43 results found
Pawlak J, Sivakumar A, Ciputra W, et al., 2023, Feasibility of transition to electric mobility for 2-wheeler taxis in Sub-Saharan Africa: a case study of rural Kenya, Transportation Research Record, Vol: 2677, Pages: 359-370, ISSN: 0361-1981
Electric mobility transition has been gradually gaining momentum, driven by several considerations, including the urgency to combat climate change impacts attributed to private transport based on the internal combustion engine. The nature and impacts of such a transition will inevitably vary across countries because of differences in the mobility patterns and preferences in the societies, as well as the policy landscape. In Sub-Saharan Africa, paratransit is one of the dominant forms of transport. This motivates the need to assess its viability for electric mobility transition, focusing on electric motorcycles in particular. Using Kenya as case study, in conjunction with mobility data collected in several Sub-Saharan countries, this research provides insight on the potential adoption and impacts of electric motorcycles in the taxi industry, based on the observed trip characteristics and relative fuel and electricity costs. The economic benefits for taxi drivers as well as the capability of the electricity infrastructure to support such transition are considered. The paper concludes that the transition to electric mobility among motorcycle taxis is feasible in Kenya. The paper also discusses implications for the electricity grid, in relation to the possible increase in the electricity consumption and power needs under various electric two-wheeler proliferation scenarios.
Wang H, Pawlak J, Faghih Imani A, et al., 2023, When does it pay off to use electricity demand data with rich information about households and their activities? A comparative machine learning approach to demand modelling, Energy and Buildings, Vol: 295, Pages: 1-15, ISSN: 0378-7788
Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage a
Manca F, Sivakumar A, Pawlak J, et al., 2023, Will we fly again? modeling air travel demand in light of COVID-19 through a London case study, Transportation Research Record: Journal of the Transportation Research Board, Vol: 2677, Pages: 105-117, ISSN: 0361-1981
The COVID-19 pandemic and associated travel restrictions have created an unprecedented challenge for the air transport industry, which before the pandemic was facing almost the exact opposite set of problems. Instead of the growing demand and need for capacity expansion warring against environmental concerns, the sector is now facing a slump in demand and the continuing uncertainty about the impacts of the pandemic on people’s willingness to fly. To shed light on consumer attitudes toward air travel during and post the pandemic, this study presents an analysis that draws on recently collected survey data (April–July 2020), including both revealed and stated preference components, of 388 respondents who traveled from one of the six London, U.K., airports in 2019. Several travel scenarios considering the circumstances and attitudes related to COVID-19 are explored. The data is analyzed using a hybrid choice model to integrate latent constructs related to attitudinal characteristics. The analysis confirms the impact of consumers’ health concerns on their willingness to travel, as a function of travel characteristics, that is, cost and number of transfers. It also provides insights into preference heterogeneity as a function of sociodemographic characteristics. However, no significant effects are observed concerning perceptions of safety arising from wearing a mask, or concerns over the necessity to quarantine. Results also suggest that some respondents may perceive virtual substitutes for business travel, for example video calls and similar software, as only a temporary measure, and seek to return to traveling as soon as it is possible to do so safely.The ongoing COVID-19 pandemic has affected air travel to an unprecedented extent, leading to the worst-ever crisis of the air transport sector (1). Airlines worldwide have faced a huge drop in demand, for example 98% drop in passengers for 6 weeks in a row over April and May 2020, as stated by the Airpor
Manca F, Pawlak J, Sivakumar A, 2023, Impact of perceptions and attitudes on air travel choices in the post-COVID-19 era: a cross-national analysis of stated preference data, Travel Behaviour and Society, Vol: 30, Pages: 220-239, ISSN: 2214-367X
The COVID-19 pandemic and the consequent travel restrictions have had an unprecedented impact on the air travel market. However, a rigorous analysis of the potential role of safety perceptions and attitudes towards COVID-19 interventions on future air passenger choices has been lacking to date. To investigate this matter, 1469 individuals were interviewed between April and September 2020 in four multi-airport cities (London, New York City, Sao Paulo, Shanghai). The core analysis draws upon data from a set of stated preference (SP) experiments in which respondents were asked to reflect on a hypothetical air travel journey taking place when travel restrictions are lifted but there is still a risk of infection. The hybrid choice model results show that alongside traditional attributes, such as fare, duration and transfer, attitudinal and safety perception factors matter to air passengers when making future air travel choices. The cross-national analysis points towards differences in responses across the cities to stem from culturally-driven attitudes towards interpersonal distance and personal space. We also report the willingness to pay for travel attributes under the expected future conditions and discuss post-pandemic implications for the air travel sector, including video-conferencing as a substitute for air travel.
