce vehicles, while maintaining the current travel patterns. This can be translated into a 9.5% reduction in private vehicles in the study region. Meanwhile, 29.8 unoccupied VMT will be induced per day per reduced vehicles. A majority of the unoccupied VMT will be loaded on interstate highways and expressways and the largest percentage inflation in VMT will occur on minor local roads. The results can provide implications for evolving trends in household vehicles uses and the location of dedicated AV lanes in the PAV dominated future.
Autonomous driving is being discussed as a promising solution for transportation related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent travelling since they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and few empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated choice experiments. Two use cases were addressed – a privately owned and a shared autonomous vehicle – compared to other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in the VTTS for commuting trips. We found that driving autonomously in a privately owned vehicle might reduce the VTTS compared to driving manually and is perceived similarly to in-vehicle time in public transportation. Also, riding in a shared autonomous vehicle is perceived less negatively than driving manually. The study provides important insights on VTTS by autonomous driving for commuting trips and can be a base for future research to build upon.
Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation (3300) to their current vehicles.
Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios (1, 2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.
Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5-10 years, and under most conditions within 10-20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.
In the recent years many developments took place regarding automated vehicles (AVs) technology. In fact AVs are expected to become available on the market in the next decades. It is however unknown to which extent the share of the existing modes will change as result of AVs introduction. To the best of our knowledge this study is the first where traveller preferences for AVs are explored and compared to existing modes. Thereby its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes. Because there are no fully-automated vehicles currently on the market, we apply a stated preference choice experiment where we explore the role of classic instrumental variables such as different travel time components and travel cost. In our study we focus on positioning AVs in the context of last mile transport at the activity-end in multimodal train trips. We can conclude that first class train travellers on average prefer using an automated vehicle as egress transport between train station and final destination, compared to using other egress modes. Second class train travellers on average prefer the use of bicycle and bus/tram/metro as egress mode instead of automated vehicles. Especially for first class train passengers, implementing AVs as last mile transport therefore has potential. Second, sensitivity of travellers for in-vehicle time is considerably higher for an automatically driven AV, compared to a manually driven AV. As consequence, the willingness-to-pay for a certain travel time reduction in an automatically driven AV is considerably higher, compared to a manually driven AV. Despite theoretical advantages of using travel time more efficiently in an automatically driven AV, it might be that psychological concepts, like attitudes, play a role here. Since automated driving is a very new and innovative way of transportation, the classic instrumental attributes like travel time might not tell the whole story.
Having entered public and scientific debate only recently, automated driving is a fresh topic. It is increasingly acknowledged that automated driving requires broader examination, beyond technical issues, to include individual and societal acceptance, which is a prerequisite for its implementation. The challenge for empirical research, however, is that nonprofessional individuals have little knowledge of and usually no experience with driverless cars. The paper presents an innovative approach to explore and structure the acceptance of automated driving. This study was made by analyzing comments on German and U.S. print media website articles. The statements were scanned for topics discussed by the participants. The method applied three steps: statement collection, issue categorization, and importance and connotation assessment (positive and negative). Further analysis involved contrasting German and U.S. comments. The results indicated a heterogeneous discussion of automated driving in both countries without, however, being evidently for or against it. Although the U.S. statements addressed societal aspects much more, the Germans often focused on liability questions. The method proved relevant in structuring the topic and laying a solid foundation for empirical research on a new mode of transportation's acceptance.
Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach 4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
An application-oriented evaluation of the implications of fully automated driving is of growing importance, particularly with regard to future user acceptance and usage. One of the most remarkable benefits of fully automated driving discussed is the potential of alternative travel-time use. Radical changes in time valuation and a rise in productivity are being discussed, which are highly relevant when evaluating transport-related measures, infrastructure investments and mode choice behavior. However, the lack of empirical examination and the inclusion of important aspects about the usage context, constraints and perceived benefits make predictions difficult. This paper aims to bridge the gap by examining user acceptance and perceived 42 advantages of automated vehicles on the basis of four specific use cases within the course of a quantitative online survey in Germany. The study includes detailed information on respondents’ current use of, and attitudes towards, today’s available transport modes, as well as their perception of the advantages of automated vehicles. Special focus is placed on current and future travel-time use. Significant differences found in the adaption of specific use cases emphasize the importance of a differentiation of fully automated driving. Perceived benefits with respect to time use were mainly window gazing and relaxing, whereas the possibility of working seemed to be less valued. A probit model analysis examined influencing factors on the propensity of regarding working while traveling as an advantage - a strong significance of the current time use on public transport and long-distance train trips, gender and rational attitude towards public transport was revealed.
