Bus Arrival Time Prediction Research Articles

Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

Bus Arrival Time Prediction Based on the Optimized Long Short‐Term Memory Neural Network Model With the Improved Whale Algorithm

Accurate prediction of bus arrival time is essential to achieve efficient bus dispatch and improve bus trip sharing rate. This article proposes using the improved whale optimization algorithm–long short‐term memory (IWOA–LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. Initially, the bus arrival process and its associated influencing factors are analyzed, with certain factors being quantified to serve as input features for the prediction model. After processing the GPS data of the No. 220 bus in Nanchang, Jiangxi, China, the proposed prediction model is analyzed and validated using an example and compared with other prediction models. The results show that the IWOA–LSTM prediction model has the best‐fitting effect between the predicted values and actual values in all time periods. Its MAPE, RMSE, and MAE have been reduced by at least 9.47%, 12.77%, and 8.93%, respectively, and the overall R2 has been improved by at least 10.65%. These results indicate that the model has the best predictive performance.

Analysis of Time-Based Public Transport Demand Prediction Using OPTUNA Framework

Buses are the most popular and easy mode of transportation in all over the world. The state government operates bus service in all routes with low-cost fare. Traffic congestion has risen at an alarming rate due to an increase in the number of automobiles. Travel time have increased as a result, while accessibility and mobility have worsened. The primary challenge encountered by passengers is the absence of information regarding bus numbers that are accessible on a certain route and the approximate time of bus departure. The delay in bus operations, could have several reasons which are inclement weather, traffic jam, and breakdowns. Neither the arrival time of the bus nor the delay are known to the people waiting at the bus stop. In order to address this problem, encouraging the usage of public transportation seems to be a feasible way. Over the past ten years, prediction of bus arrival time has become a fascinating subject around the world. In the transportation sector, Machine Learning (ML) technologies have already shown great promise and have additionally shown to yield a larger return on investment than traditional methods. In this research work , authors propose and develop predictive models to predict public transport demand for passenger transit based on bus arrival time. The dataset shows the proportion of buses operated by the Rochester-Genesee Regional Transportation Authority (RGRTA) that arrive on time. Initially, lazy predict classifier is used for solving regression-based dataset for predicting the bus demand in On-time based passenger transit. Based on the examination of lazy classifiers, the Decision Tree Regressor (DTR) has been identified as the best model. It is assessed using the most advanced hyperparameter optimization framework (OPTUNA). The proposed OPTUNA based DTR which is utilized to identify On-time performance of bus services-based passenger transit. Using OPTUNA for search is an efficient and beneficial approach considering the search speed and the improvement in model accuracy. According to the experimental data, the proposed approach performs better where the R-squared score is 0.9878 with best hyperparameter to be optimized.

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.

A Sequence and Network Embedding Method for Bus Arrival Time Prediction Using GPS Trajectory Data Only

Recently, achieving high accuracy in bus arrival time prediction has become a hot research topic. Extensive research has been conducted around this topic, among which fusion of multi-source heterogeneous information is the mainstream. However, using only GPS trajectory data to predict the arrival time of buses is meaningful for small cities since they do not have easy access to external sources of data. Therefore, we propose a novel deep-learning model based on sequence and network(i.e., graph) embedding (SGE-net) which adopts only GPS trajectories as input and achieves accurate bus arrival time prediction without external data. Specifically, the sequence embedding part, composed of the Echo State Network (ESN), the Auto-Encoder (AE), and the k-means cluster method, is constructed to extract the complex and potential sequence patterns from a large number of trajectory sequences, and cluster different patterns of trajectories. Then, the network embedding part with the node2vec algorithm is proposed to capture the spatial correlation among bus stops on different lines under the condition of limited data. Finally, a fusion prediction part based on the specifically designed parallel Gated Recurrent Unit (GRU) networks is introduced to combine sequence and network embedding vectors with hidden outputs of the GRUs and then get the prediction. Both real-world offline and online experiments are conducted based on GPS data of bus lines in QiTaiHe city and ShiJiaZhuang city. The results clearly show that the SGE-net outperforms the baseline models, and validate the effectiveness and robustness of the SGE-net.

A data analytics framework for reliable bus arrival time prediction using artificial neural networks

A data analytics framework for reliable bus arrival time prediction using artificial neural networks

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.

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.

