Current Traffic Data Research Articles

Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited.

PRYORTIE: Public on-demand smart transportation service via a scalable fog-based platform

Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traffic, number of existing drivers en-route, and local weather which may cause a huge delay. Dial-A-Ride Problem with Transfers (DARPT) allows passengers to change vehicles during their trip in a bid to minimize the delay. However, DARPT relies solely on using the existing Global Positioning System (GPS) that suffers from delay in propagating raw traffic data (e.g., route coordinates, geodesic distance, and time stamp) on time and ultimately causes traffic disruption and increases ride time constraint. To this end, this work introduces a novel interactive Fog-based navigation simulator, PRYORTIE. PRYORTIE uses roadside smart units in the form of fog nodes as a means for collecting and sharing real-time traffic data comprising of vehicles’ location and speed within the area, they each cover. These nodes can calculate an optimal route for a vehicle using a novel path recommender algorithm that utilizes a vehicle's current position, its destination, and current traffic data. The nodes can update the route in real-time using the same algorithm if traffic conditions within their radius change. A package delivery case study has been implemented and simulated in PRYORTIE to demonstrate the impact of using fog nodes in routing and navigation. Compared with the state-of-the-art, the proposed approach shows on average 46.34% improvement in saving travel time. The proposed method clearly demonstrates tangible benefits of communicating real-time data to drivers and lays the ground for subsequent future work in smart transportation and navigation especially in the context of last mile delivery. It can easily be extended to autonomous vehicles and can be easily integrated into existing transportation frameworks that employ a fog-cloud paradigm for smart transportation.

A Model for Identifying Road Risk Class

Abstract In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

Solution for Reduction of Traffic Congestion: A Case Study of Intersection on Badlapur- Katai Road

Abstract: The increasing vehicular traffic on roads is growing issue in today’s life. This leads to the congestion on the streets and degrades safe and efficient movement of traffic. This is becoming an important issue in the urban premises. It is observed that traffic congestions take place majorly at the intersections where the entering and exiting traffic from the towns to the highways creates conflicts due to improper movement. This study intended to examine flow of traffic at an intersection on Badlapur – Katai road in state of Maharashtra. The location is a part suburban areas of Mumbai metropolitan city. The Majority population is of working class also the industrial development in this area is high due to MIDC and thus the road carries huge traffic during peak hours. The study comprises of collection of the current traffic volume data, projection of the traffic for next 20 years and analysing the intersection for its serviceability. The outcomes may show the requirement of improvement of intersection as grade separated intersection.

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.

DAMPAK PEMBANGUNAN PINTU TOL JATIKARYA TERHADAP KINERJA SIMPANG 4 KRANGGAN

Kranggan intersection in Cibubur City is an intersection with four arms equipped with traffic light signals. Kranggan intersection is the confluence of four roads, namely Jl. Kranggan, Jl. Raya Kalimanggis Jl. Transyogi Barat, and Jl. Transyogi East. This intersection has a condition where long queues often occur at this intersection. especially after the existence of the Jatikarya toll gate, the behavior of the Kranggan 4 intersection has changed the direction of traffic flow, of course, so it is necessary to study how many changes have occurred. The research method used is a quantitative method by collecting road geometric data, intersection geometry and current traffic data in 2021 through direct surveys at the research location as well as traffic data on the Alternative Cibubur (Transyogi) road.Before the existence of the Jatikarya toll gate, it was known that the degrees of saturation for the north and west approaches were 0.80, 1.04 (ds > 0.75 ; saturated), while the south and east approaches produced a degree of saturation (ds) of 0.16 respectively. , 0.30 (ds < 0.75 ; unsaturated). The highest queue length is 387.82 m, the average delay of the resulting intersection is 51.47 sec/pcu and the level of service (LOS) is entered with level E (40-60 sec/pcu). After the presence of the Jatikarya toll gate, it is known that the degree of saturation for the South approach is 0.81 (ds > 0.75 ; saturated), while the North, East and West approaches produce degrees of saturation (ds) of 0.56, 0.11 respectively. , 0.38 (ds < 0.75 ; unsaturated). The highest queue length is 167.87 m, the average delay of the resulting intersection is 13, 72 sec/pcu and enter the the level of service (LOS) with level B (5 – 15 sec/pcu). In traffic conditions in the next 10 years, the performance of the intersection will be very bad, it is known that the degrees of saturation for the north and south approaches are 0.94, 1.37 (ds > 0.75 ; saturated), while the East and West approaches produce degrees of saturation (ds > 0.75 ; saturation). ) respectively 0.19, 0.65 (ds < 0.75 ; unsaturated). The highest queue length is 1353.94 m, the average delay of the resulting intersection is 128.33 sec/pcu and the the level of service (LOS) is entered with the F level (>60 sec/pcu). while the East and West approaches produce degrees of saturation (ds) respectively 0.19, 0.65 (ds < 0.75 ; unsaturated). The highest queue length is 1353.94 m, the average delay of the resulting intersection is 128.33 sec/pcu and the the level of service (LOS) is entered with the F level (>60 sec/pcu). while the East and West approaches produce degrees of saturation (ds) respectively 0.19, 0.65 (ds < 0.75 ; unsaturated). The highest queue length is 1353.94 m, the average delay of the resulting intersection is 128.33 sec/pcu and the the level of service (LOS) is entered with the F level (>60 sec/pcu).