This paper presents a new concept of urban shared-taxi services. The proposed system has a new organisational design and pricing scheme that aims to use the capacity in traditional taxi services in a more efficient way. In this system, a taxi acting in ‘sharing’ mode offers lower prices to its clients, in exchange for them to accept sharing the vehicle with other persons who have compatible trips (time and space). The paper proposes and tests an agent-based simulation model in which a set of rules for space and time matching between a request of a client and the candidate shared taxis is identified. It considers that the client is only willing to accept a maximum deviation from his or her direct route and establishes an objective function for selecting the best candidate taxi. The function considers the minimum travel time combination of pickup and drop-off of all the pool of clients sharing each taxi while allowing to establish a policy of bonuses to competing taxis with certain number of occupants. An experiment for the city of Lisbon is presented with the objectives of testing the proposed simulation conceptual model and showing the potential of sharing taxis for improving mobility management in urban areas. Results show that the proposed system may lead to significant fare and travel time savings to passengers, while not jeopardising that much the taxi revenues. Copyright © 2014 John Wiley & Sons, Ltd.
This paper introduces a conceptual framework for the systematic analysis of multitasking behavior, and the corresponding degree of preference for doing multiple activities simultaneously (polychronicity). A typology of
multitasking is developed along the two dimensions “share of time” and “share of resources” allocated to each
task. We discuss the heterogeneous nature of resources and the importance of the time scale and time granularity
used for measuring multitasking, among other considerations. An illustrative library of examples of multitasking
situations is provided. Finally, we discuss the measurement of polychronicity as a time- and context-dependent
vector, rather than as a single score.
If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle.
421 French drivers (153 males, M = 40.2 years, age range 19–73) answered an online questionnaire.
68.1% Of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R2 = .671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender.
FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.
This paper investigates the relationship between multitasking during travel and the valuation of travel time (VOT). By building on the literature on travel time valuation and multitasking, both the attitude toward multitasking and actual multitasking behavior are hypothesized to have a potential impact on the VOT. The basic assumption is that if travel time can be used for other purposes or if it becomes more enjoyable as it is increasingly accommodated by information and communication technology tools, travel time is valued less negatively, and the VOT will be lower. To test these hypotheses, a stated preference survey was carried out among commuters in the Dutch Eindhoven region. With estimated discrete choice models, the analyses indicate that monochronic individuals, who dislike engaging in activities simultaneously, have a higher VOT, as one would expect. Commuters who listen to music while commuting have a lower VOT. Commuters who read for their work while commuting have a higher VOT, probably related to a more task-oriented attitude. Overall, the findings suggest that actual multitasking behavior and attitudes toward multitasking have a significant impact on the VOT and cannot be neglected in policy analyses.
Deployment of advanced technologies has enabled wireless internet access for commuters on various transportation modes. Such networked environments have enabled riders to engage in productive activities in transit. The ability to perform activities while traveling, especially paid work, may significantly affect the value of travel time (VOTT), with potential impacts on mode choice and commute patterns. In this study, we develop a model of the VOTT grounded in utility theory and activity choice analysis. We use an efficiency factor which represents the ratio of the efficiency of working on transit to the efficiency of working at the workplace. This efficiency factor is used extensively in our models. Internet connection is expected to increase factor efficiency by providing access to real time information and enhanced communication. The model developed is used to explore the effects on VOTT of working in an enhanced networked environment while commuting. The results show that utility increases and VOTT decreases with increase in the efficiency of work while in transit, as is intuitive. An indirect utility function has been derived to represent travel on modes with internet access. The derivation permits an elegant introduction of internet access as an attribute in utility based choice models. Finally, the proposition that internet access may influence mode choice is corroborated by a survey conducted in June 2005 on Capitol Corridor trains.
We provide a framework of alternative underlying explanations and hypotheses related to the value of travel time changes caused by the widespread adoption of autonomous vehicles (AVs). This framework supports a scenario-based approach for infrastructure planning based on various assumptions about the value of AV travel time. The main focus of this paper is on how the value of time may remain unchanged or even increase with the advent of AVs. This is in sharp contrast to existing views about how meaningful positive utility will be associated with traveling in AVs. In future, as field data reveals behavioral changes related to AV adoption, the most appropriate hypothesis within the framework will be validated and refined for future planning and system management. The framework proposed in this paper is supported by justifications from existing and emerging theoretical concepts and hypotheses, as well as a meta-analysis extracted from contemporary but isolated reports in the literature about the possibility that AV use will increase the value of travel time.
