Reserve Capacity Model for Optimizing Traffic Signal Timings with an Equity Constraint

This paper represents a solution algorithm for optimizing traffic signal timings in urban road networks by considering reserve capacity with an equity constraint. It is well known that the variation of signal timings in a road network may cause an inequity issue with regard to the travel costs of road users travelling between origin-destination (O-D) pairs. That is, the users may be influenced differently by changing traffic signal timings. In this context, the bilevel programming model is proposed for finding reserve capacity for signalized road networks by taking into account the equity issue. In the upper level, the reserve capacity is maximized with an equity constraint, whereas deterministic user equilibrium problem is dealt in the lower level. In order to solve the proposed model, a heuristic solution algorithm based on harmony search combined with a penalty function approach is developed. The application of the proposed model is illustrated for an example road network taken from a literature

Enhanced content bio-imaging tools: realising the potential for high-throughput zebrafish bioassays

Abstract Estrogenic endocrine disrupting chemicals (EEDCs) are environmental contaminants that can alter hormone signalling in both humans and wildlife, exerting their action through estrogen receptors (ERs). A wealth of evidence has indicated that EEDCs are capable of producing a broad range of adverse outcomes by interacting with, and disrupting, the normal functioning of the estrogen system. Fish are particularly vulnerable to endocrine disruption due to EEDCs being frequently discharged into waterways. With more than 900 chemicals identified as being endocrine disruptors, of which ~200 may exert estrogenic effects, there is an urgent need for screening processes that can assess the estrogenic potential of chemicals in order to avoid human and environmental health risks. In vivo models capable of demonstrating the physiological effects of EEDCs hold great utility for understanding the potential health impacts of estrogens, and transgenic (TG) zebrafish (Danio rerio) models are particularly well-suited for the screening of EEDCs via bioimaging approaches. The pigment-free estrogen-responsive ERE:GFP:Casper model represents a promising transgenic line for qualifying and quantifying EEDC-induced fluorescence responses in larval fish and is amenable to high-throughput screening (HTS). We optimised a medium-throughput semi-automated in vivo bioimaging assay using the model, while simultaneously generating important data concerning estrogen-driven responses to an EEDC (EE2). Through refinement of assay parameters, including the use of various image-masking and pixel-thresholding approaches, controlled-breeding to reduce genetic variability and standardised larval orientation for image acquisition, we established the most sensitive and robust approaches for screening of the EE2-exposed model using a semi-automated imaging modality. Our optimised assay was capable of detecting a significant GFP response in 4 day old zebrafish larvae at an environmentally relevant (5ng/L) concentration of EE2. These specifications were then adopted for investigating the influence of varying incubation temperatures (24, 28 and 32°C) on EE2-exposed ERE:GFP:Casper larval growth and GFP responses. This analysis provided information concerning the potential for an EEDC to interact with temperature in a fish model, with important implications for subsequent interpretation of results. We screened the same animal over a series of timepoints generating valuable data concerning estrogen-induced fluorescence responses and specific larval growth. Incubation temperature was found to have a significant effect on GFP induction, both alone and in interaction with EE2. The findings of this thesis help to outline an improved approach for further development of higher-throughput in vivo estrogenic screening of a transgenic zebrafish model.AstraZenec

Investigating acceptable level of travel demand before capacity enhancement for signalized urban road networks

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since signalized intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for capacity enhancement on urban road networks to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different common origin-destination demand multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying capacity enhancement countermeasures

Minimization Problems in Signalized Road Networks

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. C1 [Baskan, Ozgur; Ceylan, Huseyin] Pamukkale Univ, Dept Civil Engn, Fac Engn, TR-20160 Denizli, Turkey. [Ozan, Cenk] Adnan Menderes Univ, Dept Civil Engn, Fac Engn, TR-09100 Aydin, Turkey. Document type: Articl

Reinforcement learning approach for optimising traffic signal timings at isolated intersections