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.

Traffic Road Emission Estimation Through Visual Programming Algorithms and Building Information Models: A Case Study

Emissions from transportation have a severe impact on the current climate crisis. Therefore, the estimation of these pollutants requires precise measurements that integrate both traffic and vehicle fleet information within a specific country or area. However, the current estimation tools continue using vehicle fleet standards based on recommendations or local studies. A problem for the current estimation models arises due to the difficulty of centralizing the large number of vehicle statistics. This article has taken advantage of the capabilities of both visual programming tools and building information modeling (BIM) to centralize databases from different sources, generating a model that integrates current traffic data and vehicle fleet statistics. The proposed platform estimates emissions and the carbon footprint using TIER 1 emission factors recommended by the European Environmental Agency (EEA). This platform has been successfully applied to a case study to estimate the carbon footprint of the B-20 road in Barcelona, using current vehicle restriction scenarios. This case study presents a maximum difference of −2.72% compared with the estimations made by another similar report. This proposed platform more completely automates the communication among the equations and databases required to estimate traffic road emissions.

Existing Road Performance of the New Development of Sibu Central

The proposed development of Sibu Central hinges on the fact that it attracts high number of visitors from all over Sarawak. The development of Sibu Central would definitely improve the surrounding areas in future as Sibu Central will be one of the landmarks for Sibu. This development therefore will automatically generate more road traffic which will affect the performance of the existing road. The performance of the existing road was evaluated by two signalized intersections surrounding the proposed development of Sibu Central. It was carried out in order to determine the current traffic volume data, upon the completion of Sibu Central and 5 years after the development was completed. The road performance of Intersection 1 and Intersection 2 was analysed by using Sidra software. It was found that the existing road was still within the satisfactory performance, as the Level of Service (LOS) was in the range of LOS A until LOS D. This was due to the current traffic volume that was still under the capacity of the existing road which also influenced the satisfactory result for Volume by Capacity (V/C) ratio, average delay and queue length of all directions in Intersection 1 and Intersection 2. This result will not be adequate as the capacity of the road still remains unchanged due to the fact that the traffic volume keeps on increasing every year. The deterioration of the existing road in Intersection 1 and Intersection 2 was established in this study based on the performance of LOS, V/C ratio, average delay and queue length.

Investigation on road damage due to vehicle overloading of high volume Ipoh state road

Traffic load was a major factor in thickness design due to the main function of pavement which was to resist traffic load. Although efforts to repair the road damage had been done continuously, the recovering effects were almost meaningless if the road was continuously receiving overloading from vehicles. Issues of road damage due to vehicle overloading had been addressed by most agencies in developing countries. However, there were no available study to address this issue on Perak state road. Therefore, this research aimed to determine the current traffic composition, the percentage of overloading vehicle, the Equivalence Factor (E.F.), the adequacy of existing pavement thickness and the reduction of pavement service life due to overloading of the road by referred to AASHTO and Arahan Teknik (Jalan) 5/85. The selected study area was at Jalan Tuanku Abdul Rahman, Ipoh (also known as Jalan Kuala Kangsar). Data were employed which acquired from traffic count survey, axle load survey, coring test and dynamic cone penetrometer test. Review on current traffic count data showed that vehicles with 2-axle contributes more than 60% of the overall daily traffic while percentage of overloading vehicle revealed that more than 50% of vehicles contributed from 4-axle, 5-axle and 6-axle exceeded the maximum permissible gross vehicle weight (PGVW). The analysis on the E.F. showed that primary and secondary directions gained E.F. value of 4 and 3 times higher than the 3.0 E.F. design value which were 12.4 and 9.2 respectively. This also denoted that additional overlay pavement thickness was required about 70mm and 50mm for primary and secondary direction respectively to ensure the target design life was achieved. This study also discovered the road pavement experienced 7 and 6 years of reduction of service life for both directions respectively.