Predicting public transit arrival: A nonlinear approach

Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation etc. The developing world in particular is plagued by additional factors like excess vehicles, poor lane discipline, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. A data-driven approach for BATP in real-time is proposed in the current work. We explicitly learn both spatial and temporal correlations in a general (non-linear) fashion, unlike most existing approaches. The real-time BATP is next intelligently reposed as a hidden-state estimation (or inference) problem on a related non-linear dynamical system (NLDS) model. We propose an Extended Kalman Filter (EKF) and Particle Filter (PF) based solution to the above inference problem. We demonstrate utility of this general framework of learning and prediction using (i)support vector regression and (ii)feed-forward ANN to learn spatial and temporal correlations. The EKF based inference under support-vector and feed-forward approximations to the system maps of the associated NLDS model reveals interesting computational structure, which goes beyond the BATP application. Specifically, the Jacobian computation at each step of the EKF can be carried out exactly and efficiently using either (i)forward recursion (ii)backward recursion (back-propagation) under feed-forward approximations. Under support-vector approximation, this Jacobian evaluation is possible in closed form. The effectiveness of the proposed algorithms is illustrated on real field data collected from challenging traffic conditions. Our experiments demonstrate similar prediction performance of both the proposed EKF and PF while they outperform diverse existing state-of-art data-driven approaches proposed for the same problem.

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction.

Predicting bus travel time using machine learning methods with three-layer architecture

The increase in population and the crowding of cities bring along transportation problems. Thus, people are directed to public transportation to reduce the burden on transportation. Being informed correctly about the arrival time at the stops attracts passengers. In this study, machine learning methods with three-layer architecture were used to predict bus arrival time. The first layer processes the measured data and gives the prediction results of actual data. In the second layer, the residuals are predicted at the specified depth. In the third layer, the results of the previous two layers are integrated with three different approaches to calculate the final prediction. The case study was carried out on the data obtained from Istanbul public transportation and various machine learning methods were applied to the data using the traditional and the three-layer architecture. The experimental results showed that the three-layer architecture provided successful results with approximately 2.552 MAPE.

A review of bus arrival time prediction using artificial intelligence

AbstractBuses are one of the important parts of public transport system. To provide accurate information about bus arrival and departure times at bus stops is one of the main parameters of good quality public transport. Accurate arrival and departure times information is important for a public transport mode since it enhances ridership as well as satisfaction of travelers. With accurate arrival‐time and departure time information, travelers can make informed decisions about their journey. The application of artificial intelligence (AI) based methods/algorithms to predict the bus arrival time (BAT) is reviewed in detail. Systematic survey of existing research conducted by various researchers by applying the different branches of AI has been done. Prediction models have been segregated and are accumulated under respective branches of AI. Thorough discussion is presented to elaborate different branches of AI that have been applied for several aspects of BAT prediction. Research gaps and possible future directions for further research work are summarized.This article is categorized under: Application Areas > Science and Technology Technologies > Artificial Intelligence Technologies > Prediction

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.

Bus arrival time prediction and reliability analysis: An experimental comparison of functional data analysis and Bayesian support vector regression

To maintain the stability and punctuality of bus systems, an accurate forecast of arrival time is essential to devise control strategies to prevent bus bunching especially under congested traffic conditions. Transit agencies provide travelers with accurate and reliable bus arrival times to downstream stations to improve transit service quality so as to attract more transit riders. Varieties of approaches have been dedicated to providing high prediction accuracy while the measure of the associated uncertainty is ignored. Noting that the quantification of uncertainty is vital for robust performance, this paper proposes data-driven approaches based on the Functional Data Analysis (FDA) and Bayesian Support Vector Regression (BSVR) for short-term bus travel time prediction while anticipating various uncertainties. To capture spatial–temporal dynamic traffic conditions along the route so as to increase the accuracy of the journey time prediction and to capture the skewness in journey time distribution, a probabilistic nested delay operator is adopted. Journey time reliability analysis is then conducted using the skewness of dynamic journey time distribution. An empirical study is carried out by fusing the bus transit date of No. 261 bus route and Floating Car Data (FCD) in Guangzhou. The proposed FDA and BSVR methods applied in conjunction with the probabilistic nested delay operator turn out to be highly competitive when performing forecasts under various traffic conditions. Comparative studies indicate that FDA provides more accurate prediction results and tends to anticipate uncertainties in journey time distribution more effectively.

