Multi agent attack to clip base vehicle agreement for cab companInternet Explorers.

Introduction & A ; Background

In present, taxi service is extremely increased, most demand service in the universe.

Technological support such as SMS, CLI, GPS and Taxi metre services are support to increase the demand of this service.

More than 70 % of cab market handling by reputed cab companies in relevant states.

90 % of reputed cab companies base on economical country of states.

So, so it go more competitory concern in the universe.

Reputed cab companies ever see the client satisfaction to derive the more clients and survive in the market.

For maintain the bing client base, company has usage complex programming method with extremely experient starters ( who coordinate the drivers ) .

But, day-to-day hire cancelation ratio up to 6-8 % and hire cancellation after despatch up to 2-2.5 % .

70-75 % of cancelation after despatch due to late reaching. ( late reaching mean call centre operator inform to client sensible clip period, But driver transcend that clip period pick up the client. Some clip, client has caught by another rival )

This multi agent attack to

1 ) minimise the cancellation ration

2 ) minimise the cancellation ratio after despatch

3 ) arranged the vehicle, base on clip

4 ) increase the quality of service ( QoS ) in cab industry

5 ) increase the efficiency and dependability of current system.

with multi agent attack and partly utilize more dependable cyberspace services.

Problem

Leading cab company in Colombo, srilanka has successfully developed and implemented call direction application for them.

It has installation to

1 ) manage client information

2 ) manage hire engagement information

3 ) mange hire dispatching.

4 ) manage SMS every bit good

Nature of cab service as follows.

1 ) Call Centre operators having orders at the same time.

2 ) Considerable fleet of more than 100 autos.

3 ) 1300 – 1500 orders per twenty-four hours. Erstwhile order flow exceed the rate of 300 per hr.

4 ) The order properties are client nomadic figure, pick up reference, pressing or progress engagement and particular demands ( VIP client or non, normal or intercrossed auto, marked or un-marked auto )

5 ) Types of clients likely personal or corporate

6 ) Drivers properties are capableness to finish particular occupations, driver experience ( knowing of route of colombo country or non )

7 ) Cars properties are type of auto ( intercrossed or non ) , free location of auto. Car has to go through several province of in the on the job period. ( waiting for occupation, one the manner to occupation, at the topographic point, rider on board, occupation coating, traveling to liberate )

8 ) Approximate clip for choice up the client, decided by call centre operators seen after location board or naming after driver.

9 ) Road traffic jam status decides by good experient dispatching operators.

10 ) Rearrange the new vehicle, when the failure of the ordered vehicle.

11 ) Always, starters responsible for economical dispatching to cut down the doomed kilometres. In that instance, starters try to apportion last hire for drivers, which end from driver populating manner or closer country.

Agreement of vehicle under above conditions such a complex procedure. Which is no manner to accomplish any direct mathematical theoretical account to here. In instance miss up any twenty-four hours status cancellation ration traveling high. Every things handle by the experiences of operators. But it besides blind determination comparison with statics. New multi agent theoretical account base on past experience of everyone and existent clip traffic update utilizing traffic agent.

Literature Review

[ 1 ] This focused on independent cab drivers to derive their clients utilizing the centralize database system which update by clients and drivers position at the same time utilizing their Mobiles. This attack is better to utilize for better the efficiency of street hailing. In to boot they will see the existent clip traffic congestion in future surveies.

[ 2 ] This paper explained, Evaluated by agencies of a microscopic multi-agent conveyance simulator Coupled with a dynamic vehicle routing optimizer, which allows to realistically simulate dynamic cab services. This reappraisal of without cognizing client finish, quality of service can keep every bit same as earlier. In to boot, future work goes to widening platform functionality with online vehicle tracking faculty to be simulate.

[ 3 ] This paper propose the dispatching schemes for progress and current engagement in cab industry. The microscopy simulation of traffic is adopt as mold and analysis intent. Simulation consequences serve as the proficient support for cab operators to do strategic determinations.

[ 4 ] This paper has described ‘shared-ride construct ‘ to maximise the efficiency of the cab despatching during peak hours. In to boot, It has expected to convey significant benefits operation efficiency and energy ingestion. This reappraisal has explained taxi services can be maximized by suggesting shared-taxi algorithm and prevent inordinate rider roundabout waies. In this papers they assumed all riders willing to portion them rides, but in the existent universe it ‘s non practical.

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Methodology

Imitating above procedure could non easier in simple manner. Then suggesting existent clip multiagent procedure to each subdivision. Such that

Locator agentfor happening existent clip location of driver, auto and the client. Agent responsible for communicate the each GPS devices, Smart phones ( which download the app ) , Car smart phones ( which hole by company ) and Driver mobile phone and update the location inside informations in existent clip with following properties.

Longitude, Latitude, timestamp, land velocity, header, ignition and mileometer

Traffic update agentfor update the existent clip traffic on each route. Agent responsible for update the approximative traffic status on each route as a velocity.

