Identification of Metro-Bikeshare Transfer Trip Chains by Matching Docked Bikeshare and Metro Smartcards

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics.

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Emergence of Application Based Transportation and its Impacts on Travelling Trends: A Case Study of Lahore

Global Regional Review ◽

10.31703/grr.2020(v-iii).22 ◽

2020 ◽

Vol V (III) ◽

pp. 214-229

Author(s):

Hammna Jillani ◽

Hesan Zahid ◽

Nosheen Rasool

Keyword(s):

Public Transportation ◽

Structural Equation ◽

Sharing Economy ◽

Significant Shift ◽

Travel Patterns ◽

Female Population ◽

The Sustainable Development ◽

Equation Modelling ◽

The Social

The urban transportation system impacts the sustainable development of a country. Ride sourcing is a transportation model that operates under the notion of sharing economy. This study attempts to identify the changes in travel patterns of the users, particularly female users and their access to space. Focusing on how for the women in Lahore, the mobility has changed? The data for this research has been collected from passengers and drivers of ride-sourcing in Lahore through structured questionnaires. Structural equation modelling (SEM) was used to do the econometric analysis of consumers and drivers. Main findings indicate that for females, there is a significant shift in travel patterns from conventional modes (family car, public transportation) towards ride-sourcing. The results indicate that Uber and Careem has improved mobility as women feel secure in ride-sourcing services compared to public transportation. The female population of Lahore have started taking more trips because of car availability. The paper also tries to calculate the carbon emissions of ride-sourcing. The increasing number of cars is contributing to the city's worsening air pollution as the concept of 'one person in one car' prevails. The social impacts are positive, where women have become more mobile and independent because of app-based transportation.

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Injury Severity Prediction of Traffic Collision by Applying a Series of Neural Networks: The City of London Case Study

PROMET - Traffic&Transportation ◽

10.7307/ptt.v31i6.3032 ◽

2019 ◽

Vol 31 (6) ◽

pp. 643-654

Author(s):

Meisam Siamidoudaran ◽

Ersun İşçioğlu

Keyword(s):

Neural Networks ◽

Prediction Accuracy ◽

Injury Severity ◽

Support Vector ◽

Multi Layer Perceptron ◽

Vehicle Type ◽

Severity Prediction ◽

City Of London ◽

The City

This paper focuses on predicting injury severity of a driver or rider by applying multi-layer perceptron (MLP), support vector machine (SVM), and a hybrid MLP-SVM method. By correlating the injury severity results and the influences that support their creation, this study was able to determine the key influences affecting the injury severity. The result indicated that the vehicle type, vehicle manoeuvre, lack of necessary crossing facilities for cyclists, 1st point of impact, and junction actions had a greater effect on the likelihood of injury severity. Following this indication, by maximising the prediction accuracies, a comparison between the models was made through exerting the most sensitive predictors in order to evaluate the models’ performance against each other. The outcomes specified that the proposed hybrid model achieved a significant improvement in terms of prediction accuracy compared with other models.

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Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

Future Internet ◽

10.3390/fi11120247 ◽

2019 ◽

Vol 11 (12) ◽

pp. 247

Author(s):

Xin Zhou ◽

Peixin Dong ◽

Jianping Xing ◽

Peijia Sun

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Public Transportation ◽

Prediction Accuracy ◽

Arrival Time ◽

Attention Mechanism ◽

Experimental Results ◽

Support Vector ◽

Data Set ◽

Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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Increasing T-Method Accuracy Through Application of Robust M-Estimatior

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.25.17468 ◽

2018 ◽

Vol 7 (3.25) ◽

pp. 44

Author(s):

N Harudin ◽

Jamaludin K R ◽

M Nabil Muhtazaruddin ◽

Ramlie F ◽

S H Ismail ◽

...

