Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Road space optimisation for multiclass and multimodal traffic networks

© 2017 Dr Saeed Asadi BaloeeTraffic congestion has become a serious concern and hindrance to the prosperity of many societies. Among a variety of solutions two approaches are of significant importance: constructing new roads and bridges to ease traffic congestion and promoting public transport. For the latter, the aim is to provide more space in the heart of cities for public transport (buses, trams, etc) aiming to get more commuters to their destinations. Therefore, two central questions have been addressed in this research; (i) investment in the road construction: given a number of candidate projects associated with construction expenses and a limited budget, what is the best choice of projects. This is known as the road network design problem (NDP), and (ii) transit priority lanes: given a road network, which roads should be selected to provide a lane to be exclusively used by public transport modes such that the overall performance of the transport system is not adversely affected. This problem is called the, “transit priority lane design problem” (TPLDP). For the former, (NDP) a hybridized method consisting of the branch and bound algorithm and Benders decomposition method has been developed. For the latter (TPLDP), the concept of Braess paradox was employed to seek for “mis-utilized” space in congested networks to be utilized by public transport. To this end, a merit index aiming to spot potentially some Braess-tainted roads is introduced first. Then a branch and bound algorithm was developed to find the best subset of the Braess tainted roads that have no adverse impact on the overall performance of the network. This study advances the state of knowledge in the above mentioned problems in five areas: (i) the authenticity of the traffic model is enhanced by subjecting all the analysis to multimodal and multiclass traffic circulation, (ii) the methodologies developed in this study are tailored to real world applications as illustrated with numerical analysis, (iii) a RAM-efficient branch and bound algorithm (BB) has been developed such that the expansion of the BB’s tree structure becomes memoryless, (iv) inclusion of the Braess paradox in the pursuit of the transit priority lane would nullify possible adverse effects on the private modes, and (v) a new method for the capacitated traffic assignment has been developed which is called inflated travel time

Heterogeneous parking market subject to parking rationing

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Minimization of water pumps\u27 electricity usage: a machine-learning approach

Due to pervasive deployment of electricity-propelled water-pumps, water distribution systems (WDSs) are energy-intensive technologies which are largely operated and controlled by engineers based on their judgments and discretions. Hence energy efficiency in the water sector is a serious concern. To this end, this study is dedicated to the optimal operation of the WDS which is articulated as minimization of the pumps’ energy consumption while maintaining flow, pressure, and tank water levels at a minimum level, also known as pump scheduling problem (PSP). This problem is proved to be of the most difficult problem (namely NP-hard). To this end, we develop a hybrid methodology consists of machine learning techniques as well as optimization methods, that is to address real life and large sized WDSs. Other main contributions of this research are (i) also, variable speed pumps can be modeled and optimally controlled, (ii) operational rules such as water allocation rules can also be explicitly considered in the methodology. This methodology is tested using a large sized dataset in which the results are found to be highly promising. This methodology has been coded as a user-friendly software composed of MS-Excel (as a user interface), MS-Access (a database), MATLAB (for machine learning), GAMS (with CPLEX solver for solving optimization problem) and EPANET (to solve hydraulic models)

Side constrained optimisation to capture capacity of choices in the multinomial logit model: case study of income tax policy in the USA prior to the 2009 economic crisis

The choices' limited capacities of the discrete choice models must be taken into account. We propose a convex optimisation formulation in which the exponential formulation of the logit model is upheld in the Karush-Kuhn-Tucker (KKT) conditions. The capacities of the choices are then added to the formulation as side constraints. A solution algorithm based on the successive coordinate descent is proposed. For numerical evaluation, we investigate US income tax policies for the years prior to the 2009 crisis using multinomial logit models. The tendency of the states for choice of income tax versus other tax sources is assessed and it is found that: 1) all states show a propensity to levy more income tax; 2) this propensity has a ceiling cap similar to what is already known from the 'Laffer curve'; 3) residents in the states with already high income tax are more likely to be subjected to even heavier income tax within caps

Minimization of water pumps\u27 electricity usage: A hybrid approach of regression models with optimization

Due to pervasive deployment of electricity-propelled water-pumps, water distribution systems (WDSs) are energy-intensive technologies which are largely operated and controlled by engineers based on their judgments and discretions. Hence energy efficiency in the water sector is a serious concern. To this end, this study is dedicated to the optimal operation of the WDS which is articulated as minimization of the pumps\u27 energy consumption while maintaining flow, pressure, and tank water levels at a minimum level, also known as pump scheduling problem (PSP). This problem is proved to be NP-hard (i.e. a difficult problem computationally). We therefore develop a hybrid methodology incorporating machine-learning techniques as well as optimization methods to address real-life and large-sized WDSs. Other main contributions of this research are (i) in addition to fixed-speed pumps, the variable-speed pumps are optimally controlled, (ii) and operational rules such as water allocation rules can also be explicitly considered in the methodology. This methodology is tested using a large dataset in which the results are found to be highly promising. This methodology has been coded as a user-friendly software composed of MS-Excel (as a user interface), MS-Access (a database), MATLAB (for machine-learning), GAMS (with CPLEX solver for solving optimization problem) and EPANET (to solve hydraulic models)

An efficient hybrid heuristic method for prioritising large transportation projects with interdependent activities

Transportation projects are generally large, with limited resources and highly interdependent activities. The complexities and interdependencies apparent in large transportation projects have prohibited effective application of management science and economics methods to these problems. We propose a heuristic method with several hybrid components. We formulate the problem as a Travelling Salesman Problem (TSP). A Neural Network (NN) is used to cope with the interdependency concerns. An algorithm with an iterative process is confined to search for the longest path (most benefit or most reduction in the user-time) in the NN as a solution to the TSP. The solution from each iteration step is utilised to update and train the NN and enhance its prediction. A search engine inspired by the concept of Ant Colony (AC) and hybridised with Genetic Algorithm (GA) is developed to find a suitable solution to the TSP. The hybrid heuristic method proposed in this study is applied to the real data for the city of Winnipeg in Canada to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms