Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. Method A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. Concluding remarks The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4-10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.
Accurate estimation of traffic in intelligent transportation system applications, such as the advanced traveler information system and the advanced traffic management system, requires fixed location-based measurements, vehicle-based measurements, or both. Using both data sources is too expensive for most government agencies, especially in developing countries such as India, and also leads to issues related to installation and maintenance, especially on urban roads. The main drawback of vehicle-based measurements is the potential lack of participation because of privacy concerns; lack of participation would limit data collection to a sample of the population, primarily on public transport vehicles. The study aims to overcome such difficulties by using only location-based flow data for the estimation of spatial parameters, such as density and travel time. These parameters are difficult to measure or estimate on an urban arterial, especially under heterogeneous traffic conditions, because of lack of lane discipline and because of complex interactions among different vehicle types. The Lighthill–Whitham–Richards macroscopic traffic flow model discretized in both space and time was employed in the estimation scheme. The resulting partial differential equations were solved numerically with the finite difference formulation of forward–time backward–space. Both linear and exponential speed–density relationships were considered and incorporated into the macroscopic model. Linear and cubic spline interpolations of input flow values were compared. The estimated density was corroborated with the density obtained from input–output analysis. Estimated travel times were compared with manually observed travel times and travel times obtained from probe vehicles fitted with Global Positioning System devices.
In recent years, the problem of bus travel time prediction is becoming more important for applications such as informing passengers regarding the expected bus arrival time in order to make public transit more attractive to the urban commuters. One of the popular techniques reported for such prediction is the use of time series analysis. Most of the studies on the application of time series techniques for bus arrival time prediction used Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) models, which are presently not suited for real time implementation. This is mainly due to the necessity and dependence of ARIMA models on a time series modelling software to execute. Moreover, the ARIMA model building process is time consuming, making it difficult to use for real-time implementations. Alternatively, Exponential Smoothing (ES) methods can be used, as they are easy to understand and implement when compared to ARIMA models. The present study is an attempt in this direction, where the basic equation of ES is used, as the state equation with Kalman filtering to recursively update the travel time estimate as the new observation becomes available. The proposed algorithm of state space formulation of ES with Kalman filtering for bus travel time and arrival time prediction was field tested using 105 actual bus trips data along a particular bus route from Chennai, India. The results are promising and a comparison of the proposed algorithm with ES alone without state space formulation and Kalman filtering has also been performed. An information system based on a webpage for real-time display of bus arrival times has been designed and developed using the proposed algorithm.
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