Traffic operations and capacity analysis in India

In India rapid urbanization and improved socio-economic status from past few decades has resulted in high vehicular growth. India is urbanizing at rapid pace with urban population rising much faster than its total population. Level of urbanization has increased from 17.29% in 1951 to 31.6% in 2011. India is competing with the fastest growing countries in the world. As reported in (National Transit-Oriented Development) NTOD policy (MOUD (Ministry of Urban Development) [2016]), the urban population in India, which is nearly 377 million, is poised to grow to 600 million by 2030. The urban population of India contributes 65% of country’s Gross Domestic Product (GDP), which is expected to grow to 75% as the Indian cities are growing at a rate faster than other cities in the world. Obviously, with this widespread development, negative impacts are becoming more severe with mixed traffic behavior on urban streets as pedestrian, bicycles, buses, cars, motor cycles/scooters, auto rickshaw, cycle rickshaw share the same street space creating inefficient and unsafe mobility conditions. These impacts have offered lot many new opportunities to researchers, taking up good research studies, which are directed toward understanding transportation-related problems in a better way.

The knowledge of traffic flow characteristics is a very important basic input required for planning, analysis, and operation of roadway systems. The road traffic in developing countries like India is highly heterogeneous comprising vehicles of wide ranging static and dynamic characteristics (Arasan and Arkatkar [2010]). The different types of vehicles of the heterogeneous traffic on Indian roads may be grouped into the following categories: (1) Buses, (2) Trucks, (3) Light commercial vehicles comprising large vans and small trucks, (4) Cars including jeeps and small vans, (5) Motorized three-wheelers, which include three-wheeled motorized vehicles to carry passengers and three-wheeled motorized vehicles to carry small quantities of goods, (6) Motorized two-wheelers, which include motorcycles, scooters, and mopeds, (7) Bicycles, (8) Tricycles, to carry passengers or small quantities of goods (Arasan and Arkatkar [2010]). The speeds of these vehicles vary from just five to more than 100 km/h. Due to the highly varying physical dimensions and speeds, it becomes difficult to make these vehicles to follow traffic lanes and the vehicles occupy any convenient lateral position on the road depending on the availability of road space at a given instant of time. Hence, expressing traffic volume as number of vehicles passing a given section of road or traffic lane per unit time will be inappropriate when several types of vehicles with widely varying static and dynamic characteristics are comprised in the traffic. In order to estimate the traffic volume or capacity of roadway sections under heterogeneous traffic conditions, it is necessary to study the interaction between the moving vehicles over a wide range of roadway and traffic conditions (Arasan and Arkatkar [2008]). The vehicles, of heterogeneous traffic with widely varying physical and operational characteristics such as the one prevailing on Indian roads, as mentioned earlier occupy, any convenient lateral position on the road based on the availability of space without any lane discipline. Hence, study of the interaction between moving vehicles under such heterogeneous traffic condition is highly complex. The problem of measuring volume at different levels including capacity of such heterogeneous traffic is addressed by converting the different types of vehicles into equivalent passenger cars and expressing the volume in terms of Passenger Car Unit (PCU) per hour (Arasan and Arkatkar [2010]; Dhamaniya and Chandra [2013]; Kumar et al. [2017]). The microscopic and macroscopic level analysis of traffic flow and vehicle operations for different type roadway and traffic conditions is a key aspect for studying any typical traffic operation. This also leads to understand the variation in capacity and LOS based on a particular type of roadway element/facility and its associated traffic conditions. The results obtained from detailed analysis on traffic operations for various roadway elements and facilities can be extremely helpful in demonstrating the innovative ideas on traffic flow modeling under Indian conditions.

With the advent in technology and its deployment, there is substantial rise in the quality of research outputs, since last two decades. The enduring research works in India, from the specialization of “Traffic and Transportation Engineering,” mainly include (i) mixed traffic flow modeling on varying roadway classes and traffic conditions, (ii) traffic flow parameter predictions, (iii) traffic operation control and management, (iv) traffic safety and vulnerable road users, (v) ITS applications and traffic management. For instance, a maiden attempt (from India) toward development of “Indian-Highway Capacity Manual” is undertaken on priority by group of premier institutions in India, in the form of a mission mode project for different categories of roads like Expressways, National Highways, State Highways, Rural and Urban Roads, separately. The principal goal of this research is to contemplate the nationwide characteristics of road traffic and further develop a manual for determining the roadway capacity and LOS for varying types of inter-urban and urban roads. This also includes controlled as well as uncontrolled Intersections, coupled with pedestrian facilities. Since, last decade in India, there is considerable rise in the number of events, such as conferences and workshops, organized for making available a solid platform for dissimilating research findings among researchers in transportation community. With this motivation, a National conference-cum-workshop titled, “Recent Advances in Traffic Engineering-2015 (RATE 2015)” was held at Sardar Vallabhbhai National Institute of Technology Surat (SVNIT Surat) during 3–4 July, 2015, in association with CSIR-Central Road Research Institute (CSIR-CRRI). The Conference was intended to bring together professional engineers, academicians, researchers, and others having keen interest in the field of Traffic and Transportation and have fruitful deliberations. Hence, it is the most apposite juncture to have a focused theme of the special issue as “Traffic Operations and Capacity Analysis in India,” for which the quality papers are selected after a rigorous review procedure prescribed by the journal. After this standard review procedure, I accepted total nine papers for this special issue, out of which six papers are to be published in this special issue and rest three will be published in regular issues, with a mention of its reference with this special issue. The remainder of this foreword gives an outline of six studies included in this special issue, which are connected to envisaging traffic operations for different roadway elements and transport facilities.

