Transportation infrastructure significantly influences the development of sustainable transportation systems and the effective management of road networks. Accurate segmentation of road infrastructure provides valuable data and a structured approach for the effective management of road deterioration. However, existing methods are limited in segmenting road deterioration data due to the lack of probabilistic assessment and the inability to effectively handle outliers. To address this gap, the Gaussian mixture segmentation (GMS) model is introduced, applying a Gaussian mixture distribution to model the variability in road deterioration. Our approach utilizes spatial line segmentation to effectively segment large-scale road networks into meaningful segments. The GMS model was applied to road deterioration data from the South West region of Western Australia, identifying and segmenting roads based on their distribution characteristics using the Jensen-Shannon (JS) divergence. To assess the performance of the GMS model, metrics such as the number of segments, coefficient of variation (CV), Caliński-Harabasz Index, and Davies-Bouldin Index were evaluated. The results demonstrate that the GMS model outperformed existing segmentation methods, achieving an increase in the average percentage of segments with a CV lower than 0.25 by 23.5% to 98.8%, along with a reduction in the average Davies-Bouldin Index by 25.2% to 64.4% and improvements in the average Caliński-Harabasz Index, which increased by 23.0% to 131.1%. This approach enhances the understanding of the spatial distribution of road deterioration, informing maintenance strategies for large-scale road networks and addressing the complexities of road infrastructure management, including traffic dynamics and environmental impacts.

Road transport infrastructure is a crucial component of the entire infrastructural network. Timely and efficient maintenance of roads requires accurate and effective evaluation of pavement health, of which cracking is an important aspect. However, accurately assessing pavement cracks across large-scale road networks remains challenging due to spatial variations, which diminish the effectiveness of traditional machine learning methods. This study developed a novel spatial machine learning (SML) model and employed laser scanning data and satellite remote sensing images to assess road segment-based crack severity across the state-level road network in the Wheatbelt of Western Australia. Geocomplexity is introduced to measure the complexity of local patterns and spatial dependence among neighboring road segments. Results showed that SML can accurately and effectively predict pavement cracks on a large spatial scale with an accuracy (AC) from 0.524 to 0.701. In the SMLs, laser-scanning pavement variables contributed 34.15% to 43.33% of the total explainable variations, and geocomplexity variables also contributed significantly, ranging from 27.35% to 49.92%. The SML model exhibited the highest coefficient of determination ($R^{2}$) and AC for crack prediction compared with Multiple Linear Regression (MLR), Generalized Additive Model (GAM), Bayesian Regularized Neural Network (BRNN) and Support Vector Regression (SVR). The findings provided a deep insight into large-scale crack deterioration by considering the spatial characteristics and achieved high-resolution crack assessment to support road maintenance decision-making. The spatial machine learning approach and the concept of geocomplexity can be widely applied to address large-scale spatial tasks in road engineering.

The explanation of spatial errors in geospatial modelling has long been a challenge. This study introduces an index that captures the complexity of local spatial distribution, which can partially provide insight into spatial errors. While previous studies have explored the complexity of geographical data from various perspectives, there is limited knowledge on assessing the complexity while taking spatial dependence into account. This study proposes a measure of geocomplexity, i.e. the spatial local complexity indicator, which characterizes the complexity of local spatial patterns while considering spatial neighbor dependence. We used both aspatial and spatial models to estimate the economic inequality in Australia, and applied the spatial local complexity indicator to explain spatial errors in these models. Results show that the developed geocomplexity indicator, using a binary spatial matrix, can effectively explain spatial errors arising from models, including 17%-47% of errors in aspatial models and 14% in a spatial model. The experiments in this study support our hypothesis that geocomplexity is an essential component in explaining spatial errors. The proposed geocomplexity indicator, along with our hypothesis, has the potential for advancing the understanding complex geospatial systems and enabling applications in various fields related to spatial data analysis.

Strategic maintenance is essential for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. Road deterioration is affected by sophisticated factors, but accurate investigation of the integrated deterioration factors is limited. This study developed a dynamic trade-off model (DTOM), a hybrid nonlinear and machine learning method, for quantifying temporally varied impacts of factors and examining maintenance effects at the network level. Pavement deterioration factors are classified into three categories: (i) historical observations of roughness, (ii) pavement age, and (iii) traffic, climate and environment factors. Their respective impacts on pavements are estimated using a non-linear least square regression, a joinpoint regression and a random forest model, respectively. Vehicle-based laser scanner monitored high-resolution deterioration data was collected for a large spatial scale road network in Western Australia from 2007 to 2018. Results show that the resurfacing and rehabilitation are essential for strategic reduction of deterioration. Twelve-year maintenance activities reduced the distress of roughness by 7.5% and increased road performance (the percentage of roads with roughness lower than 2.085 IRI) by 14.5% for the whole road network. The DTOM has great potentials in accurately assessing infrastructure maintenance effects and predicting deterioration scenarios.

