Search

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]

Abstract Free-floating carsharing is a rapidly growing urban mobility service. It has emerged at commercial scale more recently than traditional 'round-trip' carsharing, and at present its growth trajectory is steeper. The evidence base regarding its impacts on sustainable transport indicators is, however, less well-developed. This issue is topical for a variety of reasons, including the importance of public policy to the success of this form of carsharing. The research objective of this study is to establish the early-stage impact of free-floating carsharing on private car ownership. We report findings from a point in time three months following the initiation of a free-floating carsharing service in London (UK). We investigate characteristics of FFCS users that are associated with having one's car ownership impacted, as well as the distinction between deterrence of increased car ownership and sale/disposal of a previously owned private car. We find that 37% (n =347; 95% confidence interval:±5%) of users indicate that free-floating carsharing has impacted their ownership of private cars. Of this 37%, a large majority (83%) indicated that the mechanism of impact was that they decided not to buy a car that they otherwise would have purchased. 11% reported that they had disposed of a car in the past three months, and 6% stated that they will sell a private car within the next three months. The average income and education level of users are both higher than for the general population. Within the population of service-users, multivariate analysis demonstrates that, net of confounding effects, heavier (more-frequent) service-users are more likely to indicate impacts on car ownership, and that being highly-educated and higher-income than other users were both (independently) associated with maintaining one's car ownership level. Additional findings are presented that relate car ownership impacts to further demographic characteristics as well as behavioural indicators. Our findings should be interpreted to pertain to the 'early adopter' cohort of FFCS users, and as free-floating carsharing services mature and grow further research will be needed to ascertain how user characteristics, behaviour and impacts are evolving. [ABSTRACT FROM AUTHOR]

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]

Recent developments in mobile information and communication technologies (ICT), vehicle automation, and the associated debates on the implications for the operation of transport systems and for the appraisal of investment has heightened the importance of understanding how people spend travel time and how productive they are while travelling. To date, however, no approach has been proposed that incorporates the joint modelling of in-travel activity type, activity duration and productivity behaviour. To address this critical gap, we draw on a recently developed PPS framework (Pawlak et al., 2015) to develop a new joint model of activity type choice, duration and productivity. In our framework, we use copulas to provide a flexible link between a discrete choice model of activity type choice, a hazard-based model for activity duration, and a log-linear model of productivity. Our model is readily amenable to estimation, which we demonstrate using data from the 2008 UK Study of Productive Use of Rail Travel-time. We hence show how journey-, respondent-, attitude-, and ICT-related factors are related to expected in-travel time allocation to work and non-work activities, and the associated productivity. To the best of our knowledge, this is the first framework that both captures the effects of different factors on activity choice, duration and productivity, and models links between these aspects of behaviour. Furthermore, the convenient interpretation of the parameters in the form of semi-elasticities enables the comparison of effects associated with the presence of on-board facilities (e.g., workspace, connectivity) or equipment use, facilitating use of the model outputs in applied contexts. [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]

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]

• We propose an analytical framework to quantify different social influence processes. • An accelerated reality experience captures the effects of social interactions on stated choices. • Hypothetical adoption rate of peers increases the bike sharing preference. • Benefits and drawbacks of bike sharing influence the propensity to change mode. • Diffusion and translation of information affect the bike sharing adoption. The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an "accelerated reality" experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the "Reflexive Layers of Influence" (RLI) framework, the processes generated by social interactions (diffusion, translation and reflexivity) are measured and incorporated in the model. We finally show the effect of these social influence variables on the goodness-of-fit of the models and choice simulation for prediction. We also draw conclusions about the value of such enhanced choice models in understanding and predicting the impacts of social interactions on choice behaviour in the context of new transport technologies. [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]

The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO 2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers. [ABSTRACT FROM AUTHOR]

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]