We explore how the hypothetical provision of Wi-Fi on transit affects the willingness of non-transit commuters (solo drivers, ridesharers, and cyclists) in Northern California (N = 1402) to switch from their current mode to public transit with internet access (PTWIA). Beyond the prima facie interpretation of our survey results, they shed light on the heterogeneously-perceived utility of hands-free travel more generally, and, as such, speak to an automated-vehicle future. We develop latent class binary choice models of the likelihood of switching to PTWIA, stratified by current commute mode. Each model identifies two latent classes, based largely on work-related attitudes: solo drivers divide into the work-oriented (22%) and pleasure-oriented (78%); ridesharers into the over-traveled monotaskers (77%) and multitasking commuters (23%); and cyclists into work-oriented (28%) and non-work-oriented (72%). Thus, non-work-oriented commuters are a sizable majority of non-transit users, and also have a much lower weighted probability of switching to PTWIA (0.17, on average) compared to the work-oriented commuters (0.48). In sum, work-friendly hands-free travel can be an appealing alternative to those who are oriented toward working (and especially on the commute), but (1) not for all of them, and (2) such people only constitute about a quarter of the non-transit commuters (in Northern California). These results provide empirical insight into the extent to which the productive use of travel time made possible by automated vehicles will be exploited by future commuters.
Many studies have begun investigating possible transportation landscapes in the autonomous vehicle (AV) era, but empirical results on longer-term decisions are limited. We address this gap using data collected from a survey designed and implemented for Georgia residents in 2017–2018. Focusing on a hypothetical all-AV future, this section of the survey included questions regarding advantages/disadvantages of AVs, short-term mode choice impacts, medium-term impacts on activity patterns, and long-term behavioral changes – specifically, whether/how AVs will influence individuals to change residential location and the number of cars in the household. We hypothesize that AVs could act in concert with attitudinal preferences to stimulate changes in these long-term decisions, and that some medium-term activity changes triggered by AVs could motivate people to relocate their residence or shed household vehicles. We applied exploratory factor analysis to measure the perceived likelihood that AVs would prompt various medium-term changes. We then included some of those measures, among other variables, in a cross-nested logit (CNL) model of the choice of the residential location/vehicle ownership bundle. Although more than half of respondents expected “no change” in their bundle, we found that younger, lower income, pro-suburban, and pro-non-car-mode individuals were more likely to anticipate changing their selections. In addition, some expected medium-term impacts of AVs influenced changes in these longer-term choices. We further applied the CNL model to two population segments (Atlanta and non-Atlanta-region residents). We found notable improvement in goodness of fit and different effects of factors across segments, signifying the existence of geography-related taste heterogeneity.
Increasing population and travel demand has prompted new efforts to model travel demand across the United States. One such model is rJourney that estimates travel demand among thousands of regions and models mode and destination choice. rJourney includes records representing 1.17 billion long-distance trips throughout the year 2010. Although inter-regional impacts caused by an increase of automated vehicles (AVs) has been investigated, there is little research on inter-regional travel and how longer distance destination and mode choices will change. Because of conveniences offered by AVs, the value of travel time of drivers is expected to fall, thus reducing the generalized cost of AV travel. To initially analyze the impacts of AVs in the United States, a new AV mode was added to a subset of the rJourney mode and destination choice models. With an initial scenario assuming an operating cost of AVs that is 118% of traditional cars, two outcomes are observed that are solely based on model results. First, the attractiveness of AVs severely digs into the airline travel market, reducing airline revenues to 53%. Second, the introduction of AVs results in a shift of destination choice, increasing travel in further distances for personal vehicles, but favoring closer distances across all modes, for an overall 6.7% decline in US passenger-miles traveled on existing long-distance trips. While this preliminary research has revealed an initial perspective on how an existing model can support AVs, the increasing availability of data as AVs emerge will refine nationwide long-distance modeling.
The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a consequence, the generalised cost of car travel may decrease, and car users may become less sensitive to travel time. By facilitating private motorised mobility, AVs may eventually impact land use and households’ residential location choices. This paper seeks to advance the understanding of the potential impacts of AVs on travel behaviour and land use by investigating stated preferences for combinations of residential locations and travel options for the commute in the context of autonomous automobile travel. Our analysis draws from a stated preference survey, which was completed by 512 commuters from the Sydney metropolitan area in Australia and provides insights into travel time valuations in a long-term decision-making context. For the analysis of the stated choice data, mixed logit models are estimated. Based on the empirical results, no changes in the valuation of travel time due to the advent of AVs should be expected. However, given the hypothetical nature of the stated preference survey, the results may be affected by methodological limitations.
