Urban Road Infrastructure Research Articles

A methodology for the characterization of urban road safety through accident data analysis

As is known, road accidents essentially depend on four interrelated factors: human behaviour, vehicle efficiency, environmental conditions and the characteristics of the infrastructure. Although the vast majority of accidents is attributable to the first three factors, almost always attributable to improper user behaviour, it is of fundamental importance to try to reduce that part of the risk attributable to the infrastructure. In this research, the problem concerning the assessment of urban road safety in existing roads, in order to identify the dangerous sections on which to concentrate resources to make the functional adjustments deemed necessary, is addressed through the analysis of the accident rate found in operation. In particular, the research proposes a new model that characterizes the intrinsic risk of a urban road infrastructure through the assessment of accidents, disaggregated into different types, with a risk index that is a function of the two most significant variables that represent the accidents: the frequency with which they occur and the severity of the damages produced. The study also provides three aspects for achieving improved urban road safety. The first identifies the critical road sections (blackspots), through the application of the new model to the classic methods of accident assessment. The second defines the functional adjustments necessary to reduce the causes of accidents by comparing the risk of accidents, determined with the new methodology for each type of accident, and the technical characteristics of the road network in question. The third establishes the intervention priorities, based on an economic planning linked to the available budget, among the functional adjustments identified to reduce the risk of accident.

Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks

Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.

Road construction and air quality: Empirical study of cities in China

Traffic-related air pollution has become an urgent problem in many developing countries with rapid industrialization and urbanization. Traffic infrastructure construction, especially road reconstruction, is a prominent instrument of improving the road capacity and then affecting air quality. We employ the road area as the proxy variable of road infrastructure construction and further divide the road reconstruction pattern into lengthening pattern and widening pattern, respectively to evaluate the impact on urban air quality. The empirical result shows that the coefficient of road infrastructure construction represented by the road area is between −0.0818 and −0.0905, which means increasing road area has an emission-alleviating effect on air pollution. Moreover, we also find that the effect of road lengthening on air quality is better than road widening in the eastern China, but the improvement of road widening in the Central and Western China is better than the road lengthening. The result provides a valuable reference for policy recommendations on urban road infrastructure planning to improve air quality.

Optimal bicycle trip impediments resolution by data fusion

We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a traffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists.

Identifying interactions between policy, accountability and outcomes for adaptation of urban roads

Several decision-making frameworks are available to foster climate adaptation of road infrastructure, but these focus mainly on the adaptation of national or regional roads. Urban road infrastructure is equally important for the efficient functioning of society and it is imperative to understand the peculiarities of adapting these types of roads, however, existing frameworks may not be appropriate for this purpose. This paper presents a conceptual framework called the “Expanded Adaptation Action Cycles” (EAAC) that conceptualizes in a simple, yet comprehensive manner the adaptation planning processes of urban road infrastructure. A case study of the decision-making processes for the development and maintenance of the road network of Bogotá, Colombia was conducted to demonstrate the practical utility of this framework as a diagnostic tool. Analysing the evidence collected using the EAAC framework indicates that the city is currently at the early stages of its adaptation planning and that the adaptation measures being considered suggest that, at most, the city is aiming to implement incremental adaptation. This demonstrates how the EAAC framework provides the ability to understand the relative position of a city’s approach to adaptation planning within a wider spectrum of possibilities, providing insight into the potential impact of possible planning interventions.

Spillovers of Urban Road Infrastructure Investment and Operation: a Case Study Using Synthetic Control Method

This study evaluates the impacts of urban road investment and operation in China, especially the spillover effect attributable to the investment of urban road projects. Using the synthetic control method and difference-in-differences technique and taking the opening of Jiaozhou Bay Bridge and its Subsea Tunnel in China on 30 June 2011 as a natural experiment, this paper investigates the causal effect between urban road investment and its economic impacts. Results show that the project has a positive externality in terms of its contribution to the output and employment: taken the industrial relative output as outcome variable, no matter whether the covariates are controlled or not, the parameters of the interactive terms are positive; taken the industrial relative employment rate as outcome variable, the gap between the treated unit and its counterpart indicates a direct program effect for the treated city as well as a spillover effect across the cities within the sample province. Furthermore, the permutation test ascertains that the probability of achieving a spillover effect as large as the treated city is around 5.88 per cent. Overall, the investment and operation of urban road transportation infrastructure has a noticeable spillover effect. Our results are robust across a series of placebo tests.

Road Tests of a Two-Wheeled Vehicle with the Use of Various Urban Road Infrastructure Solutions

Road Tests of a Two-Wheeled Vehicle with the Use of Various Urban Road Infrastructure Solutions

IMPROVING CLASSIFICATION OF MULTISPECTRAL IMAGES BASED ON SELECTED RELATIONS

Abstract. A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.