One of effective ways to prevent congestion and delay on urban areas is signal control at intersections. Signal systems are operated according to state of intersections either isolated or coordinated signal systems. Many researches have been investigated to improve traffic signal systems based on delay minimization or capacity maximization throughput. Due to complexity of the system, new methods are needed to improve efficiency of signalization in aroad network.Signal setting parameters are usually obtained by minimizing total delay on anintersection. The delay is the key parameter which determines the level of service of an intersection. Delay is defined with two parts as an uniform and non-uniform. The uniform partof the delay is determined basically using conventional delay formulas. But the non-uniformpart is not easily determined and cannot be represent due to the nature of the problem and randomness in arrivals.In this study, Reinforcement Learning Signal Optimizer (RLSO) is used to optimize signal timings in isolated intersection because of reflecting the effect of non-uniform part ofdelay. Reinforcement Learning (RL) which is an approach to artificial intelligence that emphasizes learning by the individual from its interaction with its environment. This contrasts with classical approaches to artificial intelligence and machine learning, which have downplayed learning from interaction, focusing instead on learning from a knowledgeable teacher, or on reasoning from a complete model of the environment. RL is learning what todo-how to map situations to actions-so as to maximize a scalar reward signal. The learner isnot told which action to take, as in most forms of machine learning, but instead must discoverwhich actions yield the most reward by trying them.The aim of this paper is to minimize delay on intersections controlled by isolated signal system and to obtain operational parameters such as cycle time, green split rate. For thispurpose, the RLSO is applied to an example intersection which has four approaches and threestages. The results of RLSO were compared with field observations. The results showed thatthe RLSO is able to optimize traffic signal timings on an intersection. The proposed model also holds promise for successful application to optimize traffic signal timings at isolated intersections according to delay minimization

Improving the Performance of the Bilevel Solution for the Continuous Network Design Problem

For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.</p

Identification and Prioritization of Key Performance Indicators for the Construction Small and Medium Enterprises

The central purpose of this study is to propose a set of key performance indicators (KPIs) to measure the performance of construction small and medium enterprises (SMEs) that have been ignored in the performance management literature so far. Secondly, this study aims to determine the most crucial KPIs by using the fuzzy VIKOR method to improve cost-effectiveness in the performance measurement of construction SMEs. At the first stage of this study, KPIs proposed by the existing studies were identified via a literature survey. Then, the KPIs extracted from the literature survey were verified, and eight new KPIs were proposed as a result of focus group discussions with 12 participants who are owners/managers of construction SMEs. Additionally, the Balanced Scorecard (BSC) was modified in line with the needs of construction SMEs, and each KPI was grouped into a BSC perspective. A questionnaire survey followed this grouping to gather data associated with the KPIs. Based on these data, KPIs were prioritized by using the fuzzy VIKOR. It is found out that external indicators such as “effectiveness of monitoring market conditions” are determined as the most important KPIs, in contrast to the findings in the studies about large-scale companies. Furthermore, “Attracting new customers”; “Reliability of financial performance” and, “Competency of managers” are identified as important indicators. Four KPIs proposed by experts during the focus group discussion are placed among the most important KPIs, which highlights the need for a specific performance measurement system (PMS) for construction SMEs

Selective COX-2 inhibition with different doses of rofecoxib does not impair endothelial function in patients with coronary artery disease.

In this study, we investigated the effects of both 25 and 50 mg daily doses of rofecoxib on the endothelial functions of patients with coronary artery disease (CAD). For this purpose, 34 patients with documented severe CAD and who were under aspirin treatment (300 mg/day) were randomized to receive 4 weeks of treatment with a placebo (n = 10, group I), rofecoxib 25 mg/day (n = 12, group II), and rofecoxib 50 mg/day (n = 12, group III). Brachial artery vasodilator responses were measured in order to evaluate endothelial function. The percentage of change in endothelial-dependent vasodilation in groups I, II, and III were similar at the baseline level and showed no significant change after treatment (6.2+/-3.9% vs. 5.9+/-3.1% and 5.8+/-3.3% vs. 5.6+/-3.8% and 6.1+/-4.5% vs. 5.8+/-4.1%, respectively; P &#62; 0.05). Compared with the baseline, endothelium-independent vasodilatation, as assessed by nitroglycerine (NTG), remained unchanged after the treatment period (11.2+/-6.9% vs. 10.3+/-7.1% and 11.2+/-6.3% vs. 9.9+/-5.1% and 9.5+/-4.9% and 8.8+/-4.6%, respectively; P&#62; 0.05). Treatment with both doses also showed no significant effects on high-sensitivity C-reactive protein (hs-CRP) levels and resting arterial diameters (P &#62; 0.05). In conclusion, 4 weeks of treatment with standard and high doses of rofecoxib showed no significant effects on either endothelial-dependent or independent vasodilator response or plasma hs-CRP levels in patients with severe CAD taking concomitant aspirin.</p