Search

Free-floating carsharing (FFCS) systems are characterised by volatile fleet distribution as well as customers' heterogeneous price sensitivity and spatiotemporal flexibility. There is thus an opportunity for operators to employ dynamic pricing to manage various aspects of fleet allocation: which customer is provided which vehicle, at what time and price, and the agreed pick-up and drop-off location. While there are emerging examples of dynamic pricing in FFCS, there is as yet no general framework for the interaction of consumer and operator behaviours in this context, most particularly consumer response to the inherent risks and uncertainties in the journey characteristics noted above. In this study, we propose a choice-based framework for modelling the supply/demand interaction, drawing on behavioural models of decision-making in risky choice contexts and empirical stated-choice data of user preferences in a dynamically priced FFCS market. In addition to the 'spot market' mechanism of dynamic pricing, the proposed framework is capable of evaluating operator strategies of allowing (at an agreed price) customers to make guaranteed advance reservations. We demonstrate that this approach allows the system operator to set an optimal pricing strategy regardless of whether user risk preferences are risk-seeking or risk-averse. We also demonstrate the applicability of the proposed framework when the operator seeks to maximise revenue (as with a private operator) vs social welfare (as with a public operator). In the case study which employs empirical user preferences, we show that users' risk preferences have a relatively small impact on revenue, however the impacts are much larger if there is a mismatch between users' actual risk preferences and the system operator's assumptions regarding users' risk preferences. [ABSTRACT FROM AUTHOR]

In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend: (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual "top 40"), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within 'Rock' lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper. [ABSTRACT FROM AUTHOR]

The study evaluates, in the context of freeway segments, the interaction between automated cars' kinematic capabilities and the standard legal requirement for the operator of an automobile to not strike items that are in its path (known as the 'Assured Clear Distance Ahead' criterion). The objective is to characterize the impacts of ACDA-compliant driving behavior on the system-level indicator of roadway-network capacity. We assess the barriers to automated cars operating non-ACDA-compliant driving strategies, develop a straightforward ACDA-compliant automated-driving model to analytically estimate freeway 'pipeline' capacity, compare this behavior to human drivers, and interpret quantitative findings which are based on a range of rationally-specified parameter values and explicitly account for kinematic uncertainty. We demonstrate that automated cars pursuing ACDA-compliant driving strategies would have distinctive "fundamental diagrams" (relationships between speed and flow). Our results suggest that such automated-driving strategies (under a baseline set of assumptions) would sustain higher flow rates at free-flow speeds than human drivers, however at higher traffic volumes the rate of degradation in speed due to congestion would be steeper. ACDA-compliant automated cars also would have a higher level of maximum-achievable throughput, though the impact on maximum throughput at free-flow speed depends on the specific interpretation of ACDA. We also present a novel quantification of the tradeoff between freeway-capacity and various degrees of safety (one failure in 100,000 events, one failure in 1,000,000, etc.) that explicitly accounts for the irreducible uncertainty in emergency braking performance, by drawing on empirical distributions of braking distance testing. Finally, we assess the vulnerability of ACDA-compliant automated cars to lateral 'cut-ins' by vehicles making lane changes. The paper concludes with a brief discussion of policy questions and research needs. [ABSTRACT FROM AUTHOR]

Despite growing prevalence of online shopping, its impacts on mobility are poorly understood. This partially results from the lack of sufficiently detailed data. In this paper we address this gap using consumer panel data, a new dataset for this context. We analyse one year long longitudinal grocery shopping purchase data from London shoppers to investigate the effects of online shopping on overall shopping activity patterns and personal trips. We characterise the temporal structure of shopping demand by means of the duration between shopping episodes using hazard-based duration models. These models have been used to study inter-shopping spells for traditional shopping in the literature, however effects of online shopping were not considered. Here, we differentiate between shopping events and shopping trips. The former refers to all types of shopping activity including both online and in-store, while the latter is restricted to physical shopping trips. Separate models were estimated for each and results suggest potential substitution effects between online and in-store in the context of grocery shopping. We find that having shopped online since the last shopping trip significantly reduces the likelihood of a physical shopping trip. We do not observe the same effect for inter-event durations. Hence, shopping online does not have a significant effect on overall shopping activity frequency, yet affects shopping trip rates. This is a key finding and suggests potential substitution between online shopping and physical trips to the store. Additional insights on which factors, including basket size and demographics, affect inter-shopping durations are also drawn. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including information and communications technology (ICT), shared-mobility markets and business model innovation.

