Road Network Level Research Articles

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

Pedestrian fatalities and injuries are a major public health burden in developing countries. In the safety literature, pedestrian crashes have been modelled predominately using single equation regression models, assuming a single underlying source of risk factors. In contrast, the fatal pedestrian crash counts at a site may be an outcome of multiple sources of risk factors, such as poor road infrastructure, land use type, traffic exposures, and operational parameters, site-specific socio-demographic characteristics, as well as pedestrians’ poor risk perception and dangerous crossing behavior, which may be influenced by poor road infrastructure and lack of information, etc. However, these multiple sources are generally overlooked in traditional single equation crash prediction models.In this background, this study postulates, and demonstrates empirically, that the total fatal pedestrian crash counts at the urban road network level may arise from multiple simultaneous and interdependent sources of risk factors, rather than one. Each of these sources may distinctively contribute to the total observed crash count.Intersection-level crash data obtained from the “Kolkata Police”, India, is utilized to demonstrate the present modelling methodology. The three-components mixture model and a joint econometric model are developed to predict fatal pedestrian crashes. The study outcomes indicate that the multiple-source risk models perform significantly better than the single equation regression model in terms of prediction ability and goodness-of-fit measures. Moreover, while the single equation model predicts total fatal crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk factors and predicts crashes by a particular source.

Utilizing Effective Technology In High-Standard Road Defects Inventory System In Estonia

Road pavement is expected to withstand enormous traffic loads for long time but sooner or later the deterioration reaches levels when its optimal to apply treatment. While easy to measure roughness or rutting in normal traffic speed, defects are in most countries still collected by means of time-consuming visual inspection in low traffic speeds or expensive and difficult- to-use equipment. Also, most visual inspection systems only operate with aggregated inspection data. That makes data-collection expensive and defects-based decision-making inefficient. In Estonia, defects inventory system utilizes high quality panoramic and orthogonal images to enable data collection in traffic speeds and detailed mapping of pavement defects in 10 classes. Defects mapped in full detail means, that location, shape and size of each defect is known and classified data can be effectively used twice in pavement maintenance planning: for section selection planning in road network level when aggregated and for work method selection in design process when analyzed in detail. Combined with measured lidar-based point-cloud data, detailed 3d-basemap saves both road-owner's and road designer’s valuable time in design phase. In period of 2016-2020, around 35000km of state roads were analyzed with one of the most efficient road defects inventory systems in the world. Also, around 25000 km of municipal and forest roads have been captured with same technology covering several pavement types from bicycle paths to multilane streets and motorways. Current presentation discusses outcomes of Estonian defects inventory study in 2020.

Proactive pedestrian safety evaluation at urban road network level, an experience in Kolkata City, India

In developing nations, road traffic crashes involving pedestrians have become a foremost worry. Presently, most of the road safety assessment projects and selection of interventions are still restricted to traditional methods that depend on historical crash data. However, in low and middle-income countries such as India, the availability, reliability, and accuracy of crash data are uncertain. Alternatively, Post Encroachment Time (PET) has added attention as a proximal indicator to examine pedestrian-vehicular potential crashes and address pedestrian risk under mixed traffic conditions. Hence, it will be meaningful to examine if the PET is a good substitute for pedestrian-vehicular crashes and if so, what built environment and pedestrian-level factors influence PET. In this background, the present study establishes a mathematical association between the average PET value of the urban road network level and actual crashes. Afterward, multiple linear regression models are developed to study the impact of the built environment, traffic parameters, and pedestrian-level attributes on PET. The outcomes indicate that vehicle speed, lack of enforcement, absence of traffic signal (for traffic as well as pedestrians), land use type, slum population, inadequate sight distance, pedestrian’s state of crossing, and pedestrian’s risky crossing behaviour substantially affect the average PET at road network-level.

Drivers and Barriers to Implementation of Connected, Automated, Shared, and Electric Vehicles: An Agenda for Future Research

Several converging trends appear to reshape the way citizens and goods move about. These trends are social, including urbanization and population growth, and technological, such as increased automation and connectivity. All these factors influence the market for connected, automated, shared and electric (CASE) vehicles, which presents many opportunities and challenges. The pace of the shift to a profoundly penetrated market for CASE vehicles is far from secure. Such transformation depends on the development of technologies, consumer attitudes, and policies. An expanding body of research has investigated the potential social and behavioral results of deploying CASE vehicles. However, most academic literature to date concentrates on technological issues linked to these vehicles. There are several teams from federal and state agencies, OEMs, academia, startups, and consortiums working on this complex subject. This study investigates several academic papers, as well as federal and industry reports, considering all the stakeholders mentioned above. Its aim is to present a comprehensive picture of the implementation barriers and drivers of CASE vehicle usage and provide suggestions to solve them. The findings confirm that several issues are currently affecting the implementation of CASE vehicles on the road. Although there have been significant partnerships and collaborations between CASE vehicle stakeholders, namely technology companies, federal-state agencies, and academic scholars, considerable work is still required to solve the remaining barriers facing CASE-related technologies. This would enable decision-makers to create effective policies for future transportation networks and increase the speed of CASE vehicle market penetration to enhance road network's level of service.

Travel flow aggregation: Nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level

Origin–destination datasets representing millions of travel desire lines and routes are common in transport planning, but visualising such datasets is challenging. Existing methods often produce illegible results, low spatial resolution, or only a relative indication of the variation of flow on each road. This paper presents a new open-source algorithm called overline along with an accompanying method, to efficiently convert disparate geographical transport data into a policy-relevant summary form. Specifically, overline aggregates many individual routes into a route network map. These vector and raster maps provide total flow counts for each road and junction and are scalable to regional or national datasets. The method is demonstrated by visualising four million routes for a publicly accessible web mapping application, the Propensity to Cycle Tool, across the whole of England and Wales.

Comprehensive Study of Risk Factors for Fatal Pedestrian Crashes in Urban Setup in a Developing Country

In developing nations, pedestrian safety is a matter of major concern. The present study investigates historical crash data (2011–2016) obtained from Kolkata police, India, and identifies the key risk factors for fatal pedestrian crashes at the road network level (i.e., intersections and midblock crossings). To develop an understanding of the risk factors associated with fatal pedestrian crashes, a set of safety performance functions (SPF) are developed for both intersection and midblock level. In these SPFs, several attempts are executed to identify a host of risk factors ranging from road infrastructure to land use, traffic exposure, and operational parameters, pedestrian-level attributes, and spatial characteristics of the road network. Based on the study outcomes, there is strong evidence that approaching speed of vehicles, pedestrian-vehicular volume ratio, overtaking tendency of vehicles, inaccessibility of pedestrian crosswalk, land use type, lower post-encroachment time (PET), longer waiting time before crossing, a high share of “pedestrian is not following zebra crossing,” pedestrians’ perceived crossing difficulty, the presence of “pedestrian attraction zone” (e.g., hospital, educational institute, shopping mall, bars, etc.), and high population density significantly affect the fatal pedestrian crash frequency at the intersection level. On the other hand, over-speeding and overtaking tendency of vehicles, inadequate pavement marking, lack of visibility during night-time, lower PET, higher crossing difficulty, longer waiting time before crossing, and several spatial features such as slum population and the presence of “pedestrian attraction zone”, significantly increase the likelihood of fatal pedestrian crashes at the midblock level.

Simulation study on the effect of automated driving in a road network environment

Automated driving, which is considered to be able to reduce driving workload, enhance driving safety and improve traffic efficiency, has become a research hotspot in recent years. It is believed that traffic flow will consist of manual vehicles and automated vehicles at different automation levels in the near future. Researchers have carried out many studies on mixed traffic; most of them focus on the highway scenario. However, the majority of traffic occurs in urban/suburban road networks, which contains many different scenarios, such as expressways, merging/diverging areas, signalised intersections, trunk roads, branch roads, etc. To evaluate the effect of automated driving in a more realistic way, the authors first take adaptive cruise control (ACC) and cooperative ACC as representatives of automated driving. Then, the authors make simulations at the road network level and investigate macroscopic fundamental diagram in different scenarios. Results show that compared to a highway, the improvement of traffic flow is significantly limited in the road network.

Modelling risk factors for fatal pedestrian crashes in Kolkata, India

In developing countries, pedestrian safety is an issue of major concern. Although an extensive body of literature is available on the identification of elements related to the pedestrian’s risk; the studies are primarily conducted in urban areas of developed countries. The findings from these studies may only be partially relevant to the cities of an emerging country such as India. The present study analyzes historical crash records obtained from the “Kolkata Police” and identifies the risk factors at the road network level for the hazardous corridors posing a high risk to the pedestrians. The study findings reveal that pedestrians’ fatalities at intersections are associated with a high vehicular volume, higher pedestrian-vehicular interaction, high approach speed, overtaking tendency of vehicles, certain land-use type, encroachment of footpath, inadequate sight distance, inaccessible pedestrian crosswalk, wider minor carriageway, the absence of a pedestrian signal head, and lack of enforcement. On the other hand, the models outcomes reveal that pedestrians’ fatalities at midblock road segments are associated with low pedestrian volume, high approach speed, overtaking tendency of the vehicle, encroachment of footpath, on-street parking, wider road width, certain land-use type, inadequate sight distance, insufficient lighting, and inadequate pavement markings.

