Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes

Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.     

Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies

ABSTRACT

Automated vehicles (AVs) could completely change mobility in the coming years and decades. As AVs are still under development and gathering empirical data for further analysis is not yet possible, existing studies mainly applied models and simulations to assess their impact. This paper provides a comprehensive review of modelling studies investigating the impacts of AVs on travel behaviour and land use. It shows that AVs are mostly found to increase vehicle miles travelled and reduce public transport and slow modes share. This particularly applies to private AVs, which are also leading to a more dispersed urban growth pattern. Shared automated vehicle fleets, conversely, could have positive impacts, including reducing the overall number of vehicles and parking spaces. Moreover, if it is assumed that automation would make the public transport system more efficient, AVs could lead to a favouring of urbanisation processes. However, results are very sensitive to model assumptions which are still very uncertain (e.g. the perception of time in AVs) and more research to gain further insight should have priority in future research as well as the development of the models and their further adaptation to AVs.     

Could you also have made this trip by another mode? An investigation of perceived travel possibilities of car and train travellers on the main travel corridors to the city of Amsterdam,The Netherlands

We investigated perceived travel possibilities (or subjective choice-sets, consideration-sets) of car and train travellers on the main corridors to the city of Amsterdam, The Netherlands, and associations with traveller and trip characteristics. We conducted secondary analysis on a survey sample consisting of 7950 train and 19,232 car travellers. Forty-five percent of train travellers had a car in their objective choice-set, 27% of them would however never use it for this trip. Trip destination city centre, trip purpose, paying for the trip, public transport commitment, traffic congestion and parking problems were associated with consideration of car as alternative. Forty-two percent of car travellers had public transport in their subjective choice-set. The ratio between perceived public transport and objective car travel time stood out as determinant of consideration-sets, next to destination city centre, trip purpose, travel time and private versus company car ownership. On average, car travellers’ perceptions of public transport travel time exceeded objective values by 46%. We estimated that if perceptions would be more accurate, two out of three car travellers that currently do not see public transport as an alternative would include it in their choice-set, and use it from time to time. This effect has strong theoretical and policy implications.     

Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

A Time-use Model for the Automated Vehicle-era

Automated Vehicles (AVs) offer their users a possibility to perform new non-driving activities while being on the way. The effects of this opportunity on travel choices and travel demand have mostly been conceptualised and modelled via a reduced penalty associated with (in-vehicle) travel time. This approach invariably leads to a prediction of more car-travel. However, we argue that reductions in the size of the travel time penalty are only a crude proxy for the variety of changes in time-use and travel patterns that are likely to occur at the advent of AVs. For example, performing activities in an AV can save time and in this way enable the execution of other activities within a day. Activities in an AV may also eliminate or generate a need for some other activities and travel. This may lead to an increase, or decrease in travel time, depending on the traveller’s preferences, schedule, and local accessibility. Neglecting these dynamics is likely to bias forecasts of travel demand and travel behaviour in the AV-era. In this paper, we present an optimisation model which rigorously captures the time-use effects of travellers’ ability to perform on-board activities. Using a series of worked out examples, we test the face validity of the model and demonstrate how it can be used to predict travel choices in the AV-era.     

Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively?

ABSTRACT

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.     

A disaggregate study of urban rail transit feeder transfer penalties including weather effects

Transfers between urban rail transit (URT) and its feeder modes represent a considerable barrier to its ridership and the network-wide usage of public transit. The aim of this research is to quantify the time-independent transfer penalty between URT system and feeder modes and to explore its variability by different factors. Based on Melbourne URT origin and destination survey data, this study focused on URT access and egress journeys and estimated URT feeder transfer penalties by formulating feeder mode choice models. With three-hourly weather data and demographical data introduced, this paper conducted disaggregate analyses to investigate the variability of URT feeder transfer penalty across weather conditions, trip types and individual characteristics. According to the model estimation results, the values of transfer penalty vary according to the direction of transfer and the preference ordering for different transfer combinations is URT-tram, URT-bus, tram-URT, bus-URT and auto-URT. It found that local weather elements in terms of air temperature and precipitation are significant factors resulting in the variability of the transfer perception by URT travellers. Transfer penalties for access journeys increase with the rise of air temperature. The non-linear effects of precipitation on URT feeder transfer penalties were observed. In addition, commuters perceive smaller transfer penalties than other travellers for all of the transfer combinations except for bus-URT transfers. Travelers from remote areas perceive smaller transfer penalties for access trips. Travellers’ loyalty to public transit restrains transfer penalties. The male travellers perceive higher transfer penalties than the female. The elderly travellers impose low transfer penalties to access journeys but high transfer penalties for egress journeys. Finally, the paper explored policy implications and details areas for future research.     