Hou H, Pawlak J, Sivakumar A, et al., 2022, Building occupancy modelling at the district level: A combined copula-nested hazard-based approach, Building and Environment, Vol: 225, ISSN: 0007-3628
Planning and managing an energy system in a district require a comprehensive understanding and accurate modelling of people's occupancy and circulation among multiple buildings. Due to the lack of occupancy modelling tools for district scale analysis, energy models still use simplified occupancy patterns provided in building codes and standards. However, the simplified information restricts the reflection of complex occupancy patterns driven by urban heterogeneity. This paper fills this research gap and presents a hazard-based model combined with nested copula dependence to describe the complex occupants' interactions between buildings in a district, enabling the characterisation of irregular occupancy patterns in special cases. The proposed model is calibrated using Wi-Fi authentication data from the Imperial College London (UK) South Kensington campus and is validated using the following days of the same data by evaluating the performance of predicted occupancy patterns both on average and day by day. The validation results demonstrate that the model can accurately capture the effects of the urban environment on occupancy duration and choice of transition within a district. Mean Absolute Percentage Errors (MAPEs) of average-pattern predictions are between 7% and 16% for most buildings, though a bit lower in accuracy for the Library and Food Hall predictions with MAPEs of 32%–36%. We also discuss the contributions of the proposed occupancy model to potential future applications, including efficient building space use, local energy planning and management.
Abeille A, Pawlak J, Sivakumar A, 2022, Exploring the meaning and drivers of personal (Un-)Productivity of knowledge workers in mobile settings, Travel Behaviour and Society, Vol: 27, Pages: 26-37, ISSN: 2214-367X
It is now recognised in travel behaviour research that travel time can be, and in fact is, used to undertake productive, work-related activities. The phenomenon, also referred to as travel-based multitasking, has in recent years been compounded by the proliferation and sophistication of mobile information and communication technologies (ICT). Accordingly, several research efforts have made attempts to measure and model the effectiveness of work activities during travel. Yet reliance of those studies on rather simple and proxy metrics has led to a limited understanding of mobile work productivity. This has been especially the case for knowledge workers, whose job involves handling or using information and is often characterized by intangible work outputs. To address this shortcoming, the current paper presents a systematic analysis of 22 semi-structured interviews of employees of a major IT company regarding their mobile work practices, use of ICT and perception of productivity with use of ICT. Analysis of the interviews led us to adopt an ‘inverse’ approach, i.e. discussing factors hampering productivity. This emerged from our observation that individuals experienced difficulties speaking about productivity and productive tasks while finding it easier to discuss what made them unproductive. With the lens of what we term ‘unproductivity’, we are able to provide a new perspective on how to characterize the impacts of journey, technology and individual factors on productivity during episodes of mobile work. In addition, we find a strong link between productive mobile work, planning of the journey and the working activities during the course of travel.
Calastri C, Pawlak J, Batley R, 2022, Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel, Transportation, Vol: 49, Pages: 61-87, ISSN: 0049-4488
Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation.
