This document discusses a system for predicting bus arrival times using mobile phone-based participatory sensing. The key points are:
1) The system avoids using GPS or other location services by instead mapping bus routes to sequences of nearby cellular towers detected by phones. It collects these sequences from multiple phone users to classify buses and predict arrival times.
2) Bus detection on phones uses audio and accelerometer sensors to identify a bus's beep sound and detect its bumpier ride compared to trains.
3) The system was implemented and evaluated in Singapore and London, showing it could predict arrival times within 40-60 seconds on average without special hardware or cooperation from transit agencies.
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system.
This application uses the GPS function.
This application at a specific pickup point will send the current location of the bus to students when they request.
This app generate predictions of bus arrivals at stops along the route.
This application uses a variety of technologies to track the locations of buses in real time.
Intelligent Bus Tracking System Using AndroidAM Publications
Intelligent bus tracking system using android is an application that tracks a bus and collects the distance to each station. Tracking system involves the installation of an electronic device in a bus, with an installed Android App on any smart phone to enable a user to track the bus location. There are two applications one for server and other for the client. The user can get flexibility of planning travel using the app, to decide on which bus to take or when to catch the bus. The waiting time of the user can be reduced. By using this application user get the information about buses, bus numbers, bus route, bus arrival and bus delay timing information etc [1]. It provides information about which bus coming to the stop. By the presently existing system we are dealing with three terminals, a device on bus, a device at bus stop and a device on the master bus stand so as to keep the track on the all city busses. By employing this tracking system the arrival of the bus is detected near the bus stop and also can be seen on the PC at the master bus stop. GSM modem can also transmit the bus information to the registered mobile numbers. Hence, we can control the bus traffic and can detect the arrival of particular bus at the bus stop.
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system.
This application uses the GPS function.
This application at a specific pickup point will send the current location of the bus to students when they request.
This app generate predictions of bus arrivals at stops along the route.
This application uses a variety of technologies to track the locations of buses in real time.
Intelligent Bus Tracking System Using AndroidAM Publications
Intelligent bus tracking system using android is an application that tracks a bus and collects the distance to each station. Tracking system involves the installation of an electronic device in a bus, with an installed Android App on any smart phone to enable a user to track the bus location. There are two applications one for server and other for the client. The user can get flexibility of planning travel using the app, to decide on which bus to take or when to catch the bus. The waiting time of the user can be reduced. By using this application user get the information about buses, bus numbers, bus route, bus arrival and bus delay timing information etc [1]. It provides information about which bus coming to the stop. By the presently existing system we are dealing with three terminals, a device on bus, a device at bus stop and a device on the master bus stand so as to keep the track on the all city busses. By employing this tracking system the arrival of the bus is detected near the bus stop and also can be seen on the PC at the master bus stop. GSM modem can also transmit the bus information to the registered mobile numbers. Hence, we can control the bus traffic and can detect the arrival of particular bus at the bus stop.
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.
An IoT based school bus tracking and monitoring system developed as part of my course work for my B.Tech in Computer Science from Cochin University of Science & Technology
For college going students, employees of a company or for a common man, bus is the most comprehensive and affordable mode of transport. The use of bus for transport reduces private vehicle usage and thus reduces fuel consumption. It also curbs traffic congestion. The user usually wants to know the accurate arrival time of the bus. Long time waiting at the bus stops discourage the use of buses. Also many unpredictable factors delay the schedule of a bus like harsh weather situation, traffic conditions etc. Towards this aim of reducing this problem, we are proposing a project which will assist the bus travellers in predicting bus timings. The system described uses DriverSideApp, ClientSideApp and a server.
In this project, we propose an innovative method for predicting the bus information. No specific device is required for this purpose. Our sole objective is to build a application that will help student to access the current location of the bus. Moving forward our application focus on to providing them more convenience with bus schedules, bus location information so that they may not get delayed. . Further, the recent advent and popularity of Android technology motivates us to create an Android application for the same.
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system. This application uses the GPS function. This application at a specific pickup point will send the current location of the bus to students when they request. This app generate predictions of bus arrivals at stops along the route. This application uses a variety of technologies to track the locations of buses in real time
The Presentation provides insight of actual implementation of the bus cluster scheme in Delhi. The government launched the scheme in 2008 . However, the project saw the light of the day in May 2011...it is expanding rapidly...please share your feedback at info@valoriserconsultants.com.
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.
An IoT based school bus tracking and monitoring system developed as part of my course work for my B.Tech in Computer Science from Cochin University of Science & Technology
For college going students, employees of a company or for a common man, bus is the most comprehensive and affordable mode of transport. The use of bus for transport reduces private vehicle usage and thus reduces fuel consumption. It also curbs traffic congestion. The user usually wants to know the accurate arrival time of the bus. Long time waiting at the bus stops discourage the use of buses. Also many unpredictable factors delay the schedule of a bus like harsh weather situation, traffic conditions etc. Towards this aim of reducing this problem, we are proposing a project which will assist the bus travellers in predicting bus timings. The system described uses DriverSideApp, ClientSideApp and a server.
In this project, we propose an innovative method for predicting the bus information. No specific device is required for this purpose. Our sole objective is to build a application that will help student to access the current location of the bus. Moving forward our application focus on to providing them more convenience with bus schedules, bus location information so that they may not get delayed. . Further, the recent advent and popularity of Android technology motivates us to create an Android application for the same.
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system. This application uses the GPS function. This application at a specific pickup point will send the current location of the bus to students when they request. This app generate predictions of bus arrivals at stops along the route. This application uses a variety of technologies to track the locations of buses in real time
The Presentation provides insight of actual implementation of the bus cluster scheme in Delhi. The government launched the scheme in 2008 . However, the project saw the light of the day in May 2011...it is expanding rapidly...please share your feedback at info@valoriserconsultants.com.
SmartTask Attend is a simple to use proof of attendance solution for Community Carers.
