Lelitha Devi Vanajakshi

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.

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.

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.

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%.

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.

In the absence of pharmaceutical interventions for the Novel Coronavirus (COVID-19), countries have taken drastic steps like quarantine, prohibit large-scale gatherings, limited transport, social distancing, curfews, and lockdowns to curtail the spread of the virus. In light of these events, the current study attempts to understand the short-term changes in road traffic patterns, using data from two Wi-Fi MAC Scanners deployed at strategic locations in Chennai, India. The results indicate that the road traffic activities significantly reduced due to the restrictions in non-essential trips, workplace suspensions, and strict surveillance during lockdowns. However, as the lockdown rules eased, the road traffic activities began to recover. It is found that complete closedown is most effective in reducing road travel activity, but ad-hoc short duration complete closedowns may only yield temporary benefits. Also, extended lockdowns without proper enforcement may be ineffective since the public appeared to ignore the advisory after a while.

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.

Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the I-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model. © 2009 ASCE.