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PhD Candidate in Smart Urban Mobility/Artificial Intelligence (AI) in Transport, with a Bachelor's degree (Hons) focused in Civil Engineering from Swinburne University of Technology.
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.
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.
This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.
Rusul L. Abduljabbar; Hussein Dia; Pei-Wei Tsai. Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction. Journal of Advanced Transportation 2021, 2021, 1 -16.
AMA StyleRusul L. Abduljabbar, Hussein Dia, Pei-Wei Tsai. Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction. Journal of Advanced Transportation. 2021; 2021 ():1-16.
Chicago/Turabian StyleRusul L. Abduljabbar; Hussein Dia; Pei-Wei Tsai. 2021. "Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction." Journal of Advanced Transportation 2021, no. : 1-16.
Micro-mobility is increasingly recognised as a promising mode of urban transport, particularly for its potential to reduce private vehicle use for short-distance travel. Despite valuable research contributions that represent fundamental knowledge on this topic, today’s body of research appears quite fragmented in relation to the role of micro-mobility as a transformative solution for meeting sustainability outcomes in urban environments. This paper consolidates knowledge on the topic, analyses past and on-going research developments, and provides future research directions by using a rigorous and auditable systematic literature review methodology. To achieve these objectives, the paper analysed 328 journal publications from the Scopus database covering the period between 2000 and 2020. A bibliographic analysis was used to identify relevant publications and explore the changing landscape of micro-mobility research. The study constructed and visualised the literature’s bibliometric networks through citations and co-citations analyses for authors, articles, journals and countries. The findings showed a consistent spike in recent research outputs covering the sustainability aspects of micro-mobility reflecting its importance as a low-carbon and transformative mode of urban transport. The co-citation analysis, in particular, helped to categorise the literature into four main research themes that address benefits, technology, policy and behavioural mode-choice categories where the majority of research has been focused during the analysis period. For each cluster, inductive reasoning is used to discuss the emerging trends, barriers as well as pathways to overcome challenges to wide-scale deployment. This article provides a balanced and objective summary of research evidence on the topic and serves as a reference point for further research on micro-mobility for sustainable cities.
Rusul L. Abduljabbar; Sohani Liyanage; Hussein Dia. The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transportation Research Part D: Transport and Environment 2021, 92, 102734 .
AMA StyleRusul L. Abduljabbar, Sohani Liyanage, Hussein Dia. The role of micro-mobility in shaping sustainable cities: A systematic literature review. Transportation Research Part D: Transport and Environment. 2021; 92 ():102734.
Chicago/Turabian StyleRusul L. Abduljabbar; Sohani Liyanage; Hussein Dia. 2021. "The role of micro-mobility in shaping sustainable cities: A systematic literature review." Transportation Research Part D: Transport and Environment 92, no. : 102734.
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.