This paper presents a global optimization framework for identifying the most critical combination of vulnerable links in a transportation network. The problem is formulated as a mixed-integer nonlinear program with equilibrium constraints, aiming to determine the combination of links whose deterioration would induce the most increase in total travel cost in the network. A global optimization solution method applying a piecewise linearization approach and range reduction technique is developed to solve the model. From the numerical results, it is interesting and counterintuitive to note that the set of most vulnerable links when simultaneous multiple-link failure occurs are not simply the combination of the most vulnerable links with single-link failure, and the links in the critical combination of vulnerable links are not necessarily connected or even in the neighbourhood of each other. The numerical results also show that the ranking of vulnerable links will be significantly affected by certain input parameters.
Density, speed, and flow are the three critical parameters for traffic analysis. High-performance traffic management and control require the estimation–prediction of space mean speed and density for large spatial and temporal coverage. Speed, including spot mean speed and space mean speed, and flow estimation are relatively easy to measure and estimate, while less attention has been devoted to measuring and estimating density. Because IntelliDrive (previously known as vehicle infrastructure integration) is a promising technology for providing a new type of real-time traffic data, and loop detector systems have already been widely deployed, this paper proposes a method to estimate freeway traffic density with both loop detector data and IntelliDrive-based probe vehicle data. The proposed method has been validated with Berkeley Highway Laboratory loop detector data combined with field-collected probe vehicle data in the first validation study and next-generation simulation video trajectory data in the second validation test. The algorithm can be used offline and in real time.
This study investigates analytical dynamic system optimal assignment with departure time choice in a rigorous and original way. Dynamic system optimal assignment is formulated here as a state-dependent optimal control problem. A fixed volume of traffic is assigned to departure times and routes such that the total system travel cost is minimised. Solution algorithms are presented and the effect of time discretisation on the quality of calculated assignments is discussed. Calculating dynamic system optimal assignment and the associated optimal toll is shown to be difficult for practical implementation. We therefore consider some practical tolling strategies for dynamic management of network traffic. The tolling strategies considered include uniform and congestion-based tolling strategies. This study contributes to the literature on dynamic traffic modelling and management, and to support further analysis and model development in this area.
SUMMARYThis paper presents an empirical assessment of urban traffic congestion in Central London, UK. Compared with freeways or motorways, urban networks are relatively less studied because of its complexity and availability of required traffic data. This paper introduces the use of automatic number plate recognition technology to analyze the characteristic of urban traffic congestion in Central London. We also present the use of linear regression to diagnose the observed congestion and attribute them to different causes. In particular, we distinguish the observed congestion into two main components: one due to recurrent factors and the other due to nonrecurrent factors. The methodologies are illustrated through a case study of Central London Area. It is found that about 15% of the observed congestion in the region is due to nonrecurrent factors such as accidents, roadwork, special events, and strikes. Given the significance of London, the study will be valuable for transport policy evaluation and appraisal in other global cities.
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