Transport infrastructure is a fundamental component of the whole infrastructure system and socio-economic development. However, it is still a challenge to identify factors affecting large-scale infrastructure due to the lack of high-quality data and inconsistent methods. To address this issue, this study developed a comparison of data- and knowledge-driven approaches in exploring factors affecting road infrastructure performance in Western Australia using network-level high-resolution road defects data. In data-driven analysis, an optimal parameters-based geographical detectors (OPGD) model, developed based on spatial heterogeneity, was developed to investigate the contributions of explanatory factors from a spatial data perspective. In knowledge-driven analysis, a questionnaire survey was performed through group interviews with regional road management teams to analyze potential explanatory factors. A spatial analytic hierarchy process (S-AHP) approach was implemented to quantify the contributions of factors based on the survey. Finally, the consistency and difference between data- and knowledge-driven approaches are evaluated based on contributions of factors and the predictions of defect risks across the road network. The results indicate that the contributions of factors tend to be similar in both approaches, and the spatial distributions of defect risks predicted by both approaches are highly correlated. The factors and risks analyzed using both methods in rural areas are more consistent than those in urban areas due to the complexity and uncertainty of road defects. Findings from this study are critical to understanding data- and knowledge-driven approaches in transport infrastructure management and determining reasonable approaches for decision-making.

Geographical variables are usually not independent of each other. Hence, it is necessary to investigate the effect of interactions among explanatory variables on a response variable to characterize spatially enhanced or weakened relationships among all variables. The geographical detector (GD) model identifies zones for each explanatory variable, divides the study area into spatial units by overlapping these zones, and quantifies spatial associations as the power of interactive determinant (PID) between a response variable and explanatory variables. Consequently, the PID values depend upon the distributions of explanatory variables (i.e. spatial characteristics) and the subsequent division of spatial units out of these explanatory variables. This study has therefore proposed an Interactive Detector for Spatial Associations (IDSA) to optimize spatial division and improve PID. IDSA utilizes spatial autocorrelation of each explanatory variable and optimizes spatial units based on spatial fuzzy overlay to compute PID. We test the IDSA on both a simulation study and practical case that analyzes road deterioration in Australia. Results showed that the IDSA model could effectively assess the PID while existing GD overestimated PID. Hence, the IDSA improves the GD with refined spatial units based on explanatory variables to enhance their local spatial associations with a response variable.

Road network conditions and road quality are directly linked with the performance of an entire infrastructure system. As sensor monitoring of road deteriorations has rapidly increased, road infrastructure performance can now be assessed using multiple measures. However, more effective and accurate quantitative analysis methods are increasingly required. This research explores road infrastructure performance using road deterioration network data in the Mid West Gascoyne region, Australia. A spatial heterogeneity-based segmentation (SHS) model is developed for redefining road segments across the network in terms of sensor monitoring data, and for both project-level and network-level infrastructure systems management. To evaluate the model effectiveness and accuracy, an evaluation system is proposed from four aspects: segment number, homogeneity within segments, heterogeneity among segments, and segment morphology. The SHS model is compared with two widely used road network segmentation methods. The results show that the SHS model can use fewer segments to ensure higher homogeneity within segments and heterogeneity among segments across the network. Meanwhile, the segment lengths are more uniformly distributed as compared with results from other methods. The developed model and findings from this research can significantly improve the utilization of sensor monitoring network data and support multi-scale infrastructure systems management.

Geostatistical methods have been widely used for spatial prediction and the assessment of traffic issues. Most previous studies use point-based interpolation, but they ignore the critical information of the road segment itself. This can lead to inaccurate predictions, which will negatively affect decision making of road agencies. To address this problem, segment-based regression kriging (SRK) is proposed for traffic volume prediction with differentiation between heavy and light vehicles in the Wheatbelt region of Western Australia. Cross validations reveal that the prediction accuracy for heavy vehicles is significantly improved by SRK (R2= 0.677). Specifically, 78% of spatial variance and 53% of estimated uncertainty are improved by SRK for heavy vehicles compared with regression kriging, a best performing point-based geostatistical model. This improvement shows that SRK can provide new insights into the spatial characteristics and spatial homogeneity of a road segment. Implementation results of SRK-based predictions show that the impact of heavy vehicles on road maintenance is much larger than that of light vehicles and it varies across space, and the total impacts of heavy vehicles account for more than 82% of the road maintenance burden even though its volume only accounts for 21% of traffic.

Urban forms reflect spatial structures of cities, which have been consciously and dramatically changing in China. Fast urbanisation may lead to similar urban forms due to similar habits and strategies of city planning. However, whether urban forms in China are identical or significantly different has not been empirically investigated. In this paper, urban forms are investigated based on two spatial units: city and block. The boundaries of natural cities in terms of the density of human settlements and activities are delineated with the concept of ‘redefined city’ using points of interests (POIs), and blocks are determined by road networks. Urban forms are characterised by city-block two-level spatial morphologies. Further, redefined cities are classified into four hierarchies to examine the effects of different city development stages on urban forms. The spatial morphology is explained by urbanisation variables to understand the effects. Results show that the urban forms are spatially clustered from the perspective of city-block two-level morphologies. Urban forms tend to be similar within the same hierarchies, but significantly varied among different hierarchies, which is closely related to the development stages. Additionally, the spatial dimensional indicators of urbanisation could explain 41% of the spatial morphology of redefined cities.