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There is a large body of literature, spanning multiple disciplines, concerned with the relationship between traditional (physical) shopping and associated travel behaviour. However, despite the recent rapid growth of digital retailing and online shopping, the impact on travel behaviour remain poorly understood. Although the issue of the substitution and complementarity between conventional and virtual retail channels has been extensively explored, few attempts have been made to extend this work so as to incorporate virtual retail channels into modelling frameworks that can link shopping and mobility decisions. Here, we review the existing literature base with a focus on most relevant dimensions for personal mobility. How online activity can be incorporated into operational transport demand models and benefits of such effort are discussed. Existing frameworks of shopping demand are flexible and can, in principle, be extended to incorporate virtual shopping and the associated additional complexities. However, there are significant challenges associated with lack of standard ontologies for crucial concepts and insufficiencies in traditional data collection methods. Also, supply-side questions facing businesses and policy-makers are changing as retailing goes through a digital transformation. Opportunities and priorities need to be defined for future research directions for an assessment of existing tools and frameworks. [ABSTRACT FROM AUTHOR]

Travel time is an important indicator of network performance used in traffic operations and management. Commonly deployed inductive loop detectors (ILDs) measure time-mean-speed (TMS), whereas space-mean-speed (SMS) is required to calculate the travel time. A well-known relationship between the TMS and the SMS was derived by Wardrop. However, this relationship cannot be used in practice to estimate travel times as it requires knowledge of the variance of the SMS. The variance of the SMS is not measured by the ILDs and is normally not available in practice. A novel formulation is presented in this paper to estimate the SMS using TMS obtained from ILDs. In addition, two additional models based on the formulation are developed to improve the estimation performance by taking traffic states into account. The initial results show that the proposed formulation can used to estimate the SMS, and hence the travel time, accurately using real-world data. [ABSTRACT FROM AUTHOR]

Over the past several years a number of research projects have attempted to empirically measure behavioural responses to changes in travel time variability. These have generally been built on theoretical models of scheduling choice that account for changes in departure time in response to the expected costs associated with variability. This paper reviews both the theory and empirical results of several projects that estimated coefficients on various measures of variability using stated preference techniques. Gaps in the understanding of these issues are identified and discussed. [ABSTRACT FROM AUTHOR]

The growing availability of geotagged big data has stimulated substantial discussion regarding their usability in detailed travel behaviour analysis. Whilst providing a large amount of spatio-temporal information about travel behaviour, these data typically lack semantic content characterising travellers and choice alternatives. The inverse discrete choice modelling (IDCM) approach presented in this paper proposes that discrete choice models (DCMs) can be statistically inverted and used to attach additional variables from observations of travel choices. Suitability of the approach for inferring socioeconomic attributes of travellers is explored using mode choice decisions observed in London Travel Demand Survey. Performance of the IDCM is investigated with respect to the type of variable, the explanatory power of the imputed variable, and the type of estimator used. This method is a significant contribution towards establishing the extent to which DCMs can be credibly applied for semantic enrichment of passively collected big data sets while preserving privacy. [ABSTRACT FROM PUBLISHER]

This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior. [ABSTRACT FROM PUBLISHER]

Car use per person has historically grown year-on-year in Great Britain since the 1950s, with minor exceptions during fuel crises and times of economic recession. The ‘Peak Car’ hypothesis proposes that this historical trend no longer applies. The British National Travel Survey provides evidence of such an aggregate levelling off in car mileage per person since the mid-1990s, but further analysis shows that this is the result of counter trends netting out: in particular, a reduction in per capita male driving mileage being offset by a corresponding increase in female car driving mileage. A major contributory factor to the decline in male car use has been a sharp reduction in average company car mileage per person. This paper investigates this aspect in more detail. Use of company cars fell sharply in Britain from the 1990s up to the 2008 recession. Over the same period, taxation policy towards company cars became more onerous, with increasing levels of taxation on the benefit-in-kind value of the ownership of a company car and on the provision of free fuel for private use. The paper sets out the changes in taxation policy affecting company cars in the UK, and looks at the associated reductions in company car ownership (including free fuel) and patterns of use. It goes on to look in more detail at which groups of the population have kept company cars and in which parts of the country they have been most used, and how these patterns have changed over time. A preliminary investigation is also made of possible substitution effects between company car and personal car driving and between company car use and rail travel. Clearly, the role of the company car is only one of many factors that are contributing to aggregate changes in levels of car use in Great Britain, alongside demographic changes and a wide range of policy initiatives. But, company car use cannot fall below zero, so the effect of declining year-on-year company car mileage suppressing overall car traffic levels cannot continue indefinitely. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads. [ABSTRACT FROM PUBLISHER]

