The Spatiotemporal Evolution of Sydney's Tram Network Using Network Econometrics

This paper examines the evolution of Sydney trams using network econometrics approaches. Network econometrics extends spatial econometrics by developing weight matrices based on the physical structure of the network, allowing for competing and complementary elements to have distinct effects. This research establishes a digitized database of Sydney historical tramway network, providing a complete set of geo-referenced data of the opening and closing year and frequencies by time of day for every line. An autoregressive distributed lag model is specified and reveals that the combination of correlation strength and magnitude of lagged flow change on correlated links is a significant predictor of future tram service. The results indicate that complementary and competitive links play distinct roles in shaping the network structure. A link is more likely to undergo the same structural change highly complementary (upstream or downstream) links underwent previously, where the influence is measured by a combination of correlation strength and link importance, reflected by historical service levels.     

Measuring the Structure of Road Networks

Spatial networks display both topologic and geometric variations in their structure. This study investigates the measurement of a road network structure. Existing measures of heterogeneity, connectivity, accessibility, and interconnectivity are reviewed and three supplemental measures are proposed, including measures of entropy, connection patterns, and continuity. The proposed measures were applied to 16 test networks, which were derived from four idealized base networks: 90°, 45°, 30°, and completely connected. The results show that the differentiated structures of road networks can be evaluated by the measure of entropy; predefined connection patterns of arterial roads can be identified and quantified by the measures of ringness, webness, beltness, circuitness, and treeness. A measure of continuity evaluates the quality of a network from the perspective of travelers. Proposed measures could be used to describe the structural attributes of complicated road networks quantitatively, to compare different network structures, and to explore the structural evolution of networks in the spatial and temporal context. These measures can find application in urban planning and transportation practice.     

An Introduction to the Network Weight Matrix

This study introduces the network weight matrix as a replacement for the spatial weight matrix to measure the spatial dependence between links of a network. This matrix stems from the concepts of betweenness centrality and vulnerability in network science. The elements of the matrix are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrix has distinctive characteristics, which are capable of reflecting spatial dependence between traffic links: (1) elements are allowed to have negative and positive values capturing the competitive and complementary nature of links, (2) diagonal elements are not fixed to zero, which takes the self‐dependence of a link upon itself into consideration, and (3) elements not only reflect the spatial dependence based on the network structure, but they acknowledge the demand configuration as well. We verify the network weight matrix by modeling traffic flows in a 3 × 3 grid test network with 9 nodes and 24 directed links connecting 72 origin‐destination (OD) pairs. Models encompassing the network weight matrix outperform both models without spatial components and models with the spatial weight matrix. The network weight matrix represents a more accurate and defensible spatial dependency between traffic links, and offers the potential to augment traffic flow prediction.     

Density and dispersion: the co-development of land use and rail in London

This article examines the changes that occurred in the railnetwork and density of population in London during the 19thand 20th centuries. It aims to disentangle the ‘chickenand egg’ problem of which came first, network or landdevelopment, through a set of statistical analyses clearly distinguishingevents by order. Using panel data representing the 33 boroughsof London over each decade from 1871 to 2001, the research findsthat there is a positive feedback effect between populationdensity and network density. Additional rail stations (eitherUnderground or surface) are positive factors leading to subsequentincreases in population in the suburbs of London, while additionalpopulation density is a factor in subsequently deploying morerail. These effects differ in central London, where the additionalaccessibility produced by rail led to commercial developmentand concomitant depopulation. There are also differences inthe effects associated with surface rail stations and Undergroundstations, as the Underground was able to get into central Londonin a way that surface rail could not. However, the two networkswere weak (and statistically insignificant) substitutes foreach other in the suburbs, while the density of surface railstations was a complement to the Underground in the center,though not vice versa.     

Primal and Dual Access

Accessibility, measuring the ease of reaching potential destinations, is increasingly being considered as an effective indicator to evaluate the performance of transport and land use interactions. Primal accessibility, a generalization of the first accessibility formulation proposed by Hansen, has been widely used in many studies and demonstrated to be a reliable tool for project, program, and policy evaluation. The dual of accessibility, measuring the time required to reach a given number of opportunities, is less often considered but can be used for optimization in location covering-type problems. This article, hence, clarifies the definitions of primal and dual access, and applies both measures to the Minneapolis—St. Paul metropolitan area for auto and transit to demonstrate their practicality as a metropolitan-level measurement. We explore the correlations and differences between the primal and dual access to better understand the relative strengths of the measures. It is found that, as with primal accessibility, dual accessibility is an efficient approach to evaluate accessibility, which is straightforward to calculate and to explain to policy-makers and the public.