Research on distributed dynamic traffic data mining based on vehicular ad hoc network

There are some problems in current traffic data mining methods, such as poor accuracy, high energy consumption, high packet loss rate and lengthy times. A distributed dynamic traffic data mining method based on vehicular ad hoc network is proposed. Before mining, congestion, routing changes, data channel errors and connection interruptions in vehicular ad hoc networks are detected respectively. Particle Swarm Optimisation (PSO) is introduced to realise traffic data classification mining based on vehicular ad hoc networks under network coding and clustering fusion. The simulations results show that the method has high accuracy and efficiency, low-energy consumption and packet loss rate, and high feasibility.

Research on distributed dynamic traffic data mining based on vehicular ad hoc network

There are some problems in current traffic data mining methods, such as poor accuracy, high energy consumption, high packet loss rate and lengthy times. A distributed dynamic traffic data mining method based on vehicular ad hoc network is proposed. Before mining, congestion, routing changes, data channel errors and connection interruptions in vehicular ad hoc networks are detected respectively. Particle Swarm Optimisation (PSO) is introduced to realise traffic data classification mining based on vehicular ad hoc networks under network coding and clustering fusion. The simulations results show that the method has high accuracy and efficiency, low-energy consumption and packet loss rate, and high feasibility.

Stabilization analysis and modified KdV equation of a car-following model with consideration of self-stabilizing control in historical traffic data

In this paper, an extended car-following model which depends not only on the difference of the optimal velocity and the current velocity but also on self-stabilizing control is presented and analyzed in detail. The self-stabilizing control is constructed into the new model by utilizing the historical traffic data (the historical velocity and the historical optimal velocity of the considered vehicle). We derive the stability condition of the extended model against a small perturbation around the homogeneous flow. Theoretical results reveal that the self-stabilizing control in historical optimal velocity difference can further stabilize traffic system on the basis of the self-stabilizing control in historical velocity difference. It is also derived that the time gap between the current traffic data and the historical ones has an important impact on the stability criterion. We clarify the advantages of the self-stabilizing control over the cooperatively driving control and the flexibility in the choice of suppressing in traffic jams. Moreover, from the nonlinear analysis to the proposed model, the historical traffic data dependence of the propagating kink solutions for jam waves is achieved by deriving the modified KdV equation near critical point by using the reductive perturbation method. Finally, theoretical results are confirmed by direct simulations.

A new traffic route analyzer for commuter’s guidance in developing countries: application study in Islamabad, Pakistan

Growth of population in the capital city of Pakistan—Islamabad—is too high. This growth rate has caused a negative impact on the smooth flow of traffic system. The aim of this paper is to provide a solution to facilitate the car-based commuters to pick the route with minimal bottlenecks and to minimize the distance to reach the destination. The proposed solution is to manage and control the traffic system in the city of Islamabad. The current traffic data collection system in Islamabad and other urban areas does not provide timely and reliable data that can be useful to the Regional Transportation Authority for planning activities. To overcome this problem, our approach is based on collecting the missing data using a custom-built mobile app. Our system analyses and manipulates the collected information based on artificial neural network scheme that can indicate the bottlenecks for each route and predicts the shortest route based on the user’s severity levels. To validate our proposed approach, we tested six different randomly selected routes in Islamabad with different bottlenecks.

Port-IO: an integrative mobile cloud platform for real-time inter-terminal truck routing optimization

Besides the transport of containers between transshipment areas, an increasing amount of containers needs to be moved to value-added logistics and auxiliary service areas leading to additional container flows within a seaport. Both real-time information exchange and optimization are necessary to efficiently coordinate actors and container movements being involved in respective inter-terminal transport (ITT). However, there is no decision support system facilitating real-time planning and management of ITT taking advantage of modern information technologies and optimization algorithms. In this paper, we formulate the inter-terminal truck routing problem as a novel optimization problem and propose two greedy heuristics and two hybrid simulated annealing algorithms. The computational experiments, conducted using real locations from the Port of Hamburg (Germany), are evaluated extensively. They indicate that the proposed hybrid simulated annealing algorithms are able to report feasible and improved routes within seconds. The optimization component is embedded into a scalable cloud platform that integrates both real-time data from truck drivers using a mobile app and current traffic data. As such, the proposed mobile cloud platform realizes the vision of a decision support system facilitating real-time communication and context-aware ITT planning for reducing costs and the carbon footprint.