Losa Rovira Y, Faghih Imani A, Sivakumar A, et al., 2022, Do in-home and virtual activities impact out-of-home activity participation? Investigating end-user activity behaviour and time use for residential energy applications, Energy and Buildings, Vol: 257, Pages: 1-11, ISSN: 0378-7788
The ability to accurately model and predict timing and duration of activities for different individuals is essential for successful and widespread Demand Side Response (DSR) policies, especially in the residential sector. Understanding what people do during the day and what factors influence their activity participation decisions is important for planning an effective DSR strategy to harness the end-user flexibility. The recent Covid-19 pandemic has shown how much activities can be shifted to a virtual mode in the presence of mobility restrictions. Further, participation in activities via digital devices (virtual activity participation) has spread across society. Such virtual activities, including teleworking, online shopping, and virtual social interactions, are observed to explicitly impact travel behaviour and activity scheduling. And yet, activity-based models of mobility and energy demand do not accommodate the trade-offs between activity types, location and virtual activity participation. This paper presents a model of activity participation that captures the relationship between the three dimensions of: activity type (such as work, study, shopping), activity location (in-home, out-of-home), and activity modality (in-person, virtual). A Multiple-Discrete Continuous Extreme Value (MDCEV) model structure is applied, and the empirical analysis is undertaken using the 2015 United Kingdom Time Use Survey (UKTUS). The model results provide insights for better understanding of the trade-offs made by individuals as they participate in and allocate time across a set of activity type-location-modality alternatives, and the heterogeneity in these trade-offs. Further, holdout sample validation and policy scenario analysis exercises are presented to demonstrate the reliability and suitability of the model for policy implications. The empirical results presented in our paper suggest that this framework embedded in an activity and agent-based simulator of energy demand will
Zhao Y, Pawlak J, Sivakumar A, 2022, Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach, Transportation Research Part B: Methodological: an international journal, Vol: 155, Pages: 101-134, ISSN: 0191-2615
In light of the growing availability of big data sources and the essential role of socio-demographic information in travel behaviour and transport demand modelling more broadly, the enrichment of socio-demographic attributes for anonymous big datasets is a key issue that continues to be explored. The common shortcoming of existing socio-demographic enrichment approaches concerns their lack of consistent theory that can link their enrichment performance (i.e. the ability to correctly enrich the required attribute) to the underlying covariance structure in the anonymous big datasets. In other words, existing approaches are unable to indicate, prior to the enrichment, to what extent it will be successful. Instead, they require undertaking the enrichment itself to assess and validate it post factum, incurring the effort and cost of the activity. An alternative and arguably preferable way would be to have a prior indicator as to whether an enrichment is likely to be sufficiently effective for the desired application.Towards this end, this paper draws upon the Inverse Discrete Choice Modelling (IDCM) approach to demonstrate what is termed as the IDCM performance theory, which systematically and in a tractable manner links the socio-demographic enrichment performance of the IDCM approach to the structure of the underlying datasets. This is achieved by recalibration of the constant, a technique adopted from conventional discrete choice modelling practice, while also drawing upon information theory employed in the context of communication systems. The established IDCM performance theory is validated in two empirical applications where performance of the IDCM approach in enriching several socio-demographic attributes, given travel behaviour patterns, is successfully estimated. Additionally, the IDCM approach is found to perform comparably to commonly used methods in previous socio-demographic enrichment efforts. It is thus argued that the capability of the IDCM performance th
Pawlak J, Faghih Imani A, Sivakumar A, 2021, How do household activities drive electricity demand? Applying activity-based modelling in the context of the United Kingdom, Energy Research and Social Science, Vol: 82, Pages: 1-18, ISSN: 2214-6296
Driven by the necessity to increase utilisation of the existing networks and accommodation of volatility in renewable energy generation, the energy sector is undergoing a shift from an unconstrained infrastructure expansion to accommodate growth in demand towards demand management strategies. Such strategies, for example nudging demand using incentives such as price signals, or Demand Side Response (DSR), rely on the ability to accurately understand and harness flexibility in demand. Activity-based demand modelling frameworks can provide this capability, as they enable the detailed modelling and simulation of individuals and their activities. However, to date, no modelling approach has been proposed that can link energy consumption of a household to the activities undertaken, heterogeneity of the household residents, presence and use of household appliances and devices as well as weather and energy system-related variables. This paper addresses the gap by proposing a log-linear mixed-effects model of energy consumption based on reported household activities alongside a comprehensive set of attributes and contextual variables that might influence household energy consumption. Application of the model is demonstrated using joint time-use and residential electricity consumption data from 160 households, collected between 2016 and 2018 in the UK. The modelling results prove the value of incorporating time-use (activities) in modelling residentialelectricity demand, when compared against modelling without such considerations. Furthermore, the model provides (semi-)elasticities of demand and marginal changes in electricity consumption due to activities, which are of direct policy value or serve as inputs into activity-based energy demand simulation.