Keyword(s):

Sample Size ◽

Case Studies ◽

Prediction Accuracy ◽

Study Data ◽

Inverse Regression ◽

Limited Sample ◽

Limited Sample Size ◽

Lower Accuracy ◽

M Estimator ◽

Prediction Techniques

Mahalanobis Taguchi System is an analytical tool involving classification, clustering as well as prediction techniques. T-Method which is part of it is a multivariate analysis technique designed mainly for prediction and optimization purposes. The good things about T-Method is that prediction is always possible even with limited sample size. In applying T-Method, the analyst is advised to clearly understand the trend and states of the data population since this method is good in dealing with limited sample size data but for higher samples or extremely high samples data it might have more things to ponder. T-Method is not being mentioned robust to the effect of outliers within it, so dealing with high sample data will put the prediction accuracy at risk. By incorporating outliers in overall data analysis, it may contribute to a non-normality state beside the entire classical methods breakdown. Considering the risk towards lower prediction accuracy, it is important to consider the risk of lower accuracy for the individual estimates so that the overall prediction accuracy will be increased. Dealing with that intention, there exist several robust parameters estimates such as M-estimator, that able to give good results even with the data contain or may not contain outliers in it. Generalized inverse regression estimator (GIR) also been used in this research as well as Ordinary Lease Square Method (OLS) as part of comparison study. Embedding these methods into T-Method individual estimates conditionally helps in enhancing the accuracy of the T-Method while analyzing the robustness of T-method itself. However, from the 3 main case studies been used within this analysis, it shows that T-Method contributed to a better and acceptable performance with error percentages range 2.5% ~ 22.8% between all cases compared to other methods. M-estimator is proved to be sensitive with data consist of leverage point in x-axis as well as data with limited sample size. Referring to these 3 case studies only, it can be concluded that robust M-estimator is not feasible to be applied into T-Method as of now. Further enhance analysis is needed to encounter issues such as Airfoil noise case study data which T -method contributed to highest error% prediction. Hence further analysis need to be done for better result review.

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Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Sustainability ◽

10.3390/su13010225 ◽

2020 ◽

Vol 13 (1) ◽

pp. 225

Author(s):

Rafidah Md Noor ◽

Nadia Bella Gustiani Rasyidi ◽

Tarak Nandy ◽

Raenu Kolandaisamy

Keyword(s):

Machine Learning ◽

Travel Time ◽

Public Transportation ◽

Transportation System ◽

Route Optimization ◽

Support Vector ◽

Smart Transportation ◽

Svm Algorithm ◽

Artificial Neural Network Ann

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

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Resident travel mode prediction model in Beijing metropolitan area

PLoS ONE ◽

10.1371/journal.pone.0259793 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259793

Author(s):

Xueyu Mi ◽

Shengyou Wang ◽

Chunfu Shao ◽

Peng Zhang ◽

Mingming Chen

Keyword(s):

Prediction Model ◽

Metropolitan Area ◽

Public Transportation ◽

Prediction Accuracy ◽

Two Dimensions ◽

Family Characteristics ◽

Support Vector ◽

Travel Mode ◽

Traffic Demand ◽

Beijing Metropolitan Area

With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.

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The Application of Neural Network in Dam Safety Monitoring

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.304.84 ◽

2011 ◽

Vol 304 ◽

pp. 84-89

Author(s):

Wei Zhang ◽

Dong Jian Zheng ◽

Cong Cong Wang

Keyword(s):

Prediction Accuracy ◽

Strain Analysis ◽

Safety Monitoring ◽

Support Vector ◽

Dam Safety ◽

Strain Monitoring ◽

Dam Safety Monitoring ◽

Important Means ◽

High Prediction ◽

Accuracy Of Prediction

Dam safety monitoring is an important means for remaining the dam safe, while stress-strain monitoring has been an extremely important part in the dam monitoring. Sometimes the traditional forecasting methods are not high accuracy, so, in order to improve the accuracy of prediction. This paper presents a dam strain prediction model based on Least Squares Support Vector Machines(LS-SVM). Applied in one dam, LS-SVM shows the advantages of good robustness and high prediction accuracy. The strain prediction accuracy improves a lot than using the traditional stepwise regression method, so it provides reliable and effective ways and means in dam strain analysis.

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