Estimating control delays at signalised intersections using low‐resolution transit bus‐based global positioning system data

Intersection control delay is one of the most important performance indicators for evaluating the traffic level of service and intersection capacities. In current traffic data detection infrastructure, control delay is not directly measurable. Although video-based detection approaches have been applied, their detection accuracy and reliability are constrained by application conditions and detection environments. Manual control delay data collection is labour-intensive, tedious, and time-consuming. High-resolution global positioning system (GPS) data provide an effective means of estimating control delays at intersections, but computationally intensive algorithms and hardware support are needed to handle a large network and impede their wide applications. In this study, a computationally cost-effective control delay estimation algorithm is developed based on low-resolution GPS-based transit bus trajectory data. Transit bus travelling behaviour is formulated to facilitate delay estimation. The effectiveness of the proposed algorithm is examined and verified by the field data and the results indicate that the proposed algorithm provides accurate and reliable control delay estimation at intersections under various conditions.

An Artificial Intelligence Techniques and Simulation Model to Control a Traffic Jam System in Malaysia

Traffic jam in Malaysia is a huge and complicated problem nowadays, due to the rapid increase in the demand for transportation. This causes a longer vehicle travel times, increased energy consumption, growing environmental pollution, reduced traffic safety, and a decrease in the efficiency of transportation infrastructure. Hence, controlling the flow of traffic has become a very important issue under a growing pressure to relieve traffic jam. In this study, a new Artificial Intelligence Techniques (AIT) and Simulation Model (SM) are applied in order to elicit a general diagnosis for the traffic congestion problem in Kuala Lumpur and Kuantan. An integrated model involves a Neural Network (NN), Fuzzy Logic (FL), Genetic Algorithm (GA), and Simulation Model (SM) is used. The current traffic demand data will be captured by strategically placed cameras. By receiving and processing data, we plan to use our integrated model to adjust traffic lights timing to optimize traffic flow in coordinated traffic lights systems, in order to minimize the traffic congestion through controlling traffic lights. The results of this study will be reported and used to suggest and apply more efficient transportation policies with the aim of providing useful insights on traffic congestion problem, and to assist the Malaysian decision makers to elaborate the best transportation policies.

Agent based Intelligent Routing Algorithm for Energy Efficient Routing in Wireless Sensor Network

In wireless sensor network, massive sensor nodes are deployed to sense the environment for various applications, so that there is a reduction in energy when performing routing which leads to decrease in network lifetime. However, if routing is performed through all the nodes, energy will be wasted. To avoid the wastage of resources such as energy or battery lifetime of nodes, various energy efficient routing algorithms were formulated to find the suboptimal path to reach the destination nodes with shortest time. Hence, finding an optimal routing with low energy is an important challenge in wireless sensor networks. Therefore, we propose a new routing algorithm in this paper by integrating agent based intelligent decision making with energy efficient routing algorithm which provides freedom to choose the optimized path over the network traffic with limited energy consumption. In all available routes, intelligent agents are deployed in nodes which help to predict the best path by making a suitable decision on whether to take congestion free path or shortest path. Moreover, every node is considered as an independent node to choose flexible route in a distributed environment with the help of moving average (MA) model. The Moving Average model is used in this paper, since it is a parametric time series model which can predict the network traffic based on historical traffic data and current traffic data. The main advantage of the proposed algorithm is the reduction in energy consumption, time taken to reach destination and packet drop ratio.

Real-time traffic estimation using data expansion

This paper presents a method for estimating missing real-time traffic volumes on a road network using both historical and real-time traffic data. The method was developed to address urban transportation networks where a non-negligible subset of the network links do not have real-time link volumes, and where that data is needed to populate other real-time traffic analytics. Computation is split between an offline calibration and a real-time estimation phase. The offline phase determines link-to-link splitting probabilities for traffic flow propagation that are subsequently used in real-time estimation. The real-time procedure uses current traffic data and is efficient enough to scale to full city-wide deployments. Simulation results on a medium-sized test network demonstrate the accuracy of the method and its robustness to missing data and variability in the data that is available. For traffic demands with a coefficient of variation as high as 40%, and a real-time feed in which as much as 60% of links lack data, we find the percentage root mean square error of link volume estimates ranges from 3.9% to 18.6%. We observe that the use of real-time data can reduce this error by as much as 20%.