Bus Arrival Time Prediction Model Research Articles

Prediction of Arrival Time of Pure Electric Bus Based on FA-BP Algorithm

To establish a suitable pure electric bus arrival time prediction model, this paper takes pure electric bus as the research object. Based on the analysis of the influencing factors of the arrival time of the pure electric bus, the BP neural network arrival time prediction model optimized by the firefly algorithm (FA-BP prediction model) is established by selecting vehicle type, SOC value, battery age, and time as input conditions. The model is trained and tested by using bus operation data. The root mean square error of the Kalman filter model is 0.351, of the BP neural network model is 0.059, and of the FA-BP prediction model is 0.04. The results show that the model in this paper effectively improves the prediction accuracy and has good reliability and feasibility. It can provide some theoretical references for pure electric bus operation and managers and provide some basis for improving bus reliability.

An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

Real-time bus arrival information systems at transit stops can be useful to passengers for efficient trip planning and reducing waiting times. The accuracy of such systems depends upon the ability of the model to account for variations in the data series and to adjust according to changing traffic conditions. Many of the existing studies on passenger information systems have modeled the system based on stationary relations, not taking into account the cyclic variations in data, which is often suitable for demonstration purposes but not for long-term implementation. The present study models the changing relationships using Double-Seasonal Holt-Winter’s Exponential Smoothing approach, which allows for self-updating parameters at four levels. It accounts for both long-term and short-term seasonal fluctuations in data while maintaining the dynamic treatment of real-time bus information. The model also takes into account delays using the real-time running information of the bus and incorporates it into subsequent forecasts for better accuracy. Real-time data from GPS transmitters in buses were used for validation of the proposed model. The results show that the proposed model performs better than the currently-used elementary field methods and is able to forecast bus travel times with a reasonable accuracy.

RHMX: Bus arrival time prediction via mixed model

With the widespread use of information technologies such as IoT and big data in the transportation business, traditional passenger transportation has begun to transition and upgrade into intelligent transportation, providing passengers with a better riding experience. Giving precise bus arrival times is a critical link in achieving urban intelligent transportation. As a result, a mixed model-based bus arrival time prediction model (RHMX) was suggested in this work, which could dynamically forecast bus arrival time based on the input data. First, two sub-models were created: bus station stopping time prediction and interstation running time prediction. The former predicted the stopping time of a running bus at each downstream station in an iterative manner, while the latter projected its running time on each downstream road segment (stations as the break points). Using the two models, a group of time series data on interstation running time and bus station stopping time may be predicted. Following that, the time series data from the two sub-models was fused using long short-term memory (LSTM) to generate an approximate bus arrival time. Finally, using Kalman filtering, the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time. The experimental findings showed that the suggested model's accuracy and stability were both improved by 35% and 17%, respectively, over AutoNavi and Baidu.

An automatic real-time bus schedule redesign method based on bus arrival time prediction

Bus travel time often fluctuates greatly due to disturbance factors such as traffic conditions and passenger flow, which causes a reduction in the transit service as it cannot be provided as planned. Bus dispatching is necessary to handle this problem. Two crucial factors for real-time bus dispatching have been analysed in this study namely the bus arrival time prediction and timetable redesign. First, a bus arrival time prediction model combining Support Vector Regression (SVR) and Kalman Filter (K-SVR) was proposed. The headway is selected as an intermediate variable to improve the prediction accuracy by limiting the disturbance factors between two adjacent bus stops. Besides, by adjusting the parameters of the Kalman Filter, the predictive results are more suitable for practice to avoid the frequency of unnecessary timetable adjustment. Furthermore, an automatic timetable redesign method is given based on the proposed circle search algorithm, which can minimise the impact on the initial schedule by finding the minimum adjustment range in consideration of the normal working order. A case study in Shenzhen, China was conducted and the results verify that the K-SVR method could improve the prediction accuracy especially in peak hours. On this basis, in comparison with the traditional method, the timetable redesign algorithm could allocate the transit resources appropriately and perform well on indicators such as the headway fluctuation, compliance rate of headways, and schedule completeness. Moreover, it is more operable and valuable as a result of sending the departure instructions appropriately and maintaining the normal working order of drivers realistically.

