Digital Road Network Research Articles

Developing a Web-Based GIS for Tourist Attractions and Cultural Heritage in Oyo Town, Nigeria

This research focused on developing a web-based GIS for tourist attractions and cultural heritage in Oyo town, Oyo state, Nigeria. The study considered the present situation of tourism in the study area as it seems almost dead due to the lack of awareness programmes to push the state's tourism to the world market. Before tourism can be well-accepted by all and sundry, understanding the locations of the various tourist attractions need to be made known. The town lacks an up-to-date tourist guide map, including the non-availability of digital information for tourism. The study aimed at developing a web-based Geographic Information System (GIS) for tourist attractions and cultural heritage in Oyo town. The methodology involved database design and creation, satellite image georeferencing and onscreen digitizing in ArcGIS 10.8, spatial analysis, and publishing of a web map in ArcGIS online. Products generated included tourist maps, digital road network maps, and other thematic maps showing hotels, restaurants, markets, and police stations. The paper recommended that government should properly document all the tourist sites in Oyo town, develop a functional tourism database using GIS technology to better enhance the tourist experience, and that detailed information about tourism activities should be easily made available and accessible from the tourism database.

Agent-Based Modelling of Interacting Unmanned Vehicles Dynamics with the FLAME GPU

The article presents an approach to modelling traffic flows based on the intelligent analysis of unmanned ground vehicles (UGVs) dynamics within of which a novel decision-making system on maneuvering for agent-UGVs is designed. The proposed system uses clustering methods for an estimation of a traffic congestion density on alternative routes of the digital road network (DRN) to detour problem areas and minimisе risks of traffic accidents appearing. For the first time, the spatial dynamics of interacting UGVs is modelled with complex schemes for constructing the DRNs, including multiple intersecting ring, straight and diagonal sections that form various alternative routes for road users. The de­veloped model is implemented with the supercomputer agent-based modelling framework FLAME GPU. An algorithm for the behaviour of UGVs during the formation of traffic congestions has been developed, in particular, it provides an effective splitting of traffic flows by increasing the probability of choosing less pressed alternative routes for UGVs. As a result of numerical experiments, the following important regularity was obtained: an increase in the share of UGVs in the DRN using intelligent maneuvering based on cluster analysis of traffic flows with a controlled radius of an agent personal space provides a decrease in the total density of traffic congestion and contributes to a decrease in accidents.

Optimization of Municipal Waste Streams in Achieving Urban Circularity in the City of Curitiba, Brazil

The municipal solid waste (MSW) remains a great challenge in most cities of developing countries, as the majority of the generated waste is either not collected or is dumped in open uncontrolled non-engineered landfill sites, creating significant pollution due to the leakage of landfill leachate in the surrounding environment. In developing countries, a complete transition to a zero-landfill scenario is less likely to happen in the near future due to various socio-economic challenges. Therefore, the existing landfills in developing countries need holistic waste management thinking with more efforts on waste to energy conversions. This study highlights the challenges with existing MSW management practices of Curitiba, Brazil, and suggests some holistic and sustainable landfill management techniques. This is accomplished through the (i) identification of the suitable sites for setting up transfer stations (TSs), (ii) route optimization for MSW transportation, and (iii) analysis of the life expectancy of the existing landfill with waste valorization techniques for enhancing circularity of MSW of the city. The study has identified six potential TSs, making use of various geological criteria and constraints as suggested by the United States Environmental Protection Agency using GIS-based spatial analysis, which could save fuel cost of approximately 1.5 million Brazilian Real (BRL) per year for the solid waste transportation (from the source to the landfill site). This research has also made a value addition in this specific field with the preparation of a digitized road network map of the study region. Further, the sensitivity-based scenario analysis highlights that the lifespan of the existing landfill (until 2030) might be extended to 2058 if the city achieves the targeted recycling rate of 85% compared with the current rate of 23%. The results would be useful for policy-makers to adopt the crucial MSW scenario to achieve a circular economy in the waste management of the city of Curitiba.

