A New Approach of Web App Based Road Traffic Analysis

India possesses one of the world’s largest road networks, which is vital to the country’s economic development. Transportation of products and people is primarily centred on this network, which has about 5.8 million kilometres of road. Data analytics and machine learning approaches can be used to control and optimise this network. In this regard, we explored the application of classification algorithms to predict road surface quality using a dataset of synthetic road surface data. Several classification methods have been trained and evaluated, including Decision Trees, Random Forest, Logistic Regression, Support Vector Machine, XGBoost, CatBoost, and LGBM. The accuracy, precision, recall, and F1-score measures were used to compare the performance of various methods. Researchers have investigated the use of feature augmentation techniques such as Variational Autoencoder (VAE) to improve the performance of classification models. VAE is a deep generative model capable of learning a low-dimensional latent representation of high-dimensional input. Researchers can develop new features that represent the underlying structure of data by using VAE to road-related data such as traffic patterns and road conditions. Our results indicates the potential of applying machine learning algorithms to forecast road surface condition, which may be utilised to improve road network maintenance and safety.

Vehicle non-exhaust emissions are a major component of particle matter, including the direct wear of tires, brakes, road, and the resuspension of deposited particles. It is suggested that resuspended PM (RPM) emissions can be at the same magnitude or even larger than combustion emissions in urban centers. Factors affecting RPM can be included in four categories: road characteristics, traffic condition, land use, and meteorology. In order to study and evaluate these influencing factors, road dust less than 10 micrometers (RD10) was collected in 41 sites across Bogotá. The sampling points had diverse characteristics. RD10 levels varied between 1.0 and 45.8 mg/m2 with an average of 8.9 ± 8.4 mg/m2. Lower RD10 values were observed when vegetation density was high, pavement condition good, driving speeds fast and construction activities absent. On the contrary, RD10 increased under heavy-duty traffic influence and dry conditions. Among dust mitigation measures, management of land-use variables could be as important as traffic control and road maintenance. Implications: This study documented for the first time in Latin America dust loadings less than 10 micrometers, information that can be used to estimate resuspended particle matter emissions in the region. The influence of meteorology, traffic characteristics, road condition, and land-use variables was analyzed and quantified. The management of land-use variables could be as important as traffic control and road maintenance for road dust mitigation. Further research interests are discussed.

We demonstrate our novel video-based real-time traffic event notification and verification system LOOK2. It generates fast and reliable traffic information about relevant traffic state and road conditions changes on observed roads. It utilizes installed road-side sensors providing low-level traffic and environmental data, as well as video sensors which gain high-level traffic information from live video analysis. Spatio-temporal data fusion is applied on all available traffic and environmental data to gain reliable traffic information. This traffic information is published by a DATEXII compliant web service to a web-based traffic desk application. Road network and traffic channel operators receive real-time and relevant traffic event notifications by using this application. The system also enables a visual verification of the notified situations.

In the last decade, naturalistic driving studies (NDSs) have given researchers an unprecedented way to study the behavior of drivers through the deployment of, and capturing the data from, on-board vehicle sensors and cameras. The ability to determine the dominant driving risk facto rs can play an essenti al role in shaping transportation policies and education programs for drivers. This paper presents a cohort study statistical analysis to determine the risks associated with traffic and road surface conditions, quanti fied in terms of crash and near crash events. Two risk quantification measures, odds ratio (OR) and relative risk (RR, are utilized to signify the associated risk. For this research we used the 100-CAR data set, with a total of 829 crash and near crash and 19616 baseline events, which are driving events captured randomly in normal driving episodes. In the 100-CAR data set, traffic density is divid ed into six levels according to the traffic flow con dition. Similarly, road su rface condition is divided into four categories. To quantify the statistical significance of the results, measures such as the p-value are employed. The results show that icy roads with level-of-service (LOS) A, wet roads with LOS D, and dry roads with LOS D have the highest risk for crashes and near crashes. These results are proven to be of statistical significance.

The Eastern Development Region (WP Timur) of Bogor Regency, consisting of 7 districts, holds a strategic position and is experiencing rapid development. WP Timur is currently in the process of forming a new "Bogor Timur" Regency and is also the site of several national strategic projects. Infrastructure development, particularly road networks, must be able to anticipate the region's growth and changing needs. To improve the management of road infrastructure, an evaluation of the road network's performance is required.. The methodology involves (1) comparing the existing road variable values with the performance standards set by government regulations, (2) analyzing the connectivity index of the county road network, and (3) conducting an integrative assessment based on the analysis of various variables. The results of this research indicate that (1) Cariu District meets the standards for 8 road performance variables, including having good connectivity. Jonggol, Sukamakmur, and Tanjungsari Districts meet 5 variables with deficiencies in accessibility, mobility, road condition, and connectivity. Gunung Putri, Cileungsi, and Klapanunggal Districts meet only 3 variables with deficiencies in accessibility, mobility, and road condition. Gunung Putri and Cileungsi have low traffic loads, while Klapanunggal lacks connectivity. (2) New road construction is needed in Gunung Putri, Cileungsi, Klapanunggal, and Jonggol Districts based on Accessibility SPM, and in Gunung Putri, Cileungsi, Klapanunggal, Sukamakmur, and Tanjungsari Districts based on Mobility SPM. The construction of new roads aims to anticipate increased traffic volume and enhance connectivity. (3) Efforts are required to improve road conditions and maintenance in the 6 districts other than Cariu based on Road Condition SPM

Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions

Road traffic sensors provide rich multivariable datastreams about the current traffic conditions. Occasionally, there are unusual traffic events (such as accidents, jams, and severe weather) that disrupt the expected road traffic conditions. Detecting the occurrence of such events in an online and real-time manner is useful to drivers in planning their routes and in the management of the transportation infrastructure. We propose a new method for detecting traffic events that impact road traffic conditions by extending the Bayesian robust principal component analysis (RPCA) approach. Our method couples multiple traffic datastreams so that they share a certain sparse structure. This sparse structure is used to localize traffic events in space and time. The traffic datastreams are measurements of different physical quantities (e.g., traffic flow and road occupancy) by different nearby sensors. Our proposed method processes datastreams in an incremental way with small computational cost; hence, it is suitable to detect events in an online and real-time manner. We experimentally analyze the detection performance of the proposed coupled Bayesian RPCA (BRPCA) using real data from loop detectors on the Minnesota I-494. We find that our method significantly improves the detection accuracy when compared with the traditional PCA and noncoupled BRPCA.

The main task in ensuring the appropriate level of organization of traffic on the street and road network of cities is to minimize traffic delays, increase traffic safety, provide transport and pedestrian communication between the planning elements of the city. Problems caused by the deterioration of the street and road network significantly affect the work of the entire transport complex of the city. Significant traffic delays, congestion, characterized by increased travel time, deterioration of transport services, increased pollution of urban environment due to increased emissions and noise, increasing the number of road accidents indicate the inconsistency of the road network of cities to the modern level of motorization. To ensure the required capacity of the elements of the road network, there is a need to create appropriate road conditions, namely the construction of new and reconstruction of existing engineering structures and elements of streets and roads. However, addressing these issues may not always improve the performance of the entire network, as, in parallel with technical measures based on the construction and reconstruction of transport infrastructure, the application of effective traffic management measures to manage traffic in urban areas should be addressed streets. When creating conditions for optimizing the operation of regulated intersections, it is necessary to take into account the technical condition of vehicles, road conditions and the condition of the road surface. One of the main tasks of any traffic organization is to increase the capacity of intersections and reduce vehicle delays, ie the passage of traffic lights with minimal delay. To solve the problem of congestion of the street and road network of the city is to increase the efficiency and use of coordinated management of traffic and pedestrian flows, improving the operation of traffic lights. To implement the introduction of coordinated traffic light regulation, or the so-called green wave, it is necessary to establish the intensity and composition of traffic flows, the capacity of the elements of the road network and the modes of operation of traffic light facilities. The paper considers the results of the study of the impact of coordinated traffic management in the central part of Rivne on reducing traffic delays when passing intersections by vehicles and proposed technical solutions to improve road safety by equipping intersections with additional technical means of traffic control.

The ability to forecast traffic congestion ahead of time given road conditions has remained a prominent problem in road traffic analysis. In this work, we leverage mobility traces of public transport vehicles tracked by the New York City MTA and formulate Message-Passing Recurrent Neural Nets (MPRNN) to produce long-term traffic forecasting on data that is sparse but wide in coverage. We model the interactions among road segments spread over the entirety of Manhattan, New York over a period of 3 months, such that traffic conditions can be propagated to > 90% of examined segments from just a few observations. In comparison to other competing algorithms, MPRNN achieves the lowest mean error of < 0.3 mph when predicting ahead in 10 minute intervals, for up to 3 road segments ahead (message passing across 3 hops). The MPRNN model further offers compelling results when forecasting traffic speeds several hours ahead given distant observations up to approximately 1 kilometer away (three consecutive bus stops) with a mean error of about 2 mph.

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

The traffic road network is abstracted as a physical network, the intersection is the node, and the road segment is the edge. A road network set G(V, E) consisting of a node set and a road segment set is established, where V is a node (road junction) set and E is a side (road segment) set. Since some nodes in the road network will have a significant impact on the operation of the entire road network, it is important to identify the energy levels of the urban road network nodes. This paper proposes the concept of energy level, and uses the energy level recognition algorithm based on h-index to calculate the energy level of the road network node. Quickly and accurately judge and predict the traffic status and service level of urban road network intersections, which will help to induce traffic flow on urban road network, thereby improving the energy level of the entire city road network.

A collaborative approach to moving [formula omitted]-nearest neighbor queries in directed and dynamic road networks

Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

SummaryShort‐term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio‐temporal‐based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high‐density road networks. Therefore, we propose a spatio‐temporal variable selection‐based support vector regression (VS‐SVR) model fed with the high‐dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two‐stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high‐dimensional spatio‐temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real‐world traffic volume collected from a sub‐area of Shanghai, China, demonstrate that the proposed spatio‐temporal VS‐SVR model outperforms the state‐of‐the‐art. Copyright © 2015 John Wiley &amp; Sons, Ltd.