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Over the past decade, digital innovations have challenged traditional concepts of urban transport and introduced new opportunities for improving people’s access to services and economic opportunities. In response, cities have redefined their technology roadmaps and business strategies and are increasingly encouraging more collaboration between the public and private sectors. Technology and service-led disruptions are not only impacting private car usage models, they are also enhancing multimodal journey planning and payment options which will drive new mobility initiatives in future cities. From data analytics, through to machine learning, on-demand shared mobility, Blockchain, Fog Computing, and connected vehicles, these technologies are set to transform the landscape of urban transport and reduce dependence on private cars. These technologies offer opportunities to measure and monitor city functions, manage the performance of transport infrastructure, and identify where services need improvements. This chapter discusses the role of disruptive innovations, some established and others emerging, and their impacts on transport operations. The chapter also describes how digitalization of physical assets provides opportunities to enable real-time monitoring and analysis of urban mobility and movement of freight. The chapter draws on practical applications and explains the key behavioral, societal, and technological impacts and benefits. Finally, the chapter provides insights into the potential value derived from typical use cases in smart mobility solutions enabled by technology-led innovations.
Hussein Dia; Saeed Bagloee; Hadi Ghaderi. Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility. Handbook of Smart Cities 2021, 1163 -1198.
AMA StyleHussein Dia, Saeed Bagloee, Hadi Ghaderi. Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility. Handbook of Smart Cities. 2021; ():1163-1198.
Chicago/Turabian StyleHussein Dia; Saeed Bagloee; Hadi Ghaderi. 2021. "Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility." Handbook of Smart Cities , no. : 1163-1198.
The transport sector is a significant contributor to global emissions. In Australia, it is the third largest source of greenhouse gases and is responsible for around 17% of emissions with passenger cars accounting for around half of all transport emissions. Governments at all levels have identified a need for a reduction in transport carbon emissions to meet their net zero emissions targets. This research aims to help decision makers estimate the carbon footprint of transport networks within their jurisdictions and evaluate the impacts of emission-reduction interventions, through development of a simulation-based low carbon mobility assessment model. The model was developed based on a framework that integrates multiple mobility components including individual travel preferences, traffic simulation, and an assessment interface to create a seamless tool for the end-user. The feasibility of the assessment model was demonstrated in a case study for a local city council in Melbourne. In one of many scenarios reported in this paper, the model showed that maintaining current levels of emissions would require a 20% reduction in vehicle trips by 2030, and a much larger reduction would be required to reduce the levels of greenhouse gas emissions and achieve desired emissions reduction targets. The paper concludes with recommendations and future directions to extend the model’s capabilities and applications.
Damian Moffatt; Hussein Dia. Development and Evaluation of Simulation-Based Low Carbon Mobility Assessment Models. Future Transportation 2021, 1, 134 -153.
AMA StyleDamian Moffatt, Hussein Dia. Development and Evaluation of Simulation-Based Low Carbon Mobility Assessment Models. Future Transportation. 2021; 1 (2):134-153.
Chicago/Turabian StyleDamian Moffatt; Hussein Dia. 2021. "Development and Evaluation of Simulation-Based Low Carbon Mobility Assessment Models." Future Transportation 1, no. 2: 134-153.
Autonomous Mobility-on-Demand (AMoD) systems hold potential promise for addressing urban mobility challenges. Their key principle is to utilize fleets of shared self-driving vehicles to respond to customer demand on flexible routes in real-time. This research investigates station-based AMoD car-sharing systems and uses scenario analyses to identify plausible future paths for their deployment. A traffic simulation model which implements real-time rebalancing of idle vehicles is developed to evaluate their performance under uncertain travel demands. Unlike other literature which assumed homogeneous demand and resulted in low increases in vehicle kilometers travelled (VKT), this study relied on realistic heterogeneous demand and showed a significant increase in VKT. A case study for Melbourne demonstrated the impacts and showed that while AMoD can meet the demand for travel using only 16% of the current vehicle fleet, they would produce 77% increase in VKT. This would significantly increase congestion in any real-world scenario and goes against the hype of AMoD being the answer to congestion problems.