A study by Asaithambi et al. (2017) focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model, and Das and Asundi were selected for this study. These models were executed in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. The study concludes that Gipps and IDM models represent the observed conditions reasonably. Also, the capacity is found to be higher, when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. They give reasonable critical parameter values. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane. The findings have interesting implications for capacity and PCU estimation and hence LOS analysis.

A. Gautam et al. (2017) conducted a study to understand the influencing factors for estimating PCU values of different vehicle types on hill roads. The authors reviewed different methods for estimating PCUs followed by data collection on hill roads of the northeastern state of India (Meghalaya state). PCU values are estimated based on the three important methods identified. These are, (i) Speed–area method, (ii) modified density method, and (iii) area occupancy method. The authors concluded that the PCE values obtained from speed–area method are on the higher side for heavy vehicles and on lower side for motorized two wheelers compared with the modified density method and area occupancy methods. Modified density method and area occupancy methods show different PCU values yet these two methods confirm that in hilly terrains having low level of traffic volume, range of PCU values for heavy vehicles, and motorized two wheelers decreases by a considerable extent in comparison to other method.

Mohan and Chandra (2017) compared three methods of estimating PCU factors for different types of vehicles on unsignalized intersections under highly heterogeneous traffic conditions. The first method is based on the occupancy time of a vehicle, while clearing the intersection. The second method is based on the capacity of a priority movement estimated in terms of different vehicle categories. Queue clearance rate is used as the basis for PCU estimation in the third method. The authors used data, collected at two unsignalized intersection in semi-urban area of two cities of India and PCU for different types of vehicles is determined using the three methods. The resulting values of PCU factors were found to be logical and are representatives of the actual field conditions. Finally, the authors concluded on the applicability of different methods under varying traffic conditions at unsignalized intersections.

Sonu et al. (2017) estimated PCU values of different vehicle categories at a typical four-legged roundabout based on the concept of time occupancy. A stream equivalency factor (k) is also developed based on the estimated PCU to convert the heterogeneous traffic flow into a homogenous stream equivalent without making use of PCU factors. Relationship between entry and circulatory flows is developed using observed data corresponding to the time period in which there was queue formation in the approach. Further, estimated critical gap and follow-up time are used to derive the capacity by HCM 2010 equation. A multiplicative adjustment factor (modification factor) is suggested for the use of HCM 2010 equation directly in the field to estimate entry flow under heterogeneous traffic condition. The authors recommended that, the study results may be suitable for revising the code of practice, named IRC 65–1976.

A study by, Babu and Vedagiri (2017) is focused on safety evaluation of an unsignalized intersection using two surrogate measures, Post Encroachment Time (PET) and the speed of corresponding conflicting through vehicle. A term critical speed is proposed to identify critical conflicts. Critical speed for a particular PET value is determined using stopping/braking distance concept. The authors reported that drivers of right-turning vehicles do take risks in accepting small gaps in through traffic at the intersection, which is dangerous. Further, they also inferred that right-turning light motor vehicles are at higher risk compared to two wheelers and heavy vehicles at the intersection. Finally, authors demonstrated that safety evaluation by conflict study using surrogate measures is a proactive method which does not require any accident data. Also, they emphasized that identifying critical conflicts using a threshold value of PET is good and correct for highways and major roads where traffic follows posted speed, but on highways with mixed traffic for varied speeds this is not correct.

Providing travelers with real-time information on travel time is an imperative constituent of Intelligent Transportation Systems (ITS), which allow them to make better and more informed travel decisions related to choice of routes and modes. This in turn aids to reduce congestion on the roads. It is not only involved in assisting traveling decisions, but also in maintaining the demand-supply equilibrium of roadway capacity in a given network having different routes for the identical origin and destination. In connection with this, Dhivyabharathi, Hima, and Vanajakshi (2017) proposed a method to predict stream travel time using particle filtering approach, which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The authors reported that the performance of this method was compared to a basic speed-based approach, where travel time is obtained by time–distance relationship using space–mean speed, and the proposed method showed better performance. Finally, authors recommended that the particle filter approach can be considered as a promising tool for developing ATIS applications.

Guest Editor
Shriniwas S. Arkatkar
Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India
[email protected], [email protected]