Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Transport poverty meets the digital divide : accessibility and connectivity in rural communities

Peer reviewedPublisher PD

Predicting phase durations of traffic lights using live Open Traffic Lights data

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Developing Bus Tracking System that predicts the arrival times of public buses at Universiti Teknologi PETRONAS (UTP) bus stops.

This dissertation shows about the project details in developing a Bus Tracking System (BTS) by predicting the time arrival of a bus using simulation data. The main elements in this report consist of introduction, literature review, methodology and results and discussion. The project is developed by using research activities, findings analysis and developing prototype. All the basic elements in the system such as Automatic Vehicle Location (AVL) System, Global Positioning System (GPS) and the Short Messaging Services (SMS) Server have been analyzed. Based on the research, these elements are discussed further to relate the usage with the system. This project is developed based on the advancement of Information Technology (IT) system today that has enabled people to have an intelligent transport system which assists them in traveling. Travel Information System (TIS) has been used widely in certain countries such as Singapore and United States of America (Stephanie Yap, 2003). Basically, the system works by locating and tracking any transportation using GPS technology installed in that transport. By collecting all the dataneeded such as speed and location, an algorithm can be developed to predict when the transport will reach a certain checkpoint. The combination usage of the algorithm, AVL, GPS and SMS server, the information regarding the time arrival of the transport can easily being retrieve by users. In this project, an algorithm will be developed to predict the amount of time needed for the incoming public bus to arrive, based on the current location of the bus and the current time. Public bus users, especially students in UTP will be able to know when the bus is coming by using their cell phones. The usage will allowthem to access the system that manipulates this algorithm. The scope of the project includes developing an algorithm, the research and analysis of GPS and the use of SMS. Basically, the methodology that has been chosen is the combination of waterfall and spiral methodology. The development of the BTS will hopefully help to increase bus ridership, improve accuracy, timeliness and availability of public buses at UTP

LocateMyBus: IoT-Driven Smart Bus Transit

Uncertainty of traffic in cities makes it difficult for metropolitan buses to adhere to predetermined schedules, making it strenuous for commuters to plan travel reliably. The proposed LocateMyBus system leverages Internet of Things(IoT) set-ups at bus stops and buses, and Machine Learning(ML) to assuage this uncertainty by allowing commuters to track live-runningstatus of buses, disseminate tentative and live-status to commuters through Public Announcement(PA) systems at bus-stops and a web-application interface. The schedule prediction module provides a tentative schedule of buses with stop-wise arrival times estimated using ML based on historic and real-time route data. Arrival times of two bus-routes in the Massachusetts Bay Area were collected for a period of four months by periodically querying its real-time General Transit Feed Systems(GTFS). This dataset was used to train and validate the proposed ML methods. The IoT system was modeled on Proteus, and validated with a miniature prototype. LocateMyBus is proposed as a step forward toward minimal intervention algorithmic set-ups to ease the uncertainty associated with bus commute in cities. It enables commuters to track live running status and avail ML-predicted tentative schedules. Furthermore, it eradicates the computation requirements of GPS-based systems, whilst ensuring stop-level tracking granularity. LocateMyBus\u27s ability to log bus arrival times at each stop paves the way to building real-time GTFSs

A systematic approach for improving predicted arrival time using historical data in absence of schedule reliability

Public transit operations are susceptible to change, both in traffic flow and other conditions that could affect operations such as bridge openings, road floods, and torrential downpours. Traditionally, riders at waiting stops are not informed of the transit vehicles’ status along the route. Although it is normally not needed for daily transit operations, live location information is particularly useful in cases when vehicles are running behind schedule. This thesis introduces a method for gathering and analyzing historical location and telemetry data of public transit vehicles to better determine estimated arrival time for a vehicle on a closed-loop public transit pattern. The research creates a system for sending real-time locations of transit vehicles to riders through a wide array of mediums including web pages, computer programs, graphical information displays in public locations, mobile phone applications, mobile text messaging, and internet feeds. The system incorporates a weighted estimated arrival time for one route in the city, the University of North Carolina Wilmington campus loop shuttle route, which serves as a working demonstration of these concepts. The approach shows improvement over an arrival time estimate using only average speed