By using the latest Near Field Communication (NFC) technology installed on a simple mobile phone carers can simply swipe their phones over NFC tags/labels unique to service users homes to record arrival and departure
Supervisors have the ability to see arrivals and departures for each caregiver as they happen in real-time
Free trial now available http://contact.skillweb.co.uk/introducing-smarttask-attend/
Ai Workshop Slides Used By John Loty In 2008.John Loty
These slides together with a workbook were used in a 2 day Introductory Workshop on Appreciative Inquiry and how AI is being used for change management and organisational development.
There is perchance no area under agriculture which does not benefit from the ubiquitous influence of mobile technology. The ease with which data can be monitored, stored and shared at anytime, anywhere in the world via a smartphone or tablet has taken precision agriculture to another level altogether!
Having a connected farm - with a flawless sync amongst on-site workers, the office, and handheld computers - is an option that is gaining paramount importance. Mobile technology is increasingly becoming a hot favorite for anyone and everyone involved with farming.
The use of such technology has enabled farmers worldwide to not only procure information on news, markets, pricing, as well as the weather, but also maximize production, and reduce dependencies.
Our expert teams at Extentia have successfully been able to club mobility with agriculture and have helped clients achieve high productivity by providing an appropriate solution to their needs.
Let us know how Extentia can help you! We can work with you to take your iPhone, iPad, Android, and Windows Phone application ideas and concepts straight to the application store. Reach out to us at http://www.extentia.com/contact-us.
Agricultural productivity and mobile for agriculture (mAgric) innovations in ...John Kieti
Agricultural productivity by measure of value added per worker has stagnated at below $400 in East Africa for decades. Many mobile innovations have been created in the last half decade directly or indirectly targeting improvement of this productivity. The results have not yet reached the scale of the runaway success seen in the use of mobile for financial inclusion.
These are my thoughts as my studies drag on. The slides were presented at the monthly entrepreneurial forum held at iHub for 27th September 2014 at the request of Dr. Bitange Ndemo
Artificial Intelligence in Computer and Video GamesLuke Dicken
This lecture was given at the April meeting of the Glasgow branch of the British Computer Society on 12th April 2010. The lecture was supposed to be given by Dr. Darryl Charles, who fell ill a couple of days before the event, and I was asked to take the lecture instead.
In the presentation I cover the basics of why AI and Games are well suited and give a brief discussion of different types of AI as I see it. I discuss briefly how AI fits into the context of the game in terms of execution.
The bulk of the talk presents case studies in the format of Commercial game -> Theoretical technique used -> Research project using this technique.
It should be noted that the section on Left4Dead was omitted from the lecture as it was presented at the time due to concerns about the length
This seminar slide will give you the ideas how much the ARTIFICIAL INTELLIGENCE is important gaming and what hard work should do for movements of other persons in a game.
Smart bus system pilot project (BUSKET)Minh S. Dao
BUSKET is a wireless system aimed at supporting the quality of bus transportation service in Brunei Darussalam. BUSKET exploits low-cost wireless technologies for the real-time estimation of the buses position throughout the road network and to communicate to the users when one bus approaches the bus stop. The goal of BUSKET is to make smarter the bus transportation system and to improve the user awareness about the status of the bus service. From the user perspective, BUSKET will reduce the waiting time of citizens at the bus stops, while from the bus management viewpoint will enable a more centralized and pervasive control of the whole transportation network. BUSKET will use commercial smartphones (already diffused among citizens) as well as low-cost wireless GPS sensors to enable the acquisition of position information and to communicate with the users.
BUSKET is the joint-research between UTB, ELEDIA, and AITI
Real time tracking based on gsm and ir module for running traineSAT Journals
Abstract
The manual process of filling and submission of the Outdoor Duty Forms is very common today. Also there are many alternatives available for automated process of remote attendance and data analysis. But there is no solution yet that provides the integrated solution for automation of all these process. We therefore came with the concept of an application that provides these services. This application is about automated process of Outdoor duty forms, remote attendance, submission of sales and marketing data and its analysis. The application will be designed for corporate usage. A database system will be maintained regarding the submission of data and this data will be used for further analysis and intelligent results.
Keywords: ODForms (Outdoor Duty Forms), BI (Business Intelligence), RFID (Radio Frequency Identification), RTO (Regional Transport Office), KPI (Key Point Indicators).
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
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Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
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https://www.rttsweb.com/jmeter-integration-webinar
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
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How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing
1. How Long to Wait?
Predicting Bus Arrival Time With Mobile
Phone Based Participatory Sensing
IEEE TRANSACTIONS ON MOBILE
COMPUTING, VOL. 13, NO. 6, JUNE 2014
Speaker: Caroline
2. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
2
3. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
3
4. Advantage in Bus System
• Public transport, especially the bus transport, has
been well developed in many parts of the world.
• Reduce the private car usage and fuel consumption.
• Alleviate traffic congestion
• Serves over 3.3 million
bus rides every day with
around 5 million residents.
4
5. • When traveling with buses, the travelers usually
want to know the accurate arrival time of the bus.
• Excessively long waiting time at bus stops may drive
away the anxious travelers and make them
reluctant to take buses.
5
Problem in Bus System
6. Timetable at web
• Most bus operating companies have been providing
their timetables on the web freely available for the
travelers.
• Only provide very limited information
• Typically not timely updated
6
7. Public services
• There are many public services (e.g., Google Maps)
are provided for travelers.
• But they are still far from satisfactory to the bus travelers.
• The schedule of a bus may be delayed due to many
unpredictable factors.
7
8. Real-time bus arrival info
• The accurate arrival time of next bus will allow
travelers to take alternative transport choices, and
improve their experience.
• Many commercial bus information providers offer
the real-time bus arrival time to the public.
• Requires cooperation of bus operating companies
• installing special location tracking devices on the buses
• Incurs substantial cost.