A novel framework for modelling traffic flows with the participation of unmanned ground vehicles

The article presents a new software platform for modelling traffic flows involving unmanned vehicles, using a number of advanced technological solutions, in particular, the FLAME GPU supercomputer agent modelling framework, intelligent software modules based on fuzzy and hierarchical clustering, genetic optimization algorithms, a subsystem for visualizing the state of agents-vehicles based on OpenGL, etc. As a result, it is possible to simulate the dynamics of road accidents and output traffic for various configurations of digital road networks within the “smart city” scale. The possibility of using the proposed software platform (implemented using C++ and FLAME GPU) is demonstrated for the problem of estimating the sensitivity of the total number of accidents in relation to important control parameters: arrival rate and average speed of unmanned ground vehicles interacting with usual ground vehicles and other agents of the intelligent transport system.

Simulation of rates of traffic accidents involving unmanned ground vehicles within a transportation system for the ‘smart city’

This article presents an approach to simulation of the rates of road traffic accidents involving unmanned ground vehicles within a multi-agent intelligent transportation system for the ‘smart city.’ A new simulation model of an intelligent transport system has been developed which makes it possible to significantly reduce the number of potential road traffic accidents (TAs) and implements the concept of socially-centered management of the urban economy. The software implementation of such a large-scale agent-based model was carried out using the FLAME GPU framework, which allows us to effectively parallelize the agents’ behaviour logic and consider their individual decision-making systems when modelling the spacial dynamics of an ensemble of unmanned ground vehicles (UGVs) interacting with other road users: the usual manned ground vehicles (MGVs), unexpected obstacles (e.g., pedestrians, etc.). Various scenarios of such agents’ behaviour in an intelligent transportation system are studied, including the occurrence of an accident under certain conditions (e.g., under the high speed and traffic intensity of UGVs, etc.) and various configurations of the digital road network (DRN). We determine the parameter values that provide for the individual decision-making system of UGVs remaining stable with respect to the characteristics of the external environment (including in extreme situations), ensuring the safety of other road users on the scale of the ‘smart city.’

Driving Speeds in Urgent and Non-Urgent Ambulance Missions during Normal and Reduced Winter Speed Limit Periods-A Descriptive Study.

Objective: Most traffic research on emergency medical services (EMS) focuses on investigating the time saved with emergency response driving. Evidence regarding driving speed during non-urgent ambulance missions is lacking. In contrast, this descriptive study compared registered driving speeds to the road speed limit in urgent A-missions and non-urgent D-missions. Specifically, the study examined driving speeds during normal speed limits, periods of reduced winter speed limits, and speeding during non-urgent D-missions. Methods: Urgent A-missions and non-urgent D-missions were included. Registered ambulance locations and speed data from Pirkanmaa Hospital District, Finland between 1 January 2018 and 31 December 2018 were used. Ambulance locations were linked to OpenStreetMap digital road network data. The registered driving speed distribution was reported as quartiles by the effective road speed limit. Furthermore, the results during the normal speed limit and reduced winter speed limit periods were reported separately. Driving speeds in non-urgent missions were compared with current Finnish traffic violation legislation. Results: As expected, the urgent A-missions exceeded the speed limits during both the normal speed limit and reduced winter speed limit periods. On the smallest streets with speed limits of 30 km/h, the driving speeds in urgent missions were lower than the speed limit. The driving speeds in non-urgent D-missions were broadly similar throughout the whole year on high-speed roads, and mostly on lower speed limit roads. However, within the 30 km/h speed limits, the mean speed in non-urgent missions appeared to increase during the winter. One-fifth of the registered non-urgent D-missions were speeding. Conclusions: Speeding is common in urgent A-missions and non-urgent D-missions throughout the year. Stricter guidelines for EMS are needed to increase driving safety.

