THE EFFECT OF SIDE FRICTION ON DELAYS IN ONE-WAY URBAN ROAD SECTIONS

The effect of side friction activities on delays due to the reduced free-flow speed was investigated by conducting a series of traffic surveys and referring to the 1997 Indonesian Highway Capacity Manual (IHCM) parameters. The movement of pedestrians, parking/stopping vehicles, vehicles entering and exiting the road, and the slow-and-stop motion of vehicles on the road sections was observed in video footage and analyzed to estimate the effect of side friction on the delays occurring. A weighting factor was used to determine the total value of side friction on the road to test the combined effect of all activities. This study used a regression model for estimating vehicle delays, as a performance parameter, on urban road sections, taking into account the effect of side friction on the road section's vehicle free-flow speed (FFS). It was found that vehicle speed decreased when side friction increased at all levels of traffic volume. Low side friction produced a higher vehicle free-flow speed (FFS), and analysis of the free-flow speed (FV) showed a lower vehicle free-flow speed (FFS) than that in the 1997 IHCM analysis. Delays at undersaturated inseparable one-way road sections (2/1 UD) with low to high side friction were described by the equation: Y = 0.002 X + 0.028 (R2 = 0.704) for Panca Usaha Road, and Y = 0.0022 X + 0.0104 (R2 = 0.774) for Pejanggik Road, where Y = Delay, X = Traffic Flow. The results of the above study indicate the existence of new performance parameters on urban road segment type 2/1 UD in the form of delay, and that can be an early indication as input in the update of IHCM 1997 and other research that the author has done before, as well as other authors who have also written about similar topics about this manual that is more than 20 years old (1997-2021), in analyzing the performance of road networks in Indonesia.

Fundamental Diagram Estimation Based on Random Probe Pairs on Sub-Segments

A new statistical algorithm is proposed in this paper with the aim of estimating fundamental diagram (FD) in actual traffic and dividing the traffic state. Traditional methods mainly focus on sensor data, but this paper takes random probe pairs as research objects. First, a mathematical method is proposed by using probe pairs data and the jam density to determine the FD on a stationary segment. Second, we applied it to the near-stationary probe traffic state set through linear regression and expectation maximisation iterative algorithm, estimating the free flow speed and the backward wave speed and dividing the traffic state based on the 95% confidence interval of the estimated FD. Finally, simulation and empirical analyses are used to verify the new algorithm. The simulation analysis results show that the estimation error corresponding to the free flow speed and the backward wave speed are 1.0668 km/h and 0.0002 km/h respectively. The empirical analysis calculates the maximum capacity of the road and divides the traffic into three states (free flow state, breakdown state, and congested state), which demonstrates the accuracy and practicability of the research in this paper, and provides a reference for urban traffic monitoring and government decision-making.

Calculating TIN-Based 3D Sight Distance and Its Evaluation on Drivers Free-Flow Speed

Sight distance during driving may be limited by side factors such as mountain slopes or trees and buildings in horizontal curves and by the dome of the arc in vertical curves, and the night vision also can be limited in the sag vertical curves by the vehicle’s light. Analyzing driver’s sight distance in the road is very important for traffic safety. In this regard, in order to help the designer, the current rules and guidelines propose the two-dimensional analysis model for the sight distance. In this analysis, the sight distance is calculated separately in the combination of horizontal and vertical curves, and the smallest amount is considered as the sight distance. While, after constructing and operating the road, drivers control their vehicle according to the conditions in their 3D space. Nowadays, given the remarkable advances in computer science, there are many possibilities for 3D modeling of the route. In this research, the goal is to calculate the three-dimensional stopping sight distance at each spatial point by computer modeling the existing roads. The speed of various drivers with conventional riding vehicles under free traffic conditions was obtained by a GPS device. The results showed that, in places such as curves, given the provision of sufficient stopping sight distance, driver’s free-flow speed reduces. Thus, another factor affecting the speed of the drivers is the curvature change rate. Finally, using nonlinear regression modeling a logical relationship was determined and extracted between the three factors of driver’s free-flow speed, 3D stopping sight distance, and curvature change rate of the path.

An Efficient Scheduling of Flyover at Grade Intersection under Mixed Traffic Environment

A flyover is a bridge constructed along an intersecting highway over an at-grade intersection. It allows two –direction traffic to flow at free flow speed on the bridge. The flyover is one of the methods for solving traffic problems at at-grade junctions on highways including capacity, congestion, long delay and queue length. Traffic signalization at the upgraded intersection often uses the same fixed time control plans, even after the installation of a flyover over the intersection. Most of the flyovers in India are constructed at the junctions on highway bypasses of big cities. The present work deals with a efficient scheduling of flyover at the grade intersection under the mixed traffic environment. From the results and the modeling carried out in the “SIDRA Intersection” software different points are observed. The present work consists of the Proposed Intersection at Rajkamal Square, Amravati.