8
9. Phone-based transit tracking
• EasyTracker [13] presents an automatic system for
arrival time prediction using GPS traces collected by
in-vehicle smartphones.
• Thiagarajan et al. [24] present a solution by utilizing
accelerometer and GPS modules.
• The difference is our system use cell tower
sequence information, and no explicit location
services are invoked so as to reduce the overhead.
9
10. Drawback of using GPS
• A good number of mobile phones are still shipped
without GPS modules [26].
• Besides, GPS module consumes substantial amount of
energy, significantly reducing the lifetime of mobile
phones [26].
• May perform poorly when they are placed without line-of-
sight paths to GPS satellites [10].
10
11. Absolute location is not necessary
• In this system design, they avoid the need for
accurate bus localization.
• Since the buses travel on certain bus routes (1D
routes on 2D space) ,the knowledge of
• current position (1D knowledge)
• average velocity
are enough to predict its arrival time at a bus stop.
11
12. Absolute location is not necessary
• The bus is currently at bus stop 1, and a user wants
to know its arrival time at bus stop 6.
• Accurate prediction of the arrival time requires :
• Distance between bus stop 1 and 6 along the 1D bus
route (but not on the 2D map)
• Average velocity of the bus.
12
13. Use Cellular Tower Number
• Instead of pursuing the accurate 2D physical
locations,
• Logically map the bus routes to a space featured by
sequences of nearby cellular towers.
• They classify and track the bus statuses in such a
logical space so as to predict the bus arrival time.
• The bus route will be map to the
cell tower sequence
{1, 2, 4, 7, 8, 4, 5, 9, 6}
13
14. Contribution
• Present a novel bus arrival time prediction system
based on crowd-participatory sensing.
• Independent of the bus operating companies or
other third-party service providers.
• Allowing easy and inexpensive adoption of the proposed
approach over other region
14
15. Contribution
• Does not need special hardware or extra vehicle
devices
• Can substantially reduces the cost and more energy-friendly.
• Does not require the explicit human inputs from
the participants
• Can facilitates the involvement of participatory parties.
15
16. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
16
18. • Sharing users :
• Contributes the mobile phone
sensing information to the system.
• First detect whether the current user
is on a bus or not.
• Then the data collection module
starts to collect a sequence of nearby
cell tower IDs.
• Ideally, the mobile phone will
automatically performs the data
collection and transmission.
18
Three major components (1/3)
19. Three major components (2/3)
• Querying users :
• Query the bus arrival time by sending the request to the
backend server.
• The querying user indicates the interest bus route and
bus stop to receive the predicted bus arrival time.
19
20. Three major components (3/3)
• Backend server : Pre-processing
• Survey the corresponding bus routes offline
• Construct a basic database associates particular bus routes
to cell tower sequence signatures.
• We can war-drive the bus routes and record the
sequences of observed cell tower IDs.
20
21. Pre-Processing Cell Tower Data
• The connected cell tower might be different from
time to time due to varying cell tower signal
strength.
• Instead of using the associated cell tower, they
record a set of cell tower IDs that the mobile phone
can detect.
21
22. Pre-Processing Cell Tower Data
22
Cell tower coverage
Connection at position A
Connection at position B
23. Top-3 strongest cell towers
• Mobile phone is more likely to connect to the cell
tower with first and second strongest signal
strength.
• During 7-week experiments, they consistently
observe that mobile phones almost always connect
to the top-3 strongest cell towers.
• Choose the set of the top-3 strongest cell towers as
the signature for route segments.
23
25. Three major components (3/3)
• Backend server : Online processing
• Processes the cell tower sequences from sharing.
• Classifies the uploaded bus routes primarily with the
reported cell tower sequence information.
• The bus arrival time on various bus stops is derived
based on the current bus route statuses.
25
26. The challenges - Bus detection
• Is the sharing user on the bus?
• We need to first let their mobile phones accurately detect
whether or not the current user is on a bus.
• Without accurate bus detection,
• mobile phones may cause unnecessary energy
consumption
• System may collect irrelevant information, leading to
inaccuracy in prediction results.
26
27. Bus Detection - Audio Detection
• On a public bus in Singapore, several card readers
are deployed for collecting the fees.
• When a passenger taps the transit card on the
reader, the reader will send a short beep audio
response to indicate the successful payment.
• Choose to let the mobile phone detect the beep
audio response of the card reader.
27
28. Bus Detection - Audio Detection
28
• Bottom figure plots the raw audio signal in the time
domain.
• Figure (b) shows when convert the time domain
signal to the frequency domain through 512pt FFT.
29. Bus Detection - Audio Detection
• We can lower down the audio sampling rate of the
mobile phone to 8kHz (8000 samples/s).
• 128pt FFT suffices to detect the IC card reader on
the bus with tractable computation complexity.
• Sliding window w=32
• Threshold ε = 3σ
29
30. Bus Detection - Audio Detection
• We can lower down the audio sampling rate of the
mobile phone to 8kHz (8000 samples/s).
• 128pt FFT suffices to detect the IC card reader on
the bus with tractable computation complexity.
• Sliding window w=32
• Threshold ε = 3σ
30
31. Bus Detection - Accelerometer Detection
• The most possible false positives are from MRT
system, because the IC card systems are also
deployed in rapid train stations in the entrances.
• Use the accelerometer sensor on the mobile phone
to reduce such false detection.
• Rapid trains are moving at
relatively stable speeds with
few abrupt stops or sharp turns.
31
32. Bus Detection - Accelerometer Detection
• Collect the accelerometer data at a moderate
sampling rate of 20Hz.
• Made orientation-independent by computing the
L2-norm (or magnitude) of the raw data [23].
• The variance on the bus is significantly larger than
that on the train.
• Threshold ε = 3σ
32
33. The challenges - Bus classification
• When a sharing user gets on the bus, the mobile
phone samples a sequence of cell tower IDs and
reports the information to the backend server.
• The backend server aggregates the inputs from
massive mobile phones and has to classify the
inputs into different bus routes.