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems. [ABSTRACT FROM AUTHOR]

Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of 'hard' policies such as the provision of parking facilities against 'soft' measures such as cycle promotion schemes. [ABSTRACT FROM AUTHOR]

There is growing interest amongst both practitioners and researchers in the correlates of young adults' driving-licence-acquisition. One aspect of the ongoing scholarly debate is whether taking part in online (i.e. virtual) activities may be associated with young adults feeling less need to drive and hence to acquire a driving licence. This paper addresses this issue by drawing on analysis of two distinct datasets. Both contain rich pseudo-diary instruments in which people indicate detailed characteristics of their unique online-activity profile. This includes both indicators of the types of online activities in which respondents participate, and a separate metric of internet-use intensity. The latter is defined in one dataset as the amount of time/week spent online, and in the other dataset the frequency of their internet use. On the basis of a set of multivariate regression analyses, a positive (i.e. complementary) cross-sectional relationship between young adults' online activity and licence-holding was found. We find that young adults who use the internet are, net of confounding effects, more likely to hold a driving licence than otherwise identical young adults who do not use the internet. Both datasets show this type of effect, and it is robust across a range of model specifications, including multi-stage estimations to address cross-correlation between indicators of internet usage. In addition to the positive net statistical association, we also report several other noteworthy effects. Of the six effects associated with online-activity types that are directly comparable between the two datasets, we find that the correlation in the parameter estimates across the two datasets is 0.63. This suggests similar types of relationships across the two datasets. Also, in several (but not all) of our analyses we found an inverted 'U' shaped ceteris paribus relationship between intensity-of-internet-use and licence-holding. The positive net statistical association between internet use and licence-holding is a different relationship than previously reported in the literature, and therefore further research is needed to reconcile the differences (which are likely due at least in part to different methodological approaches and data resources). Further research is also needed to continue to resolve between the relative saliency of other hypothesised determinants of licence-holding (e.g. economic and socio-demographic explanators, as well as licence-acquisition regimes that vary by time and place). [ABSTRACT FROM AUTHOR]

Recent work on risky choice modelling has sought to address the shortcomings of expected utility theory (EUT) by using non-expected utility theoretic (non-EUT) approaches. However, to date these approaches have been merely tested on stated choice data which is flexible and cheap. In this study, we empirically investigate the feasibility and validity of non-EUT approaches in a revealed preference (RP) context in which travel time distribution is extracted from observed historical travel time data, and subsequently present systematic comparisons between EUT, weighted utility theory, rank-dependent expected utility theory, and prospect theory (PT). The empirical evidence indicates that each non-EUT model has important behavioural insights to offer, moreover, EUT as well as non-EUT models can be applied to the RP context. However, the EUT and non-EUT model fits are generally similar with only PT providing a marginally improved model fit over EUT. The key findings presented in this study reinforce the importance of exploring non-EUT models within a revealed preference context before they can be applied reliably to modelling risky choices in the real world. [ABSTRACT FROM AUTHOR]

In this paper, we extend the standard discrete choice modelling framework by allowing for random variations in the substitution patterns between alternatives across respondents, leading to increased model flexibility. The paper shows how such a Mixed Covariance model can be specified either with purely random variation or with a mixture of random and deterministic variation. Additionally, the model can be based on an underlying GEV or ECL structure. Finally, the model can be specified as a continuous mixture or as a discrete mixture. An application on Stated Preference data for the choice of departure time and travel mode shows that important gains in model performance can be obtained by allowing for random covariance heterogeneity. Furthermore, the approach leads to significant differences in the implied willingness to pay measures, and the substitution patterns between alternatives. [ABSTRACT FROM AUTHOR]

Modelling the temporal response of travellers to transport policy interventions has rapidly emerged as a major issue in many practical transport planning studies and is recognised to hold particular challenges. The importance of congestion and its variation over the day, together with the emergence of time-dependent road user charging as a policy tool, emphasise the need to understand whether and how travellers will change the timing of their journeys. For practical planning studies, analysts face a major issue of relating temporal changes to other behavioural changes that are likely to result from policy or exogenous changes. In particular, the relative sensitivity of time and mode switching has been difficult to resolve. This paper describes a study undertaken to determine the relative sensitivity of mode and time of day choice to changes in travel times and costs and to investigate whether evidence exists of varying magnitudes of unobservable influences in time of day switching. The study draws on data from three related stated preference studies undertaken over the past decade in the United Kingdom and the Netherlands and uses error components logit models to investigate the patterns of substitution between mode and time of day alternatives. It is concluded that the magnitude of unobserved influences on time switching depends significantly on the magnitudes of the time switches considered. With time periods of the magnitude generally represented in practical modelling, i.e. peak periods of 2–3 hours, time switching is generally more sensitive in these data than mode switching. However, the context of the modelling and the extent to which relevant variables can be measured will strongly influence these results. [ABSTRACT FROM AUTHOR]

