Author
Lina Kattan
Other affiliations: University of Toronto
Bio: Lina Kattan is an academic researcher from University of Calgary. The author has contributed to research in topics: Public transport & Computer science. The author has an hindex of 24, co-authored 95 publications receiving 1706 citations. Previous affiliations of Lina Kattan include University of Toronto.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a variable speed limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment is presented, where a multi-objective optimization function is formulated with the aim of finding a balanced trade-off among mobility and sustainability.
Abstract: This paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver’s behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver’s compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6–11% of safety improvements and 5–16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.
214 citations
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TL;DR: In this article, a multinomial logit model was used to identify the factors determining the severity of pedestrian-vehicle crashes in South Korea, and the results showed that relative to minor crashes, fatal and serious crashes were associated with collisions involving heavy vehicles; drivers who were drunk, male or under the age of 65; pedestrians who were over 65 or female.
203 citations
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TL;DR: This paper provides a crystallized, comprehensive overview of the concept of RL and its related methods and presents related successful applications in the field of traffic control and transportation engineering.
Abstract: The aim of this paper is to develop insight into the potential of reinforcement learning (RL) agents and distributed reinforcement learning agents in the domain of transportation and traffic engineering and specifically in Intelligent Transport Systems (ITS). This paper provides a crystallized, comprehensive overview of the concept of RL and presents related successful applications in the field of traffic control and transportation engineering. It is divided into two parts: the first part provides a thorough overview of RL and its related methods and the second part reviews most recent applications of RL algorithms to the field of transportation engineering. Finally, it identifies many open research subjects in transportation in which the use of RL seems to be promising.Key words: reinforcement learning, machine learning, traffic control, artificial intelligence, intelligent transportation systems.
102 citations
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TL;DR: In this article, a critical literature review of the relationship between public transportation and sustainability is presented, which offers a review of key sustainable transportation concepts and how public transport contributes to sustainability goals.
Abstract: Public transportation is often framed as a key component of building sustainable cities. Conversely, the social, economic, and environmental impacts of transport are framed as critical issues that can challenge the sustainability of cities and regions. This paper presents a critical literature review of the relationship between public transportation and sustainability. First the paper offers a review of key sustainable transportation concepts and how public transport contributes to sustainability goals. Second, the paper reviews past studies that analyse sustainable transportation in order to develop recommendations for planning, engineering, and researching sustainable public transport. Finally, the paper concludes by offering suggestions for future research into the sustainability performance of public transit.
102 citations
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TL;DR: The authors conclude that improving the connectivity of train service, reducing multimodal transfers, and increasing dedicated right-of-ways for transit would effectively increase transit ridership in Calgary and the use of an intelligent transportation system including transit signal priority, advance traveler’s information system, and real-time information on bus arrival times would increase positive perceptions of the transit reliability and convenience.
Abstract: This article, from a special issue on the modeling and optimization of transportation systems, presents a multinomial logit model that can be used to measure personal attitudes towards transit service quality. The authors describe their investigation of reasons for using transit by residents of the City of Calgary, Canada and the development of the model from this investigation. Reasons for using transit are expressed as functions of people's perceptions and attitudes towards transit service quality and attributes. The approach models the reasons for choosing transit and tests the significance of two individual specific latent variables: perceptions of ‘reliability and convenience’ and ‘ride comfort’. The authors focus on the behavioral details that have policy implications. Their research found that the people of Calgary value ‘reliability and convenience’ over ‘ride comfort’. The authors conclude that improving the connectivity of train service, reducing multimodal transfers, and increasing dedicated right-of-ways for transit would effectively increase transit ridership in Calgary. In addition, the use of an intelligent transportation system (ITS) including transit signal priority, advance traveler’s information system (ATIS), and real-time information on bus arrival times would increase positive perceptions of the transit reliability and convenience.
100 citations
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01 Jan 2016TL;DR: In this paper, the authors compare TBL approaches and principles-based approaches to developing such sustainability criteria, concluding that the latter are more appropriate, since they avoid many of the inherent limitations of the triple-bottom-line as a conception of sustainability.
Abstract: Sustainability assessment is being increasingly viewed as an important tool to aid in the shift towards sustainability. However, this is a new and evolving concept and there remain very few examples of effective sustainability assessment processes implemented anywhere in the world. Sustainability assessment is often described as a process by which the implications of an initiative on sustainability are evaluated, where the initiative can be a proposed or existing policy, plan, programme, project, piece of legislation, or a current practice or activity. However, this generic definition covers a broad range of different processes, many of which have been described in the literature as 'sustainability assessment'. This article seeks to provide some clarification by reflecting on the different approaches described in the literature as being forms of sustainability assessment, and evaluating them in terms of their potential contributions to sustainability. Many of these are actually examples of 'integrated assessment', derived from environmental impact assessment (EIA) and strategic environmental assessment (SEA), but which have been extended to incorporate social and economic considerations as well as environmental ones, reflecting a 'triple bottom line' (TBL) approach to sustainability. These integrated assessment processes typically either seek to minimise 'unsustainability', or to achieve TBL objectives. Both aims may, or may not, result in sustainable practice. We present an alternative conception of sustainability assessment, with the more ambitious aim of seeking to determine whether or not an initiative is actually sustainable. We term such processes 'assessment for sustainability'. 'Assessment for sustainability' firstly requires that the concept of sustainability be well-defined. The article compares TBL approaches and principles-based approaches to developing such sustainability criteria, concluding that the latter are more appropriate, since they avoid many of the inherent limitations of the triple-bottom-line as a conception of sustainability.
859 citations
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TL;DR: The review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial and the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.
607 citations
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TL;DR: This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC), and shows unprecedented reduction in the average intersection delay.
Abstract: Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.
437 citations