To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
(Slides) P-Tour: A Personal Navigation System for TouristNaoki Shibata
http://ito-lab.naist.jp/themes/pdffiles/041019.atsu-mar.ITSWC2004.pdf
Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K. and Ito, M.: P-Tour: A Personal Navigation System for Tourism, Proceedings of 11th World Congress on ITS Nagoya, pp.18-21 (October 2004)
We propose a personal navigation system for tourism called P-Tour. When a tourist specifies multiple destinations with relative importance and restrictions on arrival/staying time, P-Tour computes the nearly best schedule to visit part of those destinations. In addition to the map-based navigation, P-Tour provides temporal guidance according to the schedule, and automatically modifies the schedule when detecting the situation that the tourist cannot follow the schedule. We have developed a route search engine as a Java Servlet which can compute a semi-optimal schedule in reasonable time using techniques of genetic algorithms.
Our project is the complete study about both Spot speed studies and Speed delay time survey. This topic is a part of Transportation Engineering. This report helps you to understand this topic in detail. This report will also help you to make project on associated topics in traffic engineering. In spot speed, We discussed regarding various methods available to perform the test, Our team practically performed test and established a speed limit zone near a school. Coming to speed delay time survey, we conducted a survey at a selected stretch and came out with solutions to the problems faced by the vehicle users using that stretch.
GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing ...Naoki Shibata
Jiaxing Xu, Weihua Sun, Naoki Shibata and Minoru Ito : "GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion," in Proc. of IEEE Vehicular Networking Conference 2014 (IEEE VNC 2014), pp. 179-186.
Serious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. GreenWave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. GreenWave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with GreenWave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system.
Presentation delivered at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30, during the session entitled Goods Movement - Reaching Destinations Safely and Efficiently.
Prepared by
François Bélisle, Eng., B. Sc., M.A.
Marilyne Brosseau, Eng., M.Eng.
Steve Careau, Eng.
Philippe Mytofir, techn.
Validated by:
Stephan Kellner, Eng., M.Eng.
A new conceptual algorithm for adaptive route changing in urban environmentseSAT Journals
Abstract In this paper an attempt is shown in a Mathematical Model of an alternating route adapting for large scale, which was previously done in small scale. This evaluation is done considering 75 and above traffic junctions with a scale of 4 to 6 vehicular density per second in each junction. The main objective of this paper is to show how an Adaptive Route Changing [ARC] application responds to assumed scenario & also proposing effective software architecture to monitor traffic in all the junctions at real time to minimise traffic congestion. Keywords : ARC, Intelligent transport system, Traffic Monitoring.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Transit Signalisation Priority (TSP) - A New Approach to Calculate GainsWSP
Presentation by François Bélisle, Eng. , B.Sc., M.A. and Stephan Kellner, Eng., P.Eng., MS delivered at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30.
Cloudsim t-drive enhancing driving directions with taxi drivers’ intelligenceecway
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(Slides) P-Tour: A Personal Navigation System for TouristNaoki Shibata
http://ito-lab.naist.jp/themes/pdffiles/041019.atsu-mar.ITSWC2004.pdf
Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K. and Ito, M.: P-Tour: A Personal Navigation System for Tourism, Proceedings of 11th World Congress on ITS Nagoya, pp.18-21 (October 2004)
We propose a personal navigation system for tourism called P-Tour. When a tourist specifies multiple destinations with relative importance and restrictions on arrival/staying time, P-Tour computes the nearly best schedule to visit part of those destinations. In addition to the map-based navigation, P-Tour provides temporal guidance according to the schedule, and automatically modifies the schedule when detecting the situation that the tourist cannot follow the schedule. We have developed a route search engine as a Java Servlet which can compute a semi-optimal schedule in reasonable time using techniques of genetic algorithms.
Our project is the complete study about both Spot speed studies and Speed delay time survey. This topic is a part of Transportation Engineering. This report helps you to understand this topic in detail. This report will also help you to make project on associated topics in traffic engineering. In spot speed, We discussed regarding various methods available to perform the test, Our team practically performed test and established a speed limit zone near a school. Coming to speed delay time survey, we conducted a survey at a selected stretch and came out with solutions to the problems faced by the vehicle users using that stretch.
GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing ...Naoki Shibata
Jiaxing Xu, Weihua Sun, Naoki Shibata and Minoru Ito : "GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion," in Proc. of IEEE Vehicular Networking Conference 2014 (IEEE VNC 2014), pp. 179-186.
