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In recent years there has been substantial growth in the prevalence of ad-hoc data exchange arrangements between local traffic authorities and commercial traffic information service providers. Although these arrangements are widely regarded as mutually beneficial, in fact to date, no comprehensive analysis exists of the operation of this information market, nor of its consequences for the different market participants involved. To address this gap, this paper presents a new framework which enables the analysis of the long-term outcomes of various collaboration schemes for traffic information service providers, local traffic authorities and network users. The framework is based on a bi-level non-cooperative Nash game in which the upper level represents the data exchange arrangements between local traffic authorities and commercial traffic information service providers and the lower level represents the impact of the provided information services on network users. The framework is flexible and can accommodate a variety of different market structures and commercial behaviours. The game theoretic model is formulated and solved as an equivalent equilibrium problem with equilibrium constraints. Numerical experiments are undertaken using this framework to explore the consequence of a number of commonly observed real-world data exchange arrangements. This analysis leads to three general conclusions. First, the results suggest that when a local traffic authority seeks only to minimise the total network travel time and offers free collaboration schemes, it should collaborate with all the cooperating service providers in the market. Second, if conversely, a local traffic authority seeks only to maximise its revenue from selling its data to service providers, it should be aware that its revenue does not always increase by selling the data to more service providers, since the willingness of service providers to pay for data declines as more providers are granted access. And finally, if a local traffic authority seeks to establish paid schemes that balance the benefit to network users and its own revenue benefits, then circumstances can easily arise in which these two objectives are in conflict with one another. [ABSTRACT FROM AUTHOR]

The nature of shopping activity is changing in response to innovation in retailing and the growth in online channels. There is a growing interest from transport researchers, policy makers, marketing and retail businesses in understanding the implications of this change. However, existing tools and techniques developed for analysing behaviour in traditional retail environments do not adequately represent emerging complexities resulting from digital innovation. In this paper, we advance existing destination and mode choice models by incorporating online channels in a unified framework. This is a critical extension to existing transport literature on destination choice which largely ignores online activity. Specifically, we develop discrete choice models using elemental store (including both online and in-store) alternatives for joint choice of channel, store, and travel mode. We demonstrate the use of a widely-accepted consumer panel dataset with minor modifications, for the first time in transport research, together with API based data mining tools that offer great potential for enrichment. The analysis focuses on grocery shopping and uses consumer data collected from two selected boroughs in London; results from multinomial logit and nested logit estimations are reported. The extension presented here provides the tools to quantify the effects of increased online shopping on traditional store formats and travel patterns. Our results showed virtual alternatives currently offer an attractive substitute among early adopters for large basket shopping mostly for high income groups. This might suggest a significant reduction in shopping trips to hypermarkets often associated with large basket shopping potentially leading to store closures. Online deliveries mostly draw from driving trips and less so from walking and public transport trips. The present study also confirmed previous findings related to smaller stores and longer travel distances being associated with declining utilities, agglomeration and competition significantly influencing store choice. [ABSTRACT FROM AUTHOR]

This paper proposes a novel concept of congestion pricing based on voluntary peer-to-peer exchange of money between motorists in exchange for one ceding priority to another in a traffic stream. While in the classical congestion charging paradigm payments are compulsory and flow only towards the system operator, in the proposed marketplace participation is voluntary and motorists directly compensate each other. A particular motorist may find that he/she is a ‘payer’ at certain points in a given journey and a ‘payee’ at others. Humans would not be expected to successfully seek, negotiate and execute a continuous series of peer-to-peer trades involving micro-payments while also handling the cognitively-demanding task of driving; real-world implementation will therefore require vehicles operating under fully-automated control in both the longitudinal and lateral dimensions during the time periods that they seek and engage in trades. The automated vehicle control algorithms must be sufficiently intelligent and adaptable to enable alternative maneuvers on short timescales, given the inherent uncertainty of whether or not a potential trade will in fact be executed. The peer-to-peer trading would be executed algorithmically, subject to strategic-level guidance given by a vehicle’s occupant(s) regarding the occupant’s relative valuation of money and priority in the traffic stream. In this paper we detail the prospective marketplace and present a simple simulation model to expose its properties. We show that the proposed peer-to-peer marketplace could lead to both desirable and undesirable outcomes; which of these would be predominant is a matter requiring empirical study. The paper concludes with a discussion of further research needs to refine and develop these concepts into practice. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies (ICT) has been argued to affect time use patterns in a variety of ways, with consequent impacts on travel behaviour. While there exists a significant body of empirical studies documenting these effects, theoretical developments have lagged this empirical work and in particular, microeconomic time allocation models have not to date been fully extended to accommodate the implications of an increasingly digitised society. To address this gap, we present a modelling framework, grounded in time allocation theories and the goods–leisure framework, for joint modelling of the choice of mode of activity (physical versus tele-activity), travel mode and route, and ICT bundle. By providing the expression for a conditional indirect utility function, we use hypothetical scenarios to demonstrate how our framework can conceptualise various activity–travel decision situations. In our scenarios we assume a variety of situations such as the implications of severe weather, the introduction of autonomous vehicles, and the interaction between multiple decision makers. Moreover, our approach lays the microeconomic foundations for deriving subjective values of ICT qualities such as broadband speed or connection reliability. Finally, we also demonstrate the means by which our framework could be linked to various data collection protocols (stated preference exercises, diaries of social interactions, laboratory experiments) and modelling approaches (discrete choice modelling, hazard-based duration models). [ABSTRACT FROM AUTHOR]

