CAN ICT LEADING TRANSPORT INNOVATION

1 Running head: CAN ICT LEADING TRANSPORT INNOVATION?

CAN ICT LEADING TRANSPORT INNOVATION?

Research Paper

Mohammed Selim

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Abstract

In recent years, governments and transport experts have expressed alarm about the growing problem of how to exploit ICT technologies in transport roads to avoid accidents, centralized traffic management, and congestion. While most agree that the issue deserves attention, consensus dissolves around how to respond to the problem.

This research paper examines different technologies to resolving transport problems: ITS. The paper presents the effectiveness for applying ICT technologies in transport roads, and focuses on leading countries in applying ICT technologies in transport.

This current application of ICT in transport in different courtiers points out the needs of more practical research and suggests the need for a comprehensive solution of advanced systems to this growing problem.

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Can ICT Lead Transport Innovation?

Introduction

ICT services for vehicular infrastructures are very important to human life in the present and the future, as its goal is to provide innovative services related to various modes of transportation and traffic routes management, also to help different drivers and persons to be well informed, safe and more intelligent user of road networks through using advanced ICT applications. This new definition of implementing ICT service not just in vehicular infrastructures, but in rail, water and air transport, including navigation systems, is called Intelligent Transport Systems (ITS).

This research paper considers whether the use of ICT is a promising approach for leading Transport innovation by responding to the following questions:

1. What are ITS dimensions? 2. What is the Feasibility of applying ITS? 3. ITS enabling technologies and Algorithms. 4. ITS stating innovation and the future 5. The proposed system

What are ITS dimensions?

systems for collection of data (monitoring and positioning systems) systems and protocols for communicating data (e.g. between traffic control centres

and to and from vehicles) Quality of the data (accuracy, timeliness)

The following image has more details of ITS dimensionsITS dimensions (Mallik, Sumit, 2014).

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What is the Feasibility of applying ITS?

“For Japan, ITS have been crucial as the country strives to meet its goal to reduce, by 2010, CO2 emissions by 31million tons below 2001 levels, with 11 million tons of Savings come from improved traffic flow and another11 million tons of savings from more effective use of vehicles. For many countries, ITS represents a rapidly expanding, export-led growth sector which contributes directly to national economic competitiveness and employment growth. For example, the U.S. Department of Transportation has estimated that the field of ITS could create almost 600,000 new jobs over the next 20 years, and a study of ITS in the United Kingdom found that a £5 billion investment in ITS would create or retain 188,500 jobs for one year. Intelligent transportation systems deliver superior benefit-cost returns when compared to traditional investments in highway capacity. Overall, the benefit-cost ratio of systems-operations measures (enabled by intelligent transportation systems) has been estimated at about 9 to 1, far above the addition of conventional highway capacity, which has a benefit-cost ratio of 2.7 to 1. A 2005 study of a model ITS deployment in Tucson, Arizona, consisting of 35 technologies that would cost $72 million to implement, estimated that the average annual benefits to mobility, the environment, safety, and other areas would total $455 million annually, a 6.3 to 1 benefit-cost ratio. If the United States were to implement a national real-time traffic information program, the GAO estimates the present value cost of establishing and operating the program would be $1.2 billion, but would deliver present value benefits of $30.2 billion, a 25 to 1 benefit-cost ratio”, (Ezell, Stephen, January 2010, p.3).

ITS enabling technologies and Algorithms

The following examples presenting updated ITS enabling technologies in different projects and fields since 2012 till present:

1. Artificial Co-Drivers as a Universal Enabling Technology for Future Intelligent Vehicles and Transportation Systems (2014)

Summary: A system capable of determining how an expert human would drive could be regarded either as a kind of “holistic driver model” or as an “artificial human-like driver.” Enabling technologies: an artificial driver would be sufficient for developing autonomous vehicles that move and react as if driven by a human. Results:Co-drivers are potentially suited to more sophisticated applications. The system could use its emulation capacity (in a way similar to human rebind with cooperative systems, this ability may also enable the co-driver to analyze accident scenarios that rarely happen, thus improving their overall motor strategy. (Mauro Da Lio, & Francesco Biral, & Enrico Bertolazzi, & Marco Galvani, & Paolo Bosetti, & David Windridge, & Andrea Saroldi, & Fabio Tango. 2014).

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2. Estimating Speed Using a Side-Looking Single-Radar Vehicle Detector (2014) Summary: using a side-looking single-beam microwave vehicle detector (VD) system for estimation of per-vehicle speed and length. Enabling technologies: The proposed VD system is equipped with a 2-D range Doppler frequency-modulated continuous-wave (FMCW) radar using a squint angle. The associated Fourier processor uses an inverse synthetic aperture radar (ISAR) algorithm to extract range and speed data for each vehicle using a single-beam FMCW radar. Results: The proposed method has excellent detection capability for small moving targets, such as bikes and pedestrians, at speeds down to 5 km/h.

