An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved

Comparative evaluation of microscopic car-following behavior

Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport, systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Statiten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative drift and goal-seeking behavior test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings

Increasing Students’ Understanding of the Simple Past Tense Using Discovery Learning at VII grade students of SMP Negeri 4 Doloksanggul in Academic Year 2017/218

The purpose of this research is to know whether students\u27 understanding of the simple past tense could be increased by discovery learning. This research was conducted at VII grade students of SMP Negeri 4 Doloksanggul in Academic Year 2017/218.which consisted of 36 students as respondent. This research used Classroom Action Research (CAR) method in solving the students\u27 problem in understanding of the simple past tense. The researcher used the Kurt Lewin\u27s model that consists of four phases, planning, acting, observing and reflecting. There are two kinds of data in this research, namely quantitative and qualitative data. The quantitative data can be derived from the test result. Besides, the qualitative data can be derived from the observation, interview and field notes. In analyzing the data, the researcher used descriptive analysis and statistic analysis to know the result of the implementation the Classroom Action Research (CAR) to the students. The result of this study showed that the students\u27 progress during teaching and learning process using discovery learning to increase the students\u27 understanding of the simple past tense was good. It was proved by three data results, first, from the observation result, it showed that the students were more motivated, active and interested in learning simple past tense in the classroom. Second, from interview result, it could be seen that students\u27 skill in understanding of the simple past tense has improved than before in which suitable with interview result with the English teacher. Last, from the test result. It consisted of three tests, namely pretest, posttest 1 and posttest 2. There was found 22.78 point of improvement of students\u27 mean score after using discovery learning. The mean score of the pre-test was 48.19. There were only 8.33% of the whole students who could pass Kriteria Ketuntasan Minimal (KKM). Then the mean score of posttest 1 was 59.86. The percentage of students was 33.33% who could get the score above Kriteria Ketuntasan Minimal (KKM). Next, the mean score of posttest 2 was 70.97. In this test, there were 77.78% students who got the score above Kriteria Ketuntasan Minimal (KKM)

Prehabilitation for frail patients undergoing colorectal surgery: lessons learnt from a randomised feasibility study

There is substantial interest by clinicians to improve the health outcomes of older and frail patients followingmajor surgery, with prehabilitation a potential and important component of future standard patient care. We studied the feasibility of a randomised controlled trial of pre-operative prehabilitation in frail patients scheduled for colorectal surgery in regional Australia. We conducted a single blind, parallel arm, randomised controlled trial in a regional referral centre where colorectal surgical patients aged over 50 were invited to participate and screened for frailty. Frail patients were randomised to undertake either a 4-week supervised exercise program with dietary advice, or usual care. The primary outcome was 6-min-walk-distance at baseline, pre-surgery (4 weeks later) and at follow-up (4–6 weeks post-operation). Secondary outcomes included physical activity level, health-related quality of life, and post-surgical complications. Feasibility outcomes were numbers of patients reaching each stage and barriers or reasons for withdrawal. Of 106 patients eligible for screening during the 2-year study period, only five were able to be randomised, of which one alone completed the entire study to follow-up. Fewer patients than expected met the frailty criteria (23.6%), and many (22.6%) were offered surgery in a shorter timeframe than the required 4 weeks. Physical and psychological aspects of frailty and logistical issues were key for patients declining study participation and/or not complying with the intervention and/or all outcome assessments. Feasibility for a large randomised controlled trial of prehabilitation for frail colorectal patients was poor (~5%) for our regional location. Addressing barriers, examination of a large, dense population base, and utilisation of a frailty-screening tool validated in surgical patients are necessary for future studies to identify the impact of prehabilitation for frail patients

A Reactive Agent-based Neural Network Car Following Model

This paper presented a novel approach to develop car following models using reactive agent techniques for mapping perceptions to actions. The results showed that the model outperformed the Gipps and Psychophysical family of car following models. The standing of this work is highlighted by its acceptance and publication in the proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITS), which is now recognised as the premier international conference on ITS. The paper acceptance rate to this conference was 67 percent. The standing of this paper is also evidenced by its listing in international databases like Ei Inspec and IEEE Xplore. The paper is also listed in Google Scholar. Dr Dia co-authored this paper with his PhD student Sakda Panwai

An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Punching Shear Characterization of Steel Fiber-Reinforced Concrete Flat Slabs

Punching shear failure in thin slabs under concentrated loads can cause shear stresses near columns. The use of steel fiber is a practical way to improve a slab-column connection's punching strength and deformation capacity. In this study, the capacity and behavior of steel fiber-reinforced concrete flat slabs are examined under punching shear force. Ten small-scale flat slabs were tested, eight of which had steel fiber and two without. Two parameters are studied in this paper, which are the fiber volume ratio (from 0% to 2%) and the stub column load shape (circle and square). The test results include the concrete compressive strength, crack patterns, punching shear, and load-defection behavior of the slabs. Based on the experimental results, it was found that the punching shear capacity of slabs with steel fiber (S5) increased by 21.8% compared to slabs without steel fiber (S1), and the slabs with steel fiber had more ductility compared to the slabs without fiber. Doi: 10.28991/HIJ-2022-03-04-08 Full Text: PD

Training set cleansing of backdoor poisoning by self-supervised representation learning

A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small number of samples that each possess the backdoor pattern (usually a pattern that is either imperceptible or innocuous) and which are mislabeled to the attacker's target class. When trained on a backdoor-poisoned dataset, a DNN behaves normally on most benign test samples but makes incorrect predictions to the target class when the test sample has the backdoor pattern incorporated (i.e., contains a backdoor trigger). Here we focus on image classification tasks and show that supervised training may build stronger association between the backdoor pattern and the associated target class than that between normal features and the true class of origin. By contrast, self-supervised representation learning ignores the labels of samples and learns a feature embedding based on images' semantic content. %We thus propose to use unsupervised representation learning to avoid emphasising backdoor-poisoned training samples and learn a similar feature embedding for samples of the same class. Using a feature embedding found by self-supervised representation learning, a data cleansing method, which combines sample filtering and re-labeling, is developed. Experiments on CIFAR-10 benchmark datasets show that our method achieves state-of-the-art performance in mitigating backdoor attacks

Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis

Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale

The cetaceans of Guinea, a first check-list of documented species. Scientific Committee document SC/58/O15, International Whaling Commission, May-June 2006, St. Kitts

A CMS workshop on West African Cetacea (Conakry, May 2000), called for i.a. ‘carrying out .. inventory of cetacean species; collection, treatment and compilation of data for each state.’ The present paper is a preliminary faunal checklist of cetaceans occurring in Guinea’s EEZ. Information was gleaned from strandings, bycatches, scientific and opportunistic sightings and a literature review. Ten species are included for which supporting voucher material and data were available for examination. These are, three baleen whales: Balaenoptera brydei, Balaenoptera acutorostrata and Megaptera novaeangliae; and seven species of odontocetes: Kogia breviceps, Tursiops truncatus, Sousa teuszii, Stenella frontalis, Delphinus delphis, Steno bredanensis and Globicephala macrorhynchus. Another two species, Physeter macrocephalus and Stenella attenuate were sighted off Guinea but no photographic evidence was obtained. The current account is thought to reflect an incomplete picture of Guinea’s cetacean biodiversity. Future surveys are expected to update and investigate spatial and temporal distribution patterns for each species along Guinea’s coast. A few bycatches landed by artisanal fishers were utilised locally, but there are no signs of any substantial captures. Nonetheless, monitoring should be continued. The set-up of a national reference collection and database is recommended. The population identities of the encountered Atlantic humpback dolphin, minke whale and humpback whale are of particular interest