77 results found
Latinopoulos C, Sivakumar A, Polak J, 2021, Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models, Energies, ISSN: 1996-1073
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, ISSN: 0968-090X
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.
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.
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
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
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.
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.
Zhu L, Krishnan R, Sivakumar A, et al., 2019, Traffic monitoring and anomaly detection based on simulation of Luxembourg Road network, IEEE Intelligent Transportation Systems Conference - ITSC 2019, Publisher: IEEE, Pages: 1-6
Traffic incidents which commonly result fromtraffic accidents, anomalous construction events and inclementweather can cause a wide range of negative impacts on urbanroad networks. Developing a high efficiency and transferabletraffic incident detection system plays an important role insolving the imbalance caused by traffic incidents betweentraffic demand and capacity. However, the existing literatureon transferability of traffic incident detection is rather limited.The objective of this paper is to provide an accurateand transferable incident detection approach based on therelationship between traffic variables and observed trafficincidents, in particular at a network level. We propose a deeplearning based method which has been calibrated using partof the collected traffic variables and the pre-assigned trafficincidents and then tested against the rest of the dataset. Theproposed method is compared to other benchmarks commonlyused in traffic incident detection, in terms of detection rate, falsepositive rate, f-measurement and detection time. The resultsindicate that the proposed method is significantly promising fortraffic incident detection with high accuracy and transferabilitycompared to the more widely used techniques in the literature.
Zhu L, Krishnan R, Guo F, et al., 2019, Early identification of recurrent congestion in heterogeneous urban traffic, IEEE Intelligent Transportation Systems Conference - ITSC 2019, Publisher: IEEE, Pages: 1-6
Urban traffic congestion has become a criticalissue that not only affects the quality of daily lives but alsoharms the environment and economy. Traffic patterns arerecurrent in nature, so is congestion. However, little attentionhas been paid to the development of methods that wouldenable early warning of the formation of congestion and itspropagation. This paper proposes a method for automatedearly congestion detection operating over time horizons rangingfrom half an hour to three hours. The method uses a deeplearning technique, Convolutional Neural Networks (CNN), andadapts it to the specific context of urban roads. Empiricalresults are reported from a busy traffic corridor in the city ofBath. Comprehensive evaluation metrics, including DetectionRate, False Positive Rate and Mean Time to Detection, areused to evaluate the performance of the proposed methodcompared to more conventional machine learning methodsincluding Feed-forward Neural Network and Random Forest.The results indicate that recurrent congestion can indeed bepredicted before it occurs and demonstrates that CNN basedmethod offers superior detection accuracy compared to theconventional machine learning methods in this context.
Dong Y, Polak J, Sivakumar A, et al., 2019, Disaggregate short-term location prediction based on recurrent neural network and an agent-based platform, Transportation Research Record: Journal of the Transportation Research Board, Vol: 2673, Pages: 657-668, ISSN: 0361-1981
With the growing popularity of mobile and sensory devices, there has been a strong research interest in short-term disaggregate-level location prediction. Such predictive models have huge application potential in several sectors to change and improve people’s daily life and experience. Existing methods in this research stream have mainly focused on the prediction of sequence of location, with valuable temporal information overlooked. In addition, data limitations have constrained the development and understanding from different algorithms. In this paper, the authors propose a recurrent neural network-based method (RNN and LSTM, long short-term memory) for the next and future location prediction. This model predicts the sequence in time, thus it can predict both when and where an individual will be in the future and the duration of the stay at each location. The predictive model is developed based on an agent-based simulation platform that can produce realistic spatial-temporal trajectory data at the individual level. Analysis of the simulated data has shown that RNN and LSTM are capable of predicting future locations with better results than other comparative methods, especially for agents with high location variability. Online prediction with true location information fed into the model later in the day would greatly improve the predicted results. However, significant variations can be observed at the zonal level, with all methods performing much better on frequently visited locations than less visited locations or irregular visits.
Manca F, Sivakumar A, Polak J, 2019, The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population, Transportation Research Part C: Emerging Technologies, Vol: 105, Pages: 611-625, ISSN: 0968-090X
The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an “accelerated reality” experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the “Reflexive Layers of Influence” (RLI) framework, the processes generated by
Li T, Guo F, Krishnamoorthy R, et al., 2019, Right-of-Way Reallocation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles, 51st Annual Conference of the Universities-Transport-Study-Group (UTSG)
Wu C, Le Vine S, Sivakumar A, et al., 2019, Traveller preferences for free-floating carsharing vehicle allocation mechanisms, Transportation Research Part C: Emerging Technologies, Vol: 102, Pages: 1-19, ISSN: 0968-090X
Free-floating carsharing (FFCS) fleets are inherently volatile spatio-temporally, which presents both a logistical challenge for operators and a service reliability issue for customers. In this study we present a stated-choice survey to investigate the attractiveness to customers of two mechanisms for managing fleet volatility: Virtual Queuing (VQ) and Guaranteed Advance Reservation (GAR). We investigate socio-demographic features and “Big Five” personality traits that are associated ceteris paribus with choosing to use the existing FFCS service model, willingness-to-pay (WTP) for VQ and GAR, and risky-choice behaviour under the uncertainty of FFCS systems. Data (n = 289; 232 employed in analysis) are sourced from existing users of a FFCS service in London, UK. Within the survey context, we found that customers are on average not willing to pay for VQ (i.e. negative WTP), however have £0.54 per journey WTP for GAR, with low-frequency FFCS users and users scoring highly on the Big Five “Conscientiousness” dimension having larger WTP for GAR. When analysing the two dimensions of uncertainty, we found that respondents exhibit risk-seeking behaviour towards price and weaker and insignificant risk-aversion towards walking time. This pattern holds across the three standard model types of nonlinear risky-choice behaviour that we investigated. The results are intended to be useful both to policymakers and carsharing operators who are likely, as the industry matures, to seek mechanisms to differentiate their service offers to better serve individual market segments with distinctive characteristics.
Sivakumar A, 2019, Handbook on Transport and Urban Planning in the Developed World, JOURNAL OF TRANSPORT GEOGRAPHY, Vol: 77, Pages: 143-143, ISSN: 0966-6923
Manca F, Sivakumar A, Hess S, 2019, Travel demand modelling, data collection and well-being, Transportation, Vol: 46, Pages: 303-305, ISSN: 0049-4488
Karamanis R, Angeloudis P, Sivakumar A, et al., 2018, Dynamic pricing in one-sided autonomous ride-sourcing markets, 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 3645-3650, ISSN: 2153-0009
Dynamic pricing has been used by Transportation Network Companies (TNCs) to achieve a balance between the volume of ride requests with numbers of available drivers on two-sided TNC markets. Given the desire to reduce operating costs and the emergence of Autonomous Vehicles (AVs), the introduction of TNC-owned AV fleets could convert such services into one-sided markets, where operators have full control of service supply. In this paper we investigate the impact of utility-based dynamic pricing for Autonomous TNCs (ATNCs) in one-sided markets. We test the method using an Agent-Based Model (ABM) of Greater London in conditions of monopoly and competition, focusing on a statically priced ATNC service that offers a mix of private and shared ride services. Public transport is considered as an alternative mode of transportation in both scenarios. Results indicate that in monopoly, dynamic pricing provides higher revenues than static pricing at non-peak hours when average waiting times are low. On the contrary, in competition, dynamic pricing is superior at peak hours where increased waiting times are observed, thus increasing the value of low waiting time rides. Overall, in both market structures, it is found that shared trips are more popular in dynamic pricing compared to static pricing.
Sivakumar A, Hess S, 2018, New and emerging methods for the improved modelling of travel behaviour: Transportation Research Part B: Special issue of papers from the IATBR 2015 conference, Transportation Research Part B: Methodological, ISSN: 0191-2615
Daina N, Latinopoulos C, Manca F, et al., 2018, An analysis of the joint dynamics of attitudes, intentions and behaviour in e-cycling, hEART 2018 - 7th Symposium of the European Association for Research in Transportation
Manca F, Sivakumar A, Polak J, et al., 2018, An empirical exploration of endogeneity in hybrid choice models with social influence measures, 15th International Conference on Travel Behaviour Research
Karamanis R, Angeloudis P, Sivakumar A, et al., 2018, Market dynamics between public transport and competitive ride-sourcing providers, 7th Symposium of the European Association for Research in Transportation, Publisher: hEART
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.