Travel-based multitasking, or the performance of activities while traveling, is more feasible than ever before, as the expanding availability of shared ride services and increasing vehicle automation coincide with the ubiquity of portable information and communication technology devices. However, the question of whether, and if so, how these increasingly blurred boundaries between activities are truly helping rather than hurting us is not presently well-understood. Using an attitudinally-rich travel survey of Northern California commuters (N ≈ 2500), we develop a conceptual and empirically-based framework for studying benefits and disadvantages of travel-based multitasking. Through latent variable models of reported benefits and disadvantages of activities conducted on a recent commute, we identify constructs associated with hedonic and productive benefits, and with affective and cognitive disadvantages. This empirically-developed framework informs the definition of binary variables indicating the presence/absence of each construct for a given traveler on the commute in question. We then present two bivariate binary probit models that examine the effects of person and trip attributes such as personality traits, chosen mode, commute preferences, and activities conducted while traveling on the presence of those benefits and disadvantages, respectively. Notably, we find evidence that conditions/activities that may facilitate multitasking benefits can also simultaneously yield disadvantages; for example, several activities – conspicuously including talking on or otherwise using a phone – increase the probability of receiving benefits while also increasing the probability of experiencing cognitive disadvantages. This finding resonates with the general multitasking literature, and empirically corroborates the suggestion that travel-based multitasking may not uniformly increase trip utility.
Although the influence of compact development on travel behavior has been extensively studied over the past decades, additional empirical evidence is needed to better understand that effect. This study explores the influences of the built environment (BE) on zone-based travel patterns using the 2010 Household Travel Survey data in Korea, . We apply seemingly unrelated regression (SUR) and censored regression models to reveal relationships between the BE and zone-based travel patterns in the SMA. Results indicate that: (1) transit-oriented design promotes public transit and suppresses auto trips; (2) the rail mode is more predominant relative to the bus mode; (3) mixed land use with high-density development and gridded pattern streets have a positive impact on walking; and (4) high density and easy access to rail have positive influences on reducing distance traveled by automobiles. Our results suggest that a differentiated strategy is needed to improve urban sustainability in the SMA.
Developing sustainable transportation systems in a city can be substantially assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transit rather than private cars. Strategies for promoting these desirable transportation modes can be effectively identified based on a sound understanding of how citizens choose a travel mode. In this study, we sought to enhance this understanding by exploring factors associated with commute mode choice utilizing data from a general social survey in Seoul, South Korea. Based on the data, parametric and non-parametric statistical models based on classification tree and multilevel logistic regression approaches, respectively, were developed as a way to capture influential factors associated with the mode choice decision. The models illustrate that commuters’ socio-demographic characteristics such as income, occupation, gender, and residence duration tend to significantly influence mode choice. In addition, they showed that respondents’ attitudes and behaviors, including the amount of time spent on the internet and self-assessed social status, can be strongly associated with mode choice. This study is also meaningful in that it demonstrates the potential applicability of general social survey data for investigating travel behavior considering various factors that are rarely included in general transportation surveys.
Many experts believe the transport system is about to change dramatically. This change is due to so-called fully-automated vehicles (AVs). However, at present, there are numerous important knowledge gaps that need to be solved for the successful integration of AVs in our transport systems, in particular regarding the impacts of AVs on travel demand. For instance, full automation will enable passengers to perform other, non-driving, related tasks while traveling to their destination. This may substantially change the way in which passengers experience traveling by car, and, in turn, may lead to considerable changes in the so-called Value of Travel Time (VOTT). Many experts anticipate the VOTT to decrease substantially due to AVs. However, the extent to which VOTT will change is currently far from clear. This study aims to develop new insights on the potential impacts of fully automated vehicles on the VOTT for commute trips. To do so, we first look at the existing microeconomics theory on the perceived VOTT and analyze the expected changes accrued from the effect of working and having leisure in an AV. We conclude that the VOTT of a work vehicle should be lower than what is experienced today in a conventional vehicle but the leisure one could stay the same. Then we conduct a stated choice experiment, specifically designed and administered for measuring the VOTT, and analyze these data using discrete choice models (DCMs). In total, we collected data from about 500 respondents. In the experiment, respondents were presented choice tasks consisting of three alternatives: an AV with office interior, an AV with leisure interior, and a conventional car. The same experiment was also given to another sample of respondents but this time describing a chauffeur-driven vehicle. Overall we find the average VOTT for an AV with an office interior (5.50€/h) to be lower than the VOTT for the conventional car (7.47€/h), however the AV with leisure interior is not found to decrease the value of time (8.17€/h) which confirms the theoretical results. The VOTT for the chauffeur experiment is systematically lower than for the AV experiment which we attribute to some distrust that people have regarding the AVs.
