Influence of Lane and Vehicle Subclass on Free-flow Speeds for Urban Roads in Heterogeneous Traffic

Abstract

Free-flow speed (FFS) is the speed of vehicles under low volume conditions, when the drivers tend to drive at their desired speed without being affected by control delay. Estimation of FFS is important in several applications. FFS varies extensively across various road facilities as they are influenced by driver behaviour, vehicle characteristics, road factors, landuse, geometric features, control factors, etc.The estimation of FFS in homogeneous traffic is comparatively simpler as the speed variation across vehicles is limited. However, in heterogeneous traffic conditions existing in countries such as India, the FFS distribution varies across vehicle classes. The studies conducted by the authors explored the FFS distribution of various vehicle classes such as two-wheelers, three-wheelers, cars, buses, etc. However, detailed analysis revealed that the variation in FFS can be better explained by further classification of vehicles into subclasses. The study also found that the vehicle's lane position is a factor affecting FFS.The study was conducted on four-and six-lane divided roads in Chennai, India. A total of 24 study sections were chosen for data collection. Speed data were collected during early morning hours to ensure free-flow conditions. The vehicle movements were recorded using video cameras. The details regarding site factors such as carriageway width, link length, landuse, presence of kerb and type of area were collected manually. The speed and lane data were extracted and tabulated from the video recordings. The authors studied the speeds of about 17,800 vehicles (36% two-wheelers, 8% three-wheelers, 8% buses, 33% cars, 10% light commercial vehicles and 5% trucks). The vehicles were classified into 14 subclasses and speeds were analysed. The study also evaluated the effect of lane position on FFS of different classes of vehicles. It was found that vehicles on kerb lanes experienced lower speeds than those on inner lanes. Furthermore, FFS models for four-and six-lane divided roads were developed using multiple linear regression. Significant difference in speeds was observed within and across subclasses of vehicles. The models also evaluated the effects of various road factors such as carriageway width, link length, adjacent landuse type and presence of kerb on FFS. Models such as these can find applications in planning and operational analysis of urban road facilities.

The Development and Assessment of Free-Flow Speed Models under Heterogeneous Traffic in Facilitating Sustainable Inter Urban Multilane Highways

“…It can be observed from the trend of the scatterplot that a higher percentage of heavy vehicles and motorcycles causes a reduction in the free-flow speed of passenger cars. The findings of this study are consistent to some extent with the findings of Balakrishnan and Sivandan [18], who mentioned that, for heterogeneous traffic, a combined class-wise model is more efficient in predicting the overall free-flow speed compared to the base model, which is predominantly focused on passenger cars. However, in this study, the proportions of different vehicle classes were not considered in the free-flow speed estimation model as a traffic composition factor, and, together with the peak hour factor, is applied during the calculation of the demand flow rate to determine the level-of-service of multilane highways.…”

“…However, in this study, the proportions of different vehicle classes were not considered in the free-flow speed estimation model as a traffic composition factor, and, together with the peak hour factor, is applied during the calculation of the demand flow rate to determine the level-of-service of multilane highways. Therefore, by adopting the level-ofservice criteria given in the Malaysian Highway Capacity Manual [24] (as shown in The findings of this study are consistent to some extent with the findings of Balakrishnan and Sivandan [18], who mentioned that, for heterogeneous traffic, a combined class-wise model is more efficient in predicting the overall free-flow speed compared to the base model, which is predominantly focused on passenger cars. However, in this study, the proportions of different vehicle classes were not considered in the free-flow speed estimation model as a traffic composition factor, and, together with the peak hour factor, is applied during the calculation of the demand flow rate to determine the level-of-service of multilane highways.…”

“…Subsequently, multiple linear regression analyses were conducted to develop the equation to estimate free-flow speed. A similar approach was adopted by Balakrishnan and Sivanandan [18] to develop free-flow speed models for urban roads under heterogeneous traffic conditions in India. This approach was also used in the Highway Capacity Manual In order to develop free-flow speed models under prevailing conditions, the factors affecting free-flow speed, such as lane width, shoulder width, median clearance, access point density, and lane position, were investigated and are included in the model.…”

“…However, free-flow speed studies under heterogeneous traffic conditions for inter urban multilane highways are very limited, and only a few similar studies conducted on urban roads have been conducted. In a study conducted in Chennai, India, Balakrishnan and Sivanandan [18] found that the free-flow speed on urban roads varies for different vehicle classes. The authors also identified that the average free-flow speed of cars is the highest at 61 km/h, and the lowest is for three-wheelers at only 40 km/h.…”

Developing Free-Flow Speed Models for Urban Roads Under Heterogeneous Traffic Conditions

“…However, the heterogeneity of vehicles is not captured in the model. In order to study the effect of factors such as vehicle subclass, carriageway width, link length, landuse, presence of kerb, and lane position on FFS, (Balakrishnan and Sivanandan, 2015) developed disaggregate models for divided urban roads in Chennai. However, the models assumed that the effect of all road factors is uniform across vehicle types, which warrants further scrutiny.…”