A Survey of Bus Arrival Time Prediction Methods (original) (raw)

Bus Arrival Prediction – to Ensure Users not to Miss the Bus

International Journal of Electrical and Computer Engineering (IJECE)

Predicting arrival times of buses is a key challenge in the context of building intelligent public transportation systems. The bus arrival time is the primary information for providing passengers with an accurate information system that can reduce passenger waiting times. In this paper, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.

IJERT-Predicting Bus Arrival Time based on Traffic Modelling and Real-time Delay

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/predicting-bus-arrival-time-based-on-traffic-modelling-and-real-time-delay https://www.ijert.org/research/predicting-bus-arrival-time-based-on-traffic-modelling-and-real-time-delay-IJERTV4IS060512.pdf To know the arrival time of bus to a bus stop is the basic information every transit users want to know. Waiting for a long time at a bus stop discourages the people to rely upon public transports. In this paper, we present a system which can predict the arrival time of bus to a particular bus stop considering the real time parameters that affects the travel time of bus. With bus module, the real time parameters that affect travel time are continuously collected and used to predict the bus arrival time at various bus stops. A mobile application is developed in order to assist the querying users in getting the arrival time of bus to a particular stop. As there will be delay in travel time due to the vehicles on road, road traffic is modeled using M/M/1 queuing theory in order to calculate the delay caused by the other vehicles travelling in the same road. Server predicts the arrival time based on real time information updated by bus module and the information present in the database of server. Server is coded such that it sends the predicted arrival time of bus to the queried user's cell phone at every bus stop it passes till it reaches the stop queried by user. Updated arrival time received by users helps the transit users to plan their schedule and reach the bus stop in time. Such a predicting system motivates the non users of public transport systems to use them and reduce the usage of private vehicles in their day to day life.

Bus Arrival Time Prediction with Limited Data Set using Regression Models

Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, 2018

The increase of population has intensified everyday rush. Traffic congestions are still a problem in cities and are one of the main cause for public transport delays. City residents and visitors have experienced time loss by using public transport buses, because of waiting at the bus stops and not knowing if the bus is delayed or already serviced the stop. Therefore it is valuable for people to know at what time the bus should arrive (or is it already missed) at specific bus stop. Real-time public bus tracking and management system development has been the focus of many researchers, and many studies have been done in this area. This paper focuses on bus travel time prediction comparison between linear regression and support vector regression models (SVR), when using limited data set. Data were limited in a way that only historical GPS (Global Positioning System) coordinates of bus location (recorded each 30 seconds) and driven distance were used, there were no information about arrival/departure times, delays or dwell times. Distance between stops and delay (assumed values based on route observations by authors) were used as inputs for both models. It was concluded that SVR algorithm showed better results, but the difference was not significantly large.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Bus arrival time prediction algorithm based on Markov chain

Modern Problems of Russian Transport Complex

Developing public transport is an effective way to solve traffic congestion and improve travel efficiency. Improving bus service quality can attract passengers to travel by public transport. In the past, as the bus arrival time is unknown, and the buses often arrive inaccurate, passengers feel anxious and the quality of public transport service declined. Even though some bus stations equipped with electronic bus stop boards, the predicted bus arrival time is often inaccurate. Therefore, in order to convenient for people travel by public transit, this paper puts forward the method of bus travel time prediction based on the Markov chain which considers the spatial-temporal characteristics of the bus travel time. The prediction method can improve the quality of the bus service, help the travelers to make travel planning and reduce the waiting time. The algorithm is verified by the actual operation data of No.114 bus line in Harbin. The results show that the prediction error is small, and the algorithm is easy to implement.

A Suitable Approach for Evaluating Bus Arrival Time Prediction Techniques in Egypt

Accurate Bus arrival time prediction depending on a real-time basis has become an essential and important element in management transportation systems. This paper demonstrates the results of field tests for evaluating bus arrival time prediction in Egypt. One of the most difficult aspects of evaluating an operational field test is obtaining consumer response to products or services that are not market ready or even completely functional. Field tests were performed under real traffic situations in order to test the system in terms of prediction of bus arrival time to stations depending on two techniques consisting of Kalman filter and Neural Network. Findings from the field test at the real-world sites indicated that the system would be capable estimate the prediction time.

Pattern Based Spatial Formulation for Bus Travel Time Prediction Under Mixed Traffic Conditions

In recent times, congestion has become a serious problem in Indian cities due to rapid changes in urbanization. There is a need to explore better traffic operation and management systems to overcome congestion related problems such as delays and pollution. In this regard, attracting more people towards public transport is one of the option to reduce the congestion levels. To attract more people, the public transit should provide quality services to passengers. This can be achieved in one way by providing real-time information to the passengers about bus arrival details at bus stops. The effectiveness of such an information provided to passengers highly depends on the prediction method used, which in turns depends on the input data used in the prediction method. Thus, identifying correct input and incorporating them in the prediction model is important. Using the identified significant patterns in the data, a model based algorithm was developed to predict next bus travel time. The model is tested for a selected MTC bus route, 5C, which connects Taramani and Parry's corner in Chennai city, India. The performance the proposed algorithm showed a clear improvement in prediction accuracy when compared with a prediction method that uses only previous two trips as input to predict next bus travel time.

A Dynamic Bus-Arrival Time Prediction Model Based on APC Data

Computer-Aided Civil and Infrastructure Engineering, 2004

Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-ofweek, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.

A Survey on Prediction of Bus Arrival Time using Global Positioning System (GPS)

2016

Buses are an affordable means of public transport used by majority of the population in cities. Bus services are provided in many cities and have been proved to be an excellent means of transport; however, the commuters are uncertain about the arrival time of the buses, which leads to usage of private vehicles or taxis, thus leading to an increase in fuel consumption and pollution. Rather than waiting for buses it would be beneficial for passengers to know the tentative arrival times of the buses. Thus, for the convenience of citizens this application is proposed, which tracks the locations of the user as well as the bus using GPS sensors, and then calculate the approximate time required by the bus to reach the stop including the traffic analysis and various other parameters. Thus, the commuters can be aware about the waiting time for their respective buses, helping them in planning their journey accordingly.

Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study

Sustainability

The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.