Estimation Impact of Capital Development Projects on Adjacent Street and Road Network in Organization of Traffic by Means of Signal Controlled Intersections

The paper discusses problems of assessing the impact of mass attraction centers on the adjacent street and road network in the process of their functioning, expansion or conversion. The choice of criteria for assessing the organization of traffic flow, given in the Russian and foreign literature, has been substantiated, in particular, it is proposed to use v/c ratio for adjacent junction and corresponding level of traffic service (LOS). The main models for estimating capacity of signalized intersections are presented. The procedures of forming a mathematical model for estimating the load factor of signalized intersections is shown. The concepts of lane group capacity, total lost time per cycle, phase coefficients, saturation flow rate, and coefficients taking into account the decrease in the ideal saturation flow rate are explained. A mathematical model for estimating transport demand is presented, which allows to calculate the intensity of traffic flow to and from the center of mass attraction on the basis of the total traffic flow of correspondence, share of visitors in individual transport, average filling of individual transport, and coefficient of daily irregularity upon arrival and departure of visitors on an individual transport. An integrated mathematical model of loaf factor is proposed which includes parameters for estimating transport demand for centers of mass embarrassment and parameters that determine the signalized intersections capacity. The uniqueness of the integrated model is that it simultaneously involves parameters reflecting the demand and capacity of loading intersection. Recommendations are made on assessing the level of traffic service flows and the v/s ratio, based on the data of transport demand and capacity, adjacent to the centers of mass attraction of the road network. The presented method of estimating the LOS based on the capacity of the signalized intersections allows us to estimate the influence degree of mass attraction centers on the adjacent urban road network.

Impact of Turning Lane Storage Length and Turning Proportions on Throughput at Oversaturated Signalized Intersections

During oversaturated conditions, common objectives of signal timing are to maximize vehicle throughput and manage queues. A common response to increases in vehicle volumes is to increase the cycle length. Because the clearance intervals are displayed less frequently with longer cycle lengths and fewer cycles, more of the total time is used for green indications, which implies that the signal timing is more efficient. However, previous studies have shown that throughput reaches a peak at a moderate cycle length and extending the cycle length beyond this actually decreases the total throughput. Part of the reason for this is that spillback caused by the turning traffic may cause starvation of the through lanes resulting in a reduction of the saturation flow rate within each lane. Gaps created by the turning traffic after a lane change may also reduce the saturation flow rate. There is a relationship between the proportions of turning traffic, the storage length of turning lanes, and the total throughput that can be achieved on an approach for a given cycle length and green time. This study seeks to explore this relationship to yield better signal timing strategies for oversaturated operations. A microsimulation model of an oversaturated left-turn movement with varying storage lengths and turning proportions is used to determine these relationships and establish a mathematical model of throughput as a function of the duration of green, storage length, and turning proportion. The model outcomes are compared against real-world data.

Investigation of saturation flow rate using video camera at signalized intersections in Jordan

This study aimed to investigate a potential list of variables that may have an impact on the saturation flow rate (SFR) associated with different turning movements at signalized intersections in Jordan. Direct visits to locations were conducted, and a video camera was used. Highway capacity manual standard procedure was followed to collect the necessary traffic data. Multiple linear regression was performed to classify the factors that impact the SFR and to find the optimal model to foretell the SFR. Results showed that turning radius, presence of camera enforcement, and the speed limit are the significant factors that influence SFR for shared left- and U-turning movements (LUTM) with R2 = 76.9%. Furthermore, the presence of camera enforcement, number of lanes, speed limit, city, traffic volume, and area type are the factors that impact SFR for through movements only (THMO) with R2 = 69.6%. Also, it was found that the SFR for LUTM is 1611 vehicles per hour per lane (VPHPL),which is less than the SFR for THMO that equals to 1840 VPHPL. Calibration and validation of SFR based on local conditions can improve the efficiency of infrastructure operation and planning activities because vehicles’ characteristics and drivers’ behavior change over time.

New Approach to Estimating the Saturation Flow Rate of a Shared Lane with Permitted Left Turns

The estimation of the saturation flow rate is of utmost importance when defining the signal plan at intersections. Because of the numerous influential factors, the values of which are hard to be determined, the subject problem is to be regarded as an extremely complex one. This research deals with the estimation of a saturation flow rate of a shared lane with permitted left turns. The suggested algorithm is based on the application of the artificial neural networks where the data for training are received by simulation. The results obtained by the neural networks are compared with multiple linear regression and the known HCM 2010 approach for determining the saturated flow of a shared lane. The testing data have shown that the approach based on the artificial neural networks foresaw statistically significantly better values than the ones obtained by multiple linear regression, with an error of 27 veh/h against 49 veh/h. The HCM 2010 approach is significantly worse than the two others included in this research. The ways of the future development of the suggested method could include additional factors, such as the grade of the traffic lane, the proximity of the bus stops, and others.

Effect of U-Turns and Heavy Vehicles on the Saturation Flow Rates of Left-Turn Lanes at Signalized Intersections

The Saturation Flow Rate (SFR) is a primary measure that can be used when estimating intersection capacity. Further, the efficiency of signal control parameters also depends on the accuracy of assumed SFR values. Driver behavior, type of movement, vehicle type, intersection layout, and other factors may have a significant impact on the saturation flow rate. Thus, it is expected that driving environments that have heterogeneous driver populations with different driving habits and cultures may have different SFRs. In practice, the proposed SFRs based on US standards (Highway Capacity Manual, 2016) have been adopted in the State of Qatar without validation or calibration to consider the local road environment and the characteristics of the driving population. This study aims to empirically analyze the saturation flow rates for exclusive left-turn lanes and shared left- and U-turn lanes at two signalized intersections in Doha city, while considering the effects of heavy vehicles and U-turn maneuvers. Empirical observations revealed that the average base SFR, i.e., when the influences from heavy vehicles and U-turns were excluded, could vary approximately from 1800 vehicles per hour per lane (vphpl) to 2100 vphpl for exclusive left-turning lanes and approximately from 1800 vphpl to 1900 vphpl for shared left- and U-turning lanes. Furthermore, this study proposed different adjustment factors for heavy vehicle and U-turn percentages which can be applied in practice in designing signalized intersections, particularly in the State of Qatar.