33
A
B
C
D
34. Bus classification - Cell Tower Sequence Matching
• We match the received cell tower sequences to
those signature sequences store in the database.
34
35. • Design a top-k cell tower sequence matching scheme
by modifying the Smith-Waterman algorithm [28].
• Top-k set S = {c1, c2, . . . , ck} ordered by signal trength
(si ≥ sj, 1 ≤ i ≤ j ≤ k), where ci and si denote cell tower I
and its signal strength
• Sequpload = u1,u2, . . . ,um where m is the seq length
• Seqdatabase = S1,S2, . . . ,Sn where n is the set seq length
35
Bus classification - Cell Tower Sequence Matching
36. • If ui = cw ∈ Sj, ui and Sj are considered matching with
each other, and mismatching otherwise.
• We assign a score f (sw) for a match, where f (sw) is
a positive non-decreasing scoring function and w is
the rank of signal strength.
• In practice, f (sw) = 0.5w−1
• The penalty cost for mismatches is set −0.5 which
balances the robustness and accuracy in practice.
36
Bus classification - Cell Tower Sequence Matching
37. • Uploaded cell tower sequence is Sequpload = { 1, 8,
10, 15, 16 }
• After running the sequence matching algorithm
across all bus route sequences in the database,
• The backend server selects the bus route with the
highest score.
37
Bus classification - Cell Tower Sequence Matching
38. • One problem of the cell tower sequence matching
is some bus routes may overlap with each other.
• Over 90% of the overlapped route are shorter than 1400
meters, and over 80% are less than 1000 meters
• The coverage range of each cell tower in urban area
is about 300-900 meters,
• Set the ε of cell tower
sequence length to 7
38
Bus classification - Cell Tower Sequence Matching
39. • Vary the length cell tower sequence from 2 to 9
• The matching accuracy is low when the cell tower
sequence length is small (e.g, <4)
• When the cell tower sequence length reaches 6,
the accuracy increases substantially to around 90%.
39
Bus classification - Cell Tower Sequence Matching
40. The challenges - Cell Tower Sequence Concatenation
• The length of the cell tower sequence obtained by
a single sharing user, may be insufficient for
accurate bus route classification.
• Concatenate several cell tower sequences of
different sharing users on the same bus to form a
longer cell tower sequence.
40
41. Cell Tower Sequence Concatenation
• A simple way of concatenating the cell tower
sequences is to let the mobile phones of sharing
passengers locally communicate with each other
(e.g., over Bluetooth) [20].
• But this approach, mandates location exposure
among sharing passengers and might raise privacy
concerns.
41
42. Cell Tower Sequence Concatenation
• Reuse audio signal information to detect whether
the sharing passengers are on the same.
• The frequency domain of the signals are highly
cross-correlated.
42
• The backend server detects
and groups the sharing
passengers on the same bus
by comparing both cell tower
sequences and the time
intervals of the beep signals.
43. Arrival Time Prediction
• After the cell tower sequence matching, the
backend server classifies the uploaded information
according to different bus routes.
• When receiving the request from querying users
the backend server looks up the latest bus route
status, and calculates the arrival time at the
particular bus stop.
43
44. Arrival Time Prediction
• The server needs to estimate the time to travel
from its current location to the queried bus stop.
• Backend server estimates its arrival time at the bus
stop according to both historical data and the latest
bus route status.
44
45. Arrival Time Prediction
• The dwelling time of the bus at the current cell
denoted as t2.
• The traveling time of the bus in the cell that the bus
stop is located denoted as tbs.
• The historical dwelling time of the bus at cell 3 is
denoted as T3.
• T = T2 − t2 + T3 + tbs.
45
46. Arrival Time Prediction
• The dwelling time in cell i as Ti, 1 ≤ i ≤ n,
• The bus’s current cell number as k
• The queried bus stop’s cell number as q
• The server can estimate the arrival time of the bus
as this equation:
46
47. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
47
48. Experimental Methodology
• Implement the mobile phone applications with the
Android platform using Samsung Galaxy S2 i9100
and HTC Desire.
• Both types of mobile phones are equipped with
accelerometers and support 16-bit 44.1kHz audio
signal sampling from microphones.
• Implement the backend server in Java running on
the DELL Precision T3500 workstation with 4GB
memory and Intel Xeon W3540 processor.
48
49. Experimental Methodology
• Experiment on both campus shuttle buses and
public transport buses (SBS Transit bus service in
Singapore).
49
50. Audio Detection Accuracy
• Collect more than 200 beep signals on different
public transit buses.
• Set the audio sampling rate to be 8kHz, and use
128-pt FFT to detect the IC card reader.
50
51. Bus vs. MRT Train
• Evaluate the accelerometer based bus detection
method that is used to distinguish the buses from
the MRT trains.
51
52. Bus Classification
• 85% of Route A is overlapped with Route C, but
they travel in the opposite directions.
• Route D has large portion overlapped with Route A
and C, and buses travel in the same direction
• Buses along Route D might be misclassified to Route A or
Route C.
52
53. Arrival Time Prediction
• The median prediction errors vary approximately
from 40s (Bus B) to 60s (Bus D).
• The average estimation error increases as the
length of bus route increases.
53
54. Arrival Time Prediction
• The public buses (LTA) are periodically tracked with
on-bus localization devices and respond to the
queries for the bus information.
54
55. System Overhead - Mobile phone
• The major computational complexity is attributed
to performing FFT on mobile phones which is O(n
log n).
• The power consumption is use for continuously
sampling microphone, accelerometer, GPS, and
cellular signals.
55
56. System Overhead - Backend server
• The computation overhead of backend server is mainly
bounded by the bus classification algorithm
• Uploaded cell tower sequence length l,
• Cell tower set sequence length k,
• Number of cell tower set sequences in the database N.
• The computation complexity of sequence matching
using dynamic programming is O(lk)
• Need to compare with N candidate sequences in
database the overall computation complexity is O(lkN).