Farid Javanshour; Hussein Dia; Gordon Duncan; Rusul Abduljabbar; Sohani Liyanage. Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -12.
AMA StyleFarid Javanshour, Hussein Dia, Gordon Duncan, Rusul Abduljabbar, Sohani Liyanage. Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-12.
Chicago/Turabian StyleFarid Javanshour; Hussein Dia; Gordon Duncan; Rusul Abduljabbar; Sohani Liyanage. 2021. "Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.
Several past studies developed acceleration/deceleration rate models as a function of a single explanatory variable. Most of them were spot speed studies with speeds measured at specific locations on curves (usually midpoint of the curve) and tangents to determine acceleration and deceleration rates. Fewer studies adopted an estimated value of 0.85 m/s2 for both deceleration and acceleration rates while approaching and departing curves, respectively. In this study, instrumented vehicles with a high-end GPS (global positioning system) device were used to collect the continuous speed profile data for two-lane rural highways. The speed profiles were used to locate the speeds at the beginning and end of deceleration/acceleration on the successive road geometric elements to calculate the deceleration/acceleration rate. The influence of different geometric design variables on the acceleration/deceleration rate was analysed to develop regression models. This study also inspeced the assumption of constant operating speed on the horizontal curve. The study results indicated that mean operating speeds measured at the point of curvature (PC) or point of tangency (PT), the midpoint of curve (MC), and the end of deceleration in curve were statistically different. Acceleration/deceleration rates as a function of different geometric variables improved the accuracy of models. This was evident from model validation and comparison with existing models in the literature. The results of this study highlight the significance of using continuous speed profile data to locate the beginning and end of deceleration/acceleration and considering different geometric variables to calibrate acceleration/deceleration rate models.
Vinayak Malaghan; Digvijay S. Pawar; Hussein Dia. Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data. Journal of Advanced Transportation 2021, 2021, 1 -17.
AMA StyleVinayak Malaghan, Digvijay S. Pawar, Hussein Dia. Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data. Journal of Advanced Transportation. 2021; 2021 ():1-17.
Chicago/Turabian StyleVinayak Malaghan; Digvijay S. Pawar; Hussein Dia. 2021. "Modeling Acceleration and Deceleration Rates for Two-Lane Rural Highways Using Global Positioning System Data." Journal of Advanced Transportation 2021, no. : 1-17.
Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.
Rusul Abduljabbar; Hussein Dia; Pei-Wei Tsai; Sohani Liyanage. Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction. Future Transportation 2021, 1, 21 -37.
AMA StyleRusul Abduljabbar, Hussein Dia, Pei-Wei Tsai, Sohani Liyanage. Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction. Future Transportation. 2021; 1 (1):21-37.
Chicago/Turabian StyleRusul Abduljabbar; Hussein Dia; Pei-Wei Tsai; Sohani Liyanage. 2021. "Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction." Future Transportation 1, no. 1: 21-37.
Blockchain is an emerging technology expected to unleash disruptive and transformational forces in many fields. As a decentralized distributed digital ledger, Blockchain technology has the potential to become an underlying operating system that governs the way our cities function in the future. This paper explores the potential of Blockchain as a technology for enabling sustainable future cities and empowering citizen co-creation and participation, promoting civic engagement and achieving Smart Cities' efficiency and transparency objectives. The article first provides background to the Blockchain concept, current and emerging trends in its development, followed by a survey of potential urban applications with particular attention to the domains of governance, transport, supply chain and logistics. Next, the paper presents the challenges that must be overcome to enable widespread adoption and deployment of the technology. The paper is intended for urban researchers and practitioners alike and will be a contribution that can be used by a broad range of stakeholders who wish to understand the disruptive forces of Blockchain, opportunities and potential applications, as well as challenges in the context of Smart Cities.
Saeed Asadi Bagloee; Mitra Heshmati; Hussein Dia; Hadi Ghaderi; Chris Pettit; Mohsen Asadi. Blockchain: The operating system of smart cities. Cities 2021, 112, 103104 .
AMA StyleSaeed Asadi Bagloee, Mitra Heshmati, Hussein Dia, Hadi Ghaderi, Chris Pettit, Mohsen Asadi. Blockchain: The operating system of smart cities. Cities. 2021; 112 ():103104.
Chicago/Turabian StyleSaeed Asadi Bagloee; Mitra Heshmati; Hussein Dia; Hadi Ghaderi; Chris Pettit; Mohsen Asadi. 2021. "Blockchain: The operating system of smart cities." Cities 112, no. : 103104.
The geometric elements of the road, such as tangents and curves, play a vital role in road safety because significant crashes are reported on the horizontal curves and tangent-to-curve transitions. Literature reveals that inconsistent geometric design of roads violates driver’s expectation of operating speed leading to crashes. For safe manoeuver, it is necessary to achieve consistent operating speed with road geometry based on the driver’s expectations rather than the designer’s perception. Estimation of reliable operating speeds in the design phase will help to design safer road alignments. Several past studies developed operating speed models on the curves and tangent-to-curve transitions. However, these models used spot speed data with the assumption that the constant speed persists on the horizontal curves and entire deceleration/acceleration occurs on the approach/departure tangents. In this study, an instrumented vehicle with a high-end GPS (global positioning system) device was used to obtain the continuous speed profiles for passenger cars which resulted in reliable and robust peed prediction models on a tangent, curve, and tangent-to-curve. to speed prediction models for a tangent, curve, and tangent-to-curve. The study also establishes a relationship between the differential of the 85th percentile speed (ΔV85) and 85th percentile speed differential (Δ85V). The analysis results revealed that ΔV85 underestimates Δ85V by 5.32 km/h, and Δ85V predicted the actual speed reduction from tangent-to-curve transitions. Statistical analysis results showed low errors, variations, and strong correlation of the proposed models with the field data. The models developed in the present study were validated and compared with various other models available in the literature. The comparative study highlights the importance of using continuous speed profile data to calibrate the operating speed models.
Vinayak Malaghan; Digvijay S. Pawar; Hussein Dia. Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India. Journal of Transportation Engineering, Part A: Systems 2020, 146, 04020124 .
AMA StyleVinayak Malaghan, Digvijay S. Pawar, Hussein Dia. Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India. Journal of Transportation Engineering, Part A: Systems. 2020; 146 (11):04020124.
Chicago/Turabian StyleVinayak Malaghan; Digvijay S. Pawar; Hussein Dia. 2020. "Modeling Operating Speed Using Continuous Speed Profiles on Two-Lane Rural Highways in India." Journal of Transportation Engineering, Part A: Systems 146, no. 11: 04020124.
Over the past decade, digital innovations have challenged traditional concepts of urban transport and introduced new opportunities for improving people’s access to services and economic opportunities. In response, cities have redefined their technology roadmaps and business strategies and are increasingly encouraging more collaboration between the public and private sectors. Technology and service-led disruptions are not only impacting private car usage models, they are also enhancing multimodal journey planning and payment options which will drive new mobility initiatives in future cities. From data analytics, through to machine learning, on-demand shared mobility, Blockchain, Fog Computing, and connected vehicles, these technologies are set to transform the landscape of urban transport and reduce dependence on private cars. These technologies offer opportunities to measure and monitor city functions, manage the performance of transport infrastructure, and identify where services need improvements. This chapter discusses the role of disruptive innovations, some established and others emerging, and their impacts on transport operations. The chapter also describes how digitalization of physical assets provides opportunities to enable real-time monitoring and analysis of urban mobility and movement of freight. The chapter draws on practical applications and explains the key behavioral, societal, and technological impacts and benefits. Finally, the chapter provides insights into the potential value derived from typical use cases in smart mobility solutions enabled by technology-led innovations.