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers' travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neural network prediction model based on convolutional LSTM (ConvLSTM) is proposed. For improving the prediction accuracy, the implicit correlation between travel time and OD flow is explored and they are combined as inputs of the prediction model. Moreover, in order to realize OD flows prediction at the road network level, and solve the problem that grid-based representation method cannot distinguish traffic flow at different heights, such as in multi-layer overpass areas, this paper presents a grid and road nested method to represent ODs. With the time of day partitioned into time slots, OD flows are extracted and predicted in both spatial and temporal domain. In the experiment, real taxi data are used to verify the proposed model and prediction method fully. And the experimental results show that the proposed model can effectively predict city-wide taxi OD flow, and outperforms the typical time sequence models and existing deep neural network models.

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

Evaluation of the impact of pavement degradation on driving comfort and safety using a dynamic simulation model

The dynamic effects exerted by the vehicles on the road pavement have been thoroughly investigated over years. The main reason for this investigation is the major influence on the propagation and worsening of the pavement damages exerted by the dynamic loads rather than the static ones. To date, the modelling theories of systems have evolved, along with the computational capability of the modern calculators. To this effect, three-dimensional simulations of the tire-surface interaction are commonly used. These simulations take into account the dynamics of the load and the consequent deformation of the pavement. However, previous studies aimed at analyzing the above interaction for the optimisation strategies of the maintenance activities within the context of effective road asset management. On the contrary, this work focuses on the safety-related issues linked with the dynamic effects suffered by the vehicle, when passing on different road defects. The goal of this study is to analyse numerically the kinematic and the dynamic effects of the pavement degradations (and in particular rutting) on the driving safety. The simulation of the main characteristics and the evolution of the pavement damages over the time (e.g., the simulation of the tire-pavement contacts and the dynamic response on the vehicles) is a useful method for the development of safe and comprehensive rehabilitation programs. These are of paramount importance to limit the accident rates. The effects on the driving safety was analysed using a simulation model for the vehicles behaviour in the case of damaged pavements. Different road geometries and vehicle types were considered to evaluate the rutting effects on safety, as a function of the evolution stage of this type of pavement damage. In more detail, the performance characteristics of the vehicles, the dynamic and kinematic parameters (e.g., the vehicle trajectory and the vertical acceleration), were collected for pavement conditions with progressively high levels of rutting. The study proposes a theoretical model of qualitative relationships between differing stages and location of rutting, and the consequent effects on driving safety for different types of vehicles (passenger cars and powered two wheelers). It is worth to emphasize the relevance of this research for maintenance prioritization purposes at the road network level. Priority is based on the level of risk associated with the pavement degradations and with different types of vehicle.

Network-wide traffic and environmental impacts of acceleration and deceleration among Eco-Driving Vehicles in different road configurations

ABSTRACTEco-Driving, a driver behaviour-based method, has featured in a number of national policy documents as part of CO2 emission reduction or climate change strategies. This investigation comprises a detailed assessment of acceleration and deceleration in Eco-Driving Vehicles at different penetration levels in the vehicle fleet, under varying traffic composition and volume. The impacts of Eco-Driving on network-wide traffic and environmental performance at a number of speed-restricted road networks (30 km/h) is quantified using microsimulation. The results show that increasing levels of Eco-Driving in certain road networks result in significant environmental and traffic congestion detriments at the road network level in the presence of heavy traffic. Increases in CO2 emissions of up to 18% were found. However, with the addition of vehicle-to-vehicle or vehicle-to-infrastructure communication technology which facilitates dynamic driving control on speed and acceleration/deceleration in vehicles, improvements in CO2 emissions and traffic congestion are possible using Eco-Driving.