Procedures for planning multi-leg journeys with fixed-route and demand-responsive passenger transport services

This paper describes journey-planning procedures designed for use in a traveller information system covering fixed-schedule and demand-responsive public transport modes. The task is to construct a sequence of journey-legs to meet a traveller’s requirements with the least possible generalised cost, subject to time-window and other constraints. A journey may be carried out in a single leg by walking or by taking a taxi all the way from the origin to the destination, or by a sequence of one or more legs carried by public transport services sandwiched between walked segments connecting an initial pickup and final setdown stop. The public transport services may include fixed-route modes such as bus and train, and demand-responsive services running between fixed points. The main planning procedures are a high-level request-broker and a branch and bound procedure to handle multi-legged journeys; the request-broker also invokes a fleet-scheduling module to obtain bookings on demand-responsive services. The paper describes planning conditions, the planning procedures, and reduction techniques that are used to obtain acceptable computational performance. Tests with simulated demand suggest that the procedures are well suited for use in a real-time traveller information system.     

Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach

The paper presents valuing of qualitative and quantitative travel attributes influencing the attractiveness of suburban train service in Mumbai city, India. A stated preference experiment is designed to capture the data of sub-urban train mode choice behavior. The behavioral data are analyzed using different modeling techniques such as multinomial logit (MNL) and mixed logit (ML). In ML model, the random parameters are assumed to follow constrained triangular distribution, where mean equals its spread. The decomposition of preference heterogeneity around the mean estimate of random parameter is also investigated using ML model. The study shows the influence of headway time and train ride time associated with a particular crowding level (expressed in density of standing passengers/m²) in choosing the sub-urban train mode by calculating their willingness-to-pay (WTP) values and highlights the importance of WTP for addressing policy issues in the reduction of in-vehicle crowding level. The present study documents new findings of the effect of crowding level on train ride time in the context of a developing country and suggests some important directions for future suburban train transport crowding valuation research.     

“I can do perfectly well without a car!”

This article presents the results of a study exploring travellers’ preferences for middle-distance travel using Q-methodology. Respondents rank-ordered 42 opinion statements regarding travel choice and motivations for travel in general and for car and public transport as alternative travel modes. By-person factor analysis revealed four distinct preference segments for middle-distance travel: (1) choice travellers with a preference for public transport, (2) deliberate-choice travellers, (3) choice travellers with car as dominant alternative, and (4) car-dependent travellers. These preference segments differ in terms of the levels of involvement and cognitive effort in travel decision making, the travel consideration-set and underlying motivations. The study showed that for most people there is more to travel than getting from point A to point B, and that there is considerable heterogeneity in middle-distance travel preferences. Policy implications for reducing the need for travel and promoting a modal shift from car to other travel modes are discussed.     

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

Transportation - Automated vehicles (AVs) are expected to change personal mobility in the near future. Most studies on the mobility impacts of AVs focus on fully automated (SAE L5) vehicles, but...     

