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 resolve 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 over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.

/pdf/demo-how-long-to-wait-predicting-bus-arrival-time-with-cml9hw1w3l.pdf

Demo: how long to wait?: predicting bus arrival time with mobile phone based participatory sensing

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.

/pdf/how-long-to-wait-predicting-bus-arrival-time-with-mobile-1ieoelv1nd.pdf

How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing

Discovering time-dependent shortest path on traffic graph for drivers towards green driving

The ability to obtain accurate predictions of bus arrival time on a real time basis is vital to both bus operations control and passenger information systems. Several studies have been devoted to this arrival time prediction problem in many countries; however, few resulted in completely satisfactory algorithms. This paper presents an effective method that can be used to predict the expected bus arrival time at individual bus stops along a service route. This method is a hybrid scheme that combines a neural network (NN) that infers decision rules from historical data with Kalman filter (KF) that fuses prediction calculations with current GPS measurements. The proposed algorithm relies on real-time location data and takes into account historical travel times as well as temporal and spatial variations of traffic conditions. A case study on a real bus route is conducted to evaluate the performance of the proposed algorithm in terms of prediction accuracy. The results indicate that the system is capable of achieving satisfactory performance and accuracy in predicting bus arrival times for Egyptian environments.

Online Bus Arrival Time Prediction Using Hybrid Neural Network and Kalman filter Techniques

https://people.eng.unimelb.edu.au/vkostakos/files/papers/pmc16.pdf

Cyclist-aware traffic lights through distributed smartphone sensing

This paper presents a statistical approach to predict the public bus arrival time based on traffic information management system. It considers a number of factors affecting bus travel time, such as departure time, work day, current bus location, number of links, number of intersections, passenger demand at each stop and traffic status of the urban network, etc. A linear model is given to describe the bus arrival time. The parameters of the model are trained by the historical bus arrival times. A prototype system is built to verify the practicability and efficiency of the approach. The approach has been proved relatively accurate and efficient by experiments.

Yuan Yu

Papers

Public bus arrival time prediction based on traffic information management system