A Transit Signal Priority Strategy With Right-Turn Lane Sharing

Transit signal priority (TSP) has become an increasingly popular way to tackle bus operation issues. Many efforts have been conducted to design TSP strategies, yet challenges still exists in real-life applications due to the difficulty in realizing the control logics and the limitation of road infrastructures. This paper proposes a novel TSP strategy considering lane sharing and real-time bus arrival time prediction. First, a right-turn lane sharing method is presented, it shares the right-of-way of a dedicated right-turn lane with through buses at the approaches of a signalized intersection. Next, a control logic based on phase insertion is proposed, and the new signal plan for next signal cycle is generated by judging the requirement of an exclusive bus phase based on predicted bus arrivals. Finally, the Kalman filter is used to establish a bus arrival time prediction model by using RFID and GPS data. The proposed method was implemented on an arterial road of real-life traffic network in Kunshan, China. Test results show that the proposed TSP strategy can achieve satisfactory performances. Bus delay decreases significantly compared to the general traffic delay, especially in peak hours. Further investigation shows that the findings of sensitivity analysis can provide beneficial guidance for practical applications.

Improving the quality of service in public road transportation using real time travel information system

The impact of road public transport in urban transportation cannot be ignored, however, the quality of services provided is not high enough. This has made it unattractive to prospective passengers and less competitive to private transport. One of the ways of resolving these challenges with a view to increasing the quality of service is by the provision of platforms that provides travel information in real time to prospective users. This was achieved in this paper using web, mobile, and short messaging service platforms. The developed platforms employed an artificial neural network-based bus arrival time prediction model for predicting bus arrival time. The accuracy of the prediction model was evaluated using mean absolute error and mean absolute percentage error while the performance and impact of the developed platforms were assessed from users' perspective through a survey based on the unified theory of acceptance and use of technology (UTAUT) model.

Model of Delay Prediction for Signalized Intersection Based on GPS Data

To facilitate travel, many domestic bus transits have been fit with station prompt means. With this function, the bus arrival time can be forecast in real time based on the GPS data. However, the current bus arrival time prediction model features high assumption, not well adapted to urban roadway traffic congestion and delay at signalized intersection, thus compromising the accuracy of the prediction. A real time delay estimation for signalized intersection is proposed in this paper, combining the physical model derivation and data fitting, integrating the waiting time at signalized intersections into the existing prediction model. It is shown in the experiments that compared with previous models, the accuracy of our model increases by 29.1%. The mass GPS data is processed via the Hadoop platform, the performance is improved by 3600%~4000% compared with the serial program.

The Error Correction Model on Kalman Filter

In this paper, authors extracted the data from the GPS equipment on the bus and established the real-time bus arrival time prediction model and bus running speed prediction model based on Kalman filtering technique. Analyse the error and build the error correction model. Firstly the bus running speed was predicted in the next section with the bus running speed prediction model, and then the bus arrival time was predicted with the real-time bus arrival time prediction model. Applying the newest information of bus running speed and bus arrival time, we were able to predict the real-time bus arrival time dynamically. The bus running speed prediction model and the real-time bus arrival time prediction model were assessed with the data of transit route NO.300 in Beijing. Lastly we assessed the real-time bus arrival time with the error between bus arrival time and real-time bus arrival time so that the prediction error was improved to 10 seconds which has higher prediction accuracy.

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time

This study develops a dynamic bus arrival time prediction model using the data collected by the automatic vehicle location and automatic passenger counter systems. It is based on the Kalman filter algorithm with a two-dimensional state variable in which the prediction error in the most recent observation is used to optimize the arrival time estimate for each downstream stop. The impact of schedule recovery is considered as a control factor in the model to reflect the driver's schedule recovery behavior. The algorithm performs well when tested with a set of automatic vehicle location-automatic passenger counter data collected from a real-world bus route. The algorithm does not require intensive computation or an excessive data preprocessing effort. It is a promising approach for real-time bus arrival time prediction in practice.

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time

This study develops a dynamic bus arrival time prediction model using the data collected by the automatic vehicle location and automatic passenger counter systems. It is based on the Kalman filter algorithm with a two-dimensional state variable in which the prediction error in the most recent observation is used to optimize the arrival time estimate for each downstream stop. The impact of schedule recovery is considered as a control factor in the model to reflect the driver's schedule recovery behavior. The algorithm performs well when tested with a set of automatic vehicle location–automatic passenger counter data collected from a real-world bus route. The algorithm does not require intensive computation or an excessive data preprocessing effort. It is a promising approach for real-time bus arrival time prediction in practice.