Hussein Dia; Saeed Bagloee; Hadi Ghaderi. Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility. Handbook of Smart Cities 2020, 1 -36.
AMA StyleHussein Dia, Saeed Bagloee, Hadi Ghaderi. Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility. Handbook of Smart Cities. 2020; ():1-36.
Chicago/Turabian StyleHussein Dia; Saeed Bagloee; Hadi Ghaderi. 2020. "Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility." Handbook of Smart Cities , no. : 1-36.
On-demand multi-passenger shared transport options are increasingly being promoted as an influential strategy to reduce traffic congestion and emissions and improve the convenience and travel experience for passengers. These services, often referred to as on-demand public transport, are aimed at meeting personal travel demands through the use of shared vehicles that run on flexible routes using advanced tools for dynamic scheduling. This paper presents an agent-based traffic simulation model that was developed to evaluate the performance of on-demand public transport and compare it with existing scheduled bus services using a case study of the inner city of Melbourne in Australia. The key performance measures used in the comparative evaluation included quality of service and passenger experience in terms of waiting times, the efficiency of service and operations in terms of hourly vehicle utilization, and system efficiency in terms of trip completion rates, passenger kilometers travelled and total passenger trip times. The results showed significant benefits for passengers who use on-demand bus services compared to scheduled bus services. The on-demand bus service was found to reduce average total passenger waiting times by 89% during the Morning Peak; by 78% during the Mid-Day period; by 81% during the Afternoon Peak; and by more than 95% during other periods of the day. From an operator’s perspective, the on-demand services were found to achieve around 70% vehicle utilization rates during peak hours compared to a utilization rate not exceeding 16% for the scheduled bus services. Even during off-peak periods, the occupancies for on-demand services were almost twice the vehicle occupancies for scheduled bus services. In terms of system efficiency, the on-demand services achieved a trip completion rate of 85% compared to a trip completion rate of 67% for the scheduled bus services. The total passenger-kilometers travelled was similar for both scheduled and on-demand bus services, which refutes claims that on-demand bus services induce more kilometers of travel. The trip completion times were around 55% shorter for on-demand bus services compared to scheduled services, which represents a significant saving in travel time for users. Finally, the paper presents average emissions per completed trip for both types of services and shows a significant reduction in emissions for on-demand services compared to conventional bus services. These include, on average, a 48% reduction in CO2 emissions per trip; 82% reduction in NO emissions per trip; and 41% reduction in p.m.10 emissions per trip. These findings clearly demonstrate the superior benefits of on-demand bus services compared to scheduled bus services.
Sohani Liyanage; Hussein Dia. An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services. Sustainability 2020, 12, 4117 .
AMA StyleSohani Liyanage, Hussein Dia. An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services. Sustainability. 2020; 12 (10):4117.
Chicago/Turabian StyleSohani Liyanage; Hussein Dia. 2020. "An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services." Sustainability 12, no. 10: 4117.
Hussein Dia. Research Agenda for Shaping the Future of Smart Mobility. Journal of Transport & Health 2019, 14, 100656 .
AMA StyleHussein Dia. Research Agenda for Shaping the Future of Smart Mobility. Journal of Transport & Health. 2019; 14 ():100656.
Chicago/Turabian StyleHussein Dia. 2019. "Research Agenda for Shaping the Future of Smart Mobility." Journal of Transport & Health 14, no. : 100656.
Trends in vehicle electrification have gathered pace over the past few years and are set to continue. This has been the result of a number of converging factors: (1) changing consumer preferences and attitudes in support of electric vehicles; (2) improved battery costs making them more affordable; (3) stricter environmental policies curbing production and sale of petrol vehicles by 2040; and (4) broader access to charging infrastructure. The on-going momentum suggests an inflection point in the early 2030s when around 30% of all vehicles sold will be electrified. This chapter provides a cohesive body of work on the changing landscape of vehicle electrification, and pathways for unlocking their widespread deployment.