56
57. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
57
58. London bus system
• London Buses Services Limited (London Buses)
manages one of the largest bus networks in the
world.
• Oyster cards [4] are widely used for paying the
transit fees on London buses.
58
60. Bus Detection
• The beep from London buses is a single frequency
audio signal of 2.4kHz unique frequency.
• Record the beep audio signal at 44.1kHz sampling
rate and extract the frequencies using 512pt FFT.
60
61. Bus Classification
• There are many overlapped route segments
between the 5 bus routes.
• Use different lengths of cell tower sequences to
perform the bus classification.
61
62. Bus Classification
• The classification accuracy increases as the cell
tower sequence length grows.
• The cell tower sequence collected from the
overlapped bus route segments contributes less to
those collected from non-overlapped segments.
62
63. Bus Classification
• The route 27 has the shortest overlapped route
segments while route 23 has the longest
• Route 27 has highest classification accuracy of 94%
• Route 23 has lowest classification accuracy of 82%
63
64. Bus Arrival Time Prediction
• The backend server estimates the bus location with
the uploaded cell tower sequences and predicts the
bus arrival time based on the cell tower sequence
stored in the database.
64
65. Trial in London
• The overall prediction error of bus arrival time in
London is higher than that in Singapore mainly due
to the following reasons :
• The lengths of the experimental bus routes in London are
much longer than in Singapore, which brings more
unpredictable factors influencing the bus operation.
65
London
Singapore
66. Trial in London
• Second, the time duration between two adjacent
buses of some bus routes in London is much longer
than that in Singapore,
• usually results in far away buses from the querying user.
• Third, the traffic conditions in London are much
more complicated than in Singapore.
• Many unpredictable factors like traffic jam, adaptive
traffic lights may affect the system performance.
66
67. Outline
• Introduction & Related Works
• System Design
• Implement & Evaluation in Singapore
• Implement & Evaluation in London
• Conclusion & Future Work
67
68. Conclusion
• Present a crowd-participated bus arrival time
prediction system by using inexpensive and widely
available cellular signals.
• provides cost-efficient solutions to the problem
• provides a flexible framework for participatory
contribution of the community
• The only requirement of this system is there exist a
backend server and an IC card based bus system.
68
69. Future work
• How to encourage more participants to bootstrap
the system ?
• At the initial stage, they may encourage some
specific passengers (like the bus drivers) to install
the mobile phone clients.
69
Editor's Notes
Hi everyone, I’m today’s first speaker Caroline
The paper I want to present is How Long to Wait?
Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing.
It comes from IEEE Transactions on Mobile Computing at June 2014
This is the outline:
First is introduction, and the related work.
And then I will explain the design and challenges of this system and describe the technical solutions.
And see the implement and evaluation at Singapore and London,
finally is the conclusion of this paper.
Ok Let’s begin at the introduction and related works
Public transport, especially the bus transport, has been well developed in many parts of the world.
The bus transport services reduce the private car usage and fuel consumption, and alleviate traffic congestion.
As one of the most well-known means of public transport,
The bus system serves over 3.3 million bus rides every day with around 5 million residents in Singapore.
When traveling with buses, the travelers usually want to know the accurate arrival time of the bus.
Excessively long waiting time at bus stops may drive away the anxious travelers and make them reluctant to take buses.
In order to fix this problem, there are some kinds of solution:
Most bus operating companies have been providing their timetables on the web freely available for the travelers.
However, the bus timetables only provide very limited information (like operating hours, time intervals)
which are typically not timely updated according to instant traffic conditions.
Other than those official timetables, there are many public services like Google Maps are provided for travelers.
Although such services offer useful information, they are far from satisfactory to the bus travelers.
For example, the schedule of a bus may be delayed due to many unpredictable factors (like traffic conditions).
The public services might not know the information in time.
The accurate arrival time of next bus will allow travelers to take alternative transport choices, and improve their experience.
So towards this aim, many commercial bus information providers offer the real-time bus arrival time to the public.
But providing such services, usually requires the cooperation of the bus operating companies
And need to install special location tracking devices like GPS on the buses
Which will come with a lot of cost.
There are some related work about this issue, one is Phone-based transit tracking.
And there are some kind of system has been proposed, the difference is our system use cell tower sequence information.
And no explicit location services are invoked so as to reduce the overhead.
The drawbacks of GPS include:
Although many GPS-enabled mobile phones are available on the market, a good number of mobile phones are still shipped without GPS modules [26].
Besides, GPS module consumes substantial amount of energy, it will significantly reduce the lifetime of mobile phones [26].
The mobile phones in vehicles may perform poorly when they are placed without line-of-sight paths to GPS satellites [10].
In this system design, they avoid the need for accurate bus localization.
Since the buses travel on certain bus routes, the knowledge of the current position and
the average velocity of the bus are enough to predict its arrival time at a bus stop.
For instance, say the bus is currently at bus stop 1, and a user wants to know its arrival time at bus stop 6.
Accurate prediction of the arrival time requires the distance between bus stop 1 and 6 along the 1D bus route
and the average velocity of the bus.
In general, the physical positions of the bus and the bus route on the 2D maps are not strictly necessary.
In this paper, instead of pursuing the accurate 2D physical locations
they logically map the bus routes to a space featured by sequences of nearby cellular towers.
They classify and track the bus statuses in such a logical space so as to predict the bus arrival time.
So the bus route will be map to the cell tower sequence {1, 2, 4, 7, 8, 4, 5, 9, 6}
So in this paper, they present a novel bus arrival time prediction system based on crowd-participatory sensing.
The system is independent of the bus operating companies or other third-party service providers.
Allowing easy and inexpensive adoption of the proposed approach over other region.
Second, based on the mobile phones, the system doesn’t need special hardware or extra vehicle devices
which can substantially reduces the cost and be more energy-friendly.