Analysis and Prediction of Vehicles Speed in Free-Flow Traffic

Speed is a crucial factor in the frequency and severity of road accidents. Light and heavy vehicles speed in free-flow traffic at six locations on Poland’s national road network was analyzed. The results were used to formulate two models predicting the mean speed in free-flow traffic for both light and heavy vehicles. The first one is a multiple linear regression model, the second is based on an artificial neural network with a radial type of neuron function. A set of the following input parameters is used: average hourly traffic, the percentage of vehicles in free-flow traffic, geometric parameters of the road section (lane and hard shoulder width), type of day and time (hour). The ANN model was found to be more effective for predicting the mean free-flow speed of vehicles. Assuming a 5% acceptable error of indication, the ANN model predicted the mean free-flow speed correctly in 84% of cases for light and 89% for heavy vehicles.

ANALISIS KAPASITAS DAN KINERJA LALU LINTAS DI JALAN H.R. RASUNA SAID JAKARTA

Some of the main roads in South Jakarta, one of which is Jalan H.R. Rasuna Said, which is always crowded with passing vehicles, both residents of Jakarta and Jabodetabek, traffic on Jalan HR Rasuna Said Jakarta often experiences congestion, one of the causes is the increase in the number of vehicles in the city and also traffic performance which is not matched by an increase in number. road users. The increase in the volume of traffic will cause a change in behavior towards traffic performance, based on the guidelines of MKJI 1997 (Manual of Indonesian Road Capacity) the value of road capacity is C = 6968 pcu/km with a free flow speed of Fv = 59,55 km/hour while the capacity Greenberg's selected model produces a capacity of C = 11384 pcu/km with a free flow speed of Sff = 49,72 km/hour while the condition of the passing vehicle exceeds the basic capacity based on MKJI 1997, namely the maximum volume of V = 10635 pcu/km and with a speed of S = 18,25 km/hour so that the research model that will be used as a reference for the next calculation is the Greenberg model. ABSTRAKBeberapa ruas jalan raya di Jakarta Selatan salah satunya adalah jalan H.R. Rasuna Said yang selalu dipadati dengan kendaraan yang melintas baik itu warga Jakarta maupun dari Jabodetabek, lalu lintas di Jalan H.R Rasuna Said Jakarta sering sekali mengalami kemacetan salah satu penyebabnya adalah peningkatan jumlah kendaraan di dalam kota dan juga kinerja lalu lintas yang tidak diimbangi oleh meningkatnya jumlah pengguna jalan. Peningkatan jumlah volume lalu lintas akan menyebabkan perubahan perilaku terhadap kinerja lalu lintas, berdasarkan pedoman MKJI 1997 (Manual Kapasitas Jalan Indonesia) nilai kapasitas ruas jalan sebesar C = 6968 smp/km dengan kecepatan arus bebas sebesar Fv = 59,55 km/jam sedangkan kapasitas model terpilih Greenberg menghasilkan kapasitas C = 11384 smp/km dengan kecepatan arus bebas Sff = 49,72 km/jam sedangkan kondisi kendaraan yang melintas melebihi kapasitas dasar bedasarkan MKJI 1997 yaitu volume maksimum sebesar V = 10635 smp/km dan dengan kecepatan S = 18,25 km/jam sehingga model penelitian yang akan digunakan sebagai acuan perhitungan selanjutnya adalah model Greenberg.

Influence Factor Analysis of Bicycle Free-Flow Speed for Determining the Design Speeds of Separated Bicycle Lanes

To provide a knowledge basis for updating the design speed in bicycle facility codes, this paper examines factors that influence bicycle free-flow speed. We investigated six segments of Nanjing’s separated bicycle lane and established a generalized linear model of the relationship between bicycle free-flow speed and bicyclists’ gender, age, bicycle type, lane width, bicycle lateral position, and travel period. With the model, we determined the statistical significance of each factor and assessed each factor’s impact extent. Through comparing the 85th percentile speeds of different groups, we proposed the recommended values and a method for calculating the design speed of separate bicycle lanes. The following results and conclusions were obtained: (1) The significant influential factors of bicycle free-flow speed were bicyclists’ gender and age, bicycle type, lane width, and bicycles’ lateral position. (2) Bicycle type had the greatest impact on bicycle free-flow speed, following by bicycle lateral position, gender, age, and lane width in sequence. (3) The recommended design speeds for separate lanes of less than 3.5 m and the wider lanes were 25 km/h and 30 km/h, respectively.