Hussein Dia. Rethinking Urban Mobility: Unlocking the Benefits of Vehicle Electrification. Decarbonising the Built Environment 2019, 83 -98.
AMA StyleHussein Dia. Rethinking Urban Mobility: Unlocking the Benefits of Vehicle Electrification. Decarbonising the Built Environment. 2019; ():83-98.
Chicago/Turabian StyleHussein Dia. 2019. "Rethinking Urban Mobility: Unlocking the Benefits of Vehicle Electrification." Decarbonising the Built Environment , no. : 83-98.
This chapter presents a cohesive body of work on the policy principles and practical applications for low carbon mobility in both urban and suburban contexts. The chapter identifies policy instruments that prioritise pathways and solutions with the likelihood of greatest impact in achieving reductions in transport energy use. These pathways focus on integrated land-use and transport policies; transit-oriented and pedestrian-oriented cities; public transport and active travel options. They also recognise the role of the sharing economy and digital innovation in addressing the modern-day demands of urban living and travel. The chapter concludes by summarising the impacts of these interventions in meeting people’s needs for travel while reducing their carbon footprint.
Hussein Dia; Michael Taylor; John Stone; Sekhar Somenahalli; Stephen Cook. Low Carbon Urban Mobility. Decarbonising the Built Environment 2019, 259 -285.
AMA StyleHussein Dia, Michael Taylor, John Stone, Sekhar Somenahalli, Stephen Cook. Low Carbon Urban Mobility. Decarbonising the Built Environment. 2019; ():259-285.
Chicago/Turabian StyleHussein Dia; Michael Taylor; John Stone; Sekhar Somenahalli; Stephen Cook. 2019. "Low Carbon Urban Mobility." Decarbonising the Built Environment , no. : 259-285.
On-demand shared mobility is increasingly being promoted as an influential strategy to address urban transport challenges in large and fast-growing cities. The appeal of this form of transport is largely attributed to its convenience, ease of use, and affordability made possible through digital platforms and innovations. The convergence of the shared economy with a number of established and emerging technologies—such as artificial intelligence (AI), Internet of Things (IoT), and Cloud and Fog computing—is helping to expedite their deployment as a new form of public transport. Recently, this has manifested itself in the form of Flexible Mobility on Demand (FMoD) solutions, aimed at meeting personal travel demands through flexible routing and scheduling. Increasingly, these shared mobility solutions are blurring the boundaries with existing forms of public transport, particularly bus operations. This paper presents an environmental scan and analysis of the technological, social, and economic impacts surrounding disruptive technology-driven shared mobility trends. Specifically, the paper includes an examination of current and anticipated external factors that are of direct relevance to collaborative and low carbon mobility. The paper also outlines how these trends are likely to influence the mobility industries now and into the future. The paper collates information from a wide body of literature and reports on findings from actual ‘use cases’ that exist today which have used these disruptive mobility solutions to deliver substantial benefits to travellers around the world. Finally, the paper provides stakeholders with insight into identifying and responding to the likely needs and impacts of FMoD and informs their policy and strategy positions on the implementation of smart mobility systems in their cities and jurisdictions.
Sohani Liyanage; Hussein Dia; Rusul Abduljabbar; Saeed Bagloee. Flexible Mobility On-Demand: An Environmental Scan. Sustainability 2019, 11, 1262 .
AMA StyleSohani Liyanage, Hussein Dia, Rusul Abduljabbar, Saeed Bagloee. Flexible Mobility On-Demand: An Environmental Scan. Sustainability. 2019; 11 (5):1262.
Chicago/Turabian StyleSohani Liyanage; Hussein Dia; Rusul Abduljabbar; Saeed Bagloee. 2019. "Flexible Mobility On-Demand: An Environmental Scan." Sustainability 11, no. 5: 1262.
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.
Rusul Abduljabbar; Hussein Dia; Sohani Liyanage; Saeed Asadi Bagloee. Applications of Artificial Intelligence in Transport: An Overview. Sustainability 2019, 11, 189 .