This Briefing Paper explores the impactthe COVID-19 pandemic had on the UK’senergy sector over the course of thefirst government-mandated nationallockdown that began on 23 March 2020.Research from several aspects of theIntegrated Development of Low-carbonEnergy Systems (IDLES) programme atImperial College London is presented inone overarching paper. The main aim isto determine what lessons can be learntfrom that lockdown period, given theunique set of challenges it presented inour daily lives and the changes it broughtabout in energy demand, supply, anduse. Valuable insights are gained intohow working-from-home policies,electric vehicles, and low-carbon gridscan be implemented, incentivised, andmanaged effectively.
Pawlak J, Circella G, 2021, ICT, Virtual and In-Person Activity Participation, and Travel Choice Analysis, International Encyclopedia of Transportation, Editors: Vickerman, Publisher: Elsevier, ISBN: 9780081026724
In an increasingly globalised world, despite reductions in costs and time, transportation has become even more important as a facilitator of economic and human interaction; this is reflected in technical advances in transportation systems, ...
Hou H, Pawlak J, Sivakumar A, et al., 2020, An approach for building occupancy modelling considering the urban context, Building and Environment, Vol: 183, Pages: 1-18, ISSN: 0360-1323
Building occupancy, which reflects occupant presence, movements and activities within the building space, is a key factor to consider in building energy modelling and simulation. Characterising complex occupant behaviours and their determinants poses challenges from the sensing, modelling, interpretation and prediction perspectives. Past studies typically applied time-dependent models to predict regular occupancy patterns for commercial buildings. However, this prevalent reliance on purely time-of-day effects is typically not sufficient to accurately characterise the complex occupancy patterns as they may vary with building’s surrounding conditions, i.e. the urban environment. Therefore, this research proposes a conceptual framework to incorporate the interactions between urban systems and building occupancy. Under the framework, we propose a novel modelling methodology relying on competing risk hazard formulation to analyse the occupancy of a case study building in London, UK. The occupancy profiles were inferred from the Wi-Fi connection logs extracted from the existing Wi-Fi infrastructure. When compared with the conventional discrete-time Markov Chain Model (MCM), the hazard-based modelling approach was able to better capture the duration dependent nature of the transition probabilities as well as incorporate and quantify the influence of the local environment on occupancy transitions. The work has demonstrated that this approach enables a convenient and flexible incorporation of urban dependenciesleading to accurate occupancy predictions whilst providing the ability to interpret the impacts of urban systems on building occupancy. Keywords: Urban system; Competing risk hazard model; Building occupancy simulation; Wi4 Fi connection data
Pawlak J, Circella G, Mahmassani H, et al., 2020, Information and Communication Technologies(ICT), Activity Decisions,and Travel Choices: 20 years into the Second Millennium and where do we go next?, Washington DC, Publisher: Transportaton Research Board, Pages: 1-12
CENTENNIAL PAPERSStanding Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices (ADB20)Giovanni Circella, ChairInformation and Communication Technologies(ICT), Activity Decisions,and Travel Choices: 20 years into the Second Millennium and where do we go next?JACEKPAWLAK,Imperial College LondonGIOVANNICIRCELLA, University of California, Davis andGeorgia Institute of TechnologyHANIS.MAHMASSANI, Northwestern UniversityPATRICIAL.MOKHTARIAN, Georgia Institute of TechnologyABSTRACTInformation and Communication Technologies, or ICT,have rapidly emerged asan integral element of everyday life, interactingin an essential manner with mobility and the activity patterns that engender it. The current paper reflects uponthistrendandthe opportunities and challenges itrepresents.Givenmore than three decades of research in the domain of interactions between ICT, activity decisions and travel choices, we acknowledgethe elaborate, disruptiveand oftenunexpected waysalong which ICT interact with society.Tosupport the objective of theADB20 Committee, namely tosupportand promote theemerging research questions, we identifya number of technological, societal and behavioral trends related to ICT and mobility that are likelyto be major driving forces for activity-travel behavior considerations in the next 15 years. Those include democratization of technology; personalization; shared and commoditized mobility; automation;data as the new currency; next generation connectivity, including 5G; evolving social media and socialization; new forms of shopping; digital twins;activity fragmentation; andmultitasking.We also observe that inevitably, theincreasingly interlocking relationshipbetween ICT and mobility will bring challengesrelated to balancing efficiency vs. redundancy and resilience, ensuring transparency, susceptibility to malicious activitiesandtackling the digital divide. We argue that those should not be seen as barriers to realization of the ulti
