A Framework for Scalable Data Analysis and Model Aggregation for Public Bus Systems

2019 ◽  
Author(s):  
Mayuri A. Morais ◽  
Raphael Y. Camargo
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Information Quality ◽  
Prediction Models ◽  
Smart Cities ◽  
Travel Time Prediction ◽  
Network State ◽  
Time Prediction ◽  
Bus Bunching ◽  
Composite Models

Urban mobility through quality public transportation is one of the major challenges for the consolidation of smart cities. Researchers developed different approaches for improving bus system reliability and information quality, including travel time prediction algorithms, network state evaluations, and bus bunching prevention strategies. The information provided by these approaches are complementary and could be aggregated for better predictions. In this work, we propose the architecture and present a prototype implementation of a framework that enables the integration of several approaches, which we call models, into scalable and efficient composite models. For instance, travel time prediction models can use estimators of bus position, network state, and bus headways to deliver more accurate and reliable predictions. We evaluate the scalability of the framework, the CPU usage of the framework components, and the predictions of the travel time models. We show that real-time predictions using this framework can be feasible in large metropolitan areas, such as Sao Paulo city.

scholarly journals Scalable Data Analysis for Public Bus Systems

2020 ◽  
Author(s):  
Mayuri A. Morais ◽  
Raphael Y. de Camargo
Keyword(s):  
Public Transportation ◽  
System Reliability ◽  
Information Quality ◽  
Smart Cities ◽  
Urban Mobility ◽  
Prevention Strategies ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Bunching ◽  
Composite Models

Urban mobility through quality public transportation is one of the major challenges for the consolidation of smart cities. Researchers developed different approaches for improving bus system reliability and information quality, including travel time prediction algorithms, network state evaluations, and bus bunching prevention strategies. The information provided by these approaches are complementary and could be aggregated for better predictions. In this work, we propose the architecture and a present a prototype implementation of a framework that enables the integration of several approaches, which we call models, into scalable and efficient composite models.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

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.

Performance Comparison of Bus Travel Time Prediction Models across Indian Cities

2018 ◽  
Vol 2672 (31) ◽  
pp. 87-98 ◽  
Author(s):  
Jairam R ◽  
B. Anil Kumar ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transportation ◽  
Prediction Models ◽  
Road Traffic ◽  
Performance Comparison ◽  
Heterogeneous Traffic ◽  
Transport Systems ◽  
Intelligent Transport System ◽  
Travel Time Prediction

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities—Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are k-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics.

Development of a Hybrid Model for Dynamic Travel-Time Prediction

2003 ◽  
Vol 1855 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Chandra Mouly Kuchipudi ◽  
Steven I. J. Chien
Keyword(s):  
New York ◽  
Travel Time ◽  
Hybrid Model ◽  
Prediction Models ◽  
New York State ◽  
State Variables ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Historic Data ◽  
Better Than

Travel-time prediction has been an interesting research subject for decades, and various prediction models have been developed. A prediction model was derived by integrating path-based and link-based prediction models. Prediction results generated by the hybrid model and their accuracy are compared with those generated by the path-based and link-based models individually. The models were developed with real-time and historic data collected from the New York State Thruway by the Transportation Operations Coordinating Committee. In these models, the Kalman filtering algorithm is applied for travel-time prediction because of its significance in continuously updating the state variables as new observations. The experimental results reveal that the travel times predicted with the path-based model are better than those predicted with the link-based model during peak periods, and vice versa. The hybrid model derives results from the best model at a given time, thus optimizing the performance. A prototype prediction system was developed on the World Wide Web.

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

2021 ◽  
pp. 1-16
Author(s):  
Milad Baradaran Shahidin
Keyword(s):  
Regression Model ◽  
Travel Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Linear Regression Method ◽  
Ann Model ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Artificial Neural Network Ann ◽  
High Level

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

scholarly journals Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xu Miao ◽  
Bing Wu ◽  
Yajie Zou ◽  
Lingtao Wu
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Statistical Models ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Learning Models ◽  
Periodic Functions ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Machine Learning Models

Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

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