Serious traffic congestion is a major social problem in large cities. Inefficient setting of traffic signal cycles, especially, is one of the main causes of congestion. GreenWave is a method for controlling traffic signals which allows one-way traffic to pass through a series of intersections without being stopped by a red light. GreenWave was tested in several cities around the world, but the results were not satisfactory. Two of the problems with GreenWave are that it still stops the crossing traffic, and it forms congestion in the traffic turning into or out of the crossing streets. To solve these problems, we propose a method of controlling traffic signals, GreenSwirl, in combination with a route guidance method, GreenDrive. GreenSwirl controls traffic signals to enable a smooth flow of traffic through signals times to turn green in succession and through non-stop circular routes through the city. The GreenWave technology is extended thereby. We also use navigation systems to optimize the overall control of the city's traffic. We did a simulation using the traffic simulator SUMO and the road network of Manhattan Island in New York. We confirmed that our method shortens the average travel time by 10%-60%, even when not all cars on the road are equipped to use this system.
Presentation delivered at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30, during the session entitled Goods Movement - Reaching Destinations Safely and Efficiently.
Prepared by
François Bélisle, Eng., B. Sc., M.A.
Marilyne Brosseau, Eng., M.Eng.
Steve Careau, Eng.
Philippe Mytofir, techn.
Validated by:
Stephan Kellner, Eng., M.Eng.
A new conceptual algorithm for adaptive route changing in urban environmentseSAT Journals
Abstract In this paper an attempt is shown in a Mathematical Model of an alternating route adapting for large scale, which was previously done in small scale. This evaluation is done considering 75 and above traffic junctions with a scale of 4 to 6 vehicular density per second in each junction. The main objective of this paper is to show how an Adaptive Route Changing [ARC] application responds to assumed scenario & also proposing effective software architecture to monitor traffic in all the junctions at real time to minimise traffic congestion. Keywords : ARC, Intelligent transport system, Traffic Monitoring.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Transit Signalisation Priority (TSP) - A New Approach to Calculate GainsWSP
Presentation by François Bélisle, Eng. , B.Sc., M.A. and Stephan Kellner, Eng., P.Eng., MS delivered at the 2015 Transportation Association of Canada (TAC) Conference & Exhibition, from September 27 to 30.
Cloudsim t-drive enhancing driving directions with taxi drivers’ intelligenceecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
Android t-drive enhancing driving directions with taxi drivers’ intelligenceecway
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Neural Network Based Parking via Google Map GuidanceIJERA Editor
Intelligent transportation systems (ITS) focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS) uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
(Paper) A Method for Sharing Traffic Jam Information using Inter-Vehicle Comm...Naoki Shibata
Shibata, N., Terauchi, T., Kitani, T., Yasumoto, K., Ito, M., Higashino, T.: A Method for Sharing Traffic Jam Information Using Inter-Vehicle Communication, The 2nd International Workshop on Vehicle-to-Vehicle Communications (V2VCOM) (Mobiquitous2006 Workshop), pp. 1-7, DOI:10.1109/MOBIQ.2006.340428 (July 2006) (invited paper).
http://ito-lab.naist.jp/themes/pdffiles/060725.shibata.v2vcom06.pdf
In this paper, we propose a method for cars to autonomously and cooperatively collect traffic jam statistics to estimate arrival time to destination for each car using inter-vehicle communication. In the method, the target geographical region is divided into areas, and each car measures time to pass through each area. Traffic information is collected by exchanging information between cars using inter-vehicle communication. In order to improve accuracy of estimation, we introduce several mechanisms to avoid same data to be repeatedly counted. Since wireless bandwidth usable for exchanging statistics information is limited, the proposed method includes a mechanism to categorize data, and send important data prior to other data. In order to evaluate effectiveness of the proposed method, we implemented the method on a traffic simulator NETSTREAM developed by Toyota Central R&D Labs, conducted some experiments and confirmed that the method achieves practical performance in sharing traffic jam information using inter-vehicle communication.
This paper proposes development of an android app to improve the transportation services for
bus rental companies that lift Taibah University students. It intends to reduce the waiting time
for bus students, thereby to stimulate sharing of updated information between the bus drivers
and students. The application can run only on android devices. It would inform the students
about the exact time of arrival and departure of buses on route. This proposed app would
specifically be used by students and drivers of Taibah University. Any change in the scheduled
movement of the buses would be updated in the software. Regular alerts would be sent in case of
delays or cancelation of buses. Bus locations and routes are shown on dynamic maps using
Google maps. The application is designed and tested where the users assured that the
application gives the real time service and it is very helpful for them.
This paper proposes development of an android app to improve the transportation services for
bus rental companies that lift Taibah University students. It intends to reduce the waiting time
for bus students, thereby to stimulate sharing of updated information between the bus drivers
and students. The application can run only on android devices. It would inform the students
about the exact time of arrival and departure of buses on route. This proposed app would
specifically be used by students and drivers of Taibah University. Any change in the scheduled
movement of the buses would be updated in the software. Regular alerts would be sent in case of
delays or cancelation of buses. Bus locations and routes are shown on dynamic maps using
Google maps. The application is designed and tested where the users assured that the
application gives the real time service and it is very helpful for them.