Map Matching Algorithm: Empirical Review Based on Indian Open Street Map Road Network Data

Locating devices on the road network is crucial for any location-based system. Accuracy of map matching algorithms may highly affect the accuracy of any location-based service. This paper includes an empirical review of five major map matching algorithms for locating a device on a digital road network. A standard dataset was used to simulate the working of map matching algorithms. After ascertaining the accuracy of map matching algorithms, it was tested on a real road network. Six different routes varying from 0.6 kilometers to 32 kilometers, covering a total distance of 82.2 kilometers were included in the experiment. Performance of map matching algorithms was evaluated on a total of 2094 road nodes with 1271070 Global Positioning System (GPS) points on the basis of matched, unmatched nodes with root mean square error. It was concluded that Hidden-Markov Model based map matching algorithms has reasonably good accuracy (96% using global data and 89% using Indian dataset) and execution time in comparison to geometric, topological, Kalman filter and Frechet distance based algorithms.

Using land-use machine learning models to estimate daily NO2 concentration variations in Taiwan

It is likely that exposure surrogates from monitoring stations with various limitations are not sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal resolution of air pollution modelling approaches must be improved in order to achieve more accurate estimates. If not, the exposure assessments will not be applicable in future health risk assessments. To deal with this challenge, this study featured Land-Use Regression (LUR) models that use machine learning to assess the spatial-temporal variability of Nitrogen Dioxide (NO2). Daily average NO2 data was collected from 70 fixed air quality monitoring stations, belonging to the Taiwanese EPA, on the main island of Taiwan. Around 0.41 million observations from 2000 to 2016 were used for the analysis. Several datasets were employed to determine spatial predictor variables, including the EPA environmental resources dataset, the meteorological dataset, the land-use inventory, the landmark dataset, the digital road network map, the digital terrain model, MODIS Normalized Difference Vegetation Index database, and the power plant distribution dataset. Regarding analyses, conventional LUR and Hybrid Kriging-LUR were performed first to identify important predictor variables. A Deep Neural Network, Random Forest, and XGBoost algorithms were then used to fit the prediction model based on the variables selected by the LUR models. Lastly, data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were applied to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 65% and 78%, respectively, of NO2 variation. When the XGBoost algorithm was further incorporated in LUR and hybrid-LUR, the explanatory power increased to 84% and 91%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed all other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm to estimate the spatial-temporal variability of NO2 exposure. For practical application, the associations of specific land-use/land cover types selected in the final model can be applied in land-use management and in planning emission reduction strategies.

Map matching for travel route identification based on Earth Mover's Distance algorithm using wireless cell trajectory data

Precise travel route identification is the guiding basis of feasible urban traffic planning and modeling. A range of map matching (MM) algorithms have been studied in previous research to integrate coordinate-based GPS data with digital road network information to identify personal travel route. However, these methods significantly depend on the high-density positioning data, which are usually unavailable or costly for transportation planning projects. Fortunately, mobile phones under 3 G, 4 G and 5 G networks can generate massive cell-based travel trajectory data with almost no additional cost. This data is potentially valuable for travel route identification if proper MM algorithms can be developed. This paper proposes an MM method to detect travel route by using handoff trajectory data from the cellular phone network. First, a wireless communication simulation model for dynamic cellular handoff-based traffic monitoring is developed for handoff data collection. Second, the Earth Mover’s Distance (EMD) algorithm is used to identify travel route by finding the time-space relationship among the cellular handoff patterns of the road network. Finally, by comparing the performance of the proposed method with classical sequence similarity algorithms, results indicate that the EMD-based MM algorithm is much more efficient for travel route identification, and it can detect small spacing parallel roads with a high accuracy.

The application of geographic information system as an intelligent system towards emergency responses in road traffic accident in Ibadan

Background: The high influx of private and public vehicles within global cities (including Ibadan) has resulted in traffic congestion within the city centre. Ibadan North-East, an urban local government area (LGA) within the metropolitan land area of Ibadan, has been experiencing automobile accidents and traffic congestion which in turn limits emergency responders. Objectives: The broad objective of the study was to develop a digital road network database for emergency response to road traffic accident, Ibadan North East, Nigeria. Methods: This study made use of primary and secondary data to generate spatial and non-spatial data. ArcGIS 10.3 was used to create the database, analyse data and display the results. Spatial search and network analysis were carried out using a kernel density estimated tool to generate a black-spot area within Ibadan North-East. Dijkstra’s shortest path algorithm in the ArcGIS Network Analysis was utilised to identify the closest health facility to the road traffic crash scene. Results: The most cost-effective haven introduced route impedance and is defined as the best route. Study findings show that dynamic distance varies from the best route to an alternative route. Also, kernel density estimation reveals that crashes widely occur in the northeastern and southwestern parts of Ibadan North-East (Iwo Road and Orita-Aperin). Incorporating live traffic data using GPS technology and traffic cameras was identified to enhance road traffic data documentation and safety. Conclusion: The study identifies GIS as a decision support system that is useful in the area of planning, mitigation, preparedness, response and recovery for decision-making in road traffic-related emergency management and emergency planning services in Ibadan.

