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 Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3354 ◽  
Author(s):  
Jianqing Wu ◽  
Qiang Wu ◽  
Jun Shen ◽  
Chen Cai
Keyword(s):  
Information Systems ◽  
Travel Time ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Series Data ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Time Prediction ◽  
Long Short Term Memory

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

scholarly journals A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

2021 ◽  
Vol 7 ◽  
pp. e689
Author(s):  
Asad Abdi ◽  
Chintan Amrit
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Selection Process ◽  
Influence Factors ◽  
Transportation Systems ◽  
Future Research ◽  
Extensive Literature ◽  
Travel Time Prediction ◽  
Significant Information ◽  
Time Prediction

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes

2005 ◽  
Vol 1935 (1) ◽  
pp. 147-153 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Karl Petty
Keyword(s):  
Regression Model ◽  
Travel Time ◽  
Traditional Approach ◽  
Computational Cost ◽  
Computation Time ◽  
Prediction Algorithm ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Freeway Networks

A travel time prediction algorithm scalable to large freeway networks with many nodes with arbitrary travel routes is proposed. Instead of constructing separate predictors for individual routes, it first predicts the whole future space–time field of travel times and then traverses the required subsection of the predicted travel time field to compute the travel time estimate for the requested route. Compared with the traditional approach that offers the same flexibility, the proposed method substantially reduces storage and computation time requirements at the relatively small computational cost at the time of actual prediction. It is first established that travel times computed by traversing travel time fields are compatible with more direct measurements of travel times from a vehicle reidentification technique based on electronic toll collection tags. This provides a conceptual justification of the proposed approach. When applied to the loop data from an 8.7-mi section of the I-80 freeway, the proposed approach with a time-varying coefficient (TVC) linear regression model as the component predictor not only improves the baseline historical travel time predictor substantially, with a 40% to 60% reduction in the prediction error, but also improves the traditional whole-route predictor based on the same TVC regression model by 6% to 9%. The result suggests that the proposed algorithm not only achieves the scalability but also improves prediction accuracy, both of which are critical for successful deployment of the advanced traveler information system for large freeway networks.

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 Travel time prediction of urban public transportation based on detection of single routes

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262535
Author(s):  
Xinhuan Zhang ◽  
Les Lauber ◽  
Hongjie Liu ◽  
Junqing Shi ◽  
Meili Xie ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Travel Time ◽  
Public Transportation ◽  
Service Reliability ◽  
Travel Time Prediction ◽  
Filter Model ◽  
Time Prediction ◽  
Study Results ◽  
Source Data

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

Sign in / Sign up

Export Citation Format

Share Document