Publications
101 results found
Singh A, Faghih Imani A, Sivakumar A, et al., 2024, A joint analysis of accessibility and household trip frequencies by travel mode, Transportation Research Part A: Policy and Practice, Vol: 181, ISSN: 0191-2607
This paper examines the endogenous relationship between residential level of accessibility and household trip frequencies to tease out the direct and indirect effects of observed behavioural differences. We estimate a multivariate ordered probit model system, which allows dependence in both observed and unobserved factors, using data from the 2016 Transportation Tomorrow Survey (TTS), a household travel survey in the Greater Golden Horseshoe Area (GGH) in Toronto. The modelling framework is used to analyse the influence of exogenous variables on eight outcome variables of accessibility levels and trip frequencies by four modes (auto, transit, bicycle and walk), and to explore the nature of the relationships between them. The results confirm our hypothesis that not only does a strong correlation exist between the residential level of accessibility and household trip frequency, but there are also direct effects to be observed. The complementarity effect between auto accessibility and transit trips, and the substitution effect observed between transit accessibility and auto trips highlight the residential neighbourhood dissonance of transit riders. It shows that locations with better transit service are not necessarily locations where people who make more transit trips reside. Essentially, both jointness (due to error correlations) as well as directional effects observed between accessibility and trip frequencies of multiple modes offer strong support for the notion that accessibility and trip frequency by mode constitute a bundled choice and need to be considered as such.
Li T, Guo F, Krishnan R, et al., 2024, An analysis of the value of optimal routing and signal timing control strategy with connected autonomous vehicles, Journal of Intelligent Transportation Systems, Vol: 28, Pages: 252-266, ISSN: 1547-2450
With the emergence of connected and automated technologies, Connected Autonomous Vehicles (CAVs) are able to communicate and interact with other vehicles and signal controllers. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications open up an opportunity to improve routing and signal timing efficiency with additional information from CAVs, such as prior travel time and signal green time. Most of the existing research on routing and signal timing for Human Driven Vehicles (HDVs) has to face the fact that human drivers only have partial knowledge about travel costs and traffic status on the road network, which typically reduces the system efficiency. In this paper, the impacts of additional information from CAVs on routing and signal timing efficiency in terms of total travel time have been investigated. An Optimal Routing and Signal Timing (ORST) control strategy for CAVs has been proposed and compared with four existing routing and signal timing strategies where drivers have different levels of information. The results of the simulation demonstrate that with additional information from CAVs, ORST can reduce about 49% of the total travel time compared with Stochastic User Equilibrium (SUE) and about 10% of the total travel time compared with User Equilibrium (UE).
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.
Yu L, Guo F, Sivakumar A, et al., 2023, Few-Shot traffic prediction based on transferring prior knowledge from local network, Transportmetrica B: Transport Dynamics, Vol: 11, Pages: 1664-1686, ISSN: 2168-0566
Short-term traffic prediction has been widely studied in the community of Intelligent Transport Systems for decades. Despite the advances in machine learning-based prediction techniques, a challenging problem that affects the applications of such methods in practice is the prevalence of insufficient data across an entire road network. To address this few-shot traffic prediction problem at a local network scale, we develop a hybrid framework in conjunction with the prior knowledge transferring algorithm and two widely used models, i.e. Long-short Term Memory and Spatial–Temporal Graph Convolutional Neural Network. The proposed modelling framework is trained and tested using five-minute interval traffic flow data collected from London under different few-shot learning scenarios. Results show that transferring local network prior knowledge can improve the accuracy of both one-step prediction and multi-step prediction under inadequate data conditions, regardless of the deep-learning tool used.
Matthews B, Hall J, Batty M, et al., 2023, DAFNI: a computational platform to support infrastructure systems research, Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction, Vol: 176, Pages: 108-116, ISSN: 2397-8759
Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a ‘multi-systems’ approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users’ models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project.
