•Geo-physical risk and community vulnerabilities defines GEOPHRIV index.•Optimization of pavement management minimizes the pavement deterioration and GEOPHRIV values of roads.•Annual budget of USD ...2.49 million ensures having good average roads condition in the study area for 30 years.•Roads with high and medium GEOPHRIV values are given priority.•Most M&R budget will be invested for overlay and resealing.
Roads in poor road condition disrupt emergency operations in disaster-prone areas during emergency periods. Prolonged inundation of pavements from storm surge accelerates deterioration of pavements and increases maintenance cost. The objective of this study is to propose an optimized decision support system for pavement maintenance and rehabilitation (M&R) operations guided by geo-physical risk and community vulnerabilities. A case study of regional highways, arterial and collector roads at the district of Barguna, in Bangladesh is selected given the frequency of cyclones and storm surges in this area. A geo-physical risk and vulnerability (GEOPHRIV) index was estimated for each road’s segment by integrating the geo-physical risk; community, structure and infrastructure vulnerabilities; and damage indices. Linear programming was applied to optimize M&R strategies to ensure good pavement condition for all roads at a minimum M&R budget. Lifecycle optimization of M&R operations estimated that USD 2.49 million is the minimum annual budget that ensures having good average road’s condition in the study area. Most of the annual M&R budget will be invested for overlay and resealing treatments on the roads at high and medium GEOPHRIV areas. This study helps transportation authorities to identify deteriorated pavement sections, maintain the pavement periodically to prevent or minimize damage before storm surge, and allocate resources for M&R operations.
This study demonstrates through a case study that detailed analyses, even after the construction of a project, are feasible using current technologies and available data. A case study of highway 25 ...is used to illustrate the method and verify the levels of air contaminants from additionally induced traffic during and after the construction of highway. Natural traffic growth was removed from the effect of observed gas emissions by comparing observed levels on other further locations in the same metropolitan area. This study estimates air pollution from the additional traffic during and after the construction of A-25 extension project. NO2 levels were spatially interpolated during peak and off-peak hour traffic and traffic density simulated on the road network for four scenarios. Comparing the four scenarios, it was found that levels of NO2 concentrations were reduced at neighbor areas due to less traffic during the construction period. Levels of NO2 after the construction were higher than those in 2008. The simulated traffic density for four scenarios revealed that traffic density was significantly increased on both arterial and access roads within the close vicinity of the extension project during and after its construction.
The objective of this study is to apply the Backpropagation Neural (BPN) network with Generalised Delta Rule learning algorithm for reducing the measurement errors of pavement performance modelling. ...The Multi-Layer Perceptron network and sigmoid activation function are applied to build the BPN network of Pavement Condition Index (PCI). Collector and arterial roads of both flexible and rigid pavements in Montreal City are taken as a case study. The input variables of the PCI are Average Annual Daily Traffic (AADT), Equivalent Single Axle Loads (ESALs), Structural Number (SN), pavement age, slab thickness and difference in the PCI between the current and preceding year (ΔPCI). The BPN networks estimate that the PCI has inverse relationships with AADT, ESALs and pavement age. The PCI has positive relationships with these variables for roads that have recent treatment operations. The PCI has positive relationships with SN and slab thickness that imply the increase in pavement condition with increasing structural strength and slab thickness. The ΔPCI significantly influences the estimation of PCI values. The AADT and ESALs have considerable importance; however, pavement age and structural characteristics of the pavement have an insignificant influence in determining the PCI values, except in the case of flexible arterial roads.
•A quantitative model is proposed to assess the comfort level of railway journeys.•Indices could be used for transit asset management and mode choice analysis.•The newer trains in Montreal metro are ...more comfortable; but still could be better.•Proposed model could be used by decision makers in transit maintenance and planning.•Model is applicable to railway systems as well as bus-rapid-transit or tramways.
Transit agencies concentrate their efforts to satisfy most commuters and convince them to abandon the use of the private car in daily trips, which is a key element in promoting environmentally sustainable cities. Travel time and cost have been widely investigated as travelers’ satisfaction factors, while human aspects such as comfort are mostly neglected. Management of urban transit systems should ensure a convenient transit service with adequate levels of ride and quality (time and cost) as well as users’ comfort, health and safety to achieve sustainable societies. This study proposes an approach to quantitatively measure railway riders’ comfort in terms of humidity, temperature, vibration, the concentration of CO2, noise, and lighting. Such indexes can be used to direct the allocation of investments for improvement of comfort in urban transit infrastructures as well as capture reality in demand prediction modeling. A case study of several lines in the Montreal metro network is presented to illustrate the applicability and usefulness of the proposed approach. The newer trains in the Montreal metro are an improvement over older trains when it comes to rider comfort; however, they could still be better, especially when it comes to auditory comfort.
This study improves the pavement management system by developing a linear programming optimization for the road network of the City of Montreal with simulated traffic for a period of 50 years and ...deals with the uncertainty of pavement performance modeling. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. A backpropagation neural network (BPN) with a generalized delta rule learning algorithm is applied to develop pavement performance models without uncertainties. Linear programming of life-cycle optimization is applied to develop maintenance and rehabilitation strategies to ensure the achievement of good levels of pavement condition subject to a given maintenance budget. The BPN network estimated that PCI values were predominantly determined by the differences in pavement condition index, AADT, and equivalent single axle loads. Dynamic linear programming optimization estimated that CAD$150 million is the minimum annual budget required to keep most of the arterial and local roads in good condition in Montreal.
Lean construction (LC) targets the reduction and eventual removal of non-value adding activities (otherwise known as waste) in construction projects to increase value to stakeholders. The application ...of LC principles could lead to significant gains in road maintenance projects where the effect of delay in service is profound and the consequences are far-reaching for all project stakeholders. However, classical practices in the infrastructure asset management domain have less embraced lean thinking when planning for maintenance and rehabilitation. This paper presents a novel decision-making model to integrate LC principles in a classical formalized road maintenance planning and scheduling to facilitate the reduction of non-value adding activities. A case study is adopted to validate the proposed approach. Analysis of the case study revealed a reduction in the cost allocation for the selected highway with similar condition levels in comparison with the classical practices. The model could drop non-value activities, limit the gap on lead time variability, and reduce operational complexity as three main principles of lean thinking. Also, the presented approach could assist road agencies in minimizing delays, optimally assign project resources, and select qualified construction contractors more efficiently.
Public transit plays a significant role in the sustainability of an urban region which requires high pedestrian safety at the interchange points. This research studies the magnitude of pedestrian ...collisions in the proximity of Public Transit Access Points (PTAPs) and address how Traffic Calming strategies and road elements improve pedestrian safety at PTAPs. Getis- Ord Hotspot Analysis and the Negative Binomial models are applied to address research questions. Pedestrian collisions occur more frequently at intersections with the presence of a PTAP and with a higher volume and number of bus routes. Traffic calming strategies such as road width reduction, sidewalk width increase, median refugees, pedestrian crossing phase, and vehicle stop signs could improve pedestrian safety of PTAP. Besides, pedestrians are at more risk in PTAP at locations where high road gradients and in proximity to intersections with a higher number of directions of vehicle traffic flow.
Several cities around the world have announced strategies to extend and (or) upgrade their bikeway networks in response to the rapid increase of bicycle users. However, there is a disconnection ...between these strategies and management systems, often used for the scheduling of maintenance and rehabilitation of roads. Traditional pavement management systems fail to incorporate bicycle pathways considering bicycling demand, along with pavement condition, as a driving element to budget for improvements. More convenient and safer bicycling facilities can encourage more individuals to shift their daily commuting habits to bicycling. In this study, we incorporate bicycling demand into pavement management systems to produce strategic plans for the maintenance and improvement of the bicycle networks. Furthermore, here we employ smartphones to represent bicycling demand using GPS trajectories of bicycles. In addition, goal optimization is applied to schedule interventions and improvements. Two scenarios are investigated with different annual budgets.
This paper proposes the use of multi-level Bayesian modeling for calibrating mechanistic model parameters from historical data while capturing reliability by estimating a desired confidence interval ...of the predictions. The model is capable of estimating the parameters from the observed data and expert criteria even in cases of missing data points. This approach allows rapid generation of several deterioration models without the need to partition the data into pavement families. It estimates posterior distributions for model coefficients and predicts values of the response for unobserved levels of the causal factors. A case study from the New Brunswick Department of Transportation is used to calibrate a simplified mechanistic pavement roughness progression model based on 6-year international roughness index (IRI) observations. The model incorporates the effects of pavement structural capacity in terms of deflection basin parameter (AREA) in place of the modified structural number, traffic loading (ESAL) and environmental factors. The results of the model showed that, as expected, chipseal roads have higher as built roughness and deteriorate faster than asphalt roads. Sensitivity analysis of the deterministic (the mean predictions) part of the model showed that in New Brunswick where traffic is relatively low the environment is the most important factor.
•Exposure to Weather-Accident Severity is the product of accident and weather severities.•Poor light and road surface in bad weather increase the weather-accident severity.•Female, aged and ...inexperienced drivers are more vulnerable.•CGCM3 (A2 & A1B) and HadCM3 (A2 & B2) models to simulate climate change in 21st century.•Rainy days will increase and snowy and freezing days will decrease or remain same.
The objective of this research is to study the impact of climate change on the hazardous weather-related road accidents in the New Brunswick, Canada. We develop an Exposure to Weather-Accident Severity (EWAS) index multiplying accident and weather severity. The Negative Binomial Regression and Poisson regression models are applied to estimate the spatial–temporal relationship between the EWAS index and weather-related explanatory variables of road accidents. The regression results show that the surface-weather condition, weather, driver’s gender, weather-driver’s age, weather-driver’s experience, and weather-vehicle’s age have strong positive correlation with the EWAS index, while the surface-road alignment and surface-road characteristics have negative relationship with the EWAS index. The climate change model also indicates that the number of accidents declines during snowy and freezing days—most people stay at home and those who travel extra cautious—accidents do occur. The study suggests that the Road Safety Strategy 2015 of the Transport Canada should take a holistic approach to help minimize the incidences of severe road accident during the normal as well as hazardous weather conditions.
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