The discrete choice analysis of residential location choice forms an important part of land use-transport modelling systems but gives rise to a number of significant modelling challenges, one of which is the choice set formation problem. A number of alternative approaches exist to addressing the choice set formation problem, but to the best of our knowledge, there have been no efforts to empirically compare their performance using real choice data. In this paper, we propose to address this gap by examining the performance of several choice set formation methods within the context of residential location choice in London. The paper presents a brief review of the relevant theoretical and empirical literature on the treatment of the choice set formation problem in the context of spatial choice and sets out the characteristics of the specific modelling approaches implemented in the empirical research. In particular, we compare a recently proposed hazard-based screening model with more conventional pure statistical treatments. The empirical research uses a specially constructed data-set on residential location choice, developed by the authors for the Greater London area, which draws on information from a number of separate data sources (including revealed preference data from the London Area Travel Survey). The alternative choice set formation approaches are assessed in terms of their prediction performance on a hold-out validation sub-sample and also in terms of estimated model parameters. [ABSTRACT FROM PUBLISHER]

Vehicular Ad-Hoc Networks (VANETs) attract considerable research and commercial interest with promising applications in a number of areas including cooperative vehicle-highways systems, sensor networks, and safety systems. However, as a result of high speed and variable driver behavior, automotive ad-hoc networks behave in fundamentally different ways to the most prevalent models in Mobile Ad-Hoc Network (MANET) research. Previous work in MANETs has mostly assumed that the mobile nodes move randomly with an unconstrained mobility model, and it is clear that a random mobility model is not adequate to represent the major characteristics of real-world vehicle motions and may therefore lead to unreliable results. Recent studies of VANETs have attempted to introduce macro- and micromobility constraints to model vehicle motions, but they have mostly focused on modeling the mobility of generic private vehicles. Given the potential for the coordinated deployment of network nodes on centrally managed fleet vehicles, it has become important to model the characteristics of a VANET featuring vehicles of different types, with systematically different behavior patterns. In this article, the authors study the connectivity of mobile ad-hoc networks that consist of buses moving in urban area and examine the implications for transport-related services. Buses have a unique set of behavior characteristics (e.g., fixed routes, schedules, bus stops, specific priorities), which gives rise to distinct effect on node connectivity in the communication network. Through extensive simulations on the basis of real bus routes in central London, the authors (a) demonstrated the effect of the locations of stops and the prevailing traffic patterns on node connectivity (including the distributions of contact duration and intercontact time between buses) and (b) explored its implication on the design of a dissemination system to capture and disseminate data. Their results give a key message that the mobility of buses has to be modeled explicitly, and such kind of knowledge of connectivity among buses will be significant for the studies of routing algorithms and other networking functions in interbus communication networks. [ABSTRACT FROM AUTHOR]

This study draws on respondents from Greater London within the 2016/17 wave (n = 2640) of Britain's Annual Survey of carsharing users, which we enrich with external data from the 2011 England and Wales Census and small-area income estimates. Focusing on round-trip carsharing users, we present multivariate analyses of frequency-of-usage of carsharing vehicles and impacts on annual vehicle miles traveled (VMT). Published attribute effects from other geographic and contextual circumstances are compiled and compared (where direct comparison is possible) with the specific attribute effects that we report in this paper. We demonstrate a statistically significant link between customer satisfaction with proximity of carsharing vehicles and VMT impacts. Car ownership (both current, and changes upon joining a carsharing service) is shown to have intuitive structural impacts. We find that frequent usage is associated ceteris paribus with increasing VMT after joining a carsharing service, and that subscribing to multiple types of carsharing is associated with frequent carsharing usage and a reduction in VMT. Interestingly, we did not find any significant effect of household income on either frequency-of-usage or VMT impacts. [ABSTRACT FROM AUTHOR]

This paper presents a simple formulation in the form of a pipe network for modelling the global container-shipping network. The cost-efficiency and movement-patterns of the current container-shipping network have been investigated using heuristic methods. The model is able to reproduce the overall incomes, costs, and container movement patterns for the industry as well as for the individual shipping lines and ports. It was found that the cost of repositioning empties is 27% of the total world fleet running cost and that overcapacity continues to be a problem. The model is computationally efficient. Implemented in the Java language, it takes one minute to run a full-scale network on a Pentium IV computer. [ABSTRACT FROM AUTHOR]

This paper describes tailpipe emission results generated by the Vehicle Performance and Emissions Monitoring system (VPEMS). VPEMS integrates on-board emissions and vehicle/driver performance measurements with positioning and communications technologies, to transmit a coherent spatio-temporally referenced dataset to a central base station in near real time. These results focus on relationships between tailpipe emissions of CO, CO2, NOx and speed and acceleration. Emissions produced by different driving modes are also presented. Results are generally as one would expect, showing variation between vehicle speed, vehicle acceleration and emissions. Data is based upon a test run in central London on urban streets with speeds not exceeding about 65 km/h. The results presented demonstrate the capabilities of the system. Various issues remain with regard to validation of the data and expansion of the system capability to obtain additional vehicle performance data. [ABSTRACT FROM AUTHOR]