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
Jin Z, Guo F, Sivakumar A, et al., 2023, Car-following model calibration with load effect as additional optimisation objective, Structure and Infrastructure Engineering, ISSN: 1573-2479
Calibrating a car-following model is an essential component in the traffic load analysis of bridges, since the parameter set fundamentally determines the spatiotemporal distributions of the vehicles and the magnitude of the load effects. In bridge engineering, car-following models are adapted from transportation engineering so that they do not consider load effects in the calibration process. This paper proposes a novel framework that factors vehicle motions and load effects into a multi-objective optimisation problem to calibrate car-following models. The Intelligent Driver Model (IDM) and the Gipps’ model are introduced for comparison. In the testing, three different types and spans of bridges are selected to compare the models in terms of fitness, robustness, and compactness of the solution. Within the proposed framework, the Gipps’ model proves to be superior in terms of fitness but achieves poor performance in robustness, while the IDM exhibits the opposite pattern. The solution compactness of the Gipps’ model improves with higher truck weights only in the circumstance of the load effects type with a unimodal influence line. Overall, the Gipps’ model is recommended for analysis with abundant data. Otherwise, the IDM can be adopted for a non-optimal but robust result.
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.
Luan J, Daina N, Reinau KH, et al., 2022, A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph, Expert Systems with Applications, Vol: 210, Pages: 1-17, ISSN: 0957-4174
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.
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.
Wu C, Le Vine S, Sivakumar A, et al., 2022, Dynamic pricing of free-floating carsharing networks with sensitivity to travellers’ attitudes towards risk, Transportation, Vol: 49, Pages: 679-702, ISSN: 0049-4488
Free-floating carsharing (FFCS) systems are characterised by volatile fleet distribution as well as customers’ heterogeneous price sensitivity and spatiotemporal flexibility. There is thus an opportunity for operators to employ dynamic pricing to manage various aspects of fleet allocation: which customer is provided which vehicle, at what time and price, and the agreed pick-up and drop-off location. While there are emerging examples of dynamic pricing in FFCS, there is as yet no general framework for the interaction of consumer and operator behaviours in this context, most particularly consumer response to the inherent risks and uncertainties in the journey characteristics noted above. In this study, we propose a choice-based framework for modelling the supply/demand interaction, drawing on behavioural models of decision-making in risky choice contexts and empirical stated-choice data of user preferences in a dynamically priced FFCS market. In addition to the ‘spot market’ mechanism of dynamic pricing, the proposed framework is capable of evaluating operator strategies of allowing (at an agreed price) customers to make guaranteed advance reservations. We demonstrate that this approach allows the system operator to set an optimal pricing strategy regardless of whether user risk preferences are risk-seeking or risk-averse. We also demonstrate the applicability of the proposed framework when the operator seeks to maximise revenue (as with a private operator) vs social welfare (as with a public operator). In the case study which employs empirical user preferences, we show that users’ risk preferences have a relatively small impact on revenue, however the impacts are much larger if there is a mismatch between users’ actual risk preferences and the system operator’s assumptions regarding users’ risk preferences.
Manca F, Sivakumar A, Polak J, 2022, Capturing the effect of multiple social influence sources on the adoption of new transport technologies and services, Journal of Choice Modelling, Vol: 42, Pages: 1-18, ISSN: 1755-5345
This paper presents a conceptual and modelling framework that makes it possible to disentangle and quantify multiple social influence effects affecting the individual’s choice behaviour. The proposed structure simultaneously accounts for live social interaction effects, social influence processes of diffusion, translation and reflexivity, conformity processes related to social norms, such as the hypothetical adoption rate within a social network, and correlated effects related to psychometric attitudinal characteristics of peers. The modelling framework is applied to investigate the adoption of bike-sharing in a student cohort during a public transport strike. A joint hybrid choice model is estimated using a two-wave stated preference dataset and incorporating latent variables, social influence measures and live social interactions with dynamic ‘inertia’ processes. In this empirical context, results and sensitivity analyses show that the social influence variables are highly significant and explain part of the heterogeneity in choosing bike-sharing. A greater utility for this travel mode is associated with a greater hypothetical bike-sharing adoption and with live social interactions improving the understanding of the bike-sharing benefits. The results also suggest that conformity processes and social interaction effects can have a higher impact on the choice than correlated effects related to the attitudinal ‘propensity towards cars’ in the social network. This study, therefore, provides further evidence that, in the context of new technology adoptions, the choice is not only driven by explanatory variables that can be generally observed but may well be affected by social factors facilitating the exchange of information and the understanding of the individuals.
