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.

The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions.

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.

Real-time bus travel time prediction has been an interesting problem since past decade, especially in India. Popular methods for travel time prediction include time series analysis, regression methods, Kalman filter method and Artificial Neural Network (ANN) method. Reported studies using these methods did not consider the high variance situations arising from the varying traffic and weather conditions, which is very common under heterogeneous and lane-less traffic conditions such as the one in India. The aim of the present study is to analyse the variance in bus travel time and predict the travel time accurately under such conditions. Literature shows that Support Vector Machines (SVM) technique is capable of performing well under such conditions and hence is used in this study. In the present study, nu-Support Vector Regression (SVR) using linear kernel function was selected. Two models were developed, namely spatial SVM and temporal SVM, to predict bus travel time. It was observed that in high mean and variance sections, temporal models are performing better than spatial. An algorithm to dynamically choose between the spatial and temporal SVM models, based on the current travel time, was also developed. The unique features of the present study are the traffic system under consideration having high variability and the variables used as input for prediction being obtained from Global Positioning System (GPS) units alone. The adopted scheme was implemented using data collected from GPS fitted public transport buses in Chennai (India). The performance of the proposed method was compared with available methods that were reported under similar traffic conditions and the results showed a clear improvement.

An urban arterial can be considered a series of intersections, intermediate sections (defined as those that link intersections and mid-block areas), and mid-block sections. The intermediate section is characterized by varying speed characteristics at its entry and exit. A vehicle exiting the intersection and entering the intermediate section would accelerate and gradually attain uniform speed when it leaves the intermediate section (entry to the mid-block). Most of the reported travel time studies considered the delays at intersections and the travel times in mid-blocks to get the network level travel time without paying attention to the variability of speeds and travel time at intermediate sections. The present study concentrates on the travel time estimation of the intermediate section, which is not well-discussed in the literature. A speed estimation scheme was developed for the entry and exit of the intermediate section (characterized by varying speed ranges) using a dynamic model-based estimation scheme that captured the speed variations effectively. Intermediate link travel time was then estimated using a weighted average speed-based method. The effects of heterogeneity and limited lane discipline in the traffic stream of mixed traffic were considered using two model formulations, one that represented the traffic stream in Passenger Car Units (PCU) and the other in different vehicle classes for travel time estimation. The developed estimation schemes can be used as a possible application in Intelligent Transportation Systems (ITS) for real-time estimation of travel time at urban arterials under mixed traffic conditions.

Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway. A 5.5 km road stretch in Chennai city was selected as study stretch and data were collected for a week’s period. The present study develops models using linear regression and artificial neural network (ANN) techniquesFto estimate stream travel time using bus travel time obtained from GPS. ANN performed better compared to the linear regression for all sizes of segments. Most of the Indian cities have an integrated network of buses traveling on most of the road segments with on-board tracking devices, making this a useful development for real-time travel time estimation.

Estimating delays at different road segments and intersections is a primary step in any traffic management system. Delay along a path can be estimated using sensors such as Global Positioning System (GPS) sensors, Camera sensors, Wi-Fi sensors and On-Board Diagnostics (OBD) data of the vehicle. The former three techniques have been used for some time now, but the latter one has not been explored much, especially for estimating delay. The accuracy of methodologies using OBD, Wi-Fi and Camera was calculated using data from GPS as ground truth. Results obtained showed the performance of all these methods to be comparable. Choice of the right sensor may depend on various other factors such as cost, weather factors, accuracy, range, etc. Sometimes more than one sensor needs to be used for the desired results. This paper explores three techniques in more detail and summarizes the advantages and disadvantages of each one of them in various scenarios.

Congestion has become a common experience to all road users in urban cities. In this regard, travelers desire travel time consistency—a reliability in the daily or hourly travel times. Travel time reliability measures are calculated from the day-to-day distribution of travel times on a particular route. Traditionally, travel time reliability measures are classified into five broad categories (1) statistical range measures, (2) buffer time measures, (3) tardy trip measures, (4) probabilistic measures, and (5) congestion/volume-based measures. This chapter introduces these measures and discuss and depict the applicability of these measures through a case study using data from the city of Chennai, India. To begin with, a preliminary data analysis was conducted to identify the spatial and temporal variations in travel time. Then the reliability metrics were calculated and discussed their advantages, disadvantages, and limitations.

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.