In September of 2000 the UK experienced a blockade of oil refineries in response to rising fuel prices. These protests resulted in severe fuel supply disruptions that intensified over the course of about one week. During the peak of the crisis, travel activity by car was curtailed. This paper analyzes survey data collected about two months after the crisis utilizing the recent memory of respondents as to how they would expect this sort of disruption to affect their participation in daily activities. Specifically, we focused on a variety of non-discretionary and discretionary activities and examined what factors are associated with respondents expecting disruption to those activities. Statistical models were developed to analyze how demographic factors, commute mode selection, vehicle characteristics, and various other factors can explain how individuals expect disruption to their activities. Results suggest that the majority of individuals do not expect major disruptions, although for more car-dependent individuals, disruption was expected to be substantial, especially for work-related trips. These results have implications for the potential success and benefits of an integrated transport policy. [ABSTRACT FROM AUTHOR]

In many urban centers the demand for parking increases sharply before Christmas mainly due to shopping activity - causing parking congestion. One way to ameliorate such congestion is by disseminating parking information. Informed drivers may divert to relatively under-utilized parking facilities relieving the pressure on congested facilities. The City of Nottingham in England tested a real-time parking information system designed to alleviate congestion in the City Center parking facilities. Real-time information was disseminated through the radio, while historical information regarding parking locations was disseminated through newspaper advertisements and leaflets. The objective of this study is to assess impacts of the parking information system on travelers' knowledge and decisions. Survey research was used to understand traveler response. Respondents' levels of knowledge regarding car parks were analyzed by developing Poisson regression models. Drivers were more likely to have greater knowledge of city center car parks if they used several information sources (radio broadcasts, newspaper advertisements or leaflets and word-of-mouth), were active seekers of parking information, and searched for parking rather than going directly to a parking facility. In addition, the study of behavior showed that drivers were more inclined to use the relatively under-utilized Park-and-Ride facilities instead of the city center car parks if they received parking information from Newspaper advertisements/leaflets. Overall, the parking information service in Nottingham was effective; it seems reasonable to establish such information dissemination and monitoring systems at parking facilities in other urban areas. Furthermore, to support informed travel and activity participation decisions, parking information should be integrated with traffic and transit information. [ABSTRACT FROM AUTHOR]

This paper describes a study into the effects of pre-trip information on travel behaviour, carried out as part of the DRIVE project EURONETT. The aim of the study was to investigate travellers' requirements for different types of travel information and methods of enquiry and to relate the process of information acquisition to changes in travel behaviour. The study was carried out using a stated preference approach, built on the use of a microcomputer based simulation of an in-home pre-trip information system offering information on travel times from home to City Centre, by bus and car, at different times of the day. A novel feature of the stated preference exercise was that respondents effectively generated their own choice set of alternatives through the process of information acquisition. Surveys were undertaken in parallel in Birmingham and Athens, thus allowing a comparison to be made between behaviour in typical Southern and Northern European settings. The first part of the paper discusses some of the fundamental behavioural and modelling issues raised by the introduction of advanced traveller information systems. It then describes the study methodology and the stated preference experiment. Results are presented from an analysis of the information acquisition process itself and from choice models relating the acquired information to effects on different dimensions of travel behaviour. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In a recent article in this journal Supernak (1983a) advanced a forceful critique of contemporary urban transport modelling. This note re-considers some of the issues discussed by Supernak, but from a different philosophical point of view. The resultant analysis of contemporary modelling differs importantly form Supernak's and leads to a different set of prescriptions for the reformation of the field. [ABSTRACT FROM AUTHOR]

Cities are home to an increasing majority of the world's population. Currently, it is difficult to track social, economic, environmental and health outcomes in cities with high spatial and temporal resolution, needed to evaluate policies regarding urban inequalities. We applied a deep learning approach to street images for measuring spatial distributions of income, education, unemployment, housing, living environment, health and crime. Our model predicts different outcomes directly from raw images without extracting intermediate user-defined features. To evaluate the performance of the approach, we first trained neural networks on a subset of images from London using ground truth data at high spatial resolution from official statistics. We then compared how trained networks separated the best-off from worst-off deciles for different outcomes in images not used in training. The best performance was achieved for quality of the living environment and mean income. Allocation was least successful for crime and self-reported health (but not objectively measured health). We also evaluated how networks trained in London predict outcomes three other major cities in the UK: Birmingham, Manchester, and Leeds. The transferability analysis showed that networks trained in London, fine-tuned with only 1% of images in other cities, achieved performances similar to ones from trained on data from target cities themselves. Our findings demonstrate that street imagery has the potential complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of policies that aim to address them. [ABSTRACT FROM AUTHOR]

The article presents information on the contributions of professor Ryuichi Kitamura in the development of travel behaviour research. He was commited to the development of international research community and it is visible in the range of his research collaborations, which is reflected in his support for the activities of the International Association for Travel Behaviour Research.

A correction to the article "What is the relationship between online activity and driving-licence-holding amongst young adults?," published in a previous issue of the journal is presented.