Publications
39 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.
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
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
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.
Afghari AP, Imani AF, Papadimitriou E, et al., 2021, Disentangling the effects of unobserved factors on seatbelt use choices in multi-occupant vehicles, JOURNAL OF CHOICE MODELLING, Vol: 41, ISSN: 1755-5345
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- Citations: 6
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.
Harding C, Imani AF, Srikukenthiran S, et al., 2021, Are we there yet? Assessing smartphone apps as full-fledged tools for activity-travel surveys, TRANSPORTATION, Vol: 48, Pages: 2433-2460, ISSN: 0049-4488
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- Citations: 8
Chibwe J, Heydari S, Imani AF, et al., 2021, An exploratory analysis of the trend in the demand for the London bike-sharing system: From London Olympics to Covid-19 pandemic, SUSTAINABLE CITIES AND SOCIETY, Vol: 69, ISSN: 2210-6707
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- Citations: 27
Rahman M, Yasmin S, Faghih-Imani A, et al., 2021, Examining the Bus Ridership Demand: Application of Spatio-Temporal Panel Models, JOURNAL OF ADVANCED TRANSPORTATION, Vol: 2021, ISSN: 0197-6729
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- Citations: 4
Vaughan J, Imani AF, Yusuf B, et al., 2020, Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data, EUROPEAN JOURNAL OF TRANSPORT AND INFRASTRUCTURE RESEARCH, Vol: 20, Pages: 269-285, ISSN: 1567-7133
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- Citations: 4
Faghih Imani A, Harding C, Srikukenthiran S, et al., 2020, Lessons from a Large-Scale Experiment on the Use of Smartphone Apps to Collect Travel Diary Data: The "City Logger" for the Greater Golden Horseshoe Area, TRANSPORTATION RESEARCH RECORD, Vol: 2674, Pages: 299-311, ISSN: 0361-1981
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- Citations: 3
Faghih-Imani A, Eluru N, 2020, A finite mixture modeling approach to examine New York City bicycle sharing system (CitiBike) users’ destination preferences, Transportation, Vol: 47, Pages: 529-553, ISSN: 1572-9435
Given the recent growth of bicycle-sharing systems (BSS) around the world, it is of interest to BSS operators/analysts to identify contributing factors that influence individuals’ decision processes in adoption and usage of bicycle-sharing systems. The current study contributes to research on BSS by examining user behavior at a trip level. Specifically, we study the decision process involved in identifying destination locations after picking up the bicycle at a BSS station. In the traditional destination/location choice approaches, the model frameworks implicitly assume that the influence of exogenous factors on the destination preferences is constant across the entire population. We propose a finite mixture multinomial logit (FMMNL) model that accommodates such heterogeneity by probabilistically assigning trips to different segments and estimate segment-specific destination choice models for each segment. Unlike the traditional destination choice based multinomial logit (MNL) model or mixed multinomial logit (MMNL), in an FMMNL model, we can consider the effect of fixed attributes across destinations such as users’ or origins’ attributes in the decision process. Using data from New York City bicycle-sharing system (CitiBike) for 2014, we develop separate models for members and non-members. We validate our models using hold-out samples and compare our proposed FMMNL model results with the traditional MNL and MMNL model results. The proposed FMMNL model provides better results in terms of goodness of fit measures, explanatory power and prediction performance.
Faghih-Imani A, Eluru N, 2020, Examining the impact of sample size in the analysis of bicycle sharing systems
Faghih-Imani A, Eluru N, 2020, A latent segmentation multinomial logit approach to examine bicycle sharing system users' destination preferences
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.
Anowar S, Faghih-Imani A, Miller EJ, et al., 2019, Regret minimization based joint econometric model of mode choice and departure time: a case study of university students in Toronto, Canada, TRANSPORTMETRICA A-TRANSPORT SCIENCE, Vol: 15, Pages: 1214-1246, ISSN: 2324-9935
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- Citations: 10
Imani AF, Miller EJ, Saxe S, 2019, Cycle accessibility and level of traffic stress: A case study of Toronto, JOURNAL OF TRANSPORT GEOGRAPHY, Vol: 80, ISSN: 0966-6923
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- Citations: 9
Clarry A, Imani AF, Miller EJ, 2019, Where we ride faster? Examining cycling speed using smartphone GPS data, SUSTAINABLE CITIES AND SOCIETY, Vol: 49, ISSN: 2210-6707
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- Citations: 19
Hasnat MM, Faghih-Imani A, Eluru N, et al., 2019, Destination choice modeling using location-based social media data, JOURNAL OF CHOICE MODELLING, Vol: 31, Pages: 22-34, ISSN: 1755-5345
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- Citations: 8
Reynaud F, Faghih-Imani A, Eluru N, 2018, Modelling bicycle availability in bicycle sharing systems: A case study from Montreal, SUSTAINABLE CITIES AND SOCIETY, Vol: 43, Pages: 32-40, ISSN: 2210-6707
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- Citations: 22
Tetreault L-F, Eluru N, Hatzopoulou M, et al., 2018, Estimating the health benefits of planned public transit investments in Montreal, ENVIRONMENTAL RESEARCH, Vol: 160, Pages: 412-419, ISSN: 0013-9351
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- Citations: 9
Paleti R, Imani AF, Eluru N, et al., 2017, An integrated model of intensity of activity opportunities on supply side and tour destination & departure time choices on demand side, JOURNAL OF CHOICE MODELLING, Vol: 24, Pages: 63-74, ISSN: 1755-5345
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- Citations: 4
Faghih-Imani A, Anowar S, Miller EJ, et al., 2017, Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 101, Pages: 11-21, ISSN: 0965-8564
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- Citations: 97
Faghih-Imani A, Eluru N, Paleti R, 2017, How bicycling sharing system usage is affected by land use and urban form: analysis from system and user perspectives, Conference of the International-Association-for-Travel-Behavior-Research (IATBR), Publisher: EDITORIAL BOARD EJTIR, Pages: 425-441, ISSN: 1567-7133
Shekarrizfard M, Faghih-Imani A, Tetreault L-F, et al., 2017, Modelling the Spatio-Temporal Distribution of Ambient Nitrogen Dioxide and Investigating the Effects of Public Transit Policies on Population Exposure, ENVIRONMENTAL MODELLING & SOFTWARE, Vol: 91, Pages: 186-198, ISSN: 1364-8152
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- Citations: 13
Faghih-Imani A, Hampshire R, Marla L, et al., 2017, An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 97, Pages: 177-191, ISSN: 0965-8564
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- Citations: 150
Shekarrizfard M, Faghih-Imani A, Tetreault L-F, et al., 2017, Regional assessment of exposure to traffic-related air pollution: Impacts of individual mobility and transit investment scenarios, SUSTAINABLE CITIES AND SOCIETY, Vol: 29, Pages: 68-76, ISSN: 2210-6707
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- Citations: 34
Faghih-Imani A, Eluru N, 2017, Examining the impact of sample size in the analysis of bicycle-sharing systems, TRANSPORTMETRICA A-TRANSPORT SCIENCE, Vol: 13, Pages: 139-161, ISSN: 2324-9935
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- Citations: 13
Faghih-Imani A, Eluru N, 2016, Determining the role of bicycle sharing system infrastructure installation decision on usage: Case study of montreal BIXI system, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 94, Pages: 685-698, ISSN: 0965-8564
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- Citations: 46
Shekarrizfard M, Faghih-Imani A, Crouse DL, et al., 2016, Individual exposure to traffic related air pollution across land-use clusters, TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, Vol: 46, Pages: 339-350, ISSN: 1361-9209
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- Citations: 12
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