Multi-concentric optimal charging cordon design

The performance of a road pricing scheme varies greatly by its actual design and implementation. The design of the scheme is also normally constrained by several practicality requirements. One of the practicality requirements which is tackled in this paper is the topology of the charging scheme. The cordon shape of the pricing scheme is preferred due to its user-friendliness (i.e. the scheme can be understood easily). This has been the design concept for several real world cases (e.g. the schemes in London, Singapore, and Norway). The paper develops a methodology for defining an optimal location of a multi-concentric charging cordons scheme using Genetic Algorithm (GA). The branch-tree structure is developed to represent a valid charging cordon scheme which can be coded using two strings of node numbers and number of descend nodes. This branch-tree structure for a single cordon is then extended to the case with multi-concentric charging cordons. GA is then used to evolve the design of a multi-concentric charging cordons scheme encapsulated in the twostring chromosome. The algorithm developed, called GA-AS, is then tested with the network of the Edinburgh city in UK. The results suggest substantial improvements of the benefit from the optimised charging cordon schemes as compared to the judgemental ones which illustrate the potential of this algorithm

A congestion-pricing problem with a polycentric region and multi-class users: a continuum modelling approach

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Reliable Network Design Problem: case with uncertain demand and total travel time reliability

In the reliable network design problem (RNDP) the main sources of uncertainty are variable demand and route choice. The objective is to maximize network total travel time reliability (TTR), which is defined as the probability that the network total travel time will be less than a threshold. A framework is presented for a stochastic network model with Poisson-distributed demand and uncertain route choice. The travelers are assumed to choose their routes to minimize their perceived expected travel cost following the probit stochastic user equilibrium condition. An analytical method is presented for approximation of the first and second moments of the total travel time. These moments are then fitted with a log-normal distribution. Then the design problem is tackled in which the analytical derivative of the TTR is derived with the sensitivity analysis of the equilibrated path choice probability. This derivative is then supplied to a gradient-based optimization algorithm to solve the RNDP. The algorithm is tested with a small network exampl

Environmentally sustainable toll design for congested road networks with uncertain demand

This article proposes a new road toll-design model for congested road networks with uncertain demand that can be used to create a sustainable urban transportation system. For policy assessment and strategic planning purposes, the proposed model extends traditional congestion pricing models to simultaneously consider congestion and environmental externalities due to vehicular use. Based on analyses of physical and environmental capacity constraints, the boundary conditions under which a road user on a link should pay either a congestion toll or an extra environmental tax are identified. The sustainable toll design model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of the future travel demand aims to minimize a risk-averse objective by determining the optimal toll. The second stage after the uncertain travel demand has been determined is a scenario-based route choice equilibrium formulation with physical and environmental capacity constraints. A heuristic algorithm that combines the sample average approximation approach and a sensitivity analysisbased method is developed to solve the proposed model. The upper and lower bounds of the model solution are also estimated. Two numerical examples are given to show the properties of the proposed model and solution algorithm and to investigate the effects of demand variation and the importance of including risk and environmental taxation in toll design formulations. © Taylor & Francis Group, LLC.postprin

Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases

Do professional drivers suffer from dilemma zone problem while facing amber light?

Author name used in this publication: W. T. HungVersion of RecordPublishe

A Genetic Algorithm Approach for Optimising Traffic Control Signals Considering Routing

It is well known that coordinated, area-wide traffic signal control provides great potential for improvements in delays, safety, and environmental measures. However, an aspect of this problem that is commonly neglected in practice is the potentially confounding effect of drivers re-routing in response to changes in travel times on competing routes, brought about by the changes to the signal timings. This article considers the problem of optimizing signal green and cycle timings over an urban network, in such a way that the optimization anticipates the impact on traffic routing patterns. This is achieved by including a network equilibrium model as a constraint to the optimization. A Genetic Algorithm (GA) is devised for solving the resulting problem, using total travel time across the network as an illustrative fitness function, and with a widely used traffic simulation-assignment model providing the equilibrium flows. The procedure is applied to a case study of the city of Chester in the UK, and the performance of the algorithms is analyzed with respect to the parameters of the GA method. The results show a better performance of the signal timing as optimized by the GA method as compared to a method that does not consider rerouting. This improvement is found to be more significant with a more congested network whereas under a relatively mild congestion situation the improvement is not very clear

Chinese herbal recipe versus diclofenac in symptomatic treatment of osteoarthritis of the knee: a randomized controlled trial [ISRCTN70292892]

BACKGROUND: Duhuo Jisheng Wan (DJW) is perhaps the best known and most widely used Chinese herbal recipe for arthralgia, but the clinical study to verify its efficacy is lacking. The purpose of this study was to compare the efficacy of DJW versus diclofenac in symptomatic treatment of osteoarthritis (OA) of the knee. METHODS: This study was a randomized, double-blind, double-dummy, controlled trial. The 200 patients suffering from OA of the knee, were randomized into the DJW and diclofenac group. The patients were evaluated after a run-in period of one week (week 0) and then weekly during 4 weeks of treatment. The clinical assessments included visual analog scale (VAS) score that assessed pain and stiffness, Lequesne's functional index, time for climbing up 10 steps, as well as physician's and patients' overall opinions on improvement. RESULTS: Ninety four patients in each group completed the study. In the first few weeks of treatment, the mean changes in some variables (VAS, which assessed walking pain, standing pain and stiffness, as well as Lequesne's functional index) of the DJW group were significantly lower than those of the diclofenac group. Afterwards, these mean changes became no different throughout the study. Most of the physician's and patients' overall opinions on improvement at each time point did not significantly differ between the two groups. Approximately 30% of patients in both groups experienced mild adverse events. CONCLUSION: DJW demonstrates clinically comparable efficacy to diclofenac after 4 weeks of treatment. However, the slow onset of action as well as approximately equal rate of adverse events to diclofenac might limit its alternative role in treatment of OA of the knee