An extensive body of literature addresses the income elasticity of road traffic, in which income is typically treated as a homogenous quantity. Here we report evidence of heterogeneity in cross-sectional estimates of the elasticity of vehicle-kilometres of travel (VKT) with respect to income, when household income is disaggregated on the basis of income source. The results are generally intuitive, and show that the cross-sectional income elasticity of road traffic is not homogeneous as is typically specified in transport planning models. We show that in a number of circumstances the cross-sectional elasticity with respect to aggregate household income is of the opposite sign in comparison to more refined estimates of elasticity disaggregated by income source. If further research confirms that the elasticities we report here are causal in nature, neglecting the elemental effects could result in misleading results affecting practical infrastructure-investment and policy decisions, particularly as the mix of income sources shifts (e.g. if, as society ages, pension income increases as a share of all income). These results are of interest to both researchers and forecasters of travel demand, as well as designers of future travel survey instruments; the latter group must decide how to generate data about respondents’ income. Current expert guidance is to collect a single estimate of aggregate income at the household level. Future travel survey design choices will bound the analyses that can be supported by the resulting survey data, and therefore methodological research to re-visit the trade-offs associated with such choices is warranted. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a model of activity and trip scheduling that combines three elements that have to date mostly been investigated in isolation: the duration of activities, the time-of-day preference for activity participation and the effect of schedule delays on the valuation of activities. The model is an error component discrete choice model, describing individuals’ choice between alternative workday activity patterns. The utility function is formulated in a flexible way, applying a bell-shaped component to represent time-of-day preferences for activities. The model was tested using a 2001 data set from the Netherlands. The estimation results suggest that time-of-day preferences and schedule delays associated with the work activity are the most important factors influencing the scheduling of the work tour. Error components included in the model suggest that there is considerable unobserved heterogeneity with respect to mode preferences and schedule delay. [Copyright &y& Elsevier]

Abstract: In this paper, we discuss some of the issues that arise with the computation of the implied value of travel-time savings in the case of discrete choice models allowing for random taste heterogeneity. We specifically look at the case of models producing a non-zero probability of positive travel-time coefficients, and discuss the consistency of such estimates with theories of rational economic behaviour. We then describe how the presence of unobserved travel-experience attributes or conjoint activities can bias the estimation of the travel-time coefficient, and can lead to false conclusions with regards to the existence of negative valuations of travel-time savings. We note that while it is important not to interpret such estimates as travel-time coefficients per se, it is nevertheless similarly important to allow such effects to manifest themselves; as such, the use of distributions with fixed bounds is inappropriate. On the other hand, the use of unbounded distributions can lead to further problems, as their shape (especially in the case of symmetrical distributions) can falsely imply the presence of positive estimates. We note that a preferable solution is to use bounded distributions where the bounds are estimated from the data during model calibration. This allows for the effects of data impurities or model misspecifications to manifest themselves, while reducing the risk of bias as a result of the shape of the distribution. To conclude, a brief application is conducted to support the theoretical claims made in the paper. [Copyright &y& Elsevier]

• We propose an analytical framework to account for correlated effects within a social network. • We quantify the correlated effects by combining individual's peer attitudes and social proximity. • The social influence variable has been systematically tested in different hybrid choice model specifications in the context of EV purchase preferences. • An 'open to innovation' person is likely to be part of a social network in which peers are also 'open to innovation'. • Social proximity and a larger number of 'open to innovation' peers can positively affect the preference for EVs. While the importance of social influence on transport-related choices is commonly acknowledged within the transport and travel behaviour research community, there remain several challenges in modelling influence in practice. This paper proposes a new analytical approach to measure the effects of attitudes of peers on the decision making process of the individual. Indeed, while most of the previous literature focused its attention on capturing conformity to a certain real or hypothetical choice, we investigate the subtle effect of attitudes that underlies this choice. Specifically, the suggested measure enables us to model the correlated effect that might indirectly affect the individual's choice within a social group. It combines detailed information on the attitudes in the individual's social network and the social proximity of the individuals in the social network. To understand its behavioural implications on the individual's choice, the individual's peer attitude variable is tested in different components of a hybrid choice model. Our results show that the inclusion of this variable indirectly affects the decision making process of the individual as the peers' attitudes are significantly related to the latent attitude of the individual. On the other hand, it does not seem to directly affect the utility of an alternative as a source of systematic heterogeneity nor does it work as a manifestation of the latent variable, i.e. as an indicator. [ABSTRACT FROM AUTHOR]