Autonomous vehicles (AVs) are expected to reshape travel behaviour and demand in part by enabling productive uses of travel time—a primary component of the “positive utility of travel” concept—thus reducing subjective values of travel time savings (VOT). Many studies from industry and academia have assumed significant increases in travel time use and reductions in VOT for AVs. In this position paper, I argue that AVs’ VOT impacts may be more modest than anticipated and derive from a different source. Vehicle designs and operations may limit activity engagement during travel, with AV users feeling more like car passengers than train riders. Furthermore, shared AVs may attenuate travel time use benefits, and productivity gains could be limited to long-distance trips. Although AV riders will likely have greater activity participation during travel, many in-vehicle activities today may be more about coping with commuting burdens than productively using travel time. Instead, VOT reductions may be more likely to arise from a different “positive utility”—subjective well-being improvements through reduced stresses of driving or the ability to relax and mentally transition. Given high uncertainty, further empirical research on the experiential, time use, and VOT impacts of AVs is needed.
This study looks into the multitasking patterns for the developing world, while providing empirical evidences of the effect of multitasking on the value of travel time savings (VTTS). The multitasking behaviour during travel was studied, ascertaining the effect of various socio-economic variables, access to information and communication technologies (ICT), and travel related factors. Travel diary data was collected across the city of Mumbai, India for 1123 individuals capturing their revealed preferences on travel and multitasking during travel. It was observed that having a smartphone with an internet usage of more than one GB data had positive significant impacts on ICT dependent multitasking activities. In addition, the proportion of no-activity also significantly reduced with higher access to ICT. It was observed that the VTTS reduced by 26% for individuals who performed multitasking. Furthermore, for reading on a mobile device, usage of social media, messaging or talking to someone on phone, and for gaming, the VTTS reduced by 25%, 37%, and 16% respectively. Findings were used to make cross country comparisons and discuss policy implications.
Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.
Autonomous Vehicle (AV) technology is quickly becoming a reality on US roads. Testing on public roads is currently undergoing, with many AV makers located and testing in Silicon Valley, California. The California Department of Motor Vehicles (CA DMV) currently mandates that any vehicle tested on California public roads be retrofitted to account for a back-up human driver, and that data related to disengagements of the AV technology be publicly available. Disengagements data is analyzed in this work, given the safety-critical role of AV disengagements, which require the control of the vehicle to be handed back to the human driver in a safe and timely manner. This study provides a comprehensive overview of the fragmented data obtained from AV manufacturers testing on California public roads from 2014 to 2017. Trends of disengagement reporting, associated frequencies, average mileage driven before failure, and an analysis of triggers and contributory factors are here presented. The analysis of the disengagements data also highlights several shortcomings of the current regulations. The results presented thus constitute an important starting point for improvements on the current drafts of the testing and deployment regulations for autonomous vehicles on public roads.
This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:
(1) Continue to commute using a regular car that you have in your possession.
(2) Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
(3) Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.
A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.
Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.
Although vehicle automation technology has experienced rapid gains in recent years, little research has been conducted on the potential impacts of self-driving vehicles on long-distance personal travel, a major area of travel growth in the United States. Automated vehicles (AVs) offer flexible trip time and origin destination pairings at travel time costs perceived to be lower; thus, AVs have the potential to dramatically change how travelers pursue long-distance tours. This study analyzed travel surveys and then developed a statewide simulation experiment of long-distance travel to anticipate the impact of AVs on long-distance travel choices. The research explored the Michigan State 2009 Long-Distance Travel Survey and estimated a long-distance trip generation model and a modal-agnostic long-distance mode-choice model. These models were applied in a statewide simulation experiment in which AVs were introduced as a new mode with lower perceived travel time costs (via lowered values of travel time en route) and higher travel costs (to reflect the initially high price of complete vehicle automation). This experiment highlighted the potential shifts in mode choices across different trip distances and purposes. For travel of less than 500 mi, AVs tended to draw from the use of personal vehicles and airlines equally. Airlines were estimated to remain preferred for distances greater than 500 mi (43.6% of trips greater than 500 mi were by air, and 70.9% of trips greater than 1,000 mi were by air). Additionally, at certain AV travel time valuations, travel cost was not a significant factor. The findings showed that as the perceived travel time benefits from hands-free travel rose, monetary costs became less important.
