Prediction of Bus Travel Time Using ANN: A Case Study in Delhi

Johar, Amita; Jain, Saurabh; Garg, Pradeep

doi:10.1016/j.trpro.2016.11.091

Cited by 44 publications

(23 citation statements)

References 6 publications

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“…are considered as the basic parameters for BAT prediction. In [16], Amita et al selected bus stop dwell time and passenger feature to establish a linear regression model to predict BAT. However, the simple linear relationship could not denote the complexity of real driving environment.…”

Section: B Bat Predictionmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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Bus arrival time prediction not only provides convenience for passengers, but also helps to improve the efficiency of intelligent transportation system. Unfortunately, the low precision of bus-mounted GPS system, lack of real-time traffic information and poor performance of prediction model lead to low estimation accuracy-greatly influence bus service performance. Hence, in this paper, a GPS calibration method is put forward, while projection rules of specific road shapes are discussed. Moreover, two traffic factors, travel factor and dwelling factor, are defined to express real-time traffic state. Then, considering both historic data and real-time traffic condition, a hybrid dynamic BAT prediction factor, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is defined. A LSTM training model is construct to realize BAT prediction. Experiment results demonstrate that our technique can provide a higher level of accuracy compared to methods based on traditional time-ofarrival techniques, especially in the accuracy of multi-stops BAT prediction. INDEX TERMS Bus arrival time prediction, LSTM model, GPS data calibration.

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Section: B Bat Predictionmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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“…There exists a considerable amount of research papers that address the problem of travel time prediction for transport applications. Accurate travel time information is essential as it attracts more commuters and increases commuter's satisfaction [1]. The majority of these works are on short-term travel time prediction [19], aimed at applications in advanced traveler information systems.…”

Section: Application Area Of Travel Time Predictionmentioning

confidence: 99%

Reconciling Predictions in the Regression Setting: An Application to Bus Travel Time Prediction

Mendes‐Moreira

Baratchi

2020

Lecture Notes in Computer Science

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In different application areas, the prediction of values that are hierarchically related is required. As an example, consider predicting the revenue per month and per year of a company where the prediction of the year should be equal to the sum of the predictions of the months of that year. The idea of reconciliation of prediction on grouped time-series has been previously proposed to provide optimal forecasts based on such data. This method in effect, models the time-series collectively rather than providing a separate model for time-series at each level. While originally, the idea of reconciliation is applicable on data of time-series nature, it is not clear if such an approach can also be applicable to regression settings where multi-attribute data is available. In this paper, we address such a problem by proposing Reconciliation for Regression (R4R), a two-step approach for prediction and reconciliation. In order to evaluate this method, we test its applicability in the context of Travel Time Prediction (TTP) of bus trips where two levels of values need to be calculated: (i) travel times of the links between consecutive bus-stops; and (ii) total trip travel time. The results show that R4R can improve the overall results in terms of both link TTP performance and reconciliation between the sum of the link TTPs and the total trip travel time. We compare the results acquired when using group-based reconciliation methods and show that the proposed reconciliation approach in a regression setting can provide better results in some cases. This method can be generalized to other domains as well.

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“…The sigmoidal function, such as logistic and tangent hyperbolic is common because of its ability to normalize the input values to the range of -1 to 1. Most of the studies dealing with urban traffic prediction prefer to use logistic and tangent hyperbolic because they produce positive and negative value and are faster in training (Fan and Gurmu 2015;Amita et al, 2016;Čelan and Lep, 2017;Zhu et al, 2018).…”

Section: Artificial Neural Network and Travel Time Predictionmentioning

confidence: 99%

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

2020

IJTTE

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Providing real and accurate travel time information usually, assists road users to plan their trips and choose the appropriate mode of transport. However, accurate prediction of travel time is a challenging problem, especially in developing countries where heterogeneous flow conditions exist and there are no records of information about the travel time for travelers. Most of the dynamic travel-time prediction models developed emphasize on link travel time without taking into account delay time at the intersections and waiting time at the bus stops. The objective of this study was to compare Multi-Linear Regression and Artificial Neural Network models to obtain a suitable model for developing a dynamic travel-time prediction model using waiting time at the bus stop, intersection delay time, link distance, traffic volume, link travel time, peak hours and off-peak hours as model inputs. Link travel time was modeled by a well-trained Neural Network and Kalman filtering dynamic algorithm using field survey data collected by employing public buses in Dar es Salaam city. The model was validated by using data collected in five main routes in Dar es Salaam City. The Root Mean Square Error and Mean Absolute Percent Error were used to evaluate the performance of the model by comparing it with other prediction models. Findings indicate that the integration of the Artificial Neural Network and Kalman Filter algorithm model (ANN-KF) promised to be a reasonable model for predicting dynamic travel time in Dar es Salaam city.

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Prediction of Bus Travel Time Using ANN: A Case Study in Delhi

Cited by 44 publications

References 6 publications

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

Reconciling Predictions in the Regression Setting: An Application to Bus Travel Time Prediction

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

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