Pawlak J, 2020, Travel-based multitasking: review of the role of digital activities and connectivity, Transport Reviews, Vol: 40, Pages: 429-456, ISSN: 0144-1647
Travel-based multitasking, also referred to as travel time use, is now a well-established concept, whose existence is supported by the technological trajectories, with mobile information and communication technologies (ICT) and vehicle automation working together to allow travel time to be more productive and enjoyable. Despite existence of reviews of travel-time multitasking studies, the systematic overview of the role digital activities, i.e. those that necessarily require modern ICT equipment to participate, has been limited, often wrapped under the umbrella term “use of ICT”, potentially obscuring their complexity and sophistication. Similarly, the role of connectivity and its attributes, e.g. speed (bandwidth), reliability, price, ease of use, data allowance or security, deserves a more systematic overview given its key role in enabling digital online activities and hence the travel-based multitasking options. This paper provides a review of 77 empirical travel-based multitasking-studies that explored the role of digital activities or connectivity. In particular, the review discusses the existing typologies of digital activities, dividing them into hardware-centric, function-centric or a combination of both (mixed). Subsequently, key contributions are discussed with respect to the treatment of digital activities and connectivity and its attributes. Based on the review, it is possible to observe that the existing studies have looked only at a handful of rather restricted online activities that do not sufficiently capture the sophistication with which individuals interact with the virtual world nowadays. Furthermore, the role of connectivity, although deeply embedded in the “C” of the “ICT” concept, has not been looked at or modelled in any detail in studies related to travel time use or its quality. This existing shortcoming might have resulted in an insufficient understanding of the mechanisms driving travel time use, the ass
Pawlak J, Imani AF, Sivakumar A, 2020, A microeconomic framework for integrated agent-based modelling of activity-travel patterns and energy consumption, Procedia Computer Science, Vol: 170, Pages: 785-790, ISSN: 1877-0509
The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK.
Psyllou E, Pawlak J, 2019, Congestion, safety, economic, and environmental challenges of vehicle automation in transport systems: Comment on "Driverless cars will make passenger rail obsolete," by Yair Wiseman [Opinion], IEEE Technology and Society Magazine, Vol: 38, Pages: 28-35, ISSN: 0278-0097
Driverless cars are expected to have the advantage of lifting requirements for driver's license ownership and fitness to drive. As such they may offer improved accessibility and mobility for those currently unable to drive, e.g., the elderly and disabled. Despite the postulated benefits, the role of driverless cars in future transport systems remains debatable, in terms of their potential to replace other transport modes or have a novel, unique, and complementary functionality.
Abeille A, Pawlak J, Polak J, 2018, The genome of an occupation: A task-based approach to modelling travel behaviour and work in mobile settings, 15th International Conference on Travel Behavior Research (IATBR)
Pawlak J, Abeille A, Polak J, et al., 2018, 300 Mbps+ connectivity on train: pre- and post- assessment of travel time use, productivity and ridership implications from a trial implementation in Scotland’, 15th International Conference on Travel Behavior Research (IATBR)
Zhao Y, Pawlak J, Polak J, 2018, Enrichment of transport big data: exploring performance of the inverse discrete choice modelling approach using Monte Carlo simulation, Enrichment of transport big data: exploring performance of the inverse discrete choice modelling approach using Monte Carlo simulation’
Pawlak J, Polak J, 2018, Actions speak louder than words: Modelling the relationship between rail passenger satisfaction and productivity and the intended and actual Internet use while travelling, Modelling
Pawlak J, Polak J, Sivakumar A, 2017, A framework for joint modelling of activity choice, duration, and productivity while travelling, Transportation Research Part B: Methodological: an international journal, Vol: 106, Pages: 153-172, ISSN: 0191-2615
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.