Evaluating the impact of Mobike on automobile-involved bicycle crashes at the road network level

As a booming system, free-floating bicycle-sharing (denoted as Mobike) attracts a large number of users due to the convenient utilization procedure. However, it brings about a rapid increase of bicycle volume on roadways, resulting in safety problems especially on road segments shared by automobiles and bikes. This study aimed to evaluate impacts of Mobike on automobile-involved bicycle crashes on shared roadways at a macro level, the network level. Relation between traffic volumes and crashes was first established. Then, the travel mode choice before and after supplying Mobike in the market was analyzed, based on which the multi-class multi-modal user equilibrium (MMUE) models were formulated and solved. Two attributes of Mobike, supply quantity and fare, were investigated via various scenarios.Results suggested the Mobike attracted more walkers than auto-users in travel mode choices, which caused the volume increase of bicycles but few volume decline of automobiles and resulted in more crashes. The supply quantity of Mobike had a negative impact on safety, while the fare had a positive effect. The total supply of Mobike in the market should be regulated by governments to avoid over-supply and reduce bicycle crashes. The fares should be also regulated by including taxes and insurances, which can be used to build up more separated bicycle facilities and cover the Mobike accidents, respectively. The findings of this study provide useful information for governments and urban transportation managers to improve bicycle safety and regulate the Mobike market.

A multi-criteria analysis framework including environmental and health impacts for evaluating traffic calming measures at the road network level

ABSTRACTRecent interests in both vehicle emissions and public health have facilitated the development of more eco-friendly transportation systems. This study developed a multi-criteria evaluation framework to evaluate the effectiveness of traffic calming measures (TCMs) from the various perspectives at the road network level; operational efficiency, traffic safety, environmental and health impacts. The proposed methodology employs four-step sequential simulation experiments, including driving, traffic flow, emissions, and air dispersion simulations. The results obtained from these four simulations are used to evaluate the effectiveness in terms of safety and operational efficiency in addition to environmental and health impacts. A multi-criteria value function based on the weights estimated from the analysis of an analytical hierarchy process (AHP) is applied in the evaluation framework. As an application, chicanes and speed humps widely implemented in Korean school zones were evaluated at the road network level. The proposed simulation-based approach is expected to be effectively used for the decision-making process in selecting better alternatives for TCM.

Relationship between man-made environment and slope stability: the case of 2014 rainfall events in the terraced landscape of the Liguria region (northwestern Italy)

ABSTRACTIn the autumn of 2014, a series of rainfall events affected several sectors of the Liguria region, triggering many shallow landslides and causing three casualties and severe structural damages. The most intensely unstable area covered 385 km2, in which more than 1600 landslides have been identified. After these events, an airborne Light Detection and Ranging survey was carried out. The survey yielded a high-resolution digital terrain model (DTM) and aerial images that provided a means of identifying and mapping all the occurred landslides. The distribution analysis of slope instabilities highlighted the link with various human activities. In fact, the majority of the detected landslides occurred in man-modified areas. Geospatial and statistical analyses provided the identification of three main anthropic factors: terraces, their level of maintenance and road network. Moreover, they quantified their role in landslide triggering. These factors were not analysed as separate elements, but as a continuous process, overlapping in time, in man-made influence on landscape. The identification of such factors is a key element for a correct behaviour characterization of this landscape towards extreme flash floods events.

Determination of refurbishment priorities for passes on road network level

Determination of refurbishment priorities for passes on road network level

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

An accurate estimation of service life is of primary interest in pavement management systems limiting the time frame for maintenance and rehabilitation (M&R) treatments. Common condition prediction models are derived by regression analysis at the road network level based on empirical data from periodic condition surveys. If a particular section has not failed prior to the last survey or the condition has improved (e.g. due to treatment), it is considered as censored. If censoring is neglected the performance functions, service lives and estimated costs may show substantial bias. The authors who acknowledge this problem have used standard statistical (survival analysis) techniques accounting for censoring. However, any road section may fail due to different but dependent competing failure causes (risks), each leading to treatments. This constitutes a special type of censoring that cannot be addressed with traditional survival analysis methods relying on the assumption of independent censoring. As the number of failure causes usually exceeds one (e.g. fatigue, permanent deformation, thermal cracking), this case is quite common. Moreover, the time until a first failure depends on the sign and degree of correlation between present failure types being modelled by the overall survival function. This paper presents a critical review and comparison of common regression, Markov chain and survival analysis models with and without correlated competing risks based on computer-generated data. Using performance history and distress progression models at the section level in combination with survival analysis improves the accuracy of predictions in comparison. Furthermore, the paper proposes a simultaneous modelling of joint and marginal service life distributions based on copula functions as generalised solution accounting for dependence between competing risks. As the focus of this paper is on condition prediction with censored data, the distress-specific planning and optimisation of treatments will be covered in forthcoming papers.