Route-Saver: Leveraging Route APIs for Accurate and Efficient Query Processin...1crore projects
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3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Vampire attacks draining life from w...IEEEGLOBALSOFTTECHNOLOGIES
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JAVA 2013 IEEE DATAMINING PROJECT T drive enhancing driving directions with taxi drivers’ intelligence
1. T-Drive: Enhancing Driving Directions with Taxi Drivers’
Intelligence
Abstract
This paper presents a smart driving direction system leveraging the intelligence of experienced drivers.
In this system,GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi
drivers’ intelligence in choosing drivingdirections in the physical world. We propose a time-dependent
landmark graph to model the dynamic traffic pattern as well as theintelligence of experienced drivers so as to
provide a user with the practically fastest route to a given destination at a given departuretime. Then, a
Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between
twolandmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute
the practically fastest andcustomized route for end users. We build our system based on a real-world trajectory
data set generated by over 33,000 taxis in aperiod of three months, and evaluate the system by conducting both
synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our
method are faster than the competing methods, and 20 percent of the routes share the sameresults. On average,
50 percent of our routes are at least 20 percent faster than the competing approaches.
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2. Existing System
The efficient driving directions has become a dailyactivity and been implemented as a key
feature in manymap services like Google and Bing Maps. A fast drivingroute saves not only the time of
a driver but also energyconsumption (as most gas is wasted in traffic jams). Therefore,this service is
important for both end users andgovernments aiming to ease traffic problems and
protectenvironment.Essentially, the time that a driver traverses a routedepends on the following three
aspects: 1) the physicalfeature of a route, such as distance, capacity (lanes), and thenumber of traffic
lights as well as direction turns; 2) thetime-dependent traffic flow on the route; and 3) a user’sdriving
behavior. Given the same route, cautious driverswill likely drive relatively slower than those
preferringdriving very fast and aggressively. Also, users’ drivingbehaviors usually vary in their
progressing driving experiences.For example, traveling on an unfamiliar route, a userhas to pay attention
to the road signs, hence drive relativelyslowly. Thus, a good routing service should consider thesethree
aspects (routes, traffic, and drivers), which are farbeyond the scope of the shortest/fastest path
computing.Usually, big cities have a large number of taxicabstraversing in urban areas.
Disadvantages
That is, we cannot answeruser queries by directly mining trajectory patterns from thedata. Therefore,
how to model taxi drivers’ intelligence thatcan answer a variety of queries is a challenge
We cannot guarantee there aresufficient taxis traversing on each road segment even if wehave a large
number of taxis.
That is, we cannot accuratelyestimate the speed pattern of each road segment
Proposed System
The preprocessed taxi trajectories, we detect thetop-k frequently traversed road segments, which
are termedas landmarks. The reason why we use “landmark” to modelthe taxi drivers’ intelligence is
that: first, the sparseness andlow-sampling rate of the taxi trajectories do not support usto directly
calculate the travel time for each road segmentwhile we can estimate the traveling time between
twolandmarks (which have been frequently traversed by taxis).Second, the notion of landmarks follows
the naturalthinking pattern of people.The threshold _ is used to eliminate the edges seldomtraversed by
taxis, as the fewer taxis that pass two landmarks,the lower accuracy of the estimated travel time(between
the two landmarks) could be. Additionally, we setthe tmax value to remove the landmark edges having a
verylong travel time. Due to the low-sampling rate problem,sometimes, a taxi may consecutively
3. traverse three landmarkswhile no point is recorded when passing the middle(second) one. This will
result in that the travel time betweenthe first and third landmark is very long. Such kinds of edgeswould
not only increase the space complexity of a landmarkgraph but also bring inaccuracy to the travel time
estimation.
Advantages
The almost sharesimilar traffic patterns while the weekdays and weekendshave different patterns.
Therefore, we build two differentlandmark graphs for weekdays and weekends, respectively.
The travel times of transitions pertaining to alandmark edge clearly gather around some values (like a
setof clusters) rather than a single value or a typical Gaussiandistribution, as many people expected.
This aims to handle the situationthat a taxi was stuck in a traffic jam or waiting at a trafficlight where
multiple points may be recorded on the sameroad segment
Module
1. Intelligence modeling
2. Low Sampling Rate Problem
3. The Landmark Graph
4. Route Generation
5. Path logging
6. Route Computing
Module Description
Intelligence modeling
A user can select any place as asource or destination, there would be no taxi trajectoryexactly passing
the query points. That is, we cannot answeruser queries by directly mining trajectory patterns from thedata.