(Shyr-Long Jeng,& Wei-HuaChieng, & Hsiang-Pin Lu. April 2014).

3. Cooperative Collision Avoidance at Intersections: Algorithms and Experiments (2013) Summary: V2V communication, and the ability to automatically actuate the throttle and brake. Enabling technologies: computer running a Linux operating system; Differential Global Positioning System (DGPS) for position, absolute time, and heading measurement; Denso Wireless Safety Unit (WSU) capable of V2V and V2I dedicated short-range communications (DSRC); connection to the Controller Area Network (CAN) bus to read information from vehicle sensors (velocity, acceleration, brake pedal position, transmission state, etc.); and a CAN bus interface with brake and throttle actuators.

System Model and Safety Specification

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Results: The linear complexity algorithms for evaluating the capture set and control actions are fast enough for real-time implementation, which is a feature that is necessary forth practical applicability of this approach. (Michael R. Hafner, &Drew Cunningham,& Lorenzo Caminiti, &Domitilla Del Vecchio. September 2013)

4. An Agentmining Framework for Intelligent Vehicular System (2013)

Summary: The Vehicular Ad-hoc Network (VANET), a subset of Mobile Ad-hoc Network, is used in many applications such as assisting driver with signage, road traffic reporting, telling the way etc. Enabling technologies: Vehicular Ad-hoc Network provides a unique opportunity to establish communication-based cooperative safety systems. VANET comprises two modes of communication: Vehicle to-Vehicle (V2V) communication and Vehicle-to-Infrastructure (V2I) communication. Results: it can effectively optimize agent collaboration performance and enhance capabilities for tackling exceptions and conflicts.

Working model of intelligent vehicular system have the following components

(SwathiLankaa,& S.K. Jenab. 2013)

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5. Commercialization of Intelligent Transportation Systems: The Case of Cooperative

Systems (2012) Summary: CooperativeITS is a technology of the futurein Sweden carried out by national authorities, regional road authorities, vehicle OEMs and telecommunications companies. Enabling technologies:The project presents the necessary means in taking one more step towards publically available cooperative ITS, Results: The project highlighting questions such as “who has the motivation and takes the initiative to create an ecosystem for cooperative ITS systems?” and “what are the technical boundaries to comply to enable implementation by 2014?.(Maria Nilsson, & Mats Williander, &CristoferEnglund. 2012).

6. The Fusion Model of Intelligent Transportation Systems Based on the Urban Traffic Ontology (2012)

Summary:They are utilizing the semantic completeness of the ontology to build urban traffic ontology model to resolve the problems as ontology mergence and equivalence verification in semantic fusion of traffic information integration. Enabling technologies: car flow rate, car speed, distance between cars, car types, road occupancy, car information of regulation violation, weather condition of road, traffic GIS, traffic video, public transportation, traffic induction, GPS navigation and so on. Results: the semantic fusion based on ontology increases the effect and efficiency of the urban traffic information integration and reduce the storage quantity, improve query efficiency and information completeness. (Wang-Dong Yang, &Tao Wang. 2012)

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ITS stating innovation and the future

The adoption of mobile communications in IP based networks can have a major impact on improving the efficiency of multimodal logistics operations especially at a time where government agencies are engaged in launching initiatives that will contribute towards efficient freight transportation and better use of resources. In recent years and as part of their own ITS initiatives, the US, Japan and Europe have emphasized the future adoption and deployment of emerging wireless vehicular technology such as DSRC to enable vehicle integration with the possibility of achieving significant reduction in road congestion, traffic accidents and vehicle wear. Although the deployment of technologies such as Global Positioning Systems (GPS), cellular networks and Wi-Fi among others have had a significant impact on track and trace capabilities

The increasing processing power of smartphones combined with their wireless communication features has resulted in the rapid development of new applications and services hosted as smartphone “apps”. Initially, these driving-related apps focused on green driving and fuel use logs, to Global Positioning Satellite (GPS) related apps such as navigation and vehicle location features. However, recently, safety-related apps have emerged in the market, which offer feedback to the driver on aspects such as lane departure, headway (also known as following distance), and speed violations. The interesting occurrence here is that while safety features or Advanced Driver Assistance Systems (ADAS) have traditionally been the domain of the vehicle manufacturer, this may now be starting to change, suggesting that as sensors add to the initial cost of a vehicle and cannot be affordably upgraded, smartphone technology can be used as an alternate device for ADAS assisting the driver and complementing any existing active safety features.

