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.

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.

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.

Departure Time Planner (DTP) helps to efficiently manage the commute plan by providing smart travel assistance which suggests a departure from a given origin to a destination, given the desired arrival time at the destination. Towards this end, a DTP system is developed using a quasi-connected vehicle system where traffic data is collected from sparse sensor infrastructure. Novel methods and algorithms were developed accounting for the heterogeneous traffic conditions found in India. The traffic state prediction methodology based on the second-order traffic flow model shows that the system can reliably estimate the departure time for Indian conditions.

Traffic stream models provide relationship among the three basic macroscopic traffic stream characteristics namely traffic speed, volume and density. Traffic stream models are the basic building blocks of traffic flow modeling, design of road systems and traffic management systems. A number of research papers are available proposing different traffic stream models. However, majority of them are based on homogeneous traffic conditions and they may not be directly suitable for the Indian traffic condition. Not much research has been done on this important aspect so far in India. The present study is an attempt to develop a traffic stream model suitable for the heterogeneous traffic flow condition, such as the one existing in India, taking Chennai as case study. Traffic data for the present study are collected using video graphic technique. Flow, speed and occupancy are manually extracted from collected videos and density estimated using the relation connecting occupancy and density. The results show an exponential speed-density relation and parabolic relations for the other two. © 2010 ASCE.

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. ©2007 IEEE.

The process of predicting or simulating traffic conditions, based on current and past traffic observations is an important component of any of the Intelligent Transportation System (ITS) applications. There are several methods to predict traffic count. However, each method in different literatures may use different datasets, different time intervals of input traffic flow/count. One of the aims of this study is to provide a review and performance analysis of parametric and non-parametric approaches on traffic prediction. Second, to explore the possibilities in the implementation of Advanced Traffic Management Systems (ATMS), one of the functional areas of Intelligent Transportation Systems (ITS), by predicting traffic count on toll plazas for optimizing of toll-plaza operations. An RFID (Radio-Frequency Identification) based Electronic Toll Collection (ETC) system gives timely varying traffic counts observed at toll plazas, which has been utilized to develop prediction models based on historic data. An empirical differentiation of four methods, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) model based on time series analysis, Monte Carlo Simulation (MCS), Random Forest (RF) based on tree ensemble learning technique and KNN non-parametric regression-based machine learning technique, are proposed. Performance analysis at varying time intervals (5, 10, and 15 minutes) of input traffic count, for all the aforesaid models were compared with Simple Average Technique (SAT) using the historic data collected from two different toll-plazas in India. It was observed that K Nearest Neighbors (KNN) non-parametric regression performed better than other methods in most of the cases.

This paper presents a novel inductive loop sensor which detects large (e.g., bus) as well as small (e.g., bicycle) vehicles and help a traffic control management system in optimizing the best use of existing roads. To accomplish the sensing of large as well as a small vehicle, a multiple loop inductive sensor system is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle or motor cycle or car or bus but also enables accurate counting of the number of vehicles that too in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed using a virtual instrumentation scheme and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus the suitability of the proposed multi loop sensor system for any type of traffic has been established. © 2011 IEEE.

Studying patterns in traffic data is a basic analysis to understand the system. In this study, a large amount of bus travel data collected using vehicle tracking devices is analyzed for patterns. Travel time, in general, follows both spatial and temporal patterns. Spatial patterns are expected because travel times in particular sections on a roadway can be following similar patterns. For example, sections with a bus stop in it may show similar patterns due to stopping at the bus stops. The present study explores the use of data-driven approaches, primarily clustering, to identify the spatial patterns in bus travel times. Discrete Wavelet Transform (DWT) is used to extract trends from the travel time measurements. Two popular clustering algorithms - k-means and hierarchical clustering algorithms are used in this study to identify the spatial patterns and group sections with similar characteristics. Once the spatial patterns are obtained, the historic database is searched to identify similar cluster patterns and travel time trends are predicted using Pattern Sequence-based Forecasting (PSF) algorithm. The performance of the proposed algorithm for the prediction of travel time trends of trips occurring during peak and off-peak hours of a day was then compared based on prediction errors.