It is widely recognised that congestion pricing could be an effective measure to solve environmental and congestion problems in urban areas—a reform that normally also would generate a net welfare surplus. Despite this the implementation of congestion pricing has been very slow. One reason for a low public and political acceptance could be that equity impacts have not been given enough concern. In studies of distributional impacts of congestion pricing it has often been claimed that the reform is regressive rather than progressive even if there are studies claiming the opposite. We develop a method for detailed, quantitative assessment of equity effects of road pricing and apply it to a real-world example, namely a proposed congestion-charging scheme for Stockholm. The method simultaneously takes into account differences in travel behaviour, in preferences (such as values of time) and in supply of travel possibilities (car ownership, public transport level-of-service etc.). We conclude that the two most important factors for the net impact of congestion pricing are the initial travel patterns and how revenues are used. Differences in these respects dwarf differences in other factors such as values of time. This is accentuated by the fact that the total collected charges are more than three times as large as the net benefits. With respect to different groups, we find that men, high-income groups and residents in the central parts of the city will be affected the most. If revenues are used for improving public transport, this will benefit women and low-income groups the most. If revenues are used for tax cuts, the net benefits will be about equal for men and women on the average, while it naturally will benefit high-income groups. Given that it is likely that the revenues will be used to some extent to improve the public transport system, we conclude that the proposed congestion-charging scheme for Stockholm is progressive rather than regressive.

Equity effects of congestion pricing: Quantitative methodology and a case study for Stockholm

We review recent studies on transportation network performance under perturbations. Three representative concepts relating to network performance are covered: reliability, vulnerability, and resilience. With an overview of the definitions and the quantitative indices of these three concepts, we analyse and compare their similarities and differences in the context of transportation. These concepts differ from each other in terms of focus, measurement, and application scenario. Numerical examples are conducted to assess these concepts under different perturbation scenarios. The results indicate their rationale in reflecting network performance under perturbations, yet their outputs differ. Moreover, the relationship among the three concepts is intuitively illustrated by the analysis results.

Performance of transportation network under perturbations: Reliability, vulnerability, and resilience

The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be studied in the spatiotemporal dimension. Investigating the clustering problem over time in the dynamic domain is critical to better understand and reveal the hidden information during the process of congestion formation and dissolution. The primary motivation of the paper is to study the spatiotemporal relation of congested links, observing congestion propagation from a macroscopic perspective, and finally identifying critical pockets of congestion that can aid the design of peripheral control strategies. To achieve this, we first introduce a static clustering method to partition the heterogeneous network into homogeneous connected sub-regions. The proposed framework guarantees connectivity of the cluster in different steps, which eases the development of a dynamic framework. The proposed clustering approach has 3 steps; firstly, it obtains a set of homogeneous connected components in the network. Each component has a form of sequence which is built by sequentially adding neighboring links with similar level of congestion. Secondly, the major skeleton of clusters is obtained out of this feasible set by minimizing a heterogeneity index. Thirdly, a fine-tuning step is designed to complete the clustering task by assigning the unclustered links of the network to proper clusters while keeping the connectivity. The optimization problem in both second and third step is formulated as a mixed integer linear programming. The approach is also extended to capture spatiotemporal growth and formation of congestion. The dynamic clustering is based on an iterative and fast procedure that considers the spatiotemporal characteristics of congestion propagation and identifies the links with the highest degree of heterogeneity due to time dependent conditions and finally re-cluster them to guarantee connectivity and minimize heterogeneity. An implementation of the developed methodologies in a megacity based on more than 20,000 taxis with GPS highlights the quality of the method due to its fast computation and proper integration of physical properties of congestion.

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Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

Redundancy is vital for transportation networks to provide utility to users during disastrous events. In this paper, we develop two network-based measures for systematically characterizing the redundancy of transportation networks: travel alternative diversity and network spare capacity. Specifically, the travel alternative diversity dimension is to evaluate the existence of multiple modes and effective routes available for travelers or the number of effective connections between a specific origin-destination pair. The network spare capacity dimension is to quantify the network-wide residual capacity with an explicit consideration of travelers’ mode and route choice behaviors as well as congestion effect. They can address two fundamental questions in the pre-disaster transportation system evaluation and planning, i.e., "how many effective redundant alternatives are there for travelers in the normal or disruptive event?" and "how much redundant capacity does the network have?" To implement the two measures in practice, computational methods are provided to evaluate the network redundancy. Numerical examples are also presented to demonstrate the features of the two redundancy measures as well as the applicability of the computational methods. The analysis results reveal that the two measures have different characterizations on network redundancy from different perspectives, and they can complement each other by providing meaningful information to both travelers and planners.

Transportation network redundancy: Complementary measures and computational methods

The public transport system (PTS) consists of different types of transport subsystems, such as high-speed rail (HSR), air transport (AT), conventional railway, expressway and waterway in a state, which exerts significant impacts on distribution of demographic and economic activities. And its interruption or failure may cause severe social and economic disruption. So the vulnerability analysis of the system attracts an increasing attention. However, most of previous researches mainly concentrate on the vulnerability modeling and analyzing of a single subsystem, neglecting the simultaneous interruption of components, and less considering the complementary effects of other subsystems from systematic perspective and the geographic interdependency among components. For trying to fill these gaps, this paper proposes a method to analyze the vulnerability of PTS composed of two or more transport subsystems and identity the critical area within the system based on the data of running timetable and position coordinates. Finally, taking HSR and AT coupling system in mainland China as a case study, we investigate the differences of vulnerability and critical areas between individual subsystem and overall system. Results reveal that the vulnerability variations and the critical areas distribution of the system are different from these of the subsystems. The research also finds that whether a component is critical depends not only on the system’s feature, but also on the component’s feature, whether an area is critical depends on economic development and population density of the area. These findings are beneficial to prioritize the plan, budget and maintenance of PTS, and prepare emergency response plans.

Vulnerability analysis and critical area identification of public transport system: A case of high-speed rail and air transport coupling system in China

Transport networks operating at or near capacity are vulnerable to disruptions, so flow bottlenecks are potent sources of vulnerability. This paper presents an efficient method for finding transport network cuts, which may constitute such bottlenecks. Methods for assessing network vulnerability found in the literature require origin-destination demands and path assignment. However, in transport network planning and design, demand information is often missing, out of date, partial or inaccurate. Capacity weighted spectral partitioning is proposed to identify potential flow bottlenecks in the network, without reference to demand information or path assignments. This method identifies the network cut with least capacity, taking into account the relative sizes of the sub-networks either side of the cut. Spectral analysis has the added advantage of tractability, even for large networks, as shown by numerical examples for a five-node illustrative example, the Sioux Falls road network and the Gifu Prefecture road network.

Investigating transport network vulnerability by capacity weighted spectral analysis

This paper serves as an introduction to a special issue containing selected proceedings of a symposium that was held in August 2003 in London to discuss congestion charging. The fourteen papers in this issue address a variety of theoretical and practical aspects of congestion charging. The meeting was organized to celebrate the life of Professor Peter Hills, a well-known researcher in the study of congestion charging, and to capitalize on the 2003 introduction of a congestion charge scheme in London.

Walter K.I. Wong

Papers

Investigating transport network vulnerability by capacity weighted spectral analysis

The Theory and Practice of Congestion Charging