There are a wide range of technologies and applications that are being used in current ITS functions that are intended to benefit the driver. However, what is as yet unknown is whether these ITS will have any measurable effects on driving performance in the real world.

The existing works providing solutions either for traffic congestion, or for alternative route generation, journey time estimations. There must be a framework with Driving assistance must provide all necessary information about the road-traffic to the vehicle driver and a continuous monitoring on individual vehicle movement has to be done as well. Existing works generating road information with Variable Message Signs. Intelligent report includes information regarding road traffic, flow rate, road obstacles (accident, damage), speed, lane changing assistance, vehicle breakdown, vehicles rerouting. Intelligent report makes vehicle as an intelligent vehicle since each vehicle will be equipped with advance information, which will guides vehicle throughout journey.

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References

Adrian E. Coronado Mondragona, & Chandra S. Lalwanib, & Etienne S. Coronado Mondragonc, & Christian E. Coronado Mondragond, &Kulwant S. Paware.(2012).“ Intelligent transport systems in multimodal logistics: A case of role and contribution through wireless vehicular networks in a sea port location”. Available from http://www.sciencedirect.com/science/article/pii/S0925527311004683.accessed 8 August 2014. Ezell, Stephen. (January 2010). “Explaining International IT Application Leaderhip: Intelligent Transportation Systems”.Available from http://www.itif.org/files/2010-1-27-ITS_Leadership.pdf.accessed 2 August 2014. European Commission. (12 June 2014). “Intelligent transport systems”.Available from http://ec.europa.eu/transport/themes/its/road/application_areas/ict_infrastructure_en.htm.accessed 6 August 2014. European Telecommunications Standards Institute. (April 2013). “Intelligent Transport Systems”.Available from http://www.etsi.org/index.php/technologies-clusters/technologies/intelligent-transport.accessed 4 August 2014. Falment, Maxime. (2013). “Benefits of Intelligent Transport Systems (ITS)”.Available from http://ec.europa.eu/transport/road_safety/pdf/stake_8_3_2013/session_2_maxime_flament.pdf.accessed 7 Aug 2014. Mallik, Sumit. (2014). “Intelligent Transportation System”.Available from http://www.ripublication.com/ijcer_spl/ijcerv5n4spl_10.pdf.accessed 4 August 2014. Maria Nilsson, & Mats Williander, &CristoferEnglund. (2012).”Commercialisation of Intelligent Transportation Systems: The Case of Cooperative Systems”. Available from http://www.sciencedirect.com/science/article/pii/S1877042812027863.accessed 11 August 2014. Mauro Da Lio, & Francesco Biral, & Enrico Bertolazzi, & Marco Galvani, & Paolo Bosetti, & David Windridge, & Andrea Saroldi, & Fabio Tango.(2014).”Artificial Co-Drivers as a Universal Enabling Technology for Future Intelligent Vehicles and Transportation Systems”.Available from http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853398 .accessed 8 August 2014. Michael R. Hafner, &Drew Cunningham,& Lorenzo Caminiti, &Domitilla Del Vecchio. (September 2013).”Cooperative Collision Avoidance at Intersections:Algorithms and Experiments”. Available from http://ieeexplore.ieee.org/stamp/stamp.jsp?reload=true&tp=&arnumber=6495719 .accessed 7 August 2014. Mayville, Casey. (27 January 2010) . “Intelligent Transportation Systems: U.S. Not Leading the Pack”. Available from http://www.digitalcommunities.com/articles/Intelligent-Transportation-Systems-US-Not-Leading.html.accessed 3 August 2014. Shyr-Long Jeng,& Wei-HuaChieng, & Hsiang-Pin Lu. (April 2014).”Estimating Speed Using a Side-Looking Single-Radar Vehicle Detector”.Available from http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6636147.Accessed 6 August 2014. SwathiLankaa,& S.K. Jenab. (2013).”An Agentmining Framework for Intelligent Vehicular System”.Available from http://www.sciencedirect.com/science/article/pii/S2212017313005410.accessed 10 august 2014. Wang-Dong Yang, &Tao Wang. (2012).”The Fusion Model of Intelligent Transportation Systems Based on the Urban Traffic Ontology”. Available from http://www.sciencedirect.com/science/article/pii/S1875389212005949.accessed 9 August 2014.

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XuBina, & Chen Xiaohongb, & Lin Hangfeic, & Yang Chaod. (2013).”Decision Oriented Intelligent Transport Information Platform Design Research – Case study of Hangzhou City”.Available from http://www.sciencedirect.com/science/article/pii/S1877042813023781.accessed 9 August 2014.

CAN ICT LEADING TRANSPORT INNOVATION

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