Análisis del sistema de transporte urbano para optimizar el tiempo de viaje del pasajero de la ciudad de Puno-2018

El objetivo de la investigación fue analizar el sistema de transporte urbano de la ciudad de Puno, se propone de infraestructura vial urbano para tener una capacidad vial de acuerdo al tipo de vía y así poder optimizar el tiempo de viaje del pasajero según la tendencia de movilidad, movilidad de origen destino según zonas, movilidad del transporte urbano. Se analizó el recorrido de rutas de transporte público urbano para determinar la velocidad promedio y así poder conocer los tiempos de viajes. La hipótesis que se ha formulado es: con el análisis del sistema de transporte urbano se propone infraestructura vial urbana para optimizar el tiempo de viaje del pasajero de la ciudad de Puno-2018, se analiza la tendencia de movilidad según origen destino, la movilidad del transporte urbano, rutas de transporte urbano público e infraestructura vial urbana. La investigación centró su metodología, bajo un enfoque cuantitativo, diseño no experimental. Se aplicó un cuestionario como un instrumento para el análisis; de los cuales permitieron recoger información y analizar las variables de estudio y efectuar las correlaciones. La muestra estuvo conformada de 384 pasajeros y 8 microbuses que brindan servicio en las principales rutas de transporte público urbano. Como resultado se tiene que el sistema de transporte urbano de la ciudad de Puno es concentrado debido al uso de suelo en la zona céntrica, la movilidad vehicular tiene obstáculos en el flujo debido al uso de la calzada como estacionamiento y que las vías superan su capacidad vial la cual no optimiza los tiempos de viajes en ciudad de Puno para lo cual se requiere de infraestructura vial urbana como son: terminales terrestres, pavimentado de las vías paraderos, declarar zonas rígidas las vías que son utilizadas por las rutas de transporte urbano e implementar un sistema inteligente de gestión de tráfico en las intersecciones principales de la ciudad.

Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.

A topological characterization of flooding impacts on the Zurich road network

Infrastructure systems are the structural backbone of cities, facilitating the flow of essential services. Because those systems can be disrupted by natural hazards, risk management has been the prevailing approach for assessing the consequences and expected level of damage. Although this may be a valuable metric, the practice of risk assessment does not represent how hazards affect a network of assets on a larger scale. In contrast, network topology metrics are useful because they evaluate the performance of network infrastructures by looking at the system as a whole. As described here, we began this study to improve our understanding of how flooding events affect the topological properties of road networks, in this case, the urban road infrastructure of Zurich, Switzerland. Using maps of flooding risk, we developed a procedure to extract the damaged networks and analyze the centrality metrics for peak water levels on the surface of the city. Our approach modelled roads as edges and junctions between roads as nodes. The betweenness centrality metric characterizes the importance of nodes or edges for any type of exchange within a network, whereas the closeness centrality metric measures the accessibility of a specific node to all the other nodes. This investigation produced three main findings. First, descriptive analyses showed that the characteristics and patterns of nodes and edges changed under the flooding events. Second, the distribution function of centrality metrics became heavier in the tails as the flood magnitude increased. Third, the associated strain shifted critical nodes to areas in which those nodes would not be important under normal conditions. These findings are essential for identifying crucial locations and devising plans to address risks. Future projects could expand our approach by including traffic flow to move the analysis closer to real-world flows, and by studying the accessibility under emergency conditions at local levels.

Automation-ready framework for urban transport and road infrastructure planning

Abstract “Automation-ready” is currently defined in the H2020 CoEXist project as the capability to conduct transport and infrastructure planning with the incorporation of connected and automated vehicles (CAV) in the same comprehensive manner as for existing modes in future plans, where they complement and coexist with conventional vehicles, public transport, pedestrians and cyclists, to achieve and support higher sustainable mobility goals. This definition will be fine-tuned through stakeholder engagement processes. The H2020 CoEXist project started in May 2017 and will run until April 2020. This paper introduces this project and covers its progress until January 2018, with a focus on the methodology of the “Automation-ready framework” that provides a planning framework for urban road authorities to prepare for the introduction of CAVs on the road network. The two Stuttgart use cases (among the eight use cases in CoEXist) are also elaborated in this paper. The framework includes elements about strategic urban mobility planning for CAVs and a clear guide for urban transport planners with a list of concrete actions that cities can do now to plan for CAVs on their road network.

Urban Road Infrastructure Maintenance Planning with Application of Neural Networks

The maintenance planning within the urban road infrastructure management is a complex problem from both the management and technoeconomic aspects. The focus of this research is on decision-making processes related to the planning phase during management of urban road infrastructure projects. The goal of this research is to design and develop an ANN model in order to achieve a successful prediction of road deterioration as a tool for maintenance planning activities. Such a model is part of the proposed decision support concept for urban road infrastructure management and a decision support tool in planning activities. The input data were obtained from Circly 6.0 Pavement Design Software and used to determine the stress values (560 testing combinations). It was found that it is possible and desirable to apply such a model in the decision support concept in order to improve urban road infrastructure maintenance planning processes.