AMA StyleRusul Abduljabbar, Hussein Dia, Sohani Liyanage, Saeed Asadi Bagloee. Applications of Artificial Intelligence in Transport: An Overview. Sustainability. 2019; 11 (1):189.
Chicago/Turabian StyleRusul Abduljabbar; Hussein Dia; Sohani Liyanage; Saeed Asadi Bagloee. 2019. "Applications of Artificial Intelligence in Transport: An Overview." Sustainability 11, no. 1: 189.
This paper aims to explore the performance of Autonomous Mobility on-Demand (AMoD) systems under uncertain travel demands in an urban environment using a case study of Melbourne, Australia. The results of the simulation model developed in this study showed that an AMoD system could reduce the current fleet size by 84% while still meeting the same demand for travel. This, however, comes at a cost of more Vehicle-Kilometres Travelled (VKT). The increase in VKT is significant and amounts to around 77% for scenarios in which the vehicles are used in car-sharing systems, and 29% for the scenarios in which vehicles are used as ride-sharing systems. These findings show that the benefits reported in other studies have mainly been overestimated. The current study has also discovered a strong quadratic relationship between AMoD fleet size and VKT.
Farid Javanshour; Hussein Dia; Gordon Duncan. Exploring the performance of autonomous mobility on-demand systems under demand uncertainty. Transportmetrica A: Transport Science 2018, 15, 698 -721.
AMA StyleFarid Javanshour, Hussein Dia, Gordon Duncan. Exploring the performance of autonomous mobility on-demand systems under demand uncertainty. Transportmetrica A: Transport Science. 2018; 15 (2):698-721.
Chicago/Turabian StyleFarid Javanshour; Hussein Dia; Gordon Duncan. 2018. "Exploring the performance of autonomous mobility on-demand systems under demand uncertainty." Transportmetrica A: Transport Science 15, no. 2: 698-721.
Christopher Pettit; Ashley Bakelmun; Scott Lieske; Stephen Glackin; Karlson ‘Charlie’ Hargroves; Giles Thomson; Heather Shearer; Hussein Dia; Peter Newman. Planning support systems for smart cities. City, Culture and Society 2018, 12, 13 -24.
AMA StyleChristopher Pettit, Ashley Bakelmun, Scott Lieske, Stephen Glackin, Karlson ‘Charlie’ Hargroves, Giles Thomson, Heather Shearer, Hussein Dia, Peter Newman. Planning support systems for smart cities. City, Culture and Society. 2018; 12 ():13-24.
Chicago/Turabian StyleChristopher Pettit; Ashley Bakelmun; Scott Lieske; Stephen Glackin; Karlson ‘Charlie’ Hargroves; Giles Thomson; Heather Shearer; Hussein Dia; Peter Newman. 2018. "Planning support systems for smart cities." City, Culture and Society 12, no. : 13-24.
This chapter presents the development and evaluation of traffic simulation frameworks which can be used to assess the impacts of smart mobility strategies. A number of case studies are presented based on simulation models for road networks in Australia. The models were used to examine the effectiveness of selected smart mobility strategies including intelligent transport systems applications such as traffic information dissemination combined with route diversions, and variable speed limit systems. The chapter also reports on investigations into the impacts of autonomous mobility on-demand systems as a means to enhance travel in urban areas. The work reported in this chapter demonstrates the feasibility of the simulation-based approaches for evaluating the impacts of technology-based mobility strategies before they are implemented in the field. The work also shows that the impacts of these strategies are network-dependent and have the potential to provide substantial economic benefits in terms of reduction in travel times and delays, and improvements in safety conditions.
Hussein Dia; Farid Javanshour; Sakda Panwai; Konstantinos Gakis; Panos Pardalos. Simulation-based Assessments of Smart Mobility Strategies. Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets 2017, 189 -218.
AMA StyleHussein Dia, Farid Javanshour, Sakda Panwai, Konstantinos Gakis, Panos Pardalos. Simulation-based Assessments of Smart Mobility Strategies. Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets. 2017; ():189-218.