The impact of extreme weather conditions on long distance travel behaviour

This paper examines traveller attitudes and responses towards disruption from weather and natural events. An internet-based travel behaviour survey was conducted with more than 2000 respondents in London and Glasgow. Of these respondents, 740 reported information on over 1000 long distance trips affected by extreme weather and natural events over the previous three years. Results show respondents are generally cautious towards travelling during extreme weather events. For a slight majority in the case of air and public transport, and a greater one in the case of car, travellers did not considerably alter their travel plan following the disruption. This was explained not only by less disruptive weather conditions (with heavy snow and volcanic ash being the most disruptive) and impact, but also by the relative importance of their trips. Differences between transport modes were not substantial. Business trips sometimes appeared to give travellers more flexibility, some other times not. Origin and destination did have an impact on reaction, as well as the presence of children whilst travelling. Mixed results were obtained about socio-economic and attitudinal variables. Age in particular did not appear to have a significant effect. Whilst most respondents did acknowledge no external influence in their decision, results showed an important contribution of transport organisation staff, as well as home and mobile internet technology. A limited but still considerable number of respondents indicated their closest friends/relatives as the main influence of their decisions. The results will help planners deploy strategies to mitigate the negative effects of weather related disruptions.     

Knowledge-based systems for transport network analysis: A fifth generation perspective on transport network problems

This paper considers the formative steps in the development of an expert system for route selection in transport networks. It discusses the application of knowledge-based system (KBS) technology to the production of an expert system to provide route guidance information for some groups of urban travellers. There is a focus on the possibilities for applying a ‘fifth generation’ programming language, such as PROLOG, to the solution of network path selection problems, and the maintenance of a dynamic network database. There are significant arguments both for and against the use of the fifth generation languages, and the alternatives are discussed in speculative terms, on the basis of trials and experiments with PROLOG. The paper develops the specifications for a route guidance KBS for urban road travel, noting that one particular concern is that of providing the most suitable advice to a given traveller, rather than the system-wide rating of ‘best’ advice. Driver attitudes, behaviour, preferences, and vehicle characteristics differ widely, and the degree of satisfaction with any advice provided would depend largely on how closely the expert system could match the preferences of the individual traveller. Thus the system would need to gauge the characteristics of the user as well as those of the network under consideration. Further, prototype route guidance KBS are probably better directed at some clearly defined groups of urban travellers, such as commercial vehicle operations, rather than as community-wide systems.     

Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment

This paper develops an integrated model to characterize the market penetration of autonomous vehicles (AVs) in urban transportation networks. The model explicitly accounts for the interplay among the AV manufacturer, travelers with heterogeneous values of travel time (VOTT), and road infrastructure capacity. By making in-vehicle time use more leisurely or productive, AVs reduce travelers’ VOTT. In addition, AVs can move closer together than human-driven vehicles because of shorter safe reaction time, which leads to increased road capacity. On the other hand, the use of AV technologies means added manufacturing cost and higher price. Thus, traveler adoption of AVs will trade VOTT savings with additional out-of-pocket cost. The model is structured as a leader (AV manufacturer)-follower (traveler) game. Given the cost of producing AVs, the AV manufacturer sets AV price to maximize profit while anticipating AV market penetration. Given an AV price, the vehicle and routing choice of heterogeneous travelers are modeled by combining a multinomial logit model with multi-modal multi-class user equilibrium (UE). The overall problem is formulated as a mathematical program with complementarity constraints (MPCC), which is challenging to solve. We propose a solution approach based on piecewise linearization of the MPCC as a mixed-integer linear program (MILP) and solving the MILP to global optimality. Non-uniform distribution of breakpoints that delimit piecewise intervals and feasibility-based domain reduction are further employed to reduce the approximation error brought by linearization. The model is implemented in a simplified Singapore network with extensive sensitivity analyses and the Sioux Falls network. Computational results demonstrate the effectiveness and efficiency of the solution approach and yield valuable insights about transportation system performance in a mixed autonomous/human driving environment.     

A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection

The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy.     

Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria,Australia

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

Automated vehicles: exploring possible consequences of government (non)intervention for congestion and accessibility

ABSTRACT

Academic research on automated vehicles (AVs) has to date been dominated by the fields of engineering and computer science. Questions of how this potentially transformative technology should be governed remain under-researched and tend to concentrate on governing the technology’s early development. We respond in this paper by exploring the possible longer-term effect of government (lack of) intervention.