An integrated intersection design for promoting bus and car traffic

Signal priority and exclusive bus lanes are common measures used to promote bus travel at signalized intersections. However, these bus priority measures create significant damage to the car traffic especially at intersections with heavy bus and car traffic. For this reason, bus priority is not welcome at busy intersections. We examine a novel intersection approach design that can solve the above dilemma. The design integrates a bus lane, bus signal priority, and a midblock pre-signal for sorting different car traffic streams in tandem in the approach. Car capacity gains from the use of pre-signal can potentially recover the car capacity lost to bus lane and signal priority schemes. This paper first presents how the pre-signal and main signal can be timed to realize bus signal priority, and where the pre-signal should be placed. Models are then formulated for estimating the expected bus delay and car capacity under the integrated design. They are compared against three alternative designs, including a conventional intersection design without bus priority or pre-signal. Numerical results unveil that the integrated design produces not only significant bus delay savings, but also higher car capacities in most instances. Even greater car capacity gains are observed with higher bus frequencies. Moreover, the benefits are fairly robust when real-world operating features, such as bus arrival time prediction error, are considered. Thus, the integrated design can potentially promote both bus and car traffic at congested intersections.

Intellectualization of the urban and rural bus: The arrival time prediction method

Abstract To improve the intelligence of urban and rural buses, it is necessary to realize the accurate prediction of bus arrival time. This paper first introduced urban and rural buses. Then, the arrival time prediction was divided into two parts: road travel time and stop time, and they were predicted by the support vector regression method and k-nearest neighbor (KNN) method. A section of a bus route in Pingdingshan city of Henan province was taken as an example for analysis. The results showed that the method designed in this study had better accuracy, and the result was closer to the actual value, with a maximum error of 84 s, a minimum error of 10 s, an average error of 42.5 s, and an average relative error of 5.74%, which could meet the needs of passengers. The results verify the reliability of the designed method in predicting the arrival time of urban and rural buses, which can be popularized and applied in practice.

Gps Enabled Vehicle Location Identification Using Gsm And Fare Collection Using Smart Card

This project explains that the prediction of bus arrival time and bus location information to the passengers in their respective bus stops through GSM and GPS and by measuring the speed by accelerometer. It includes that the unmanned vehicle(i.e. bus) driver and passengers journing without conductor. It also include an automated fare collection system through passengers onboard smart card tickets. Our project provides vehicle location updated information to the passengers and other additional information's are, number of passengers onboard while starting the bus in the starting point and how many of them off board and onboard in point to point stopping information and also provides final passengers information through GSM to ticket checker and other higher officials and also an information viewed by driver through electronic LCD board.

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.

Multistep Prediction of Bus Arrival Time with the Recurrent Neural Network

Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly and improve travel efficiency. Thus, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system. However, due to many variables disturbing the scheduled transportation, accurate prediction is challenging. For accurate prediction of the arrival time of a bus, this research adopted a recurrent neural network (RNN). For the prediction, the variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. Then, a stacked multilayer RNN model was created with the variables that were categorized into four groups. The RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. The result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. The propagation of error and the number of prediction steps influenced the prediction accuracy.

The bus arrival time prediction using LSTM neural network and location analysis

The accurate bus arrival time information is crucial to passengers for reducing waiting times at the bus stop and improve the attractiveness of public transport. GPS-equipped buses can be considered as mobile sensors showing traffic flows on road surfaces. In this paper, we present an approach that predicts bus arrival time using historical bus GPS information and real-time situation on the road. In this study, we divide bus arrival time into bus dwelling time at bus stops and bus travel time between stations and predict each of them separately. The clustering approach used to predict the travel time between stations, and then for each cluster, we apply LSTM NN to predict walking time between stations. The latency at each bus stop we evaluate by historical dwelling time and using location analysis to find the importance of the bus stop as a point of interest during prediction time. The study is trained and tested on GPS data collected from 1200 buses in a period of 3 months. According to tests results our method show small mean absolute error for buses that not far from departure station. The outcomes of this work can be used as an additional information for bus passengers to know possible bus coming time and to estimate possible travel time in bus journey. The method for arrival time prediction proposed in this research has several advantages. It considers historical bus travel time information, real time information, bus dwelling time, riding time, traffic lights and city facilities.