And this system can automatically detecting environments and generating bus route related reports
Means it doesn’t require the human inputs from the participants, which can facilitates the involvement of participatory parties.
Though the idea is intuitive, the design of such a system in practice entails substantial challenges.
In this section, I will describe the major components of the system design.
Explain the challenges and present several techniques to cope with them.
This is the architecture of bus prediction system. There are 3 major components.
Sharing users, Querying users and backend server
I will introduce them step by step
The sharing user contributes the mobile phone sensing information to the system.
Since the sharing user may travel with different means of transport, the mobile phone needs to first detect whether the current user is on a bus or not.
After a sharing user gets on a bus, the data collection module starts to collect a sequence of nearby cell tower IDs.
The collected data is transmitted to the server via cellular networks.
Ideally, the mobile phone of the sharing user will automatically performs the data collection and transmission without the manual input from the sharing user.
And querying users query the bus arrival time by sending the request to the backend server.
The querying user needs to indicates the interest bus route and bus stop to receive the predicted bus arrival time.
We shift most of the computation burden to the backend server.
There are two stages are involved in this component.
In order to bootstrap the system, we need to survey the corresponding bus routes in the offline pre-processing stage.
And the server will construct a basic database that associates particular bus routes to cell tower sequence signatures.
Since we do not require the absolute physical location reference
we can mainly war-drive the bus routes and record the sequences of observed cell tower IDs
This can significantly reduce the initial construction overhead.
In the experiments they found out that even if a passenger travels by the same place,
the connected cell tower might be different from time to time due to varying cell tower signal strength.
So instead of using the associated cell tower, they record a set of cell tower IDs that the mobile phone can detect.
To validate such a point, they do an initial experiment.
They measure the cell tower coverage at two positions A and B within the university campus, which are about 300 meters.
The right picture report the cell tower that the mobile phone can detect,
as well as their average signal strength and connection time at A and B, respectively
The position A and position B are both covered by 6 cell towers with divergent signal strength.
At position A the mobile phone is connected to the cell tower 5031 over 99% of the time,
while its signal strength remains consistently the strongest during the 10-hour measurement.
And the mobile phone at position B observes two cell towers with comparable signal strength.
They find that the mobile phone is more likely to connect to the cell tower with stronger signal strength,
and also may connect to the cell tower with the second strongest signal strength.
Nevertheless, during 7-week experiments, they consistently observe that mobile phones almost always connect to the top-3 strongest cell towers.
Therefore, in practice we choose the set of the top-3 strongest cell towers as the signature for route segments.
This figure shows the cell tower sequence collected on their campus bus traveling from school to a rapid train station off the campus.
The whole route of the bus is divided into several sub-route segments according to the change of the top-3 cell tower set.
They are marked in red and black in this figure.
For example, the mobile phone initially connects to cell tower 5031 in the first sub-route and the top-3 cell tower set is {5031, 5092, 11141}.
Later the mobile phone is handed over to cell tower 5032.
Such a sequence of cell tower ID sets identifies a bus route in the database.
By war-driving along different bus routes, it can easily construct a database of cell tower sequences associated to particular bus routes.
When the database is ready, the backend server can processes the cell tower sequences from sharing users in the online processing stage.
Receiving the uploaded information, the backend server first classifies the uploaded bus routes
primarily with the reported cell tower sequence information.
The bus arrival time on various bus stops is then derived based on the current bus route statuses.
Implementing such a participatory sensing based system, however, entails substantial challenges.
First is bus detection , since the sharing users may travel with diverse means of transport
we need to first let their mobile phones accurately detect whether or not the current user is on a bus.
Without accurate bus detection, mobile phones may collect irrelevant information,
leading to unnecessary energy consumption or even inaccuracy in prediction results.
Nowadays, IC cards are commonly used for paying transit fees in many areas
On a public bus in Singapore, several card readers are deployed for collecting the fees.
When a passenger taps the transit card on the reader, the reader will send a short beep audio response to indicate the successful payment.
In our system, we choose to let the mobile phone detect the beep audio response of the card reader,
since such distinct beeps are not widely used in other means of transportation such as non-public buses and taxis.
The bottom figure plots the raw audio signal in the time domain,
where the IC card reader starts beeping about from 11000th sample and lasts to 18000th sample.
After we convert the time domain signal to the frequency domain through 512pt Fast Fourier Transform (FFT),
we observe clear peaks at 1kHz and 3kHz frequency bands.
For comparison, figure (a) is no beep signal.
We find no peaks at 1kHz and 3kHz frequency bands.
With the knowledge of the frequency range of the beep signal sent by the IC card reader
we can lower down the audio sampling rate of the mobile phone to 8kHz (8000 samples/s) which is sufficient to capture the beep signals with maximum frequency of 3kHz.
And in practice 128pt FFT suffices to detect the IC card reader on the bus with tractable computation complexity on mobile phones.
We use the standard sliding window averaging technique with window size w = 32 samples to filter out the noises.
And use an threshold ε of three standard deviation (i.e., 99.7% confidence level of noise) to detect beep signals.
In this figure, when the IC card reader starts beeping, the signal strengths in both 1kHz and 3kHz frequency bands jump significantly and therefore can be detected.
The most possible false positives are from MRT system, because the IC card systems are also deployed in rapid train stations in the entrances.
We want to use the accelerometer sensor on the mobile phone to reduce such false detection.
The rapid trains are moving at relatively stable speeds with few abrupt stops or sharp turns.
We collect the accelerometer data at a moderate sampling rate of 20Hz.
The raw accelerometer readings are first made orientation-independen.
This figure plots the accelerometer readings on a rapid train and a public transit bus.
The bottom figure shows the variance of the accelerometer.
The variance on the bus is significantly larger than that on the train.
Therefore, we can distinguish the buses from the trains using the variance of accelerometer readings by setting a proper threshold.
After experiment, they select an threshold 0.03 to balance the false negative and false positive.