Abeille A, Pawlak J, Polak J, 2017, A framework for modelling the role of tasks associated with occupations in determining the propensity to and productivity of work in mobile settings, 50th Universities’ Transport Study Group Conference
Zhao Y, Pawlak J, Polak J, 2017, Inverse Discrete Choice Modelling (IDCM): Theoretical and Practical Considerations for Imputing Respondent Attributes from the Patterns of Observed Choices, Transportation Planning and Technology, Vol: 41, Pages: 58-79, ISSN: 0308-1060
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.
Mikolajczak P, Pawlak J, 2017, Factors affecting outcomes of EU-supported investments in innovation among SMEs in the Greater Poland (Wielkopolska) region, Poland, Journal of Research in Marketing and Entrepreneurship, Vol: 19, Pages: 140-160, ISSN: 1471-5201
PurposeThe European Union offers support mechanisms to help small and medium sized enterprises (SMEs) to innovate and grow. Given the substantial contribution of SMEs to national economies, the present paper explores what factors tend to be associated with the success of EU-supported innovation by SMEs in Poland during its early post-accession period.Design/methodology/approachA conceptual model relating the type of innovation, investment purpose, funding type and financial readiness, location and collaboration possibilities, company size and sector of operation to changes in the capital base, employment, unit price and revenue is proposed. This model is operationalised and estimated as a structural equations model and estimated using a sample of 110 SMEs surveyed in 2008 in the Greater Poland (Wielkopolska) region in Poland.FindingsTwo approaches to the successful use of innovation support have been observed among the studied companies. The first approach implements market innovations to establish a presence in foreign markets and to move the product or service up the value chain. The second approach uses the funding to de-risk workforce expansion and increase production capacity.Originality/valueThe paper provides the first systematic disaggregate level analysis of an early post-accession context where impacts of EU support for SME innovation are decomposed into effects of specific investment conditions and innovation type on changes in capital base, employment, unit price and ultimately revenue. The insights provided here are valuable for managers developing business and innovation strategies on the one hand, but also for policymakers responsible for creating an entrepreneurship friendly environment in emerging economies.
Zhao Y, Pawlak J, Polak J, 2017, Privacy-preserving socioeconomic attribute enrichment for mapping of passively-derived OD matrices, Royal Geographical Society with Institute of British Geographers Annual International Conference
Bris M, Pawlak J, Polak J, 2017, How is ICT use linked to household transport expenditure? A cross-national macro analysis of the influence of home broadband access, Journal of Transport Geography, Vol: 60, Pages: 231-242, ISSN: 0966-6923
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.
Pawlak J, 2016, Book review: ICT for Transport: Opportunities and Threats Edited by Nikolas Thomopoulos, Moshe Givoni, and Piet Rietveld, 2015. Cheltenham, UK: Edward Elgar Publishing Limited. £95.00 (hardback). ISBN 978 1 78347 128 7, Journal of Transport Geography, Vol: 55, Pages: 191-192, ISSN: 0966-6923
Pawlak J, Circella G, Polak J, et al., 2016, Is there anything exceptional about ICT use while travelling? A time allocation framework for and empirical insights into multitasking patterns and well-being implications from the Canadian General Social Survey, 95th Annual Meeting of Transportation Research Board, Publisher: Transportation Research Board
Pawlak J, Le Vine S, Polak J, et al., 2015, ICT and Physical Mobility – State of Knowledge and Future Outlook, ICT and Physical Mobility – State of Knowledge and Future Outlook, Munich, Publisher: Institute for Mobility Research ifmo: A Research Facility of the BMW Group
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