Understanding of the interactions between Information and Communication Technologies (ICT) and physical mobility is a major area of research with practical applications in a number of fields. Very little, however, is known regarding how these relationships vary on a cross-national basis, including across countries at different stages in development. To address this gap, this paper presents an analysis of household transport expenditure as a function of the available variables, with a particular focus on the ICT. This analysis is based on a cross-sectional dataset from 2010 comprising information on 33 countries including average household transport expenditure, ICT represented by the percentage of households with Internet access at home, and a number of contextual macroeconomic and infrastructural variables. Using a log-log framework we find that, in our sample of countries, household transport expenditure is negatively associated with Internet penetration with an elasticity of − 0.394. We verify this to be robust to endogeneity using presence of restrictions on foreign ownership in the Internet market as an instrumental variable. We also control for potential differences in data quality across countries using the Corruption Perceptions Index. To the best of our knowledge, this is the first attempt to quantify this relationship at a cross-national level while also controlling for endogeneity and data quality issues. Among the control variables, we observe the estimated effects to be intuitive, and consistent with existing research and microeconomic understandings of the behaviour of individuals and households. [ABSTRACT FROM AUTHOR]

In the literature electric vehicle use is modelled using of a variety of approaches in power systems, energy and environmental analyses as well as in travel demand analysis. This paper provides a systematic review of these diverse approaches using a twofold classification of electric vehicle use representation, based on the time scale and on substantive differences in the modelling techniques. For time of day analysis of demand we identify activity-based modelling (ABM) as the most attractive because it provides a framework amenable for integrated cross-sector analyses, required for the emerging integration of the transport and electricity network. However, we find that the current examples of implementation of AMB simulation tools for EV-grid interaction analyses have substantial limitations. Amongst the most critical there is the lack of realism how charging behaviour is represented. [ABSTRACT FROM AUTHOR]

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks. In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation. On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda. This paper concludes with a brief discussion of research needs to advance this line of inquiry. [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]

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments. [ABSTRACT FROM AUTHOR]

• 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]

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]

Highlights • Evaluation of two vehicle allocation mechanisms in free-floating carsharing. • A stated-choice survey with two interactive uncertain attributes. • Risk preference for two uncertain attributes. • Factors correlating with respondents' preference for vehicle allocation mechanisms. Abstract Free-floating carsharing (FFCS) fleets are inherently volatile spatio-temporally, which presents both a logistical challenge for operators and a service reliability issue for customers. In this study we present a stated-choice survey to investigate the attractiveness to customers of two mechanisms for managing fleet volatility: Virtual Queuing (VQ) and Guaranteed Advance Reservation (GAR). We investigate socio-demographic features and "Big Five" personality traits that are associated ceteris paribus with choosing to use the existing FFCS service model, willingness-to-pay (WTP) for VQ and GAR, and risky-choice behaviour under the uncertainty of FFCS systems. Data (n = 289; 232 employed in analysis) are sourced from existing users of a FFCS service in London, UK. Within the survey context, we found that customers are on average not willing to pay for VQ (i.e. negative WTP), however have £0.54 per journey WTP for GAR, with low-frequency FFCS users and users scoring highly on the Big Five "Conscientiousness" dimension having larger WTP for GAR. When analysing the two dimensions of uncertainty, we found that respondents exhibit risk-seeking behaviour towards price and weaker and insignificant risk-aversion towards walking time. This pattern holds across the three standard model types of nonlinear risky-choice behaviour that we investigated. The results are intended to be useful both to policymakers and carsharing operators who are likely, as the industry matures, to seek mechanisms to differentiate their service offers to better serve individual market segments with distinctive characteristics. [ABSTRACT FROM AUTHOR]

• A new approach based on a trailer capacity graph for matching shipments and trailer trips. • A data-based engine for identifying real-time horizontal freight collaboration opportunities. • Approximating and simplifying routes enables balancing between the computational performance and the effectiveness. • This approach identifies many more opportunities than the traditional OD-based matching approach. • Enhanced efficiencies via collaborative logistics are possible using a pure data-based approach. Logistics operators participating in horizontal collaboration can gain economic benefits and being better placed to meet environmental goals. Data-based approaches provide a viable, albeit suboptimal, solution that can enable real-time collaborative order sharing. Conventional data-based approaches for identifying collaboration (order sharing) opportunities are typically based on origin-destination (OD) matching between trips and shipments from different collaborating companies. This, however, prevents the exploitation of en-route collaboration opportunities. Hence, we propose a practical data-based engine for identifying collaboration opportunities during shipment planning stages that enables shipments to be matched according to both the OD and trailer trip routes. The engine is based on a multigraph approach, called the trailer capacity graph (TCG) approach. We further enhance the engine to improve its computational performance for real-time operations. Numerical experiments based on real-world data from two logistics companies show that the TCG approach identifies a significantly larger number of opportunities, and provides a higher total distance saving than conventional OD-based matching. The experiments also demonstrate that with trailer route approximation and route shape simplification, this engine allows trade-offs between the computational performance and the effectiveness of opportunity identification, which implies that the engine can be flexibly tailored according to user preferences. [ABSTRACT FROM AUTHOR]

