Lelitha Devi Vanajakshi

Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging. © 2011 IEEE.

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.

Traffic congestion is a serious problem which traffic engineers all over the world are trying to solve. Congestion increases the uncertainty in travel times leading to human stress and unsafe traffic situations. Better management of traffic through Intelligent Transportation Systems (ITS) applications, especially by predicting the congestion on various roads and informing the travelers regarding the same is one possible solution. Accurate and quick prediction is one of the important factors on which the reliability of such a system depends. If one is able to predict congestion on a roadway, then the travelers can be warned of the same either pre-trip or enroute so that they can take well informed travel decisions. The number of vehicles in a given stretch of a roadway (usually referred to as "traffic density") is one of the most commonly used congestion indicator. Also, the travelers in general will be more interested to know what they can expect when they make the trip in future rather than the present scenario. This makes the short term prediction to future time intervals important. In this study, some of the reported techniques for density prediction under homogeneous traffic conditions are attempted under heterogeneous traffic conditions in order to determine their feasibility under the Indian traffic scenario. © 2009 IEEE.

Data fusion is one of the recent approaches in traffic analysis for the accurate estimation and prediction of traffic parameters. In this approach, the parameters are estimated using the data from more than one source for better accuracy. This paper discusses a model based approach to estimate the parameters of heterogeneous traffic using both location data and spatial data using data fusion. The proposed method uses the Kalman filtering technique for the estimation of traffic density. Traffic density is a spatial parameter which is difficult to measure directly from field and can be measured only using aerial photography. Hence, it is usually estimated from other easily measurable parameters such as speed, flow, etc., or from a combination of such parameters. The present study estimates density using the flow values measured from video and the travel time obtained from Global Positioning System (GPS) equipped vehicles. The study also reports density estimation using flow and Space Mean Speed (SMS) obtained from location based data alone without fusing with spatial data, using the Extended Kalman filter technique. The estimates are corroborated using actual values and the results show data fusion performing better while estimating density. © 2011 IEEE.

Reliable estimation of performance measures such as queue and delay at intersections is important for the proper management of traffic. The information about these variables is valuable for the development of various traffic control strategies. The spatial nature of queue and delay makes their direct measurement a challenging task. The present study estimated these performance measures for the scenario when the queue ends within the advance detector using the data obtained from loop detectors installed at the entry and the exit of the intersection. A detailed analysis of the data obtained from loop detectors revealed that there were errors in the data. Two model based schemes, namely the occupancy based method and the queue clearance based method, were used for estimation of queue and delay using the erroneous data obtained from loop detectors. The results showed that the queue clearance based method was performing better while estimating queue and delay compared to the occupancy based method. Thus, the queue clearance based method would be valuable for the estimation of queues and delays while implementing with erroneous field data.

Passenger information systems about bus arrival at bus stops, which is an integral part of any Advanced Public Transportation Systems (APTS) application, is catching the attention of traffic engineers in India in recent years. APTS, by making the public transport system more attractive, will encourage people shift from personal mode to public mode for their transport, thus relieving congestion. There are different approaches of APTS that try to make public transport systems more desirable to commuters and one among them is accurate arrival time prediction at bus stops. This will reduce the wait time and associated uncertainties. There have been many studies reported which looked into the problem of bus arrival time prediction or travel time prediction. However, studies which deal with automatic incorporation of delays explicitly into travel time prediction are limited. Further, studies focusing on travel time prediction under heterogeneous traffic conditions are scarce. The present study attempts to identify delays automatically and explicitly incorporate them in predicting the total travel time of buses under heterogeneous traffic conditions such as those existing in India. The results obtained are corroborated with actual data and found to be promising. © 2009 IEEE.

Congestion has become a serious problem in the context of urban transport around the world. As more and more vehicles are being introduced into the urban streets every year, the mode share of the public transportation sector is declining at an alarming rate. Particularly in developing countries, more people have moved to personalized mode since it is becoming easily affordable and the quality of service offered by the public transit is not improving. To attract more people, the public transit should provide a high level of quality service to the passengers. One way of achieving this is by using Advanced Public Transport Systems (APTS) applications such as providing accurate real-time bus arrival information to the passengers which will improve the service reliability of the public transit. Travel time prediction has been a well-renowned topic of research for years. However, studies which were model based and incorporating dwell times at bus stops explicitly for heterogeneous traffic conditions are limited. The present study tries to explicitly incorporate the bus stop delays associated with the total travel times of the buses under heterogeneous traffic conditions. This will help in obtaining a reliable algorithm which can be adopted for bus arrival time prediction under Indian conditions. © 2009 IEEE.

Traffic density, defined as the number of vehicles per unit length, is the primary measure used for quantifying road congestion. However, the direct measurement of this variable is difficult due to its spatial nature and the only method to directly measure it from field is aerial photography. Hence, it is usually estimated from other easily measurable variables such as speed or flow. Some of the reported approaches to obtain density include the input output analysis, fundamental traffic flow relation, and occupancy-based measurements in addition to those based on statistics, machine learning or model-based approaches. However, for better performance, all these methods require the careful selection of the relevant input variables/parameters and their relationships. One way of obtaining these relationships is to perform a dimensional analysis of the variables/parameters involved, identifying the non-dimensional variables/parameters and then obtaining a relationship between them using experimental data. This approach has been attempted for estimating road traffic density in this paper. The appropriate non-dimensional variables/parameters that characterize road traffic flow were first determined and the relation between them was then found out using simulated data. This relationship was subsequently used to estimate density for other datasets and the results were found to be promising.

Regulation of heterogeneous traffic is a challenging task on urban roads, particularly those where traffic congestion is routinely encountered. In this paper, a model based traffic signal control scheme via state feedback controller is presented. An original contribution of this study is the use of area occupancy as the measurement variable, which is apt for characterising heterogeneous and lane less traffic. An adaptive Kalman filter is used to estimate traffic density. The developed control scheme was implemented on a road stretch simulated in VISSIM, a commercial microscopic traffic simulation software, and interfaced with MATLAB using VISSIM COM interface. The implementation was shown to satisfy the objective of maintaining the desired density in the study stretch which demonstrated the effectiveness of the developed control scheme.

Real time traffic state estimation is important to facilitate better traffic management in urban areas and is a prime concern from a traffic engineer's viewpoint. Traffic density is a key traffic variable that can be used to characterize the traffic system and can be a valuable input to the functional areas of Intelligent Transportation Systems (ITS). However, measurement of density in the field is difficult due to several practical limitations. This creates a need for inferring density from other traffic variables that are easily measurable in the field. In this paper, model based approaches for the estimation of traffic density are discussed. The non-linear model equations are based on the conservation principle and the fundamental traffic flow. The technique used for recursive estimation of density in real time plays a key role in terms of estimation accuracy. The Extended Kalman Filter (EKF) is a common tool for recursive estimation for nonlinear systems. This study investigates the application of particle filter (PF) and Unscented Kalman Filter (UKF) as alternatives to (EKF) for non-linear traffic state estimation in the context of traffic conditions in India. The estimated density values were corroborated using manually extracted field density values. The performance of these methods was also compared with a base model, where the fundamental traffic flow equation was used for calculating density. The convergence properties of these filters were also analyzed.