Gender difference in the attitude toward technology use has long been a concern in education. The last meta-analysis on this issue covered the empirical studies up to about 20 years ago. Since then, technology use has increased exponentially, and many more empirical studies have examined this issue, but showed inconsistent findings. As a result, there is a lack of clear understanding about if such gender difference still persists. The purpose of this research is to re-examine this issue by meta-analyzing the empirical research studies on this issue in the last two decades, and to examine the potential moderators that may have contributed to the heterogeneity of the research findings. A total of 50 articles from 1997 to 2014 were identified and used in this meta-analysis. The findings indicated that males still hold more favorable attitudes toward technology use than females, but such different would be characterized as small effect sizes. The comparison between this study and the last meta-analysis of about two decades ago suggested that there was only minimal reduction in the gender attitudinal gap in general. But when the general attitude was broken down to different dimensions of attitude, the present study showed a reduction of gender difference in the dimension of Affect and Self-efficacy, but not in the dimension of Belief. The limitations of the study were noted, and the implications and future research directions were discussed.
Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.
This paper reports the most extensive meta-analysis of values of time yet conducted, covering 3109 monetary valuations assembled from 389 European studies conducted between 1963 and 2011. It aims to explain how valuations vary across studies, including over time and between countries. In addition to the customary coverage of in-vehicle time in review studies, this paper covers valuations of walk time, wait time, service headway, parking space search time, departure time switching, time in congested traffic, schedule delay early and late, mean lateness and the standard deviation of travel time. Valuations are found to vary with type of time, GDP, distance, journey purpose, mode, the monetary numeraire and a number of factors related to estimation. Model output values of time compare favourably with earnings data, replicate well official recommended values obtained from major national studies, and are transferable across countries. These implied monetary values serve as very useful benchmarks against which new evidence can be assessed and provide parameters and values for countries and contexts where there is no other such evidence.
In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.
Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.
This paper reviews British evidence regarding the value of travel time available from models developed since 1980. There are two main aspects to the review. First, a regression model has been developed to explain variations in the value of lime across studies. Second, the author reports on a review of variations in the value of time within specific studies. The review also places the research in historical perspective, with reference to work conducted elsewhere.
We derive values of travel time savings (VOT) for the Madrid-Barcelona corridor, linking the two largest cities in Spain, based on the estimation of discrete choice models among the main public transport services in the corridor: air transport, high speed rail (HSR) and bus. The new HSR alternative (which started to operate in February 2008) competes directly with one of the densest airline domestic markets in the world, and its introduction produced substantial improvements in level of service, achieving reductions in travel time of more than 50% over the conventional train.
Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.
After decades of study, the value of travel time remains incompletely understood and ripe for further theoretical and empirical investigation. Research has revealed many regularities and connections between willingness to pay for time savings and other economic factors including time of day choice, aversion to unreliability, labor supply, taxation, activity scheduling, intra-household time allocation, and out-of-office productivity. Some of these connections have been addressed through sophisticated modeling, revealing a plethora of reasons for heterogeneity in value of time rooted in behavior at a micro scale. This paper reviews what we know and what we need to know. A recurrent theme is that the value of time for a particular travel movement depends strongly on very specific factors, and that understanding how these factors work will provide new insights into travel behavior and into more general economic choices.
The value of travel time savings (VTTS) is a critical parameter in transport project appraisal and through its application produces the dominating user benefit, typically 60 per cent of traditionally quantified user benefits. Beesley's work in the 1960s laid the foundation for much of the subsequent applied research, especially in respect of measurement and interpretation. This paper revisits Beesley's contribution in the context of the 1960s and shows the subsequent development of his ideas. © The London School of Economics and the University of Bath 2001
The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.
The value of travel time savings (VTTS) is the monetary value attached to the possibility to save a determined amount of travel
time. VTTS is also the most important benefit category aimed at justifying investments in transport infrastructures by public
administrations. Hence VTTS played a significant role in various economic studies, both analytical and empirical. The present
paper introduces a brief history of meta-analysis and describes the microeconomic formula used in VTTS estimations. It also
provides a meta-analytical estimation of a selection of empirical studies, emphasizing the similarities and the differences
between European and North-American observations.