Therefore, how to model taxi drivers’ intelligence thatcan answer a variety of queries is a challenge.
Low Sampling Rate Problem
To save energy and communicationloads, taxis usually report on their locations in a very lowfrequency,
like 2-5 minutes per point. This increases theuncertainty of the routes traversed by a taxi. As shown inthere
could exist four possible routes.
4. The Landmark Graph
To save energy and communication loads, taxis usually report on their locations in a very low frequency,
like2-5 minutes per point. This increases the uncertainty of theroutes traversed by a taxi. Meanwhile, we
cannotguarantee there are sufficient taxis traversing on each roadsegment anytime even if we have a large
number of taxis.That is, we cannot directly estimate the speed pattern of eachroad segment based on taxi
trajectories.
Route Generation
The traffic condition of a road, the travel time of aroute also depends on drivers. Sometimes, different
driverstake different amounts of time to traverse the same route atthe same time slot. The reasons lie in a
driver’s drivinghabit, skills and familiarity of routes. For example, peoplefamiliar with a route can usually pass
the route faster than anewcomer. Also, even on the same path, cautious peoplewill likely drive relatively slower
than those preferring todrive very fast and aggressively.
Path logging
The cloud sends the computed drivingroutes along with the travel time distributions of theLandmark
edges contained in the driving route to thephone. Later, the mobile phone logs the user’s drivingpath with a
GPS trajectory, which will be used forrecalculate the user’s custom factor. The more a driveruses this system,
the deeper this system understandsthe driver; hence, a better driving direction servicescan be provided.
Route computing
According to the departure time,start and destination point, the cloud chooses a properlandmark graph
considering the weather informationand whether it’s a holiday or a workday. Based on thelandmark graph, a
two-stage routing algorithm isperformed to obtain a time-dependent fastest route
5. Flow chart
Conclusion
This paper describes a system to find out the practicallyfastest route for a particular user at a given departure
time.Specifically, the system mines the intelligence of experienceddrivers from a large number of taxi
6. trajectories andprovide the end user with a smart route, whichincorporatesthe physical feature of a route, the
time-dependent trafficflow as well as the users’ driving behaviors (of both the fleetdrivers and of the end user
for whom the route is beingcomputed). We build a real system with real-world GPStrajectories generated by
over 33,000 taxis in a period ofthree months, then evaluate the system with extensiveexperiments and in-the-
field evaluations. The results showthat our method significantly outperforms the competingmethods in the
aspects of effectiveness and efficiency infinding the practically fastest routes. Overall, more than60 percent of
our routes are faster than that of the existingonline map services, and 50 percent of these routes are atleast 20
percent faster than the latter. On average, ourmethod can save about 16 percent of time for a trip, i.e.,5 minutes
per 30-minutes driving.
REFERENCE
[1] J. Yuan, Y. Zheng, C. Zhang, W. Xie, G. Sun, H. Yan, and X. Xie,“T-Drive: Driving Directions Based on
Taxi Trajectories,” Proc.18th SIGSPATIAL Int’l Conf. Advances in Geographic InformationSystems (GIS),
2010.
[2] T. Hunter, R. Herring, P. Abbeel, and A. Bayen, “Path and TravelTime Inference from GPS Probe Vehicle
Data,” Proc. NeuralInformation Processing Systems (NIPS), 2009.
[3] Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang“Map-Matching for Low-Sampling-Rate GPS
Trajectories,” Proc.Int’l Conf. Advances in Geographic Information Systems (GIS),2009.
[4] J. Yuan, Y. Zheng, C. Zhang, and X. Xie, “An Interactive-VotingBased Map Matching Algorithm,” Proc.
Int’l Conf. Mobile DataManagement (MDM), 2010.
[5] E. Kanoulas, Y. Du, T. Xia, and D. Zhang, “Finding Fastest Pathson a Road Network with Speed Patterns,”
Proc. Int’l Conf. DataEng. (ICDE), 2006.
[6] A. Orda and R. Rom, “Shortest-Path and Minimum-DelayAlgorithms in Networks with Time-Dependent
Edge-Length,”J. ACM, vol. 37, no. 3, pp. 607-625, 1990.
[7] N. Leibowitz, B. Baum, G. Enden, and A. Karniel, “TheExponential Learning Equation as a Function of
Successful TrialsResults in Sigmoid Performance,” J. Math. Psychology, vol. 54,
no. 3, pp. 338-340, 2010.
[8] B.C. Dean, “Continuous-Time Dynamic Shortest Path Algorithms,”master’s thesis, MIT, 1999.