In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk- neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk- seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices, and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision-making.

The Neural Basis of Financial Risk Taking

Transportation research. part c, emerging technologies

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

Planning, operation, and control of bus transport systems: A literature review

This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model.

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Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process

Overview of Studies on the Wi-Fi Probe Data Analysis for Transport Problems

A Lagrangian-Based Approach for Reliable User Equilibrium Considering Link Travel Time Variance

Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters

With the growing attention toward developing a multimodal transport system to enhance urban mobility, there is an increasing need to construct new infrastructures, rebuild or expand the existing ones, to accommodate the current and newly generated travel demand. Therefore, this study develops a bi-level model that simultaneously determines the location and capacity of the transfer infrastructure to be built considering the elastic demand in a multimodal transport network. The upper-level problem is formulated as a mixed-integer linear programming problem, whereas the lower-level problem is a combined trip distribution/modal split/assignment model that depicts both the destination and route choices of travellers via a multinomial logit model. Numerical studies are conducted to demonstrate the occurrence of two Braess-like paradox phenomena in a multimodal transport network. The first one states that under fixed demand, constructing new parking spaces to provide the usage of park-and-ride services could deteriorate the system performance measured by the total passengers’ travel time, while the second one reveals that under elastic demand, increasing the parking capacity for park-and-ride services to promote its usage may fail, which would be represented by the decline in their modal share. Meanwhile, a numerical example also suggests that constructing transfer infrastructures at distributed stations outperforms building a large transfer centre in terms of attracting travellers using sustainable transit modes.

Joint optimisation of transfer location and capacity for a capacitated multimodal transport network with elastic demand: a bi-level programming model and paradoxes

Zhiyuan Liu

Papers

Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process

Overview of Studies on the Wi-Fi Probe Data Analysis for Transport Problems

A Lagrangian-Based Approach for Reliable User Equilibrium Considering Link Travel Time Variance

Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters

Joint optimisation of transfer location and capacity for a capacitated multimodal transport network with elastic demand: a bi-level programming model and paradoxes