Hess S, Sivakumar A, 2017, Recent developments and future topics in choice modelling for travel behaviour research, Journal of Choice Modelling, Vol: 25, Pages: 1-2, ISSN: 1755-5345
Manca F, Sivakumar A, Axsen J, et al., 2017, Investigating social influence mechanisms and their inclusion in discrete choice models: recent findings on electric vehicle purchase preferences, 50th Universities’ Transport Study Group Annual Conference
Daina N, Sivakumar A, Polak JW, 2017, Electric vehicle charging choices: modelling and implications for smart charging services, Transportation Research Part C: Emerging Technologies, Vol: 81, Pages: 36-56, ISSN: 0968-090X
The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers’ response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers’ charging and travel behaviour. We propose a random utility model for joint EV drivers’ activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers.
Latinopoulos C, Sivakumar A, Polak JW, 2017, Response of electric vehicle drivers to dynamic pricing of parking and charging services: risky choice in early reservations, Transportation Research Part C: Emerging Technologies, Vol: 80, Pages: 175-189, ISSN: 0968-090X
When clusters of electric vehicles charge simultaneously in urban areas, the capacity of the power network might not be adequate to accommodate the additional electricity demand. Recent studies suggest that real-time control strategies, like dynamic pricing of electricity, can spread the demand and help operators to avoid costly infrastructure investments. To assess the effectiveness of dynamic pricing, it is necessary to understand how electric vehicle drivers respond to uncertain future prices when they charge their vehicle away from home. Even when data is available from electric vehicle trials, the lack of variability in electricity prices renders them insufficient for this analysis. We resolve this problem by designing a survey where we observe the stated preferences of the respondents for hypothetical charging services. A novel feature of this survey is its interface, which resembles an online or smartphone application for parking-and-charging reservations. The time-of-booking choices are evaluated within a risky-choice modelling framework, where expected utility and non-expected utility specifications are compared to understand how people perceive price probabilities. In the progress, we bring together theoretical frameworks of forward-looking behaviour in contexts where individuals were subject to equivalent price uncertainties. The results suggest that a) the majority of the electric vehicle drivers are risk averse by choosing a certain price to an uncertain one and b) there is a non-linearity in their choices, with a disproportional influence by the upper end of the price distribution. This approach gives new perspectives in the way people plan their travel activities in advance and highlights the impact of uncertainty when managing limited resources in dense urban centres. Similar surveys and analyses could provide valuable insights in a wide range of innovative mobility applications, including car-sharing, ride-sharing and on-demand services.
Manca F, Sivakumar A, Daina N, et al., 2017, Exploring the inclusion of social influence in a hybrid choice model of electric vehicle (EV) purchase preferences, 6th Symposium of the European Association for Research in Transportation
Calastri C, Hess S, Sivakumar A, 2017, Editorial: Triggers of behavioural change in an evolving world, European Journal of Transport and Infrastructure Research, Vol: 17, Pages: 304-305, ISSN: 1567-7141
Daina N, Sivakumar A, Polak JW, 2017, Modelling electric vehicles use: a survey on the methods, Renewable and Sustainable Energy Reviews, Vol: 68, Pages: 447-460, ISSN: 1364-0321
In the literature electric vehicle use is modelled using of a variety of approaches in power systems, energy and environmental analyses as well as in travel demand analysis. This paper provides a systematic review of these diverse approaches using a twofold classification of electric vehicle use representation, based on the time scale and on substantive differences in the modelling techniques. For time of day analysis of demand we identify activity-based modelling (ABM) as the most attractive because it provides a framework amenable for integrated cross-sector analyses, required for the emerging integration of the transport and electricity network. However, we find that the current examples of implementation of AMB simulation tools for EV-grid interaction analyses have substantial limitations. Amongst the most critical there is the lack of realism how charging behaviour is represented.
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