When a sharing user gets on the bus, the mobile phone samples a sequence of cell tower IDs and reports the information to the backend server.
The backend server aggregates the inputs from massive mobile phones and classifies the inputs into different bus routes.
We need to carefully classify the bus route information from the mixed reports of participatory users.
Without users’ manual indication, such automatic classification is more difficult.
We match the received cell tower sequences to those signature sequences store in the database.
This picture shows an example where a sharing passenger gets on the bus at location A.
The backend server will receive a cell tower sequence of {7, 8, 4, 5} when the sharing user reaches location B.
The cell tower sequence of the bus route stored in the database is 1, 2, 4, 7, 8, 4, 5, 9, 6,
then the sequence 7, 8, 4, 5 matches this bus route as a sub-segment.
In practical scenarios, the sequence matching problem becomes more complicated due to the varying cell tower signal strength.
In the matching process, we design a top-k cell tower sequence matching scheme by modifying the Smith-Waterman algorithm [28].
In a top-k set S = {c1, c2, . . . , ck} ordered by signal strength which si ≥ sj, 1 ≤ i ≤ j ≤ k, where ci and si denote cell tower I and its signal strength.
The uploaded cell tower sequence from a sharing user is denoted as Sequpload = {u1u2 . . . um)} where m is the sequence length.
We also denote a cell tower set sequence in database as Seqdatabase = {S1,S2 . . . Sn} where n is the set sequence length.
If ui = cw ∈ Sj, ui and Sj are considered matching with each other, and mismatching otherwise.
We assign a score f (sw) for a match, where f (sw) is a positive non-decreasing scoring function and w is the rank of signal strength.
In practice, f (sw) = 0.5w−1 as the scoring function according to the signal strength order in the set.
The penalty cost for mismatches is set to be −0.5 which balances the robustness and accuracy in practice.
Table 2 shows a cell tower set sequence matching instance.
In the example, the uploaded cell tower sequence is Sequpload = {1, 8, 10, 15, 16 }
And the cell tower ID set is shown in the first three rows sorted in decreasing order of the associated cell tower signal strength.
One problem of the cell tower sequence matching is some bus routes may overlap with each other.
So they survey 50 bus routes in Singapore and measure their overlapped road segments using Google Maps.
This picture shows the distribution of the lengths of overlapped road segments,
which suggests that over 90% of the overlapped route segments are shorter than 1400 meters, and over 80% are less than 1000 meters.
They set the threshold of cell tower sequence length to 7.
This picture shows the cell tower sequence matching accuracy in classifying the bus routes
We vary the length cell tower sequence from 2 to 9.
And find that the matching accuracy is low when the cell tower sequence length is small (e.g, <4) because of the problem of route overlap.
When the cell tower sequence length reaches 6, the accuracy increases substantially to around 90%.
In many practical scenarios, the length of the cell tower sequence obtained by a single sharing user, may be insufficient for accurate bus route classification.
So we can concatenate several cell tower sequences of different sharing users on the same bus to form a longer cell tower sequence.
In this example, both cell tower sequences of sharing user A and B are short,
But by concatenating the two cell tower sequences the backend server may obtain an long cell tower sequence which can be used for more accurate bus classification.
A simple way of concatenating the cell tower sequences is to let the mobile phones of sharing user locally communicate with each other (e.g., over Bluetooth) [20].
But this approach, mandates location exposure among sharing user and might raise privacy concerns.
So we shift such a job to the backend server.
Recall that the mobile phone needs to collect audio signals for bus detection.
Here, we reuse this information to detect whether the sharing passengers are on the same bus.
This picture shows an example of the audio signals captured by three different mobile phones on the same bus.
We depict the raw audio signals in Fig. 12(a), and corresponding frequency domain signals in Fig. 12(b)–(d).
We can see that in the frequency of domain the signals are highly cross-correlated and thus can be used to determine whether the phones are on the same bus.
Such beep interval information is reported along with the cell tower sequences to the backend server.
Receiving the uploaded sensing data from sharing user,
the backend server detects and groups the sharing user on the same bus by comparing both cell tower sequences and the time intervals of the beep signals.
After the cell tower sequence matching, the backend server classifies the uploaded information according to different bus routes.
When receiving the request from querying users the backend server looks up the latest bus route status, and calculates the arrival time at the particular bus stop.
This figure illustrates the calculation of bus arrival time prediction.
The server needs to estimate the time to travel from its current location to the queried bus stop.
Suppose that the sharing user on the bus is in the coverage of cell tower 2, the backend server estimates its arrival time at the bus stop according to both historical data and the latest bus route status.
The server first computes the dwelling time of the bus at the current cell (i.e., cell 2 in this example) denoted as t2.
The server also computes the traveling time of the bus in the cell that the bus stop is located denoted as tbs.
The arrival time of the bus at the queried stop is then estimated as this equation.
In generality, we denote the dwelling time in cell i as Ti, 1 ≤ i ≤ n,
the bus’s current cell number as k, and the queried bus stop’s cell number as q.
The server can estimate the arrival time of the bus as this equation
The server periodically updates the prediction time according to the latest route report from the sharing users and responds to querying users.
And now we can move on to the Implement & Evaluation
They implement the mobile phone applications with the Android platform using Samsung Galaxy S2 and HTC Desire.
Both types of mobile phones are equipped with accelerometers and support audio signal sampling from microphones.
And they implement the backend server in Java running on the DELL Precision.
They experiment on both campus shuttle buses and public transport
As this picture show, there are 4 shuttle bus routes in campus.
The bus route lengths span about from 3.8km to 5.8km with cell tower set sequence lengths varying from 9 to 22.
Table 3 gives the details of the bus routes.
Table 4 summarizes the shared route segments between each pair of bus routes
They collect more than 200 beep signals on different public transit buses.
And set the audio sampling rate to be 8kHz, and use 128-pt FFT to detect the IC card reader.
