Deployment of Inductive Loop Vehicle Traffic Counters Along Trunk Roads in Tanzania

As Tanzania's population grows, there is a greater demand for efficient, effective, and strong road design, as well as a smooth transportation system with a balanced traffic management network architecture. To plan for maintenance and expansion of such a vast network, a credible data inventory and network condition must be established. The deployment of vehicle inductor loop counters along Tanzanian trunk roads is presented in this paper. At test sites, inductor loop sensors and control cards were used to capture vehicle traffic data, which was then wirelessly sent using GSM technology for vehicle classification verification and storage for future road traffic management. The traffic count statistics from selected test sites acquired by the base station monitoring software were examined in real-time and the findings over a one-year period were provided. According to the study's findings, inductive loop vehicle counter technology can count and categorize vehicles along a section of roadway and provide reliable performance indicators for monitoring progress at the local, state, and national levels. The vehicle traffic data collected by this system will be used to project future situations within a transportation system and to keep a record of historical trends that will be used to project into the future what is likely to happen based on actual observations of the past and using other socio-economic data obtained from census information or economic indicators

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

Vehicle classification in intelligent transport systems: an overview, methods and software perspective

Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods

Design and implementation of an inductive-based human postures recognition system

This paper describes the design and implementation of an inductive-based human postures recognition system during Muslim prayers or ‘Solat’. Inductive sensors are preferred over contact sensors as they allow remote detection of postures. An array of inductive sensors are placed underneath a prayer mat to sense four different postures namely Woquf, Rokoo, Sojod and Qood. Each inductive proximity sensor comprises of a modified inductive loop, with inner and outer loops and three capacitors. The design of the sensing circuit was simulated using both MATLAB and Multisim. Nine identical sensors, with each sensor placed on a different zone on the prayer mat, are connected in parallel to a ChipKit Max32 development board. The sensors send analog signals that are digitized by the board and sent to a PC as frequency plots. Posture identification was done by analyzing the triggered zones. Experimental results are in agreement with both the analytical and simulation results and can successfully distinguish the different postures remotely

DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems

DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

Abstract. Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems. Document type: Articl

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality--vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces