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Papers by Sohani Liyanage

Research paper thumbnail of Neural network approaches for forecasting short-term on-road public transport passenger demands

Edward Elgar Publishing eBooks, Oct 12, 2023

Research paper thumbnail of AI-based neural network models for bus passenger demand forecasting using smart card data

Journal of Urban Management

Research paper thumbnail of A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities

Research paper thumbnail of Obtaining the Optimum Block Length of the Chet Network: An At-Grade Transportation Network without Signalized Intersections, Roundabouts, or Stop Signs

Engineer: Journal of the Institution of Engineers, Sri Lanka, 2018

A typical at-grade transportation network forms a grid consisting of intersections. The majority ... more A typical at-grade transportation network forms a grid consisting of intersections. The majority of delays and accidents in the network are caused by traffic movements through those intersections. Even though the use of controlling systems gets rid of conflict movements at intersections, it increases the congestion due to capacity reduction. To overcome this important issue, designing zeroconflict transportation network is essential. A novel design of an at-grade transportation network without signalized intersections, roundabouts, or stop signs was proposed. It is called the Chet network, which can be used as an alternative form of urban streets for a built environment. Within the Chet network, a car can move from one place to another without facing any conflict movement at any junction while still maintain the low cost of at-grade infrastructure. The network is composed of hexagon blocks tiling together with a unique arrangement of one-way or two-way directional links to avoid conflict movements at all junctions. This study aims further to explore the concept of the Chet network by constructing several testing cases in microscopic traffic simulation to obtain the optimum block length in forming hexagons in the Chet network, which is an important step in moving forward to implementation in real-life.

Research paper thumbnail of Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems

Research paper thumbnail of The role of micro-mobility in shaping sustainable cities: A systematic literature review

Transportation Research Part D-transport and Environment, 2021

Micro-mobility is increasingly recognised as a promising mode of urban transport, particularly fo... more 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 ne...

Research paper thumbnail of Smart Mobility: Unlocking the Value of the Internet of Things

The Internet of Things (IoT) – or digitising the physical world – has experienced rapid growth in... more The Internet of Things (IoT) – or digitising the physical world – has experienced rapid growth in recent years, and has benefited from the fast pace of innovations and scientific advances in a number of areas including sensor technologies, algorithms and data analytics. IoT has the potential to change fundamentally the way people interact with their environments. The ability to embed sensors in the physical world makes it possible to monitor, measure, manage and transform the performance of critical infrastructure and processes, saving time and resources and improving the quality of life for citizens. The impacts and benefits will be profound. Infrastructure in cities around the world is gradually being instrumented with sensors and devices that communicate with each other in real-time. This offers new opportunities to measure performance of infrastructure and assets and transform operations. We are already witnessing how converging the physical transport assets and the digital worl...

Research paper thumbnail of Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Future Transportation

Traffic forecasting remains an active area of research in the transport and data science fields. ... more 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 i...

Research paper thumbnail of Flexible Mobility On-Demand: An Environmental Scan

Sustainability

On-demand shared mobility is increasingly being promoted as an influential strategy to address ur... more 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 e...

Research paper thumbnail of Applications of Artificial Intelligence in Transport: An Overview

Sustainability

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportu... more 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) Th...

Research paper thumbnail of An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services

Sustainability

On-demand multi-passenger shared transport options are increasingly being promoted as an influent... more 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 trav...

Research paper thumbnail of Neural network approaches for forecasting short-term on-road public transport passenger demands

Edward Elgar Publishing eBooks, Oct 12, 2023

Research paper thumbnail of AI-based neural network models for bus passenger demand forecasting using smart card data

Journal of Urban Management

Research paper thumbnail of A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities

Research paper thumbnail of Obtaining the Optimum Block Length of the Chet Network: An At-Grade Transportation Network without Signalized Intersections, Roundabouts, or Stop Signs

Engineer: Journal of the Institution of Engineers, Sri Lanka, 2018

A typical at-grade transportation network forms a grid consisting of intersections. The majority ... more A typical at-grade transportation network forms a grid consisting of intersections. The majority of delays and accidents in the network are caused by traffic movements through those intersections. Even though the use of controlling systems gets rid of conflict movements at intersections, it increases the congestion due to capacity reduction. To overcome this important issue, designing zeroconflict transportation network is essential. A novel design of an at-grade transportation network without signalized intersections, roundabouts, or stop signs was proposed. It is called the Chet network, which can be used as an alternative form of urban streets for a built environment. Within the Chet network, a car can move from one place to another without facing any conflict movement at any junction while still maintain the low cost of at-grade infrastructure. The network is composed of hexagon blocks tiling together with a unique arrangement of one-way or two-way directional links to avoid conflict movements at all junctions. This study aims further to explore the concept of the Chet network by constructing several testing cases in microscopic traffic simulation to obtain the optimum block length in forming hexagons in the Chet network, which is an important step in moving forward to implementation in real-life.

Research paper thumbnail of Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems

Research paper thumbnail of The role of micro-mobility in shaping sustainable cities: A systematic literature review

Transportation Research Part D-transport and Environment, 2021

Micro-mobility is increasingly recognised as a promising mode of urban transport, particularly fo... more 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 ne...

Research paper thumbnail of Smart Mobility: Unlocking the Value of the Internet of Things

The Internet of Things (IoT) – or digitising the physical world – has experienced rapid growth in... more The Internet of Things (IoT) – or digitising the physical world – has experienced rapid growth in recent years, and has benefited from the fast pace of innovations and scientific advances in a number of areas including sensor technologies, algorithms and data analytics. IoT has the potential to change fundamentally the way people interact with their environments. The ability to embed sensors in the physical world makes it possible to monitor, measure, manage and transform the performance of critical infrastructure and processes, saving time and resources and improving the quality of life for citizens. The impacts and benefits will be profound. Infrastructure in cities around the world is gradually being instrumented with sensors and devices that communicate with each other in real-time. This offers new opportunities to measure performance of infrastructure and assets and transform operations. We are already witnessing how converging the physical transport assets and the digital worl...

Research paper thumbnail of Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Future Transportation

Traffic forecasting remains an active area of research in the transport and data science fields. ... more 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 i...

Research paper thumbnail of Flexible Mobility On-Demand: An Environmental Scan

Sustainability

On-demand shared mobility is increasingly being promoted as an influential strategy to address ur... more 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 e...

Research paper thumbnail of Applications of Artificial Intelligence in Transport: An Overview

Sustainability

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportu... more 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) Th...

Research paper thumbnail of An Agent-Based Simulation Approach for Evaluating the Performance of On-Demand Bus Services

Sustainability

On-demand multi-passenger shared transport options are increasingly being promoted as an influent... more 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 trav...