Saving Travel Time as an Urban Planning Instrument. Case Study: Manizales, Colombia

This research explores the benefits of a proposal for urban road infrastructure which aims to improve road connection between northwest and western neighborhoods of the city of Manizales, Colombia, as well as to expand the ring of urban mobility that runs through the city. By calculating the global average accessibility and comparing the current and future situation, by averages of savings gradient, timesaving generated by this alternative are obtained in terms of average travel time. There is evidenced that suggest the road infrastructure proposal would generate savings in the average travel times for the entire city, especially to the neighborhoods located in the area of direct influence.

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

ABSTRACTLand use change models enable the exploration of the drivers and consequences of land use dynamics. A broad array of modeling approaches are available and each type has certain advantages and disadvantages depending on the objective of the research. This paper presents an approach combining cellular automata (CA) model and support vector machines (SVMs) for modeling urban land use change in Wallonia (Belgium) between 2000 and 2010. The main objective of this study is to compare the accuracy of allocating new land use transitions based on CA-SVMs approach with conventional coupled logistic regression method (logit) and CA (CA-logit). Both approaches are used to calibrate the CA transition rules. Various geophysical and proximity factors are considered as urban expansion driving forces. Relative operating characteristic and a fuzzy map comparison are employed to evaluate the performance of the model. The evaluation processes highlight that the allocation ability of CA-SVMs slightly outperforms CA-logit approach. The result also reveals that the major urban expansion determinant is urban road infrastructure.

The traffic of school buses in the urban area of Tunis: modelling and simulating with meta-heuristic means

We present a model for the capacity of an urban road infrastructure using data from the urban area of Tunis. A simulation, implemented using the C language and meta-heuristics is applied to school bus itineraries. We incorporated various factors to ensure the smooth operation of school bus traffic, such as the roads, bus travel times, the infrastructure, congestion, waiting times, the age of the vehicles and the availability of drivers. The results show the genetic algorithm to be more adept at resolving urban transport problems, with its superiority also manifesting in terms of a shorter resolution time and faster convergence to an optimal solution. This algorithm enabled a reduction in the number of buses needed and found the shortest path in the least simulation time. In addition to reducing operating costs, it also managed to ensure a transport network that provided an adequate quality of service with minimal journey times. Then, this improvement of the circuit of transport leads to lower bus running costs and it improves the living conditions of students and their social welfare.

The traffic of school buses in the urban area of Tunis: Modeling and simulating with meta-heuristic means

We present a model for the capacity of an urban road infrastructure using data from the urban area of Tunis. A simulation, implemented using the C language and meta-heuristics is applied to school bus itineraries. We incorporated various factors to ensure the smooth operation of school bus traffic, such as the roads, bus travel times, the infrastructure, congestion, waiting times, the age of the vehicles and the availability of drivers. The results show the genetic algorithm to be more adept at resolving urban transport problems, with its superiority also manifesting in terms of a shorter resolution time and faster convergence to an optimal solution. This algorithm enabled a reduction in the number of buses needed and found the shortest path in the least simulation time. In addition to reducing operating costs, it also managed to ensure a transport network that provided an adequate quality of service with minimal journey times. Then, this improvement of the circuit of transport leads to lower bus running costs and it improves the living conditions of students and their social welfare.

Impact analysis of two kinds of failure strategies in Beijing road transportation network

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

Road Infrastructure and Urban Development in Akure, Nigeria: “Performance Indices and Sustainable Development”

Transport is essential to the functioning of any society and road transport in particular plays a significant role in the economic growth and physical development of the urban Centre’s as well as transport furniture and safety aid in their surrounding regions. Road transport infrastructure influence the location and range of productive and leisure activities, it affects the location of commercial business and residents; it influences the range and provision of goods and services available for consumption. It inevitably impacts on safety, movement travelling time and quality of city life. The paper examines the models that describe the internal structure of Akure city and its urban road infrastructure development and performance indices, its sustainable management developmentwith comparison of old Akure in the 19th Century and the new Akure metropolitan of 21st Century, and recommends the needs for the extension of road construction and expansion to other area of the city. The provisionof functioning road traffic light at various junctions and the need for professional skilled manpower to manage the road information furniture’s and waiting bus stop shades for neatness and maximum utilization, public enlightenment on usage, policy formulation, rules and regulations for both users and operators.

The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability

Making a supply chain more resilient and making it more efficient are often diametrically opposed objectives. Managers have to make informed trade-offs when designing their supply chain networks. There are many methods available to quantify and optimise efficiency. Unfortunately the same cannot be said for vulnerability and resilience. We propose a method to quantify the impact that a supply chain’s dependence on the underlying transport infrastructure has on its vulnerability. The dependence relationship is modelled using multilayered complex network theory. We develop two metrics relating to the unique collection of shortest path sets namely redundancy and overlap. To test the relationships between these metrics and supply chain vulnerability we simulate progressive random link disruption of the urban road network and assess the impact this has on Fully Connected, Single Hub and Double Hub network archetypes. The results show that redundancy and overlap of the collection of shortest paths are significantly related to supply chain resilience, however under a purely random disturbance regime they hold no predictive power. This paper builds a foundation for a new field of inquiry into supply chain vulnerability by presenting a flexible mathematical formulation of the multilayered network and defining and testing two novel metrics that could be incorporated into supply chain network design decisions.