Dispatch agentfor position the most economical despatch options to dispatcher. Agent responsible for happening the most suited dispatching option find the current status such that route status, distance, driver waiting clip and etc.

Time Frame

Mentions

[ 1 ] Ruibin Bai, Jaiwei Li, Jason AD Atkin & A ; Graham Kendall ( 2014 ) “ A fresh attack to independent cab scheduling job based on stable matching ” .Journal of the Operational Research Society( 2014 ) , 65, 1501–1510.

[ 2 ] Micha cubic decimeter Maciejewski & A ; Kai Nagel ( 2014 ) “ The influences of multi-agent cooperation on the efficiency of cab dispatching ” ,Parallel Processing and Applied Mathematics Lecture Notes in Computer Science Volume8385, 751-760

[ 3 ] Xian WU & A ; Der-Horng LEE ( 2013 ) “ An Integrated Taxi Dispatching Strategy Handling both Current and Advance Bookings ” .Proceedings of the Eastern Asia Society for Transportation Surveies, Vol.9, 2013

[ 4 ] Jaeyoung Jung, R. Jayakrishnan, Ji Young Park ( 2013 ) “ Design and Modeling of Real-time Shared-Taxi Dispatch Algorithms ” , Transportation Research Board 92nd Annual Meeting, Washington, DC. volume.

[ 5 ] Antonio Nelson Rodrigues da Silva & A ; Ronaldo Balassiano ( 2011 ) “ GLOBAL TAXI SCHEMES AND THEIR INTEGRATION IN SUSTAINABLE URBAN TRANSPORT SYSTEMS ” ,BNDES, Rio de Janeiro, 18-19 May 2011

[ 6 ] Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie and Guangzhong Sun, “ Where to Find My Next Passenger? “ ,Microsoft Research Asia

[ 7 ] Kiam Tian Seow, Nam Hai Dang & A ; Der-Horng Lee, “ A Collaborative Multiagent Taxi-Dispatch System ” , Automation Science and Engineering, IEEE Transactions on ( Volume:7, Issue: 3 ) , 607 – 616

[ 8 ] Andrey Glaschenko, Anton Ivaschenko, George Rzevski & A ; Petr Skobelev, “ Multi-Agent Real Time Scheduling System for Taxi Companies ” ,Conference: 8th International Conference on Autonomous Agents and Multiagent Systems ( AAMAS 2009 ) , Budapest, Hungary

[ 9 ] Aamena Ali Ahmed Omran Alshamsi, “ Self-organization and Multi-Agent Reinforcement Learning for Taxi Dispatch ” , 2009

[ 10 ] Darshan Santani, Rajesh Krishna Balan and C Jason Woodard, “ Spatio-temporal Efficiency in a Taxi Dispatch System ” ( 2005 ) , Research Collection School Of Information Systems ( SMU Access Merely ) . Paper 9.

[ 11 ] Aamena Alshamsi, Sherief Abdallah & A ; Iyad Rahwan, “ Multiagent Self-organization for a Taxi Dispatch System ” ( 2008 ) .

[ 12 ] Anurag Mandle, Akshay Jaiswal, Bhushan Dod, Roshan Lokhande ( 2006 ) , “ Taxi Automation Using Real Time Adaptive Scheduling ” ,International Journal on Recent and Innovation Trends in Computing and Communication IJRITCC| March 2014, ISSN: 2321-8169 Volume: 2 Issue: 3 592 – 594.

[ 13 ] Der-Horng Lee, Hao Wang, Ruey Long Cheu & A ; Siew Hoon Teo ( 2002 ) , “ A TAXI DISPATCH SYSTEM BASED ON CURRENT DEMANDS AND REAL-TIME TRAFFIC CONDITIONS ”,Transportation Network Modeling for presentation at the82nd Annual Meeting of the Transportation Research Board and consideration of publication in Transportation Research Record( 2002 )

[ 14 ] MichaA‚ Maciejewski1, “ BENCHMARKING MINIMUM PASSENGER WAITING TIME IN ONLINE TAXI DISPATCHING WITH EXACT OFFLINE OPTIMIZATION METHODS ” , THE ARCHIVES OF TRANSPORT. Volume 30. Issue 2. 2014. 67-75

[ 15 ] Patryk Filipiak, “ A Multiagent Simulation for Traffic Flow Management with Evolutionary Optimization ” ( 2014 ) ,

[ 16 ] Jeroen Brouwer, “ Measuring real-time traffic informations quality based on Floating Car Data ” ( 2014) ,ATEC ITS France Congress, Paris, France, January 29th 2014

[ 17 ] Mahmood Rahmani, Haris N. Koutsopoulos, Anand Ranganathan, “ Requirements and Potential of GPS-based Floating Car Data for Traffic Management ” ( 2010 ) ,Annual Conference on Intelligent Transportation Systems Madeira Island, Portugal, September 2010, 19-22.

[ 18 ] YAO Zhi-gang, “ Measuring riders perceptual experiences of cab service quality with SERRQUAL weighted by SERVPERF ” ( 2012 ) , Journal of Wuhan University of Technology.

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