The Evaluation of Redundancy for Road Traffic Networks

Abstract

This paper presents two redundancy indices for road traffic network junctions and also an aggregated network redundancy index. The proposed redundancy indices could be implemented to identify optimal design alternatives during the planning stage of the network junctions whereas the aggregated network redundancy index could assess the best control and management policies under disruptive events. Furthermore, effective measures of network redundancy are important to policy makers in understanding the current resilience and future planning to mitigate the impacts of greenhouse gases. The proposed junction indices cover the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The proposed redundancy indices are based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and Relative Link Speed (RLS) were examined. However, the two redundancy indices developed from the combined RLS and relative link spare capacity showed strong correlation with junction delay and volume capacity ratio of a synthetic road transport network of Delft city. Furthermore, the developed redundancy indices responded well to demand variation under the same network conditions and supply variations. Another case study on Junction 3A in M42 motorway near Birmingham demonstrated that the developed redundancy index is able to reflect the impact of the Active Traffic Management (ATM) scheme introduced in 2006.

A composite resilience index for road transport networks

“…The use of real-time travel information (updating every 900 seconds) generally leads to an improvement in as shown in Figure 3. This is as intuitively expected and in line with the M42 (Junction 3a) motorway case study results presented in EL Rashidy and Grant-Muller (2016). However, the level of improvement varies according to different departure rates in each scenario as explained below:  Between 7:00am and 7:15am, has responded inversely to the increase in demand but with no notable changes arising from the use of real-time travel information (e.g.…”

“…They should have the ability to reflect the impact of a certain policy or technology on the targeted system. In previous investigations, the authors developed indicators to assess the three resilience characteristics using various attributes and methodologies briefly described below, while the full details are available in EL Rashidy and Grant-Muller (2014, 2015and 2016. These indicators of the characteristics of resilience are used as the basis for the composite resilience index developed in this paper.…”

“…The approach proposed by El Rashidy and Grant-Muller (2016) to quantify the redundancy characteristic will be adopted in the composite resilience index. A brief explanation of this approach is covered below, see (El Rashidy and Grant-Muller, 2016) for full details. In this method, a junction redundancy indicator is first developed by taking into account both traffic flow variations and network topology using the concept of entropy.…”

“…An OmniTRANS task was written to obtain the full set of routes for each OD pair, with the fraction of the demand used for each route for each time period under different scenarios (22760 routes for every scenario). The data obtained from OmniTRANS were then implemented in MATLAB code to calculate the network redundancy indicator , vulnerability indicator and mobility indicator using the methodologies detailed in EL Rashidy and Grant-Muller (2014, 2015and 2016.…”

Route Redundancy-Based Network Topology Measure of Metro Networks

“…Freckleton et al [23] defined redundancy as the ability of a traveller to adjust routes as necessary to detour around the affected sections of the network under disruptions. Actually, the concept of redundancy has been widely studied but there is no consensus definition for transportation networks, which comes down to two aspects: the number of alternatives and the network spare capacity (e.g., [10,18,24,25]). For quantifying the redundancy of transportation networks, El-Rashidy and Grant-Muller [25] proposed a redundancy index of various nodes in road networks covering the static aspect of redundancy (i.e., Journal of Advanced Transportation 3 alternative paths) and the dynamic feature of redundancy (i.e., the availability of spare capacity under different network loading and service levels).…”