Manca F, Daina N, Sivakumar A, et al., 2022, Using digital social market applications to incentivise active travel: Empirical analysis of a smart city initiative, Sustainable Cities and Society, Vol: 77, ISSN: 2210-6707
Information and communication technologies (ICTs), such as mobile communication networks, and behaviour-based approaches for citizen engagement play a key role in making future cities sustainable and tackling persistent problems in high-density urban areas. In the context of Sharing Cities, an EU-funded programme aiming to deliver smart city solutions in areas such as citizen participation and infrastructure improvements of buildings and mobility, a prominent intervention has been the deployment and monitoring of a Digital Social Market (DSM) tool in Milan (Italy). The DSM allows cities to engage with residents and encourage sustainable behaviours by offering non-monetary rewards. This paper aims to evaluate the effectiveness of the DSM approach to promote active travel (cycling and walking) by analysing the data collected through the app as well as through participant surveys. Our model results show that a broader engagement with the DSM app (number of claps to posts, number of posts made, non-monetary rewards earned by participating in non-travel events) is positively correlated with the monitored level of active travel. Lifestyles, attitudes, and social influence also explain the variability in cycling and walking. This highlights the importance of investigating these factors when replicating such initiatives on a large scale.
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.
Latinopoulos C, Patrier A, Sivakumar A, 2021, Planning for e-scooter use in metropolitan cities: a case study for Paris, Transportation Research Part D: Transport and Environment, Vol: 100, ISSN: 1361-9209
E-scooters are a growing part of transportation networks, offering a sustainable solution for first and last mile trips. However, without caution in operation and regulation they can become a liability for riders and cities as a whole. Inappropriate implementations led to a surge in policy initiatives to mitigate the negative effects and enable the harmonic coexistence of e-scooters with other travel modes. This paper brings together information on the current state of the market and knowledge from successful and failed trials. It proposes an evaluation framework that classifies all the aspects of interest for planners and policymakers. A case study is built to assess this framework for one of the pioneers in micromobility, the city of Paris. The results along with the discussion at the end, aim to provide insights to researchers and stakeholders associated with designing new e-scooter systems or optimizing the performance of existing ones.
Trask A, Wills K, Green T, et al., 2021, Impacts of COVID-19 on the Energy System, Impacts of COVID-19 on the Energy System
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.
Li J, Guo F, Sivakumar A, et al., 2021, Transferability Improvement in Short-term Traffic Prediction using Stacked LSTM Network, Transportation Research Part C: Emerging Technologies, Vol: 124, ISSN: 0968-090X
Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to provide proactive traffic state information to road network operators. A variety of methods to predict traffic variables in the short-term can be found in the literature, ranging from time-series algorithms, machine learning tools and deep learning methods to a selective hybrid of these approaches. Despite the advances in prediction techniques, a challenging problem that affects the application of such methods in the real world is the prevalence of insufficient data across an entire network. It is rare that extensive historical training data required for model training are available for all the links in a city. In order to address this data insufficiency problem, this paper applies transfer learning techniques to machine learning methods in short-term traffic prediction. All the traffic data used in this paper were collected from Highways England road networks in the UK. The results show that through improving the transferability of machine learning-based models, the computational burden due to the model training process can be significantly reduced and the prediction accuracy under data deficient scenarios can be improved for one-step ahead prediction. However, the prediction accuracy gradually decreases in multi-step ahead prediction. It is also found that the accuracy of the proposed hybrid method is highly dependent upon consistency between datasets but less dependent on geographical attributes of links.
Latinopoulos C, Sivakumar A, Polak J, 2021, Optimal pricing of vehicle-to-grid services using disaggregate demand models, Energies, Vol: 14, ISSN: 1996-1073
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.