The distances between the IC card reader and the mobile phones vary from 1 meter to 7 meters.
We also consider the scenarios where mobile phones may be held in hand and inside bags.
We see that the detection rate is over 95% when mobile phones are close to the IC card reader, even when they are placed in bags.
As the distance increases further, the detection accuracy drops substantially.
Next we evaluate the accelerometer based bus detection method that is used to distinguish the buses from the MRT trains.
This figure plots the accuracy in detecting the buses.
We find that accelerometer based method can distinguish the buses from the MRT trains with an accuracy of over 90% on average.
//The main reason for falsely detecting public buses as MRT trains, is because it happens mostly when the buses are driving along long straight routes late during night time.
//The accelerometer readings may be relatively stable and very similar to those on the MRT trains.
Although 85% of Route A is overlapped with Route C, the bus classification accuracy for Bus A and C are still around 94%.
The main reason is that Bus A and C travel in the opposite directions.
Since Route D shares a large portion of overlapped road segments with A and C,
and buses travel in the same direction on the shared road segments, buses along Route D might be misclassified to A or C.
The median prediction errors vary about from 40s (Bus B) to 60s (Bus D).
The 90th percentiles are about from 75s (Bus B) to 115s (Bus D).
The average estimation error increases as the length of bus route increases.
We observe that as the bus moves closer to the querying user, the prediction error becomes smaller.
We experiment with commercial bus system as well.
For comparison, we also query the arrival time of public transit buses provided by LTA of Singapore.
The public buses are periodically tracked with on-bus localization devices and respond to the queries for the bus information.
Both prediction errors of LTA and our system are measured and plot in this Figure.
According to the results, the average prediction error of our system is about 80 seconds, while the prediction result of LTA is around 150 seconds.
Means our system is more accurate than GPS-based prediction.
The major computational complexity is attributed to performing Fast Fourier Transform on mobile phones which is O(n log n).
Current mobile phones can finish the computation task in real-time.
Table 5 illustrates the measured battery lifetime when the mobile phones continuously trigger different sensors.
We can see the battery lifetime is substantially reduced when the GPS module in the phone is enabled.
The computation overhead of backend server is mainly bounded by the bus classification algorithm :
The uploaded cell tower sequence length l, cell tower set sequence length k,
And number of cell tower set sequences in the database N.
The computation complexity of sequence matching using dynamic programming is O(lk)
Need to compare with N candidate sequences in database the overall computation complexity is O(lkN).
Since in practice both l and n are usually small, the computation complexity increases almost linearly.
This is another implement in London
In addition to Singapore, this paper also do trial experiments with London bus system.
London Buses Services Limited manages one of the largest bus networks in the world.
Oyster cards [4] are widely used for paying the transit fees on London buses.
As this figure, the experiment is with London bus route 27, 7, 36, 23 and 159.
They collect the audio beeps on buses and the cellular signals observed along the bus routes.
The bus route details are summarized in Table 6.
//The bus route lengths span from 11km to 20km and the cell tower sequence lengths along the bus routes are 48 to 67.
//The average bus speed is about 23km/h. The overlapped bus route segments are mainly in the city center.
Different from Singapore buses, the beep from London buses is a single frequency audio signal of 2.4kHz unique frequency.
So they record the beep audio signal from the card readers at a sampling rate of 44.1kHz and extract the frequencies using 512pt FFT.
The distances to the nearest card reader to collect beep audio is vary from 1m to 5m.
Some of the London buses has second floor.
For all the testing positions, we consider the scenarios where the mobile phone may be held in hand or placed inside bags.
Table 7 summarizes the average detection accuracy of single beeps.
The average detection accuracy (Table 7) is above 80% when the distance is within 4m.
The audio detection accuracy decreases as the distance increases.
In this picture we can see that, there are many overlapped route segments between the 5 bus routes.
So we use different lengths of cell tower sequences to perform the bus classification.
The classification accuracy increases as the cell tower sequence length grows.
When the length is longer than 8, the classification accuracy becomes higher than 90%.\
The cell tower sequence collected from the overlapped bus route segments
contributes less to those collected from non-overlapped segments.
Fig b plots the individual performance of bus classification of the 5 experimented bus routes.
The route 27 has the shortest overlapped route segments while route 23 has the longest
This cause route 27 has highest classification accuracy, and route 23 has lowest classification accuracy
But the overall bus classification accuracy is higher than 85% for all bus routes.
The backend server estimates the bus location with the uploaded cell tower sequences
and predicts the bus arrival time based on the cell tower sequence stored in the database.
The overall prediction error in this graph is calculated by comparing the predicted and the actual bus arrival time.
We can see that the prediction error of route 27 is the lowest and the route 36 is the highest.
For the 5 bus routes, the median prediction error varies from 65s (route 27) to 125s (route 36)
The overall prediction error of bus arrival time in London is higher than that in Singapore mainly due to the following reasons.
First, the lengths of the experimental bus routes in London are much longer than in Singapore, which brings more unpredictable factors influencing the bus operation.
Second, the time duration between two adjacent buses of some bus routes in London is much longer than that in Singapore, which usually results in far away buses from the querying user.
Third, the traffic conditions in London are much more complicated than in Singapore. Many unpredictable factors like traffic jam, adaptive traffic lights, pedestrians, etc., may affect the system performance.
And finally is the conclusion
In this paper, the author present a crowd-participated bus arrival time prediction system
by using inexpensive and widely available cellular signals, which provides cost-efficient solutions to the problem.
And being independent of any support from bus operator, the proposed scheme provides a flexible framework for participatory contribution of the community.
For a particular city, the only requirement of the system implementation is that there exist a backend server and an IC card based bus system.
Future work includes how to encourage more participants to bootstrap the system
because the number of sharing passengers affects the prediction accuracy in the system.
At the initial stage, they may encourage some specific passengers (like the bus drivers) to install the mobile phone clients.
The more precise they can predict, the more user will want to participate in.