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Evaluation of roundabout capacity holds significant importance because it directly affects the performance, safety, and environmental issues at the intersections. Several researchers have presented models for estimation of the entry capacity of roundabouts, but most of them have been limited to traffic conditions in developed countries having proper lane discipline, and upto single-lane and two-lane roundabouts only. There is dearth of such studies for developing countries, such as India, where traffic conditions are entirely different from developed countries. This study aims to evaluate the capacity of both single-lane and multilane roundabouts in handling different levels of heterogeneous traffic flow. Data were collected at 10 roundabouts spread across three Indian cities. All the roundabouts have different geometrical features and lane configurations, thereby ensuring the applicability of this study across different roundabouts. The formation of queues was used to indicate that the approach is operating at its maximum capacity. Four different models (linear regression model, non-linear regression model, VISSIM simulated model, and calibrated Highway Capacity Manual [HCM] model) were developed for each roundabout, and results were compared with the field capacity values. Careful consideration was given to statistical characteristics of the data and different statistical tests were conducted to evaluate the reliability of these models. A separate set of roundabouts was used to test and validate the models. Results reveal that a non-linear regression model, based on both the geometrical and traffic flow characteristics, outperforms all other models. Additionally, sensitivity analysis was performed to check the effect of various parameters on the roundabout capacity.
Evaluation of roundabout capacity holds significant importance because it directly affects the performance, safety, and environmental issues at the intersections. Several researchers have presented models for estimation of the entry capacity of roundabouts, but most of them have been limited to traffic conditions in developed countries having proper lane discipline, and upto single-lane and two-lane roundabouts only. There is dearth of such studies for developing countries, such as India, where traffic conditions are entirely different from developed countries. This study aims to evaluate the capacity of both single-lane and multilane roundabouts in handling different levels of heterogeneous traffic flow. Data were collected at 10 roundabouts spread across three Indian cities. All the roundabouts have different geometrical features and lane configurations, thereby ensuring the applicability of this study across different roundabouts. The formation of queues was used to indicate that the approach is operating at its maximum capacity. Four different models (linear regression model, non-linear regression model, VISSIM simulated model, and calibrated Highway Capacity Manual [HCM] model) were developed for each roundabout, and results were compared with the field capacity values. Careful consideration was given to statistical characteristics of the data and different statistical tests were conducted to evaluate the reliability of these models. A separate set of roundabouts was used to test and validate the models. Results reveal that a non-linear regression model, based on both the geometrical and traffic flow characteristics, outperforms all other models. Additionally, sensitivity analysis was performed to check the effect of various parameters on the roundabout capacity.
This paper addresses the gap in the scientific literature regarding construction cost estimates for the construction of underground metro stations. It provides preliminary cost estimation models using linear regression for use by the Greek underground metro public transport authority for planning future extensions to the Athens and Thessaloniki networks. At the same time, it contributes to the body of knowledge by proposing material quantity prediction models and presents a two-stage preliminary cost estimation model for the construction of civil engineering works of underground metro stations. Stage one uses the construction cost budgets of six metro stations in Greece to develop a multilinear regression equation for the prediction of the overall cost for construction of civil engineering works; stage two provides estimates of material quantities using linear regression, key quantity ratios, and artificial neural networks. The data analyzed are from the prior measurements of quantities for the construction of the Chaidari to Piraeus extension of the Athens Metro Line 3. After comparing the actual values of costs and quantities with the corresponding predictions, acceptable discrepancies are observed. All models provide estimates within ±25% discrepancies, which are acceptable at the conceptual planning phase in order to initiate project funding quests.
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