Lelitha Devi Vanajakshi

In this paper, a model based estimation scheme has been proposed to estimate density incorporating the heterogeneity and lane indiscipline observed in Indian traffic. In order to incorporate lane indiscipline, the road stretch under study was considered as multiple parallel strips. Time occupancy and composition based weighted vehicle length were used to incorporate heterogeneity. Then, using these, a single state non-continuum macroscopic model was developed with density as the state variable and time occupancy as the output variable. The Kalman filtering technique was used for dynamic estimation of density. The estimator was corroborated using data generated from a microscopic traffic simulation software, VISSIM. Results obtained showed that the proposed approach could provide accurate density estimates and reproduced traffic characteristics better than without considering lane indiscipline.

Even though new infrastructure is being developed to meet demand, increased urbanization and vehicle ownership have increased the congestion levels in Indian cities. Attracting more travelers to public transport is an option to reduce congestion but still remains a challenge, mainly because of the uncertainty of service. A reliable and accurate system for predicting vehicle arrival can help make public transportation more attractive. An accurate prediction method should be used to provide reliable information to passengers, and accuracy depends on the input data used. Therefore, identifying the optimum inputs and incorporating them in the prediction method become important. The optimum number of inputs required for best prediction performance was identified with an analytical approach. A model-based algorithm motivated by the Kalman filter was used to predict bus travel time with the use of GPS data. A case study was conducted on two selected bus routes in the city of Chennai, India, to evaluate the prediction accuracy of the proposed method. Results obtained from the algorithm were promising and showed the prediction accuracy to be ±5 min for a prediction window of 30 min in 92% of instances. The predicted travel time can be used to provide realtime bus arrival information to the public through various media, including web pages, mobile applications, and display boards.

Predicting and providing information about bus arrival time to passengers accurately is a very important aspect of advanced public transportation systems (APTS), a major functional area of intelligent transportation systems. However, the information provided to passengers should be reliable. The reliability of such information provided to passengers depends on the prediction method used, which in turns depends on the input data used in the method. This means that identifying the most significant/appropriate input data and using them in the method are important. So, in the present study, travel time pattern analysis was carried out to find the most significant inputs by performing statistical tests for each day of the week separately. Also, a model-based Kalman filtering algorithm was developed to predict bus travel time by using the identified patterns effectively based on temporal discretization under heterogeneous traffic conditions. The performance of the proposed algorithm showed a clear improvement in prediction accuracy when compared with a prediction method using space discretization.

The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.

Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.

Dynamic scheduling of buses, which adapts to the current passenger demand and traffic conditions, will help in ensuring an efficient service to the commuters and maximizing the profit for operators. A fixed schedule with a constant headway, which is currently used widely, may lead to inadequate number of buses during peak periods and under-utilization of the system in off-peak periods. To overcome this, the present study proposes a demand and travel time responsive model to maximize the benefit of the operator by preparing an optimal schedule that can adapt to the variations in passenger demands and traffic conditions, subjected to minimizing the waiting time of the passengers, capacity constraints of the buses to achieve the maximum financial benefit as well as social satisfaction. For this, the study analyses the data received from real-time tracking devices that were fitted in a selected bus route in Chennai. Results showed that the waiting times were reduced up to 10 min per passenger and the percentage utilization of bus capacity was increased by 8% on an average across a day.

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities-Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are κ-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics.

In recent times, bus arrival time prediction (BATP) systems are gaining more popularity in the field of advanced public transportation systems, a major functional area under intelligent transportation systems. BATP systems aim to predict bus arrival times at various bus stops and provide the same to passenger’s pre-trip or while waiting at bus stops. A BATP system, which is accurate, is expected to attract more commuters to public transport, thus helping to reduce congestion. However, such accurate prediction of bus arrival still remains a challenge, especially under heterogeneous and lane-less traffic conditions such as the one existing in India. The uncertainty associated with such traffic is very high and hence the usual approach of prediction based on average speed will not be enough for accurate prediction. To make accurate predictions under such conditions, there is a need to identify correct inputs and suitable prediction methodology that can capture the variations in travel time. To accomplish the above goal, a robust framework relying on data analytics is proposed in this study. The spatial and temporal patterns in travel times were identified in real time by performing cluster analysis and the significant inputs thus identified were used for the prediction. The prediction algorithm used the Adaptive Kalman Filter approach, to take into account of the high variability in travel time. The proposed schemes were corroborated using real-world GPS data and the results obtained are very promising.

Efficient modelling of vehicular traffic is a challenging task in the context of Indian traffic conditions. One of the approaches for modelling traffic is using simulation. Though there are several traffic simulation software available, all of them are developed for the lane based and homogeneous traffic conditions. However, traffic conditions in many countries are heterogeneous and lane-less and for simulating such traffic, either specific software needs to be developed or calibration of existing software for such traffic conditions is required. For example, one of the commonly used software, namely VISSIM can be calibrated for such traffic conditions and is already reported in literature. However, VISSIM being licensed software, researchers have developed an open source software, namely Simulation of Urban MObility (SUMO). Though the initial development of SUMO focused on homogeneous and lane disciplined traffic, later researchers started developing modules for the Indian traffic with its wide mix of vehicle types and improper lane discipline. This paper presents a methodology for the calibration of SUMO for Indian heterogeneous traffic conditions by calibrating its parameters. Data from a 2 km segment in Chennai was used for the calibration. In the first level, parameters that can affect the driving behaviour under such conditions were identified using sensitivity analysis and one-way ANOVA test. Then optimal combination of parameters were identified using Genetic Algorithm (GA). Performance comparison was done with calibrated VISSIM for the same test bed. Average speed obtained from both the simulation software (VISSIM and SUMO) were compared and the errors were calculated in terms of Mean Absolute Percentage Error (MAPE) with respect to actual speed values. Results were found to be comparable, indicating that SUMO can be calibrated for simulating Indian traffic.