Bilevel optimization approach to design of network of bike lanes

A bike lane is an effective way to improve cycling safety and to decrease greenhouse gas emissions with the promotion of cycling. Improvements include high-quality off-road facilities and on-road bike lanes. Whereas construction of off-road lanes is not always possible because of urban land constraints and construction costs, on-road lanes can be a cost-effective alternative. An optimization framework for the design of a network of bike lanes in an urban road network was proposed. This framework identified links on which a bike lane could be introduced. Allocation of a lane to cyclists would increase the use of cycling, although it could disadvantage auto traffic. The presented approach balances the effects of a bike lane for all stakeholders. A bilevel optimization was proposed to encompass the benefits of cyclists and car users at the upper level and a model for traffic and bike demand assignment at the lower level. The objective function was defined by a weighted sum of a measure for private car users (total travel time) versus a measure for bike users (total travel distance on bike lanes). A genetic algorithm was developed to solve the bilevel formulation, which included introduction of a special crossover technique and a mutation technique. The proposed optimization will help transport authorities at the planning stage to quantify the outcomes of various strategies for active transport

Scoping review of indicators and methods of measurement used to evaluate the impact of dog population management interventions

Background: Dogs are ubiquitous in human society and attempts to manage their populations are common to most countries. Managing dog populations is achieved through a range of interventions to suit the dog population dynamics and dog ownership characteristics of the location, with a number of potential impacts or goals in mind. Impact assessment provides the opportunity for interventions to identify areas of inefficiencies for improvement and build evidence of positive change. Methods: This scoping review collates 26 studies that have assessed the impacts of dog population management interventions. Results: It reports the use of 29 indicators of change under 8 categories of impact and describes variation in the methods used to measure these indicators. Conclusion: The relatively few published examples of impact assessment in dog population management suggest this field is in its infancy; however this review highlights those notable exceptions. By describing those indicators and methods of measurement that have been reported thus far, and apparent barriers to efficient assessment, this review aims to support and direct future impact assessment

Optimal Design of STATCOM Based OFD Controller using Quantum Particle Swarm Optimization

Abstract: This study present the optimal design of OFD (OFD) controller for static synchronous compensator (STATCOM) connected to a power system, in order to increase the damping of low frequency electromechanical oscillations. The design process is converted to an optimization problem with the time domain-based objective function which is solved by a Quantum-behaved Particle Swarm Optimization (QPSO) technique that has fewer parameters and stronger search capability than the Classical Particle Swarm Optimization (CPSO), as well as is easy to implement. To guarantee the robust performance of the OFD controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results demonstrate the effectiveness of proposed controller in comparison with designed Classical PSO (CPSO) based STATCOM controller

Delay function for signalized intersections in traffic assignment models

The consideration of delay at signalized intersections improves the accuracy of equilibrium flows obtained from traffic assignment models. This delay depends not only on physical specifications of the intersection, intersection signal timing, and traffic volume and its composition but on drivers' behavior and specific traffic culture in each country. This paper proposes a delay function for traffic assignment models, which considers the special drivers' behavior in Iran. Unlike the existing models such as the Highway Capacity Manual model, the proposed delay function has modest data requirements on intersection characteristics, and this ensures the easy use of this function in a real traffic assignment model. To evaluate the proposed function in a real network, the traffic assignment model of the city of Mashhad, Iran is used. In two different scenarios, the equilibrium flows obtained when the proposed function is applied and those obtained when a simple uniform delay function is used are compared to the observed traffic flows. In a comparative sense, the results show the better performance of the proposed function in estimating the equilibrium flows. DDS 181376

Impact of heavy vehicles on surrounding traffic characteristics

This work examines the impact of heavy vehicle movements on measured traffic characteristics in detail. Although the number of heavy vehicles within the traffic stream is only a small percentage, their impact is prominent. Heavy vehicles impose physical and psychological effects on surrounding traffic flow because of their length and size (physical) and acceleration/deceleration (operational) characteristics. The objective of this work is to investigate the differences in traffic characteristics in the vicinity of heavy vehicles and passenger cars. The analysis focuses on heavy traffic conditions (level of service E) using a trajectory data of highway I-80 in California. The results show that larger front and rear space gaps exist for heavy vehicles compared with passenger cars. This may be because of the limitations in manoeuvrability of heavy vehicles and the safety concerns of the rear vehicle drivers, respectively. In addition, heavy vehicle drivers mainly keep a constant speed and do not change their speed frequently. This work also examines the impact of heavy vehicles on their surrounding traffic in terms of average travel time and number of lane changing manoeuvres using Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks (AIMSUN) microscopic traffic simulation package. According to the results, the average travel time increases when proportion of heavy vehicles rises in each lane

Tram track deterioration modeling

Perceiving track geometry deterioration decisively influences the optimization of track maintenance operations. The effective management of this deterioration and increasingly utilized system with limited financial resources is a significant challenge. This paper provides a review of degradation models relevant for railroad tracks. Furthermore, due to the lack of long term information on the condition development of tram infrastructures, presents the methodology which will be used to derive degradation models from the data of Melbourne tram network

Influence of the surrounding traffic characteristics on lane changing decision of heavy vehicle drivers

Lane changing manoeuvres have major impacts on traffic flows. Despite the increasing number of heavy vehicles on freeways, previous lane changing studies are predominantly associated with passenger cars. This study uses a detailed vehicle trajectory data analysis to investigate the effects of microscopic characteristics of the surrounding traffic on lane changing decision of heavy vehicle drivers. Lane changing decision can be divided into two stages: the motivation to change lanes and the selection of the target lane. To investigate the factors which influence heavy vehicle drivers' lane changing decision, 42 heavy vehicle lane changes are analysed. Heavy vehicles are classified into heavy trucks and light trucks based on their length. The results show that the speeds of the front and the rear vehicles in the current lane may motivate the heavy vehicle drivers to change lanes. Furthermore, the speed difference between the lead and the lag vehicles in both adjacent lanes has an impact on selection of the target lane. In general, different influencing traffic characteristics are observed in lane changing decision of heavy truck and light truck drivers

A genetic algorithm-based support vector machine for bus travel time prediction

Information about public transport travel time is a key indicator of service performance, and is valued by passengers and operators. Among many different approaches, Support Vector Machines (SVM) has recently gained attention in predicting bus travel times. The training process of SVMs involves solving a quadratic programming problem which is slow when dealing with large training data. This paper proposes a Least Squares SVM (LS-SVM) method that expedites the training process by simplifying the quadratic programming problem using a linear regression technique. Also, to ensure the accuracy of the prediction results, a Genetic Algorithm (GA) is used to determine the optimal set of model parameters. The GA based LS-SVM approach is tested using real-world travel time data from a bus route in Melbourne, Australia. The comparison of the results in this paper to those obtained in a previous study using artificial neural networks shows that the proposed method produces more accurate results

Modeling degradation of tracks for maintenance planning on a tram line

There is a need among transportation maintenance decision makers for a better understanding of the long-term behavior of railroad tracks. The use of accurate techniques to predict track conditions increases track safety and maintenance effectiveness. This paper provides a review of degradation models relevant for railroad tracks. Furthermore, due to the lack of long term information on the condition development of tram infrastructures, presents the methodology which will be used to derive degradation models from the data of Melbourne tram network