The paper tests the hypothesis that a “laissez-faire” governance approach is likely to produce less desirable outcomes in a scenario of mass uptake of AVs than would a well-planned set of government interventions. This is done using two prominent themes in transport policy – traffic flow and accessibility – in a scenario of high market penetration of Level-5 automated vehicles in capitalist market economies. The evidence used is drawn from a literature review and from the findings of a set of workshops with stakeholders.

We suggest that a laissez-faire approach will lead to an increase in traffic volume as a result of a growing population of “drivers” and a probable increase in kilometres driven per passenger. At the same time, the hoped-for increases in network efficiency commonly claimed are not guaranteed to come about without appropriate government intervention. The likely consequence is an increase in congestion. And, with respect to accessibility, it is likely that the benefits of AVs will be enjoyed by wealthier individuals and that the wider impacts of AV use (including sprawl) may lead to a deterioration in accessibility for those who depend on walking, cycling or collective transport.

We consider the range of possible government intervention in five categories: Planning/land-use; Regulation/policy; Infrastructure/technology; Service provision; and Economic instruments. For each category, we set out a series of interventions that might be used by governments (at city, region or state level) to manage congestion or protect accessibility in the AV scenario described. Many of these (e.g. road pricing) are already part of the policy mix but some (e.g. ban empty running of AVs) would be new. We find that all interventions applicable to the management of traffic flow would also be expected to contribute to the management of accessibility; we define a small number of additional interventions aimed at protecting the accessibility of priority groups.

Our general finding is that the adoption of a package of these interventions could be expected to lead to better performance against generic traffic-flow and accessibility objectives than would a laissez-faire approach, though questions of extent of application remain.

In our conclusions, we contrast laissez-faire with both anticipatory governance and “precautionary” governance and acknowledge the political difficulty associated with acting in the context of uncertainty. We point out that AVs do not represent the first emerging technology to offer both opportunities and risks and challenge governments at all levels to acknowledge the extent of their potential influence and, in particular, to examine methodically the options available to them and the potential consequences of pursuing them.     

Modelling route choice behaviour in multi-modal transport networks

The paper presents new findings on the influence of multi-modal trip attributes on the quality and competitiveness of inter-urban multi-modal train alternatives. The analysis covers the entire trip from origin to destination, including access and egress legs to and from the train network. The focus is on preferences for different feeder modes, railway station types and train service types as well as on the relative influence of time elements and transfer penalties. Data from dedicated surveys are used including individual objective choice sets of 235 multi-modal homebound trips in which train is the main transport mode. The observed trips have origins and destinations within the Rotterdam–Dordrecht region in The Netherlands with an average total trip time of 50 minutes. Hierarchical Nested Logit models are estimated to take account of unobserved similarities between alternatives at the home-end and the activity-end of the trip respectively, resulting in two-level nesting structures which differentiate between intercity (IC) and non-intercity railway station types at the upper level and between transit and private access modes at the lower level. In order to reflect the multi-dimensional structure of the data a more advanced so-called Multi-Nested GEV model according to the Principles of Differentiation has been estimated which significantly improves the explanatory power and stresses the importance of the home-end of the multi-modal trip.     

Barriers to Passenger Rail Use: A Review of the Evidence

Research has shown that even when rail travel is the most cost-effective mode of transport for a particular journey, many travellers will still choose other modes. This indicates the existence of non-financial barriers to rail use, and this paper reviews the evidence on the importance of such barriers, focusing particularly on the UK but also considering research from other countries. A total of 37 distinct barriers were identified, and these can be divided into “hard”, “soft” and “complementary” factors. Travellers are unlikely to consider these barriers individually, viewing them instead as a package, which can make it difficult to identify which barriers are most significant. In many cases, all barriers which exist for a particular traveller will need to be addressed before mode shift occurs. After considering the relative importance of the different barriers, the paper concludes by making some suggestions as to the most effective ways in which these barriers can be overcome and mode shift to rail achieved. This has key implications for transport policy, as it can inform the targeting of the limited funds available to influence travel behaviour and increase the sustainability of travel patterns.