Abstract

The rationale of road-pricing schemes is to reduce the negative externalities of road traffic by increasing costs to decrease demand. Although in the transportation literature this is a well-acknowledged means of relieving cities from congestion, only few cities have introduced such schemes so far. One of the most notable examples in Europe is London’s congestion charge. Motorists entering London’s city center are required to pay a fixed levy during working hours. The revenue generated by this levy is partly dedicated to improvements of the public transport system. The benefits of this congestion charge have only been analyzed from an economic perspective without reference to its impact on macroscopic traffic indicators. The recently introduced macroscopic fundamental diagram (MFD) and its extension to multimodal traffic, the 3D-MFD, offer a novel framework to address this gap."br" In this paper, we analyze the performance of London’s overground traffic with the empirical 3D-MFD covering both car traffic and buses. Data is acquired from loop detectors (for car traffic) and automated vehicle location devices (for buses).

Abstract

The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and "jats:italic"k"/jats:italic"-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results. Document type: Article