Chicago/Turabian StyleHussein Dia; Farid Javanshour; Sakda Panwai; Konstantinos Gakis; Panos Pardalos. 2017. "Simulation-based Assessments of Smart Mobility Strategies." Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets , no. : 189-218.
Hussein Dia; Farid Javanshour. Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study. Transportation Research Procedia 2017, 22, 285 -296.
AMA StyleHussein Dia, Farid Javanshour. Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study. Transportation Research Procedia. 2017; 22 ():285-296.
Chicago/Turabian StyleHussein Dia; Farid Javanshour. 2017. "Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study." Transportation Research Procedia 22, no. : 285-296.
This paper presents findings from a simulation-based comparative evaluation of driving behaviours and their impacts on road safety, environmental quality and network efficiency. Driving behaviour was represented by driver speed, acceleration, lane changing and gap acceptance actions. A fourmode elemental emissions model was used to collect second-bysecond data on fuel consumption and CO2 emissions. Surrogate measures of safety, expressed in terms of the number of lane changes and severe decelerations, were used to describe the degree of safety in the simulation experiments. Aggressive drivers were found to be 35 times more likely to be involved in a crash on the motorway, and two times more likely to be involved in a crash on the urban network. The results for the motorway simulations also showed that aggressive drivers achieved only a 3.8 percent reduction in travel times (62 seconds on a 26 minute trip) at the expense of 85 percent more lane changes and 332 percent increase in fuel consumption and CO2 emissions. The reduction in travel times for urban conditions was lower at around 1.6 percent (7 seconds on a 434 second trip) at the expense of 300 percent more lane changes and 138 percent increase in fuel consumption and CO2 emissions. Sensitivity analysis of the impacts of varying proportions of drivers was also conducted. The results showed that the negative impacts of aggressive driving behaviour outweigh by a factor of three any benefits that can be obtained through reductions in travel times.
Hussein Dia; Sakda Panwai. Impact of Driving Behaviour on Emissions and Road Network Performance. 2015 IEEE International Conference on Data Science and Data Intensive Systems 2015, 355 -361.
AMA StyleHussein Dia, Sakda Panwai. Impact of Driving Behaviour on Emissions and Road Network Performance. 2015 IEEE International Conference on Data Science and Data Intensive Systems. 2015; ():355-361.
Chicago/Turabian StyleHussein Dia; Sakda Panwai. 2015. "Impact of Driving Behaviour on Emissions and Road Network Performance." 2015 IEEE International Conference on Data Science and Data Intensive Systems , no. : 355-361.
The convergence of physical and digital worlds is creating unprecedented opportunities to enhance the travel experience for millions of people every day. A key to the success of these systems is a good understanding of driver behaviour under the influence of travel information. This paper presents the application of a new generation of driver behaviour models, based on neural agent (neugent) techniques, to describe drivers' decisions and compliance with travel information. The new models enhance the capabilities of existing simulation tools in modelling the behaviour of heterogeneous drivers and dealing with the vagueness inherent in driver decision making and the information received from sensors and the road environment. This paper also describes the traffic simulation and practical applications of the new models and how they serve to assess the impacts of smart and sustainable transport interventions.
Hussein Dia; Sakda Panwai. Intelligent Mobility for Smart Cities: Driver Behaviour Models for Assessment of Sustainable Transport. 2014 IEEE Fourth International Conference on Big Data and Cloud Computing 2014, 625 -632.
AMA StyleHussein Dia, Sakda Panwai. Intelligent Mobility for Smart Cities: Driver Behaviour Models for Assessment of Sustainable Transport. 2014 IEEE Fourth International Conference on Big Data and Cloud Computing. 2014; ():625-632.
Chicago/Turabian StyleHussein Dia; Sakda Panwai. 2014. "Intelligent Mobility for Smart Cities: Driver Behaviour Models for Assessment of Sustainable Transport." 2014 IEEE Fourth International Conference on Big Data and Cloud Computing , no. : 625-632.