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.

Travel time estimation in urban arterials is challenging compared to freeways and multilane highways. This becomes more complex under Indian conditions due to the additional issues related to heterogeneity, lack of lane discipline, and difficulties in data availability. The fact that most of the urban arterials in India do not employ automatic detectors demands the need for an effective, yet less data intensive way of estimating travel time. An attempt has been made in this direction to estimate total travel time in an urban road stretch using the location based flow data and sparse travel time data obtained using GPS equipped probe vehicles. Three approaches are presented and compared in this study: (1) a combination of input-output analysis for mid-blocks and Highway Capacity Manual (HCM) based delay calculation at signals named as base method, (2) data fusion approach which employs Kalman filtering technique (nonhybrid method), and (3) a hybrid data fusion HCM (hybrid DF-HCM) method. Data collected from a stretch of roadway in Chennai, India was used for the corroboration. Simulated data were also used for further validation. The results showed that when data quality is assured (simulated data) the base method performs better. However, in real field situations, hybrid DF-HCM method outperformed the other methods. © 2012 S. P. Anusha et al.

This paper proposes methods for detecting platoons in a traffic stream using automated techniques. Three different methods are tested for platoon detection, namely Cluster based approach, modified Gaur and Mirchandani approach, and an Image processing based technique. Corroboration was carried out using dataset from an arterial in Chennai, India. The results obtained showed all the methods working well for platoon identification. The cluster based approach proved to work very well but it is an offline method for platoon identification. For real time environments, the modified G&M method as well as the image processing solution showed promising results for the Indian traffic conditions with the modified G&M approach performing slightly better.

The accurate estimation of travel time of different types of vehicles in a traffic stream has always been of interest in various stages of planning, design, operations and evaluation of transportation systems. The traditional way of travel time data collection by means of active test vehicles or license plate matching techniques has its own limitations in terms of cost, manpower, geographic coverage, sample size and accuracy. With the growing need for real-time travel time data, the passive probe vehicles with onboard global positioning systems (GPS) are increasingly being used. However, due to privacy issues and participation requirements, the public transit vehicles are the only ones which can be equipped with GPS devices and this could possibly be used as a source to estimate the travel time of other types of vehicles. The present study is an attempt in this direction. Two approaches have been proposed: one based on the ratio of the section travel times of personal vehicles to public transit and the other based on the quantifiable relationship between the public transit and personal vehicles section travel times. The results showed that the approach-2 which is based on the relationship between the bus travel time and other vehicles travel time outperforms the approach-1, with 98% of the times the deviation of estimated travel time of personal vehicle with respect to observed/actual travel time being less than ±5 min and mean absolute percentage error (MAPE) within the acceptable range of 10–15%.

A new multiple inductive loop detector system that uses the mutual inductances between an outer loop and multiple inner loops is presented in this paper. Automated detection, classification and speed measurement of vehicles are a challenging task in a no-lane and heterogeneous traffic. A recently reported multiple loop scheme is a solution but it is complex and less reliable due to large number of electrical connections required to realize the system. This paper proposes a loop sensor wherein small inner loops are placed within a large outer loop. In the new system the outer loop alone is connected to the measurement unit and all the inner loops are simply coupled inductively to the outer loop. This scheme is simple and can be easily employed to convert an existing single loop system to a multiple loop system by incorporating the inner loops. A suitable measurement scheme based on a synchronous detection is employed that guarantees accurate measurement. A special excitation that ensures parallel resonance of the whole inductive system is employed to keep the power consumption minimum. A prototype of the proposed system has been built and the practicality has been tested. The new system correctly sensed the vehicles, categorized and counted them in an undisciplined traffic. © 2012 IEEE.

The phenomenon of platoon dispersion deals with the spreading out of groups of vehicles discharged together from a signal (platoon) as they move along the roadway during normal traffic operations. Understanding and analyzing this behavior is important in efficient traffic operations and management. There are different platoon dispersion models reported in the literature, out of which Robertson's model is one of the oldest and widely used. However, all the existing studies were from homogeneous and lane-based traffic conditions and few studies studied platoon dispersion behavior under traffic conditions such as those existing in India. In this study, data were collected in a typical Indian urban arterial road. To account for the heterogeneous and laneless Indian traffic, Roberston's model was modified and an optimization approach was used to obtain the coefficients. The proposed model's performance was evaluated and compared with the original Robertson's model after calibrating for the specific traffic conditions under consideration. Platoon parameters were also estimated using both proposed and calibrated models and the results were compared, which showed a better performance of the proposed model compared to the calibrated Robertson's model. Though the proposed model was tested for heterogeneous and laneless traffic data, it is in no way constraining and is generic enough to be applied for other traffic conditions.

Bus travel times are prone to high variability, especially in countries that lack lane discipline and have heterogeneous vehicle profiles. This leads to negative impacts such as bus bunching, increase in passenger waiting time and cost of operation. One way to minimize these issues is to accurately predict bus travel times. To address this, the present study used a modelbased approach by incorporating mean and variance in the formulation of the model. However, the accuracy of prediction did not improve significantly and hence a machine learning-based approach was considered. Support vector machines were used and prediction was done using ν-support vector regression with linear kernel function. The proposed scheme was implemented in Chennai using data collected from public transport buses fitted with global positioning system. The performance of the proposed method was analysed along the route, across subsections and at bus stops. Results show a clear improvement in performance under high variance conditions.

Providing accurate and reliable travel time information to travellers is essential to improve the quality of public transit systems. With the availability of the latest technologies, it has become possible to collect a large amount of traffic data to analyze and understand these systems better. Traffic in India is characterized by lack of lane discipline and the presence of vehicles of varying static and dynamic characteristics, which makes prediction of bus travel time especially challenging. The aim of this study is to identify both a prediction algorithm that can handle high variability and suitable inputs or regressors to be used. Earlier studies performed offline manual grouping considering the patterns observed, which leads to limitations for automated field implementations. The present study explores the use of data-driven approaches, primarily clustering, to address the challenges for the prediction of bus travel time trends. Discrete wavelet transform (DWT) was used to extract trends from the travel time measurements. Three popular clustering algorithms—k-means, hierarchical, and self organizing maps (SOM)—were used to identify patterns. Travel time trends were then predicted by searching for similar cluster patterns within the historical database using pattern sequence-based forecasting (PSF). A comparison of the performance of these algorithms was carried out based on prediction errors. The clustering +prediction framework developed was also compared with the case when no clustering was done on the regressor dataset.

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.