Using a land use regression model with machine learning to estimate ground level PM2.5.

Ambient fine particulate matter (PM2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM2.5. Daily average PM2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM2.5 exposures.

Improving the Maneuverability of Unmanned Vehicles at Various Road Network Configurations

The article presents an approach to improving maneuverability of unmanned ground vehicles (UGVs) interacting with manned ground vehicles (MGVs) within the proposed multi-agent intelligent transport system (MITS) at various configurations of the digital road network. With the developed models, the abilities of improving maneuverability of agent-vehicles in MITS under various control scenarios for the most important characteristics of the system, such as, the arrival intensities of agents, speeds of UGVs and MGVs, clustering methods for the traffic density estimation and intelligent maneuvering are studied. The proposed MITS is implemented based on the reconfigurable digital road network (DRN). There is proposed an approach to improving the maneuverability of the UGVs, aimed to effectively maneuvering in changing lanes and choosing the best routes to bypass traffic congestions and other problem areas with providing the required level of output traffic and minimizing the number of potential traffic accidents in the MITS. The possibilities of improving maneuverability of UGVs at various configurations of the DRN (for example, the roundabouts, "Manhattan Lattice", etc.) are studied.

Map-matching approach based on link factor and hidden Markov model

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates.

Which quality is a route? A methodology for assessing route quality using spatio‐temporal metrics

AbstractIn the context of intelligent transport systems, dynamic route planning is an eagerly researched topic. While the research community during the last decade has focused on performance and scalability of dynamic route planning algorithms, the question concerning route qualities has been widely neglected. The current work contributes to this question by proposing a methodology to assess the quality of arbitrary routes based on seven spatio‐temporal route quality metrics (five spatial and two temporal ones). The methodology is evaluated by calculating quality metrics for 45 routes being derived from three dynamic route planning systems for three selected origin–destination pairs during one day. For objectively assessing route qualities, the approach matches the route planning results to a reference digital road network and uses reference travel times for calculating quality metrics. A spatio‐temporal cross‐evaluation illustrates different quality aspects in the context of both quality dimensions and demonstrates the usefulness of the proposed approach.

Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city

ABSTRACT Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.

An enhanced weight-based real-time map matching algorithm for complex urban networks

A map-matching algorithm is used to map the inaccurate raw coordinate data to the digital road network. It is an indispensable part of Location Based Service applications and Intelligent Transportation Systems, such as navigation systems. Accuracy and performance (running speed) are usually traded off in traditional algorithms. An enhanced weight-based real-time map matching algorithm only employing GPS data is proposed to guarantee both. The algorithm has two steps: initialization and tracking match, each step is mainly composed of three parts. Firstly, segments near the GPS point are selected as candidate segments. Secondly, four criteria (distance, heading difference, direction difference and segment connectivity) are used to identify the best segment among candidates. Considering the reliability of each criterion, four dynamic weight coefficients are introduced. Finally, before assigning a candidate segment to each GPS point, a confidence level is calculated and considered based on the density and complexity of roads around the point. We evaluate the algorithm with field data collected from the city of Chongqing, China. The results demonstrate that it can identify correct segment from complicated and dense urban road networks, with an average matching accuracy of 97.31% and a latency of 3.20ms per location estimate.

