Intelligent Transportation System for Smart-Cities using Fuzzy Logic

According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation

Design, development, and validation of a remotely reconfigurable vehicle telemetry system for consumer and government applications

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 67-68).This thesis explores the design and development of a cost-effective, easy-to-use system for remotely monitoring vehicle performance and drivers' habits, with the aim of collecting data for vehicle characterization and traffic shaping. Vehicular congestion and concerns over fuel reserves, pollution, and carbon emissions have recently emerged as prominent sociopolitical concerns. These problems are formidable, but could be addressed more fruitfully with better information about vehicles and drivers habits, leading to policies such as vehicle-specific congestion charging or an odometer-based road tax. Despite the proliferation of sensors in cars, data is often hidden due to the antiquated nature of the federally-required On-Board Diagnostics (OBD). Systems to log and process such data exist, but no well known reconfigurable systems augment OBD with additional sensor data and transmit it over a cellular network. This thesis proposes a system wherein vehicles become distributed sensors, each transmitting a rich supply of information. The standardization of OBD and decreasing cost of bandwidth make now an opportune time to develop a real-time logging system. Inexpensive processors make it possible to provide privacy through onboard calculation, obfuscating much personally-identifiable data. This document discusses the planning process, experimental configurations of hardware and software, results, and conclusions associated with the development of a cellular diagnostic system capable of supporting an "app" model for information feedback. I present a Bluetooth-OBD logger, a cellular logger, and a web interface capable of representing live and historical data from vehicles, including example applications for calculating congestion pricing. This project proves the feasibility of capturing data using a remotely reconfigurable controller area network (CAN) to general packet radio service (GPRS) interpreter, visualizing the information in real-time, and writing applications to make use of the incoming data. The hardware and software were proven successful in meeting the goals set for the project. The hardware proved robust, gathering data without issue for hundreds of miles. The sample data demonstrated low bandwidth use, identified network weaknesses, and pointed out issues with the currently-legislated OBD standard. This thesis closes by exploring future possibilities suggested by the development of this system, including wireless odometry and next-generation OBD.by Joshua Eric Siegel.S.B

The Design and Implementation of a PCIe-based LESS Label Switch

With the explosion of the Internet of Things, the number of smart, embedded devices has grown exponentially in the last decade, with growth projected at a commiserate rate. These devices create strain on the existing infrastructure of the Internet, creating challenges with scalability of routing tables and reliability of packet delivery. Various schemes based on Location-Based Forwarding and ID-based routing have been proposed to solve the aforementioned problems, but thus far, no solution has completely been achieved. This thesis seeks to improve current proposed LORIF routers by designing, implementing, and testing and a PCIe-based LESS switch to process unrouteable packets under the current LESS forwarding engine

Road Traffic Management using Vehicle-to- Everything (V2X) Communication

Traffic congestion is the primary concern in dense cities; while the increased number of automobiles is becoming uncontrollable in some cities, it is more challenging to manage or change how people use cars. To contribute to solving traffic congestion in cities, this project examines the study of transferring vehicles to be competent in a way that can help the government entities analyze the received vehicles’ data and for better decisions on reducing traffic congestion as well as the real-time monitoring of traffic wherever it is located using the Vehicle-to-Everything (V2X) communication methodology. This study proposes a hardware “system” that can be attached to any vehicle to collect real-time data from vehicles and communicate with the Road and Transportation Authority. The hardware system, however, is connected to the cars through a wireless On-Board Diagnostics (OBD) connection in favor of collecting all the necessary information from the vehicle, such as the car speed and Revolutions Per Minute (RPM) data. On the other hand, a GPS sensor is used to inquire about the vehicle’s location, a GSM module to make sure the device is always connected to the internet for data transmission, a LiDAR sensor for distance and safety measurement, and a camera module accessed only by the driver for object detection such as cars, pedestrians, traffic signs, damaged roads, and road hazards. Moreover, system updates and maintenance can be done remotely to reduce the number of visits to the traffic department since all devices are to be connected to a single platform. As a result, it was possible to create a prototype for a single vehicle, including the sensors mentioned above, returning valuable data that include vehicle speed and exact location, which will help future researchers develop an application platform to monitor and track traffic congestion in real time

Design of an Automotive IoT Device to Improve Driver Fault Detection Through Road Class Estimation

Unsafe driver habits pose a serious threat to all vehicles on the road. This thesis outlines the development of an automotive IoT device capable of monitoring and reporting adverse driver habits to mitigate the occurrence of unsafe practices. The driver habits targeted are harsh braking, harsh acceleration, harsh cornering, speeding and over revving the vehicle. With the intention of evaluating and expanding upon the industry method of fault detection, a working prototype is designed to handle initialization, data collection, vehicle state tracking, fault detection and communication. A method of decoding the broadcasted messages on the vehicle bus is presented and unsafe driver habits are detected using static limits. An analysis of the initial design’s performance revealed that the industry method of detecting faults fails to account for the vehicle’s speed and is unable to detect faults on all roadways. A framework for analyzing fault profiles at varying speeds is presented and yields the relationship between fault magnitude and speed. A method of detecting the type of road driven was developed to dynamically assign fault limits while the vehicle traveled on a highway, city street or in traffic. The improved design correctly detected faults along all types of roads and proved to greatly expand upon the current method of fault detection used by the automotive IoT industry today

Project54 vehicle telematics for remote diagnostics, fleet management and traffic monitoring

The Project54 system was developed to introduce advanced technologies into the operations of the New Hampshire Department of Safety and other law enforcement agencies. The application of computing, sensing and telecommunication technologies within the Project54 system enables advanced telematics services that can provide benefits to vehicle operators, fleet managers and the public. This thesis describes the implementation of remote diagnostics and fleet management services for the Project54 system and investigates the use of radar equipped police vehicles as traffic probes. Aftermarket diagnostic hardware has been integrated in the Project54 system and software applications have been developed to control the hardware and record diagnostic information. An electronic data entry form has been created for tracking vehicle operating expenses and a vehicle status reporting system is described. Additionally, a traffic congestion scoring method using information from traffic radar units is presented