This study addresses the impacts of automated cars on traffic flow at signalized intersections. We develop and subsequently employ a deterministic simulation model of the kinematics of automated cars at a signalized intersection approach, when proceeding forward from a stationary queue at the beginning of a signal phase. In the discrete-time simulation, each vehicle pursues an operational strategy that is consistent with the ‘Assured Clear Distance Ahead’ criterion: each vehicle limits its speed and spacing from the vehicle ahead of it by its objective of not striking it, regardless of whether or not the future behavior of the vehicle ahead is cooperative. The simulation incorporates a set of assumptions regarding the values of operational parameters that will govern automated cars’ kinematics in the immediate future, which are sourced from the relevant literature. We report several findings of note. First, under a set of assumed ‘central’ (i.e. most plausible) parameter values, the time requirement to process a standing queue of ten vehicles is decreased by 25% relative to human driven vehicles. Second, it was found that the standard queue discharge model for human–driven cars does not directly transfer to queue discharge of automated vehicles. Third, a wet roadway surface may result in an increase in capacity at signalized intersections. Fourth, a specific form of vehicle-to-vehicle (V2V) communications that allows all automated vehicles in the stationary queue to begin moving simultaneously at the beginning of a signal phase provides relatively minor increases in capacity in this analysis. Fifth, in recognition of uncertainty regarding the value of each operational parameter, we identify (via scenario analysis, calculation of arc elasticities, and Monte-Carlo methods) the relative sensitivity of overall traffic flow efficiency to the value of each operational parameter. This study comprises an incremental step towards the broader objective of adapting standard techniques for analyzing traffic operations to account for the capabilities of automated vehicles. [ABSTRACT FROM AUTHOR]

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]

There are nearly two million subscribers to carsharing services worldwide. These services can provide large benefits both to users and the general public (e.g., through emissions reductions). There has not however previously existed a general framework for forecasting their market potential and impacts that is sensitive to the way that they re-structure the costs associated with personal car ownership. Techniques for predicting market scope and impacts ahead of field implementation are urgently required, both by entrepreneurs and the public sector, whose support, or at least acquiescence, is generally required. This paper draws on the Perceived Activity Set conceptual framework that was recently developed to address the methodological challenges posed by carsharing, and presents the first set of empirical findings from employing it to model carsharing. The empirical analysis makes uses of pooled data from the British National Travel Survey and a purpose-designed stated-choice survey. We investigate both the ‘round-trip’ and ‘point-to-point’ carsharing service models. The results suggest that the number of prospective subscribers to a point-to-point carsharing service in London is between three and four times as large as the comparable number for round-trip carsharing. The greatest reduction in overall vehicle-miles of travel – including both carsharing cars and private cars – was found from introducing round-trip carsharing across all of London. Survey respondents indicated they would use point-to-point carsharing for commuting journeys much more frequently than round-trip carsharing. Finally, point-to-point carsharing was found to be a substitute for public transport, whilst round-trip carsharing was found to be a complement. [ABSTRACT FROM AUTHOR]

• Capacity of mixed flow has been proven to increase convexly with AV penetration rate. • This property demonstrates road capacity can be increased with RoW reallocation. • Crucial points of RoW strategies are identified quantitatively for policymakers. • CCapacity can still be increased with RoW reallocation when AVs are 'worse' than HDVs. Autonomous Vehicles (AVs) are bringing challenges and opportunities to urban traffic systems. One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate, and to efficiently reallocate the Right-of-Way (RoW) for the mixed flow of AVs and Human Driven Vehicles (HDVs). Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all-AV scenario, when AVs are able to drive with smaller headways to the leading vehicle. However, this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs. In order to investigate the impacts of mixed flow conditions (AVs and HDVs), this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation. Secondly, four different RoW reallocation strategies are compared using a SUMO simulation to cross-validate the results in a numerical analysis. A range of scenarios with different AV penetration rates and traffic demands are used. The results show that road capacity on a two-lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates, compared with the do-nothing RoW strategy. [ABSTRACT FROM AUTHOR]