Road network analysis of Guwahati city using GIS

Starting from the past or medieval period to the present day situation, history clearly indicates that the development of a region was and even today is function of a good transportation network. In the present day, society demands for an efficient and unobstructed road network after experiencing major issues or problems like traffic congestion, delay, pollution, increased vehicle operating cost and road accidents. Keeping in mind the above needs and constraints to traffic movement, an analysis of a digitized road network of the concerned city/town can be one of the best remedy to solve the problems. Such analysis is best suited in ArcGIS, Geographic Information System (GIS) software for creating, analyzing and compiling maps for obtaining information. In the present study an effort was made to prepare a road network map of Guwahati city and to find the shortest route between two places by proper analysis and digitization of its existing road network system in order to solve the traffic related problems to great extent. Network Analyst is a special analysis tool in ArcGIS which not only scrutinize the closest facility available in network of digitized interconnected lines but also facilitates in optimizing route during floods and emergency responses. One of the best models that can be generated through Network Analysis is the shortest route between required origin and destination points. The analysis is done based on input of certain network attributes like traversing distance, time and cost of travel, barriers, vehicle restrictions etc. All the important roads connecting each other within the Guwahati City were digitized in the GIS environment and proceeded further to serve the purpose.

Development of a map-matching algorithm for dynamic-sampling-rate GPS signals to determine vehicle routes on a MATSim network

The rapid development and proliferation of global positioning system (GPS)-enabled systems and devices have led to a significant increase in the availability of transport data, more specifically GPS trajectories, that can be used in researching vehicle activities. In order to save data storage- and handling costs many vehicle tracking systems only store low-frequency trajectories for vehicles. A number of existing methods used to map GPS trajectories to a digital road network were analysed and such an algorithm was implemented in Multi-Agent Transport Simulation (MATSim), an open source collaborative simulation package for Java. The map-matching algorithm was tested on a simple grid network and a real and extensive network of the City of Cape Town, South Africa. Experimentation showed the network size has the biggest influence on algorithm execution time and that a network must be reduced to include only the links that the vehicle most likely traversed. The algorithm is not suited for trajectories with sampling rates less than 5 seconds as it can result in unrealistic paths chosen, but it manages to obtain accuracies of around 80% up until sampling sizes of around 50 seconds whereafter the accuracy decreases. Further experimentation also revealed optimal algorithm parameters for matching trajectories on the Cape Town network. The use case for the implementation was to infer basic vehicle travel information, such as route travelled and speed of travel, for municipal waste collection vehicles in the City of Cape Town, South Africa.

Hierarchical Sarsa Learning Based Route Guidance Algorithm

In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how to solve the large state space of digital road network is a challenge for Sarsa Learning, which is very common due to the large scale of modern road network. In this study, the hierarchical Sarsa learning based route guidance algorithm (HSLRG) is proposed to guide vehicles in the large scale road network, in which, by decomposing the route guidance task, the state space of route guidance system can be reduced. In this method, Multilevel Network method is introduced, and Differential Evolution based clustering method is adopted to optimize the multilevel road network structure. The proposed algorithm was simulated with several different scale road networks; the experiment results show that, in the large scale road networks, the proposed method can greatly enhance the efficiency of the dynamic route guidance system.

Distributed Context Tree Weighting (CTW) for route prediction

Route prediction play a vital role in many important location-based applications like resource prediction in grid computing, traffic congestion estimation, vehicular ad-hoc networks, travel recommendation etc. The goal of this work is to design scalable route prediction application based on Context Tree Weighting (CTW) modeling of user travel data. CTW is one of the widely used technique for text compression as well string sequence indexing and for prediction. CTW tree construction from the huge volume of data by sequential processing is time-consuming in practical implementation. Existing techniques are designed for single machine and their implementation on the distributed environment is still a challenge. This work focuses on achieving horizontal scalability of CTW and addresses various challenges in distributed construction like reducing I/O, parallel computation of sequences and coming up with final CTW tree in a distributed environment efficiently. Map Reduce framework running over Hadoop file system is used for processing in distributed mode. Large GPS data set is map-matched to digitized road network obtained from Open Street Map and CTW model is built. A two-step construction of CTW tree is proposed which is implemented in the map-reduce framework. Horizontally scalable CTW model is built and evaluated for route prediction from a huge corpus of historical GPS traces.