How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing android project free download | How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing android project
How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing
ABSTRACT:
The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers’ participatory sensing. With commodity mobile phones, the bus passengers’ surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS-enabled location information, we resort to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android-based mobile phones and comprehensively experiment with the NTU campus shuttle buses as well as Singapore public buses over a 7-week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions. We further adopt our system and conduct quick trial experiments with London bus system for 4 days, which suggests the easy deployment of our system and promising system performance across cities. At the same time, the proposed solution is more generally available and energy friendly.
EXISTING SYSTEM:
When travelling with buses, the travellers usually want to know the accurate arrival time of the bus. Excessively long waiting time at bus stops may drive away the anxious travellers and make them reluctant to take buses. Nowadays, most bus operating companies have been providing their timetables on the web freely available for the travellers. The bus timetables, however, only provide very limited information (e.g., operating hours, time intervals, etc.), which are typically not timely updated. Other than those official timetables, many public services (e.g., Google Maps) are provided for travelers. Although such services offer useful information, they are far from satisfactory to the bus travelers
DISADVANTAGES OF EXISTING SYSTEM:
1) The schedule of a bus may be delayed due to many unpredictable factors (e.g., traffic conditions, harsh weather situation, etc)
2) However, usually requires the cooperation of the bus operating companies (e.g., installing special location tracking devices on the buses), and incurs substantial cost.
PROPOSED SYSTEM:
In this paper, we present a novel bus arrival time prediction system based on crowd-participatory sensing. We interviewed bus passengers on acquiring the bus arrival time. Most passengers indicate that they want to instantly track the arrival time of the next buses and they are willing to contribute their location information on buses to help to establish a system to estimate the arrival time at various bus stops for the community. This motivates us to design a crowd-participated service to bridge those who want to know bus arrival time (querying users) to those who are on the bus and able to share the instant bus route information (sharing users). To achieve such a goal, we let the bus passengers themselves cooperatively sense the bus route information using commodity mobile phones. In particular, the sharing passengers may anonymously upload their sensing data collected on buses to a processing server, which intelligently processes the data and distributes useful information to those querying users.
ADVANTAGES OF PROPOSED SYSTEM:
1) Through directly bridging the sharing and querying users in the participatory framework, we build our system independent of the bus operating companies or other third-party service providers.
2) Based on the commodity mobile phones, our system obviates the need for special hardware or extra vehicle devices.
3) Automatically detecting ambient environments and generating bus route related reports, our approach does not require the explicit human inputs from the participants
SYSTEM ARCHITECTURE:
MODULES:
1. System Overview
1.1. Querying User
1.2. Sharing User
1.3. Backend server
2. Pre-Processing Cell Tower Data
3. Bus Detection
4. Bus Classification
5. Arrival Time Prediction
MODULE DESCRIPTION:
1.1. Querying User:
A querying user queries 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.
1.2. Sharing user:
The sharing user on the other hand contributes the mobile phone sensing information to the system. After a sharing user gets on a bus, the data colllection module starts. The collected data is transmitted to the server. 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. The mobile phone periodically samples the surrounding environment and extracts identifiable features of transit buses. Once the mobile phone confirms it is on the bus, it starts sampling sequences and sends the sequences to the backend server. Ideally, the mobile phone of the sharing user automatically performs the data collection and transmission without the manual input from the sharing user.
1.3. Backend server:
We shift most of the computation burden to the backend server where the uploaded information from sharing users is processed and the requests from querying users are addressed. 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. We 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 mainly war-drive the bus routes and record the sequences of observed cell tower IDs, which significantly reduces the initial construction overhead. The backend server 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.
Pre-Processing Data:
The backend server needs to maintain a database that stores sequences of cell IDs that are experienced along different bus routes. War driving along one bus route, the mobile phone normally captures several cell tower signals at one time, and connects to the cell tower with the strongest signal strength. We subsequently record the each sub-route. Such a sequence of cell ID sets identifies a bus route in our database. By war-driving along different bus routes, we can easily construct a database of cell sequences associated to particular bus routes.
Bus Detection:
During the on-line processing stage, we use the mobile phones of sharing passengers on the bus to record the cell tower sequences and transmit the data to the backend server. As aforementioned, the mobile phone should intelligently detect whether it is on a public transit bus or not and collect the data only when the mobile phone is on a bus. Some works study the problem of activity recognition and context awareness using various sensors. Such approaches, however, cannot be used to distinguish different transport modes (e.g., public transit buses and non-public buses). In this section, we explore multi-sensing resources to detect the bus environment and distinguish it from other transport modes. We seek a lightweight detection approach in terms of both energy consumption and computation complexity.
Bus Classification:
When a sharing user gets on the bus, the mobile phone samples a sequence of cell 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. The statuses of the bus routes are then updated accordingly.
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. The server needs to estimate the time for the bus to travel from its current location to the queried bus stop. Suppose that the sharing user on the bus is in the cover-age of cell, the backend server estimates its arrival time at the bus stop according to both historical data as well as the latest bus route status.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System : Pentium IV 2.4 GHz.
Ø Hard Disk : 40 GB.
Ø Floppy Drive : 1.44 Mb.
Ø Monitor : 15 VGA Colour.
Ø Mouse : Logitech.
Ø Ram : 512 Mb.
Ø MOBILE : ANDROID
SOFTWARE REQUIREMENTS:
Ø Operating system : Windows XP/7.
Ø Coding Language : Java 1.7 / PHP
Ø Tool Kit : Android 2.3 ABOVE
Ø IDE : Eclipse
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