Manca F, Sivakumar A, 2020, Modelling live social interactions and indirect social influence affecting the adoption of transport technologies and services: a bike sharing case study, hEART 2020: 9th Symposium of the European Association for Research in Transportation
Wu C, Le Vine S, Sivakumar A, 2020, Exploratory analysis of young adults' trajectories through the UK driving license acquisition process., Traffic Injury Prevention, Vol: 22, Pages: 37-42, ISSN: 1538-957X
OBJECTIVE: The UK is one of many high-income countries to experience a decline in driving license acquisition among young adults in the 2000s. This paper draws on newly available nationally representative microdata that captures the progress of individual drivers through the UK driving license acquisition process, to establish socio-demographic correlates. METHODS: Using the 2016 and 2017 editions of England's National Travel Survey data, a series of binary logit models were employed to identify factors associated with progression through the various phases of the UK's driving license acquisition process. Factors that are associated with (1) the frequency of taking the driving license tests, (2) the number of times having failed the theory and driving tests are then identified. RESULTS: The socio-demographic explanators considered were each found to be associated with driving license holding in intuitive ways that are consistent with prior literature. However, relatively few factors are significantly associated with progress through the steps of the license acquisition process, and the goodness-of-fit for progress through these intermediate phases are generally lower (indicating that other unobservable idiosyncratic personal or contextual characteristics are dominant in these processes). A consistent theme is the strong relationship with labor market participation. Links between income and the intermediate phases, however, were generally weaker. Age is negatively associated with progress through the early phases when respondents are applying for provisional license and taking theory test, but this relationship turns positive in later stages of the acquisition process. CONCLUSION: To the authors' knowledge, this study is the first opportunity to evaluate this novel data resource covering the UK's driving license acquisition process. This is an important research direction to help policymakers understand young adults' delay in acquiring licenses, particularly the exte
Manca F, Sivakumar A, Daina N, et al., 2020, Modelling the influence of peers’ attitudes on choice behaviour: theory and empirical application on electric vehicle preferences, Transportation Research Part A: Policy and Practice, Vol: 140, Pages: 278-298, ISSN: 0191-2607
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.
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
Dixit M, Sivakumar A, 2020, Capturing the impact of individual characteristics on transport accessibility and equity analysis, Transportation Research Part D: Transport and Environment, Vol: 87, Pages: 1-17, ISSN: 1361-9209
Transport accessibility experienced by an individual depends on their needs and abilities, as represented by their individual characteristics, such as age, income and gender. Although important from an equity perspective, the individual component of accessibility is currently ignored in most transport equity studies.This paper evaluates the impact of including individual characteristics into logsum-based accessibility measures for transport equity analysis. Using data from the London Travel Demand Survey (LTDS) 2011-13, two alternate log sum measures of accessibility are specified –with and without individual characteristics. An empirical analysis of spatial, social and economic equity is conducted using both the measures,and the outcomes are compared.The results clearly demonstrate that ignoring individual characteristics in logsum measures of accessibility can lead to unreliable outcomes for social and economic equity analysis,but do not add significant value when aggregated across large geographical zones for spatial equity analysis.Overall, ignoring individual characteristics masks the disparity in distribution of accessibility, as measured by the Gini index. Although not straightforward, the difference between accessibility patterns using the two logsum measures also yields insights into the possible causes of inequity, which can provide actionable inputs to policy makers.The study highlights that personal needs and abilities are often responsible for accessibility variations among individuals and ignoring them can result in a misleading picture of equity,as demonstrated quantitatively in this paper.
Li T, Guo F, Krishnan R, et al., 2020, Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles, Transportation Research Part C: Emerging Technologies, Vol: 115, ISSN: 0968-090X
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.
Kim I-C, Daina N, Sivakumar A, 2020, DECISION SUPPORT TOOL FOR OPTIMAL CHARGING SCHEDULING FOR INDIVIDUAL ELECTRIC VEHICLE USERS, 4th International Conference on Smart Grid and Smart Cities
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.
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