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. 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. 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.

A vast majority of urban transportation systems in North America are equipped with traffic surveillance systems that provide real time traffic information to traffic management centers. The information from these are processed and provided back to the travelers in real time. However, the travelers are interested to know not only the current traffic information, but also the future traffic conditions predicted based on the real time data. These predicted values inform the drivers on what they can expect when they make the trip. Travel time is one of the most popular variables which the users are interested to know. Travelers make decisions to bypass congested segments of the network, to change departure time or destination etc., based on this information. Hence it is important that the predicted values be as accurate as possible. A number of different forecasting methods have been proposed for travel time forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN). This paper examines the use of a machine learning technique, namely support vector machines (SVM), for the short-term prediction of travel time. While other machine learning techniques, such as ANN, have been extensively studied, the reported applications of SVM in the field of transportation engineering are very few. A comparison of the performance of SVM with ANN, real time, and historic approach is carried out. Data from the TransGuide Traffic Management center in San Antonio, Texas, USA is used for the analysis. From the results it was found that SVM is a viable alternative for short-term prediction problems when the amount of data is less or noisy in nature.

Public transport buses have uncertainties associated with its arrival/travel times, due to several factors such as signals, dwell times at bus stops, seasonal variations, fluctuating travel demands, and so on. In the developing world, these uncertainties are further magnified by the presence of excess vehicles, diverse modes of transport, and acute lack of lane discipline. Hence, the problem of bus arrival time prediction continues to be a challenging one especially in developing countries. This paper proposes a new methodology for bus arrival time prediction in real time. Unlike existing approaches, the proposed method explicitly learns the spatial (and temporal) correlations/patterns of traffic in a novel fashion. Specifically, it first detects the unknown order of spatial dependence and then learns linear, non-stationary spatial correlations for this detected order. It learns temporal correlations between successive trips as a function of their time difference. To make the optimal prediction feasible, the learnt predictive model is rewritten in a suitable linear state-space form, and then, an appropriate Kalman filter (KF) is applied. The performance was evaluated with real field data and compared with existing methods.

Predicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy.

Bus signal priority (BSP) is an active traffic management measure to reduce bus travel delay at signalized intersections and to improve the bus service reliability. In this paper, we present a real-time BSP system with a primary focus on its practical implementation. We tackle two inter-related issues of existing priority systems, namely, real-time computation and solution optimality, using an analytical approach. The core of the proposed priority system involves two signal controller actions – red truncation (RT) and green extension (GE), which determine the priority timings based on the objective of minimizing total person delay incurring at the subject intersection. We demonstrate the analytical approach by deriving closed-form expressions for optimal RT and GE for a two-phase signal using cumulative count curves. The inputs required for these priority models are based on average traffic and bus conditions limited to the current signal cycle alone. Solutions for the RT and GE models indicate that three dimensionless variables – ratio of bus-arrival time to traffic queuing time, ratio of bus passenger occupancy to other vehicles’ average passenger occupancy, and ratio of traffic demand to saturation flow ratio – govern the priority decisions. Simulation results showed significant delay reduction for buses (≈22%) with a negligible impact on other traffic users during low to medium traffic conditions and high bus frequencies (3 min headway).

Analysis of queue discharge characteristics at signalized intersections is a primary component of traffic signal analysis and design. On the basis of previous studies, mainly conducted in homogeneous traffic conditions, the discharge headway is assumed to be high at the start of green for the first few vehicles, mainly because of start-up lost times, and is also assumed to reach the minimum value by the fourth or fifth vehicle in the queue. The minimum headway is expected to continue until the end of the queue. However, this may not be the case under heterogeneous traffic conditions, such as those in India, which has the additional problem of lacking lane discipline. Most of the signals in India include a countdown timer that indicates the time left for the signal phase, which is also expected to affect queue discharge characteristics. This paper presents insights gained on queue discharge characteristics at signalized intersections under heterogeneous traffic conditions and on the effect of a countdown timer on the headway distribution. The analysis was carried out using data collected from two intersections, one with a timer and one without, in Chennai, India, through the use of a videographic technique. The data collected are classified into three discharge regimes: start-queue, mid-queue, and end-queue. Linear regression models are used to assess the impact of vehicle types on queue discharge characteristics. The results indicate that the accepted headway distribution is followed when there is no timer. However, with the presence of a timer, there is a clear change in the trend for reduced start-up lost time and end lost time.