Impact Analysis of Ecodriving Behaviour Using Suitable Simulation Platform (ICT-EMISSIONS Project)

Eco-driving is a way to drive that allow to use less fuel. To be eco-driver you need to follow a set of simple rules such as: upshifting to avoid engine speeds over 2500rpm, keep steady vehicle speed, anticipating traffic, accelerating and decelerating smoothly, and avoiding long idles.The promotion of an energy-efficient driving style is a measure that can have an important impact on fuel consumption.The eco-driving behaviour varies the attitude in setting speed and distance to the preceding vehicles. Speed and distance are parameters that influence, at the macroscopic level, the speed and density of traffic. To assess measure's impact at road network level we need to simulate, first the microscopic level movement of vehicles and then scale up in a suitable way the result to the macroscopic level.A suitable software platform has been developed using and enhancing the models adopted in the existing commercial tools in order to simulate traffic with drivers having different behaviour such as eco driver. The results shows that different behaviour, which imply different law for interaction between vehicle, can have different impact on emission when not a single vehicle is considered but the traffic on a real road network. Results show that Eco drive has more impact in free and normal traffic condition, but could be not useful when traffic condition is congested.

The Benefits of Long-Term Pavement Performance (LTPP) Research to Funders

Long-term pavement performance (LTPP) monitoring has been conducted in Australia for over 20 years. This research was funded by Austroads (representing federal, state and territory road agencies, local government and the New Zealand road agency) to promote improved practice and capability for the road agencies. The LTPP monitoring program measured performance by rutting, roughness, cracking and deflection. Initially the program involved a range of designated flexible pavement sites under varying conditions of environment and traffic. Many of these LTPP sites were included in the Strategic Highway Research Program (SHRP) of the United States (US). All sites were monitored in accordance with the SHRP protocols. Later long-term pavement performance maintenance (LTPPM) sites were included in the program to: (i) assess the impact of surface maintenance treatments on changes to pavement conditions (works effects, WE); and, (ii) assess the impact of maintenance on road deterioration (RD).A range of RD and WE models were developed using the LTPP/LTPPM observational data in combination with experimental data collected from Australia's Accelerated Loading Facility (ALF) that separately investigated the impact on RD of increased axle load and various typical surface maintenance treatments. The RD models cover the deterministic prediction of functional surface distress (rutting, roughness and cracking) and the loss of traffic load capacity (strength). The WE models cover the deterministic prediction of the impact of a range of typical surface treatments on improved surface conditions.The RD models are currently being used to develop probabilistic distress predictions based on the variability found in the observational data. The future use of probabilistic predictions of RD outcomes will allow road agencies to better quantify the risks involved in managing the pavement infrastructure.Both the RD and WE models are currently being installed in the pavement management systems (PMS) of both state and local government road agencies to provide efficient maintenance allocation and resourcing at a road network level. In the process of developing the RD and WE models, a number of support tools were also developed such as a tool for determining the independent variable representing climate, based on GPS locations, and a tool for estimating deterioration rates from time-series data. The RD and WE models are also being used to estimate the marginal cost of road wear when pavements are subject to increased axle load to increase road freight efficiency.A dedicated research program generally involves a long-term evolutionary process, however, when it is clearly focused on achieving measurable and practical outcomes, such as improving the practices and capabilities of road agencies, the benefits can be clearly defined and quantified when bench-marked against current and past practices. The LTPP/LTPPM research program is annually reviewed and assessed to ensure that the sites that no longer yield useful data are replaced by sites that provide new observational data with respect to environment, traffic and pavement material conditions.

The Research on Aggregate Analysis of the Regulatory Detailed Planning by considering the Traffic Capacity

The regulatory detailed planning is substantive legal planning stage of urban planning in China. The first problem is the need to check and inheritance the indicators of total planning land about construction in the process of compiling. According to city zoning rules establishment work reality, the basic thought of the system analysis, the traffic engineering and urban planning in the related theory and method on the basis of macroscopic traffic flow classification, using the road network capacity calculation method and traffic distribution forecasting method proposed with interregional channel planning level of service and the internal road network planning level of service as the control target, proposes the method by using the master planning road network control to inversely calculate the total amount for the construction of district land use. The calculation example is also given. Studies show that the presented control rules compiled for checking algorithm can play check function about the amount of land used for construction master planning achieve under the premise of the control overall situation trunk road network level of service, to avoid the amount of unnecessary in the subsequent units, neighbourhood planning adjustments.