A bi-objective turning restriction design problem in urban road networks

postprin

A turning restriction design problem in urban road networks

Turning restriction is one of the commonest traffic management techniques and an effective low cost traffic improvement strategy in urban road networks. However, the literature has not paid much attention to the turning restriction design problem (TRDP), which aims to determine a set of intersections where turning restrictions should be implemented. In this paper, a bi-level programming model is proposed to formulate the TRDP. The upper level problem is to minimize the total travel cost from the viewpoint of traffic managers, and the lower level problem is to depict travelers' route choice behavior based on stochastic user equilibrium (SUE) theory. We propose a branch and bound method (BBM), based on the sensitivity analysis algorithm (SAA), to find the optimal turning restriction strategy. A branch strategy and a bound strategy are applied to accelerate the solution process of the TRDP. The computational experiments give promising results, showing that the optimal turning restriction strategy can obviously reduce system congestion and are robust to the variations of both the dispersion parameter of the SUE problem and the level of demand. © 2010 Elsevier B.V. All rights reserved.postprin

Urban road network crisis response management: time-sensitive decision optimization

With the increasing global stock of vehicles, traffic congestion is becoming more severe and costly in many urban road networks. Road network modeling and optimization are essential tools in predicting traffic flow and reducing network congestion. Markov chains are remarkably capable in modeling complex, dynamic, and large-scale networks; Google’s PageRank algorithm is a living proof. In this article, we leverage Markov chains theory and its powerful statistical analysis tools to model urban road networks and infer road network performance and traffic congestion patterns, and propose an optimization approach that is based on Genetic Algorithm to model network-wide optimization decisions. Such decisions target relief from traffic congestion arising from sudden network changes (e.g. rapid increase in vehicles flow, or lanes and roads closures). The proposed network optimization approach can be used in time-sensitive decision making situations such as crisis response management, where decision time requirements for finding optimal network design to handle such abrupt changes typically don’t allow for the traditional agent-based simulation and iterative network design approaches. We detail the mathematical modeling and algorithmic optimization approach and present preliminary results from a sample application

Integrated Special Event Traffic Management Strategies in Urban Transportation Network

How to effectively optimize and control spreading traffic in urban network during the special event has emerged as one of the critical issues faced by many transportation professionals in the past several decades due to the surging demand and the often limited network capacity. The contribution of this dissertation is to develop a set of integrated mathematical programming models for unconventional traffic management of special events in urban transportation network. Traffic management strategies such as lane reorganization and reversal, turning restriction, lane-based signal timing, ramp closure, and uninterrupted flow intersection will be coordinated and concurrently optimized for best overall system performance. Considering the complexity of the proposed formulations and the concerns of computing efficiency, this study has also developed efficient solution heuristics that can yield sufficiently reliable solutions for real-world application. Case studies and extensive numerical analyses results validate the effectiveness and applicability of the proposed models

Adaptive performance optimization for large-scale traffic control systems

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-scale traffic control systems that are composed of distinct and mutually interacting modules. This problem usually requires a considerable amount of human effort and time to devote to the successful deployment and operation of traffic control systems due to the lack of an automated well-established systematic approach. We investigate the adaptive fine-tuning algorithm for determining the set of design parameters of two distinct mutually interacting modules of the traffic-responsive urban control (TUC) strategy, i.e., split and cycle, for the large-scale urban road network of the city of Chania, Greece. Simulation results are presented, demonstrating that the network performance in terms of the daily mean speed, which is attained by the proposed adaptive optimization methodology, is significantly better than the original TUC system in the case in which the aforementioned design parameters are manually fine-tuned to virtual perfection by the system operators

Mixed network design using hybrid scatter search

This research proposes a bi-level model for the mixed network design problem (MNDP). The upper level problem involves redesigning the current road links’ directions, expanding their capacity, and determining signal settings at intersections to optimize the reserve capacity of the whole system. The lower level problem is the user equilibrium traffic assignment problem. By proving that the optimal arc flow solution of the bi-level problem must exist in the boundary of capacity constraints, an exact line search method called golden section search is embedded in a scatter search method for solving this complicated MNDP. The algorithm is then applied to some real cases and finally, some conclusions are drawn on the model's efficiency.postprin

Guest Editorial: Special Issue on Quantitative Approaches to Environmental Sustainability in Transportation Networks

postprin

Applications of sensitivity analysis for probit stochastic network equilibrium

Network equilibrium models are widely used by traffic practitioners to aid them in making decisions concerning the operation and management of traffic networks. The common practice is to test a prescribed range of hypothetical changes or policy measures through adjustments to the input data, namely the trip demands, the arc performance (travel time) functions, and policy variables such as tolls or signal timings. Relatively little use is, however, made of the full implicit relationship between model inputs and outputs inherent in these models. By exploiting the representation of such models as an equivalent optimisation problem, classical results on the sensitivity analysis of non-linear programs may be applied, to produce linear relationships between input data perturbations and model outputs. We specifically focus on recent results relating to the probit Stochastic User Equilibrium (PSUE) model, which has the advantage of greater behavioural realism and flexibility relative to the conventional Wardrop user equilibrium and logit SUE models. The paper goes on to explore four applications of these sensitivity expressions in gaining insight into the operation of road traffic networks. These applications are namely: identification of sensitive, ‘critical’ parameters; computation of approximate, re-equilibrated solutions following a change (post-optimisation); robustness analysis of model forecasts to input data errors, in the form of confidence interval estimation; and the solution of problems of the bi-level, optimal network design variety. Finally, numerical experiments applying these methods are reported

Reliable network design under supply uncertainty with probabilistic guarantees

This paper proposes a bi-level risk-averse network design model for transportation networks with heterogeneous link travel time distributions. The objective of the network design is to minimise the total system travel time (TSTT) budget (TSTTB), which consists of the mean TSTT and a safety margin. The design is achieved by selecting optimal link capacity expansions subject to a fixed expansion budget. Users’ selfish behaviour and risk attitude are captured in the lower level traffic assignment constraints, in which travellers select routes to minimise their own path travel time budget. The properties of the design problem are analysed analytically and numerically. The analysis shows that despite the lack of knowledge of travel time distributions, the probabilities that the actual TSTT and the actual path travel time are, respectively, within the optimal TSTTB and the minimum path travel time budget under optimal design have lower bounds. The lower bounds are related to the system manager's and travellers’ risk aversion. The optimal TSTTB is proven to be bounded below even when the link expansion budget is unlimited.postprin